面向智能船舶的容错卡尔曼融合估计方法

针对智能船舶多传感器系统因未知海洋环境干扰和设备间干扰等因素导致的一个或数个传感器产生随机间歇性故障从而导致融合估计结果出现偏差甚至失真的问题,设计1种基于四分位滤波的容错方法,并针对该方法导致的观测时滞问题设计1种预报方法,提前预报观测值,进而抵消容错方法导致的时滞问题。此外,针对多传感器之间的互协方差难以准确估计的问题,采用CI融合估计方法进行融合估计。为验证算法的有效性和融合估计的精度,对带有间歇性故障的两传感器系统进行仿真试验,并与按矩阵、按对角阵和按标量3种分布式融合估计方法得到的结果进行对比。4种方法的均方误差系数大小对比结果显示,对于带间歇性故障的多传感器系统,设计的融合滤波不仅具有鲁棒性,而且具有较高的融合精度。

Fusion Estimation Method of Fault Tolerant Kalman for Inteligent Ship

In order to solve the problem that the estimation result of fusion is error or even distorted due to the random intermittent failure of one or several sensors due to unknown marine environment interference and equipment interference, A fault tolerant method based on quartile filter is proposed, and a forecasting method is designed for the observation time delay caused by the method. The forecasting value is predicted in advance, and then the time-delay problem caused by the fault-tolerant method is offset. On the other hand, aiming at the problem that it is difficult to accurately estimate the cross-covariance between multiple sensors, a CI fusion estimation method is used to estimate the fusion. In order to verify the effectiveness of the algorithm and the accuracy of the estimation, two sensor systems with intermittent faults were simulated and compared with the results of three distributed fusion estimation methods according to matrix, diagonal matrix and scalar. By comparing the mean square error coefficients of the four methods, it is proved that the proposed fusion filtering method is not only robust to multi-sensor systems with intermittent faults, but also has high fusion accuracy.