Modified Kalman Filter Research Articles

An interactive gesture control system for collaborative manipulator based on Leap Motion Controller

Gesture control is often used for the control of robotic manipulators. However conventional methods always focus on position control of robotic manipulators and use a fixed position to place gesture detection devices, which limits the flexibility to control and interact. This paper presents an interactive gesture control system based on Leap Motion Controller that can overcome these shortcomings. A coordinate transformation is performed between the position of the left palm detected by Leap Motion Controller and the tool center point (TCP) of the collaborative manipulator to obtain gesture data first. This gesture data is used to control the posture of a six-joint collaborative manipulator-Lite6. The position of gripper is controlled by grip level of the right palm. A controller is designed to manipulate a lift table on which Leap Motion Controller is placed to achieve adaptive ascending and descending movements to meet the needs of operators of different heights and arm spans. A modified Kalman filter is used to filter noise and smooth the signal captured by Leap Motion Controller. The experiments demonstrate that the system can operate stably and accurately control the position, posture, and gripper position of a collaborative manipulator in real time using human palms’ gestures.

A hybrid prognostic & health management framework across multi-level engineering systems with scalable convolution neural networks and adjustable functional regression models

With the rapid development of sensor technology and mechatronics integration, there are emerging demands for achieving prognostic and health management (PHM) within a general framework across multi-level engineering systems. However, current PHM frameworks are mainly supported either by field data or degradation models, which are always limited in inadequate robustness to systematically increase operational availability and reduce maintenance costs. For extending the scalability and flexibility of PHM services, this paper proposes a novel hybrid framework that integrates the scalable convolution neural network (SCNN) with the adjustable functional regression model (AFRM) through a modified linear filter. This proposed framework first establishes a data-driven SCNN with scalable time sequences and kernel sizes to enhance the remaining useful life (RUL) generalization. Then, enabled by an adjustable semi-parametric degradation process model, AFRM with flexible lifetime distributions is designed to evaluate time-to-failure (TTF) in real time. Finally, a modified Kalman filter has been explored to coordinate data-driven RUL results with model-based TTF values to schedule predictive maintenance (PdM). High-accuracy prediction, multi-level systems, high-flexibility evaluation are thus encompassed within the hybrid progressive modification. The proposed framework has been practically validated through multi-level studies including the component-level bearings, subsystem-level engines, and system-level machines. Results demonstrate that our hybrid PHM framework achieves significant improvements in prediction accuracy, evaluation adaptability, and scheduling effectiveness.

An enhanced video compression approach through RLAH encoding and KDENN algorithms

Recently, video transmission is going through many failures because of the limited size of the top-notch technique for storing large volume videos. Thus, video compression (VC) techniques are introduced, which try to eradicate various sorts of redundancies within or betwixt video sequences. However, the VC often falls short to maintain a good quality of compression if motion discontinuities are present in the video frames (VF). To trounce this challenge, this paper proposes an enhanced VC approach via run length-based ASCII Huffman (RLAH) encoding, Kernel-based deep Elman neural network (KDENN), together with modified Kalman filters (MKF) algorithms. Initially, the video is transmuted into frames, and the frame's color space model (CSM) is changed as of RGB to YCbCr. Next, the frames are bifurcated into [8 × 8] blocks, and the significant features are extracted as of every block. On account of these features, the KDENN identifies the motion of every block. Those blocks directly undergo a compression process in case of a single motion. Otherwise, MFK smoothens those blocks in order to eradicate the jitters and undesired movements, and then, it goes through compression. After that, RLAH encoding compresses the VF. Then, on the other side, the RLAH decoding algorithm decomposes the video. The results exhibit that the proposed work renders good quality videos with high PSNR value and also it trounces the prevailing compression techniques concerning compression ratio (CR).

