Constrained Kalman filtering and its application to tracking of ground moving targets
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.
Publication Source (Journal or Book title)
Proceedings of SPIE - The International Society for Optical Engineering
Gu, O. (2007). Constrained Kalman filtering and its application to tracking of ground moving targets. Proceedings of SPIE - The International Society for Optical Engineering, 6577 https://doi.org/10.1117/12.719884