卡尔曼滤波在光纤围栏系统去噪中的应用

提出一种基于卡尔曼滤波的统计学方法,对光纤围栏系统的状态进行实时估计并去除系统的噪声,提高光纤围栏系统的准确度。基于贝叶斯最大后验概率(MAP)和最小均方误差(MMSE)准则,通过新的测量值和量测更新方程修正后验证状态估计值。这种迭代的算法最终可以得到状态的最优估计值。该方法应用到光纤围栏系统中,实验结果表明可有效地降低光纤围栏的噪声强度,提高扰动定位精度。

A statistical method based on Kalman filter(KF)is proposed to estimate the state of the optical fiber fence system in real time and remove the noise of the system,so as to improve the accuracy of the optical fiber fence system.Based on Bayesian Maximum Posterior Probability(MAP)and Minimum Mean Square Error(MMSE)criteria,the posterior state estimates are modified by new measurement values and measurement update equations.Finally,the optimal state estimation can be obtained by this iterative algorithm.The method is applied to the optical fiber fence system and the experimental results show that the noise intensity of the optical fiber fence can be effectively reduced and the positioning accuracy of the disturbance can be improved.