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