Showing papers on "Kalman filter published in 2022"

Improved Vehicle Localization Using On-Board Sensors and Vehicle Lateral Velocity

Abstract: Vehicle localization is essential for intelligent and autonomous vehicles. To improve the accuracy of vehicle stand-alone localization in highly dynamic driving conditions during GNSS (Global Navigation Satellites Systems) outages, this paper proposes a vehicle localization system based on vehicle chassis sensors considering vehicle lateral velocity. Firstly, a GNSS/On-board sensors fusion localization framework is established, which could estimate vehicle states such as attitude, velocity, and position. Secondly, when the vehicle has a large lateral motion, nonholonomic constraint in the lateral direction loses fidelity. Instead of using nonholonomic constraint, we propose a vehicle dynamics/kinematics fusion lateral velocity estimation algorithm, which combines the advantage of vehicle dynamic model in low dynamic driving conditions and the advantage of kinematic model in highly dynamic driving conditions. Thirdly, vehicle longitudinal velocity estimated by WSS (Wheel Speed Sensor) and lateral velocity estimated by proposed method are as measurements for the localization system. All information is fused by an adaptive Kalman filter. Finally, vehicle experiments in U-turn maneuver and left-turn maneuver at a traffic intersection are conducted to verify the proposed method. Four different methods are compared in the experiments, and the results show that the estimated position accuracy of our method is below half a meter during a 5s GNSS outage and could keep a sub-meter-level during a 20s GNSS outage while the vehicle has a relatively large lateral motion.

Optimized adaptive tree seed Kalman filter for a diabetes recommendation system—bilevel performance improvement strategy for healthcare applications

Abstract: Over the past 2 decades, recommendation systems have made remarkable changes and achievements in the field of disease prevention and control. Building an accurate recommendation system for various diseases is important. The recommendation systems primarily rely on data science and conform to standard real-time measures. Suitable data, when provided to recommendation systems, yield an excellent outcomes. The results of a recommendation system will also vary by changing its adaptability to the problem and input used. In this chapter, the authors define an optimized adaptive Kalman filter technique for diabetic recommendation systems. Despite the Kalman filter being best known for its efficacy in forecasting time changes and event prediction, its dependency on static attributes makes it somewhat restricted to real-time dynamic problems. Generally, healthcare problems are dynamic. In particular, the insulin requirement of a diabetes patient is an absolutely dynamic problem, as it is usually determined by various biological parameters that vary greatly with time. It is extremely difficult to recommend any information for a solution using a Kalman filter that relies on the static attributes of a real-time problem. Thus, as an attempt to remove the drawbacks in the existing Kalman filter, the authors propose an adaptive ability mechanism to the existing Kalman filter, which makes it very suitable for dynamic problems such as the insulin recommendation for diabetes. The adaptability performance is further boosted with the usage of a bioinspired optimization algorithm called the tree seed algorithm. This inherits the best values of features among the existing available features and allows only the best features to iterate to the next phase of the process, thus improving the process optimization. The usage of optimization algorithms in selecting the features for building an efficient recommendation gives an added advantage that it will not be affected by local minima solutions. A bilevel performance improvement strategy, such as the adaptive Kalman filter and the use of the tree seed algorithm, makes the proposed method robust in the field of diabetes recommendations. The results extracted from the proposed method are compared with those from conventional methods. The comparative analysis shows that the proposed method outperforms the existing methods in all performance indicators. The proposed method not only provides higher value performance indicators but also eliminates the tradeoff between higher performance indicators and the time taken.

KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics

Abstract: State estimation of dynamical systems in real-time is a fundamental task in signal processing. For systems that are well-represented by a fully known linear Gaussian state space (SS) model, the celebrated Kalman filter (KF) is a low complexity optimal solution. However, both linearity of the underlying SS model and accurate knowledge of it are often not encountered in practice. Here, we present KalmanNet, a real-time state estimator that learns from data to carry out Kalman filtering under non-linear dynamics with partial information. By incorporating the structural SS model with a dedicated recurrent neural network module in the flow of the KF, we retain data efficiency and interpretability of the classic algorithm while implicitly learning complex dynamics from data. We demonstrate numerically that KalmanNet overcomes non-linearities and model mismatch, outperforming classic filtering methods operating with both mismatched and accurate domain knowledge.

