LAUSR

Target localization utilizing frequency diverse array (FDA) has received much attention in recent years. In this paper, we propose a tensor subspace-based multiple-target 3-D localization method with planar frequency diverse subaperturing multiple-input multiple-output (FDS-MIMO) radar. To fully exploit the inherent multidimensional FDS-MIMO radar matched filter output information, a tensor signal model is established first. Then, the FDS-MIMO radar range ambiguity problem is mitigated by applying co-prime frequency offsets along both the dimensions of the planer array. Next, a beamspace-based unitary tensor-multiple signal classification (UTMUSIC) algorithm is developed to utilize the inherent multidimensional structure through the higher order singular value decomposition (HOSVD)-based low-rank approximation. Moreover, two computationally efficient methods, namely, partial spectral search UTMUSIC and range-angle decoupling UTMUSIC algorithms, are also proposed accordingly. The superiority of the proposed approaches over conventional methods is verified with numerical results, in terms of both computational complexity and estimation accuracy.