Chingiz Hajiyev

Pico satellite attitude estimation via Robust Unscented Kalman Filter in the presence of measurement faults

In the normal operation conditions of a pico satellite, a conventional Unscented Kalman Filter (UKF) gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, UKF gives inaccurate results and diverges by time. This study introduces Robust Unscented Kalman Filter (RUKF) algorithms with the filter gain correction for the case of measurement malfunctions. By the use of defined variables named as measurement noise scale factor, the faulty measurements are taken into consideration with a small weight, and the estimations are corrected without affecting the characteristics of the accurate ones. Two different RUKF algorithms, one with single scale factor and one with multiple scale factors, are proposed and applied for the attitude estimation process of a pico satellite. The results of these algorithms are compared for different types of measurement faults in different estimation scenarios and recommendations about their applications are given.

Sensor and control surface/actuator failure detection and isolation applied to F‐16 flight dynamic

Purpose – The purpose of the paper is to present an approach to detect and isolate the aircraft sensor and control surface/actuator failures affecting the mean of the Kalman filter innovation sequence.Design/methodology/approach – The extended Kalman filter (EKF) is developed for nonlinear flight dynamic estimation of an F‐16 fighter and the effects of the sensor and control surface/actuator failures in the innovation sequence of the designed EKF are investigated. A robust Kalman filter (RKF) is very useful to isolate the control surface/actuator failures and sensor failures. The technique for control surface detection and identification is applied to an unstable multi‐input multi‐output model of a nonlinear AFTI/F‐16 fighter. The fighter is stabilized by means of a linear quadratic optimal controller. The control gain brings all the eigenvalues that are outside the unit circle, inside the unit circle. It also keeps the mechanical limits on the deflections of control surfaces. The fighter has nine state v...

Sensor/actuator fault diagnosis based on statistical analysis of innovation sequence and Robust Kalman Filtering

In this paper, an approach to detect and isolate the aircraft sensor/actuator faults affecting the mean of the Kalman filter innovation sequence is presented. The effects of the sensor and actuator faults in the innovation process of the channels are investigated, and a decision approach to isolate the sensor and actuator faults is proposed. When a Kalman filter is used, the decision statistics change regardless of whether the fault is in the sensors or in the actuators, whilst when a Robust Kalman Filter (RKF) is used, it is easy to distinguish the sensor and actuator faults. A novel feature of this diagnostic method is that the innovation sequence based fault isolation algorithm has been presented and hence, the sensor/actuator fault detection and isolation problem has been solved. The categories (or classes) of the likely faults are not demanded. The statistical characteristics of the system are not required to be known after the fault has occurred. In the simulations, the longitudinal dynamics of an aircraft control system are considered, and the detection and isolation of pitch rate gyro faults and actuator faults affecting the mean of the innovation sequence are examined.

UKF-Based Reconfigurable Attitude Parameters Estimation and Magnetometer Calibration

A reconfigurable unscented Kalman filter (UKF)-based estimation algorithm for magnetometer biases and scale factors is proposed as a part of the attitude estimation scheme of a pico satellite. Unlike existing algorithms, in this paper, scale factors are not treated, together with other parameters, as part of the state vector; they are estimated separately. After satisfying the conditions of the proposed stopping rule for bias estimation, UKF reconfigures itself for estimation of attitude parameters alone.

Robust Kalman filtering for small satellite attitude estimation in the presence of measurement faults

Abstract In normal working conditions it is possible to achieve sufficient attitude estimation accuracy for a satellite using regular Kalman filter algorithm. On the other hand, when there is a fault in the measurements, the Kalman filter fails in providing the required accuracy and may even collapse over time. In this paper, a Robust Kalman filtering method is proposed for the attitude estimation problem. By using the proposed method both the Extended Kalman Filter and Unscented Kalman Filter are modified and the new algorithms, which are robust against measurement malfunctions, are called Robust Extended Kalman Filter and Robust Unscented Kalman Filter, respectively. A multiple scale factor based adaptation scheme is preferred for adapting the filters so only the data of the faulty sensor is scaled and any unnecessary information loss is prevented. The proposed algorithms are demonstrated for attitude estimation of a small satellite and performances of these two robust Kalman filters are compared in case of different measurement faults. The application of the algorithm is discussed for small satellite missions where the attitude accuracy depends on a limited number of measurements.

