Sensor Fusion with Square-Root Cubature Information Filtering

Ienkaran Arasaratnam

Ford Motor Company of Canada Ltd., Windsor, ON, Canada.

DOI: 10.4236/ica.2013.41002                

Abstract

This paper derives a square-root information-type filtering algorithm for nonlinear multi-sensor fusion problems using the cubature Kalman filter theory. The resulting filter is called the square-root cubature Information filter (SCIF). The SCIF propagates the square-root information matrices derived from numerically stable matrix operations and is therefore numerically robust. The SCIF is applied to a highly maneuvering target tracking problem in a distributed sensor network with feedback. The SCIF’s performance is finally compared with the regular cubature information filter and the traditional extended information filter. The results, presented herein, indicate that the SCIF is the most reliable of all three filters and yields a more accurate estimate than the extended information filter.

Keywords

Kalman Filter; Information Filter; Multi-Sensor Fusion; Square-Root Filtering

Conflicts of Interest

The authors declare no conflicts of interest.

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