Distributed compressed sensing based joint detection and tracking for multistatic radar system

In this paper, we present a novel distributed compressed sensing based joint detection and tracking algorithm for multistatic radar system, which significantly reduces the computational load in a centralized fusion framework. For different receivers in the multistatic radar system, their corresponding sparse vectors, which are represented in state space, share the same locations of nonzero reflection coefficients. This fits the joint sparsity model 2 (JSM-2) in distributed compressed sensing. In this paper, a novel algorithm, named distributed general similar sensing matrix pursuit (DGSSMP) algorithm, is proposed to tackle the generalized JSM-2 model when each individual sensing matrix is different and with high coherence. In contrast to the classical greedy algorithms dealing with single subspace, the proposed DGSSMP algorithm has to tackle a union of different subspaces, with each subspace corresponding to a different sensing matrix for each individual receiver. The simulation results show that in the proposed distributed compressed sensing based joint detection and tracking framework, the proposed DGSSMP algorithm together with the track before detect (TBD) scheme can effectively distinguish true targets from clutter based on the information from multiple scans.