Papers by Keyword: Extended Kalman Flter (EKF)

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Authors: Lei Du, Nan Liu, Rui Fang, Nan Li, Xiang Hui Song
Abstract: Cooperative positioning (CP) originating in wireless sensor networks (WSN) is expected to enhance the accuracy of real-time positioning by exchanging location related information in vehicular network via wireless communication. A novel CP system based on beam-forming for vehicular networks is proposed by this work. Its application includes several roadside units equipped with a kind of transceiver based on an special dual-transmitter outphasing architecture which are utilized to broadcast the spatial directivity and correct receive angle information to vehicles with onboard wireless communication units in desired areas. The goal of enhancement positioning via vehicle-to-infrastructure communication can be acquired by a data fusion means based on the extended Kalman filter when GNSS is available and a cooperative solution based on the least-squares method under the condition that the global navigation satellite system (GNSS) is available respectively. The main process of positioning and all the key technical points of the system's application are modeled and analyzed mathematically. And the results of computer simulation confirm the technical practicability for the proposed method.
Authors: Lei Du, Nan Liu, Rui Fang, Xiang Hui Song
Abstract: Cooperative positioning (CP) is one of the core features in intelligent transportation systems (ITS) which is used to increase the positioning accuracy via wireless communication between vehicles and infrastructures. The global navigation satellite system (GNSS) is always unavailable near black spot such as the curve which needs to be solved. So, in this paper, a novel CP scheme is proposed for the curve warning scenario with limited GNSS by utilizing the information of received signal strength and pointer angular of the roadside unit which is in a special dual-transmitter outphasing architecture. An extended Kalman filter is founded to estimate the real-time position of the vehicle in the curve section. The whole warning scenario is analyzed by computer simulation, and the result shows the feasibility of the method.
Authors: Chao Shen, Lei Wan
Abstract: Battery management system (BMS) in autonomous underwater vehicle (AUV) not only can measure the main parameters of battery packs such as current, voltage, and temperature, but also estimate the state of charge (SOC) of battery packs. This paper proposes a broad approach for the design of battery management system. The new design can improve the cycle life and safety capability. With the model well designed, the parameters required are obtained and the SOC estimation is completed. Extended Kalman filter (EKF) was chosen to make the last estimate with the reliable battery model which was used to the non-linear system to estimate SOC and suitable for AUV applications. The experiments results prove that the data measured by battery management system have high precision and reliability. The estimated error of SOC was also small, which was better than other approaches for estimate.
Authors: Ling Zhang, Qiang Liu, Shuo Yang
Abstract: A new approach to risk assessment of storm surge and prediction problems was suggested. The model is based on the Extended Kalman Filter (EKF) equations, which simply linearises all nonlinear models so that the traditional Kalman filter can be applied. Key factors describing storm surge disasters are considered in the model. Numerical simulations were carried out and tested with some actual observations of recent storm surge events and related damages in coastal regions of China. The results show a reasonable fit for storm surge disaster prediction and encourage the possibility of using the method for future studies.
Authors: Yuan Yun Song, Wan Chun Chen, Xing Liang Yin
Abstract: A new type of guidance law is developed for intercepting high maneuvering target. The law takes angular acceleration of line-of-sight as a primary input in place of the acceleration of target. As the significant required input quantity of the angular acceleration guidance (AAG), the angular acceleration of line-of-sight is estimated by a developed estimation approach based on sliding mode observer (SMO). Simulation results demonstrates advantages of this AAG guidance with the estimation approach based on SMO by comparing with the conventional guidance techniques and extended Kalman filter.
Authors: Cheng Dong Wu, Peng Da Liu, Yun Zhou Zhang, Long Cheng, Jing Yu Ru
Abstract: In wireless sensor networks, NLOS propagation often enlarges the errors of position estimates when time-of-arrival (TOA) measurements are used. To mitigate the effects caused by NLOS propagation, herein, an EKF-based robust non-parametric approach is proposed. In this paper, we utilize the variable kernel method to obtain an approximate noise density function, which is inexpensively computational and then used to improve the mobile positioning accuracy. Note that the standard EKF often works well when NLOS error is adequately low. This property could also be used to improve the accuracy of mobile positioning. In the proposed algorithm, a hard decision is employed to choose the rational position estimate which may come from the non-parametric approach or the standard EKF. Numerical simulations show a significant improvement over the standard EKF.
Authors: Xin Zhong Ding, Cheng Rui Zhang, Le Hua Yu, Hu Xiu Li
Abstract: This paper presents a new permanent magnet synchronous motor (PMSM) drive technique using adaptive state estimator for high-performance motion control to estimate the instantaneous speed, position and disturbance load torque. In the proposed algorithm, the model reference adaptive control (MRAC) method is incorporated to identify the variations of inertia moment, and the identified inertia is used to adapt the extended Kalman filter (EKF), which is an optimal state estimator to provide good estimation performance for the rotor speed, rotor position and disturbance torque with low precision quadrature encoder in a random noisy environment. In addition, the disturbance–rejection ability and the robustness to variations of the mechanical parameters are discussed and it is verified that the system is robust to the modeling error and system noise. Simulation and experimental results confirm the validity of the proposed estimation technique.
Authors: Yuan Liao, Ju Hua Huang, Qun Zeng
Abstract: In this paper a novel method for estimating state of charge (SOC) of lithium ion battery packs in battery electric vehicle (BEV), based on state of health (SOH) determination is presented. SOH provides information on aging of battery packs and it declines with repeated charging and discharging cycles of battery packs, so SOC estimation depends considerably on the value of SOH. Previously used SOC estimation methods are not satisfactory as they haven’t given enough attention to the decline of SOH. Therefore a novel SOC estimation method based on SOH determination is introduced in this paper; trying to compensate the deficiency for lack of attention to SOH. Real time road data are used to compare the performance of the conventionally often used Ah counting method which doesn’t give any consideration to SOH with the performance of the proposed SOC estimation method, and better results are obtained by the proposed method in comparison with the conventional method.
Authors: Ling Zhao, Jing Zhi Ye, Wen Feng Luo
Abstract: In this paper, a real-time location feedback control system based on multi-sensor network is proposed for the precise control of a moving robot. The target tracking network system is a real test-bed that consists of a group of ultrasonic sensor nodes, a mobile robot and two laptops. In order to pursue excellent tracking performance and modify the robot’s trajectory promptly, Extended Kalman Filtering algorithm as well as a kind of scheduling scheme based on location is applied in the system. The experiment result validates the correctness of the Extended Kalman Filter (EKF) and shows that the target tracking network system is effective for robot feedback control.
Authors: Jiang Ming Kuang, Shuang Zhang
Abstract: the theory for maneuvering target tracking is significant to national defense and civil application. The filtering algorithm is one of important components in maneuvering target tracking. After the model of the maneuvering target is built, state vectors in the model are forecast and estimated through relevant filtering algorithms. The Unscented Kalman filtering is a novel filtering algorithm specially used for the nonlinear system, which is characterized by easy implementation, good generality, stable performance and so forth. Compared with the traditional Extended Kalman Filtering algorithm, the filtering algorithm can achieve less tracking error and higher tracking precision.
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