无源雷达WSN信号采集模型仿真分析

Research on Passive Radar Signal Acquisition Simulation Scheme in Improved WSN

研究基于改进的无源雷达WSN信号采集模型仿真。无源雷达WSN信号采集过程中,无源雷达信号的稀疏度很高,并且信号未知度较高。传统的信号采集方法应用到该领域时,需要进行较大规模的重构,以保证信号密度,才能完成采集,但是由于无源雷达的WSN信号的未知度过高,重构过程复杂,信号采集过程耗时。提出了一种有效的基于改进无源雷达WSN信号采集方案,首先通过稀疏压缩感知获得节点的数据测量数值,并通过随机投影进行采集测量数据,其次在节点中引入的高斯矩阵对所获的测量值进行编码,通过拉格朗日松弛函数将这些编码进行处理,得到近似最优解,从而获得了一种有效的无源雷达传感信号采集方案。仿真结果表明,基于改进模型在WSN数据收集的精度和耗能方面优于传统的采集方法。

During WSN signal acquisition of passive radar, the signal represents a highly sparse and unknown degree. When the traditional signal acquisition method is applied to the field, large- scale reconstruction is needed to ensure that the signal density and then complete the acquisition. But WSN signal of passive radar possesses a high degree of uncertainty, the reconstruction process is complex and signal acquisition process is time - consuming. This paper proposes an improved WSN signal acquisition program for passive radar. The program first gets measured values of node data by sparse compressed, and obtains measured data via random projection, matrix Gaussian is introduced in nodes to encode the obtained values. Lagrangian relaxation function is used to treat these codes and the approxi- mate optimal solution is given. In this way, an efficient program for sensor signal acquisition of passive radar is ob- tained. Simulation results show that the improved model is better than the traditional WSN data collection in terms of accuracy and energy consumption.