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

TL;DR

This paper introduces a Bayesian compressive sensing method tailored for multipath time-delay estimation in radar and sonar, effectively mitigating impulsive noise and improving accuracy over existing techniques.

Contribution

A novel Bayesian approach using heavy-tail Laplacian noise modeling enhances multipath delay estimation under impulsive noise conditions.

Findings

Outperforms benchmark methods in noisy environments

Achieves lower RMSE in impulsive noise scenarios

Provides more accurate multipath parameter estimates

Abstract

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.