加权融合鲁棒增量Kalman滤波器

在一定环境条件下，当系统的量测方程没有进行验证或校准时，使用该量测方程往往会产生未知的系统误差，从而导致较大的滤波误差。同样地，当系统的噪声方差不确定时，滤波的性能也将会变坏，甚至会引起滤波器发散。增量方程的引入可以有效消除系统的未知量测误差，从而带未知量测误差的欠观测系统的状态估计问题可以转换为增量系统的状态估计问题。该文考虑带未知量测误差和未知噪声方差的线性离散系统，首先提出一种基于增量方程的鲁棒增量Kalman滤波器。进而，基于线性最小方差最优融合准则，提出一种加权融合鲁棒增量Kalman滤波算法。仿真实例证明了所提算法的有效性和可行性。

Weighted Fusion Robust Incremental Kalman Filter

Under certain environmental conditions, when the measurement equation of the system is not verified or calibrated, the use of the measurement equation will often produce unknown system errors, resulting in large filtering errors. Similarly, when the noise variance of the system is uncertain, the performance of the filter will deteriorate, and even cause the filter divergence. The introduction of incremental equation can effectively eliminate the unknown measurement error of the system, so that the state estimation of system under poor observation condition with unknown measurement error can be transformed into the state estimation of incremental system. In this paper, a robust incremental Kalman filter based on incremental equation is proposed for linear discrete systems with unknown measurement error and unknown noise variance. Then, based on the linear minimum variance optimal fusion criterion, a weighted fusion robust incremental Kalman filtering algorithm is proposed. Simulation results show the effectiveness and feasibility of the proposed algorithm.