Parameter Estimation by Alternating Reconstruction and Sensation for Sonar System

Deconvolution algorithms often rely on conventional beamforming methods to obtain beamforming vectors, which limit their resolution. To enhance parameter estimation resolution, this article introduces the Parameter Estimation by Alternating Reconstruction and Sensation (PEARS) algorithm. In the proposed algorithm, direction estimation leverages a linearly constrained quadratic programming method and weighted L1-norm to solve the objective function, achieving higher resolution in the direction spectrum under fixed weighted vector conditions. The algorithm utilizes the gradient descent method to update the weighted vector, and the relationship among the dictionary matrix, direction spectrum, and weighted vector is computed using the chain rule. This process improves direction estimation results, particularly in scenarios with low signal-to-noise ratios. By alternating between target parameter estimation and weight vector calculation, the PEARS algorithm achieves highly accurate target azimuth estimation. Simulation results validate the algorithm's ability to accurately estimate target azimuth angles. In addition, lake and sea experimental results demonstrate the algorithm's effectiveness in correctly estimating direction in complex environments.