Abstract:

In the middle part of the ballistic trajectory, the target is a group of targets, including warheads, decoys, and fragments. Moreover, due to the long distance from the sensor, the infrared imaging of the target is a point target with less available information. Therefore, a single infrared sensor is often difficult to meet the recognition requirements, which means that multiple sensors need to be fused to complete the recognition task. In response to the fusion recognition problem of infrared multiple sensors, a fusion recognition method based on increment support vector machine-Dempster-Shafer (ISVM-DS) evidence theory is proposed. Firstly, the support vector data description (SVDD) model of infrared features of multiple band sensors is trained, and the shell vector is generated and the ISVM model is trained. Then the posterior probability of the ISVM model is used to generate basic probability assignment (BPA). Finally, the D-S evidence theory is used to fuse the BPA of multiple evidences and output classification results. Experimental results show that the proposed method can effectively improve the accuracy of target recognition.

Key words: ballistic target recognition, multi-sensor fusion, Dempster-Shafer (D-S) evidence theory, support vector machine (SVM)