A search method for a hypersonic gliding vehicle based on early warning information guidance - Zhang - 2022 - IET Radar, Sonar & Navigation - Wiley Online Library

A search method for a hypersonic gliding vehicle based on early warning information guidance - Zhang - 2022 - IET Radar, Sonar & Navigation - Wiley Online Library

Junbiao Zhang, Jiajun Xiong, Lingzhi Li, Qiushi Xi, Xin Chen, Fan Li

First published: 12 April 2022

https://doi.org/10.1049/rsn2.12251

Citations: 1

ietresearch.onlinelibrary.wiley.com

---

1 INTRODUCTION

A hypersonic gliding vehicle (HGV) usually refers to an aircraft with a speed above 5 Ma and flying airspace between 20 and 100 km [1, 2]. Due to the rapid flight speed and strong penetration ability, it poses severe challenges to the existing early warning detection system. As an important sensor in the battlefield perception system, radar plays a significant role in the searching and tracking of HGV. According to the guidance of early warning information, radar can quickly and effectively detect these targets, providing a longer time window for the defence system to improve the success rate of interception. However, owing to the limited resources (such as time and energy) of radar, it is necessary to reasonably allocate time and energy resources between tracking tasks and search tasks to achieve better detection efficiency. Therefore, reasonably allocating radar search resources based on early warning information is an important issue that needs to be addressed.

To the best of our limited knowledge, many scholars have researched the detection of HGV and the optimisation of radar resources, but these studies mainly focus on the HGV trajectory tracking and prediction [3, 4], radar task scheduling [5] and radar target allocation [6, 7]. There are few studies on the optimisation of radar search parameters. In fact, target search is an important task of a phased array radar. Therefore, this study mainly focusses on the search strategy of HGV with guidance information.

The search optimisation problem under guidance information mainly includes two parts: the radar search optimisation model and the optimisation algorithm. Jang et al. [8] proposes a target optimisation model that minimises the search load while maintaining the expected cumulative detection probability, and uses a semi-analytical method to solve the problem of the search parameters setting (such as dwell time and beam width). Yan et al. [9] proposes an integrated resource allocation method for radar when performing search and tracking tasks simultaneously, and obtains a multi-group Pareto optimal solution using the Pareto theory. Briheche [10] establishes a search optimisation model with the goal of minimising the search time, and expresses the objective function as an integer programme for solution. Tao et al. [11] establishes a search resource allocation model to minimise the average discovery time, and obtains the optimal search data rate by the Lagrange multiplier method. Huang et al. [12] proposes a radar search optimisation method under the early warning aircraft guidance and obtains the best search data rate by combining the Lagrange method with the obstacle method. Jiao et al. [13] optimises the search parameters of the space-based radar and proposes a parameter optimisation algorithm with the least search time, which considers the dwell time, search area and other factors. Fan et al. [14] establishes a radar search model with the objective of minimising the search airspace, and solves it with particle swarm optimisation (PSO) to achieve the planning of the search airspace. It can be found that the existing research studies mainly aim to establish a radar search model with the goal of simply increasing the cumulative detection probability or simply reducing the average discovery time. It lacks a comprehensive consideration of the cumulative detection probability, the average discovery time and the target priority level. When searching for targets, the radar should prioritise targets with high priority. Therefore, we propose a HGV search method based on early warning information guidance. The main contributions of this paper are as follows.

1. The priority judgement model of HGV is established. Combined with the early warning guidance information, three indicators, namely height, velocity and distance, are selected as the basis for priority judgement. The quantitative formulae corresponding to the three indicators are constructed, which can quickly judge the priority level of HGV.

2. A search optimisation model is proposed, which comprehensively considers the cumulative detection probability, average discovery time and target priority level. For HGV targets, it is equally vital to shorten the discovery time and increase the cumulative detection probability. Meanwhile, targets with high priority levels should be preferentially detected.

3. A hybrid optimisation method is proposed based on PSO and differential evolution (DE). The adaptive inertia weight and learning factors of PSO are designed to improve the search efficiency of particles. In addition, the mutation, crossover and selection operations of DE are used to perturb the particle swarm to enhance the population diversity of particles, thereby improving the global optimisation ability of the algorithm.

The rest of this paper is organised as follows. Section 2 describes the determination of the radar search airspace. Section 3 introduces the HGV priority quantification model and the radar search model. Section 4 details the hybrid optimisation algorithm based on PSO and DE. Section 5 verifies the effectiveness of the proposed method, and Section 6 concludes the paper.

2 RADAR SEARCH AIRSPACE

Early warning guidance information provides an important data basis for radar to search and detect HGV. It mainly includes HGV position information, velocity information and error information. The early warning system usually predicts the HGV trajectory according to the historical tracking data, and then provides the predicted trajectory information to the radar. As shown in Figure 1, the radar determines the search airspace based on the guidance information. Due to the high manoeuvrability of HGV, the prediction error will keep increasing and the search airspace will gradually become larger with the extension of prediction time. Assuming that the HGV position and velocity information estimated by the early warning system is , the radar needs to convert it to azimuth angle and elevation angle information.

Radar search based on early warning information guidance

The change curve of the priority level with height

The change curve of the priority level with velocity

The change curve of the priority level with distance

The relationship between single detection probability and SNR

The relationship between the number of pulses and SNR

Relationship between the cumulative detection probability and the number of revisits

Assuming the real azimuth angle and elevation angle of HGV are

, and the estimated azimuth angle and elevation angle of HGV are

. Then we can get the formula as follows.

(1)

where

is the estimated error corresponding to the azimuth angle and elevation angle.

Assuming the probability density of the HGV appearing in the radar search airspace

is

. Then the probability of HGV appearing in this search airspace is Ref. [15, 16].

