Target Recognition and Tracking based on Data Fusion and Data Mining

摘要

A system for target recognition and tracking based on radar and infrared image sensors is presented, which can make use of the complement and redundancy of data from different sensors to improve the precision of target recognition and tracking and the robustness and reliability. For data fusion at characteristic level, characteristics of a target obtained from radar can be used in the IR Image-based subsystem to improve the ability of object recognition, and vice versa. The process of target recognition based on IR image analysis is composed of image enhancement, image segmentation and recognition of segmented objects. The recognition of segmented objects is divided into two classes: recognition of dot targets and area targets. Rule-based reasoning is used to deal with the recognition of dot targets; a classifier based on neural network is used to deal with the recognition of area targets. The models for target recognition are extracted by data mining. The rules for the recognition of dot targets are extracted by decision trees. A neural network for the recognition of area targets is constructed by multi-layer preceptron and trained by training examples. After data fusion at characteristic level, a true target is recognized by the radar-based subsystem and the IR image-based subsystem. Based on these two decisions, data fusion at decision level is to make a final decision of target tracking. A factor of decision certainty is introduced. Our system for target recognition and tracking has been verified by simulation experiments to be more effective and robust than traditional systems.