Parametric GLRT for Multichannel Adaptive Signal Detection
Parametric GLRT for Multichannel Adaptive Signal Detection
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DOI：
10.1109/tsp.2007.896068
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发表时间：
2007-11
影响因子：
5.4
通讯作者：
Kwang June Sohn;Hongbin Li;B. Himed
中科院分区：
文献类型：
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作者：
Kwang June Sohn;Hongbin Li;B. Himed
本文考虑了在存在空间和时间颜色干扰的情况下检测多通道信号的问题。通过将干扰建模为多通道自回归 (AR) 过程，开发了参数广义似然比检验 (GLRT)。检查参数 GLRT 基础的最大似然 (ML) 参数估计。结果表明，备择假设的 ML 估计量是非线性的，并且不存在封闭式表达式。为了解决这个问题，提出了一种渐近 ML (AML) 估计器，它以降低的复杂性产生渐近最优参数估计。通过考虑用于参数估计的训练信号有限或没有的挑战性情况来研究参数 GLRT 的性能。这种情况（特别是当训练不可用时）对于在异构、快速变化或密集目标环境中检测信号非常感兴趣，但通常无法由大多数现有的多通道检测器处理，这些检测器更依赖于足够水平的训练。与最近推出的参数自适应匹配滤波器（PAMF）和参数Rao检测器相比，参数GLRT实现了更高的数据效率，总体上提供了改进的检测性能。
This paper considers the problem of detecting a multichannel signal in the presence of spatially and temporally colored disturbance. A parametric generalized likelihood ratio test (GLRT) is developed by modeling the disturbance as a multichannel autoregressive (AR) process. Maximum likelihood (ML) parameter estimation underlying the parametric GLRT is examined. It is shown that the ML estimator for the alternative hypothesis is nonlinear and there exists no closed-form expression. To address this issue, an asymptotic ML (AML) estimator is presented, which yields asymptotically optimum parameter estimates at reduced complexity. The performance of the parametric GLRT is studied by considering challenging cases with limited or no training signals for parameter estimation. Such cases (especially when training is unavailable) are of great interest in detecting signals in heterogeneous, fast changing, or dense-target environments, but generally cannot be handled by most existing multichannel detectors which rely more heavily on training at an adequate level. Compared with the recently introduced parametric adaptive matched filter (PAMF) and parametric Rao detectors, the parametric GLRT achieves higher data efficiency, offering improved detection performance in general.