随着雷达分辨率的提高，低分辨率下的点源模型不再适用，且由雷达分辨率提高带来的目标距离扩展现象对雷达的检测概率影响越来越大。研究分布式MIMO雷达中距离扩展目标的检测问题，将该问题建模为二元假设检验问题，并将不同发送-接收对所对应的干扰杂波协方差矩阵建模为随机矩阵。同时指定它们的先验分布为逆复Wishart分布。通过设置不同的功率水平模拟不同的发射-接收天线对之间的非均匀杂波功率。根据Rao和Wald检测器的检测标准结合贝叶斯框架在非均匀杂波背景下推导出2个新的检测器。新推导的贝叶斯检测器主要具备以下2个优点：首先，不需要训练数据；其次，在决策规则中包含杂波的先验信息，并以此实现性能增益。仿真结果表明，在非均匀杂波环境中，推导的检测器比现存的检测性能更好，计算复杂度更低。
With the increase of radar resolutions, point source models at low resolutions are no longer applicable. And the target range spread phenomenon brought by the improvement of radar resolution has an increasingly greater impact on radar detection probability. Therefore, this paper aims at the problem of detecting range spread targets in distributed MIMO radar. We model the problem as a binary hypothesis testing problem, and suppose the interference clutter covariance matrices corresponding to different transmit-receive pairs is random matrices. The prior probability density functions are also supposed as inverse complex Wishart distributions. By setting different power levels to simulate the nonhomogeneous clutter power between different transmit-receive antenna pairs. Two new detectors were derived based on the detection criteria of Rao and Wald detectors and the Bayesian frame with the nonhomogeneous clutter. The detectors mainly have two advantages:firstly, no training data is needed; secondly, the prior information of clutter is included in the decision rule which increase the performance. The simulation results show that in a nonhomogeneous clutter environment, our detectors perform better than the existing detection performance and have lower computational complexity.
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