Runnalls, Andrew R. (2006) The IGMARP Data Fusion Algorithm. In: 2006 IEEE Nonlinear Statistical Signal Processing Workshop. IEEE, Cambridge, pp. 3336. ISBN 9781424405794. (doi:10.1109/NSSPW.2006.4378814) (KAR id:14432)
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Official URL http://dx.doi.org/10.1109/NSSPW.2006.4378814 
Abstract
The IGMARP Data Fusion Algorithm Andrew R. Runnalls University of Kent Computing Laboratory Technical Report 0507 IGMARP (Iterative Gaussian Mixture Approximation of the ReducedDimension Posterior) is a data fusion algorithm for handling nonlinear measurements, particularly ambiguous measurements (i.e. measurements for which the likelihood function may be multimodal), in conjunction with a linear or linearisable system model. It is particularly well suited to system models of high dimensionality, and applications where it is desired to interoperate with existing approaches using a Kalman Filter or multihypothesis Kalman Filter. The algorithm was developed under sponsorship from QinetiQ Ltd over the period 20015 as a means of integrating data from terrainreferenced navigation systems into a multiway integrated navigation solution also comprising an inertial navigation system (INS) and GPS. An example of a terrainreferenced navigation system is terraincontour navigation (TCN), in which an air vehicle uses a radio altimeter or similar sensor to take measurements of the height above sea level of the terrain being overflown. The paper describes the mathematical foundations of the algorithm, and illustrates its application to an integrated TCN/INS system. Sec. 2 introduces the motivating application, TCN. Sec. 3 reviews the measurement update equations for the multihypothesis Kalman filter (MHKF), which represent an application of Bayes' Theorem to the case in which the prior distribution is a Gaussian mixture, and the likelihood function also has the form of a (slightly generalised) Gaussian mixture. Sec. 4 then discusses how the likelihood function can be computed for TCN, and gives the flavour of the resulting functions, which are by no means of a Gaussian mixture form; this motivates Sec. 5, which discusses how the MHKF approach can be adapted to handle more general likelihood functions, and introduces the key theorems on which the IGMARP method depends. Then Sec. 6 describes the algorithm itself, and Sec. 7 illustrates the results of applying the algorithm to TCN/INS flight data. Finally Sec. 8 discusses conclusions and possible further work.
Item Type:  Book section 

DOI/Identification number:  10.1109/NSSPW.2006.4378814 
Uncontrolled keywords:  sea measurements; radio navigation; particle measurements; covariance matrix; iterative algorithms; density measurement; vectors; laboratories; approximation algorithms; intertial navigation 
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 
Depositing User:  Mark Wheadon 
Date Deposited:  24 Nov 2008 18:03 UTC 
Last Modified:  16 Feb 2021 12:25 UTC 
Resource URI:  https://kar.kent.ac.uk/id/eprint/14432 (The current URI for this page, for reference purposes) 
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