Multi-sensor active fault-tolerant estimation method based on self-organization Kalman filtering

Abstract

The invention discloses a multi-sensor active fault-tolerant estimation method based on self-organization Kalman filtering. According to the method, a self-organization Kalman filtering structure is built, fault detection is achieved by means of a hard fault detection threshold value, and the increase and decrease rate and the change rate of soft fault detection factors, and compensating factors are designed. Meanwhile, according to signal sensing accuracy of each sensor, information distribution coefficients are designed, and integration optimal estimation among the sensors and active fault-tolerant compensation of the sensors are achieved. The method mainly comprises the steps that a parallel Kalman filtering factor system and a reference Kalman filtering system are built; multi-sensor hard fault detection based on self-organization Kalman filtering is performed; sensor soft fault detection based on the increase and decrease characteristic and the change rate of soft fault factors is performed, and correction of soft faults is achieved; the optimal estimation value X(k) is obtained. The multi-sensor active fault-tolerant estimation method based on self-organization Kalman filtering can effectively solve the problems of redundant signal fault detection and active fault tolerance of a multi-sensor system, and improve integration accuracy and fault tolerance performance of the multi-sensor system.

Description

technical field [0001] The invention belongs to the field of fault-tolerant technical estimation of redundant sensor data in an active safety system, and relates to a self-organizing Kalman filter-based multi-sensor active fault-tolerant estimation method. Background technique [0002] With the rapid development of intelligent technology, the requirements for the perception ability and robust performance of multi-sensor systems are becoming more and more stringent. In order to ensure the reliability of the multi-sensor system, it is necessary to effectively analyze the fault-tolerant function of the system and the accuracy of the input signal, so that the accuracy and system reliability of the sensor acquisition signal can be effectively guaranteed after processing. [0003] Since the random signal has no established change law, its estimation cannot be completely accurate, and the so-called optimal estimation only refers to the optimum under a certain criterion. According ...

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Problems solved by technology

[0004] (1) The state dimension of the centralized Kalman filter is high, and the calculation burden is heavy. The "dimension disaster" brought by it makes the calculation burden increase sharply, which is not conducive to the real-time operation of the filter.

Dimensionality reduction filtering will lose filtering accuracy and even cause filtering divergence

[0005] (2) The fault tolerance performance of the centralized Kalman filter is not high, which is not conducive to fault detection

Faults in either sensor subsystem can pollute other states in the centralized filter, making the sensory information output by the combined sensor system unreliable

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