Coestimation of State-of-Charge and State-of-Health for Power Batteries Based on Multithread Dynamic Optimization Method

Abstract: Accurate estimation of state-of-charge (SOC) and state-of-health (SOH) is extremely important for the state diagnosis of power batteries, which is related to the energy efficiency and safety of electric vehicles. However, in order to represent the signal noises of sensors, the most commonly used method based on Kalman filter introduces the random Gaussian noise into the estimation, which causes the uncertainty of the estimation results. In this article, the multithread dynamic optimization method is proposed to solve the problem. In addition, the fractional-order model and the unscented Kalman filter are used in SOC estimation. The Gaussian linear models based on parameters of six commonly used open-circuit-voltage models are proposed to estimate SOH. Finally, the dynamic stress test current condition and four lithium-ion batteries are implemented to verify the effectiveness of the proposed method in the experiment. For SOC estimation, root-mean-square error (RMSE) of the proposed method is 0.098 and the average value of the six models is 0.123, thus the proposed method improves the estimation accuracy by 20.32%. For SOH estimation, we compare the smallest RMSE of the six models and that of the proposed method for four experimental batteries, thus the average improvement of accuracy is 25.44%.

Adaptive Ensemble-Based Electrochemical–Thermal Degradation State Estimation of Lithium-Ion Batteries

Abstract: In this article, a computationally efficient state estimation method for lithium-ion (Li-ion) batteries is proposed based on a degradation-conscious high-fidelity electrochemical–thermal model for advanced battery management systems. The computational burden caused by the high-dimensional nonlinear nature of the battery model is effectively eased by adopting an ensemble-based state estimator using the singular evolutive interpolated Kalman filter (SEIKF). Unlike the existing schemes, it shows that the proposed algorithm intrinsically ensures mass conservation without imposing additional constraints, leading to a battery state estimator simple to tune and fast to converge. The model uncertainty caused by battery degradation and the measurement errors are properly addressed by the proposed scheme as it adaptively adjusts the error covariance matrices of the SEIKF. The performance of the proposed adaptive ensemble-based Li-ion battery state estimator is examined by comparing it with some well-established nonlinear estimation techniques that have been used previously for battery electrochemical state estimation, and the results show that excellent performance can be provided in terms of accuracy, computational speed, and robustness.

The First Carrier Phase Tracking and Positioning Results With Starlink LEO Satellite Signals

Abstract: This letter shows the first carrier phase tracking and positioning results with Starlink’s low earth orbit (LEO) satellite signals. An adaptive Kalman filter based algorithm for tracking the beat carrier phase from the unknown Starlink signals is proposed. Experimental results show carrier phase tracking of six Starlink satellites and a horizontal positioning error of 7.7 m with known receiver altitude.

Kalman filtering techniques for the online model parameters and state of charge estimation of the Li-ion batteries: A comparative analysis

