Localization in Underwater Dispersive Channels Using the Time-Frequency-Phase Continuity of Signals

Time-frequency representations constitute the main tool for analysis of nonstationary signals arising in real-life systems. One of the most challenging applications of time-frequency representations deal with the analysis of the underwater acoustic signals. Recently, the interest for dispersive channels increased mainly due to the presence of the wide band nonlinear effect at very low frequencies. That is, if we intend to establish an underwater communication link at low frequencies, the dispersion phenomenon has to be taken into account. In such conditions, the application of the conventional time-frequency tools could be a difficult task, mainly because of the nonlinearity and the closeness of the time-frequency components of the impulse response. Moreover, the channel being unknown, any assumption about the instantaneous frequency laws characterizing the channel could not be approximate. In this paper, we introduce a new time-frequency analysis tool that aims to extract the time-frequency components of the channel impulse response. The main feature of this technique is the joint use of time-amplitude, time-frequency, and time-phase information. Tests provided for realistic scenarios and real data illustrate the potential and the benefits of the proposed approach.

[1]  Benjamin Friedlander,et al.  The discrete polynomial-phase transform , 1995, IEEE Trans. Signal Process..

[2]  Evan K. Westwood,et al.  A normal mode model for acousto‐elastic ocean environments , 1996 .

[3]  Gordon R. Ebbeson,et al.  Right whale localisation using a downhill simplex inversion scheme , 2004 .

[4]  Anna Scaglione,et al.  Product high-order ambiguity function for multicomponent polynomial-phase signal modeling , 1998, IEEE Trans. Signal Process..

[5]  Gerald Matz,et al.  Time-frequency formulation, design, and implementation of time-varying optimal filters for signal estimation , 2000, IEEE Trans. Signal Process..

[6]  C. Clay,et al.  Ocean Acoustics: Theory and Experiment in Underwater Sound , 1987 .

[7]  A. Papandreou,et al.  The use of hyperbolic time-frequency representations for optimum detection and parameter estimation of hyperbolic chirps , 1994, Proceedings of IEEE-SP International Symposium on Time- Frequency and Time-Scale Analysis.

[8]  Boaz Porat,et al.  Estimation and classification of polynomial-phase signals , 1991, IEEE Trans. Inf. Theory.

[9]  Ljubisa Stankovic,et al.  On the realization of the polynomial Wigner-Ville distribution for multicomponent signals , 1998, IEEE Signal Processing Letters.

[10]  Miller,et al.  Tomographic inversion for sediment parameters in shallow water , 2000, The Journal of the Acoustical Society of America.

[11]  Antonia Papandreou-Suppappola,et al.  The power classes-quadratic time-frequency representations with scale covariance and dispersive time-shift covariance , 1999, IEEE Trans. Signal Process..

[12]  Antonia Papandreou-Suppappola,et al.  Wideband Weyl symbols for dispersive time-varying processing of systems and random signals , 2002, IEEE Trans. Signal Process..

[13]  Cornel Ioana,et al.  A Time-Frequency Characterization Framework for Signals Issued from Underwater Dispersive Environments , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[14]  Patrick J. Loughlin,et al.  Multiple window time-varying spectral analysis , 2001, IEEE Trans. Signal Process..

[15]  Cornel Ioana,et al.  Toward The Use Of The Time-Warping Principle With Discrete-Time Sequences , 2007, J. Comput..

[16]  Boualem Boashash,et al.  Polynomial Wigner-Ville distributions and their relationship to time-varying higher order spectra , 1994, IEEE Trans. Signal Process..

[17]  B. Friedlander,et al.  Multicomponent signal analysis using the polynomial-phase transform , 1996, IEEE Transactions on Aerospace and Electronic Systems.

[18]  Braham Barkat,et al.  Instantaneous frequency estimation of polynomial FM signals using the peak of the PWVD: statistical performance in the presence of additive gaussian noise , 1999, IEEE Trans. Signal Process..

[19]  Michael I. Taroudakis,et al.  On the use of matched-field processing and hybrid algorithms for vertical slice tomography , 1997 .

[20]  Imre Csiszár,et al.  Information projections revisited , 2000, IEEE Trans. Inf. Theory.

[21]  Milica Stojanovic,et al.  Acoustic (Underwater) Communications , 2003 .

[22]  Antonia Papandreou-Suppappola,et al.  Discrete Time-Frequency Characterizations of Dispersive Linear Time-Varying Systems , 2007, IEEE Transactions on Signal Processing.

[23]  Cedric Gervaise,et al.  Robust 2D localization of low-frequency calls in shallow waters using modal propagation modelling , 2008 .

[24]  Akbar M. Sayeed,et al.  Communication over Multipath Fading Channels: A Time-Frequency Perspective , 1997 .

[25]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[26]  Antonia Papandreou-Suppappola,et al.  Discrete time-scale characterization of wideband time-varying systems , 2006, IEEE Transactions on Signal Processing.