Adaptive Fading Kalman Filter Research Articles

Dynamic load altering attack detection based on adaptive fading Kalman filter in smart grid

AbstractThis paper mainly studies a detection method of dynamic load altering attacks (D‐LAAs) in smart grids. First, communication factors are considered, and a smart grid discrete system model under D‐LAA attack is established. Second, for closed‐loop D‐LAAs, an adaptive fading Kalman filter (AFKF) is designed to estimate the states of smart grids with Gaussian noise in real time, and a Euclidean distance ratio detection algorithm based on AFKF is proposed to detect D‐LAAs. Moreover, the proposed detection algorithm can identify D‐LAAs even in the presence of noise in the measurement data, significantly enhancing the speed of attack detection. Finally, take a smart grid with three generators and six buses as an example. Its feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified by simulations. The simulations are carried out through the real‐time hardware‐in‐the‐loop simulation platform, which is mainly composed of StarSim and multi‐tasking devices.

A Lag Compensation-Enhanced Adaptive Quasi-Fading Kalman Filter for Sensorless Control of Synchronous Reluctance Motor

A novel position estimation strategy based on lag compensation-assisted adaptive quasi-fading Kalman filter (LC-AQFKF) is proposed for synchronous reluctance motor (SynRM) sensorless drive in this article. In LC-QFKF, the quasi-fading factor is derived to avoid the harsh assumptions of conventional adaptive fading Kalman filter, and accessibility of the method is improved while ensuring the estimation accuracy. The computational efficiency of LC-AQFKF is promoted by introducing the quasi-fading factor into the prediction error covariance matrix. Moreover, the frequency characteristic of AQFKF-based active back electromotive force observer is analyzed, and the phase lag problem in high-speed situations caused by the low-pass filtering property of AQFKF is overcome. In this way, the estimation accuracy of the rotor position is significantly enhanced. Besides, the double dynamic position compensation method is put forward to strengthen the position estimation performance of sensorless SynRM drive under dynamic conditions. The effectiveness of the proposed scheme is validated at a 1.5 kW SynRM drive.

An Adaptive Fusion Attitude and Heading Measurement Method of MEMS/GNSS Based on Covariance Matching.

Aimed at the problem of filter divergence caused by unknown noise statistical characteristics or variable noise characteristics in an MEMS/GNSS integrated navigation system in a dynamic environment, on the basis of revealing the parameter adjustment logic of covariance matching adaptive technology, a fusion adaptive filtering scheme combining innovation-based adaptive estimation (IAE) and the adaptive fading Kalman filter (AFKF) is proposed. By setting two system tuning parameters, for the process noise covariance adaptation loop and the measurement noise covariance adaptation loop, covariance matching is sped up and achieves an effective suppression of filter divergence. The vehicle-mounted experimental results show that the mean square error of the combined attitude error obtained based on the fusion filtering method proposed in this paper is better than 0.5°, and the mean square error of the heading error is better than 1.5°. The results can provide technical support for the continuous extraction of low-cost attitude information from mobile platforms.

Dynamic load-altering attack detection based on adaptive fading Kalman filter in power systems

This paper presents an effective and feasible method for detecting dynamic load-altering attacks (D-LAAs) in a smart grid. First, a smart grid discrete system model is established in view of D-LAAs. Second, an adaptive fading Kalman filter (AFKF) is designed for estimating the state of the smart grid. The AFKF can completely filter out the Gaussian noise of the power system, and obtain a more accurate state change curve (including consideration of the attack). A Euclidean distance ratio detection algorithm based on the AFKF is proposed for detecting D-LAAs. Amplifying imperceptible D-LAAs through the new Euclidean distance ratio improves the D-LAA detection sensitivity, especially for very weak D-LAA attacks. Finally, the feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified based on simulations.

Novel interacting multiple model filter for uncertain target tracking systems based on weighted Kullback–Leibler divergence

Interacting multiple model (IMM) filter is a classical method to track targets in hybrid situations. However, it can exhibit divergence when the models are correlated or the system suffers from uncertainties. The generalized covariance intersection method based on the weighted Kullback–Leibler (K–L) divergence can solve the divergence problem of correlated estimates. A novel interacting multiple model (NIMM) filter is presented that combines two different algorithms, the adaptive fading Kalman filter and the maximum correntropy Kalman filter, based on the model interacting with the weighted K-L divergence to address the uncertainty problems of the system. The NIMM filter algorithm is designed and the stability and accuracy are analyzed. The simulation results demonstrate that the proposed filter can effectively improve the accuracy under different uncertainty conditions for classical examples and ballistic trajectory tracking scenarios.

