Abstract
In this advanced exploration,we focus on multiple parameters estimation in bistatic Multiple-Input Multiple-Output(MIMO)radar systems,a crucial technique for target localization and imaging.Our research innovatively addresses the joint estimation of the Direction of Departure(DOD),Direction of Arrival(DOA),and Doppler frequency for incoherent targets.We propose a novel approach that significantly reduces computational complexity by utilizing the Temporal-Spatial Nested Sampling Model(TSNSM).Our methodology begins with a multi-linear mapping mechanism to efficiently eliminate unnecessary virtual Degrees of Freedom(DOFs)and reorganize the remaining ones.We then employ the Toeplitz matrix triple iteration reconstruction method,sur-passing the traditional Temporal-Spatial Smoothing Window(TSSW)approach,to mitigate the single snapshot effect and reduce computational demands.We further refine the high-dimensional ESPRIT algorithm for joint estimation of DOD,DOA,and Doppler frequency,elim-inating the need for additional parameter pairing.Moreover,we meticulously derive the Cramér-Rao Bound(CRB)for the TSNSM.This signal model allows for a second expansion of DOFs in time and space domains,achieving high precision in target angle and Doppler frequency estima-tion with low computational complexity.Our adaptable algorithm is validated through simulations and is suitable for sparse array MIMO radars with various structures,ensuring higher precision in parameter estimation with less complexity burden.
基金项目
National Natural Science Foundation of China(62071476)
China Postdoctoral Science Foundation(2022M723879)
Science and Technology Innovation Program of Hunan Province,China(2021RC3080)
NETL
NSTL
万方数据