A PHD Filter for Tracking Multiple Extended Targets Using Random Matrices

Granstrom, Karl
Orguner, Umut
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 presented along with the necessary assumptions and approximations. The particularly challenging case of close extended targets is addressed with practical measurement clustering algorithms. The capabilities and limitations of the resulting extended target tracking framework are illustrated both in simulations and in experiments based on laser scans.


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 Random Matrix Measurement Update Using Taylor-Series Approximations
Sarıtaş, Elif; Orguner, Umut (2018-07-13)
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.
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...
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 ...
Posterior Cramér-Rao lower bounds for extended target tracking with random matrices
Sarıtaş, Elif; Orguner, Umut (2016-08-04)
This paper presents posterior Cramér-Rao lower bounds (PCRLB) for extended target tracking (ETT) when the extent states of the targets are represented with random matrices. PCRLB recursions are derived for kinematic and extent states taking complicated expectations involving Wishart and inverse Wishart distributions. For some analytically intractable expectations, Monte Carlo integration is used. The bounds for the semi-major and minor axes of the extent ellipsoid are obtained as well as those for the exten...
Citation Formats
K. Granstrom and U. Orguner, “A PHD Filter for Tracking Multiple Extended Targets Using Random Matrices,” IEEE TRANSACTIONS ON SIGNAL PROCESSING, pp. 5657–5671, 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41778.