Stealthy FDI attacks on modified Kalman filtering in complex networks with non-Gaussian-Lévy noise

This paper investigates the problem of network security for stealthy false data injection (FDI) attacks under modified Kalman filtering (MKF) over complex networks with non-Gaussian-Lévy noise (NGLN). Initially, a modified Kalman filter (MKFR) is proposed to estimate system states, where the estimation function is related to the probabilistic characteristics of Lévy noise, and the saturation threshold is designed in relation to Lévy noise parameters. The upper bound of error covariance is obtained, and the boundedness of the upper bound is proven through the use of mathematical induction and iterative methods. Second, based on the MKF, this paper proposes a two-channel stealthy FDI attacks (TSFAs) strategy that is related only to the system model, which is injected into the sensor-to-controller (S-C) and controller-to-actuator (C-A) transmission channels at the same time. In addition, the attacker sets two MKFRs as observers to estimate the states of the target system, which are used to adjust the attack signal. Third, a sufficient condition is obtained that demonstrates the stability of the system. Meanwhile, TSFAs can avoid being detected by the residual-based detector to guarantee stealthiness. Finally, the effectiveness of the MKF is verified by the numerical simulation, and the stealthiness of the TSFAs and the impact on the system stability are also demonstrated.

Finite element model updating based on response reconstruction using a modified Kalman filter

Finite element model updating based on response reconstruction using a modified Kalman filter

Image restoration and analysis with application to quality variable prediction in flotation process

Flotation process is commonly used to separate valuable minerals from undesirable ones. Measurement of flotation froth concentration is essential for control applications. The concentration of froth is often evaluated visually by operators and through the laboratory analysis of collected samples over a long period of time. However, the latter suffers from significant time delays and the former is not very reliable. Computer vision is widely used in industry to analyze the froth concentration based on various froth image features. In this study, we propose a computer vision model to estimate the froth concentration. Important practical challenges including contaminated images with bright lighting spots, camera noise and outliers are addressed. First, the contaminated images are restored using a modified Kalman filter. The color, texture, and deep features of froth images are then extracted using gray-level co-occurrence matrix (GLCM) and a transfer learning-based convolutional neural network (CNN), respectively. Afterward, a regression model is built to estimate the froth concentration based on the integrated visual features. An expectation–maximization (EM) algorithm is then applied to determine the unknown parameters of the model, which is also robust against outliers by using a t-distribution to model the noise. Experiments conducted on a batch flotation column for tailings bitumen extraction show that the proposed algorithm is able to estimate the bitumen concentration with reasonable accuracy. The proposed algorithm is a time-saving alternative to time-consuming and costly laboratory analysis and is essential for advanced process control applications.

Spectral Graph Filtering for Noisy Signals Using the Kalman filter

Noise is unwanted electrical or electromagnetic radiation that degrades the quality of the signal and the data. It can be difficult to denoise a signal that has been acquired in a noisy environment, but doing so may be necessary in a number of signal processing applications. This paper extends the issue of signal denoising from signals with regular structures, which are affected by noise, to signals with irregular structures by applying the graph signal processing (GSP) technique and a very wellknown filter, the standard Kalman filter, after adjusting it. When the modified Kalman filter is compared to the standard Kalman filter, the modified one performs better in situations where there are uncertain observations and/or processing noise and shows the best results. Also, the modified Kalman filter showed a higher efficiency when we compared it with other filters for different types of noise, which are not only standard Gaussian noises but also uniform distribution noise across two intervals for uncertain observation noise.

Designing a Novel Two-Stage Fusion Framework to Predict Short-Term Origin–Destination Flow

Short-term origin-destination (OD) demand predicting plays an indispensable role in intelligent transportation systems and ride-hailing service operations. However, most studies are carried out in trip ends travel demand prediction, i.e., trip generation/attraction, while paying less attention to predicting OD flows in citywide traffic management. To this end, a novel generalized framework is proposed, named two-stage fusion framework (TFF). TFF consists of two stages, where the first stage utilizes an attention-based spatio-temporal graph convolutional network (AST-GCN) to predict the trip generation/attraction, and the second stage develops a modified Kalman filter (KF) to predict OD flow which is converted into a coefficient matrix. Lastly, the final predicted OD can be obtained by integrating the trip generation/attraction and the coefficient matrix. In AST-GCN, a gated fusion mechanism and dynamical zone proximity matrix are applied to improve the capacity of capturing the spatio-temporal interdependence among traffic analysis zones. In KF, we redefine the state vector and observed vector and their covariance matrixes, and then introduce the Box-Cox technique to standardize the deviation matrix to adapt to KF. The proposed model is tested on a large-scale real-world data set from Chongqing, China, and the results indicate that TFF can achieve a satisfactory performance of short-term OD prediction. Moreover, the proposed framework has the convenient scalability, because either of the two stages is replaceable by other predictors.

