Adapitive waveform design for distributed OFDM MIMO radar system in multi-target scenario

In order to improve detection and estimation performance of distributed Orthogonal-Frequency-Division Multiplexing (OFDM) Multiple-Input Multiple-Output (MIMO) radar system in multi-target scene, we propose a novel approach of Adaptive Waveform Design (AWD) based on a constrained Multi-Objective Optimization (MOO). The sparse measurement model of this radar system is derived, and the method based on decomposed Dantzig selectors is applied for the sparse recovery according to the block structures of the sparse vector and the system matrix. An AWD approach is proposed, which optimizes two objective functions, namely minimizing the upper bound of the recovery error and maximizing the weakest-target return, by adjusting the complex weights of the emitting waveform amplitudes. Several numerical simulations are provided and their results show that the detection and estimation performance of the radar system is improved significantly when this MOO-based AWD approach is applied to the distributed OFDM MIMO radar system. Especially, we verify the effectiveness of our AWD approach when the available samples are reduced severally and the technique of compressed sensing is introduced. Keywords: Adaptive waveform design, Compressed sensing, Distributed radar, Multi-objective optimization, Multiple-input multiple-output, Orthogonal-frequency-division multiplexin