Modelling and Simulation of Multi-target Multi-sensor Data Fusion for Trajectory Tracking

An implementation of track fusion using various algorthims has been demonstrated. The sensor measurements of these targets are modelled using Kalman filter (KF) and interacting multiple models (IMM) filter. The joint probabilistic data association filter (JPDAF) and neural network fusion (NNF) algorithms were used for tracking multiple man-euvring targets. Track association and fusion algorithm are executed to get the fused track data for various scenarios, two sensors tracking a single target to three sensors tracking three targets, to evaluate the effects of multiple and dispersed sensors for single target, two targets, and multiple targets. The targets chosen were distantly spaced, closely spaced and crossing. Performance of different filters was compared and fused trajectory is found to be closer to the true target trajectory as compared to that for any of the sensor measurements of that target.