Extended Target Tracking Using Gaussian Processes

Download

index.pdf

Date

2015-08-15

Author

Wahlström, Niklas
Özkan, Emre

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

245
views

0
downloads

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 for gating and association. Furthermore, we use an efficient recursive implementation of the algorithm by deriving a state space model in which the Gaussian process regression problem is cast into a state estimation problem.

Subject Keywords

Extended target tracking, Gaussian processes, Star-convex.

URI

https://hdl.handle.net/11511/33280

Journal

IEEE TRANSACTIONS ON SIGNAL PROCESSING

DOI

https://doi.org/10.1109/tsp.2015.2424194

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

Extended Target Tracking and Classification Using Neural Networks Tuncer, Barkın; Kumru, Murat; Özkan, Emre (2019-01-01) Extended target/object tracking (ETT) problem involves tracking objects which potentially generate multiple measurements at a single sensor scan. State-of-the-art ETT algorithms can efficiently exploit the available information in these measurements such that they can track the dynamic behaviour of objects and learn their shapes simultaneously. Once the shape estimate of an object is formed, it can naturally be utilized by high-level tasks such as classification of the object type. In this work, we propose ...
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...
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...
Extended Target Tracking using a Gaussian-Mixture PHD Filter Granstrom, Karl; Lundquist, Christian; Orguner, Umut (2012-10-01) This paper presents a Gaussian-mixture (GM) implementation of the probability hypothesis density (PHD) filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. S...
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...

Citation Formats

N. Wahlström and E. Özkan, “Extended Target Tracking Using Gaussian Processes,” IEEE TRANSACTIONS ON SIGNAL PROCESSING, pp. 4165–4178, 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/33280.