JOURNAL ARTICLE

Development of GPS/INS integration module using Kalman filter

Nagesh YadavAbhishek ShanmukhaB. M. AmruthBasavaraj

Year: 2017 Journal:   2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET) Pages: 1-5

Abstract

Inertial Navigation systems combined with other navigational aids like GPS, has picked up significance due to the upgraded navigational and inertial execution. INS alone can compute the position of a certain object or vehicle with no assistance from the outside world. There are extensive number of errors that are acquainted from time to time due to which there are inadmissible floats in the yields. Henceforth GPS is utilized to help INS utilizing the Kalman channel which helps in redesigning the position precision and the errors are evaluated. Extended Kalman Filter is intended to coordinate INS and GPS in a flexible way compared with the conventional integration. In light of the loosely coupled GPS/INS integration, the proposed plan can switch between the feed forward and feed backward techniques. In this way, the framework can lessen the position and speed errors contrasted with ordinary combination strategy. The outcome of the project is the successful integration of the GPS and INS using a simulation model in the MATLAB. The result obtained demonstrates the minimized errors in the parameters when both INS and GPS are integrated using Kalman Filter.

Keywords:
GPS/INS Global Positioning System Kalman filter Computer science Inertial navigation system Position (finance) MATLAB Assisted GPS Real-time computing Inertial frame of reference Artificial intelligence Telecommunications

Metrics

8
Cited By
3.08
FWCI (Field Weighted Citation Impact)
15
Refs
0.92
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Inertial Sensor and Navigation
Physical Sciences →  Engineering →  Aerospace Engineering
GNSS positioning and interference
Physical Sciences →  Engineering →  Aerospace Engineering
Target Tracking and Data Fusion in Sensor Networks
Physical Sciences →  Computer Science →  Artificial Intelligence
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