Impact and point prediction using a neural extended Kalman filter with multiple sensors

Stephen C. Stubberud; Kathleen A. Kramer

doi:10.1117/12.713614

7 May 2007 Impact and point prediction using a neural extended Kalman filter with multiple sensors

Stephen C. Stubberud, Kathleen A. Kramer

Proceedings Volume 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI; 656702 (2007) https://doi.org/10.1117/12.713614
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

The neural extended Kalman filter is an adaptive estimation technique that has been shown to learn on-line the maneuver model of the trajectory of a target. This improved motion model can be used to better predict the location of a target at given point in time, especially when the target, such as a mortar shell, has limited maneuvering capabilities. In this paper, the neural extended Kalman filter is used to predict, with multiple-sensor-systems provided measurement reports, impact point and impact time of a ballistic-like projectile when the drag on the shell was not accurately modeled in the motion model. In previous work, the neural extended Kalman filter was shown to work well with a single sensor with a uniform sample rate. Multiple sensors can incorporate two major differences into the problem. The first difference is that of the multiple aspect angles and uncertainty that are used in the model adaptation. The second difference is that of a non-uniform update rate of the measurements to the tracking system. While most tracking systems can easily handle this difference, the adaptation of the neural network training parameters can be deleteriously affected by these variations. The first of these two differences, potential concerns to the neural extended Kalman filter's implementation, is investigated in this effort. In this effort, performance of this adaptive and predictive scheme with multiple sensors in a three dimensional space is shown to provide a quality impact estimate.

Citation Download Citation

Stephen C. Stubberud and Kathleen A. Kramer "Impact and point prediction using a neural extended Kalman filter with multiple sensors", Proc. SPIE 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI, 656702 (7 May 2007); https://doi.org/10.1117/12.713614

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Sensors

Motion models

Neural networks

Filtering (signal processing)

Systems modeling

Mathematical modeling

Motion estimation

Show All Keywords

Keywords/Phrases

Search In:

Publication Years