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An Adaptive Detector with Mismatched Signals Rejection in Compound Gaussian Clutter
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摘要: 随着雷达分辨率的提高及擦地角的减小，海杂波幅度分布明显偏离瑞利分布，表现出很强的非高斯特性，复合高斯模型得到广泛应用。因此该文以复合高斯杂波为背景，研究当信号发生失配时的雷达目标检测问题。该文基于两步广义似然比(GLRT)检验，设计了复合高斯杂波下对失配信号具有选择性的自适应检测器。为了设计选择性检测器，在零假设下引入虚假干扰以修正原始二元假设，并假设该虚假干扰与实际目标信号在白化空间正交。该文提出的检测器对海杂波纹理分量及协方差矩阵恒虚警(CFAR)。最后利用仿真及实测海杂波数据，通过蒙特卡洛实验验证该检测器的有效性。实验表明，该文所提检测器有效提高了对失配信号的选择性，同时对距离扩展目标匹配信号的检测性能也有1～3 dB的提升。Abstract: Because of the improvement in radar resolution and decrease in grazing angle, the amplitude distribution of sea clutter obviously deviates from the Rayleigh distribution and presents a significant non-Gaussian feature. In this case, the compound Gaussian model is widely used. This study investigates the problem of detecting a target when signal mismatches occur in compound Gaussian clutter and proposes a selective detector to reject mismatched signals embedded in compound Gaussian clutter based on the so-called two-step Generalized Likelihood Ratio Test (GLRT). To design the selective detector, we modified the original hypothesis test by injecting a fictitious interference under the null hypothesis. These unwanted signals are assumed to be orthogonal to the nominal steering vector in the whitened subspace. The proposed detector has a Constant False Alarm Rate (CFAR) with respect to the statistics of the texture and covariance matrix. Finally, to demonstrate the effectiveness of the proposed detector, a Monte Carlo simulation is conducted to assess its performance based on the simulated and measured sea clutter data. The experimental results show that the proposed detector effectively improves the selectivity of the mismatched signals together with the detection of matched signals in a range spread target of 1～3 dB.
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图 1 本文所提检测器在不同
$\nu ,b,\rho $ 下的检测门限(H=4, K=32)Figure 1. Detection threshold of the proposed detector under different
$\nu ,\;b\;{\rm and}\;\rho $ (H=4, K=32).
图 2 不同检测器对匹配信号的检测性能曲线(N=8, H=1, K=32,
${\cos ^2}\theta = 1$ , b=1.0,$\nu $ =1.5)Figure 2. Detection performance curve of different detectors for matched signal (N=8, H=1, K=32,
${\cos ^2}\theta = 1$ , b=1.0,$\nu $ =1.5)
图 3 不同检测器对匹配信号的检测性能曲线(N=8, H=4, K=32,
${\cos ^2}\theta = 1$ , b=1.0,$\nu $ =1.5).Figure 3. Detection performance curve of different detectors for matched signal (N=8, H=4, K=32,
${\cos ^2}\theta = 1$ , b=1.0,$\nu $ =1.5)
图 4 不同检测器检测概率随
${\cos ^2}\theta $ 的变化曲线(N=8, H=1, K=32, SCR=5 dB, b=1.0,$\nu $ =1.5)Figure 4. Detection probability of different detectors versus
${\cos ^2}\theta $ (N=8, H=1, K=32, SCR=5 dB, b=1.0,$\nu $ =1.5)
图 5 不同检测器检测概率随
${\cos ^2}\theta $ 的变化曲线(N=8, H=4, K=32, SCR=0 dB, b=1.0,$\nu $ =1.5)Figure 5. Detection probability of different detectors versus
${\cos ^2}\theta $ (N=8, H=4, K=32, SCR=0 dB, b=1.0,$\nu $ =1.5)
图 6 实测数据幅度分布拟合曲线
Figure 6. Amplitude probability density function fitting of the measured data
图 7 实测数据下不同检测器对匹配信号的检测性能曲线(N=8, H=4, K=24)
Figure 7. Detection performance curve of different detectors for matched signal under measured data (N=8, H=4, K=24)
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