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CFAR data fusion of multistatic radar system under homogeneous and nonhomogeneous backgrounds

机译:同质和非同质背景下多基地雷达系统的CFAR数据融合

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Multistatic radar with distributed sensors and data fusion are increasingly being used by surveillance systems. The performance of the detection system mainly depends on the data fusion process. There has been a great deal of theoretical study on decentralized detection networks in homogeneous and nonhomogeneous backgrounds. To solve the resulting nonlinear system, exhaustive search and some crude approximations are adopted, however, those often cause either the system to be insensitive to some parameters or produce suboptimal results. A novel flexible genetic algorithm is investigated to obtain optimal results on constant false alarm rate data fusion. Using this approach, all system parameters are directly coded in decimal chromosomes and they can be optimized simultaneously. Furthermore, our method can also be implemented for the more general situations.
机译:监视系统越来越多地使用具有分布式传感器和数据融合的多基地雷达。检测系统的性能主要取决于数据融合过程。关于均质和非均质背景下的分散检测网络已经进行了大量的理论研究。为了解决由此产生的非线性系统,采用了穷举搜索和一些粗略的近似,但是,这些通常会导致系统对某些参数不敏感或产生次优的结果。研究了一种新颖的灵活遗传算法,以获得恒定误报率数据融合的最优结果。使用这种方法,所有系统参数都直接在十进制染色体中编码,并且可以同时进行优化。此外,我们的方法也可以用于更一般的情况。

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