Multi-Ellipsoidal Extended Target Tracking Using Sequential Monte Carlo

Kara, Süleyman Fatih
Özkan, Emre
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, idea and derive an efficient filter to approximate the joint posterior density of the target kinematic states and target extent. Conditional analytical expressions, which are essential for Rao-Blackwellization, are not available in our problem. We use variational Bayes technique to approximate the conditional densities and enable Rao-Blackwellization. The performance of the method is demonstrated through simulations. A comparison with a recent method in the literature is performed.


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...
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 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...
Variational smoothing for extended target tracking with random matrices
Kartal, Savaş Erdem; Orguner, Umut; Department of Electrical and Electronics Engineering (2022-4-05)
In this thesis, two Bayesian smoothers are proposed for random matrix based extended target tracking (ETT). The proposed smoothers are based on the variational Bayes techniques and they are derived for an extended target model without and with orientation. The random matrix models of Feldman et al. and Tuncer and Özkan are used as the extended target models without and with orientation, respectively. The performance of both smoothers is evaluated using simulation results on two different scenarios. It is se...
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...
Citation Formats
S. F. Kara and E. Özkan, “Multi-Ellipsoidal Extended Target Tracking Using Sequential Monte Carlo,” presented at the 21st International Conference on Information Fusion (FUSION), Cambridge, ENGLAND, 2018, Accessed: 00, 2020. [Online]. Available: