Filter Based State Estimation Research Articles

LRPL-VIO: A Lightweight and Robust Visual-Inertial Odometry with Point and Line Features.

Visual-inertial odometry (VIO) algorithms, fusing various features such as points and lines, are able to improve their performance in challenging scenes while the running time severely increases. In this paper, we propose a novel lightweight point-line visual-inertial odometry algorithm to solve this problem, called LRPL-VIO. Firstly, a fast line matching method is proposed based on the assumption that the photometric values of endpoints and midpoints are invariant between consecutive frames, which greatly reduces the time consumption of the front end. Then, an efficient filter-based state estimation framework is designed to finish information fusion (point, line, and inertial). Fresh measurements of line features with good tracking quality are selected for state estimation using a unique feature selection scheme, which improves the efficiency of the proposed algorithm. Finally, validation experiments are conducted on public datasets and in real-world tests to evaluate the performance of LRPL-VIO and the results show that we outperform other state-of-the-art algorithms especially in terms of speed and robustness.

On distributed Kalman filter based state estimation algorithm over a bearings-only sensor network

On distributed Kalman filter based state estimation algorithm over a bearings-only sensor network

Radio Frequency Signal Strength Based Multitarget Tracking With Robust Path Planning

The proliferation of technologically advanced and mobile devices poses risks to public safety and security. These threats can be mitigated by systems equipped to perform timely identification and tracking of devices and their operators. In pursuit of these capabilities, we propose an automated sensing platform designed specifically for tracking multiple, mobile radio frequency (RF) targets. There are a number of challenges involved with tracking multiple moving RF sources. We formulate the task as an iterative state estimation and path planning process, whereby the sensor platform first estimates the positions of the targets through observation of the RF environment and then plans and executes a movement path. By developing a sensor model informed by RF propagation theory, we construct a particle filter based state estimator with the potential to track multiple targets using only signal strength observations. In addition, we propose a path planning technique rooted in uncertainty minimization and safety based constraints. Finally, we validate the proficiency of the proposed methods with simulated experiments. Through analysis of tracking metrics and localization performance we show the benefits of our proposed active sensing techniques as they apply to tracking multiple RF targets. We demonstrate the robustness of our method to various environmental scenarios by testing with a multitude of realistic and challenging experimental parameters (e.g., speed of the sensor platform, number of targets, speed of targets, level of signal-to-noise ratio (SNR)). The results indicate that our method performs better than other state-of-the-art tracking methods, with significant improvements seen in the most difficult scenarios with higher speed targets. In these and other settings, our method is more often able to localize the targets and with less error and uncertainty in position estimation. We also show that our method is computationally efficient and scales well to an increasing number of targets.

Particle filter based self sensing Shape Memory Alloy wire actuator under external cooling

Shape Memory Alloy wires are widely used as actuators due to their ability to generate a wide range of force and displacement. In practice, these wires are activated following resistive heating by passing an electrical current through them, whereas deactivation is enacted through environmental cooling or forced cooling. It is observed that for fine wires, the temperature is highly sensitive to ambient conditions, i.e., temperature, the velocity of wind, humidity etc., thus severely affecting the performance of these wire actuators. In this study, a particle filter based state estimation technique has been developed to estimate the outcome of the Shape Memory Alloy wire actuated system, by using the change in electrical resistance data of the wire during actuation. Under stationary ambience, the developed particle filter based estimator performs as good as the EKF based estimators (Mohan and Banerjee (2021)). However, both estimators fail while an unwanted external cooling is introduced through a fan. To improve on it, both filters are modified, incorporating convective heat transfer coefficient as an unknown to be estimated in addition to the system’s state. The performance of both estimators are evaluated for various loading conditions in the presence of forced cooling of different extents and intensities. Under external cooling conditions, the proposed Particle Filter based estimator outperforms the same based on EKF in almost all loading situations.

