基于Kalman滤波的数据融合研究

Multi-Sonar Data Fusion Using Kalman Filter

分析了标准kalman滤波(KF)和自适应kalman滤波(AKF)的原理,通过仿真实验,对两种滤波算法的性能进行了比较。选用凸组合融合算法作为声纳探测的航迹融合算法,将多声纳融合系统的融合结果和简单交叉定位算法进行了比较研究。仿真实验结果表明:自适应kalman滤波比标准kalman滤波具有更好的目标跟踪性能,多声纳融合结果较简单交叉定位结果的性能有大幅度提高,所选用的融合算法航迹能够较好的与真实航迹吻合。

With analysis of KF （Kalman filter） and AKF （adaptive Kalman filter）, the performances between them are compared by simulation experiment. Choosing the simple convex - combination arithmetic as the track fusion method, the fusion results of both the multi - sonar and the simple cross - orientation are compared and studied. The simulation result shows that AKF is better at targets＇ trace tracking than KF, meanwhile, multi - sonar data fusion is better than simple cross - orientation, and the track of data fusion is fit for the real track.