Algorithm

Smoothing

Pure mathematics

MIMO

Applied mathematics

Toeplitz matrix

Telecommunications

Computer science

Antenna (radio)

Beamforming

Tensor (intrinsic definition)

Mathematics

Artificial intelligence

Direction of arrival

Subspace topology

Radar

Computer vision

IEEE Transactions on Aerospace and Electronic Systems

This article addresses the problem of joint direction of departure (DOD) and direction of arrival (DOA) estimation with nested bistatic multiple input multiple output (MIMO) radar using tensor decomposition. We first employ the two-level nested transmit and receive arrays to develop the sum-difference coarray for constructing the Toeplitz and spatial smoothing matrices. We then generalize the three-way tensor model from DOD and DOA dimensions, and derive the optimized tensor by maximizing the number of detectable targets, where the existing COMFAC technique is exploited for angle estimation. We show that the proposed method can identify more targets and achieve better performance by enforcing the three-way structure information compared with the subspace-based algorithms. We also show that the conventional tensor model is just a special case. Finally, we derive the coarray Cramér–Rao Bound (CRB) for the nested MIMO radar, and also conduct a study for the conditions under which the CRB exists. Numerical simulations are provided to validate the theoretical analysis and demonstrate the performance improvement.

Nested MIMO Radar: Coarrays, Tensor Modeling, and Angle Estimation