Sensor calibration using the neural extended Kalman filter in a control loop

被引：2

作者：

Kramer, Kathleen A. ^{[1
]}

Stubberud, Stephen C. ^{[2
]}

Geremia, J. Antonio ^{[3
]}

机构：

[1] Univ San Diego, Dept Engn, 5998 Alcala Pk, San Diego, CA 92110 USA

[2] Rockwell Collins Inc, Poway, CA 12365 USA

[3] Entrop Commun Inc, San Diego, CA 92121 USA

来源：

2007 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MEASUREMENT SYSTEMS AND APPLICATIONS | 2007年

关键词：

adaptive; neural network; sensor correction; vehicle trajectory; Kalman filter; control system;

D O I：

10.1109/CIMSA.2007.4362531

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Sensor errors can adversely affect the behavior of a control system. When multiple sensors are used, a broken sensor can have its effects minimized by artificially inflating its error covariance. In this paper, a different approach to compensating for sensor errors in a multiple-sensor control system is introduced The technique, referred to as a neural extended Kalman filter (NEKF), is developed for closed-loop control systems. The NEKF learns on-line from the same residual information used in the state estimator. The improvement in the sensor report is made by the neural network being added to the measurement model. In this work, the NEKF is applied to vehicle trajectory control problem with a position sensor and a velocity sensor.

引用

页码：19 / +

页数：3

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