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Optimising the integration of terrain referenced navigation with INS and GPS

Groves, Paul D., Handley, Robin J., Runnalls, Andrew R. (2004) Optimising the integration of terrain referenced navigation with INS and GPS. In: GNSS 2004. . pp. 71-89. Cambridge University Press (doi:10.1017/S0373463305003462) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:14080)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1017/S0373463305003462

Abstract

The benefits of integrated INS/GPS systems are well known. However, the knowledge required to jam GPS is becoming public and the hardware to achieve this is basic. When GPS data are unavailable and a low grade INS is used, navigation accuracy quickly degrades to an unacceptable level. The addition of one or more terrain referenced navigation (TRN) systems to an integrated INS/GPS navigation system enables the INS to be calibrated during GPS outages, increasing the robustness of the overall navigation solution. TRN techniques are compared and integration architectures are reviewed. For the initial studies of INS/GPS/TRN integration, radar altimeter based terrain contour navigation (TCN) with a batch processing algorithm is used in conjunction with a centralised integration filter. Four different approaches for using these TCN fixes to calibrate the INS are compared. These are a best fix method, a weighted fix method using a probabilistic data association filter (PDAF) and single and multi-hypothesis versions of the Iterative Gaussian Mixture Approximation of the Posterior (IGMAP) method. Simulation results are presented showing that the single hypothesis IGMAP technique offers the best balance between accuracy, robustness and processing efficiency.

Item Type: Conference or workshop item (UNSPECIFIED)
DOI/Identification number: 10.1017/S0373463305003462
Additional information: 21-24 September 2004, Long Beach, California.
Uncontrolled keywords: TRN; GPS; INS.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: Institute of Navigation (https://ror.org/05rmpy443)
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:01 UTC
Last Modified: 05 Nov 2024 09:48 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14080 (The current URI for this page, for reference purposes)

University of Kent Author Information

Runnalls, Andrew R..

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