Block Sparse Bayesian Learning Using Weighted Laplace Prior for Super Resolution Estimation of Multi Path Parameters
In order to overcome the conventional methods of time delay estimation suffering from low resolution in a multi path environment, in this paper, we propose block sparse Bayesian learning using weighted Laplace prior (WL BSBL) We impose the weighted Laplace prior on the TOA. And a greedy iterative strategy is proposed to solve the WL BSBL model. Furthermore, to improve the computation efficiency, we incorporate block idea into the WL BSBL model by dividing the potential TOA time domain into connected blocks based on the first few iterations result. WL BSBL model can take advantage of the active sonar receiving data to estimate the number, time delay, and amplitude of multi path accurately. The advantages of WL BSBL include low computation complexity, super resolution. The simulation results show that WL BSBL runs fast and retains good performance in low signal to noise ratio (SNR) environment.
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