Kalman Filter-Based Integrity Monitoring Against Sensor Faults

2013 ◽  
Vol 36 (2) ◽  
pp. 349-361 ◽  
Author(s):  
Mathieu Joerger ◽  
Boris Pervan
Keyword(s):  
Kalman Filter ◽  
Sensor Faults ◽  
Integrity Monitoring

Integrity monitoring for Kalman filter-based localization

2020 ◽  
Vol 39 (13) ◽  
pp. 1503-1524
Author(s):  
Guillermo Duenas Arana ◽  
Osama Abdul Hafez ◽  
Mathieu Joerger ◽  
Matthew Spenko
Keyword(s):  
Kalman Filter ◽  
Time Window ◽  
Satellite System ◽  
Navigation Satellite ◽  
Robot Localization ◽  
Sensor Faults ◽  
Prior Work ◽  
Integrity Monitoring ◽  
Integrity Risk ◽  
Global Navigation Satellite

The problem of quantifying robot localization safety in the presence of undetected sensor faults is critical when preparing for future applications where robots may interact with humans in life-critical situations; however, the topic is only sparsely addressed in the robotics literature. In response, this work leverages prior work in aviation integrity monitoring to tackle the more challenging case of evaluating localization safety in Global Navigation Satellite System (GNSS)-denied environments. Localization integrity risk is the probability that a robot’s pose estimate lies outside pre-defined acceptable limits while no alarm is triggered. In this article, the integrity risk (i.e., localization safety) is rigorously upper bounded by accounting for both nominal sensor noise and other non-nominal sensor faults. An extended Kalman filter is employed to estimate the robot state, and a sequence of innovations is used for fault detection. The novelty of the work includes (1) the use of a time window to limit the number of monitored fault hypotheses while still guaranteeing safety with respect to previously occurring faults and (2) a new method to account for faults in the data association process.

scholarly journals Kalman filter-based ARAIM algorithm for integrity monitoring in urban environment

ICT Express ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 65-71 ◽  
Author(s):  
Hieu Trung Tran ◽  
Letizia Lo Presti
Keyword(s):  
Kalman Filter ◽  
Urban Environment ◽  
Integrity Monitoring

Investigating Fault Detection and Diagnosis in a Hydraulic Pitch System Using a State Augmented EKF-Approach

2019 ◽  
Author(s):  
Magnus F. Asmussen ◽  
Henrik C. Pedersen ◽  
Lina Lilleengen ◽  
Andreas Larsen ◽  
Thomas Farsakoglou
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Extended Kalman Filter ◽  
Fault Detection And Diagnosis ◽  
Sensor Faults ◽  
System Parameters ◽  
Power Regulation ◽  
Pitch System ◽  
Detection And Diagnosis ◽  
Drop Outs

Abstract Pitch systems impose an important part of today’s wind turbines, where they are both used for power regulation and serve as part of a turbines safety system. Any failure on a pitch system is therefore equal to an increase in downtime of the turbine and should hence be avoided. By implementing a Fault Detection and Diagnosis (FDD) scheme faults may be detected and estimated before resulting in a failure, thus increasing the availability and aiding in the maintenance of the wind turbine. The focus of this paper is therefore on the development of a FDD algorithm to detect leakage and sensor faults in a fluid power pitch system. The FDD algorithm is based on a State Augmented Extended Kalman Filter (SAEKF) and a bank of observers, which is designed utilizing an experimentally validated model of a pitch system. The SAEKF is designed to detect and estimate both internal and external leakage faults, while also estimating the unknown external load on the system, and the bank of observers to detect sensor drop-outs. From simulation it is found that the SAEKF may detect both abrupt and evolving internal and external leakages, while being robust towards noise and variation in system parameters. Similar it is found that the scheme is able to detect sensor drop-outs, but is less robust towards this.

Innovation approach to detect the faults in multidimensional dynamic systems

Kybernetes ◽  
2010 ◽  
Vol 39 (1) ◽  
pp. 127-139 ◽  
Author(s):  
Chingiz Hajiyev ◽  
Ali Okatan
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Dynamic Systems ◽  
Detection Algorithm ◽  
Statistical Characteristics ◽  
Sensor Faults ◽  
Content Type ◽  
Longitudinal Dynamics ◽  
Mean And Variance ◽  
The Mean

PurposeThe purpose of this paper is to design the fault detection algorithm for multidimensional dynamic systems using a new approach for checking the statistical characteristics of Kalman filter innovation sequence.Design/methodology/approachThe proposed approach is based on given statistics for the mathematical expectation of the spectral norm of the normalized innovation matrix of the Kalman filter.FindingsThe longitudinal dynamics of an aircraft as an example is considered, and detection of various sensor faults affecting the mean and variance of the innovation sequence is examined.Research limitations/implicationsA real‐time detection of sensor faults affecting the mean and variance of the innovation sequence, applied to the linearized aircraft longitudinal dynamics, is examined. The non‐linear longitudinal dynamics model of an aircraft is linearized. Faults affecting the covariances of the innovation sequence are not considered in the paper.Originality/valueThe proposed approach permits simultaneous real‐time checking of the expected value and the variance of the innovation sequence and does not need a priori information about statistical characteristics of this sequence in the failure case.

scholarly journals Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8441
Author(s):  
Susmita Bhattacharyya
Keyword(s):  
Kalman Filter ◽  
Measurement Errors ◽  
Weighted Least Squares ◽  
Temporal Variations ◽  
Global Navigation Satellite Systems ◽  
Model Parameters ◽  
Integrity Monitoring ◽  
Satellite Systems ◽  
Performance Analyses ◽  
Global Navigation Satellite

This paper evaluates the performance of an integrity monitoring algorithm of global navigation satellite systems (GNSS) for the Kalman filter (KF), termed KF receiver autonomous integrity monitoring (RAIM). The algorithm checks measurement inconsistencies in the range domain and requires Schmidt KF (SKF) as the navigation processor. First, realistic carrier-smoothed pseudorange measurement error models of GNSS are integrated into KF RAIM, overcoming an important limitation of prior work. More precisely, the error covariance matrix for fault detection is modified to capture the temporal variations of individual errors with different time constants. Uncertainties of the model parameters are also taken into account. Performance of the modified KF RAIM is then analyzed with the simulated signals of the global positioning system and navigation with Indian constellation for different phases of aircraft flight. Weighted least squares (WLS) RAIM used for comparison purposes is shown to have lower protection levels. This work, however, is important because KF-based integrity monitors are required to ensure the reliability of advanced navigation methods, such as multi-sensor integration and vector receivers. A key finding of the performance analyses is as follows. Innovation-based tests with an extended KF navigation processor confuse slow ramp faults with residual measurement errors that the filter estimates, leading to missed detection. RAIM with SKF, on the other hand, can successfully detect such faults. Thus, it offers a promising solution to developing KF integrity monitoring algorithms in the range domain. The modified KF RAIM completes processing in time on a low-end computer. Some salient features are also studied to gain insights into its working principles.

scholarly journals Robust Kalman Filter Based Estimation of AUV Dynamics in the Presence Of Sensor Faults

2018 ◽  
Vol 51 (30) ◽  
pp. 424-429
Author(s):  
Ch. Hajiyev ◽  
S.Y. Vural ◽  
A. Shumsky ◽  
A. Zhirabok
Keyword(s):  
Kalman Filter ◽  
Sensor Faults ◽  
Robust Kalman Filter
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