Innovation sequence application to aircraft sensor fault detection: comparison of checking covariance matrix algorithms

In this paper, the algorithms verifying the covariance matrix of the Kalman filter innovation sequence are compared with respect to detected minimum fault rate and detection time. Four algorithms are dealt with; the algorithm verifying the trace of the covariance matrix of the innovation sequence, the algorithm verifying the sum of all elements of the inverse covariance matrix of the innovation sequence, the optimal algorithm verifying the ratio of two quadratic forms of which matrices are theoretic and selected covariance matrices of Kalman filter innovation sequence, and the algorithm verifying the generalized variance of the covariance matrix of the innovation sequence. The algorithms are implemented for longitudinal dynamics of an aircraft to detect sensor faults, and some suggestions are given on the use of the algorithms in flight control systems.

Robust Estimation of UAV Dynamics in the Presence of Measurement Faults

This study introduces a robust Kalman filter (RKF) with a filter-gain correction for cases of measurement malfunctions. Using defined variables called measurement-noise scale factors, the faulty measurements are taken into consideration with a small weight and the estimations are corrected without affecting the characteristics of the accurate ones. In this study, RKF algorithms with single and multiple scale factors are proposed and applied for the state estimation process of an unmanned aerial vehicle (UAV) platform. The results of these algorithms are compared for different types of measurement faults, and recommendations for their utilization are given.

Adaptive Unscented Kalman Filter with multiple fading factors for pico satellite attitude estimation

Thus far, Kalman filter based attitude estimation algorithms have been used in many space applications. When the issue of pico satellite attitude estimation is taken into consideration, general linear approach to Kalman filter becomes insufficient and Extended Kalman Filters (EKF) are the types of filters, which are designed in order to overrun this problem. However, in case of attitude estimation of a pico satellite via magnetometer data, where the nonlinearity degree of both dynamics and measurement models are high, EKF may give inaccurate results. Unscented Kalman Filter (UKF) that does not require linearization phase and so Jacobians can be preferred instead of EKF in such circumstances. Nonetheless, if the UKF is built with an adaptive manner, such that, faulty measurements do not affect attitude estimation process, accurate estimation results even in case of measurement malfunctions can be guaranteed. In this study an Adaptive Unscented Kalman Filter with multiple fading factors based gain correction is introduced and tested on the attitude estimation system of a pico satellite by the use of simulations.

Adaptive Fading UKF with Q-Adaptation: Application to Picosatellite Attitude Estimation

AbstractThe unscented Kalman filter (UKF) is a filtering algorithm that gives sufficiently good estimation results for estimation problems of nonlinear systems even when high nonlinearity is in question. However, in the case of system uncertainty the UKF becomes inaccurate and diverges in time. In other words, if any change occurs in the process noise covariance, which is known a priori, the filter fails. This study introduces a novel adaptive fading UKF algorithm based on the correction of process noise covariance (Q-adaptation) for the case of mismatches with the model. By the use of a newly proposed adaptation scheme for the conventional UKF algorithm, change in the noise covariance is detected and corrected. Differently from most of the existing adaptive UKF algorithms, covariance is not updated at each step; it has only been corrected when the change in the process noise covariance is detected, and that brings about a noteworthy reduction in the computational burden. The proposed algorithm is tested ...

Kalman filter and neural network‐based icing identification applied to A340 aircraft dynamics

Purpose – The purpose of this paper is to maintain safe flight and to improve existing deicing (in‐flight removal of ice) and anti‐icing (prevention of ice accretion) systems under in‐flight icing conditions.Design/methodology/approach – A recent academic research on aircraft icing phenomenon is presented. Several wind tunnel tests of an experimental aircraft provided by NASA are used in the neural network training. Five ice‐affected parameters are chosen in the light of these experiments and researches. An offline artificial neural network is used as an identification technique. The Kalman filter is used to increase the state measurements accuracy such that neural network training performance gets better. A linear A340 dynamic model is selected in cruise conditions. This linear model is simulated in time varying manner in terms of changing icing parameters in a system dynamic matrix. The obtained data are used in neural network training and testing.Findings – Airframe icing can grow in many ways and man...