Multi-Ellipsoidal Extended Target Tracking With Variational Bayes Inference

Download

index.pdf

Date

2022-01-01

Author

Tuncer, Barkın
Orguner, Umut
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

212
views

123
downloads

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 filtering framework. We utilize the variational Bayes inference method to solve the association problem and to approximate the intractable true posterior. The performance of the proposed solution is demonstrated in simulations and real-data experiments. The results show that our method outperforms the state-of-the-art methods in terms of accuracy with lower computational complexity.

Subject Keywords

Kinematics, Covariance matrices, Shape, Target tracking, Partitioning algorithms, Signal processing algorithms, Computational modeling, Extended target tracking, random matrix, variational Bayes, OBJECT, MODEL

URI

https://hdl.handle.net/11511/99610

Journal

IEEE TRANSACTIONS ON SIGNAL PROCESSING

DOI

https://doi.org/10.1109/tsp.2022.3192617

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

Random Matrix Based Extended Target Tracking with Orientation: A New Model and Inference Tuncer, Barkın; Özkan, Emre (2021-02-01) In this study, we propose a novel extended target tracking algorithm which is capable of representing the extent of dynamic objects as an ellipsoid with a time-varying orientation angle. A diagonal positive semi-definite matrix is defined to model objects' extent within the random matrix framework where the diagonal elements have inverse-Gamma priors. The resulting measurement equation is non-linear in the state variables, and it is not possible to find a closed-form analytical expression for the true poste...
Posterior Cram'er-Rao Lower Bounds for Extended Target Tracking with Random Matrices Sarıtaş, Elif; Orguner, Umut (2016-07-08) This paper presents posterior Cram'er-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 exte...
Multi-Ellipsoidal Extended Target Tracking Using Sequential Monte Carlo Kara, Süleyman Fatih; Özkan, Emre (2018-07-10) In this paper, we consider the problem of extended target tracking, where the target extent cannot be represented by a single ellipse accurately. We model the target extent with multiple ellipses and solve the resulting inference problem, which involves data association between the measurements and sub-objects. We cast the inference problem into sequential Monte Carlo (SMC) framework and propose a simplified approach for the solution. Furthermore, we make use of the Rao-Blackwellization, aka marginalization...
Multi-dimensional hough transform based on unscented transform as a method of track-before-detect / Şahin, Gözde; Demirekler, Mübeccel; Department of Electrical and Electronics Engineering (2014) Track-Before-Detect (TBD) is the problem where target state estimation and detection occur simultaneously, and is a suitable method for the detection of low-SNR targets in unthresholded sensor data. In this thesis, a new Multi-Dimensional Hough Transform (MHT) technique based on Unscented Transform is proposed for the detection of dim targets in radar data. MHT is a TBD method that fuses Hough Transform results obtained on (x-t), (y-t) and (x-y) domains in order to detect a constant velocity target. The pro...
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

B. Tuncer, U. Orguner, and E. Özkan, “Multi-Ellipsoidal Extended Target Tracking With Variational Bayes Inference,” IEEE TRANSACTIONS ON SIGNAL PROCESSING, vol. 70, pp. 3921–3934, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/99610.