Reduced-Dimensional PARAFAC-Based Algorithm for Joint Angle and Doppler Frequency Estimation in Monostatic MIMO Radar
In this article, the problem of joint direction of arrival (DOA) and Doppler frequency estimation using parallel factor (PARAFAC) analysis in the monostatic multiple-input multiple-output radar is investigated and a reduced-dimensional PARAFAC (RD-PARAFAC) algorithm is proposed. In order to overcome the shortcoming of the heavy computational load in conventional PARAFAC algorithm, we firstly utilize a RD transformation by employing the property of uniform linear arrays, which can remove the redundant entries of steering matrix. By means of the reduced-dimensional transformation, the proposed algorithm can reduce the computational complexity, save memory capacity significantly and has very close joint DOA and Doppler frequency estimation performance when compared with conventional PARAFAC algorithm. Meanwhile, it outperforms the estimation of signal parameters via rotational invariance technique and the propagator method. Furthermore, our algorithm needs no spectral peak searching or pair matching. The complexity analysis and the Cramer-Rao Bound of the DOA and frequency estimation is derived. Simulations are provided to verify its effectiveness and superiority.
mimo radar joint doa and doppler frequency estimation parallel factor analysis low complexity