Fuzzy Kalman filtering

Multiscale fuzzy Kalman filtering

The underwater sensor network is currently a hot research field in academia and industry with many underwater applications, such as ocean monitoring, seismic monitoring, environment monitoring, and seabed exploration. Underwater target tracking is a critical component of ocean development. This paper studies the underwater target tracking problem of the wireless sensor network. The core technology of the target tracking algorithm is the filtering algorithm, which identifies the accuracy of the target tracking system. Nonlinear filtering is a hot issue in target tracking because feasible projects are mostly non-linear systems. The linearization method used in traditional Kalman filtering has serious shortcomings. Therefore, this paper presents the improved cubature Kalman filtering (ICKF) algorithm for underwater target tracking. There is uncertainty in the target movement, an adaptive forgetting factor is given into the cubature Kalman filtering algorithm to directly modify the error covariance to reduce the impact of uncertainties. Then, interactive multi-model technology is introduced to establish the IMMICKF algorithm with multiple states. Compared with other filtering algorithms, the new algorithm can effectively deal with non-linear target tracking problems and obtain better estimation accuracy. The numerical simulation is given to demonstrate the effectiveness of the IMMICKF algorithm.

An adaptive Kalman filter algorithm is adopted to estimate the state of charge (SOC) of a lithium-ion battery for application in electric vehicles (EVs). Generally, the Kalman filter algorithm is selected to dynamically estimate the SOC. However, it easily causes divergence due to the uncertainty of the battery model and system noise. To obtain a better convergent and robust result, an adaptive Kalman filter algorithm that can greatly improve the dependence of the traditional filter algorithm on the battery model is employed. In this paper, the typical characteristics of the lithium-ion battery are analyzed by experiment, such as hysteresis, polarization, Coulomb efficiency, etc. In addition, an improved Thevenin battery model is achieved by adding an extra <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RC</i> branch to the Thevenin model, and model parameters are identified by using the extended Kalman filter (EKF) algorithm. Further, an adaptive EKF (AEKF) algorithm is adopted to the SOC estimation of the lithium-ion battery. Finally, the proposed method is evaluated by experiments with federal urban driving schedules. The proposed SOC estimation using AEKF is more accurate and reliable than that using EKF. The comparison shows that the maximum SOC estimation error decreases from 14.96% to 2.54% and that the mean SOC estimation error reduces from 3.19% to 1.06%.

New estimation methodologies for well logging problems via a combination of fuzzy Kalman filter and different smoothers

In oil and gas development operation, for horizontal wells and extended reach wells, monitoring cuttings flow rate is an important method for analyzing wellbore cleanliness. However, due to complex working environment on site, the accuracy of cuttings flow rate monitoring is low. In order to solve this problem, based on the analysis of a cuttings flow rate real-time measurement method, combined with the classic Kalman filter (KF) algorithm, an adaptive KF model based on fuzzy control rules (Fuzzy-KF) is proposed, it realize real-time update of process noise <inline-formula> <tex-math notation="LaTeX">$({Q})$ </tex-math></inline-formula> and measurement noise <inline-formula> <tex-math notation="LaTeX">$({R})$ </tex-math></inline-formula> and realize real-time correction of data collected by sensors of Cuttings Flow Meters (CFM). Through the comparison of experiments, the research results show that the mean square error (MSE) of the Fuzzy-KF model is 76.2919, and the posterior error estimate be less than 0.3, and all aspects of performance are better than the KF and Sage-Husa KF models. The effects of different initial values of <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> on the experimental results were studied, the research result shows that the difference of the initial value of <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula> will affect the data correction result, but does not change the change trend of each parameter. The Fuzzy-KF model established in this paper can effectively reduce the environmental interference to CFM, and it provides new technical support for improving the accuracy of cuttings flow rate monitoring.

Wireless Sensor Network (WSN) is a new technology integrated with communications, embedded technology, and network functions; having capability of real-time collecting, communicating, and sensing, for which dynamic target locating and tracking is its critical task, as well as a basic element in its commercial and military application such as battlefield information accessing and traffic monitoring. Sensor target tracking is the technology of target location estimate based on measurements from corresponding sensors. An inaccurate estimated location may very easily lead to target tracking failure which reduces the reliability and stability of the target tracking [1]. Experts and scholars have done a lot of researches to solve problems exited in wireless sensor target tracking, and many new tracking algorithms have been proposed, among which tracking algorithms such as target contrail-fitting, linear prediction, Kalman filter, unscented Kalman filter(UKF) and particle filter[2] are most common applied. Sikdar, with his partners, has proposed target location tracking algorithm based on linear sensor which is proved better than conventional contrail fitting algorithm in its effects. Zhang Xiaoping , with his partners, has proposed WSN target tracking algorithm through modeling for quadratic polynomial motions which has improved target tracking accuracy[3]. However, there is a problem in common: all the algorithms above assume the tracking target contrail-fittings being in linear motion, while the actual contrail fitting is of uncertainty and variability, and the estimated accuracy can hardly be improved after being to a certain level. In this condition, the tracking error of the above algorithms can be greater [4]. Kalman filter algorithm can be applied for weakly nonlinear models, but for strongly nonlinear models, Unscented Kalman Filter algorithm can be better. Kalman filter work well in cases based on model-linear and Gaussian noise, thus its estimated accuracy is not that ideal [5, 6]. Particle filter algorithm is based on optimal regression Bayesian filtering algorithm in Monte Carlo simulation, which can accurately estimate the any distribution state with only a small amount of mobile anchor nodes. PF algorithm has distinguished advantages in processing non-linear, non-Gaussian cases [7]. However, in PF application process after several iterations, diversified degradation of the particles will occur, which is likely to lead a target tracking loss, thus further affect WSN target tracking accuracy [8]. In order to solve the diversified degradation of the particles in Particle Filter algorithm, this paper Research on the Target Tracking Algorithm for Wireless Sensor Network Based on Improved Particle Filter Liang Peng,Xinhan Huang, Li He

