论文部分内容阅读
对于带有不确定协方差线性相关白噪声的多传感器系统,利用Lyapunov方程提出设计协方差交叉(CI)融合极大极小鲁棒Kalman估值器(预报器、滤波器、平滑器)的一种统一方法.利用保守的局部估值误差互协方差,提出改进的CI融合鲁棒稳态Kalman估值器及其实际估值误差方差最小上界,克服了用原始CI融合方法给出的上界具有较大保守性的缺点,改善了原始CI融合器鲁棒精度.跟踪系统的仿真例子验证了所提出方法的正确性和有效性.
For a multisensor system with uncertain linear covariance and white noise, a Lyapunov equation is proposed to design a covariance cross (CI) fusion for a very small and robust Kalman estimator (predictor, filter, smoother) This paper proposes an improved CI fusion robust steady-state Kalman estimator and its minimum variance of the actual estimation error using the conservative local estimation error covariance, which overcomes the problem of using the original CI fusion method Which has the disadvantage of large conservativeness and improves the robust accuracy of the original CI fusion.The simulation example of the tracking system verifies the correctness and effectiveness of the proposed method.