Concurrent error detection and tolerance in Kalman filters using encoded state and statistical covariance checks
Concurrent error detection and tolerance in Kalman filters using encoded state and statistical covariance checks
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使用编码状态和统计协方差检查卡尔曼滤波器中的并发错误检测和容错
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发表时间：
2016
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通讯作者：
A. Chatterjee
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作者：
Sujay Pandey;Suvadeep Banerjee;A. Chatterjee
卡尔曼滤波是一种用于控制和信号处理系统的多功能工具，用于从噪声测量中预测具有统计意义的数据。在许多实际控制系统中，并不是所有的系统状态都是直接可控和可观测的。卡尔曼滤波从可观测系统状态的有限子集的噪声测量中，使用专门的矩阵算法来预测整个连续演化系统状态集的平均值和协方差。我们的目标是检测卡尔曼滤波操作中涉及的任何基础算术计算(例如加法/乘法)中的错误。虽然现有的线性状态校验和方法可用于检测卡尔曼滤波器的矩阵运算的子集中的错误，但它们不足以检测确定状态协方差所涉及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验的概念。以卡尔曼滤波、轨迹跟踪系统和线性化控制系统为例，验证了该方法的有效性。使用简单的状态恢复方法来补偿检测到的错误，从而允许整个系统在错误影响系统操作时容忍错误。
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.