Papers by Keyword: Kalman Filter (KF)

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Authors: Chen Zhang, Chang Yong Xu
Abstract: A de-noising method is proposed for 3DM-GX1 accelerometer signal based on kalman filter and average filter. This method can reduce the disturbance caused by vibration of vehicle motion and sensor itself, get acceleration accurately, and have advantages of easy realization and good real-time performance. A vehicle acceleration signal model is established with real acceleration noises as simulation signals based on kalman filter and average filter. Experiments based on real vehicle acceleration signal showed that the method could reflect the detailed information effectively by filtering noise, and it is very suitable for de-noising of vehicle acceleration signals which needs real-time application. The start, stop, advance and retreat action could be identify from the acceleration signal by using the proposed de-noising method based on kalman filter and average filter.
Authors: Muhammad Ushaq, Fang Jian Cheng, Ali Jamshaid
Abstract: The complementary characteristics of the Strapdown Inertial Navigation System (SINS) and external non-inertial navigation aids like Global Positioning System (GPS) and Celestial Navigation System (CNS) make the integrated navigation system an appealing and cost effective solution for various applications. SINS exhibits position errors owing to errors in initialization of the inertial measurement unit (IMU) and the inherent accelerometer biases and gyroscope drifts. SINS also suffer from diverging azimuth errors and an exponentially increasing vertical channel error. Pitch and roll errors also exhibit unbounded growth with time. To mitigate this behavior of SINS, periodic corrections are opted for through measurements from external non-inertial navigation aids. These corrections can be in the form of position fixing, velocity fixing and attitude fixing from external aids like GPS, GLONASS (Russian Satellite Navigation System), BEIDU(Chinese Satellite Navigation System) and Celestial Navigation Systems (CNS) etc. In this research work GPS and CNS are used as external aids for SINS and the navigation solutions of all three systems (SINS, GPS and CNS) are fused using Federated Kalman Filter (FKF). The FKF differs from the conventional Central Kalman Filter (CKF) because each measurement is processed in Local Filters (LFs), and the results are combined in a Master Filter (MF). FKF is a partitioned estimation method that uses a two stage data processing scheme, in which the outputs of sensor related LFs are subsequently combined by a large MF. Each LF is dedicated to a separate sensor subsystem, and uses data from the common reference such as SINS. The SINS acts as a cardinal system in the combination, and its data is also available as measurement input for the master filter. In this research work, information from the GPS and the CNS are dedicated to the corresponding LFs. Each LF provides its solutions to the master filter all information is fused together forming a global solution. Simulation for the proposed architecture has validated the effectiveness of the scheme, by showing the substantial precision improvement in the solutions of position, velocity and attitude as compared to the pure SINS or any other standalone system.
Authors: Li Zhen Wu, Xiao Hong Hao
Abstract: This paper studies the coordinate optimization control problem of networked control systems (NCSs) with random time delay and packet dropout. A discrete-time system model of NCSs with time-delays and data packet dropout is proposed. A method of state estimation base on extra kalman filter is given. Then a gain-schedule adaptive LQG control strategy base on effective delay-estimation online is proposed. The result illustrate that the effectiveness of the proposed controller design and the satisfactory performance of the system.
Authors: Chyuan Yow Tseng, Ting-Wei Shih, Jun-Tsun Lin
Abstract: When manufacturing electric motors, the armature must be carefully balanced before the motor is assembled to ensure that the motor remains within specified vibration limits when in operation. This study develops a novel scheme for the automatic balancing of motor armatures. In the proposed scheme, a Kalman filter is employed to make the milling system adaptive to the wear conditions. The balancing scheme is validated by performing a series of experiments using automobile starting motor armatures.
Authors: Cui Feng Du, Wen Ming Shen, Shi Bao Jiang
Abstract: The real-time prediction of micro regional market share provides decision for the analysis of micro regional marketing scheme and micro regional channel planning. More and more increasing complexion mobile network environment require real-time micro area of market share and only mastering micro regional market share can have a more comprehensive understanding of market. To solve this problem, consideration of advantages of real-time aspects of the extended Kalman filtering algorithm in predicting, we propose a real-time prediction algorithm based on the extended Kalman filter Market Share. The algorithm can be real-time prediction of mobile network market share of base station. The simulation results show that the proposed algorithm in this paper is a real-time and good prediction quality.
Authors: Hong Wei Quan, Dong Liang Peng, An Ke Xue
Abstract: A new algorithm for tracking a maneuvering target in presence of clutter or false measurements is addressed. Due to the availability of feature or attribute information in measurement vector, a joint probability density function description of the target state and target class is given. Using the joint state-class description the predictive measurement pdf can be proven to be a Gaussian mixture distribution. A Gaussian mixture Kalman filter is used for state estimation, where maneuver detection can also be avoided. In simulation the results with three tracking algorithms are compared, which have shown that proposed method here is more effective.
Authors: Zhi Kai Huang, Xing Wang Zhang, Wei Zhong Zhang, Ling Ying Hou
Abstract: In this paper, we propose a new embossing algorithm for gray images using Kalman filter. First, a 2D gray image is first converted to a one dimension vector; those vectors could be considered as a one-dimension discrete-time signal. Then, the performance of image filtering using Kalman filter for image is studied and according to its results, Canny edge detection operators are investigated to find edge map in a gray scale image. Finally, enhance contrast using histogram equalization has been applied. Compared with other conventional embossing method for images, it is an impressive experimental result using our proposed algorithm for gray image embossing. Practical results show that this algorithm can be exploited in different fields such as image pattern recognition.
Authors: Xiao Hua Nie
Abstract: The conventional perturbation and observation (P&O) method is combined by the target tracking technique of data fusion, the adaptive multi-mode MPPT control algorithm is put forward for the first time. The reasonable and long step is selected to insure tracking velocity, the target tracking algorithm is adoptted for weaken and restrain vibration, the target maneuvering detection technique is put to use to judge whether the system is “lose control ” in fast variation environments, the repeat starting up perturbation and observation (P&O) method in order to strengthen system stability .The experimental result shows the correctness and validity of the method.
Authors: Ming Xiao, Liang Pan, Yan Long Bu, Li Li Wan
Abstract: The traditional Kalman filter is able to obtain the optimal estimation of the estimated signals. However, it fails to consider their reliability. In real applications, the estimated signals may include outliers. Fortunately, we are able to know the reliability of the signals transcendentally. In this paper, we derive the one-dimensional data fusion formulas based on signals reliability which is according to minimum variance restriction. Furthermore, a corresponding data fusion scheme is proposed. Experimental results show the propose data fusion method performs much better than traditional methods.
Authors: Wan Li Li, Liang Qing Lu
Abstract: Mounting accuracy is of great importance to the performance of the Doppler-based navigation techniques. In this paper, a novel method for alignment calibration of IMU and Doppler sensors is presented. The presented scheme is based on the information from INS/GPS integration and Doppler. Different from previously reported techniques, not only the misalignments but also the scale factor error is considered in this study. By using a Kalman filter, the alignment matrix which is consisted of a misalignment matrix multiplying with a scale factor is estimated. The performance of the alignment estimates is evaluated with field experimental data over Yangzi River. Experimental results shown the estimates obtained by the proposed method perform much better than the existing solution. By using the proposed method, the accuracies of the transformed velocity and positioning are both increased.
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