Sparse Structure Inference for Group and Network Tracking

2016-07-08
Murphy, James
Özkan, Emre
Bunch, Pete
Godsill, Simon J.
This paper presents a method for inferring interaction strength and structure amongst targets in multiple target tracking (MTT) applications. By making simple assumptions, it is shown how an efficient and well-mixing MCMC inference method can be developed to learn about the relationships between tracked targets, including leader-follower relationships, group relationships and the influence of targets on others. This network structure of influence between targets is inferred in a sparse way, setting many interaction terms to zero and allowing for more efficient inference and clearer structural conclusions to be drawn. The effectiveness of the method is demonstrated on both synthetic and real animal flocking data.

Suggestions

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...
Distributed Target Tracking with Propagation Delayed Measurements
Orguner, Umut (2009-07-09)
This paper presents a framework for making distributed target tracking under significant signal propagation delays between the target and the sensors. Each sensor considered makes estimation using its own measurements compensating for the involved signal propagation delay using a deterministic sampling based algorithm proposed previously. Since the individual sensor readings might not be enough to localize the target, the sensors have to share their estimates with each other at specific time instants and co...
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...
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...
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...
Citation Formats
J. Murphy, E. Özkan, P. Bunch, and S. J. Godsill, “Sparse Structure Inference for Group and Network Tracking,” 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54318.