(2)

The range of the radar search airspace is mainly affected by the error of guidance information. The larger the search airspace is, the more conducive it is to detect HGV. However, too large search airspace will also cause a waste of radar resources and reduce radar performance. Therefore, we usually determine the range of search airspace based on the Pau Ta criterion (3σ principle) [17].

3 RADAR SEARCH MODEL

The fast beam scanning capability and fast waveform agility capability of the phased array radar enable it to be flexibly adjusted between multiple working modes [18]. The main tasks of the radar include searching and tracking. The searching task is the premise for the radar to achieve other functions. The main problem discussed in this section is establishing a radar search model so that the radar can reasonably allocate time and energy resources in the search task.

3.1 Hypersonic gliding vehicle priority judgement model

The priority of HGV is different due to different manoeuvre states. Radar will improve the detection probability of high-priority HGVs when performing the search task. Therefore, how to judge the HGV priority is the primary problem in the radar searching process. We propose three indicators to judge the priority of HGV, namely height, velocity, and distance.

• (1)

  Height

  The lower the height of HGV, the shorter the remaining flight time, the closer to the HGV attack phase and the more difficult it is to defend against interception. Therefore, the lower the HGV height, the greater the priority. The HGV usually flies in the airspace range of 20 to100 km, and its height priority is modelled as a function in the interval [0,1].

  (3)

  (4)

  where H_max indicates the upper bound of the HGV flight altitude, and its value is 100 km. H_min indicates the lower bound of the HGV flight altitude, and its value is 20 km. The change curve of the priority level with height is shown in Figure 2.

• (2)

  Velocity

  The faster the HGV velocity, the stronger its manoeuvrability and attack capability. The high speed also makes it difficult for the radar to detect and track HGV. Therefore, the faster the HGV speed, the greater the priority level. The manoeuvring speed of HGV is usually 5 to 20 Ma, and the priority level of HGV speed is modelled as,

  (5)

  where V indicates the upper bound of the HGV flight speed, and its value is 20 Ma. is the priority coefficient of speed. The range of its values is generally [0.025, 0.035]. The value of can be determined according to the needs of the system, and its value chosen in this paper is 0.03. The change curve of the priority level with velocity is shown in Figure 3.

• (3)

  Distance

  The distance is an important indicator for the priority level judgement. The smaller the distance, the greater the priority. Assuming that the maximum detection range of the radar is 1500 km, the distance priority level can be modelled as,

  (6)

  where indicates the minimum priority level and indicates the priority coefficient of distance. The distance priority level curve can be adjusted by and as shown in Figure 4. is usually set as a constant in the interval [0.1, 0.3]. is usually set as a constant in the interval [0.001, 0.003]. r indicates the distance between the HGV and radar.

  By computing the priority level of height, velocity and distance, we can get the comprehensive priority level of HGV as follows.

  (7)

  where , and are the priority level weights corresponding to the height, velocity and distance, respectively.

By normalising all the HGV priority levels, the value of the priority level corresponding to each HGV can be obtained.

3.2 Optimisation objective

When a radar is searching for a target, it usually wants to detect the target with maximum probability in the shortest time. Therefore, we take the shortest average discovery time and the largest cumulative detection probability as the optimisation criteria and optimise the search parameters of the radar.

Assuming that according to the early warning guidance information, the number of airspaces that the radar needs to search is N. The search frame period of the ith airspace is

, and the time of the target appears in the ith airspace obeys a uniform distribution. Then the average time for the target to be detected in the first search frame period can be expressed as,

(8)

Assuming that the target is discovered after waiting for k search frame periods, the time and probability of the target being detected can be expressed as,

(9)

(10)

where

is the target detection probability within a single dwell time. According to the characteristic of HGV, it is usually modelled as a Swerling III target [19]. The formula of its detection probability is taken from Ref. [20].

(11)

where

is the detection threshold,

is the incomplete gamma function, and

is the accumulated pulse number for a single dwell time. SNR is the signal-to-noise ratio.

It can be concluded that the average discovery time of the target in the ith airspace is,

(12)

The cumulative detection probability of the target in the ith airspace is,

(13)

where n is the number of radar revisits. Generally, we hope that the average discovery time is as short as possible, and the cumulative detection probability is as large as possible. Therefore, we design the optimisation criterion as,

(14)

3.3 Constraint condition

Due to the limitations of time and energy resources, the radar should also meet the following constraints when searching for targets:

• (1)

  Constraint of detection probability

  The cumulative detection probability is related to the single detection probability and the number of radar revisits n, and their relationship can be expressed as,

  (15)

  It can be seen that the cumulative detection probability can be increased by increasing the single detection probability or the number of revisits. The single detection probability is related to SNR. When the false alarm probability is constant, the relationship between the single detection probability and SNR is shown in Figure 5. Assuming that pulses are coherently accumulated, the SNR is related to the number of pulse accumulations, and their relationship is shown in Figure 6. When the single detection probability is constant, the relationship between the cumulative detection probability and the number of radar revisits is shown in Figure 7.

  At the same time, the number of radar revisits n should satisfy the following equation.

  (16)

  where T_z represents the total time resources.
• (2)

  Constraint of dwell time

  The equation of the radar detection range [21, 22] is as follows.

  (17)

  where and are the radar transmit power and the transmit antenna gain, respectively. is the receiver antenna area. is the target scattering cross-sectional area. L represents the total system loss. k = 1.38 × 10–23 J/K is the Boltzmann parameter.  = 288 K is the radar system temperature. B is the radar bandwidth.

  The radar dwell time can be expressed as,

  (18)

  where is the pulse width.

  Combining (17) and (18), the equation can be obtained as follows.

  (19)

  In order to avoid distance ambiguity, the pulse recurrence period needs to meet.

  (20)

  where C is the light velocity, and its value is 3 × 10⁸ m/s.
• (3)

  Constraint of time resource

  The total time for the radar to perform the search task is,

  (21)

  where is the number of radar beams in the ith airspace.