Optimal networked state estimation in the presence of multi-channel correlated packet losses

This paper studies the optimal estimation problem for linear time-invariant systems over lossy communication networks subject to multi-channel packet losses. In its full generality, we assume that the random packet losses are spatially correlated across different channels. The problem is addressed by employing a modified Kalman filter to estimate the system’s states, and the primary issue under study is the convergence of the state estimate under a mean-square criterion. This convergence is shown to be equivalent to the existence of a stabilizing solution to a modified algebraic Riccati equation. We provide a necessary and sufficient condition to characterize the existence of the stabilizing solution, and show that the solution can be computed by solving a set of linear matrix inequalities. Additionally, from a robust convergence perspective, we consider further packet losses whose probabilities are unknown and uncertain. We develop a matrix measure referred to as the critical matrix to characterize the maximal tolerance for the modified Kalman filter to converge. Specifically, we show that the mean-square estimation error is bounded if and only if the critical matrix satisfies a positive semi-definiteness condition.

Fault Diagnosis and Its Applications to Fault Tolerant Control of a Turbojet Engine

This paper presents a comprehensive study of model-based fault diagnosis (FD) and a fault-tolerant control (FTC) scheme for sensor and actuator faults of turbojet engines. For actuator FD, an unbiased estimation scheme with a modified Kalman filter (KF) was developed. For sensor FD, two approaches, the generalized likelihood ratio with robust KF and the pseudo actuator model with modified KF, were investigated in a comparative study. For fault detection and isolation, test statistics are commonly employed to detect fault behavior. For FTC, integral-type sliding mode control using control reconfiguration and the reconstruction of the sensor signal was adopted with the FD schemes. The effectiveness of the employed methods was demonstrated in this study and discussed with numerical simulations.

Optimal Stealthy Attacks on Industrial Cyber Physical Systems Under Random Access Protocol

This article designs the stealthy attack against industrial cyber-physical systems under the random access protocol (RAP), which is used to avoid data collisions. The aim is to design an optimal attack that degrades the estimation performance while remaining strictly stealthy. Under the RAP, only a part of the transmitted data can be utilized to construct attack signals at each time instant, which brings challenges to attack design. To obtain the precondition for attack design, a novel modified algebraic Riccati equation (MARE) is derived, where the MARE’s convergence problem is analyzed and a sufficient condition for the existence of the modified Kalman filter’s steady state is presented. Then, the optimal attack strategy is obtained by solving a convex optimization problem. Furthermore, the analytical optimal attack parameters are presented and proved. Finally, experiments and simulations are performed to verify our results.

Modified Kalman Filtering for Stochastic Nonlinear Systems Under Non-Gaussian–Lévy Noise and Cyber Attacks

This article studies the filtering problem for a class of stochastic nonlinear system under non-Gaussian–Lévy noise and cyber attacks, where the denial-of-service (DoS) attacks and the false data-injection (FDI) attacks are both considered. Since the covariance of the Lévy noise is unknown and infinite, the standard Kalman filter fails to estimate system states. By exploiting saturation functions, a modified Kalman filter is proposed, where the extremely large values of the measurement outputs caused by the Lévy noises can be clipped. In the presence of Lévy noise and cyber attacks, an upper bound for the error covariance is guaranteed and can be minimized via designing the filter parameter. Besides, a sufficient condition is provided to guarantee the boundedness of the upper bound, and the convergence analysis of the filtering error is presented. Finally, the simulation results are given to verify the algorithm.

Adaptive denoising of photoacoustic signal and image based on modified Kalman filter.

