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Performance analysis of GLRT-based adaptive detector for distributed targets in compound-Gaussian clutter

机译:基于GLRT的复合高斯杂波分布目标自适应检测器性能分析。

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摘要

The problem of adaptive detection for spatially distributed targets in compound-Gaussian clutter is studied. We first derive the optimum NP detector and suboptimum two-step GLRT detector. For the two-step detection strategy, we also introduce three covariance matrix estimation strategies and evaluate their CFAR properties and complexity issues. Next, the numerical results are presented by means of Monte Carlo simulation strategy. In particular, the simulation results highlight that the performance loss due to adaptively estimating the texture is negligible, and that the loss due to adaptively estimating covariance matrix largely depends on the estimation algorithm, the number of the secondary data vectors and the number of the scatterers.
机译:研究了复合高斯杂波中空间分布目标的自适应检测问题。我们首先得出最优的NP检测器和次优的两步GLRT检测器。对于两步检测策略,我们还介绍了三种协方差矩阵估计策略,并评估了它们的CFAR属性和复杂性问题。接下来,通过蒙特卡洛模拟策略给出了数值结果。特别地,仿真结果突出表明,由于自适应估计纹理而导致的性能损失可以忽略不计,并且由于自适应估计协方差矩阵而导致的损失在很大程度上取决于估计算法,辅助数据向量的数量和散射体的数量。 。

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