In this article, we describe the role of time-frequency distributions (TFDs) in array processing. We particularly focus on quadratic TFDs (QTFDs). We demonstrate how these distributions can be properly integrated with the spatial dimension to enhance individual source signal recovery and angular estimation. The framework that enables such integration is referred to as spatial TFD (STFD). We present the important milestones of STFDs that have been reached during the last 15 years. Most importantly, we show that array processing creates new perspectives of QTFDs and defines new roles to the autoterms and cross-terms in both problem formulation and solution. Multisensor configurations, in essence, establish a different paradigm and introduces new challenges that did not exist in a single-sensor time-frequency distribution.

Source Separation and Localization Using Time-Frequency Distributions: An Overview

Source separation and localization using time-frequency distributions

Spatial time-frequency DOA estimation based on joint diagonalization using Jacobi rotation

Direction-of-arrival (DOA) estimation is one of the classical problems in the realm of array signal processing, and its applications include such areas as navigation, tracking, communication. Conventional approaches for DOA estimation make utilize of an array of pressure sensors, which have been distributed at different locations. This paper deals with measurements obtained by an acoustical vector-sensor array and develops a new approach which exploits the spatial time-frequency information of the underlying vector-sensor array signal to achieve better DOA estimation performance even in a noisy and coherent environment. Numerical simulations are conducted to quantify the performance and evaluate the theoretical results developed in this paper. Concretely speaking, Simulation results show that under several frequently encountered scenarios, such as a single source and two closely spaced coherent sources, our proposed approach can achieve significant performance improvements. Furthermore, the superior root mean square error (RMSE) performance of our proposed algorithm against noise is demonstrated via Monte-Carlo numerical simulations.

Vector-Sensor Array DOA Estimation Based on Spatial Time-Frequency Distribution

This paper addresses the design of a signal pre-processor dedicated to passive acoustic tomography. Blind processor of channel's spatial response is proposed and adapted with wide band transient signals such as when marine mammals vocalize. Full validation of this processor is performed on realistic synthetic data.

https://ieeexplore.ieee.org/iel5/10104/32403/01513179.pdf

Contributions to passive acoustic oceanic tomography - inversion algorithms based on time frequency space representation for multiple hydrophones processing

Acoustic tomography is a way to produce a fast, accurate and cheap monitoring of water mass. This monitoring requires an inversion procedure made of two steps. The first one is to estimate acoustic properties (such as the sound speed profile of the water column) from the measurement of a propagated known acoustic waveform between fixed sources and receivers. Then a second step consists in inferring some physical ocean parameters (temperature, bottom nature) from these previous estimated acoustic characteristics. Large scales deep water and small scales shallow water configurations have been successfully studied and associated to matched delay, matched field and matched impulse response inversion processing.

/pdf/contributions-to-passive-acoustic-oceanic-tomography-ii-2mc2cijtw8.pdf

E.B. Mansour

Papers

Contributions to passive acoustic oceanic tomography - inversion algorithms based on time frequency space representation for multiple hydrophones processing

Contributions to passive acoustic oceanic tomography. II. Extraction of propagation features from single hydrophone measurement