Abstract: The state of charge (SoC) is the most commonly used performance indicator of battery used in various applications. A chronic erroneous estimation of battery SoC may result in constant over charging and discharging, which in turn causes permanent damage to the internal structure of the battery cells along with system disruptions. This paper presents a comprehensive review of different techniques for SoC estimation of batteries, followed by a review of Li-ion battery model parameter estimation methods. Then this paper classifies the Kalman filters (KFs) in a systematic manner and conducts a detailed literature review on the linear Kalman filter (LKF) and non-linear Kalman filters (NLKFs). In recent literature, the NLKFs such as extended Kalman filter (EKF), adaptive EKF (AEKF), unscented Kalman filter (UKF), and adaptive UKF (AUKF) are the most extensively established techniques for an accurate and reliable SoC estimation of batteries. However, the precise estimation of battery SoC using the Kalman filters largely relies on accurate battery modeling and its online model parameter estimation. According to the literature, the recursive least square (RLS) and the polynomial regression-based battery model (PRBM) are the most often used techniques for estimating real-time model parameters of Li-ion batteries. Therefore, this paper performs an experimental comparative performance evaluation of the most popularly used NLKFS and battery modeling techniques in terms of SoC estimation accuracy at constant and varying operating conditions. The EKF, AEKF, UKF, and AUKF techniques augmented with the popularly used RLS or PRBM are first developed and tested with offline measured data in the MATLAB platform. Then they are implemented on the LabVIEW based battery testing platform using the Math-Script feature of MATLAB for real-time parameters and SoC estimation. Rigorous experimental studies have been carried out for comparative performance evaluation of the PRBM-EKF, PRBM-AEKF, PRBM-UKF, PRBM-AUKF, RLS-EKF, RLS-AEKF, RLS-UKF, and RLS-AUKF techniques under the standard room temperature (25 °C) and a wide temperature range (−5 °C to 45 °C). Overall, the PRBM-AUKF and RLS-AUKF surpassed other approaches in terms of SoC estimation accuracy.

False Data Injection Attacks Against Partial Sensor Measurements of Networked Control Systems

Abstract: This brief concerns the problem of false data injection (FDI) attacks against partial sensor measurements of a networked stochastic system. For a Kalman filter based output tracking control system with a residual-based anomaly detector, a partial FDI attack strategy is presented to deteriorate the system performance by injecting false signals into the feedback communication channel to tamper partial sensor measurements. The stealthiness condition of the attack as well as its impact on the closed-loop system is derived, which are quite different from those of the FDI attack against all sensor measurements given in the existing work. This may be helpful for guaranteeing the secure control of a networked system by protecting partial critical sensor measurements from FDI attacks. Two numerical examples are included to verify the theoretical results.

State of charge estimation of supercapacitors based on multi‐innovation unscented Kalman filter under a wide temperature range

Abstract: Supercapacitors are characterized by a long service lifetime and high power density, which can meet the instantaneous high‐power demand during the acceleration of electric vehicles. In this study, a fractional‐order model is developed to simulate the polarization effect and charging/discharging characteristics of supercapacitors, considering the precision of the electrochemical model and the amount of calculation of the equivalent circuit model and using the adaptive genetic algorithm to identify the parameters. The accurate prediction of the state of charge (SOC) can improve efficiency, prolong the service lifetime, and ensure the safety of supercapacitors. This study proposes a multi‐innovation unscented Kalman filter algorithm based on the fractional‐order model to improve the SOC estimation accuracy. The proposed algorithm is compared with other algorithms and analyzed under different temperatures and operating conditions to verify the accuracy and effectiveness of the proposed algorithm in estimating the SOC and tracking the terminal voltage. Experimental results show that the root mean squared error and mean absolute error of the proposed algorithm are less than those of the other algorithms. The proposed algorithm accurately estimates the SOC and tracks the terminal voltage. The maximum root mean squared error and mean absolute error of SOC estimation error are 1.8% and 1.78%, respectively.

Coestimation of State-of-Charge and State-of-Health for Power Batteries Based on Multithread Dynamic Optimization Method

Abstract: Accurate estimation of state-of-charge (SOC) and state-of-health (SOH) is extremely important for the state diagnosis of power batteries, which is related to the energy efficiency and safety of electric vehicles. However, in order to represent the signal noises of sensors, the most commonly used method based on Kalman filter introduces the random Gaussian noise into the estimation, which causes the uncertainty of the estimation results. In this article, the multithread dynamic optimization method is proposed to solve the problem. In addition, the fractional-order model and the unscented Kalman filter are used in SOC estimation. The Gaussian linear models based on parameters of six commonly used open-circuit-voltage models are proposed to estimate SOH. Finally, the dynamic stress test current condition and four lithium-ion batteries are implemented to verify the effectiveness of the proposed method in the experiment. For SOC estimation, root-mean-square error (RMSE) of the proposed method is 0.098 and the average value of the six models is 0.123, thus the proposed method improves the estimation accuracy by 20.32%. For SOH estimation, we compare the smallest RMSE of the six models and that of the proposed method for four experimental batteries, thus the average improvement of accuracy is 25.44%.

