Fan Jin-Yu, Gu Hong, Su Wei-Min, Chen Jin-Li. Co-prime MIMO Radar Multi-parameter Estimation Based on Tensor Decomposition[J]. Journal of Electronics & Information Technology, 2015, 37(4): 933-938. doi: 10.11999/JEIT140826
Citation:
Fan Jin-Yu, Gu Hong, Su Wei-Min, Chen Jin-Li. Co-prime MIMO Radar Multi-parameter Estimation Based on Tensor Decomposition[J]. Journal of Electronics & Information Technology, 2015, 37(4): 933-938. doi: 10.11999/JEIT140826
Fan Jin-Yu, Gu Hong, Su Wei-Min, Chen Jin-Li. Co-prime MIMO Radar Multi-parameter Estimation Based on Tensor Decomposition[J]. Journal of Electronics & Information Technology, 2015, 37(4): 933-938. doi: 10.11999/JEIT140826
Citation:
Fan Jin-Yu, Gu Hong, Su Wei-Min, Chen Jin-Li. Co-prime MIMO Radar Multi-parameter Estimation Based on Tensor Decomposition[J]. Journal of Electronics & Information Technology, 2015, 37(4): 933-938. doi: 10.11999/JEIT140826
A novel algorithm for estimation of Direction Of Departure (DOD), Direction Of Arrival (DOA), and Doppler frequency based on bistatic MIMO radar with Co-Prime Array (CPA) is presented. The transmit and receive arrays are both composed of a pair of sparse uniform subarrays. Similarly, a pair of snapshot sequences with co-prime intervals constitutes the sampling of temporal. Three manifold matrices which contain multi-targets DODs, DOAs and Doppler frequencies respectively are estimated through tensor decomposition. From which a group of Vandermonde matrices of virtual manifold are constructed. To improve the estimation accuracy, an error depressing algorithm based on eigenvalue decomposition is proposed. Finally, the above three parameters are estimated by an Estimation of Signal Parameters via Rotation Invariant Techniques (ESPRIT) algorithm. The proposed algorithm offers better performance through virtual array and virtual snapshot without parameter ambiguous. It requires neither peak searching nor pairing processes, and the simulation results are presented to verify the effectiveness of the proposed algorithm.