A Novel Adaptive Fading Kalman Filter-Based Approach to Time-Varying Brain Spectral/Connectivity Analyses of Event-Related EEG Signals

This paper proposes a novel adaptive fading Kalman filter (AF-KF)-based approach to time-varying brain spectral and functional connectivity analyses of event-related multi-channel electroencephalogram (EEG) signals. By modeling the EEG signals as a time-varying (TV) multivariate autoregressive (MVAR) process, a new AF-KF with variable number of measurements (AF-KF-VNM) is proposed for estimating the spectra of the EEG signals and identifying their functional connectivity. The proposed AF-KF-VNM algorithm uses a new adaptive fading method to adaptively update the model parameters of the KF for improved state estimation and utilizes multiple measurements for better adaptation to the nonstationary signal observations. Experimental results on a simulated data for modeling the TV directed interactions in multivariate neural data show that the proposed AF-KF-VNM method yields better tracking performance than other approaches tested. The proposed algorithm is then integrated into a novel methodology for combined functional Magnetic Resonance Imaging (fMRI) activation maps and EEG spectrum analyses for quantifying the differences in spectrum contents and information flows between the target and standard conditions in a visual oddball paradigm. The results and findings show that the proposed methodology agrees well with the literature and is capable of revealing significant frequency components and information flow involved as well as their time variations.

Dynamic Visual Tracking for Robot Manipulator Using Adaptive Fading Kalman Filter

This paper focuses on the problem of visual tracking of a moving target with the temporary occlusion of image feature, a dynamic visual tracking control system for robot manipulator is developed by using adaptive fading Kalman filter (AFKF). The estimation of the residual covariance is used to compute the forgetting factor to automatically adjust the weight of the image observation data for improving the visual state estimation accuracy. When the target features are occluded, the prediction of missing observation sequence are generated by using the predicted compensation noise and preorder observation sequence to determine the forgetting factor for estimating the missing visual states. Then, a parameter adaptive law with projection error compensation is designed to realize the visual tracking with uncertain camera parameters. Finally, the trajectory tracking experiments based on a real robot platform is carried out to verify the performance of the proposed state estimator and tracking controller. The results show that the proposed method can accurately realize the visual tracking with the occluded trajectory and inaccurate camera parameters, which improves the flexibility of dynamic visual tracking of robot manipulator.

Model-based topography estimation in trolling mode atomic force microscopy

In this study, a novel approach based on a modified Kalman filter algorithm is presented to directly estimate and measure the surface topography of samples by trolling mode atomic force microscopy. Trolling mode atomic force microscopy was introduced as an atomic force microscopy alternative to overcome imaging problems in liquid environments by reducing the liquid-resonator interaction forces. In conventional imaging techniques, the time to reach the steady state periodic motion of the oscillating probe restricts scanning speed. To overcome this limitation, we propose a novel imaging technique for trolling mode atomic force microscopy based on the system dynamics model and using the adaptive fading Kalman filter with forgetting factor. In this approach, the sample height is estimated directly without the need for any closed loop controller. As a result, the scanning speed is improved significantly, and topography is obtained more accurately compared to the conventional imaging method. Moreover, the effects of process noise, scanning speed, and parameter uncertainties on the performance of proposed approach are investigated.

Hierarchical Fault-Tolerant Control using Model Predictive Control for Wind Turbine Pitch Actuator Faults

Wind energy is one of the fastest growing energy sources in the world. It is expected that by the end of 2022 the installed capacity will exceed 250 GW thanks to the supply of large scale wind turbines in Europe. However, there are still challenging problems with wind turbines. In particular, off-shore and large-scale wind turbines are required to tackle the issue of maintainability and availability because they are installed in harsh off-shore environments, which may also prevent engineers from accessing the site for immediate repair works. Fault-tolerant control techniques have been widely exploited to overcome this issue. This paper proposes a novel fault-tolerant control strategy for wind turbines. The proposed strategy has a hierarchical structure, consisting of a pitch controller and a wind turbine controller, with parameter estimations using the adaptive fading Kalman filter technique. The pitch controller compensates any fault with a pitching actuator, while the wind turbine controller computes the optimal reference command for pitching behavior so that the effect of the fault with a pitch actuator can be minimized. The performance of the proposed approach is demonstrated through a set of simulations with a wind turbine benchmark model.

