基于量测自适应辨识的多传感器数据融合算法

摘要

Aiming at the effective utilization and fusion of multi-sensor measurement in measurement uncertainty, a novel multi-sensor measurement adaptive dala fusion algorithm is proposed. In the new algorithm, influence of interference on sensor measurement is considered. By calculation of measurement likelihood equivalent measurement is confirmed. Statistic distance among the measurement datas is constructed to optimize the equivalent measurement. The reasonable selection and fusion of measurement datas is accomplished. The theoretical analysis and simulation experimental results show the new algorithm not only effectively improves adverse effect of interference on filtering precision, but also reduce computational complexity compared with the distributed fusion method.%针对量测不确定条件下多传感器量测数据的合理利用和有效融合问题,提出了一种量测不确定下多传感器量测自适应数据融合算法.算法实现中考虑到传感器量测受扰动影响的具体情况,通过单个传感器的量测似然度的求解确认等效量测,并利用传感器量测数据间统计距离的构建完成对等效量测优化,进而实现不含扰动影响传感器量测数据的合理选择和融合.理论分析和仿真实验验证结果表明:新算法不仅有效改善扰动对于滤波精度的不利影响,并且相对于分布式融合方式降低计算复杂度.