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研究了结构化的复合高斯杂波(CGC)背景中距离扩展目标自适应检测问题。针对异质杂波背景中的近似广义似然比检验(AGLRT-HTG)检测器应用于CGC背景中时存在一定的信杂比损失问题,结构化的复合高斯杂波采用自回归过程建模,结合近似广义似然比检验(AGLRT)方法和迭代估计思想,提出了CGC背景中距离扩展目标的迭代近似广义似然比检测器(RAGLRT-CGC)。从理论上分析了极限情况下RAGLRT-CGC虚警概率与检测门限关系的解析表达式。仿真结果表明,在CGC背景中,RAGLRT-CGC对不同多主散射点目标具有较好的鲁棒性,并且检测性能明显优于AGLRT-HTG。
The problem of self-adaptive distance detection in structured composite Gaussian clutter (CGC) is studied. There is a certain loss of signal-to-noise ratio in the application of AGLRT-HTG detector to CGC background in heterogeneous clutter background. The structured composite Gaussian noise model is modeled by an autoregressive process, Combined with approximate generalized likelihood ratio test (AGLRT) method and iterative estimation method, an iterative approximate generalized likelihood ratio detector (RAGLRT-CGC) is proposed for distance-expanded targets in CGC background. The analytical expression of the relationship between the false alarm probability and the detection threshold of RAGLRT-CGC in the limit case is theoretically analyzed. The simulation results show that RAGLRT-CGC has better robustness against different multi-primary scatterers and better detection performance than AGLRT-HTG in CGC background.