LED chip accurate positioning control based on visual servo using dual rate adaptive fading Kalman filter

In this paper, to improve the positioning accuracy of LED chip, a dual rate adaptive fading Kalman filter algorithm with delay compensation is proposed and applied to the LED chip visual servo positioning system. Firstly, a structure of dual rate Kalman filter is introduced to the visual servo control system, which compensate the visual information delay and realize the accurate time sequential coordination of encoder and visual feedback. Then, considering the inaccuracy of system mathematical model, the adaptive forgetting factor is added to the iterative process of above algorithm, and the impact of accumulated model error on system stability is consequently mitigated. Finally, the experimental results show that the proposed method obviously decreases the positioning errors of LED chip and is robust to inaccuracy and uncertainty of system model parameters.

An Adaptive Kalman Filtering Approach to Fourier Analysis for Estimating Various Chirp-Type GNSS Interference Frequencies

An adaptive Kalman filter-based frequency estimation algorithm is proposed to track the frequency of a chirp-type global navigation satellite system (GNSS) interference. The unknown interference frequency of GNSS in the received signal can be estimated by using received samples in the time domain, but the thus-obtained value can include a large amount of error caused by measurement noise and by the frequency change rate of the interference. In order to reduce the error, an adaptive fading Kalman filter that is based on the Fourier series is applied in the proposed algorithm. Furthermore, an optimal order-reduction algorithm is proposed to select the appropriate order of the Fourier series for the type of received interference. The performance of the tracking algorithm is verified by making comparisons with conventional algorithms. The proposed algorithm has better tracking performance than conventional algorithms when the jammer-to-signal ratio (J/S) is more than 30 dB, which would seriously affect the accuracy of the GNSS.

Adaptive maneuvering frequency method of current statistical model

Current statistical model U+0028 CSM U+0029 has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly converging speedy and a limited precision when using Kalman filter U+0028 KF U+0029 algorithm. In this study, a new current statistical model and a new Kalman filter are proposed to improve the performance of maneuvering target tracking. The new model which employs innovation dominated subjection function to adaptively adjust maneuvering frequency has a better performance in step maneuvering target tracking, while a fluctuant phenomenon appears. As far as this problem is concerned, a new adaptive fading Kalman filter is proposed as well. In the new Kalman filter, the prediction values are amended in time by setting judgment and amendment rules, so that tracking precision and fluctuant phenomenon of the new current statistical model are improved. The results of simulation indicate the effectiveness of the new algorithm and the practical guiding significance.

Noise adaptive fading Kalman filter for free-space laser communication beacon tracking.

We proposed a prediction algorithm for laser communication pointing, acquisition, and tracking (PAT) subsystems in order to further improve PAT accuracy and reduce the effect of processing delay. In terms of this prediction algorithm, a fading Kalman filter is employed, with the observation noise obtained by the gray value distribution of the laser images. Moreover, to better fit the dynamics of a laser target, the two-stage dynamic model has been chosen as the state transition model for Kalman filtering. In addition, the two-stage dynamic model has been modified by accommodating its form to a change of time lag, thereby compensating the effect of time delay. A series of horizontal path (17 km) experiments under different atmospheric conditions were conducted in the fields. According to the experimental results, the algorithm we proposed could effectively reduce the tracking error and improve pointing accuracy.

AFAKF for manoeuvring target tracking based on current statistical model

The fixed maximum acceleration of current statistical model (CSM) will lead to the deterioration of Kalman filter. To improve the performance of CSM in target tracking, a new modified CSM (MCSM) and a new Kalman filter (KF) are proposed. The new model, which employs innovation dominated subjection function to adaptively adjust maximum acceleration, has a better performance in target tracking, but it is very sensitive to innovation and will lead to a fluctuant phenomenon when target manoeuvres occur. The new adaptive fading Kalman filter which is formed by amendatory KF (AKF) and adaptive fading KF can weaken the fluctuant phenomenon caused by MCSM. The principle and deducing of AKF are specifically elaborated based on probability theory. Three simulations results indicate the high performance and robustness of MCSM and MCSM-adaptive fading amendatory Kalman filter in target tracking.

Fault tolerant control method of dual steering actuator motors for steer-by-wire system

A fault tolerant control method has been proposed based on the steer-by-wire system with dual steering actuator motors as presented in this paper. The paper built a real-time fault diagnosis mechanism based on the motor model and adaptive fading Kalman filter firstly. Then the diagnosis results were used to judge whether the angle feedback control and the torque feedback control need to be amended to realize fault tolerant control. Finally, hardware-in-the-loop test bench of this steer-by-wire system has been built for experiments and the results demonstrated that the fault tolerant control method was feasible and accurate.

