UAV swarm has emerged as a crucial new combat force in information warfare. To tackle the challenges of collective shape control in UAV swarms driven by multiple tasks， a phase transition control method for UAV swarms inspired by birds’ behavior is proposed. Firstly， the behavioral patterns of bird flocks are analyzed， and the characteristics of collective defense behavior in bird flocks exemplified by sparrows are summarized. Then， a phase transition control model for UAV swarms based on four rules （four-rules phase transition model， FRPT） is built by introducing centripetal/centrifugal dual force and establishing “mutation” interaction rules. Next， with control parameters and order parameters defined and key control parameters regulated， the phase transition control of UAV swarm is achieved and the effectiveness of the proposed method is experimentally proved. Finally， through the optimal parameter solving of swarm intelligence optimization algorithms， the formation period of ordered phases is shortened by nearly 70%， which validates the optimization feasibility of the proposed method.The method is anticipated to be extensively applied in tasks such as reconnaissance and emergency rescue missions for UAV swarm perception.

In recent years， the recognition of non-cooperative communication signals has become research focus in the field of communication countermeasures. In this paper， a recognition method of non-cooperative communication signals based on modulation is proposed. The characteristic parameters of non-cooperative communication signals are extracted by signal processing algorithm， and then the signal modulation mode is identified based on the decision tree combined with discriminant process. Various analog and digital modulation signals can be identified by this method. For frequency shift keying（FSK） signal， this paper proposes to construct a new signal by extracting the phase difference between the sampling points of the baseband signal， and the modulation type of the original signal is determined according to the recognition result of the constructed signal， avoiding the need for prior information. The signal modulation recognition method proposed in this paper has low computational cost and good real-time performance. Matlab simulation results show that this method has good recognition rate， and the correctness and feasibility of this method are proved.