一种噪声未知条件下的盲信号提取方法

传感器网络中的节点带宽等资源受限，使得在设计盲信号处理方法时需考虑信号量化等因素，而量化噪声的引入使得整体噪声复杂且未知。针对传感器网络中噪声统计特性未知的情况，提出了一种基于容积点变换和代价参考粒子滤波的盲信号提取方法。在滤波过程中，采用容积点变换可获得较为准确的预测粒子，通过用户自定义的权值映射规则可以实现粒子的更新和重采样，减少了算法对噪声和源信号统计特性的依赖。实验结果表明该方法可实现对源信号的有效提取，在噪声统计特性未知时的提取性能要优于其他方法。

A New Blind Signal Extraction Method Based on Cost Reference Particle Filter with Unknown Statistics

Developing the blind signal processing method in wireless sensor networks (WSNs) often needs to consider several constraints including limited communication bandwidth and energy of sensors. The processing of observed information will import a variety of quantization noise, which is always difficult to be modelled accurately by simple probabilistic models. To study the extraction issue of signal with unknown statistics in WSNs, a signal extracted method based on a cost reference particle filter (CRPF) is proposed in this paper. The method attains the accuracy of prediction particles by cubature-points transformation, and completes particles updating and propagation through cost-risk functions. Simulation results show that the proposed method has comparable performance with the other algorithms for noise of unknown statistics.