基于压缩协作表示的辐射源识别算法

针对低信噪比（SNR）条件下传统辐射源识别算法性能下降的问题，提出了基于压缩协作表示的识别算法，分别从特征提取和分类器设计两方面进行描述。首先将时域辐射源信号变换到二维时频域，通过图像处理方法提取高维特征列向量。经随机矩阵压缩到一定维度后，输入到提出的压缩协作表示分类器中得到识别结果。进而，对协作表示系数进行非负约束，提出了更符合实际应用场景的算法。仿真结果验证了所提算法的可行性与有效性，且在低信噪比条件下稳健性强、抗噪声干扰性能好、计算量较小、易于工程实现。

Emitter recognition algorithm based on compressively collaborative representation

Aimed at performance decline of traditional emitter recognition algorithms under the low signal-to-noise ratio (SNR) conditions, an algorithm based on compressively collaborative representation is proposed, which is described from feature extraction and classifier design. The time-domain emitter signal is transformed into a 2-dimensional image through time-frequency analysis, and then a high-dimensional feature vector is extracted with image processing methods. After random compression, the features are input into the proposed classifiers to obtain recognition results. Furthermore, the non-negative constraint is posed to the collaborative representation coefficients, which makes the proposed algorithm more suitable for the practical applications. The simulation results verify the validity of the proposed algorithm and show that its robustness to noise is better when SNR is low and it can be easily realized in engineering due to the low computation burden.