  The phased array radar can perform searching and tracking tasks at the same time by using time segmentation technology as shown in Figure 8.

  Assuming that the resource occupancy rate of the search task is , the total time for the radar searching task needs to meet the following equation:

  (22)

  Considering the target priority and constraints conditions, we design the optimisation equation of radar search parameters as follows.

  (23)

  (24)

4 HYBRID OPTIMISATION ALGORITHM

It can be seen that the above optimisation problem is a multi-objective and multi-constraint optimisation problem. In order to improve the solution efficiency, we propose a hybrid optimisation algorithm that combines DE and PSO.

4.1 Adaptive particle swarm optimisation

• (1)

  Particle swarm optimisation

  PSO is a simplified model of swarm intelligence [23-26], which regards the solution process of the optimisation problem as the process of birds foraging and regards the solution space as the flight space of birds. It searches for the optimal solution through the movement of particles in the solution space. PSO has the advantages of simple parameters and easy implementation. It has been widely used in the fields of function optimisation, pattern classification and control engineering.

  The PSO regards each particle as a potential best solution. It determines the best position according to the fitness value of each particle, and updates the current movement speed and position of each particle through its own best position and the global best position. Its update formula is as follows.

  (25)

  (26)

  where is the current position of the ith particle in the jth dimension. is the particle best position. is the global best position. is the current particle velocity. and are the learning factors. and are the random number distributed in the interval [0, 1]. w is the inertia weight.
• (2)

  Design of adaptive parameters

  The first term on the right side of (25) mainly represents the inertial motion of the particle, which is controlled by the inertia weight w. During the iterative process, w can be dynamically changed. In the early iteration stage, a larger value of w is beneficial to enhance the particle's inertia weight, thereby enhancing the search ability of the global best solution. In the later stage of the iteration, a smaller value of w is beneficial to the rapid convergence of the algorithm. We adopt a dynamic variable weighting strategy [27] and set w to the cosine change form. The equation is as follows.

  (27)

  where and are the maximum inertia weight and the minimum inertia weight, respectively. is the maximum number of iterations. Figure 9 shows the relationship between the inertia weight and the number of iterations.

The second term and third term on the right-hand side of (25) represent the particle cognitive and the social factors, respectively. c₁ is mainly used to control the influence of the particle's best position on the movement of the particle. The larger the value of c₁, the more the particles tend to be closer to their own best position. c₂ is mainly used to control the influence of the global best position on the movement of the particle. The larger the value of c₂, the more the particles tend to be closer to the global best position. In order to maximise the search range in the early stage of iteration and converge to the optimal position quickly in the later stage of iteration, we design the learning factors c₁ and c₂ as a trigonometric function.

(28)

(29)

where

,

,

and

are the control parameters of learning factors. Figure 10a,b shows the relationship between the learning factors and the number of iterations.

A new particle iteration formula can be obtained by introducing (27) – (29) into (25).

4.2 The proposed method

In order to overcome the shortcomings of PSO, which are the lack of population diversity and easy to fall into local optimality, we propose a hybrid optimisation algorithm based on PSO and DE.

DE is a heuristic search algorithm [28-30] that finds the best solution through population mutation, crossover and selection. It is put forward on the basis of the genetic algorithm [31]. It has a strong global search ability, and its equation is as follows.

(30)

where r₁, r₂ and r₃ are random serial numbers, and they are different from each other. F is a scaling factor designed as an adaptive regulation parameter.

(31)

(32)

where F₀ is a mutation operator.

DE is used to disturb the iteration of PSO. The formula is as follows.

(33)

where

is a random number in the interval [0,1]. When

is less than or equal to the threshold S, the particles first perform mutation operation, and then perform population crossover and selection. When

is greater than the threshold S, the particle is updated according to (25). The threshold S is designed as an adaptive function.

The relationship between the threshold S and the number of iterations is shown in Figure 11. It can be seen that at the beginning of iteration, the value of S is larger so that particles have a greater probability of performing DE to increase population diversity. At the later stage of iteration, the value of S is smaller so that the particles can converge as soon as possible and find the best value.

Time segmentation of the phased array radar

The variation curve of inertia weight

The variation curve of learning factors. (a) The variation curve of c₁; (b) The variation curve of c₂

The variation curve of the threshold S

5 SIMULATION

In order to verify the effectiveness of the proposed method, this section provides the results of numerical operation. The radar parameters are designed as shown in Table 1. Assuming that the cross-sectional area of HGV is 0.01 m², and the false alarm probability is 10⁻⁶, the following two benchmark models are selected for comparison. The first model is a radar task scheduling algorithm based on the particle swarm-annealing algorithm proposed by Meng and Tian [32], which is marked as Method 1. The second model is a sensor network deployment optimisation algorithm based on modified differential evolution proposed by Cao et al. [33], which is marked as Method 2.

TABLE 1. The radar parameters

Parameter	Value
Peak power	582 kW
Transmitting antenna gain	38.4 dB
Working frequency	450 MHz
Radar system loss	8 dB
Noise factor	2.9 dB
Noise temperature	290 K
Boltzmann constant	1.38 × 10−23 J/K
Beam width	1°
Scanning range of azimuth angle	(−60°, 60°)
Scanning range of elevation angle	(5°, 85°)

The simulation experiments are carried out in two simulation scenarios. Simulation scenario I is the optimisation of radar search resources under three HGV targets, which is a typical scenario setting. Simulation scenario II is the optimisation of radar search resources under five HGV targets, which is mainly to verify the performance of the proposed method in complex search scenarios.

