An improved de-interleaving algorithm of radar pulses based on SOFM with self-adaptive network topology  

作　　者：JIANG Wen FU Xiongjun CHANG Jiayun QIN Rui 

机构地区：[1]School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China

出　　处：《Journal of Systems Engineering and Electronics》2020年第4期712-721,共10页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(61571043);the 111 Project of China(B14010)。

摘　　要：As a core part of the electronic warfare(EW) system,de-interleaving is used to separate interleaved radar signals. As interleaved radar pulses become more complex and denser, intelligent classification of radar signals has become very important. The self-organizing feature map(SOFM) is an excellent artificial neural network, which has huge advantages in intelligent classification of complex data. However, the de-interleaving process based on SOFM is faced with the problems that the initialization of the map size relies on prior information and the network topology cannot be adaptively adjusted. In this paper, an SOFM with self-adaptive network topology(SANT-SOFM) algorithm is proposed to solve the above problems. The SANT-SOFM algorithm first proposes an adaptive proliferation algorithm to adjust the map size, so that the initialization of the map size is no longer dependent on prior information but is gradually adjusted with the input data. Then,structural optimization algorithms are proposed to gradually optimize the topology of the SOFM network in the iterative process,constructing an optimal SANT. Finally, the optimized SOFM network is used for de-interleaving radar signals. Simulation results show that SANT-SOFM could get excellent performance in complex EW environments and the probability of getting the optimal map size is over 95% in the absence of priori information.

关 键 词：de-interleaving self-organizing feature map(SOFM) self-adaptive network topology(SANT) 

分 类 号：TN95[电子电信—信号与信息处理] TP18[电子电信—信息与通信工程]