This paper deals with the problem of adaptive detection for targets with known Doppler and unknown complex amplitude in compound Gaussian clutter.The speckle component of the clutter is modeled as an autoregressive (AR) process with unknown parameters.Basing on the generalized Likelihood ratio (GLR)criterion,we first estimate the AR parameters and the unknown complex amplitude by maximum likelihood estimation,and then propose an adaptive GLR de-tector.It has been shown that for large data records,the proposed detector is Constant False Alarm Rate with respect to the product of the driving signal’s variance of AR model and the texture component of the clutter.The numerical simulations show that the proposed detector has better performance than the Normalized Adaptive Matched Filter with two different clut-ter covariance matrix estimation approaches.The impacts of the target length and the AR model order on the proposed de-tector performance are analyzed and variously simulated in this paper.%本文研究了复合高斯杂波中已知给定距离-多普勒单元多普勒频率、未知目标幅度的自适应检测目标的问题。杂波散斑分量用未知自回归系数的自回归过程建模表示，通过求解自回归系数和目标幅度的最大似然估计，推导得到复合高斯杂波中广义似然比检测器。在目标信号向量维数很大的情况下，该检测器对激励信号方差与纹理分量的乘积具有恒虚警率特性。仿真表明该检测器性能优于正则化自适应匹配滤波器。最后分析了目标信号向量维数、自回归模型阶数在不同信杂比下对检测器性能的影响。
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