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为了充分利用多入多出(multiple input multipleoutput,MIMO)雷达的空间分集技术,同时使其便于设计和实现,论文基于目标的分布源模型,在Neyman-Pearson准则下研究当MIMO雷达分集路径不完全独立时的检测方法和性能,并且利用主分量分析的思想简化了检测方法。该方法通过特征值分解,从M个正交信号中提取出独立观测分量并将其加权合并。仿真结果表明:与传统雷达相比,只要存在2个以上的独立观测分量,MIMO雷达可以更有效地对抗目标RCS(radar cross section)闪烁。同时,当发射阵元间距减小为理想条件的1/4时,MIMO雷达仍然可以利用空间分集提高其检测性能。
In order to make full use of the space diversity technology of multiple input multiple output (MIMO) radar and to make it easy to design and implement, the dissertation is based on the distributed source model of the target. Under the Neyman-Pearson criterion, when the MIMO radar diversity path is incomplete Independent of the detection methods and performance, and the use of principal component analysis to simplify the detection method. In this method, eigenvalue decomposition is used to extract the independent observation components from the M quadrature signals and weight them together. The simulation results show that the MIMO radar can more effectively combat the target RCS flicker than the conventional radar, provided there are more than two independent observations. At the same time, the MIMO radar can still improve its detection performance by using space diversity when the spacing of the transmitting elements is reduced to 1/4 of the ideal condition.