Robust Kalman Filtering for Estimation of UAV Dynamics Under Uncertain Measurement Noise Conditions

Chingiz Hajiyev

doi:10.1007/978-3-032-07678-6_57

Robust Kalman Filtering for Estimation of UAV Dynamics Under Uncertain Measurement Noise Conditions

Chingiz Hajiyev^*

^*Corresponding author for this work

Department of Aeronautical Engineering

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

Abstract

A new innovation-based recursive measurement noise covariance estimation method is proposed. The presented algorithm is used for Kalman filter tuning, as a result, the robust Kalman filter (RKF) against measurement malfunctions is derived. The proposed innovation-based RKF with recursive estimation of measurement noise covariance is applied for the model of Unmanned Aerial Vehicle (UAV) dynamics. Algorithms are examined for two types of measurement fault scenarios; constant bias at measurements (additive sensor faults) and measurement noise increments (multiplicative sensor faults). The simulation results show that the proposed RKF can accurately estimate UAV dynamics in real time in the presence of various types of sensor faults.

Original language	English
Title of host publication	Sustainable Aviation
Publisher	Springer Nature
Pages	318-322
Number of pages	5
DOIs	https://doi.org/10.1007/978-3-032-07678-6_57
Publication status	Published - 2026

Publication series

Name	Sustainable Aviation
Volume	Part F1097
ISSN (Print)	2730-7778
ISSN (Electronic)	2730-7786

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Keywords

covariance estimation
fault tolerant estimation
Kalman filter
sensor faults
UAV

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10.1007/978-3-032-07678-6_57

Cite this

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abstract = "A new innovation-based recursive measurement noise covariance estimation method is proposed. The presented algorithm is used for Kalman filter tuning, as a result, the robust Kalman filter (RKF) against measurement malfunctions is derived. The proposed innovation-based RKF with recursive estimation of measurement noise covariance is applied for the model of Unmanned Aerial Vehicle (UAV) dynamics. Algorithms are examined for two types of measurement fault scenarios; constant bias at measurements (additive sensor faults) and measurement noise increments (multiplicative sensor faults). The simulation results show that the proposed RKF can accurately estimate UAV dynamics in real time in the presence of various types of sensor faults.",

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PY - 2026

Y1 - 2026

N2 - A new innovation-based recursive measurement noise covariance estimation method is proposed. The presented algorithm is used for Kalman filter tuning, as a result, the robust Kalman filter (RKF) against measurement malfunctions is derived. The proposed innovation-based RKF with recursive estimation of measurement noise covariance is applied for the model of Unmanned Aerial Vehicle (UAV) dynamics. Algorithms are examined for two types of measurement fault scenarios; constant bias at measurements (additive sensor faults) and measurement noise increments (multiplicative sensor faults). The simulation results show that the proposed RKF can accurately estimate UAV dynamics in real time in the presence of various types of sensor faults.

AB - A new innovation-based recursive measurement noise covariance estimation method is proposed. The presented algorithm is used for Kalman filter tuning, as a result, the robust Kalman filter (RKF) against measurement malfunctions is derived. The proposed innovation-based RKF with recursive estimation of measurement noise covariance is applied for the model of Unmanned Aerial Vehicle (UAV) dynamics. Algorithms are examined for two types of measurement fault scenarios; constant bias at measurements (additive sensor faults) and measurement noise increments (multiplicative sensor faults). The simulation results show that the proposed RKF can accurately estimate UAV dynamics in real time in the presence of various types of sensor faults.

KW - covariance estimation

KW - fault tolerant estimation

KW - Kalman filter

KW - sensor faults

KW - UAV

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M3 - Chapter

AN - SCOPUS:105020973666

T3 - Sustainable Aviation

SP - 318

EP - 322

BT - Sustainable Aviation

PB - Springer Nature

ER -

Robust Kalman Filtering for Estimation of UAV Dynamics Under Uncertain Measurement Noise Conditions

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