Optimal Zonotopic Kalman Filter-based State Estimation and Fault-diagnosis Algorithm for Linear Discrete-time System with Time Delay

Optimal Zonotopic Kalman Filter-based State Estimation and Fault-diagnosis Algorithm for Linear Discrete-time System with Time Delay

Orbit Estimation for Spacecraft Based on Intermittent Measurements: An Event-Triggered UKF Approach

This article addresses a problem of spacecraft orbit estimation with measurement transmission restrictions through an event-triggered state estimation approach. Specifically, we consider the scenario that the ground station communicates with the spacecraft through multiple channels and each channel decides whether or not to transmit the measurements to the spacecraft based on separate deterministic event-triggering conditions. Considering $J_2$ perturbations, an event-triggered unscented Kalman filter-based state estimation algorithm is designed on the basis of a model of orbit dynamics and a measurement model of the ground station. The performance of the estimator is evaluated based on real-time two-line element data. The obtained results indicate that the proposed algorithm can achieve satisfactory estimation performance at reduced measurement transmission rates.

Iterated extended Kalman filter based state estimation of diode circuit

This paper presents the state estimation of diode circuit using iterated extended Kalman filter (IEKF). The root mean square error (RMSE) based performance evaluation gives the superiority of the IEKF based estimation over extended Kalman filtering (EKF) based method.

Ensemble kalman filter and particle filter-based state estimation on electrical power systems

In this article, a referential study of the sequential importance sampling particle filter with a systematic resampling and the ensemble Kalman filter is provided to estimate the dynamic states of several synchronous machines connected to a modified 14-bus test case, when a balanced three-phase fault is applied at a bus bar near one of the generators. Both are supported by Monte Carlo simulations with practical noise and model uncertainty considerations. Such simulations were carried out in MATLAB by the Power System Toolbox, whereas the evaluation of the Particle Filter and the Ensemble Kalman Filter by script files developed inside the toolbox. The results obtained show that the particle filter has higher accuracy and more robustness to measurement and model noise than the ensemble Kalman filter, which helps support the feasibility of the method for dynamic state estimation applications.

Formation control of nonholonomic unmanned ground vehicles via unscented Kalman filter-based sensor fusion approach

This paper investigates the formation control of nonholonomic unmanned ground vehicles via unscented Kalman filter-based sensor fusion approach. According to the kinematic model of single unmanned ground vehicle, the formation model of multiple unmanned ground vehicles is established. Note that the physical leader is considered instead of a virtual leader, which is more realistic. The formation control problem is converted to the stability problem of an error dynamic system. An asymptotic stability condition of the error dynamic system is derived by designing an appropriate Lyapunov function. The leader-following formation is well formed through designing effective control vectors and utilizing unscented Kalman filter-based state estimation algorithm for each follower. Some simulation examples are provided to verify the effectiveness of the proposed formation control algorithm.

전자 유압 엑추에이터를 포함하는 비선형 능동 서스펜션의 상태추정을 위한 가우시안 분포 리샘플링 기반 입자 필터 설계

This paper proposes a GDRPF (Gaussian Distribution Resampling Particle Filter) for electro-hydraulic active suspension systems. In order to estimate the state of the nonlinear/non-Gaussian suspension system effectively, a particle filter-based state estimator was developed, and the computational burden and sample impoverishment problems were overcome through the GDR algorithm. The proposed GDRPF-based state estimator not only reduces the computational burden of the particle filters efficiently, but also has better estimate performance than other linearization-based state estimators. Simulation results for nonlinear active suspension system demonstrate the effectiveness of the proposed algorithm.

Nonlinear observer-based optimal control of an active transfemoral prosthesis

This paper designs a joint controller/observer framework using a state dependent Riccati equation (SDRE) approach for an active transfemoral prosthesis system. An integral state control technique is utilized to design a tracking controller for a robot/prosthesis system. This framework promises a systematic flexible design using which multiple design specifications such as robustness, state estimation, and control optimality are achieved without the need for model linearization. Performance of the proposed approach is demonstrated through simulation studies, which show improvements versus a robust adaptive impedance controller and an extended Kalman filter-based state estimation method. Numerical results confirm the benefits of our method over the above-mentioned approaches with regard to control optimality and state estimation.

