An Improved Radar target tracking system using fusion algorithm of Kalman and Particle filters

Radars have been an important tool for mankind since its inception. Several object tracking algorithms have been overlaid over the conventional radar to improve the accuracy of detection. Bayesian methods like Kalman and Particle filters is one such example, where the occurrence of the target at various positions is tracked probabilistically. The objective here is to devise an adaptive target tracking system using a combination of Kalman and particle filters, to improve the accuracy while optimizing the rate of convergence with respect to the basic Bayesian methods. It was observed that Kalman and Particle both have good accuracy of detection at lower accelerations with accuracy decreasing sharply at accelerations above 50 m/s(2). This trends of decreasing accuracy at higher accelerations is mitigated with the proposed system where the accuracy reduces below 90% at accelerations above 100 m/s(2).