Abstract-Targets that generate multiple measurements at a given instant in time are commonly known as extended targets. These present a challenge for many tracking algorithms, as they violate one of the key assumptions of the standard measurement model. In this paper, a new algorithm is proposed for tracking multiple extended targets in clutter, that is capable of estimating the number of targets, as well the trajectories of their states, comprising the kinematics, measurement rates and extents. The proposed technique is based on modelling the multi-target state as a generalised labelled multi-Bernoulli (GLMB) random finite set (RFS), within which the extended targets are modelled using gamma Gaussian inverse Wishart (GGIW) distributions. A cheaper variant of the algorithm is also proposed, based on the labelled multi-Bernoulli (LMB) filter. The proposed GLMB/LMBbased algorithms are compared with an extended target version of the cardinalised probability hypothesis density (CPHD) filter, and simulation results show that the (G)LMB has improved estimation and tracking performance.
This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. The solution exploits the GLMB joint prediction and update together with a new technique for truncating the GLMB filtering density based on Gibbs sampling. The resulting algorithm has quadratic complexity in the number of hypothesized object and linear in the number of measurements of each individual sensors. Index TermsRandom finite sets, generalized labeled multi-Bernoulli, multi-object tracking, data association, Gibbs sampling The classical PHD and CPHD filters are developed for single-sensors. Since the multi-sensor PHD, CPHD and multi-Bernoulli filters are combinatiorial [4], [30], the most commonly used approximate multi-sensor PHD, CPHD and multi-Bernoulli filter are the heuristic "iterated corrector" versions [31] that apply single-sensor updates, once for each sensor in turn. This approach yields final solutions that depend on the order in which the sensors are processed. Multi-sensor PHD and CPHD filters that are principled, computationally tractable, and independent of sensor order have been proposed in [4] (Section 10.6).However, this approach as well as the heuristic "iterated corrector" involve two levels of approximation since the exact multi-sensor PHD, CPHD and multi-Bernoulli filters are approximations of the Bayes multi-sensor multi-object filter.An exact solution to the Bayes multi-object filter is the Generalized Labeled Multi-Bernoulli (GLMB) filter, which also outputs multi-object trajectories [32], [33]. Moreover, given a cap on the number of GLMB components, recent works show that the GLMB filter can be implemented with linear complexity in the number of measurements and quadratic in the number of hypothesized objects [34]. The GLMB density is flexible enough to approximate any labeled RFS density with matching intensity function and cardinality distribution [35], and also enjoys a number of nice analytical properties, e.g. the void probability functional-a necessary and sufficient statistic-of a GLMB, the Cauchy-Schwarz divergence between two GLMBs, the L 1 -distance between a GLMB and its truncation, can all be computed in closed form [36], [33]. Recent research in approximate GLMB filters [37], [38] as well as applications in tracking from merged measurements [39], extended targets [40], maneuvering targets [41], [42], trackbefore-detect [35], [43], computer vision [44]-[47], sensor scheduling [36], [48], field robotics [49], and distributed multi-object tracking [50], demonstrate the versatility of the GLMB filter, and suggest that it is an important tool in multi-object systems.In this work we present an implementation of the multi-sensor GLMB filter. The major hurdle in the multi-sensor GLMB filter implementation is the NP-hard multi-dimensional ranked assignment problem.A multi-sensor version of an approximation of the GLMB filter, known as the marginalized GLMB filter, was proposed in [38]. While this multi-sensor solution is scalable in the number of sensors, it...
In multi-object inference, the multi-object probability density captures the uncertainty in the number and the states of the objects as well as the statistical dependence between the objects. Exact computation of the multi-object density is generally intractable and tractable implementations usually require statistical independence assumptions between objects. In this paper we propose a tractable multi-object density approximation that can capture statistical dependence between objects. In particular, we derive a tractable Generalized Labeled Multi-Bernoulli (GLMB) density that matches the cardinality distribution and the first moment of the labeled multi-object distribution of interest. It is also shown that the proposed approximation minimizes the Kullback-Leibler divergence over a special tractable class of GLMB densities. Based on the proposed GLMB approximation we further demonstrate a tractable multi-object tracking algorithm for generic measurement models. Simulation results for a multi-object Track-Before-Detect example using radar measurements in low signal-to-noise ratio (SNR) scenarios verify the applicability of the proposed approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.