In this article, the problem of simultaneously detecting and localizing multiple targets in homogeneous noise environment is considered for non-coherent multiple-input multiple-output (MIMO) radar with widely separated antennas. By assuming that the a prior knowledge of target number is available, an optimal solution to this problem is presented first. It is essentially a maximum-likelihood (ML) estimator searching the parameters of interest in a high-dimensional state space. However, the complexity of this solution increases exponentially with the number G of targets. Besides, if the number of targets is unknown, a multi-hypothesis testing strategy to verify all the possible hypotheses on target number is required, which further complicates this method. In order to devise computationally feasible methods for practical applications, we split the high-dimensional maximization into G disjoint sub-optimization problems by sequentially detecting targets and then clearing their interference for the subsequent detection of remaining targets. In this way, we further propose two fast and robust suboptimal solutions which allow to trade performance for a much lower implementation complexity. In addition, the multi-hypothesis testing is no longer required when target number is unknown. Simulation results show that the proposed algorithms can correctly detect and accurately localize multiple targets even when targets lie in the same range bins. Experimental data recorded by three small radars are also provided to demonstrate the efficacy of the proposed algorithms.

中文翻译：

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具有广泛分离天线的非相干 MIMO 雷达的次优低复杂度联合多目标检测和定位


在本文中，考虑了具有宽距离天线的非相干多输入多输出（MIMO）雷达在均匀噪声环境中同时检测和定位多个目标的问题。通过假设目标数的先验知识可用，首先提出该问题的最优解。它本质上是一个最大似然（ML）估计器，在高维状态空间中搜索感兴趣的参数。然而，该解决方案的复杂性随着目标数量 G 呈指数增长。此外，如果目标数量未知，则需要采用多假设检验策略来验证关于目标数量的所有可能假设，这进一步使该方法变得复杂。为了设计出适合实际应用的计算上可行的方法，我们通过顺序检测目标，然后清除其干扰以进行剩余目标的后续检测，将高维最大化问题分解为 G 个不相交的子优化问题。通过这种方式，我们进一步提出了两种快速且强大的次优解决方案，它们允许以性能换取低得多的实现复杂性。此外，当目标数未知时，不再需要进行多重假设检验。仿真结果表明，即使目标位于同一范围内，所提出的算法也可以正确检测和准确定位多个目标。还提供了三个小型雷达记录的实验数据来证明所提出算法的有效性。