In autonomous vehicles, GPS is vital for navigation but vulnerable to spoofing attacks that compromise safety and system reliability. Traditional Kalman Filters (KFs) assume linear dynamics and Gaussian noise, which are inadequate for handling uncertainties and nonlinearities caused by GPS spoofing. This paper presents the SpoofGuard Framework, a GPS spoofing detection and mitigation method combining Interval Kalman Filtering (IKF) with hybrid signal modeling. Legitimate GPS signals are modeled using a Ricean distribution, while spoofed signals follow a Rayleigh distribution. The IKF adapts dynamically to both legitimate and spoofed signals, enabling precise position estimation and spoofed signal rejection. Unlike traditional KFs, which rely on point estimates, the IKF uses interval estimates, making it resilient to deviations and spoofing attempts. This approach enhances the security and reliability of autonomous vehicle navigation.

Groundwater pollution source identification using Metropolis-Hasting algorithm combined with Kalman filter algorithm

The Kalman filter is used in this paper as a framework for space time data analysis. Using Kalman filtering it is possible to include physically based simulation models into the data analysis procedure. Attention is concentrated on the development of fast filter algorithms to make Kalman filtering feasible for high dimensional space time models. The ensemble Kalman filter and the reduced rank square root filter algorithm are briefly summarized. A new algorithm, the partially orthogonal ensemble Kalman filter is introduced too. We will illustrate the performance of the Kalman filter algorithms with a real life air pollution problem. Here ozone concentrations in a part of North West Europe are estimated and predicted.

A hybrid Bayesian Kalman filter and applications to numerical wind speed modeling

The development of a Kalman filter for state and parameter estimation of a biotechnical process is discussed. Because of the large complexity of biotechnical processes, mathematical models for online estimation are based on extensive simplifications. Therefore model errors in the structure and parameters cannot be avoided. In such situations, simulations of the process in combination with the estimator are very helpful during the design phase: these permit fast examinations of the different behaviour of linear filters compared to nonlinear algorithms and also investigations of the influence of sampling interval and initial values of state and filter variables on the estimation. By the use of such simulations, the suitability of process models with various degrees of simplifications can also be easily tested. Based on the simulations, an extended Kalman filter with iteration of the output equations was chosen. Besides the states, two parameters of a third order process model are estimated online. The filter algorithm was tested during batch processes and worked well after a slight modification. The filter behaviour observed in the experiments was very similar to the simulations.

Kalman Filtering is a computational tool with widespread applications in robotics, financial and weather forecasting, environmental engineering and defense. Given observation and state transition models, the Kalman Filter (KF) recursively estimates the state variables of a dynamic system. However, the KF requires a cubic time matrix inversion operation at every timestep which prevents its application in domains with large numbers of state variables. We propose Relational Gaussian Models to represent and model dynamic systems with large numbers of variables efficiently. Furthermore, we devise an exact lifted Kalman Filtering algorithm which takes only linear time in the number of random variables at every timestep. We prove that our algorithm takes linear time in the number of state variables even when individual observations apply to each variable. To our knowledge, this is the first lifted (linear time) algorithm for filtering with continuous dynamic relational models.

A new model-based dynamic resistive wall mode (RWM) identification and feedback control algorithm has been developed. While the overall RWM structure can be detected by a model-based matched filter in a similar manner to a conventional sensor-based scheme, it is significantly influenced by edge-localized-modes (ELMs). A recent study suggested that such ELM noise might cause the RWM control system to respond in an undesirable way. Thus, an advanced algorithm to discriminate ELMs from RWM has been incorporated into this model-based control scheme, dynamic Kalman filter. Specifically, the DIII-D [J. L. Luxon, Nucl. Fusion 42, 614 (2002)] resistive vessel wall was modeled in two ways: picture frame model or eigenmode treatment. Based on the picture frame model, the first real-time, closed-loop test results of the Kalman filter algorithms during DIII-D experimental operation are presented. The Kalman filtering scheme was experimentally confirmed to be effective in discriminating ELMs from RWM. As a result, the actuator coils (I-coils) were rarely excited during ELMs, while retaining the sensitivity to RWM. However, finding an optimized set of operating parameters for the control algorithm requires further analysis and design. Meanwhile, a more advanced Kalman filter based on a more accurate eigenmode model has been developed. According to this eigenmode approach, significant improvement in terms of control performance has been predicted, while maintaining good ELM discrimination.

Efficient hybrid Kalman filter for denoising fiber optic gyroscope signal

In the paper the fuzzy Kalman filter (KF) is proposed to allow for adaptation to changing properties of the controlled process. The fuzzy KF is used to estimate both states and unmeasured disturbances of the nonlinear process. Further, a Model Predictive Control (MPC) based on the fuzzy representation of the nonlinear process is formulated. The performance of the proposed estimation fuzzy scheme and predictive controller is evaluated through computer simulations of the pH neutralization process. The pH neutralization process is widely recognized as a difficult control problem due to the strong nonlinearity of the process.