Communication signals have many modulation types in the current complex electromagnetic environment, so it is more and more difficult to recognize them accurately. Extracting features from different dimensions can describe the essence of the signals in different aspects and better distinguish various signal modulation formats. However, there may be features with poor anti-noise performance, irrelevant features and redundant features in the original high-dimensional feature set, which not only reduce the recognition probability of the signals but also make the feature selection space grow exponentially. Aiming at the problem of multi-dimensional feature selection of communication signals, this paper proposes a two-stage hybrid feature selection method based on combined scoring and improved binary whale optimization algorithm. In the first stage, the method uses the combined algorithm to initially filter the extracted signal features, and in the second stage, the improved binary whale optimization algorithm is used for further optimization. The results show that, compared with the original feature set, this method can increase the average recognition probability of the signals by up to 17%, and the feature reduction rate up to 81%. Besides, we prove that the proposed algorithm has certain generalization ability for different signal-to-noise ratio (SNR) and classifiers.

As a basic task and key link of space situational awareness, space target recognition has become crucial in threat analysis, communication reconnaissance and electronic countermeasures. Using the fingerprint features carried by the electromagnetic signal to recognize is an effective method. Because traditional radiation source recognition technologies are difficult to obtain satisfactory expert features, automatic feature extraction methods based on deep learning have become popular. Although many deep learning schemes have been proposed, most of them are only used to solve the inter-class separable problem and ignore the intra-class compactness. In addition, the openness of the real space may invalidate the existing closed-set recognition methods. In order to solve the above problems, inspired by the application of prototype learning in image recognition, we propose a novel method for recognizing space radiation sources based on a multi-scale residual prototype learning network (MSRPLNet). The method can be used for both the closed- and open-set recognition of space radiation sources. Furthermore, we also design a joint decision algorithm for an open-set recognition task to identify unknown radiation sources. To verify the effectiveness and reliability of the proposed method, we built a set of satellite signal observation and receiving systems in a real external environment and collected eight iridium signals. The experimental results show that the accuracy of our proposed method can reach 98.34% and 91.04% for the closed- and open-set recognition of eight iridium targets, respectively. Compared to similar research works, our method has obvious advantages.