A state estimation scheme with minimal sensor configuration for three-body tethered satellite formations

Tethered satellite formations in space have attracted widespread attention in recent years, as they can provide an excellent large-scale, self-stabilized space platform. At present, most studies on tethered satellite formation systems focus on dynamics, control deployment and stability analysis, and they all assume that the state information of the system is given or easily measured. However, it is not trivial to obtain accurate state measurements of tethered satellite formations in space, especially without using high-cost sensors. In this paper, we present a filter-based state estimation scheme with minimum sensor configuration for three-body tethered satellite formation. The satellite-to-satellite ranges and absolute attitude angles of tethered formation can be accurately estimated by fusing only the measurements of three gyroscopes and three tether releasing mechanisms. Particularly, the absolute attitude angles are obtained even without using any sensors that can measure absolute information. The feasibility of the proposed scheme is theoretically proven via the observability analysis of nonlinear systems, and the performance of the scheme is demonstrated through extensive simulations. This method can be readily extended to the state estimation process of other tethered formations in space.

Integration of Prognostics and Control of an Oil/CO2 Subsea Separation System

The exploitation of reserves with a high CO2 content is challenging because of the need for its separation and the environmental impact associated with its generation. In this context, a suitable use for the generated CO2 is its reinjection into the reservoir, and subsea CO2 separation improves the efficiency of this process. The main objective of this work is to investigate the health-aware control of a subsea CO2 separation system. Previously identified linear models were used in a predictive controller with Kalman filter-based state estimation and online model update, and simulations were performed to evaluate the controller tuning. Regarding prognostics, a stochastic model of pump degradation, sensitive to its operating conditions, was proposed, and a particle filter was implemented to perform online degradation state estimation and remaining useful lifetime prediction. Finally, a health-aware controller was designed, which could extend the life of the process by four months when compared to operation with a conventional model predictive controller. Some difficulties in combining reference tracking and lifetime extension objectives were also investigated. The obtained results indicate that dealing with the control problem through the multiobjective optimization theory or addressing the lifetime extension in an optimization layer may improve its performance.

Wave Excitation Force Estimator Using Kalman Filtering Approach for Point Absorber Wave Energy Converters Under Different Modeling and Operation Scenarios

In this research, one of the most important research questions that influence the effectiveness of the used control strategies in heaving wave energy converters (WECs) is addressed. A nonlinear extended Kalman filter (EKF) based state estimator to estimate the wave excitation force and heave velocity in real-time is proposed. A holistic nonlinear model involving both the mechanical and electrical sides of the WEC system is used. The proposed estimator was compared with a simpler linear Kalman filter (KF) estimator under varying sea state environment and electric loading conditions. Generally, both estimators produced "statistically" good estimates, however, the EKF estimators outperformed its counterpart in both the estimation accuracy and maintaining low incident energy drop.

Investigations on state estimation of smart structure systems

This paper aims at enlightening the properties, computational and implementation issues related to Kalman filter based state estimation algorithms and sliding mode observers, by applying them for estimating the states of a smart structure system. The Kalman based estimators considered in this work are Kalman filter and information filter and, the sliding mode observers considered are Utkin observer and higher order sliding mode observer. A fourth order linear time invariant model of a piezo actuated beam is used in this work. This structure is embedded with four number of piezo patches, of which two act as sensors, one as disturbance actuator and the other as control actuator. The performance of the state estimation algorithms is evaluated through simulation, for the first two vibrating modes of the piezo actuated structure, when the structure is maintained at first mode and second mode resonance.

Vision-integrated Navigation and Integrity Monitoring for Aircraft Final Approach

This paper proposes a tightly-integrated Vision/GNSS navigation system for aircraft final approach. It consists of: i) a Kalman filter-based state estimator which handles time-delayed vision measurements by using image-trigger signals of camera device, and ii) an integrity monitoring (IM) function for sensor fault detection and Protection Level calculation. The integrity monitoring function is founded on a batch-realization of Kalman filter. The paper introduces an IM reset mechanism which re-initializes a fault detector regularly without relying on the current state estimation result, in order to remove an influence of past undetected faults on the newly reset detector. The proposed navigation system is tested on real sensor measurements, acquired onboard an unmanned aircraft in flight, with simulated GNSS faults. The test results show an improvement in fault detectability as well as in navigation performance by adding onboard vision information to classical aircraft navigation system.

