Peak Extraction Passive Source Localization Using a Single Hydrophone in Shallow Water

In this work, we use the acoustic ray model to study passive source localization problems using a single hydrophone for shallow water waveguides. It has been found that conventional matched field processing, which requires a multi-hydrophone array, is no longer effective. In this paper, we introduce matched feature localization to achieve passive source localization using a single hydrophone. The eigenray that contains the arrival structure is the core of the acoustic ray model; however, it is difficult to estimate the relative arrival delay of the eigenray for passive localization, especially in shallow water. Therefore, we propose a novel peak extraction passive source localization method based on the autocorrelation function, which does not require estimation of the relative arrival delay of the eigenray. According to a computer simulation, the proposed method can achieve good performance using only one hydrophone. Further, its performance is validated using wideband data collected on a vertical line array during the Shallow Water 2006 experiment.

[1]  Mark Porter,et al.  The KRAKEN normal mode program , 1992 .

[2]  Mohsen Guizani,et al.  Prediction-Based Delay Optimization Data Collection Algorithm for Underwater Acoustic Sensor Networks , 2019, IEEE Transactions on Vehicular Technology.

[3]  L. Neil Frazer,et al.  Single-hydrophone localization , 1990 .

[4]  Hui Li,et al.  Moving source localization with a single hydrophone using multipath time delays in the deep ocean. , 2014, The Journal of the Acoustical Society of America.

[5]  S. Glenn,et al.  Shallow Water '06: A Joint Acoustic Propagation/Nonlinear Internal Wave Physics Experiment , 2007 .

[6]  Y. Stephan,et al.  Single hydrophone source localization , 2000, IEEE Journal of Oceanic Engineering.

[7]  Lillykutty Jacob,et al.  Localization Using Ray Tracing for Underwater Acoustic Sensor Networks , 2010, IEEE Communications Letters.

[8]  W A Kuperman,et al.  Passive acoustic tracking using a library of nearby sources of opportunity. , 2018, The Journal of the Acoustical Society of America.

[9]  Hangfang Zhao,et al.  Single Hydrophone Underwater Source Hyperbolic Passive Localization using Multipath Arrivals in Shallow Water , 2018, OCEANS 2018 MTS/IEEE Charleston.

[10]  Guangjie Han,et al.  A Stratification-Based Data Collection Scheme in Underwater Acoustic Sensor Networks , 2018, IEEE Transactions on Vehicular Technology.

[11]  Steven Finette,et al.  Stochastic matched-field localization of an acoustic source based on principles of Riemannian geometry. , 2018, The Journal of the Acoustical Society of America.

[12]  Gang Qiao,et al.  Optimal Beamforming Design for Underwater Acoustic Communication With Multiple Unsteady Sub-Gaussian Interferers , 2019, IEEE Transactions on Vehicular Technology.

[13]  Kunde Yang,et al.  Estimating marine sediment attenuation at low frequency with a vertical line array. , 2009, The Journal of the Acoustical Society of America.

[14]  Jingjing Wang,et al.  Underwater Acoustic Sparse Channel Estimation Based on DW-SACoSaMP Reconstruction Algorithm , 2019, IEEE Communications Letters.

[15]  T. Barnard,et al.  Ray Theory Results and Ray Wavefront Diagrams for the Hyperbolic Cosine Propagation Sound-Speed Profile , 2015, IEEE Journal of Oceanic Engineering.

[16]  W. Kuperman,et al.  Estimating relative channel impulse responses from ships of opportunity in a shallow water environment. , 2018, The Journal of the Acoustical Society of America.

[17]  A. Tolstoy,et al.  Matched Field Processing for Underwater Acoustics , 1992 .

[18]  Colin W. Jemmott,et al.  Single-Hydrophone Model-Based Passive Sonar Source Depth Classification , 2011, IEEE Journal of Oceanic Engineering.

[19]  Jin-bao Weng,et al.  Experimental Demonstration of Shadow Zone Localization Using Deep Water Interference Patterns Measured by a Single Hydrophone , 2018, IEEE Journal of Oceanic Engineering.

[20]  Kang Zhang,et al.  Applying improved particle swarm optimization for dynamic service composition focusing on quality of service evaluations under hybrid networks , 2018, Int. J. Distributed Sens. Networks.

[21]  Lan Zhang,et al.  Performance Evaluation of Acoustic Model-Based Blind Channel Estimation in Ocean Waveguides , 2018, IEEE Access.

[22]  Lei Yan,et al.  A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks , 2017, Sensors.

[23]  Peter Gerstoft,et al.  Adaptive and compressive matched field processing. , 2017, The Journal of the Acoustical Society of America.

[24]  I-Tai Lu,et al.  A time‐domain backpropagating ray technique for source localization , 1994 .

[25]  P. Gerstoft,et al.  Short range travel time geoacoustic inversion with vertical line array. , 2008, The Journal of the Acoustical Society of America.

[26]  Kiseon Kim,et al.  RAR: Real-Time Acoustic Ranging in Underwater Sensor Networks , 2017, IEEE Communications Letters.

[27]  Augusto Sarti,et al.  Extraction of Acoustic Sources Through the Processing of Sound Field Maps in the Ray Space , 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[28]  Npu Xi,et al.  Theory of passive localization for underwater sources based on acoustic ray modeling , 1997 .

[29]  K. M. Mridula,et al.  Sound velocity profile estimation using ray tracing and nature inspired meta-heuristic algorithms in underwater sensor networks , 2019, IET Commun..

[30]  Arthur B. Baggeroer,et al.  An overview of matched field methods in ocean acoustics , 1993 .

[31]  Mohsen Guizani,et al.  An AUV Location Prediction-Based Data Collection Scheme for Underwater Wireless Sensor Networks , 2019, IEEE Transactions on Vehicular Technology.

[32]  Christophe Laplanche A Bayesian method to estimate the depth and the range of phonating sperm whales using a single hydrophone. , 2007, The Journal of the Acoustical Society of America.