5.1 Simulation scenario I

Assuming that there are three HGV targets from different directions. The height, velocity, and distance of HGV are estimated by the early warning system. The search airspace parameters of three HGV targets are determined based on the early warning information as shown in Table 2. According to Section 3.1, the corresponding priority levels can be calculated as 0.2670, 0.3147 and 0.4183, respectively. The search resources of the three airspaces are allocated through Method 1, Method 2 and the proposed method. The relationship between the total cumulative detection probability and the search resource occupancy rate is shown in Figure 12. The relationship between the average discovery time and the search resource occupancy rate is shown in Figure 13. As the search resource occupancy rate increases, the total cumulative detection probability gradually increases and the average discovery time gradually decreases. When the search resource occupancy rate is greater than 0.7, the increasing of the total cumulative detection probability and the decreasing of the average discovery time gradually slow down. Figures 12 and 13 show that the proposed method is better than Method 1 and Method 2. Especially when the resource occupancy rate of the search task is low, the advantage of the proposed method is more prominent.

TABLE 2. Parameters of the search airspace in scenario I

Target	Search airspace		Target priority
	Elevation	Azimuth	Distance	Velocity	Height
Target 1	12°–24°	16°–31°	480 km	8 Ma	74 km
Target 2	30°–45°	−30°–−20°	390 km	6.5 Ma	65 km
Target 3	47°–57°	0°–13°	360 km	7 Ma	52 km

Total cumulative detection probability in scenario I

Average discovery time in scenario I

Figure 14a–c shows the detection probability of Method 1, Method 2 and the proposed method for the three targets under different search task resource occupancy rates. It can be found that the detection probability of target 3 is greater than that of target 1 and target 2 under the same search task resource occupancy rate. This is because the target with higher priority obtains relatively more search resources. When the search task resource occupancy rate is 0.1, both the PSO and the proposed method choose to give up the search of target 1 to maximise the detection probability of target 2 and target 3.

Detection probabilities of HGV in scenario I. (a) Detection probability of Method 1; (b) Detection probability of Method 2; (c) Detection probability of the proposed method

In order to analyse the performance of the proposed method in detail, the time resource allocation schemes corresponding to the three algorithms are given when the resource occupancy rate of the search task is 0.1 as shown in Figure 15. It can be seen that the proposed method allocates 63% of time resources to target 3%, 30% of time resources to target 2%, and 7% of time resources to target 1. Such an allocation of time resources can achieve more efficient target detection.

The time resource allocation schemes in scenario I. (a) Time resource allocation schemes of Method 1; (b) Time resource allocation schemes of Method 2; (c) Time resource allocation schemes of the proposed method

The convergence of Method 1, Method 2 and the proposed method is shown in Figure 16, when the search task occupancy rate is 0.1, 0.5 and 1, respectively. It can be found that the convergence speed of the proposed method is always better than those of Method 1 and Method 2. When the search tasks occupancy rate is lower, the convergence speed is slower.

Convergence comparison of different algorithms in scenario I. (a) Comparison of the convergence speed when the search task resource occupancy rate is 0.1; (b) Comparison of the convergence speed when the search task resource occupancy rate is 0.5; (c) Comparison of the convergence speed when the search task resource occupancy rate is 1

5.2 Simulation scenario II

Suppose there are five HGV targets from different directions. The altitude, velocity and distance of HGV are estimated by the early warning system. According to the guidance information, the scope of five search airspaces is determined. The parameters of the search airspace are shown in Table 3. The corresponding priorities of the five HGV targets are 0.1570, 0.1773, 0.2072, 0.2144, and 0.2441, respectively. Method 1, Method 2 and the proposed method are used to optimise the radar search resources. The relationship between the total cumulative detection probability and the search task resource occupancy rate is shown in Figure 17, and the relationship between the average discovery time and the search task resource occupancy rate is shown in Figure 18. It can be seen that the proposed method performs better than the other two algorithms.

TABLE 3. Parameters of the search airspace in scenario II

Target	Search airspace		Target priority
	Elevation	Azimuth	Distance	Velocity	Height
Target 1	20°–30°	10°–25°	600 km	10 Ma	82 km
Target 2	30°–45°	−30°–−20°	460 km	9 Ma	70 km
Target 3	45°–55°	−15°–0°	300 km	7 Ma	56 km
Target 4	55°–69°	21°–32°	330 km	6 Ma	49 km
Target 5	10°–22°	32°–45°	250 km	8 Ma	50 km

Total cumulative detection probability in scenario II

Average discovery time in scenario II

Figure 19 shows the variation of the detection probability with the search task resource occupancy rate. With the decline of the search tasks occupancy rate, the detection probability of target 1 and target 2 decreases most obviously, while the detection probability of target 4 and target 5 decreases more smoothly due to the higher priority.

Cumulative detection probabilities in scenario II. (a) Detection probability of Method 1; (b) Detection probability of Method 2; (c) Detection probability of the proposed method

Figure 20 shows the time allocation schemes of the three algorithms for different targets when the search task occupancy rate is 0.1. It can be seen that the DE has a low detection probability for the target with higher priority, and the PSO emphasises the target with higher priority and ignores target 1, target 2, and target 3. The resource allocation scheme of the proposed method is relatively more reasonable, so that the cumulative detection probability and average discovery time are better than DE and PSO.

The time resource allocation schemes in scenario II. (a) Time resource allocation schemes of Method 1; (b) Time resource allocation schemes of Method 2; (c) Time resource allocation schemes of the proposed method

The convergence of Method 1, Method 2 and the proposed method is shown in Figure 21, when the search task occupancy rate is 0.1, 0.5 and 1, respectively. It can be found that the convergence speed of the algorithms in scenario II is slightly slower than that in scenario I. However, the proposed method still maintains the fastest convergence rate, which is superior to the other two algorithms.

