CS quadrilinear model-based angle estimation for MIMO radar with electromagnetic vector sensors

In order to estimate the angles for bistatic MIMO radar with electromagnetic vector sensors, we link the compressed sensing (CS) theory with quadrilinear model, and propose a novel angle estimation algorithm. In the proposed algorithm, the received data is firstly arranged into a quadrilinear model and then it is compressed according to the compressed sensing theory. We then perform quadrilinear decomposition on the compressed quadrilinear data model via the quadrilinear alternating least square (QALS) algorithm and finally obtain the automatically paired angle estimates with sparsity. Owing to compression, the proposed algorithm has smaller storage requirement and lower computational complexity than the conventional quadrilinear decomposition-based algorithm. Moreover, our algorithm has higher angle estimation accuracy than the estimation signal parameters via rotational invariance techniques (ESPRIT) algorithm and its estimation performance is close to that of the conventional quadrilinear decomposition-based algorithm. Our proposed algorithm needs neither additional pair matching, nor spectral peak searching, and it can be applied to both uniform and non-uniform arrays. Effectiveness of our proposed algorithm is assessed through various simulation results.