Global navigation satellite system interference tracking and mitigation based on an adaptive fading Kalman filter

A time-domain signal tracking and mitigation algorithm is proposed to estimate the frequency of interference in a global navigation satellite system (GNSS) so as to classify different types of interference and to mitigate interference. The frequency of GNSS interference can be obtained using the properties of the trigonometric functions of received signal samples, but these values contain numerous errors caused by measurement noise and frequency changes associated with the interference. To reduce these errors, an adaptive fading Kalman filter is applied in the proposed algorithm. Furthermore, a low-pass differentiator and a pattern enhancement algorithm are used to estimate the sweep period of chirp-type interference, which is used to reset the filter parameter for estimating the frequency of the interference accurately. By estimating the sweep period, the interference identification logic is designed to select the proper system model of the Kalman filter. Finally, in order to mitigate the interference, the denoised frequency from the filter is used to design a notch filter which eliminates the interference in the received signal. The frequency tracking performance of the proposed algorithm is verified to compare with conventional algorithms and the mitigation performance of the proposed algorithm is evaluated by means of Monte Carlo simulations.

적응 페이딩 칼만 필터를 이용한 기준국 기반의 램프 형태 GNSS 기만신호 검출 알고리즘

In this paper, a GNSS interference detection algorithm based on an adaptive fading Kalman filter is proposed to detect a spoofing signal which is one of the threatening GNSS intentional interferences. To detect and mitigate the spoofing signal, the fading factor of the filter is used as a detection parameter. For simulation, the effect of the spoofing signal is modeled by the ramp type bias error of the pseudorange to emulate a smart spoofer and the change of the fading factor value according to ramp type bias error is quantitatively analyzed. In addition, the detection threshold is established to detect the spoofing signal by analyzing the change of the error covariance and the effect of spoofing is mitigated by controlling the Kalman gain of the filter. To verify the performance analysis of the proposed algorithm, various simulations are implemented. Through the results of simulations, we confirmed that the proposed algorithm works well.

An adaptive fading Kalman filter based on Mahalanobis distance

An adaptive Kalman filter with fading factor is derived to address the modeling errors. In any recursion of the proposed filter, a judging index is defined as the square of the Mahalanobis distance of the innovation vector, which is found to be chi-square distributed. Modeling errors can be detected through doing hypothesis test of the index, and then the prior covariance matrix can be artificially inflated by introducing a fading factor which is calculated iteratively using Newton’s method. The proposed method has a stronger tracking ability to the true state than the standard Kalman filter in the presence of modeling errors. Furthermore, by selecting a relatively small significance level, the efficiency of the proposed filter almost does not decrease while the adaptivity is achieved. A simple and illustrative case of kinematic positioning, which is assumed with a constant velocity model is simulated, and an unknown constant acceleration or perturbing Gaussian distribution is artificially introduced to represent the functional and stochastic modeling errors respectively. Simulation results validate the better performance of the proposed method compared to the standard Kalman filter.

A GNSS Interference Identification and Tracking based on Adaptive Fading Kalman Filter

A time domain signal tracking algorithm is proposed to estimate the frequency of a chirp type global navigation satellite system (GNSS) interference. The unknown interference frequency of GNSS is estimated by the received signal using properties of trigonometrical functions, but this value has a lot of error caused by measurement noise and the frequency change of the interference. In order to reduce the ierror, an adaptive fading Kalman filter is applied to the proposed algorithm. Furthermore, a low pass differentiator (LPD) and a pattern enhancement algorithm are used to estimate the sweep period of chirp type interference. The performance of the tracking algorithm is verified by comparing to conventional algorithms. By analyzing the results of comparisons, the proposed algorithm has better tracking performance than conventional algorithms when the jammer to signal ratio (J/S) of GNSS interference signal is more than 30dB, which seriously affects the accuracy of the GNSS.

Multiple Adaptive Fading Schmidt-Kalman Filter for Unknown Bias

Unknown biases in dynamic and measurement models of the dynamic systems can bring greatly negative effects to the state estimates when using a conventional Kalman filter algorithm. Schmidt introduces the “consider” analysis to account for errors in both the dynamic and measurement models due to the unknown biases. Although the Schmidt-Kalman filter “considers” the biases, the uncertain initial values and incorrect covariance matrices of the unknown biases still are not considered. To solve this problem, a multiple adaptive fading Schmidt-Kalman filter (MAFSKF) is designed by using the proposed multiple adaptive fading Kalman filter to mitigate the negative effects of the unknown biases in dynamic or measurement model. The performance of the MAFSKF algorithm is verified by simulation.