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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。
Aiming at the low accuracy of fusion results in multi-radar data fusion, an adaptive fusion algorithm based on improved D-S evidence theory is proposed. The algorithm combines multi-sensor time-domain fusion and multi-sensor spatial-domain fusion. Firstly, the box graph is used to classify the single sensor measurements, and the single-sensor time-domain fusion is carried out. According to the improved evidence conflict criterion proposed in the paper, the local evidence with high conflict is corrected and the corresponding multi-sensor spatial data Fusion algorithm. Simulation results show that the proposed algorithm has good feasibility and validity. Compared with the existing multi-radar data fusion algorithm, the proposed algorithm can effectively reduce the systematic errors generated during the fusion process, and the fusion results are more reliable and accurate.