Maneuver tracking using an adaptive Gaussian sum technique

Stephen C. Stubberud; Kathleen A. Kramer

doi:10.1117/12.601106

28 March 2005 Maneuver tracking using an adaptive Gaussian sum technique

Stephen C. Stubberud, Kathleen A. Kramer

Proceedings Volume 5813, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005; (2005) https://doi.org/10.1117/12.601106
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

The best method to track through a maneuver is to know the motion model of the maneuvering target. Unfortunately, a priori knowledge of the maneuver is not usually known. If the motion model of the maneuver can be estimated quickly from the measurements then the resulting track estimate will be better than the a priori static model. An adaptive function approximation technique to improve the motion model while tracking is analyzed for its potential to track through various maneuvers. The basic function approximation technique is that of a Gaussian sum. The Gaussian sum approximates the function which represents the error between the initial static model and the actual model of the maneuver. The parameters of the Gaussian sum are identified on-line using a Kalman filter identification scheme. This scheme, used in conjunction with a Kalman filter tracker, creates a coupled technique that can improve the motion model quickly. This adaptive Gaussian sum approach to maneuver tracking has its performance analyzed for three maneuvers. These maneuvers include a maneuvering ballistic target, a target going through an s-curve, and real target with a multiple racetrack flight path. The results of these test cases demonstrate the capabilities of this approach to track maneuvering targets.

Citation Download Citation

Stephen C. Stubberud and Kathleen A. Kramer "Maneuver tracking using an adaptive Gaussian sum technique", Proc. SPIE 5813, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005, (28 March 2005); https://doi.org/10.1117/12.601106

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KEYWORDS

Motion models

Filtering (signal processing)

Error analysis

Motion estimation

Mathematical modeling

Motion analysis

Algorithm development

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