Anti-interference recognition method of aerial infrared targets based on the Bayesian network

The development of infrared decoy countermeasure technologies has resulted in the complexity of the air combat environment. Therefore, higher requirements are put forward considering the infrared imaging-guided air-to-air missile anti-interference target recognition technology. The release of infrared decoys destroys the completeness, saliency and stability of target features. It is difficult for statistical pattern recognition methods based on feature fusion and matching to cover all of the confrontation conditions. In the present study, a Bayesian probabilistic recognition model is proposed that deals with uncertain information about targets and decoys. The proposed model deals with the interference of the air battlefield with the confrontation environment. Moreover, it simulates some functions of the human visual cognition and improves the target recognition ability when interference exists. Furthermore, numerous simulation data samples are utilized to solve the network structure as well as parameters of the Bayesian recognition model and the probabilistic definitions of the target and interference are achieved. Finally, a new aerial infrared target recognition algorithm is constructed based on prior information and the probability recognition model. Results of simulation experiments indicate that the recognition rate of the anti-interference recognition algorithm based on the Bayesian network reaches 90.73%, which is 6.905% higher than that of the anti-interference recognition algorithm based on Naive Bayes classifier in the tested air combat anti-interference simulation image dataset. Moreover, it can solve the problem of anti-interference target recognition to a certain extent such as false targets and target occlusion.