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针对于扩展卡尔曼滤波(Extended Kalman Filter,EKF)和无迹卡尔曼滤波(Unscented Kalman Filter,UKF),介绍了一种通过调节系统输出数据的概率密度函数(Probability Density Function,PDF)的函数形状,从而使滤波系统获得更高的跟踪精度的方法。方法中,用采集的输出数据构成神经网络来补偿系统的非线性,使得滤波具有相当优良的跟踪效果。仿真结果表明方法可以改善EKF和UKF的性能,提高跟踪精度和抗干扰能力。
For Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), a function shape of Probability Density Function (PDF) is introduced by adjusting the output data of the system , So that the filter system to obtain a higher tracking accuracy of the method. In the method, the neural network is used to compensate the nonlinearity of the system by using the collected output data so that the filtering has a rather good tracking effect. The simulation results show that the method can improve the performance of EKF and UKF, and improve the tracking accuracy and anti-interference ability.