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在噪声或杂波环境中进行自适应雷达目标检测是每部雷达接收机中非常重要的设计。在几乎所有的检测程序中,都将接收回波信号幅度与某一门限作简单比较。目标检测的主要目的是在极低的恒虚警率(CFAR)约束条件下使目标检测概率最大化。噪声和杂波背景可以用一个统计模型来加以描述,如独立相同瑞利模型,或用已知平均噪声功率的指数分布随机变量进行描述。但是在实际应用中,平均噪声或杂波功率绝对是未知的,并且还会随着距离、时间和方位角发生变化。因此,对用于几种不同背景信号情况的某些距离CFAR技术进行描述。在这些背景信号情况下,平均噪声功率和另外一些统计参数都被假设是未知的。因而所有的距离CFAR技术都通过将幅度门限应用于检测单元内的回波信号幅度,把估算流程(用以获取噪声功率的精确值或估算值)与判定步骤结合起来。许多研究工作都分析了这种通用的检测方案,对这一课题投入了大量精力。对这些重要的距离CFAR检测方案中的几种作一简短描述,然后进行技术比较。
Adaptive radar target detection in noisy or clutter environments is a very important design in every radar receiver. In almost all testing procedures, the amplitude of the received echo signal is simply compared with a certain threshold. The main purpose of target detection is to maximize the probability of target detection with very low CFAR constraints. Noise and clutter backgrounds can be described by a statistical model, such as an independent Rayleigh model, or an exponential random variable with a known average noise power. However, in practice, the average noise or clutter power is absolutely unknown, and will vary with distance, time and azimuth. Therefore, some distance CFAR techniques for several different background signal situations are described. Under these background signals, the average noise power and other statistical parameters are assumed to be unknown. Thus all distance CFAR techniques combine the decision process (to obtain the exact value or estimate of the noise power) with the decision step by applying the amplitude threshold to the amplitude of the echo signal in the detection unit. Many studies have analyzed this common testing program, put a lot of energy on this topic. Briefly describe several of these important distances to the CFAR detection protocol and then make technical comparisons.