基于迭代双模型卡尔曼滤波的惯性传感器故障诊断方法

doi:10.12305/j.issn.1001-506X.2025.10.26

摘要/Abstract

摘要：

针对惯性传感器故障导致的导航信息不准确甚至失效的问题，提出一种基于改进卡尔曼滤波的故障诊断方法。该方法通过引入迭代双模型机制，提高扩展卡尔曼滤波算法在处理非线性系统时的性能。同时，采用运动学模型，降低方法对湍流的敏感性，提升算法精度。通过对仿真实验数据的评估验证并与传统方法进行对比分析，结果表明本文所提方法在准确性和鲁棒性上具有显著优势，能够有效实现对惯性传感器突变以及渐变故障的诊断。

关键词: 惯性导航系统, 传感器故障诊断, 扩展卡尔曼滤波器, 故障检测与诊断

Abstract:

In response to the problem of inaccurate or even failed navigation information caused by inertial sensor faults, a fault diagnosis method based on improved Kalman filter is proposed. By introducing an iterative dual-model mechanism, the method enhances the performance of the extended Kalman filter （EKF） algorithm in handling nonlinear systems. Additionally, a kinematic model is adopted to reduce the sensitivity of the method to turbulence, thereby improving algorithm accuracy. Evaluation and comparison with traditional methods using simulation experiment data demonstrate that the proposed method has significant advantages in accuracy and robustness, effectively achieving the diagnosis of both abrupt and gradual faults in inertial sensors.

Key words: inertial navigation system （INS）, sensor fault diagnosis, extended Kalman filter （EKF）, fault detection and diagnosis

中图分类号:

V 241.6

张晨远, 刘海颖. 基于迭代双模型卡尔曼滤波的惯性传感器故障诊断方法[J]. 系统工程与电子技术, 2025, 47(10): 3426-3432.

Chenyuan ZHANG, Haiying LIU. Fault diagnosis method of inertial sensor based on iterative double-model Kalman filter[J]. Systems Engineering and Electronics, 2025, 47(10): 3426-3432.

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故障时间/s	故障单元	故障类型	故障程度/（m·s⁻²）
13 < t < 23	$ {f_{{{Ax}}}} $	阶跃	2
	$ {f_{{{Ay}}}} $	斜坡	0.1t
	$ {f_{{{Az}}}} $	振荡	10sin（0.5πt）/180
33 < t < 43	$ {f_{{{Ax}}}} $	阶跃	−2
	$ {f_{{{Ay}}}} $	振荡	sin（0.5πt）/180
	$ {f_{{{Az}}}} $	斜坡	1t
53 < t < 63	$ {f_{{{Ax}}}} $	振荡	sin（0.5πt）/180
	$ {f_{{{Ay}}}} $	斜坡	−0.1t
	$ {f_{{{Az}}}} $	振荡	5sin（0.5πt）/180

故障时间/s	故障单元	故障类型	故障程度/（m/s²）
13 < t < 33	$ {f_{{{Ay}}}} $	斜坡	0.005t
33 < t < 53	$ {f_{{{Ay}}}} $	斜坡	−0.005t

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