采用AKF神经网络的直扩系统抗干扰技术

Interference suppression in spread spectrum system using AKF neural network

为了消除扩频系统中的窄带干扰信号,提出了一种新的基于自适应卡尔曼滤波(AKF)学习算法的递归神经网络预测器(RNNP),其中自适应卡尔曼滤波被用于反馈修改递归神经网络的权值系数,从而准确地估计干扰信号,具有收敛速度快、预测精度高和数值鲁棒性较好的优点.仿真实验表明:基于AKF学习算法的RNNP相对于自适应线性最小均方差(LMS)干扰预测器、自适应近似条件均值(ACM)干扰预测器和基于实时递推学习(RTRL)算法的RNNP,在预测误差的均方误差、收敛速度、干噪比改善量和信噪比损失量方面上有不同程度改进.

In order to eliminate the narrowband interference, a new recurrent neural network predictor （RNNP） based on the adaptive Kalman filter （AKF） was proposed in the spread spectrum system in this paper. The adaptive Kalman filter was used to modify the weights of the RNNP and precisely estimate the interference, with the virtue of rapid convergence rate, high prediction precision and perfect numerical robustness. Simulation results show that the RNNP based on AKF learning algorithm has improvement to different extent on interference elimination capability compared with the adaptive linear least mean square （LMS） interference predictor, the adaptive approximate conditional mean （ACM） interference predictor and the RNNP based on the real-time recurrent learning （RTRL） arithmetic.