Research on application of Kalman filter to reduce random angle error of satcom station

2014 ◽  
Author(s):  
Chenchen Wang ◽  
Hao Liu ◽  
Weidong Yang
Keyword(s):  
Kalman Filter ◽  
Angle Error ◽  
Research On Application ◽  
Random Angle

scholarly journals Effectiveness of Variable-Gain Kalman Filter Based on Angle Error Calculated from Acceleration Signals in Lower Limb Angle Measurement with Inertial Sensors

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Yuta Teruyama ◽  
Takashi Watanabe
Keyword(s):  
Kalman Filter ◽  
Lower Limb ◽  
Measurement Accuracy ◽  
Inertial Sensors ◽  
Angle Measurement ◽  
Angle Error ◽  
Variable Gain ◽  
Improve Measurement ◽  
Kalman Gain ◽  
Acceleration Signals

The wearable sensor system developed by our group, which measured lower limb angles using Kalman-filtering-based method, was suggested to be useful in evaluation of gait function for rehabilitation support. However, it was expected to reduce variations of measurement errors. In this paper, a variable-Kalman-gain method based on angle error that was calculated from acceleration signals was proposed to improve measurement accuracy. The proposed method was tested comparing to fixed-gain Kalman filter and a variable-Kalman-gain method that was based on acceleration magnitude used in previous studies. First, in angle measurement in treadmill walking, the proposed method measured lower limb angles with the highest measurement accuracy and improved significantly foot inclination angle measurement, while it improved slightly shank and thigh inclination angles. The variable-gain method based on acceleration magnitude was not effective for our Kalman filter system. Then, in angle measurement of a rigid body model, it was shown that the proposed method had measurement accuracy similar to or higher than results seen in other studies that used markers of camera-based motion measurement system fixing on a rigid plate together with a sensor or on the sensor directly. The proposed method was found to be effective in angle measurement with inertial sensors.

Research on Application of Camshift and Kalman Filter Algorithm in Video Object Tracking

2014 ◽  
Vol 1049-1050 ◽  
pp. 1685-1689
Author(s):  
Cheng Yi Guo ◽  
Wen Bing Fan
Keyword(s):  
Kalman Filter ◽  
Search Window ◽  
Large Area ◽  
A Algorithm ◽  
Correct Operation ◽  
Video Object Tracking ◽  
Camshift Algorithm ◽  
Kalman Filter Algorithm ◽  
Color Background ◽  
Research On Application

When the background is complex and there is a lot color similar pixel interference, it may lead to location and size of Camshift algorithm’s search window abnormal so as to tracking failure. Aiming at these problems, this paper proposed a algorithm that combinating Camshift algorithm and Kalman filter. Kalman filter can predict the position of the moving object. Camshift algorithm adjusted the position and size of search window by using the prediction, so as to ensure the correct operation of the Camshift algorithm. Experimental results show that the proposed algorithm can effectively overcome the large area of similar color background and occlusion and many colors similar moving targets interference and other issues, improve the accuracy and robustness of target tracking algorithm.

Design and Simulation of Servo System of Based on Kalman Filter

2013 ◽  
Vol 787 ◽  
pp. 532-537 ◽  
Author(s):  
Min Tao ◽  
Xin Rong Wang ◽  
Gui Ping Zhang
Keyword(s):  
Kalman Filter ◽  
State Vector ◽  
Variance Estimation ◽  
Internal Noise ◽  
Servo System ◽  
Minimum Variance ◽  
Angle Error ◽  
Key Factor ◽  
Minimum Variance Estimation ◽  
And Control

In order to enhance metrical precision of radar on the vessel, Kalman filter based on Singer model is designed to filter angle-error signals in servo system of the radar in this paper. Precision of angle-error signal in the servo system is a key factor influencing precision of tracking and control. But it inevitably corrupted by external disturbances and internal noise during transmission and detection. Kalman filter, which is a kind of linear minimum variance estimation, can estimate state vector at any time when a parameter variable changing with time (state vector) and a linear model (system model) are provided. Therefore, its extremely suitable for filtering the angle-error signals in servo system. Experiments on practical signal show that Kalman filter can reduce noise of the angle-error signals effectively and hence enhancing metrical precision of the radar.

