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The nonlinearity estimation is a major problem in the application of Kalman filtering. Though EKF algorithm achieves the output estimation by using approximate nonlinear function, and it enhances the application in nonlinear system to some extent, for strong nonlinear systems, the EKF still has a large estimation error. Therefore, an approximate probability density of the UKF algorithm is proposed...
Power battery is the heart of electric vehicles, and the accurate state of charge (SOC) estimation is crucial for the management of the power battery. This paper proposes an adaptive strong tracking unscented Kalman filter (ASTUKF) algorithm to estimate the SOC of lithium-ion battery. This method doesn't need to compute the Jacobian matrix compared with the traditional strong tracking filter. This...
In this paper the dynamic compressed sensing (DCS) estimation of time varying underwater acoustic (UWA) channel is investigated. By modeling the time varying UWA channels as sparse set consisting with constant and time-varying supports, the estimation of time varying UWA channel is transformed into a problem of dynamic compressed sensing (DCS) sparse recovery. Employing the combination of Kalman filter...
This paper presents a new approach for broken rotor bars detection in induction motors. When broken rotor bar occurs, the rotor resistance will increase. Furthermore, thermal effects of rotor resistance are considered to address practical aspects. Therefore, a suitable method to detect broken rotor bar is rotor resistance estimation. An applicable method in state estimation is Cubature Kalman Filter...
In this paper, we present an extended Tobit Kalman filter that deals with fault detection problem in nonlinear systems with missing measurements and censored data. The missing measurements randomly occurring are regulated by individual random variables whose probability distributions are on the interval [0,1]. The censored data are characterized by the Tobit measurement model. The Tobit Kalman filter...
This paper reviews the development history of simultaneous localization and mapping (SLAM) and concentrates on two mainstream methods: the filter-based method and the vision-based graph optimization method. FastSLAM and Real-Time Appearance-Based Mapping (RTAB-MAP) as two examples are adopted in the real experiments. The experiments are implemented on TurtleBot with Kinect in a small laboratory and...
In order to estimate the attitude angle accurately, improve the accuracy of attitude and heading reference system(AHRS), aiming at larger errors of extended Kalman filter(EKF) in estimating attitude angle, a method based on iterative LM-CDKF algorithm has been proposed to estimate the attitude angle. In the paper, iterative filtering theory and Levenberg-Marquardt method is introduced into the central...
This paper focuses on the design of a linear Kalman filter and an extended Kalman filter for the estimation of an octorotor unmanned aerial vehicle's (UAV) state in the context of Synthetic Aperture Radar image reconstruction. A comparison to a linear interpolation method is also proposed. The Kalman filters are developed based on a complete nonlinear model of the UAV and its linearized form. A particularity...
Coming with the reliability enhancement and the life extension for the Photovoltaic (PV) inverters modules, diagnostic techniques will be required to derive signature identification. The reliable operation of PV inverter is very crucial to get the highest performance for the long term. A new Smart Inverter Robustness Index (SIRI) is used for verifying the performance and robustness of the grid-tied...
In order to estimate the accurate and real-time dynamic battery state of charge (SOC), owing to greater errors of extended Kalman filter (EKF) in estimating SOC, a method has been proposed to estimate the battery SOC based on LM-ICDKF algorithm. By using the two order RC equivalent circuit model, the MATLAB simulation tools are used to simulate the algorithm, this paper compares LM-ICDKF algorithm...
Gyro-less attitude and angular rate estimations are of great importance in small, low-cost spacecraft, where high performance gyroscopes are not available due to multiple limitations. Recent development of accurate, high-bandwidth attitude sensors such as high data-rate star trackers, makes this approach implementable. The gyro-less estimator propagates the estimated states by nonlinear attitude dynamics...
Estimation of electromagnetic torque using Kalman Filter for sensorless Direct Torque Controlled (DTC) brushless DC (BLDC) motor having trapezoidal back emf is presented in this paper. Predefined voltage vector from an optimal switching table is used for getting faster torque response in comparison to the conventional speed controlled drive with six step PWM technique. Quasi square wave current is...
A tracking simulator is an online simulation system that achieves a permanent state synchronization with the targeted process by dynamically calibrating the model state after comparing process measurements with model results. Tracking simulators are a powerful industrial application that can be utilized as a plant-wide virtual sensor for process monitoring and diagnosis as well as a predictive tool...
The continuous monitoring of blood pressure (BP) has been found to significantly predict the risk of severe cardiovascular disease. Pulse arrival time (PAT), generally extracted from synchronized photoplethysmogram (PPG) and electrocardiogram (ECG) signals, is widely adopted in noninvasive blood pressure studies. However, motion artifact and physical activities introduce different levels of noise...
Lithium Iron Phosphate (LiFePO4) batteries have obtained extensive interests for the high energy density, little contamination, and ready availability. To enhance the compatibility of the batteries in electrical systems, the accurate estimation of the state of charge (SOC) is remarkably significant. Conventionally, Kalman filter algorithm and its derivations can be utilized for SOC estimation. To...
The paper utilizes a novel battery model based on the electrical features of LiFePO4 battery, because Kalman filter algorithm(KF) is largely dependent on system model. Measurements of battery state are easily disturbed by colored noise which is high relevance in working condition, and the paper studies that the system noise satisfy one-order AR model. The paper proposes an adaptive extended Kalman...
State-of-charge (SOC) estimation methods based on battery model rely heavily on the accuracy of model parameters. And these parameters could vary with environment and the types of batteries. Online battery modeling methods can improve the robustness of SOC estimation algorithms through updating model constantly with real-time data. These methods have far more profound significance on algorithm adaptability...
State of charge is a significant indicator with respect to the remaining capacity for the lithium-ion battery. Nonetheless, strong nonlinearity and time-varying attributes resulting from the complicated electrochemical reactions incur the tremendous difficulty to acquire the accurate state directly. To address the above problems, a novel estimation method based on unscented Kalman filter and dual-filters...
Forty years of cross-disciplinary academic and industrial research & development work on radar tracking systems is surveyed, starting from the former implementations based on the α — β, the Kalman filter up to the modern implementations based on random set filters.
This paper provides a comparative evaluation of phasor estimation signal processing tools, namely the least square estimator and the linear Kalman filter. Comparative investigations are carried out using simulated signals for a power grid voltage sag disturbance. For both techniques, the fundamental frequency is assumed known, hence the estimated phasor parameters are amplitude and initial phase....
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