基于新息的自适应增量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。增量方程的引入可以有效解决欠观测系统的状态估计问题。该文考虑带未知噪声统计的线性离散增量系统，首先提出一种基于新息的噪声统计估计算法。可以得到系统噪声统计的无偏估计。进而，提出一种新的增量系统自适应Kalman滤波算法。相比已有的自适应增量滤波算法，该文所提算法得到的状态估计精度更高。两个仿真实例证明了其有效性和可行性。

Adaptive Incremental Kalman Filter Based on Innovation

Under certain environmental conditions, the unknown system errors often occur and yield to larger filtering errors when the unverified or uncalibrated measurement equation is used. Incremental equation can be introduced, which can effectively solve the problem of state estimation for the systems under poor observation condition. In this paper, the linear discrete incremental system with unknown noise statistics is considered. Firstly, a noise statistics estimation algorithm is proposed based on innovation. The unbiased estimation of system noise statistics can be obtained. Furthermore, a new incremental system adaptive Kalman filtering algorithm is proposed. Compared with the existing adaptive incremental filtering algorithm, the state estimation accuracy of the proposed algorithm is higher. Two simulation examples prove its effectiveness and feasibility.