A Random Matrix Measurement Update Using Taylor-Series Approximations

An approximate extended target tracking (ETT) measurement update is derived for random matrix extent representation with measurement noise. The derived update uses Taylor series approximations. The performance of the proposed update methodology is illustrated on a simple ETT scenario and compared to alternative updates in the literature.


A Variational Measurement Update for Extended Target Tracking With Random Matrices
Orguner, Umut (2012-07-01)
This correspondence proposes a new measurement update for extended target tracking under measurement noise when the target extent is modeled by random matrices. Compared to the previous measurement update developed by Feldmann et al., this work follows a more rigorous path to derive an approximate measurement update using the analytical techniques of variational Bayesian inference. The resulting measurement update, though computationally more expensive, is shown via simulations to be better than the earlier...
A PHD Filter for Tracking Multiple Extended Targets Using Random Matrices
Granstrom, Karl; Orguner, Umut (2012-11-01)
This paper presents a random set based approach to tracking of an unknown number of extended targets, in the presence of clutter measurements and missed detections, where the targets' extensions are modeled as random matrices. For this purpose, the random matrix framework developed recently by Koch et al. is adapted into the extended target PHD framework, resulting in the Gaussian inverse Wishart PHD (GIW-PHD) filter. A suitable multiple target likelihood is derived, and the main filter recursion is present...
A Gaussian mixture PHD filter for extended target tracking
Granström, Karl; Lundquist, Christian; Orguner, Umut (null; 2010-07-29)
In extended target tracking, targets potentially produce more than one measurement per time step. Multiple extended targets are therefore usually hard to track, due to the resulting complex data association. The main contribution of this paper is the implementation of a Probability Hypothesis Density ( phd) filter for tracking of multiple extended targets. A general modification of the phd filter to handle extended targets has been presented recently by Mahler, and the novelty in this work lies in the reali...
Extended Target Tracking Using Gaussian Processes
Wahlström, Niklas; Özkan, Emre (2015-08-15)
In this paper, we propose using Gaussian processes to track an extended object or group of objects, that generates multiple measurements at each scan. The shape and the kinematics of the object are simultaneously estimated, and the shape is learned online via a Gaussian process. The proposed algorithm is capable of tracking different objects with different shapes within the same surveillance region. The shape of the object is expressed analytically, with well-defined confidence intervals, which can be used ...
Extended target tracking with a cardinalized probability hypothesis density filter
Orguner, Umut; Granström, Karl (null; 2011-07-08)
This paper presents a cardinalized probability hypothesis density (CPHD) filter for extended targets that can result in multiple measurements at each scan. The probability hypothesis density (PHD) filter for such targets has already been derived by Mahler and a Gaussian mixture implementation has been proposed recently. This work relaxes the Poisson assumptions of the extended target PHD filter in target and measurement numbers to achieve better estimation performance. A Gaussian mixture implementation is d...
Citation Formats
E. Sarıtaş and U. Orguner, “A Random Matrix Measurement Update Using Taylor-Series Approximations,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34596.