As a burgeoning medical imaging method based on hybrid fusion of light and ultrasound, photoacoustic imaging (PAI) has demonstrated high potential in various biomedical applications, especially in revealing the functional and molecular information to improve diagnostic accuracy. However, stemming from weak amplitude and unavoidable random noise, caused by limited laser power and severe attenuation in deep tissue imaging, PA signals are usually of low signal-to-noise ratio, and reconstructed PA images are of low quality. Despite that conventional Kalman filter (KF) can remove Gaussian noise in time domain, it lacks adaptability in real-time estimation due to its fixed model. Moreover, KF-based denoising algorithm has not been applied in PAI before. In this paper, we propose an adaptive modified KF (MKF) targeted at PAI denoising by tuning system noise matrix Q and measurement noise matrix R in the conventional KF model. Additionally, in order to compensate the signal skewing caused by MKF, we cascade the backward part of Rauch-Tung-Striebel smoother, which utilizes the newly determined Q. Finally, as a supplement, we add a commonly used differential filter to remove in-band reflection artifacts. Experimental results using phantom and ex vivo colorectal tissue are provided to prove validity of the algorithm.

Measurement Based Flow Speed Estimation for Tidal Turbine Control

This work aims to explore effective online torque estimation techniques for tidal turbine control under varying flow conditions. A modified Kalman filter with adaptive features is developed to estimate hydrodynamic torque from a tidal turbine's available measurements, based on which a modified Newton-Rapson method is employed to calculate the effective flow speed. The rotor speed reference signal required for tidal turbine control can be produced using the estimated torque and flow speed. The Kalman filter model is constructed using on a low frequency lumped parameter model of a horizontal-axis, fixed-pitch, two-bladed variable speed tidal turbine, established from a fully characterised model of a real turbine. The adaptive feature of the Kalman filter allows the tracking of the spatial-temporal variations of the effective flow speed caused by turbulence. Simulation studies are implemented to test the developed algorithm over the full envelope of the flow speeds.

Research on innovative human capital for China’s economic development based on STI model

Abstract The study uses a modified Kalman filter to analyse the impact of innovative human capital’s contribution to China’s economic development. The Kalman filter-STI model is used, and the growth rates of labour force, physical capital and innovative human capital and their contributions to economic growth are further calculated. The analysis employing the Kalman filter-STI model leads to the following results: In 2015, the sum of innovative human capital in each region increased by 6.15% compared to 2010. From 2005 to 2021, the number of scientific and technical papers included in three international systems in China decreased from 45% to 31% in Beijing, from 34% to 21% in Shanghai, and decreased in Jilin and Gansu. Jiangsu Province is the province with the largest increase in the share, from 13% to 26%.

Estimation of water momentum and propeller velocity in bow thruster model of autonomous surface vehicle using modified Kalman filter

<span lang="EN-US">Autonomous surface vehicle (ASV) is a vehicle in the form of an unmanned on-water surface vessel that can move automatically. As such, an automatic control system is essentially required. The bow thruster system functions as a propulsion control device in its operations. In this research, the water momentum and propeller velocity were estimated based on the dynamic bow thruster model. The estimation methods used is the Kalman filter (KF) and ensemble Kalman filter (EnKF). There are two scenarios: tunnel thruster condition and open-bladed thruster condition. The estimation results in the tunnel thruster condition showed that the root mean square error (RMSE) by the EnKF method was relatively smaller, that is, 0.7920 and 0.1352, while the estimation results in the open-bladed thruster condition showed that the RMSE by the KF method was relatively smaller, that is, 1.9957 and 2.0609.</span>

Design and implementation of high-precision voltage measurement system based on Kalman filter and automatic calibration technology

