一种神经网络结合NIC的PN码估计方法

Neural Network with NIC Estimation Algorithm for PN Sequence

针对网络电磁对抗环境中直接序列扩频信号的伪随机码的估计,这里提出了一种基于神经网络结合信息更新法则的方法,对过去基于神经网络的方法进行改进,构建了新的运算模型。理论分析和计算机仿真结果表明,该方法能在较低的信噪比容限下,正确地估计出扩频信号的伪随机码序列,该方法的性能优于原有的基于神经网络模型的码估计方法,为今后能够解决低信噪比条件下扩频码序列盲处理问题提供了一种更有效的途径。

Aiming at the estimation for PN sequence of DSSS signals in cyberspace and electromagnetic countermeasure at lower SNR, the paper proposes a neural network with NIC algorithm for the estimation of PN sequence. The new method could improve the taditional one based on only neural network, and constructs a new calculation model. The theoretical analysis and computer simulations show that the proposed algorithm could be used for estimating PN sequence at lower SNR and is of greater efficiency than the traditional neural network algorithm. This could provide a more effective way in solving the blind processing problem of spread spectrum sequences at low SNR.