On Spawning and Combination of Extended/Group Targets Modeled With Random Matrices

Granstrom, Karl
Orguner, Umut
In extended/group target tracking, where the extensions of the targets are estimated, target spawning and combination events might have significant implications on the extensions. This paper investigates target spawning and combination events for the case that the target extensions are modeled in a random matrix framework. The paper proposes functions that should be provided by the tracking filter in such a scenario. The results, which are obtained by a gamma Gaussian inverse Wishart implementation of an extended target probability hypothesis density filter, confirms that the proposed functions improve the performance of the tracking filter for spawning and combination events.


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...
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...
Multi-Ellipsoidal Extended Target Tracking With Variational Bayes Inference
Tuncer, Barkın; Orguner, Umut; Özkan, Emre (2022-01-01)
In this work, we propose a novel extended target tracking algorithm, which is capable of representing a target or a group of targets with multiple ellipses. Each ellipse is modeled by an unknown symmetric positive-definite random matrix. The proposed model requires solving two challenging problems. First, the data association problem between the measurements and the sub-objects. Second, the inference problem that involves non-conjugate priors and likelihoods which needs to be solved within the recursive fil...
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...
Negative information fusion for gaussian process based three-dimensional extended target tracking
Sür, Cem Gürkan; Özkan, Emre; Department of Electrical and Electronics Engineering (2021-5-10)
Extended target tracking refers to the estimation of extent of a target as well as its position, kinematics, and orientation. In this thesis, we compare performances of Gaussian process based extended target tracking methods. Additionally, we propose a method that uses negative information fusion in three-dimensional point cloud data to enhance extent estimates of the target. Wide-ranging simulations are carried out to demonstrate the performance of the proposed algorithm. All simulations are carried out on...
Citation Formats
K. Granstrom and U. Orguner, “On Spawning and Combination of Extended/Group Targets Modeled With Random Matrices,” IEEE TRANSACTIONS ON SIGNAL PROCESSING, pp. 0–0, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/46264.