Debiased Converted Measurements Kalman Filter Algorithm for Motion Parameter Estimation

2013 ◽  
Vol 427-429 ◽  
pp. 1674-1677 ◽  
Author(s):  
Shuai Xu ◽  
Shao Hui Cui ◽  
Yuan Zhou ◽  
Zhen Bin Tang
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Coordinate System ◽  
Relative Motion ◽  
Delay Time ◽  
Relative Velocity ◽  
Control Algorithm ◽  
Kalman Filter Algorithm ◽  
Estimation Precision ◽  
Motion Parameters

The directional warhead of ATBM missile requires accurate initiation delay time and the initiating direction to obey effective damage for TBM, which needs to estimate the relative motion parameters to improve the accuracy. A debiased converted measurement Kalman Filter is presented and used for the estimation of TBMs position in body coordinate system of ATBM missile and the relative velocity. Results of simulation shows that this algorithm has high estimation precision for the parameters and satisfies the need of building initiating control algorithm for The directional warhead of ATBM missile.

Battery SOC Estimation Based on Multi-Model Adaptive Kalman Filter

2011 ◽  
Vol 403-408 ◽  
pp. 2211-2215 ◽  
Author(s):  
Ke Xin Wei ◽  
Qiao Yan Chen
Keyword(s):  
Kalman Filter ◽  
State Of Charge ◽  
Model Parameters ◽  
Adaptive Kalman Filter ◽  
Single Model ◽  
Soc Estimation ◽  
Battery Model ◽  
Kalman Filter Algorithm ◽  
Estimation Precision ◽  
State Of Charge Estimation

This paper introduces multi-model adaptive kalman filter estimation algorithm.Based on the battery thevenin model,the multi-model adaptive kalman filter is applied to the battery SOC(state of charge) estimation, which solute the battery SOC estimation in conditions that the battery model parameters change caused by temperature changing. Simulation results show that compared to the single model kalman filter algorithm, Multi-Model adaptive kalman filter algorithm improves the estimation precision and reliability greatly.

scholarly journals Adaptive Kalman Filter with L2 Feedback Control for Active Suspension Using a Novel 9-DOF Semi-Vehicle Model

Actuators ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 267
Author(s):  
Huan Yang ◽  
Jiang Liu ◽  
Min Li ◽  
Xilong Zhang ◽  
Jianze Liu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Feedback Control ◽  
Control Algorithm ◽  
Active Suspension ◽  
Adaptive Kalman Filter ◽  
Feedback Matrix ◽  
Kalman Filter Algorithm ◽  
Feedback Algorithm ◽  
Suspension Model ◽  
Rear Tire

In order to further improve driving comfort, this paper takes the semi-vehicle active suspension as the research object. Furthermore, combined with a 5-DOF driver-seat model, a new 9-DOF driver seat-active suspension model is proposed. The adaptive Kalman filter combined with L2 feedback control algorithm is used to improve the controller. First, a discrete 9-DOF driver seat-active suspension model is established. Then, the L2 feedback algorithm is used to solve the optimal feedback matrix of the model, and the adaptive Kalman filter algorithm is used to replace the linear Kalman filter. Finally, the improved active suspension model and algorithm are verified through simulation and test. The results show that the new algorithm and model not only significantly improve the driver comfort, but also comprehensively optimize the other performance of the vehicle. Compared with the traditional LQG control algorithm, the RMS value of the acceleration experienced by the driver’s limb are, respectively, decreased by 10.9%, 15.9%, 6.4%, and 7.5%. The RMS value of pitch angle acceleration experienced by the driver decreased by 6.4%, and the RMS value of the dynamic tire deflection of front and rear tire decreased by 32.6% and 12.1%, respectively.

scholarly journals Improved Relative Range Equation in Squint SAR for Moving Targets Refocusing

CONVERTER ◽  
2021 ◽  
pp. 212-220
Author(s):  
Pu Cheng, ZhentaoYu, Jie Chen
Keyword(s):  
Synthetic Aperture Radar ◽  
Target Detection ◽  
Relative Motion ◽  
Relative Velocity ◽  
Synthetic Aperture ◽  
Moving Target ◽  
Moving Targets ◽  
Sar Imaging ◽  
Motion Parameters ◽  
Range Equation

Moving target detection is difficult for synthetic aperture radar (SAR). As SAR is designed for imaging of stationary ground scene, the moving targets would be blurred and displaced in conventional SAR imaging. To increase the signal clutter ratio, the moving targets should be refocused while detecting. Based on relative range equation, one can refocus and detect the moving targets simultaneously by searching the relative velocity. This method has been derived and applied for side looking SAR. In this paper, we extend the relative range equation to squint mode. The procedures of the refocusing method are also illustrated. By introducing a parameter of relative squint angle, the imaging position of the moving target is derived. The refocusing method is validated by simulations. The moving target can be optimally refocused, and the refocused position can be parametrized by the relative motion parameters.

scholarly journals Acceleration Feature Extraction of Human Body Based on Wearable Devices

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Kalman Filter ◽  
Human Body ◽  
Wearable Devices ◽  
Human Motion ◽  
Data Set ◽  
Feature Extraction Method ◽  
Motion Data ◽  
Kalman Filter Algorithm

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

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