Supervisor:
Southwest Ordnance Industry Bureau
Organizer:
Chongqing Ordnance Industry Society
Chongqing University of Technology
Chongqing University of Technology
Target attribute recognition method based on image and track information fusion
| DOI: | 10.11809/bqzbgcxb2024.02.030 |
| Keywords: | target attribute recognition; radar; D S evidence theory; ResNet; XGBoost |
| Abstract: | In response to the problem of low target attribute recognition capability of single radar sensor, a target attribute recognition method based on D S evidence theory of the information fusion of radar trajectory and photoelectric image is proposed. ResNet network and XGBoost network are used for target attribute recognition of photoelectric images and radar trajectory features respectively, and the obtained category probability assignments are fused by D S combination rules to obtain the final target attribute recognition results. The experimental study shows that the fused model has better recognition capability than the single model before fusion in both long range or close range target attribute recognition, and the fused model can correct the problem of incorrect recognition results caused by the single model. The average recall of the fused model for each category on the test set improved by 3% over the photoelectric image classification model and by 10% over the radar track classification model, with an average recall of 95% for the fused model. |
| Published: | 2024-02-28 |