Convergence comparison of different algorithms in scenario II. (a) Comparison of the convergence speed when the search task resource occupancy rate is 0.1; (b) Comparison of the convergence speed when the search task resource occupancy rate is 0.5; (c) Comparison of the convergence speed when the search task resource occupancy rate is 1

6 CONCLUSION

This paper studies the problem of HGV search under the guidance of early warning information. Firstly, we choose the height, velocity and distance of HGV as the priority evaluation indexes, and design the priority quantitative formulae. Secondly, considering the cumulative detection probability, average discovery time and target priority, the multi-objective search optimisation model of HGV is established. Finally, a hybrid optimisation algorithm based on DE and PSO is proposed to solve the multi-objective and multi-constraint problems. The proposed method improves the search efficiency of PSO by designing adaptive weight and learning factors, and improves the diversity of population by introducing the DE algorithm. The performance of the proposed method is verified through experiments in two scenarios. The results show that the proposed method is better than the existing mainstream methods.

ACKNOWLEDGEMENTS

This study was supported by the Military Postgraduate Funding Project of China (No. JY2019B138).

CONFLICT OF INTEREST

The authors declare that they have no conflict of interest to disclose.

REFERENCES

 

 

Jindalee Operational Radar Network: New Growth from Old Roots | IEEE Conference Publication | IEEE Xplore

Jindalee Operational Radar Network: New Growth from Old Roots | IEEE Conference Publication | IEEE Xplore

Abstract:

The Jindalee Operational Radar Network (JORN) is a network of three over-the-horizon radars providing wide area surveillance to the north of Australia at ranges of 1000 to 3000 km. JORN plays a vital role in supporting the Australian Defence Force's air and maritime operations, border protection, disaster relief and search and rescue operations. JORN is currently undergoing a mid-life upgrade known as Defence Project AIR2025 Phase 6, which is intended to extend the operational life of JORN to beyond 2040. This paper provides a brief description of the genesis and evolution of JORN, and a description of the technologies being incorporated into the radar through the JORN Phase 6 project.

Published in: 2022 IEEE Radar Conference (RadarConf22)

Date of Conference: 21-25 March 2022

Date Added to IEEE Xplore: 03 May 2022

ISBN Information:

INSPEC Accession Number: 21777240

DOI: 10.1109/RadarConf2248738.2022.9764214

Publisher: IEEE

Conference Location: New York City, NY, USA

SECTION I.

Introduction

Australia has had almost 40 years of operational experience with skywave Over-The-Horizon Radar (OTHR) systems. The early Australian OTHR experiments and prototype developments were conducted by the Australian Defence Science and Technology Group (DSTG), and their challenges, political frustrations and performance highlights are well described by Sinnott [1]. Through the support of BAE Systems Australia, these early activities culminated in the development of an operational radar in the early 1980s, capable of detecting aircraft and later ships. The radar was named Jindalee, an indigenous word meaning “bare bones”. This prototype radar was located in central Australia near Alice Springs, Northern Territory. This radar has subsequently become known as the Jindalee Facility Alice Springs (JFAS) to distinguish it from the two subsequent Australian OTHR installations.

The JFAS radar incorporated the following features, many of which remain common to current generation skywave OTHRs:

• Propagation using the ionospheric E and F layers.

• Transmit and receive site separation of approximately 100 km to allow transmission of continuous waveforms.

• 1000–3000 km range coverage, and 90 degree azimuthal coverage.

• A frequency agile, high-powered electronically steerable transmit array capable of transmitting several hundred kW in the 5–28 MHz HF frequency band.

• A receive array some 3 km long, featuring 462 pairs of dual monopole antennas, and 32 receivers deployed in a receiver-per-overlapped-subarray configuration.

• Deliberately mismatched receive antennas to balance elevation performance against external noise [2].

• Receive beamforming to produce multiple simultaneous “finger-beams” within the transmit footprint.

• The ability to choose between “full radar” and two “half-radar” modes, balancing coverage against sensitivity [3].

• “Step-scan” operations, allowing observations of multiple geographic areas with a 30 second revisit rate.

• An ionospheric sounder network making real-time measurements to augment a climatological ionospheric model.

• A separate cognitive Frequency Management System (FMS) used to provide propagation advice from the highly variable ionosphere [4].

• A Clear Channel Advice system that provides candidate frequencies which will not interfere with existing users.

• Significant Signal Processing capabilities to extract the weak target signals against the Earth surface backscatter and noise floor, enable track-while-scan, and provide radar-to-ground coordinate registration.

By the mid 1980s the radar was declared ready to be operated by the No. 1 Radar Surveillance Unit (1RSU, renamed the No.1 Remote Sensor Unit in 2015) of the Royal Australian Air Force (RAAF).

JFAS soon proved its worth as an invaluable asset to surveil thousands of square kilometres of ocean directly north of the Australian coast, demonstrating a picture of both air traffic and ocean vessels entering a sector of Australian airspace or coastal waters. Consequently, in 1986 the Australian Government announced the development of two new OTHRs to augment JFAS. The contract tender was won by Telstra, who handled early development until RLM, a joint venture of Lockheed Martin Australia and the Tenix Group, assumed full responsibility for the project in 1997.

By 1995 the specified system operation of the two new OTHRs was outlined by Cameron [3], with full operational capability delivered in 2003. Named the Jindalee Operation Radar Network (JORN), the new radars were separated across the width of Australia. Radar 1 (or R1) was sited at Longreach, Queensland, and Radar 2 (R2) at Laverton, Western Australia. The radar locations were chosen to complement each other with regards to range coverage, ionospheric propagation path diversity (hours and area of coverage), and the observability of targets flying tangential to either radar. The Laverton radar features two perpendicularly oriented “aspects”, providing 180 degree coverage, while the Longreach radar features a single aspect providing 90 degree coverage (see Fig. 1). The JORN radars incorporated an extended frequency range (5–32 MHz), and were equipped with a receiver-per-antenna, providing more flexibility and full control over spatial discrimination in comparison to the receiver-per-overlapped-sub array used in JFAS.

Fig. 1.

Location of JORN radar sites, Battlespace Surveillance Centre (BSC), and radar coverage: Longreach (R1, green); Laverton (R2, blue); Alice Springs (R3, red).

