基于改进D-S 证据理论的多雷达自适应数据融合

针对多雷达数据融合时融合结果精度较低问题,提出一种基于改进D-S 证据理论的自适应融合算法。该算法将单传感器多时刻时域融合和多传感器空域融合相结合。首先,利用盒状图对单传感器测量值分类优化,进行单传感器时域融合;再根据文中提出的改进证据冲突程度判据,对高冲突的局部证据进行修正,并选择相应的多传感器空域数据融合算法。仿真分析表明,文中算法具有较好的可行性与有效性,同现有的多雷达数据融合算法相比,文中算法能够有效降低融合过程中产生的系统误差,且融合结果更加可靠、精确。

Multi-radar Adaptive Data Fusion Based on Improved D-S Evidence Theory

Aiming at the problem of low precision of multi-radar data fusion result, an adaptive data fusion algorithm based on improved D-S(Dempster) evidence theory is proposed in this paper. The proposed method integrates time-domain fusion in different time of single sensor and airspace fusion of multi-sensor. Firstly, in order to accomplish time-domain fusion of single sensor, classifying and optimizing the measurements of single sensor utilising box figure. And afterwards, according to the improved conflictive degree of evidence, correcting the local high conflict evidence, and choosing fusion algorithm of multi-sensor in the airspace adaptively. Simulation analyses show that the algorithm is feasible and effective. Comparing with other data fusion algorithm now available, the fusion error in this paper shows significant improvement, and the fusion results are much more reliable and accurate.