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Adaptive Detectors for Two Types of Subspace Targets in an Inverse Gamma Textured Background
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摘要: 该文在复合高斯海杂波背景下，以逆Gamma分布作为纹理分量的先验分布模型，研究了1阶高斯(First Order Gaussian, FOG)和2阶高斯(Second Order Gaussian, SOG)两类子空间目标的自适应检测问题。采用两步广义似然比(Generalized Likelihood Ratio Test, GLRT)推导了检测统计量，并分别采用采样协方差矩阵(Sample Covariance Matrix, SCM)、归一化采样协方差矩阵(Normalized Sample Covariance Matrix, NSCM)和定点估计(Function Point Estimation, FPE)作为协方差矩阵估计值，与GLRT相结合，构造出新的自适应检测器。由于该文检测器在设计阶段考虑了海杂波的先验分布模型，且在检测阶段采用了与工作环境相匹配的模型参数，经性能分析与验证，其在检测性能上优于已有匹配滤波(Adaptive Matched Filter, AMF)和归一化匹配滤波(Adaptive Normalized Matched Filter, ANMF)检测器。Abstract: Considering an inverse Gamma prior distribution model for texture, the adaptive detection problems for both first order Gaussian and second order Gaussian subspace targets are researched in a compound Gaussian sea clutter. Test statistics are derived on the basis of the two-step generalized likelihood ratio test. From these tests, new adaptive detectors are proposed by substituting the covariance matrix with estimation results from the Sample Covariance Matrix (SCM), normalized SCM, and fixed point estimator. The proposed detectors consider the prior distribution model for sea clutter during the design stage, and they model parameters that match the working environment during the detection stage. Analysis and validation results indicate that the detection performance of the proposed detectors out performs existing AMF (Adaptive Matched Filter, AMF) and ANMF (Adaptive Normalized Matched Filter, ANMF) detectors.
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Key words:
- Adaptive detection /
- Compound Gaussian model /
- Inverse Gamma texture /
- Subspace targets
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