Hybrid Methods Using Neural Network and Kalman Filter for the State of Charge Estimation of Lithium-Ion Battery

Abstract: With the increasing carbon emissions worldwide, lithium-ion batteries have become the main component of energy storage systems for clean energy due to their unique advantages. Accurate and reliable state-of-charge (SOC) estimation is a central factor in the widespread use of lithium-ion batteries. This review, therefore, examines the recent literature on estimating the SOC of lithium-ion batteries using the hybrid methods of neural networks combined with Kalman filtering (NN-KF), classifying the methods into Kalman filter-first and neural network-first methods. Then the hybrid methods are studied and discussed in terms of battery model, parameter identification, algorithm structure, implementation process, appropriate environment, advantages, disadvantages, and estimation errors. In addition, this review also gives corresponding recommendations for researchers in the battery field considering the existing problems.

State-of-charge estimation for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification

Abstract: The state of charge(SOC) of lithium-ion battery is an essential parameter of battery management system. Accurate estimation of SOC is conducive to give full play to the capacity and performance of the battery. For the problems of selection of forgetting factor and poor robustness and susceptibility to the noise of extended Kalman filtering algorithm, this paper proposes a SOC estimation method for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification. Firstly, the Thevenin equivalent circuit model is established and the recursive least squares with forgetting factor(FFRLS) method is used to achieve the parameter identification. Secondly, an evaluation factor is defined, and fuzzy control is used to realize the mapping between the evaluation factor and the correction value of forgetting factor, so as to realize the adaptive adjustment of forgetting factor. Finally, the noise adaptive algorithm is introduced into the extended Kalman filtering algorithm(AEKF) to estimate the SOC based on the identification results, which is applied to the parameter identification at the next time and executed circularly, so as to realize the accurate estimation of SOC. The experimental results show that the proposed method has good robustness and estimation accuracy compared with other filtering algorithms under different working conditions, state of health(SOH) and temperature.

An Adaptive Kalman Filter With Inaccurate Noise Covariances in the Presence of Outliers

Abstract: In this article, a novel variational Bayesian (VB) adaptive Kalman filter with inaccurate nominal process and measurement noise covariances (PMNC) in the presence of outliers is proposed. The probability density functions of state transition and measurement likelihood are modeled as Gaussian–Gamma mixture distributions. The VB inference is used to perform the state and PMNC simultaneously. Simulations show that the effectiveness of the proposed method with inaccurate noise covariances in the presence of outliers environments.

Unbiasedness-constrained least squares state estimation for time-varying systems with missing measurements under round-robin protocol

Abstract: In this paper, we present an unbiasedness-constrained approach to deal with the state estimation issue for a class of time-varying stochastic systems subject to missing measurements. The state estimates are generated from measurements collected by multiple sensing nodes whose information are transmitted via communication networks under scheduling of the Round-Robin protocol. The purpose of the addressed problem is to design an optimal state estimation algorithm in the sense of least mean square with an unknown initial condition in the existence of possible data missing. By resorting to the batch form technique, the optimal filter is proposed by minimising the estimation error covariance in the sense of matrix trace subject to the unbiasedness constraint. Then, a recursive computational algorithm is exploited for the optimal filter to facilitate practical realisation. Finally, simulations are carried out to demonstrate that the proposed method can be utilised to handle missing measurements in the multi-sensor systems when the initial states are unavailable, which outperforms the Kalman filter especially in the initial stage.

