基于小波包变换和BP神经网络的DSSS系统干扰抑制算法  

Interference Suppression Based on Wavelet Packets Transform and BP Neural Network for DSSS System

作　　者：史阳春[1] 吴龙胜[1] 刘佑宝[1] 

机构地区：[1]西安微电子技术研究所,陕西西安710054

出　　处：《中山大学学报（自然科学版）》2011年第5期15-20,共6页Acta Scientiarum Naturalium Universitatis Sunyatseni

基　　金：总装备部预研基金资助项目(51308010512;513160703);国家核高基计划资助项目(20092X01023)

摘　　要：提出功率分布优势小波包变换(PDP-WPT)和扩展BP神经网络(EBPNN),并基于两者提出针对直扩系统(DSSS)的变换域信息信号提取(TISI)干扰抑制算法。首先采用PDP-WPT高效跟踪直扩系统中的敌意干扰,提高算法收敛速度;然后通过EBPNN对变换系数进行信息信号的自适应识别达到干扰抑制的目的,具有复杂度低、鲁棒性好的特点。理论分析得到采用TISI后的扰信比(ISR)抑制量、信噪比(SNR)损失量和误码率(BER)的数学表达式。仿真结果表明:在相同干扰信号的情况下,与两种传统算法相比较,本算法的扰信比抑制量分别提高了43.8%和20.8%,信噪比损失量分别降低了62.5%和34.8%。To suppress the interference in a DSSS system, a transform domain information signal identify （TISI） algorithm is proposed, based on two improved algorithms： Power distributing predominance wavelet packets transform （PDP-WPT） and extend BP neural network （EBPNN）. Firstly, PDP-WPT is proposed to track the interference signal effectively, improving the convergence rate of this algorithm. Secondly, the information signal can be identified from transform domain coefficients by self-adaptive EB- PNN, which has simple structure and enhanced numerical robustness. Based on the math model of the TISL, the formulas for ISR suppression, SNR loss and BER are deduced. Results show that TISI can improve the ISR suppression by 43.8% and 20. 8% , reduce the SNR loss by 62.5% and 34. 8% separately compared with traditional algorithms in the condition of same interference input.

关 键 词：小波包变换 BP神经网络 直扩系统 干扰抑制 

分 类 号：TN914.42[电子电信—通信与信息系统]