中文摘要：

基于有序数据变率（ODV）和削减平均恒虚警（TM-CFAR）检测，提出了自适应TM-CFAR检测，它能判决自动选择参数并估计背景噪声，仿真结果表明，在均匀背景和多目标背景下，其具有较好的检测性能，能提高抗干扰目标最大容限；在强杂波边缘时，其虚警概率控制能力优于有序统计CFAR检测和单元平均CFAR检测．采用两级结构和分块并行处理思想实现时，该算法所需硬件资源和运算复杂度都低于自动删除平均ODV检测，而且具有实时处理性高和时序控制方便的优点．

英文摘要：

Adaptive trimmed mean constant false alarm rate （ATM-CFAR） detection based on TM-CFAR detection and statistics ordered data variability （ODV） is presented. These parameters and background estimations can be selected automatically. Simulation shows that the algorithm has good detection per- formance under homogeneous environment and multi-target environment, and also increases its tolerance of interfering targets. Moreover, under high clutter noise ratio at clutter edge regions, the control ability on false alarm rate is much better than that of cell average CFAR detection and ordered statistics CFAR detection. Using two-level architecture and sub-block parallel processing methods, its hardware imple- mentation and computational complexity are less than the automatic censored cell-averaging based on the statistics ODV by on-chip implementation. Furthermore, it also has the advantages of high real-time pro- cessing and is very convenient for sequential control in practice.