基于滑窗对比度的多源融合目标恒虚警检测方法
Multi-source Fusion CFAR Detection Method Based on the Contrast of Sliding Window
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摘要: 随着空天地跨域协同技术的快速发展,多基地雷达利用其构型的特殊性,可实现目标的多视角融合探测,从而改善检测信噪比,然而现有的恒虚警检测算法尚未提出适用于多站构型下的融合检测理论。文中提出一种基于滑窗对比度的多源融合目标恒虚警检测方法。首先,利用地面直角坐标系网格与多站距离环的映射关系,指出多站融合图像中目标区域与背景单元的特征分布差异。其次,通过构建指数型似然比融合检测量,有效增加了目标分辨单元和背景单元的对比度。最后,根据系统虚警指标要求,推导了基于指数型似然比二阶统计量的恒虚警检测门限,在保证虚警性能前提下给出了多站构型目标恒虚警融合检测理论。所提算法充分挖掘了多站融合图像中目标能量的分布特征,为多源融合目标增强检测提供了一种有效途径,仿真结果验证了所提算法的有效性。Abstract: With the rapid development of space-ground cross domain cooperative technology, multi-source fusion detection based on multistatic radar can be achieved by means of its special configuration. However, the existing constant false alarm ratio (CFAR) detection algorithms have not yet proposed a suitable fusion detection theory for multi-station configuration. In this paper, a novel multi-source fusion CFAR detection method based on the contrast of sliding window is presented. Firstly, the feature distribution difference between the target area and the background unit is pointed out in the fused image based on the mapping relationship between the ground grid of the rectangular coordinate and the range loop of receiving station. Then, the fusion detection quantity of exponential likehood ratio is constructed, which increases the contrast between target resolution unit and background resolution unit effectively. Finally, the CFAR detection threshold based on second-order statistics of exponential likehood ratio is derived according to the system false alarm index, and thus to form a novel fusion detection theory with regard to multi-station configuration. The proposed algorithm fully exploits the target distribution characteristics in multi-source fusion image, which provides an effective way for target enhancement detection. Simulation results verify the effectiveness of the proposed algorithm.
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