Analysis on the Influence of Random Vibration on MEMS Gyro Precision and Error Compensation

2011 ◽  
Vol 130-134 ◽  
pp. 4164-4168
Author(s):  
Xiang Yu Hao ◽  
Ming Li ◽  
Xue Feng Han ◽  
Hong Guang Jia
Keyword(s):  
Kalman Filter ◽  
Random Vibration ◽  
Low Cost ◽  
Dynamic Environment ◽  
Angle Error ◽  
Random Drift ◽  
Static Test ◽  
Attitude Angle ◽  
Aircraft Navigation ◽  
Calculation Results

In order to improve its precision in dynamic environment, a Kalman filter was designed. Firstly, two sets of random drift data of MEMS gyro were respectively analysed, and it was found that the variance of random drift under random vibration significantly increased and its mean also changed. Then calculation results show that attitude angle error under random vibration is 2.6°, while in the static test it is 0.25°. Analysis on the characteristics of random drift was carried out, and it is found that it can be treated as stable, normally distributed random signal. Finally, a corresponding Kalman filter was designed. The results indicated that after filtering the variance of random drift is reduced to 0.0282, 26.4% of pre-filtering and the attitude angle error is reduced to 1.5°, 57.7% of pre-filtering. The above method can effectively compensate for the attitude angle error of MEMS gyro caused by random vibration. This study can be a reference to the application of low-cost MEMS gyro in aircraft navigation.

Attitude Matching for Gimbaled Inertial Navigation System Transfer Alignment

2012 ◽  
Vol 190-191 ◽  
pp. 768-773
Author(s):  
Zhi Jian Ding ◽  
Hong Cai ◽  
Hua Bo Yang ◽  
Yuan Cao
Keyword(s):  
Kalman Filter ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Navigation Systems ◽  
Misalignment Angle ◽  
Inertial Navigation Systems ◽  
Angle Error ◽  
Transfer Alignment ◽  
Moving Base ◽  
Alignment Model

Abstract: Aiming at transfer alignment of gimbaled INS(Inertial Navigation Systems) on moving base, the paper proposes an attitude matching alignment model to calibrate the slave platform. This method is achieved by applying a Kalman filter, which based on the frame angle error equations, to estimate the fixed misalignment angle and obtain the misalignment angle. Firstly, the frame dynamics equations are introduced and the relation between the fixed angle and misalignment angle is discussed. Secondly, the frame angular error differential equations are built up via the frame angle information from the master and the slave INS platform. Lastly, the attitude matching alignment model is designed based on Kalman filter technology. The simulation results show that the proposed method can obtain an alignment accuracy of 40", and the corresponding alignment time is 30 seconds.

scholarly journals A FastSLAM Algorithm Based on Nonlinear Adaptive Square Root Unscented Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yu-feng Zhang ◽  
Qi-xun Zhou ◽  
Ju-zhong Zhang ◽  
Yi Jiang ◽  
Kai Wang
Keyword(s):  
Kalman Filter ◽  
Indoor Environment ◽  
Unscented Kalman Filter ◽  
Square Root ◽  
Angle Error ◽  
3 Dimensional ◽  
Localization And Mapping ◽  
Traditional Algorithm ◽  
Model Linearization ◽  
Posteriori Estimation

For fast simultaneous localization and mapping (FastSLAM) problem, to solve the problems of particle degradation, the error introduced by linearization and inconsistency of traditional algorithm, an improved algorithm is described in the paper. In order to improve the accuracy and reliability of algorithm which is applied in the system with lower measurement frequency, a new decomposition strategy is adopted for a posteriori estimation. In proposed decomposition strategy, the problem of solving a 3-dimensional state vector and N 2-dimensional state vectors in traditional FastSLAM algorithm is transformed to the problem of solving N 5-dimensional state vectors. Furthermore, a nonlinear adaptive square root unscented Kalman filter (NASRUKF) is used to replace the particle filter and Kalman filter employed by traditional algorithm to reduce the model linearization error and avoid solving Jacobian matrices. Finally, the proposed algorithm is experimentally verified by vehicle in indoor environment. The results prove that the positioning accuracy of proposed FastSLAM algorithm is less than 1 cm and the azimuth angle error is 0.5 degrees.

Application of Kalman Filter in the Large-Diameter Antenna of Ship-Borne

2013 ◽  
Vol 336-338 ◽  
pp. 408-412
Author(s):  
Qiu Qi Ding ◽  
Min Tao ◽  
Ren Long Li
Keyword(s):  
Kalman Filter ◽  
State Vector ◽  
Variance Estimation ◽  
Large Diameter ◽  
Internal Noise ◽  
Servo System ◽  
Minimum Variance ◽  
Angle Error ◽  
Key Factor ◽  
Minimum Variance Estimation

In order to enhance metrical precision of radar on the vessel, Kalman filter based on Singer model is designed to filter angle-error signals in servo system of the radar in this paper. Precision of angle-error signal in the servo system is a key factor influencing precision of tracking and control. But it inevitably corrupted by external disturbances and internal noise during transmission and detection. Kalman filter, which is a kind of linear minimum variance estimation, can estimate state vector at any time when a parameter variable changing with time (state vector) and a linear model (system model) are provided. Therefore, it’s extremely suitable for filtering the angle-error signals in servo system. Experiments on practical signal show that Kalman filter can reduce noise of the angle-error signals effectively and hence enhancing metrical precision of the radar.

Sign in / Sign up

Export Citation Format

Share Document