Target tracking with infrared imaging and millimetre-wave radar sensor

Two commonly used tracking fusion methods for Kalman filter-based multi-sensor data fusion which are weighed cross-covariance fusion and augmented measurement fusion are analysed in this paper. Based on tracking fusion of infrared sensor and millimetre wave Radar ,the fused states and measurements are compared with individual estimates. Results are presented using Monte Carlo simulation by two given virtual trajectories which show that: (1)the two fusion methods are functionally equivalent if the sensors used for data fusion have identical measurement matrix;(2)the obtained joint state-vector estimate is better than the individual sensor-based estimate. Also presented are the possible reason caused the bias between individual position estimate and true followed by the analysis of the computational advantages of each method.