Sparse Representation Based Distributed Multisensor Track Fusion

Abstract

In the distributed multisensory information fusion system, each local sensor independently forms local tracks, and multisensory track fusion refers to fusing multiple local tracks that represent the same target into one global track. By studying the theory of multisensory track fusion and signal sparse representation, a sparse representation based multisensory track fusion algorithm is proposed. This new algorithm first obtains a noisy dictionary and a noise-free dictionary by sample tracks. At each scan, the state vectors of local tracks from the same target are rewritten as a vector, and then the sparse representation coefficients of the vector in noisy dictionary are computed. At last, the global state vector at this scan is obtained by the sparse representation coefficients and noise-free dictionary. Simulation results illustrate that the fusion results of the new algorithm are better than that of Linear Minimum Mean-Square Error (LMMSE).