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LOS and NLOS Classification for Underwater Acoustic Localization

Published: 01 February 2014 Publication History

Abstract

The low sound speed in water makes propagation delay (PD)-based range estimation attractive for underwater acoustic localization (UWAL). However, due to the long channel impulse response and the existence of reflectors, PD-based UWAL suffers from significant degradation when PD measurements of nonline-of-sight (NLOS) communication links are falsely identified as line-of-sight (LOS). In this paper, we utilize expected variation of PD measurements due to mobility of nodes and present an algorithm to classify the former into LOS and NLOS links. First, by comparing signal strength-based and PD-based range measurements, we identify object-related NLOS (ONLOS) links, where signals are reflected from objects with high reflection loss, for example, ships hull, docks, rocks and so on. In the second step, excluding PD measurements related to ONLOS links, we use a constrained expectation-maximization algorithm to classify PD measurements into two classes: LOS and sea-related NLOS (SNLOS), and to estimate the statistical parameters of each class. Since our classifier relies on models for the underwater acoustic channel, which are often simplified, alongside simulation results, we validate the performance of our classifier based on measurements from three sea trials. Both our simulation and sea trial results demonstrate a high detection rate of ONLOS links, and accurate classification of PD measurements into LOS and SNLOS.

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Harekrishna Misra

While wireless communication has many advantages and has become a general-purpose technology today, it is generally challenged by the medium (ionosphere, water, and so on). This paper discusses important work, showcasing methods for addressing challenges related to underwater wireless communication with line-of-sight (LOS) and nonline-of-sight (NLOS) classification approaches. This work has primarily adopted a system development life cycle with the support of conceptualization, algorithms, implementation with due cognizance of constraints and complexities, experimentation, simulations, and performance evaluation. However, I was specifically looking for a benchmarking of the algorithm adopted. This could have been done through the related work discussed in the paper and how these related works have used algorithms. I was also expecting the influence of diffraction, reflections, and penetration by taking into account the effects of various noises and delay that can emerge through this type of propagation. The many abbreviations and notations throughout the paper make it difficult to comprehend the scenario. In order to tackle this challenge, it would have been better to introduce discussions on a scenario that compares the presented performance vis-à-vis the approaches available with parametric evaluations. Such discussions would have certainly improved the work presented in the paper. The strength of the paper, however, is in the form of sea trials. Through simulation and experimentation, this method will certainly attract the attention of academics, researchers, and practitioners interested in wireless communication. Online Computing Reviews Service

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Published In

cover image IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing  Volume 13, Issue 2
February 2014
234 pages

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 February 2014

Author Tags

  1. Underwater acoustic localization (UWAL)
  2. line-of-sight
  3. nonline-of-sight
  4. time-of-arrival classification

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