An Approach of Extended Kalman Filter in Cooperative Localization

2021 ◽  
Vol 9 (VII) ◽  
pp. 3083-3089
Author(s):  
Akanksha Katiyar
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Mobile User ◽  
Base Station ◽  
Filter Method ◽  
Cooperative Localization ◽  
Angle Of Arrival ◽  
Target Node ◽  
Target User ◽  
Kalman Filter Method

In this paper we explored the problem of localizing a mobile user within the range of a base station in 5G communication. For solving this problem we utilized Cooperative localization method over conventional localization technique to estimate the position of a mobile user. In cooperative localization we estimate position of the target user using Angle of Arrival(AOA), Time Difference of Arrival(TDOA) and Received Signal Strength(RSS) observations from nodes which are neighbour to target node and the base station. This estimation is done with the help of Extended Kalman Filter method.

scholarly journals Performance evaluation of GPS / BDS combined single-point positioning based on RTKLIB

2020 ◽  
Vol 165 ◽  
pp. 03009
Author(s):  
Li Yan-yi ◽  
Huang Jin ◽  
Tang Ming-xiu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Single Point ◽  
Least Square Method ◽  
Least Square ◽  
Filter Method ◽  
Single System ◽  
Positioning Error ◽  
Dual Mode ◽  
Kalman Filter Method

In order to evaluate the performance of GPS / BDS, RTKLIB, an open-source software of GNSS, is used in this paper. In this paper, the least square method, the weighted least square method and the extended Kalman filter method are respectively applied to BDS / GPS single system for data solution. Then, the BDS system and GPS system are used for fusion positioning and the positioning results of the two systems are compared with that of the single system. Through the comparison of experiments, on the premise of using the extended Kalman filter method for positioning, when the GPS signal is not good, BDS data is introduced for dual-mode positioning, the positioning error in e direction is reduced by 36.97%, the positioning error in U direction is reduced by 22.95%, and the spatial positioning error is reduced by 16.01%, which further reflects the advantages of dual-mode positioning in improving a system robustness and reducing the error.

Application of Improved Extended Kalman Filter for Adaptive Model of Aircraft Engine

2014 ◽  
Vol 709 ◽  
pp. 180-185
Author(s):  
Gu Ting Zhou ◽  
San Mai Su
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Extended Kalman Filter ◽  
Performance Monitoring ◽  
Aircraft Engine ◽  
Filter Method ◽  
Adaptive Model ◽  
Nominal Model ◽  
Simulation Results ◽  
Kalman Filter Method

Adaptive model is the basis of engine fault diagnosis, performance monitoring, engine control, etc. This paper presents an improved kalman filter method which uses engine measurable parameters deviation to estimate the degradation parameters to correct the nominal model, and the acquisition and application of multiple kalman filter gain matrices in the whole flight envelope is analyzed. Simulation is carried out taking a civil engine as simulation object, the simulation results show that the method used in this paper to establish unmeasured parameters adaptive model can get the engine parameters better.

scholarly journals A Progressive Extended Kalman Filter Method for Freeway Traffic State Estimation Integrating Multisource Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yingshun Liu ◽  
Shanglu He ◽  
Bin Ran ◽  
Yang Cheng
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
State Space Model ◽  
Heterogeneous Data ◽  
Filter Method ◽  
Multiple Sources ◽  
Freeway Traffic ◽  
Traffic Conditions ◽  
Kalman Filter Method

Variable techniques have been used to collect traffic data and estimate traffic conditions. In most cases, more than one technology is available. A legitimate need for research and application is how to use the heterogeneous data from multiple sources and provide reliable and consistent results. This paper aims to integrate the traffic features extracted from the wireless communication records and the measurements from the microwave sensors for the state estimation. A state-space model and a Progressive Extended Kalman Filter (PEKF) method are proposed. The results from the field test exhibit that the proposed method efficiently fuses the heterogeneous multisource data and adaptively tracks the variation of traffic conditions. The proposed method is satisfactory and promising for future development and implementation.

scholarly journals Analysis of SDEs Applied to SEIR Epidemic Models by Extended Kalman Filter Method

2016 ◽  
Vol 07 (17) ◽  
pp. 2195-2211
Author(s):  
D. Ndanguza ◽  
I. S. Mbalawata ◽  
J. P. Nsabimana
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Epidemic Models ◽  
Filter Method ◽  
Kalman Filter Method

Extended Kalman Filter Phase Unwrapping Smooth Algorithm in InSAR Image

2014 ◽  
Vol 701-702 ◽  
pp. 449-452
Author(s):  
Man Yan ◽  
Li Fen Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Least Squares Method ◽  
Phase Error ◽  
Phase Unwrapping ◽  
Filter Method ◽  
Differential Interferometry ◽  
Key Technologies ◽  
Kalman Filter Method ◽  
Range Error

InSAR phase unwrapping is one of the key technologies in precise differential interferometry measurement. However, when the noise is more, wrapped phase easily make the unwrapping result’s errors more. To solve this problem, phase error is compensated, and extended Kalman filter gain is limited within a certain range, error is suppressed in a relatively small area. Finally, extended Kalman filter smooth reduce the errors’ spread. Using interference data of ESA ERS-1 satellite to do experiments, it show that this method is superior to least squares method, quality guided method and original Kalman filter method in unwrapping precision and quality, it has higher stability.

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