This paper deals with the problem of adaptive detection for range-spread targets with unknown compjex amplitude and known Doppler in the presence of compound-Gaussian clutter modeled as an autoregressive ( AR) process with unknown parameters. Since no uniformly most powerful test exists for this problem, we devise and assess the AR-model-based detection strategy based on the Wald test. The unknown parameters are estimated by maximum likelihood criterion only under hypothesis Hl for the use in Wald test. Different to the traditional covariance-matrix-based detectors, we consider no secondary data; the AR-model-based detector adjusts itself to the environment utilizing only the primary data in the cells under test. Moreover, the AR-model-based Wald test ensure the constant false alarm rate (CFAR) property with respect to the clutter power level, and are asymptotically CFAR with respect to the clutter covariance matrix. Finally, the performance assessments, conducted by Monte Carlo simulations, also in comparison to previously proposed detectors, have shown the newly proposed detector possesses better detection performance than its counterpart resorting to the generalized likelihood ratio test (GLRT) approach. In addition, the newly proposed AR-model-based Wald test possesses the same asymptotical performance as the two-step GLRT-based detector with perfect knowledge about the clutter covariance matrix.%本文研究复合高斯杂波环境中的距离扩展目标的自适应检测问题.有色杂波采用参数未知的自回归(AR)过程描述.结合Wald检测准则,仅需对H1假设条件下的未知参数进行最大似然估计,给出了一种新的基于参数化模型的扩展目标检测器——参数化Wald检测器.该检测器的检验统计量可解释为首先针对各个待测单元分别计算检验统计量,然后将所有待测单元的输出进行非相参累加,其对杂波的随机功率起伏具有恒虚警率(CFAR)特性.相比于常规的基于协方差矩阵的检测方法,参数化检测算法的执行过程不需要依赖辅助数据,仅利用待测扩展目标数据即可实现自适应处理,有效缓解了训练压力并降低了计算量.仿真实验表明,所提出的参数化Wald检测器的检测性能优于之前提出的参数化广义似然比检测器的性能.
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