Concurrent error detection and tolerance in Kalman filters using encoded state and statistical covariance checks

Sujay Pandey, Suvadeep Banerjee, A. Chatterjee
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引用次数: 5

Abstract

The Kalman filter is a versatile tool used in control and signal processing systems to predict statistically significant data from noisy measurements. In many practical control systems, not all the system states are directly controllable and observable. From noisy measurements of a limited subset of the observable system states, the Kalman filter predicts the mean values and covariances of the complete set of continuously evolving system states using specialized matrix arithmetic. Our goal is to detect errors in any underlying arithmetic computation (e.g. addition/multiplication) involved in the operation of the Kalman filter. While prior linear state checksum methods can be used to detect errors in a subset of the matrix operations of the Kalman filter, they do not suffice for detecting errors in the majority of calculations involved in determining the state covariances. To solve this problem, we develop the notion of statistical state covariance checks. Two applications of a Kalman filter, a trajectory tracking system and a linearized control system for an inverted pendulum are used to demonstrate the proposed approach. A simple state restoration approach is used to compensate for detected errors allowing the complete system to tolerate errors as and when they affect system operation.
使用编码状态和统计协方差检查的卡尔曼滤波器并发错误检测和容错
卡尔曼滤波器是一种通用的工具,用于控制和信号处理系统,从噪声测量中预测统计上显著的数据。在许多实际的控制系统中,并非所有的系统状态都是直接可控和可观察的。从可观测系统状态的有限子集的噪声测量中,卡尔曼滤波器使用专门的矩阵算法预测连续演化的系统状态的完整集合的平均值和协方差。我们的目标是检测卡尔曼滤波器操作中涉及的任何底层算术计算(例如加法/乘法)中的错误。虽然先前的线性状态校验和方法可用于检测卡尔曼滤波器矩阵操作子集中的错误,但它们不足以检测涉及确定状态协方差的大多数计算中的错误。为了解决这个问题,我们提出了统计状态协方差检查的概念。用卡尔曼滤波、轨迹跟踪系统和倒立摆线性化控制系统的两个应用来说明所提出的方法。一种简单的状态恢复方法用于补偿检测到的错误,使整个系统能够容忍影响系统操作的错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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