摘要
In order to raise the detection precision of the extended binary phase shift keying （EBPSK） receiver, a detector based on the improved particle swarm optimization algorithm （IMPSO） and the BP neural network is designed. First, the characteristics of EBPSK modulated signals and the special filtering mechanism of the impacting filter are demonstrated. Secondly, an improved particle swarm optimization algorithm based on the logistic chaos disturbance operator and the Cauchy mutation operator is proposed, and the EBPSK detector is designed by utilizing the IMPSO-BP neural network. Finally, the simulation of the EBPSK detector based on the MPSO-BP neural network is conducted and the result is compared with that of the adaptive threshold-based decision, the BP neural network, and the PSO-BP detector, respectively. Simulation results show that the detection performance of the EBPSK detector based on the IMPSO-BP neural network is better than those of the other three detectors.
为了提高扩展的二元相移键控(EBPSK)接收机的检测精度,设计了一种基于改进粒子群算法(IMPSO)和BP神经网络的EBPSK检测器.首先,阐述了EBPSK调制特征及冲击滤波器的特殊滤波机理.然后,提出了基于logistic混沌扰动和Cauchy变异的改进粒子群算法,并利用IMPSO-BP神经网络设计了EBPSK检测器模型.最后,对IMPSO-BP检测器进行了仿真,并分别与自适应门限判决、BP神经网络和PSO-BP检测器进行了对比.仿真结果表明:基于IMPSO-BP神经网络的EBPSK检测器检测效果要明显好于其他3种检测器.
基金
The National Natural Science Foundation of China (No.60872075)
the National High Technology Research and Development Program of China (863 Program) (No. 2008AA01Z227)
