Rough fuzzy joint probabilistic association for tracking multiple targets in the presence of ECM

Abstract A novel rough fuzzy joint probabilistic data association algorithm (RF-JPDA) is presented to improve the performance of multitarget tracking in the presence of clutter, electronic countermeasures (ECM) and false alarms. The possibility data association matrix is evaluated by applying upper and lower approximations of validated measurements which are obtained from the radar. Four case studies are taken to validate the proposed data association algorithm. The proposed technique performance has been compared with conventional joint probabilistic data association filter (JPDA), fuzzy clustering means (FCM), and fuzzy Genetic Algorithm (Fuzzy-GA) approaches. A hybrid data association approach is formulated and examined for multi-target tracking using intelligent technique. Further, it is evident from the experimental results that RF-JPDA approach is providing enhanced performance in terms of position root mean square error (RMSE), velocity RMSE and execution time for all cases. The average position and velocity RMSE of RF-JPDA are 42.3% and 16.98% less when compared to conventional JPDA. Thus accomplishing novel and effective multiple target tracking algorithm based on expert systems.

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