With the rapid development of science and technology, precision measurement is of increasing importance. In this paper, we discuss noise reduction and automatic calibration issues for a high-precision voltage measurement system that covers voltages from 0.1 µV to 300 V and a frequency range from 10 Hz to 300 kHz (including DC). First, a modified Kalman filter is proposed to reduce measurement noise. Then, a self-calibration circuit is constructed to eliminate the systematic errors in DC voltage (DCV) measurements. Finally, a wideband AC calibration model is developed to overcome the defects in AC voltage (ACV) measurements caused by AC characteristics. The measurement application demonstrates the effectiveness of the proposed methods----the designed voltage measurement system has a DCV measurement accuracy of better than 9 µV/V and a high ACV accuracy of 0.25 mV/V in the frequency range of 30 Hz to 30 kHz at an ambient temperature of 25 °C. In addition, the minimum DC measurement uncertainty obtained on the DCV 10 V range is 0.4 μV/V (type A) and the minimum AC measurement uncertainty obtained on the ACV 10 V range is 2 μV/V (type A).

Evaluating Real Time Ramp Meter Queue Length and Wait Time Estimation

Models to predict ramp meter queue length from traffic detector data are potentially useful tools in improving traffic operations and safety. Existing research, however, has been based on microscopic simulation or relied on extensive calibration of Kalman filter and related models to produce reliable queue length estimates. This research seeks to develop methodologies for improving and simplifying the calibration process of existing queue length models by applying loop detector data including volume, occupancy, and the metering rate data for ramp meters along I-15 in Utah. A conservation model and several variations of a Kalman filter model generated estimated queues that were compared to observed queue lengths in 60 s bins. A modified Kalman filter model and a new heuristic model derived from cluster analysis—the models that yielded the best results—provided queue length estimates that were generally within approximately eight vehicles of the observed queue length. Using the ramp metering rate, the queue length estimates were converted into wait times that were generally within approximately 30 s of the actual wait time, producing a viable method to predict wait time from up-to-the-minute traffic detection information with relatively little required calibration. The implementation of the ramp meter queue length and wait time estimation algorithms presented in this research will allow departments of transportation to better assess freeway and ramp conditions, which can then aid in reducing congestion throughout the freeway network.

High-Speed Train Positioning Using Deep Kalman Filter With 5G NR Signals

Positioning is the most basic yet important process in a train control system. In practical train systems, the position of a train is determined with a track circuit, a radio frequency (RF) tag called a “balise”, or a tachometer. A train control center gives each train an automatic train protection (ATP) speed profile computed from the positioning information of both the train and the next train in front it. To successfully operate in a conventional train control system such as ATP/ATO, each train derives its own automatic train operation (ATO) speed profile from the ATP speed profile provided by the control center. However, existing train positioning schemes face many difficulties in terms of installation, maintenance, and repair. To address these difficulties, we consider using 5G NR (New Radio) signals which have a high probability for guaranteeing line-of-sight (LOS) as well as a high sampling rate because they do not require the additional installation of any infrastructure for positioning. In this paper, we propose two positioning schemes for high speed trains (HSTs) based on Kalman filters that make use of 5G NR signals. The first is an HST positioning scheme using a modified Kalman filter and the second is an HST positioning scheme using a deep Kalman filter. Simulation results show that the two proposed schemes achieve better performance in nonlinear non-Gaussian systems as well as in nonlinear Gaussian systems in terms of mean and worst 5% position errors when compared to the existing positioning scheme for HSTs that utilize 5G NR signals.

Speech enhancement with noise estimation and filtration using deep learning models

Speech enhancement helps in eliminating the environmental noises from the communication signals. The main intention of the augmentation system is to develop the perceptual quality of communication or speech. For this purpose, various filtering schemes, spectral restoration models and speech models were implemented. In order to improve the odds of reducing noise and restoring the original signal, artificial intelligence (AI) and machine learning algorithms (MLA) were included into every sector. Deep transfer learning was used in this work to remove noise from the data and restore the original signals. This proposed approach includes a filtration scheme instead of using a convolution layer in the RESNET-50 architecture. The filters tested for speech enhanced deep learning models are modified Kalman filter and enhanced wiener filter. The performance metrics were calculated between various algorithms and proposed models to identify which approaches to follow the better way result obtained. The performance metrics compared PESQ, LSD and segSNR for different low signal to noise ratio conditions.