Constrained Kalman filtering and its application to tracking of ground moving targets

Guoxiang Gu

doi:10.1117/12.719884

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3 May 2007 Constrained Kalman filtering and its application to tracking of ground moving targets

Guoxiang Gu

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Proceedings Volume 6577, Wireless Sensing and Processing II; 657708 (2007) https://doi.org/10.1117/12.719884
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

Localization and tracking of the ground moving target (GMT) are investigated based on measurements of TDOA (time-difference of arrival) and AOA (angle of arrival) in which the measurement noises are assumed to be uncorrelated and Gaussian distributed. An approximate MMSE algorithm is proposed via developing constrained Kalman filtering based on the pseudo-measurement model in the existing literature that leads to a nonlinear constraint imposed on the state vector for the GMT model. Randomization of the state vector suggests to replace the hard constraint by its expectation. We first derive a solution to a similar constrained MMSE problem that is used to extend the Kalman filtering to develop a linear recursive MMSE estimator subject to the nonlinear constraint as mentioned earlier which is termed as constrained Kalman filtering.

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Guoxiang Gu "Constrained Kalman filtering and its application to tracking of ground moving targets", Proc. SPIE 6577, Wireless Sensing and Processing II, 657708 (3 May 2007); https://doi.org/10.1117/12.719884

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