Optimum and Centralized Detection for Statistical MIMO Radar
Multiple Input Multiple Output (MIMO) radar is a new emerging radar technique developed recently. MIMO radar can exploit the spatial diversity of target scatterers in a variety of ways to extend the radar’s performance. This paper introduces the statistical MIMO radar concept and the method of receiving signal processing also is presented. The fundamental difference between statistical MIMO and other radar array systems is that the latter seek to maximize the coherent processing gain, while statistical MIMO radar capitalizes on the diversity of target scattering to improve radar performance. Compared to traditional phased-array radar, statistical MIMO radar can offer better parameter identifiability and higher resolution, better sensitivity to slowly moving targets, enable the direct applicability of adaptive techniques for effective interference and jamming suppression, and allow for much flexibility for transmit beam-pattern design and waveform optimization. Reaping the full benefit of the superior performance enabled by the statistical MIMO radar requires a novel design of its receive filters to minimize the impact of scatterers in nearby range bins on the received signals from the range bin of interest to minimize range compression problem by spacing the antenna elements at the transmitter and at the receiver such that the target angular spread is manifested. In this paper, we focus on the application of the target spatial diversity to improve detection performance. The optimal detector in the Neyman-Pearson sense is developed and analyzed for the statistical MIMO radar in case of stationary target. The performance improvement that can be achieved by the use of angular diversity in statistical MIMO radar is investigated. The results show that at high SNR values statistical MIMO radar provides great improvements of target detection performance over other types of array radars. Whereas, at low SNR values, phased array radar performs better than statistical MIMO radar. Finally, we present a number of numerical examples to demonstrate the effectiveness of the proposed approaches. Therefore, statistical MIMO radar can be applied to enhance radar resolution by allowing the measurement of one scatterer at a time.