一种基于“当前”模型的改进卡尔曼滤波算法

An improved Kalman filter algorithm based on the "current" model

针对"当前"模型中加速度上下限对卡尔曼算法造成的影响,提出了一种改进算法。该改进算法利用速度预测估计和速度滤波估计间的偏差进行加速度方差自适应调整,避免了加速度极限值对状态估计精度的影响。最后对具有不同加速度极限值参数的卡尔曼滤波算法进行了仿真,验证了加速度上下限对卡尔曼滤波算法精度有一定影响,并进一步对比了所提出的改进算法和基于"当前"模型的标准卡尔曼滤波算法的效果,结果表明改进算法的预测误差小,跟踪精度高。

An improved Kalman algorithm based on the ＂current＂ model was presented to avoid the influence of the ac- celeration limits. The difference between the velocity forecast estimate and the corrected velocity estimate was utilized to perform adaptive acceleration variance adjustment. The simulation of Kalman algorithms with different acceleration limit parameters proved that the performance of Kalman filter was influenced by the acceleration limits. In addition, the im- proved Kalman algorithm was compared with standard Kalman filter. The results showed that the proposed method fore- cast more accurately than the standard Kalman filter.