Fusion Using Quasilinearization Technique for the Likelihood Ratio Based T2ta in Multi Radar Data Fusion

Data fusion techniques combine data from multiple sensors, and related information from associated databases, to achieve improved accuracies and more specific inferences than could be achieved by the use of a single sensor alone. This paper presents the fusion using the generalized quasilinearization technique to obtain a monotone sequence of iterates, converging uniformly for the tracks, obtained after association. Likelihood ratio based cost for association with kinematic information [1] is used for track-to-track association (T2TA). These associated tracks and the fused track are smoothened using Kalman filter (KF). Simulated results through MATLAB are compared with the state vector fusion technique. The main advantage of the proposed method is that fusion follows the actual track where as conventional method results are based on the Kalman estimates.