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针对目前通信辐射源个体识别算法在实际试验中由于各类干扰信号和多径衰落导致识别率较低的问题,提出一种用于识别算法前端的信号分离算法,可有效地减少其他电磁信号对于识别算法输入信号的影响,从而提高在复杂电磁环境中通信辐射源个体识别的正确识别率。该算法将灾变策略和搜索状态的自适应引入量子粒子群算法,通过对混合信号的联合对角化从截获的观测信号中提取出目标通信辐射源的有用信号。为了更加系统、直观地衡量算法的分离效果,提出分离熵来量化算法的整体性能。仿真结果表明,该分离算法可以把目标通信辐射源的有用信号从复杂电磁环境中提取出来,从而提高通信辐射源个体识别在复杂电磁环境中的正确识别率,具有较好的可行性和有效性。
Aiming at the problem of low recognition rate due to various types of interference signals and multipath fading in the current experiments of individual identification algorithms for communication radiation sources, a signal separation algorithm for identifying the front of the algorithm is proposed, which can effectively reduce the influence of other electromagnetic signals on Identify the influence of the input signal of the algorithm, so as to improve the correct recognition rate of the individual identification of the communication radiation source in a complex electromagnetic environment. This algorithm introduces the adaptive of catastrophe strategy and search state into quantum particle swarm optimization algorithm, and extracts the useful signal of target communication radiation source from the intercepted observation signal through the joint diagonalization of the mixed signal. In order to measure the separation effect of the algorithm systematically and intuitively, the entropy of separation is proposed to quantify the overall performance of the algorithm. The simulation results show that the separation algorithm can extract the useful signal of the target communication radiation source from the complex electromagnetic environment and improve the correct identification rate of the communication radiation source individual identification in complex electromagnetic environment, which has good feasibility and effectiveness .