Joint DOD and DOA detection for MIMO radar based on signal subspace reconstruction and matching

In this study, the Direction Of Departure (DOD) and Direction Of Arrival (DOA) of signals detection for Multi-Input Multi-Output (MIMO) radar is discussed. A novel signal subspace reconstruction model to match the signal subspace obtained based on the covariance matrix of the array output is developed to enhance the performance of the DOD and DOA detection. In the developed scheme, the technology of beamforming is first introduced to define an objective space in mathematics for the targets to be detected. By considering the orthogonality between the signal subspace and the noise subspace and defining a reconstruction index of the signal subspace, a multi-dimensional objective function of the DOD and DOA is established. Therefore, the problem of DOD and DOA detection is transformed into an optimization of the multi-dimensional objective function. Subsequently, the Quantum-Behaved Particle Swarm Optimization (QPSO) is employed to optimize the multi-dimensional objective function and to determine an optimal signal subspace. At the same time the DOD and DOA can be fast captured. A series of simulations demonstrate that the proposed method provides significant accuracy improvements in DOD and DOA detection, especially for low signal-to-noise ratio thresholds and small snapshots.