Multipath time-delay estimation via the EM algorithm

We consider the application of the EM algorithm to the multipath time delay estimation problem. The algorithm is developed for the case of deterministic (known) signals, as well as for the case of wide-sense stationary Gaussian signals.Funding was provided by the Naval Underwater Systems Center under contract No. N00014-80-C-0381

Multipath Time-delay Estimation with Impulsive Noise via Bayesian Compressive Sensing

Multipath time-delay estimation is commonly encountered in radar and sonar signal processing. In some real-life environments, impulse noise is ubiquitous and significantly degrades estimation performance. Here, we propose a Bayesian approach to tailor the Bayesian Compressive Sensing (BCS) to mitigate impulsive noises. In particular, a heavy-tail Laplacian distribution is used as a statistical model for impulse noise, while Laplacian prior is used for sparse multipath modeling. The Bayesian learning problem contains hyperparameters learning and parameter estimation, solved under the BCS inference framework. The performance of our proposed method is compared with benchmark methods, including compressive sensing (CS), BCS, and Laplacian-prior BCS (L-BCS). The simulation results show that our proposed method can estimate the multipath parameters more accurately and have a lower root mean squared estimation error (RMSE) in intensely impulsive noise

Underwater target detection using multichannel subband adaptive filtering and high-order correlation schemes

Includes bibliographical references.In this paper, new pre- and post-processing schemes are developed to process shallow-water sonar data to improve the accuracy of target detection. A multichannel subband adaptive filtering is applied to preprocess the data in order to isolate the potential target returns from the acoustic backscattered signals and improve the signal-to-reverberation ratio. This is done by estimating the time delays associated with the reflections in different subbands. The preprocessed results are then beamformed to generate an image for each ping of the sonar. The testing results on both the simulated and real data revealed the efficiency of this scheme in time-delay estimation and its capability in removing most of the competing reverberations and noise. To improve detection rate while significantly minimizing the incident of false detections, a high-order correlation (HOC) method for postprocessing the beamformed images is then developed. This method determines the consistency in occurrence of the target returns in several consecutive pings. The application of the HOC process to the real beamformed sonar data showed the ability of this method for removing the clutter and at the same time boosting the target returns in several consecutive pings. The algorithm is simple, fast, and easy to implement.This work was supported by the Office of Naval Research (ONR 321TS) under Contract N61331-94-K-0018

OMP-type Algorithm with Structured Sparsity Patterns for Multipath Radar Signals

A transmitted, unknown radar signal is observed at the receiver through more than one path in additive noise. The aim is to recover the waveform of the intercepted signal and to simultaneously estimate the direction of arrival (DOA). We propose an approach exploiting the parsimonious time-frequency representation of the signal by applying a new OMP-type algorithm for structured sparsity patterns. An important issue is the scalability of the proposed algorithm since high-dimensional models shall be used for radar signals. Monte-Carlo simulations for modulated signals illustrate the good performance of the method even for low signal-to-noise ratios and a gain of 20 dB for the DOA estimation compared to some elementary method

Close range ship noise cross correlations with a vector sensor in view of geoacoustic inversion

Distant ship noise has been utilized for geoacoustic inversion and ocean monitoring for many years. In a shallow water experiment, Makai 2005, a 4-element acoustic vector sensor array was deployed at the stern of the research vessel R/V Kilo Moana. The recorded engine noise of R/V Kilo Moana during its dynamic positioning was analyzed by the DEMON (Detection of Envelope Modulation on Noise) method. The strongest modulation frequency band of the ship noise was found by a group of band-pass filters for further data processing. Multipath arrivals in the vertical particle velocity have higher signal-to-noise ratios than those in the horizontal particle velocities because of steep arrival directions. By exploiting this advantage, the cross-correlation of broadband ship noise between the pressure and the vertical particle velocity can be used for multipath information exploration. Since ship noise is often characterized as continuous broadband noise plus strong tonal noise, the cross-correlation of tonal noise would dominate that of broadband noise, and consequently cover the multipath arrival pattern. Therefore, spectral weighting functions are applied in order to reduce the noise contamination and ensure sharp multipath peaks in the cross-correlation. For engine noise emitted by the dynamically positioned ship, a short correlation time of 0.4s was used in order to keep the time delay fluctuation details of multipath arrivals. Clear multiple arrivals are seen in the cross-correlation of different arrivals, and verified by the ray tracing program TRACEO. The results demonstrate the potential of only one acoustic vector sensor in applications of source localization and geoacoustic inversion

Super-resolution time delay estimation in multipath environments

The problem of super-resolution time delay estimation in multipath environments is addressed in this paper. Two cases, active and passive systems, are considered. The time delay estimation is first converted into a sinusoidal parameter estimation problem. Then the sinusoidal parameters are estimated by generalizing the Multiple Signal Classification (MUSIC) algorithm for single-experiment data. The proposed method, referred to as the MUSIC-type algorithm, approximates the Cramer-Rao bound (CRB) in terms of the mean square errors (MSEs) for different signal-to-noise ratios (SNRs) and separations of muitipath components. Simulation results show that the MUSIC-type algorithm performs better than the classical correlation approach and the conventional MUSIC method for the closely spaced components in muitipath environments.published_or_final_versio