Tunable Adaptive Target Detection With Kernels in Colocated MIMO Radar
Tunable Adaptive Target Detection With Kernels in Colocated MIMO Radar
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DOI：
10.1109/tsp.2020.2975371
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发表时间：
2020
影响因子：
5.4
通讯作者：
A. Zaimbashi;Jian Li
中科院分区：
文献类型：
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作者：
A. Zaimbashi;Jian Li
本文介绍了一种利用核理论在非线性特征空间中研究共址MIMO雷达自适应目标检测的框架。核理论启发我们用非线性映射数据在特征空间的内积或其核技巧的内积来代替检验统计量的内积，以获得更好的检测性能。将该框架应用于共址MIMO雷达的可调自适应目标检测问题，根据广义似然比（GLR）、Rao和Wald（RaW）检验的原理，提出了基于核可调GLR和核可调Raw两种新的可调检测器形式下的新检测器。所提出的可调谐检测器包括了大多数的共址MIMO雷达作为特殊情况下的现有检测器。所提出的检测器的一个能力是，它们对导向矢量失配（SVM）的鲁棒性或选择性（RoS）可以通过RoS调谐参数灵活地调谐。此外，我们还能够通过PD整定参数纳入干扰协方差矩阵的先验分布（PD）（如果可用）。因此，所提出的检测器可以基于调谐RoS参数来调谐，以在存在SVM的情况下实现鲁棒性或选择性性能，以及通过PD参数在基于贝叶斯或非贝叶斯的检测器之间切换。对于实际情况，我们证明了所提出的检测器具有恒虚警性能的干扰协方差矩阵诉诸不变性原理。大量的蒙特卡罗仿真结果表明，所提出的检测器具有更好的检测性能比他们的同行在单目标和多目标的情况下。
We introduce a framework for exploring adaptive target detection in colocated MIMO radar in non-linear feature space by exploiting the theory of kernel. The kernel theory inspires us to replace the inner products of test statistics with that of nonlinear mapped data in the feature space or that of their kernel tricks to achieve better detection performance. We apply this framework to the problem of tunable adaptive target detection in colocated MIMO radar, according to the principle of the generalized likelihood ratio (GLR), Rao and Wald (RaW) tests, and propose several new detectors under two new tunable detector forms, namely kernel tunable GLR-based and kernel tunable Raw-based detectors. The proposed tunable detectors include most of the prior detectors in colocated MIMO radar as special cases. One capability of the proposed detectors is that their robustness or selectivity (RoS) to steering vector mismatch (SVM) can be tuned flexibility through an RoS tuning parameter. In addition, we are able to incorporate the prior distribution (PD) of the disturbance covariance matrix, if available, through a PD tuning parameter. Therefore, the proposed detectors can be tuned based on the tuning RoS parameter to achieve robust or selective performance in the presence of SVM as well as to switch between the Bayesian or non-Bayesian based detectors through the PD parameter. For practical situations, we show that the proposed detectors possess CFAR property against disturbance covariance matrix by resorting to the invariance principle. Extensive Monte Carlo simulation results are provided to indicate that the proposed detectors have better detection performance than their counterparts in both single-target and multi-target scenarios.