针对非高斯杂波背景中扩展目标的检测问题，将自回归(AR)模型与广义杂波分组模型相结合，提出了基于AR的广义杂波分组模型。并在该杂波模型背景下，利用近似广义似然比检测(AGLRT)原理，结合迭代估计方法，提出了广义杂波背景下迭代近似广义似然比检测器(RAGLRT-GCC)。该检测器不需要利用辅助距离单元估计杂波协参数就可以实现目标的自适应检测。RAGLRT-GCC利用了杂波分组信息，有效提高了对稀疏扩展目标的检测性能。仿真结果表明，在相同检测概率下，RAGLRT-GCC性能优于现有的复合高斯杂波背景下迭代近似广义似然比检测器(RAGLRT-CG)。

For the problem of range-spread detection in non-Gaussian clutter, the Autoregressive(AR) model and the generalized clutter-clustered model are combined and a generalized clutter-clustered model based on AR is established. Moreover, a Regressive Approximate Generalized Likelihood Ratio Test detector based on the Generalized Clutter-Clustered(RAGLRT-GCC) is proposed. The RAGLRT-GCC is an adaptive detector and it does not need secondary range cells to estimate the parameters of clutter. The RAGLRT-GCC makes full use of the clutter-clustered information and it improves the detection performance for spare scatterer targets of the detector. The simulation results also show that the detection performance of the RAGLRT-GCC is better than that of the Regressive Approximate Generalized Likelihood Ratio Test detector based on Compound Gaussian(RAGLRT-CG) at the same detection probability.