Target Registration Correction Using the Neural Extended Kalman Filter

Author

Kramer, Kathleen A. ; Stubberud, Stephen C. ; Geremia, J. Antonio

Author_Institution

Dept. of Eng., Univ. of San Diego, San Diego, CA, USA

Volume

59

Issue

7

fYear

2010

fDate

7/1/2010 12:00:00 AM

Firstpage

1964

Lastpage

1971

Abstract

Target registration can be considered a problem in aligning the reports of two sensor platforms. It is often a result of sensor misalignment and navigation errors. One technique to alleviate these errors is to continually recompute a correction with each report. In this paper, a different approach using a modification of an adaptive neural network technique is proposed and developed. The technique, which is referred to as a neural extended Kalman filter, learns the differences between the a priori model of the off-board reports and the actual model. This correction can then be added to the model to provide an improved estimate of the sensor report. The approach is applied to the problem of static-registration-applied track-level position reports.

Keywords

adaptive Kalman filters; neural nets; sensors; target tracking; a priori model; adaptive neural network technique; navigation errors; neural extended Kalman filter; sensor misalignment; sensor platforms; static-registration-applied track-level position reports; target registration correction; Adaptive; Kalman filter; neural network; sensor registration; target tracking;

fLanguage

English

Journal_Title

Instrumentation and Measurement, IEEE Transactions on

Publisher

ieee

ISSN

0018-9456

Type

jour

DOI

10.1109/TIM.2009.2030870

Filename

5280378