Propeller speed estimation for unmanned aerial vehicles using Kalman filtering

This paper presents an online propeller speed estimation system for a multirotor unmanned aerial vehicle (UAV) equipped with brushless DC (BLDC) motors and powered by a lithium-polymer battery pack. Propeller speed estimation is based on battery drain current measurement extended with averaged state-space model of brushless DC motor utilised within a Kalman filter-based state estimator. Based on the BLDC motor and propeller physical parameters and utilising corresponding mathematical model, the estimation system is implemented within the flight computer on board the UAV. The proposed propeller speed estimation algorithm is verified experimentally for a wide range of propeller operating regimes, which has shown that the proposed method is able to provide efficient estimation of UAV propeller speed.

Robust extended Kalman filter based state estimation for nonlinear dynamic processes with measurements corrupted by gross errors

Abstract The quality of on-line measurement data usually cannot meet the demands of practical process monitoring and control due to the influence of measurement noise. Although extended Kalman filter (EKF) is able to improve the quality by reconciling the measurement data to fit the dynamic process model describing nonlinear and Gaussian processes, it rarely considers the presence of different types of gross errors, such as outliers, bias and drift. However, the three types of gross errors often simultaneously appear in dynamic process systems. We propose a robust EKF combined with measurement compensation (MC-REKF) approach, in which a statistical test method is used to detect the abnormal measurement data, where gross error identification is accomplished within a moving window. Subsequently, the magnitudes of gross errors are estimated and used for measurement compensation. Finally, the compensated measurements are updated to re-estimate the accurate states via EKF. The effectiveness of the proposed MC-REKF is demonstrated through a complex nonlinear dynamic chemical process system, namely the free radical polymerization of styrene. With three different types of gross errors, the mean squared error (MSE) of reconciled measurements based on the MC-REKF decreases 37 fold compared to EKF. The magnitude of the residuals between the estimated states and the true states falls below 1.0E−6 when using the MC-REKF. The implementation results imply that the proposed MC-REKF can identify and estimate different types of gross errors and finally decreases their influence on state estimation and measurement reconciliation.

Extended Kalman filter-based state estimation of MOSFET circuit

PurposeThis paper aims to present the estimation of the output voltage of metal oxide semiconductor field effect transistor (MOSFET) using the extended Kalman filter (EKF) method.Design/methodology/approachThe method uses EKF for MOSFET output voltage estimation. To implement the EKF method, the state space model has been obtained using Kirchhoff’s current law and Enz-Krummenacher-Vittoz model of the MOSFET circuit.FindingsThe proposed method can be used for any mode of MOSFET operation besides near the quiescent point region. The nonlinearity that occurs in the saturation region of MOSFET can also be considered in the proposed method. The proposed method can also be used for a large input signal. Though Kalman filter can be used for the small amplitude input signal, it results in inaccurate estimation due to the linearization of the nonlinear system.Research limitations/implicationsThe method is able to track the parameters when they are slowly changing with time.Originality/valueThe proposed method presents maximal precision of simulation as the maximal precision of simulation requires modeling of the circuit in terms of device parameters and circuit elements.

An On-Board Model-Based Condition Monitoring for Lithium-Ion Batteries

A model-based condition monitoring for lithium-ion (Li-ion) batteries involves estimating critical model parameters (e.g., capacity and impedance) and operational states [e.g., state of charge (SOC) and state of health]. This is important to design high-performance and safety-critical battery systems. Moreover, the tuning parameters of the condition monitoring algorithms significantly influence the performance of the algorithms. This paper proposes a real-time model-based condition monitoring for Li-ion batteries based on a real-time second-order resistor–capacitor electrical circuit battery model. The proposed method consists of an extended Kalman filter based online parameter identification and a smooth variable structure filter based state estimation. The two filters are systematically integrated to cooperate with each other and named hybrid filter (HF). Furthermore, a proposed particle swarm optimization based tuning parameter optimization is employed to find the optimal tuning parameters of the HF. The proposed method is compared with the dual extended Kalman filter (DEKF) via simulations and validated by experiments using a low-priced microcontroller. The results show that the proposed method can effectively choose the tuning parameters and has less computational complexity and higher accuracy of the SOC and capacity estimation than those of the conventional DEKF. Therefore, the proposed HF can be suitable for use in real-time embedded battery management system applications.