| Distributed Compressed Sensing (DCS) extends sparsity from single signal to multi-signalensembles, and enables joint recovery. To solve multi-channel signal processing problems of MIMOradar, DCS-MIMO can effectively improve target detection performance. Not only is the parameterestimation of coherent and statistical DCS-MIMO radar studied but also adaptive design oftransmitting energy for statistical DCS-MIMO Radar. The completed major tasks are as follows:After studying on DCS background knowledge, we compare the differences between CS andDCS in multi-sensor observations of the same sparse scene, which can be identified as JSM-2. In thiscase, DCS can combine the received signal of each sensor and then achieve joint reconstruction. ButCS should reconstruct the information of each sensor, and then use data fusion model. Thus, thefusion level of DCS is higher than CS, and can get more accurate estimation.The parameter estimation of coherent DCS-MIMO radar is studied. To solve the problem ofdifferent noise power in coherent MIMO radar receiving channels, a new algorithm with SNRweighting and several SNR-based weighting methods are proposed for DCS-MIMO radar afterconstructing sparse basis.We show that the presented method offers higher estimation accuracy andprobability of exact reconstruction than CS-MIMO and DCS-MIMO. The rapid DCS algorithm is alsoproposed to reduce the complexity of the reconstruction.The parameter estimation of statistical DCS-MIMO radar is studied. We establish the jointsparsity model of statistical DCS-MIMO, and give joint reconstruction algorithms for thecorresponding scene.The realization structures of DCS-MIMO and CS-MIMO is also compared. Weshow that, compared with CS-MIMO, the signal model of DCS-MIMO and joint recovery algorithmcan improve probability of exact reconstruction of the target scene and reduce reconstruction error bytaking full utilization of information in all transmitter-receiver channels.Inspried by the notion of cognitive radar, we also investigate the adaptive mechanism for optimalenergy allocation at different transmitters of statistical MIMO radar. A method is given to optimizethe transmit energy allocation in single-target and multi-target environment. A new metric inCS-MIMO radar is also introduced and amended to analyze the performance of DCS-MIMO. Weshow that this adaptive mechanism outperforms than transmit fixed equal energy across all theantennas for MIMO radar systems. |
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