Show All

The JORN capability is managed and operated by the RAAF from the Battlespace Surveillance Centre (BSC) at RAAF Base Edinburgh, South Australia, and is acquired and sustained by the Capability Acquisition and Sustainment Group (CASG).

JORN continues to grow through ongoing innovation, driven by DSTG research, and a phased acquisition approach for transition to capability, driven by industry through phases of “Defence Acquisition Project AIR2025” (hereafter JORN Phase 3–6). Some of these innovations are captured firstly by briefly describing the prior JORN Phase 5 Enhancements in Section II, and then the in-progress JORN Phase 6 Mid-life Upgrade in Section III. Aspects of system development are discussed in Section IV and potential future developments in Section V.

SECTION II.

JORN Phase 5

With the initial JFAS prototype radar development retrospectively referred to as JORN Phase 1 and Phase 2, the design and building of JORN was known as JORN Phase 3 and 4. During the JORN Phase 3–4 period, DSTG and BAE Systems continued to improve the performance and function of JFAS, and in many respects the older JFAS had become the more capable radar. This research and development, and the need to rationalize maintenance and operations across the systems, culminated in a new Defence Project “JORN Phase 5,” which was delivered by BAE Systems and Lockheed Martin Australia. JORN Phase 5 fully incorporated JFAS into JORN as Radar 3 (R3), and introduced JFAS improvements and new capabilities into JORN [5], including:

• Reduction of signal processing losses in Doppler processing [6], [7] achieving twice the coverage capability (based on DATEX [8]).

• Enhanced signal processing capacity to improve range-depth coverage, particularly for the stacked half-radar range mode described in [3].

• Stare mode operations, where the radar observes the same geographic area to provide high revisit rates, allowing improved measurement of manoeuvring targets.

• A cognitive signal processing suite incorporating new advanced signal processing capabilities [9]–​[13].

• Two-dimensional Numerical Ray Tracing to improve radar-to-ground coordinate registration accuracy [14].

JORN Phase 5 also integrated JORN and JFAS human-machine interfaces (HMIs) to provide a consistent look-and-feel for operators and reduce training overheads.

SECTION III.

JORN Phase 6

During the JORN Phase 5 timeframe, the evolving Australian Defence capability needs and gaps were once more reviewed, with a determination that JORN was a vital asset fulfilling a long-term wide-area aircraft and surface vessel surveillance role. The in-progress acquisition project “JORN Phase 6” (Mid-life Upgrade) was created with objectives to extend JORN life-of-type to beyond 2040, together with an improvement in aircraft detection sensitivity based on DSTG R&D performed during the JORN Phase 5 period. The JORN Phase 6 project also needed to address obsolescence issues and component commonality across the three radar sites (noting some JORN components dated back to the original JFAS operational demonstrator of the mid 1980s).

The JORN Phase 6 upgrade encompasses all system components except the antennas and cabling of the radars and ionospheric sounders. The extant FMS receive array, cabling and processing will also be maintained to continue the long-term synoptic FMS data collection [15], which for JFAS now extends over three solar cycles. The following subsections describe the most significant system component upgrades and resulting capability enhancements.

A. Sampling Bandwidth

The current JORN receivers are of a heterodyne design, with analog narrowband filtering of the received signal prior to digitization. The development of receivers capable of high speed sampling with sufficient dynamic range and linearity enables the replacement of heterodyne receivers with broadband devices that digitize the entire HF bandwidth, relying on digital down-conversion techniques to extract the bandwidths of interest.

The digitization of the entire HF band greatly increases the utility of the radar system, since there is no longer the need to tune to a particular carrier frequency - because multiple down-conversions can be performed on the same data stream, and multiple bandwidths can be monitored concurrently using the same receive aperture. Accordingly, the Australian HF community refers to this capability as “Common Aperture” operation.

Common Aperture operation allows considerably increased functionality compared to the extant JORN radars, allowing simultaneous use of the full main receiver array for:

• “Half-radar transmit” observations, reducing the sensitivity loss compared to “full radar” observations from the current 9 dB to 6 dB, a 3 dB improvement.

• Passive and active channel evaluation, whereby candidate frequency channels can be evaluated using the full main receiver array gain and the operational signal processing algorithms. Passive channel evaluation assesses channel noise characteristics, while active channel evaluation assesses clutter characteristics using FMS “mini-radar” transmissions.

• Potential bistatic operation, through passive reception of transmissions from other JORN radars.

• High azimuth resolution FMS operations, thereby enhancing environmental condition assessment compared to the extant small FMS receive array.

• Real-time diagnostics by using frequencies within the three-octave 5-45 MHz receiver bandwidth, but outside the bandwidths used by the radar at the time.

Common Aperture FMS backscatter sounder ionograms allow improved parameter advice and ionospheric modeling, while the enhanced FMS mini-radar observations allow the measurement of land-sea maps [16], aiding radar-to-ground coordinate registration.

Despite its many advantages, Common Aperture operation is difficult to implement in the HF large signal environment since any out-of-band signals are no longer rejected by analog filters. This in turn places exceptionally high-performance requirements on the receivers to ensure that they can operate in such a wide spectrum environment.

As so-called Government Furnished Material, DSTG delivered a Common Aperture receiver that broke the nexus between noise figure and linearity. Using a novel architecture, BAE Systems further developed the JORN Phase 6 Common Aperture receivers to exceed the specification. With the need to produce several thousand receivers over all three radars, a large emphasis was placed on manufacturability.

