Probabilistic-Adaptive Kalman Filtering for Fault-Tolerant Estimation of UAV States

Chingiz Hajiyev

doi:10.1007/978-3-032-00618-9_17

Probabilistic-Adaptive Kalman Filtering for Fault-Tolerant Estimation of UAV States

Chingiz Hajiyev^*

^*Corresponding author for this work

Department of Aeronautical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

A probabilistic-adaptive Kalman filter (pAKF) algorithm for the fault tolerant estimation of Unmanned Aerial Vehicles (UAV) dynamics in the presence of measurement faults is proposed. The proposed pAKF based on the evaluation of the posterior probability of the normal operation of the system, given for the current measurement. This probability is proposed to calculate via the posterior probability density of the normalized innovation sequence at the current estimation step. As a result, faults in the estimation system are corrected by the system, without affecting the good estimation behaviour. The developed pAKF algorithm is applied for the fault tolerant estimation of the UAV dynamics. The proposed pAKF algorithm is tested for the two different measurement malfunction scenarios; continuous bias at measurements and measurement noise increment.

Original language	English
Title of host publication	Sustainable Aviation
Publisher	Springer Nature
Pages	119-123
Number of pages	5
DOIs	https://doi.org/10.1007/978-3-032-00618-9_17
Publication status	Published - 2025

Publication series

Name	Sustainable Aviation
Volume	Part F987
ISSN (Print)	2730-7778
ISSN (Electronic)	2730-7786

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Keywords

fault tolerant estimation
Kalman filter
Probabilistic estimation
sensor faults
UAV

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10.1007/978-3-032-00618-9_17

Cite this

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abstract = "A probabilistic-adaptive Kalman filter (pAKF) algorithm for the fault tolerant estimation of Unmanned Aerial Vehicles (UAV) dynamics in the presence of measurement faults is proposed. The proposed pAKF based on the evaluation of the posterior probability of the normal operation of the system, given for the current measurement. This probability is proposed to calculate via the posterior probability density of the normalized innovation sequence at the current estimation step. As a result, faults in the estimation system are corrected by the system, without affecting the good estimation behaviour. The developed pAKF algorithm is applied for the fault tolerant estimation of the UAV dynamics. The proposed pAKF algorithm is tested for the two different measurement malfunction scenarios; continuous bias at measurements and measurement noise increment.",

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author = "Chingiz Hajiyev",

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AB - A probabilistic-adaptive Kalman filter (pAKF) algorithm for the fault tolerant estimation of Unmanned Aerial Vehicles (UAV) dynamics in the presence of measurement faults is proposed. The proposed pAKF based on the evaluation of the posterior probability of the normal operation of the system, given for the current measurement. This probability is proposed to calculate via the posterior probability density of the normalized innovation sequence at the current estimation step. As a result, faults in the estimation system are corrected by the system, without affecting the good estimation behaviour. The developed pAKF algorithm is applied for the fault tolerant estimation of the UAV dynamics. The proposed pAKF algorithm is tested for the two different measurement malfunction scenarios; continuous bias at measurements and measurement noise increment.

KW - fault tolerant estimation

KW - Kalman filter

KW - Probabilistic estimation

KW - sensor faults

KW - UAV

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PB - Springer Nature

ER -

Probabilistic-Adaptive Kalman Filtering for Fault-Tolerant Estimation of UAV States

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