Extended Target Tracking using a Gaussian-Mixture PHD Filter

Date

2012-10-01

Author

Granstrom, Karl
Lundquist, Christian
Orguner, Umut

Metadata

Show full item record

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

Item Usage Stats

297
views

0
downloads

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. Suitable remedies are given to handle spatially close targets and target occlusion.

Subject Keywords

Filtering theory, Gaussian processes, Target tracking

URI

https://hdl.handle.net/11511/43877

Journal

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS

DOI

https://doi.org/10.1109/taes.2012.6324703

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

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 ...
Interacting multiple model probabilistic data association filter using random matrices for extended target tracking Özpak, Ezgi; Orguner, Umut; Department of Electrical and Electronics Engineering (2018) In this thesis, an Interacting Multiple Model – Probabilistic Data Association (IMM-PDA) filter for tracking extended targets using random matrices is proposed. Unlike the extended target trackers in the literature which use multiple alternative partitionings/clusterings of the set of measurements, the algorithm proposed here considers a single partitioning/clustering of the measurement data which makes it suitable for applications with low computational resources. When the IMM-PDA filter uses clustered mea...
Efficient Bayesian track-before-detect Tekinalp, Serhat; Alatan, Abdullah Aydın (2006-10-11) This paper presents a novel Bayesian recursive track-before-detect (TBD) algorithm for detection and tracking of dim targets in optical image sequences. The algorithm eliminates the need for storing past observations by recursively incorporating new data acquired through sensor to the existing information. It calculates the likelihood ratio for optimal detection and estimates target state simultaneously. The technique does not require velocity-matched filtering and hence, it is capable of detecting any targ...
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...

Citation Formats

K. Granstrom, C. Lundquist, and U. Orguner, “Extended Target Tracking using a Gaussian-Mixture PHD Filter,” IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, pp. 3268–3286, 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43877.