Multisensor fusion estimation in state monitoring
应用于状态监测的多传感器融合估计

In practical state estimation, multiple identical sensors are commonly employed to produce more accurate results. However, measurement noises from different sensors are generally correlated with covariance matrix which is difficult to be determined accurately. Moreover, the measured system may contain uncertain parts. This paper decomposes the measured system into two parts, the certain part and the uncertain part. The states of each part are estimated respectively, and the two estimated results are combined to produce the final fusion estimation. For the certain part, a simple optimal fusion estimation algorithm is proposed for computing the maximum eigenvalue of the Pei-Radman measurementnoise covariance matrix. For the uncertain part, a robust fusion algorithm is also developed in terms of the linear matrix inequality(LMI), based on the polytopic models.