A Data Association Approach Based on Pseudo Baseline Direction Consistency for Angle-Only Measurements with Unknown Sensor Positions

Data association can yield a unified target identity for sensors, which is the basis of target allocation and collaborative strikes for multiplatform systems. The targets of drone swarms and warhead swarms are small and have similar appearances, making it difficult to associate them through features such as shape, color, and texture; thus, angle-only measurements provide important information. The classic method measures the correlations between angle-only measurements on the basis of the distances between target lines of sight, and its performance depends greatly on precise knowledge of the sensor positions, making it difficult to apply this method to drone swarms and other low-cost mobile platforms. This paper presents a data association approach based on pseudo baseline direction consistency, which uses the consistency of the pseudo baseline directions determined from any two pairs of corresponding relationships in the association result as the cost function. The calculations in this method do not involve sensor position information and are therefore not affected by sensor position errors. Moreover, the method is robust to missed detections and false alarms. Both the simulation results and the experimental results validate the effectiveness of the proposed approach.