B. Noise Figure

The HF spectrum in which an OTHR operates is congested and inherently noisy due to anthropogenic, atmospheric (e.g. lightning), solar and galactic noise [17], [18]. This external noise excludes internal instrumentation noise and strong signals from users (such as communications, broadcasters and radars), and is often referred to as “ambient” or “background” noise. Using the aforementioned long-term JFAS synoptic database [15], a Design Noise Reference (DNR) was defined for JORN, with the JORN system required to be “externally noise limited” - i.e. exhibit system noise that is never greater than the external environment. The DNR is based on the knowledge that the optimal frequency for an OTHR is dependent on the time of day, with lower frequencies predominant during night-time operation due to the lower ionospheric F2-layer electron densities. Coincidentally, higher external noise levels are observed at night due to the collapse of the ionospheric D-layer, which during the day causes non-deviative absorption of HF radiowave signals [19]. This is the principle behind the deliberately mismatched receive antenna [2], which also allows the cost-effective preservation of elevation performance.

Since the total system noise is the sum of external and internal noise, and the “externally noise limited” specification requires only that internal noise be no greater than external noise, the total system noise is on occasion doubled. Thus for JORN Phase 6 the definition has been refined to produce a receiver design that features an exceptionally low noise figure over the entire dynamic range over which the receiver will be required to operate. It is expected that the reduction in total system noise will increase detection of low Radar Cross-Section aircraft, as well as simplifying the gain management algorithms required to remain externally noise limited over the full dynamic range.

C. Large Signal Environment

A demanding problem when designing and testing an OTHR is that the ionospheric conditions vary according to diurnal, seasonal and solar sunspot cycles, and geographic location. Each of these cycles influence the performance requirements over which an OTHR receiver must operate, creating large variations in the dynamic range that is necessary to endure the incidence and intensity of the full HF spectrum. Although performance verification over a diurnal cycle is possible, testing over an annual cycle becomes difficult, and testing over a solar cycle is intractable for any sensible test program. Hence for design purposes it was necessary to predict expected dynamic range requirements for the receiver.

JORN Phase 6 has addressed this problem by using the same multi-decade synoptic database used to determine the noise environment discussed above. The database also contains observations of the strong signal spectrum recorded at 2 kHz resolution. Fig. 2 shows the probability distribution of the total signal power at the receiver's input (blue) over a full solar cycle. To determine dynamic range requirements, the expected noise power at the receiver input must be estimated. This may be derived from the external noise energy database at the radar operational frequency. Assuming the radar may operate on any clear channel across the HF spectrum, we examine the full HF external noise spectra that were concurrent to the occurrence of each signal power level. Overlaid in red is the lower ventile of the external noise energy (shown as “external noise factor” with reference to −204 dBJ). Due to small sample statistics, the ventile statistic has high variance for the low occurrence signal power levels >−30 dBm and <−60 dBm. We note other quantiles may be used to inform design and that the lower ventile is used here only for indicative purposes. Performance requirements on receiver design clearly depend on location and environmental characterization.

Fig. 2.

Example of measurements of the probability distribution of the large signal and noise environment over a solar cycle at a JORN site.

Show All

To verify receiver performance over the full solar cycle, DSTG developed a Receiver Evaluation Environment that allows the reconstruction of an informed synthetic RF environment over the full operating range of the receivers (see Fig. 3). Due to linearity limitations of commercially available digital-to-analog devices compared to analog-to-digital, multiple waveform generators are used to reconstruct the full receiver bandwidth. Thus, the full performance envelope of the receivers was bench tested during hardware development. By adding other synthetic signals, secondary performance aspects such as the receiver's intermodulation and cross modulation performance were also measured.

D. Timing

JORN Phase 6 incorporates custom technology based on cryogenically cooled Sapphire Crystal Oscillators (CSOs) (see Fig. 4) developed by Australian company QuantX Labs [20], [21]. CSOs have been demonstrated to provide long-term timing stability and levels of phase noise traditionally seen only in national standards laboratory settings. This high-fidelity timing and frequency source removes prior limitations to other dependent transmitter and receiver system components.

E. Waveform Generation

Like the receiver, the JORN Phase 6 waveform generation is based on direct digital conversion, discarding traditional fixed and variable frequency oscillators. The direct digital approach is enabling exceptional levels of signal purity.

F. High-Power Amplifier Replacement

Although not part of the original JORN Phase 6 upgrade proposal, increasing maintenance and sustainment costs and the recent development of high-fidelity high-power amplifiers (HPAs) by Australian company Schach RF have motivated the decision to replace the HPAs at all JORN sites.

G. Wide Transmit Capability

In environments where the radar is clutter limited (e.g. low radial velocity surface vessel detection against a sea clutter background) the transmit system may have excess power available. This power may be exploited by adapting the transmitter phased array weights to spread the transmitter footprint over a wider azimuthal span, without losing any detection performance, since the radar is not noise limited. A consequence is of course a required increase in receive site information and communications technology (ICT) capacity to process additional receive finger-beams.

Fig. 3.

Receiver evaluation environment used to generate a synthetic HF environment to measure receiver intermodulation and cross modulation performance. The racks contain 32 waveform generators, which produce signals spanning the 5 to 32 MHz operating range of the JORN radars.

Show All

Fig. 4.

Laboratory testing of the cryogenically cooled sapphire crystal oscillators. Right: Sapphire crystal sample.

Show All

H. Signal Processing

Computer power has grown exponentially since the original JORN specification and becomes an enabler for significant signal processing improvements. As an immobile land-based asset, OTHR has no significant weight, space or power constraints. Moreover, as a wide-area early warning capability for air and maritime surveillance there are low demands in terms of processing latency.

The signal processing components that will be incorporated into JORN Phase 6 include:

• Simultaneous processing of radar, active, and passive channel evaluator dwells, including half- and wide-transmit radar dwells.

• A reduction of conventional range and beam tapering losses through auto-regressive bandwidth [22] and antenna array extrapolation, thereby enhancing range and azimuth resolution.

• Improved tracking for stare-mode operations.

• Three-dimensional Numerical Ray Tracing through the ionosphere, thereby improving the modelling of geomagnetic-field induced “out-of-plane” ray-path deviations [23].