A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries

Abstract: Lithium‐ion batteries are widely used as rechargeable energy and power storage system in smart devices and electric vehicles because of their high specific energy, high power densities, etc. The state of charge (SOC) serves as a vital feature that is monitored by the battery management system to optimize the performance, safety, and lifespan of lithium‐ion batteries. In this paper, a strong tracking adaptive fading‐extended Kalman filter (STAF‐EKF) based on the second‐order resistor–capacitor equivalent circuit model (2RC‐ECM) is proposed for accurate SOC estimation of lithium‐ion batteries under different working conditions and ambient temperatures. The characteristic parameters of the established 2RC‐ECM for the lithium‐ion battery are identified offline using the least‐squares curve fitting method with an average R‐squared value of 0.99881. Experimental data from the hybrid pulse power characterization (HPPC) is used for the estimation and verification of the proposed STAF‐EKF method under the complex Beijing bus dynamic stress test (BBDST) and the dynamic stress test (DST) working conditions at varying ambient temperatures. The results show that the established 2RC‐ECM tracks the actual voltage of the battery with a maximum error of 28.44 mV under the BBDST working condition. For the SOC estimation, the results show that the proposed STAF‐EKF has a maximum mean absolute error (MAE) and root mean square error (RMSE) values of 1.7159% and 1.8507%, while the EKF has 6.7358% and 7.2564%, respectively, at an ambient temperature of −10°C under the BBDST working condition. The proposed STAF‐EKF delivers optimal performance improvement compared to the EKF under different working conditions and ambient temperatures, serving as a basis for an accurate and robust SOC estimation method with quick convergence for the real‐time applications of lithium‐ion batteries.

Precise 3D Indoor Localization and Trajectory Optimization Based on Sparse Wi-Fi FTM Anchors and Built-In Sensors

Abstract: Indoor location-based services have become more and more important due to their potential applications in a wide range of personalized services in recent years. The accuracy of smartphone based 3D indoor localization is subjected to the poor performance of low-cost sensors and limited coverage of location sources. In order to solve these problems, this paper proposes a precise 3D indoor localization and trajectory optimization framework that uses the combination of sparse Wi-Fi Fine Time Measurement (FTM) anchors and built-in sensors (3D-LOWS). The inertial navigation system (INS) mechanization, multi-level constraints and observed values are integrated by the adaptive unscented Kalman filter to eliminate effects of cumulative error, indoor magnetic interference, and diversity of handheld modes. The Wi-Fi based ranging and landmark detection information is used to provide an accurate absolute reference to the built-in sensors based method. In addition, this paper proposes and evaluates two different trajectory optimization algorithms and compares the improved localization performance. The comprehensive experiments indicate that the proposed 3D-LOWS is proved to achieve accurate and stable 3D indoor positioning and trajectory optimization performance under complex indoor environments using sparse wireless stations.

Detection and tracking of moving cloud services from video using saliency map model

Abstract: Abstract In cloud computing, the services are observed in the video stream and clustering their pixels is the initial task in service detection. Tracking is the practice to observe or tracking the moments of a given item in each frame. Numerous false positives are included in the frame. Using the saliency map model and the Extended Kalman Filter, the proposed approach can recognize and track moving objects in video. The item is tracked using an Extended Kalman Filter. In the proposed research the evaluation is based on the delay and accuracy of the evaluation parameter. Finally, the suggested method is compared to existing object tracking methods, with an accuracy of greater than 90% attained.

An Integrated Visual System for Unmanned Aerial Vehicles Following Ground Vehicles: Simulations and Experiments

Abstract: The vision of unmanned aerial vehicles is very significant for UAV-related applications such as search and rescue, landing on a moving platform, etc. In this work, we have developed an integrated system for the UAV landing on the moving platform, and the UAV object detection with tracking in the complicated environment. Firstly, we have proposed a robust LoG-based deep neural network for object detection and tracking, which has great advantages in robustness to object scale and illuminations compared with typical deep network-based approaches. Then, we have also improved based on the original Kalman filter and designed an iterative multi-model-based filter to tackle the problem of unknown dynamics in real circumstances of motion estimations. Next, we have implemented the whole system and do ROS Gazebo-based testing in two complicated circumstances to verify the effectiveness of our design. Finally, we have deployed the proposed detection, tracking, and motion estimation strategies into real applications to do UAV tracking of a pillar and do obstacle avoidance. It is demonstrated that our system shows great accuracy and robustness in real applications.