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Multi-rank Range-spread Target Detection Method for Space/Time Symmetric Array Radar under Non-Gaussian Clutter Background
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摘要: 针对多通道阵列雷达从实际杂波中检测目标场景，该文提出了一种面向多通道阵列雷达非高斯杂波背景的多秩距离扩展目标检测方法。首先，利用秩大于1的子空间矩阵和相应距离单元的坐标向量，建立了多秩距离扩展目标模型；然后，利用雷达接收单元空间或时间中心对称探测场景下杂波协方差矩阵的反对称结构信息，通过酉变换，采取广义似然比、Rao、Wald检验准则，构建待解参数小样本估计策略，设计了面向非高斯杂波背景的多秩距离扩展目标检测方法。最后，通过理论推导证明了所提检测方法相对于杂波协方差矩阵具有恒虚警特性。基于仿真数据和实测数据的实验结果表明，所提检测方法能够保证对杂波协方差矩阵具有恒虚警特性，此外，相较于现有检测方法，所提检测方法提升了小训练支持的目标检测性能，并且在导向矢量失配条件下，有效地改善目标检测的稳健性。Abstract: This study proposes a multi-rank range-spread target detection method for multi-channel array radar under a non-Gaussian clutter background. The method aims to detect the target from real clutter using the multi-channel array radar. First, a multi-rank range-spread target model was formulated using a subspace matrix with a rank greater than one and the coordinate vectors of corresponding range bins. Then, by exploiting the persymmetric structure information of the clutter covariance matrix under the detection scenario, wherein the radar receiver units were central symmetric in space or time, a small sample estimation strategy for the parameters to be solved through the unitary transformation was constructed. Further, a non-Gaussian clutter background multi-rank range-spread target detection method was designed based on the generalized likelihood ratio, Rao, and Wald tests. Finally, a theoretical derivation proved that the proposed detection method has the constant false alarm rate property. The experimental results based on both the simulated and measured data showed that the proposed detection method can ensure the constant false alarm rate property of the clutter covariance matrix. Additionally, compared with the existing detection methods, the proposed detection method improves the target detection performance under small sample support. Besides, the proposed detection method effectively improves the robustness of target detection under the condition of steering vector mismatch.
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Key words:
- Adaptive detection /
- Range-spread target /
- Multi-rank subspace /
- Persymmetry /
- Non-Gaussian
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图 3 不同失配程度下基于仿真数据的检测概率曲线
Figure 3. Detection probability curves of the detectors for different mismatch levels based on the simulated data
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