基于压缩感知的辐射源信号数据级融合识别方法

摘要

针对协同侦察数据级融合识别中通信量大的问题,利用压缩感知可用少量测量值表征完整信号的特点,提出了一种基于压缩感知的数据级融合识别方法.接收终端采用确定测量阵对侦察信号的Gabor时频特征进行压缩测量,通过传输少量的压缩测量值以减轻通信压力,融合中心根据多源测量数据间的相关特性,采取相关性融合规则直接对多源压缩测量数据进行融合,最后再计算融合压缩测量值在不同类信号字典下的重构误差,最小重构误差对应的信号类别即识别结果.分别对融合识别方法识别性能和相关性融合规则融合效果进行仿真分析,实验结果表明:所提方法在保证识别率的同时大幅减小了数据通信代价,在低信噪比时识别性能突出、抗噪声干扰性能好;相比于其他融合规则,基于测量向量相关性的融合规则可保留更为全面的信息.%A novel data-level fusion method for emitter identification is proposed for the large scale communication in cooperative reconnaissance,which uses the superiority of compressed sensing in representing an original signal by using few measured data.In the proposed method,the Gabor time-frequency data of intercepted signal in a receiver is compressively measured with a Gaussian random measurement matrix.By transmitting few compressively measured data rather than the original signal,the large scale communication problem is alleviated.In the fusion center,a correlation fusion rule is proposed to calculate the combined weight of measured data according to the correlation among compressively measured data.To identify the signal type,a dictionary library is trained with every possible signal,and the reconstruction error in the sub-dictionary is calculated.The signal type with minimum reconstruction error is just the identification result.The simulated result proves that the proposed method achieves a pretty good balance in identification rate and communication scale,especially under low signal-to-noise ratio.Compared to the existed algorithms,the correlation fusion rule keeps more details of original signal.