论文部分内容阅读
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。
Aiming at the influence of upper and lower acceleration limits on the Kalman algorithm in the “current” model, an improved algorithm is proposed. The improved algorithm adaptively adjusts the acceleration variance by using the deviation between the speed prediction estimation and the speed filtering estimation to avoid the influence of the acceleration limit on the state estimation accuracy. Finally, the Kalman filtering algorithm with different acceleration limit parameters is simulated. It is verified that the upper and lower acceleration limits the accuracy of the Kalman filter algorithm. Furthermore, the improved algorithm and the “current” model are further compared The result of standard Kalman filter shows that the improved algorithm has less error and higher tracking accuracy.