Multiple Targets Localization Algorithm Based on Covariance Matrix Sparse Representation and Bayesian Learning

Abstract

The multi-source passive localization problem is a problem of great interest in signal processing with many applications. In this paper, a sparse representation model based on covariance matrix is constructed for the long-range localization scenario, and a sparse Bayesian learning algorithm based on Laplace prior of signal covariance is developed for the base mismatch problem caused by target deviation from the initial point grid. An adaptive grid sparse Bayesian learning targets localization (AGSBL) algorithm is proposed. The AGSBL algorithm implements a covariance-based sparse signal reconstruction and grid adaptive localization dictionary learning. Simulation results show that the AGSBL algorithm outperforms the traditional compressed-aware localization algorithm for different signal-to-noise ratios and different number of targets in long-range scenes.

Graphical abstract

Keywords

grid adaptive model / Bayesian learning / multi-source localization