基于贝叶斯理论的多雷达点迹自适应融合方法

Adaptive multiple-radar point fusion based on bayesian theory

雷达组网进行数据融合是复杂电磁环境下提高预警探测精度和容错能力的有效方法,研究人员需研究适应干扰、信噪比降低等复杂情形的数据融合方法。基于贝叶斯统计理论提出一种多雷达点迹融合方法,将贝叶斯多源数据融合方法与卡尔曼滤波结合,以卡尔曼滤波输出的航迹预测及其协方差作为贝叶斯理论的先验知识,以多雷达量测结果作为贝叶斯理论的观测值进行融合,并提出一种基于回波信噪比的点迹标准差实时估计方法,构建标准差自适应估计的点迹融合与滤波框架。仿真结果表明,多雷达点迹自适应融合方法,滤波精度优于单雷达滤波结果、优于航迹融合结果,能够适应目标距离、RCS起伏引起的标准差变化,具有较强的工程应用价值。

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.