A Probabilistic Model for Sensor Fusion Using Range-Only Measurements in Multistatic Radar

The implementation of multistatic radar architecture for the detection and tracking of targets engendered the development of numerous sensor data fusion models. This article presents a sensor model based on a probabilistic approach for the data fusion in the multistatic radar. The received polar coordinate measurements are converted to the Cartesian coordinates for the state estimation of the target using a Kalman filter. Then a distributed probabilistic maximum likelihood estimation based distributed data fusion method with five sensors is implemented for the data fusion. The simulation results validate the effectiveness of the data fusion algorithm under various measurement noise conditions. It is observed that the performance of the probabilistic data fusion algorithm performs better as compared with the traditional data fusion algorithm.