An Algorithm for Dynamic Location Based on Doppler Velocity Estimation and Kalman Filtering in Distributed Mobile Communication

2011 ◽  
Vol 30 (6) ◽  
pp. 1420-1423
Author(s):  
Wen Pan ◽  
Zhan-jun Jiang ◽  
Zheng-feng Du ◽  
Yan Wang ◽  
Xiao-hu You
Keyword(s):  
Kalman Filtering ◽  
Mobile Communication ◽  
Velocity Estimation ◽  
Doppler Velocity ◽  
Dynamic Location

scholarly journals GNSS DOPPLER VELOCITY BASED ON ADAPTIVE ROBUST KALMAN FILTERING

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1005-1012
Author(s):  
H. Liu ◽  
M. Z. Xin ◽  
J. J. Wei ◽  
Y. K. Liang ◽  
F. L. Yang
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Velocity Measurement ◽  
Measurement Errors ◽  
Threshold Value ◽  
Velocity Estimation ◽  
Least Square ◽  
Doppler Velocity ◽  
Factors Affecting ◽  
Robust Kalman Filter

Abstract. The main factors affecting the error of Doppler velocity measurement mainly come from the measurement errors of GNSS data, influence of different motion states on GNSS velocity measurement and the noise of different receiver types. To improve the precision of GNSS velocity estimation, an algorithm of adaptive robust Kalman filter based on the PDOP was put forward. PDOP value as well as the number of satellite in each epoch are used as a criterion in the velocity processing. While the PDOP value is greater than the threshold value, which means the observation accuracy is low, then the robust Kalman filter based on IGG – III scheme is introduced. While the PDOP value is between the threshold values, which means the observation precision is normal, adaptive factor could be determined normally, and the single-factor three-stage adaptive model is applied for Kalman filtering. If the above two conditions are not consistent, it indicates that the prediction accuracy of the local epoch satellite is high, and Kalman filtering can be directly used. Through the experiment of shipborne GNSS velocity measurement, it was proved that comparing with conventional least square, the algorithm based on the adaptive robust Kalman filtering can improve the accuracy and stability of the GNSS velocity determination.

Investigation of Crossbeam Multi-receiver Configurations for Accurate 3-D Vector Doppler Velocity Estimation

2016 ◽  
Vol 63 (11) ◽  
pp. 1786-1798 ◽  
Author(s):  
Bushra Hussain ◽  
Billy Y. S. Yiu ◽  
Alfred C. H. Yu ◽  
James C. Lacefield ◽  
Tamie L. Poepping
Keyword(s):  
Velocity Estimation ◽  
Doppler Velocity

scholarly journals Doppler Velocity Estimation of Overlapping Linear-Period-Modulated Ultrasonic Waves Based on an Expectation-Maximization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Natee Thong-un ◽  
Minoru K. Kurosawa
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Pulse Compression ◽  
Moving Objects ◽  
Expectation Maximization Algorithm ◽  
Ultrasonic Waves ◽  
Velocity Estimation ◽  
Doppler Velocity ◽  
The Em Algorithm ◽  
Linear Period

The occurrence of an overlapping signal is a significant problem in performing multiple objects localization. Doppler velocity is sensitive to the echo shape and is also able to be connected to the physical properties of moving objects, especially for a pulse compression ultrasonic signal. The expectation-maximization (EM) algorithm has the ability to achieve signal separation. Thus, applying the EM algorithm to the overlapping pulse compression signals is of interest. This paper describes a proposed method, based on the EM algorithm, of Doppler velocity estimation for overlapping linear-period-modulated (LPM) ultrasonic signals. Simulations are used to validate the proposed method.

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