Adaptive multiple-radar point fusion based on bayesian theory

Radar networking is an effective method to improve detection accuracy and fault tolerance in complex electromagnetism environment. It is necessary to study data fusion schemes which can address the challenges from interference and signal-to-noise ratio reduction. In this paper, a data fusion method for multiple-radar point fusion based on bayesian statistical theory is proposed. The multi-source data fusion method based on bayesian theory is combined with kalman filtering, with the prediction of kalman filter and its covariance as the prior knowledge for bayesian theory. The points of multiple-radar are regarded as the observation value of bayesian theory. A real-time estimation method for the standard deviations of radar points is also proposed based on signal-to-noise ratio. The simulation results show that the filtering accuracy of the proposed data fusion method is better than that of the individual radar track and track fusion, and it can adapt to changing standard deviations caused by target distance changing and RCS (Radar Cross-Section) fluctuating. The proposed method is of great value to area air defense.