• Increasing the number of ionospheric propagation modes ray-traced through the ionosphere, including high-ray modes [24].

I. Ionospheric Sounders and Transponder Upgrades

JORN Phase 6 will replace the transmitter and receiver hardware of all ionospheric sounders [25]–​[28] and transponders. Multichannel receiver technology has enabled a significant increase in oblique incidence pathways [27], and additional sounder sites will extend sounder geographical coverage. This will increase the real-time ionospheric model (RTIM) fidelity and improve radar-to-ground coordinate registration accuracy.

J. Open Architecture

Like many Defence Projects of the 1990s, JORN was developed using the mandated software language Ada. The Australian experience with Ada realised the need to enable more efficient software control and modification. For this reason the JORN Phase 6 software is being developed:

• In C++, allowing the project to draw from a far greater pool of qualified software engineers.

• Using a Data Distribution Service (DDS) to provide a modular and scalable framework of open system architecture to allow easier addition of software modules, and increase the reliability and performance of the radar.

• Reversing the previous Ada mindset whereby an operator request was denied unless explicitly allowed, to one where, unless it runs the risk of safety or damage to equipment, the operator's request will be honoured.

K. Enhanced Radar Site Commonality

JORN Phase 6 introduces significant JFAS technical and facilities upgrades designed to enhance radar commonality introduced in JORN Phase 5. The former includes the upgrade of the receiving system from a receiver-per-overlapped-subarray to a receiver-per-antenna system. The latter includes upgrades to the operational and accommodation facilities to a similar standard as the Longreach and Laverton radars, allowing fly-in fly-out crewing in line with those radars.

L. BSC Refresh

The Battle space Surveillance Centre will undergo a complete ICT refresh. This will incorporate new operator interfaces and improved versions of the extant displays to enhance ease of use.

SECTION IV.

System Development

JORN Phase 6 System Design has adopted an “edges-in” development approach, developing “top-down” specifications concurrently with “bottom-up” hardware development. The simultaneous development of system requirements and hardware implementability has uncovered many details in the system design that would otherwise not have been uncovered until the testing phase. Like almost any large project the start has been slower than expected, however it is believed efficiencies will be realized as lessons are learnt early.

By the time that the Critical Design Review is scheduled, a comprehensive set of test results based on real-world data will be available.

SECTION V.

Future Development

The Australian Government has recently announced the expansion of JORN to provide wide area surveillance of Australia's eastern approaches [29]. Although timescales have not been announced, multiple location and configuration options are currently under consideration by DSTG, each with its own associated benefit and cost. Modeling studies using the techniques described in [30], [31], are currently underway to inform the final design decision.

The continual funding and progression of Australian OTHR innovations have and are being captured within JORN through its acquisition phases. Continued R&D looks to further evolve and expand capability of the JORN system and may include:

• Antenna design improvements, such as “grail” transmit antennas (see Fig. 5), which may reduce aeolian-induced phase noise associated with log-periodic dipole curtain arrays [2], and thereby improve the detection of slow-moving targets.

• Regular overdense sparse receiving arrays (ROSA), providing enhanced system sensitivity through improved rejection of anthropogenic and galactic noise [32], [33].

• Mode Selective Radar, employing multiple-input multiple-output (MIMO) techniques to allow the selection of individual ionospheric propagation modes, thereby improving target detection in and around disturbed clutter, and reducing mode association problems to improve tracking performance [34].

In addition to skywave OTHR operation, HF radars can operate in line-of-sight (LOS), SkyLOS (skywave transmission, LOS reception), and multi-static combinations of these modes. HF and lower VHF LOS radars are ideally suited to high-speed weapon (HSW) detection as the sizes of HSWs are comparable to the radar wavelength, placing them in the resonant scatter region where RCS enhancements can be observed at certain aspect angles [35]. HF and lower VHF LOS radars have demonstrated detection of resident space objects in low earth orbit [36]–​[37], given the development of signal processing capabilities that tolerate the high speed (up to 8 kms⁻¹) and acceleration that are required for HSW detection. SkyLOS radar provides a low-cost means of augmenting a skywave over-the horizon radar, obviating the need for ionospheric measurement and modeling [38]. DSTG continues to develop HF LOS and SkyLOS mode capabilities for HSW detection aimed at future inclusion into JORN Phase 6 or subsequent JORN upgrades.

Another area of active DSTG research informing future development is artificial intelligence (AI) (e.g. [39]), primarily motivated by the need to increase automation to reduce the workload on JORN operators associated with a “data deluge” created with the JORN Phase 6 enhanced signal processing capabilities. The JORN Phase 6 open system architecture will allow faster and improved incorporation of new software capabilities such as AI, allowing rapid provision of these capabilities to operators.

SECTION VI.

Conclusion

The Jindalee Operational Radar Network had its foundations as a long-range wide-area surveillance operational demonstrator in the 1980s. Australia has enacted a sustained R&D program to provide innovations that have significantly improved and expanded Australia's sovereign OTHR capability. This transition of innovation to capability continues with the “AIR2025 Phase 6 JORN Mid-life Upgrade”. JORN Phase 6 is aimed at establishing life-of-type extension beyond 2040, and will deliver substantial capability enhancements by taking advantage of the significant improvements in digital processing capabilities.

Fig. 5.

Grail transmit antenna array installed at Laverton, Western Australia

Show All

The “Common Aperture” receiver, enabled by full HF band direct-digital receiver technology, allows multiple simultaneous reception channels for multiple functions, including support for cognitive and adaptive radar. The reduction in hardware required has allowed JORN Phase 6 to concentrate effort on a relatively small number of components, allowing those components to achieve exceptional performance.

ACKNOWLEDGMENT

The authors thank Mr Brett Northey and Dr Trevor Harris for figure contributions, Dr Gordon Frazer for photographs, and colleagues from BAE Systems and ISSD, DSTG for their insightful comments.