An emitter fusion recognition algorithm based on multi-collaborative representations

When signal samples are severely contaminated by interference noise, good emitter recognition can't be achieved in most cases simply by extracting distinctive features and improving the performance of a single classifier. Firstly, vectorized time-frequency features are extracted, and then representation coefficients are obtained in the frame of collaborative representation. Then, a decision-level fusion of multiple sensors is implemented under the maximum activity rule and recognition results are acquired by selecting the minimum residual. The simulation experiments validate the feasibility of the proposed algorithm and show that the recognition rate of fusion is higher than a single classifier, which indicates the good recognition performance.