&lt;title&gt;Tracking multiple targets using a particle filter representation of the joint multitarget probability density&lt;/title&gt;

Kreucher, Chris; Kastella, Keith; Hero, Alfred O.

doi:10.1117/12.502696

Cited by 40 publications

(42 citation statements)

References 7 publications

(4 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The samples for each cluster are then combined and weighted to account for the approximation involved in assuming this factorization. This approach has been used in [16], [17], and [24], where the primary motivation for separating the targets into clusters was to improve performance. When the JOID is used for each cluster the degree of performance improvement gained by clustering, while important, is perhaps of less significance than the computational savings.…”

Section: ) Joint Measurement-directed Proposalsmentioning

confidence: 99%

“…In [16] and [17], particles for each target were proposed indepedently leading to many particles being proposed in undesirable parts of the multitarget state space when multiple targets are in close proximity. A joint proposal overcomes this by taking into account the presence of nearby targets.…”

Section: ) Joint Measurement-directed Proposalsmentioning

confidence: 99%

“…Early work used a deterministic grid approximation, which is practical only for simple problems involving a small number of targets moving in one dimension [15]. More recently, particle filters (PFs) have been used to approximate the JMPD in realistic scenarios involving large numbers of targets moving in two-dimensions [16], [17]. PFs provide a recursive stochastic grid approximation to the exact solution of Bayesian state estimation problems [18], [19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Bayesian Approach to Multiple Target Detection and Tracking

Morelande

Kreucher

Kastella

2007

IEEE Trans. Signal Process.

138

View full text Add to dashboard Cite

Abstract-This paper considers the problem of simultaneously detecting and tracking multiple targets. The problem can be formulated in a Bayesian framework and solved, in principle, by computation of the joint multitarget probability density (JMPD). In practice, exact computation of the JMPD is impossible, and the predominant challenge is to arrive at a computationally tractable approximation. A particle filtering scheme is developed for this purpose in which each particle is a hypothesis on the number of targets present and the states of those targets. The importance density for the particle filter is designed in such a way that the measurements can guide sampling of both the target number and the target states. Simulation results, with measurements generated from real target trajectories, demonstrate the ability of the proposed procedure to simultaneously detect and track ten targets with a reasonable sample size.

show abstract

Section: ) Joint Measurement-directed Proposalsmentioning

confidence: 99%

Section: ) Joint Measurement-directed Proposalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Bayesian Approach to Multiple Target Detection and Tracking

Morelande

Kreucher

Kastella

2007

IEEE Trans. Signal Process.

138

View full text Add to dashboard Cite

show abstract

“…Early work used a deterministic grid approximation, which is practical only for simple problems involving a small number of targets moving in one dimension [197]. PFs have also been used to approximate the JMPD in realistic scenarios involving tracking multitarget [196,198]. PFs provide a recursive stochastic grid approximation to the exact solution of Bayesian state estimation problems.…”

Section: Bayesian Approachesmentioning

confidence: 99%

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

View full text Add to dashboard Cite

Abstract-Multitarget tracking (MTT) in surveillance system is extremely challenging, due to uncertain data association, maneuverable target motion, dense clutter disturbance, and real-time processing requirements. A good many methods have been proposed to cope with these challenges. However, no up-to-date survey is available in the literature that can help to select suitable tracking algorithm for practical problem. This paper provides a comprehensive review of the state-of-the-art motion-based MTT techniques, classifies existing methods into two groups, i.e., the detect-beforetrack (DBT) scheme and the track-before-detect (TBD) scheme. The DBT scheme is employed to achieve robust and tractable tracking performance when the signal-noise-ratio (SNR) is strong. The TBD scheme is used in the scenarios of low SNR, and it aims to cumulate target energy by multiple sensor frames. Furthermore, depending on the data association mechanism, the DBT methods can be classified into two categories, data association based approaches and finite set statistics (FISST) based approaches. And the TBD methods can be classified into non-Bayesian approaches and Bayesian approaches depending on the basis theory used for tracking. For each category, this paper provides the detailed descriptions of the representative algorithms, and examines their pros and cons. Finally, new trends for future research directions are offered.

show abstract

“…For this reason, casting the SSA estimation problem in a Bayesian paradigm has become a topic of growing interest but remains relatively unexplored so far. Besides some nontraditional approaches in the context of tracking [1,2], it has been addressed chiefly through either track-based [3,4] or set-based approaches [5][6][7][8][9][10][11]. Popular track-based solutions include the multiple hypothesis tracking (MHT) and joint probabilistic density association (JPDA) filters and follow an intuitive construction in which sequences of observations that may represent the data originating from a single specific object are maintained as tracks.…”

mentioning

confidence: 99%

Novel Multi-Object Filtering Approach for Space Situational Awareness

Delande

Frueh

Franco

et al. 2018

Journal of Guidance, Control, and Dynamics

View full text Add to dashboard Cite

Surveillance activities with ground-based assets in the context of space situational awareness are particularly challenging. The observation process is indeed hindered by short observation arcs, limited observability, missed detections, measurement noise, and contamination by clutter. This paper exploits a recent estimation framework for stochastic populations for space situational awareness surveillance scenarios. This framework shares the flexibility of the finite set statistics framework in the modeling of a dynamic population of objects and the representation of all the sources of uncertainty in a single coherent probabilistic framework and the intuitive approach of traditional trackbased techniques to describe individual objects and maintain track continuity. We present a recent multi-object filtering solution derived from this framework, the filter for distinguishable and independent stochastic populations, and propose a bespoke implementation of the multitarget tracking algorithm for a space situational awareness surveillance activity. The distinguishable and independent stochastic populations filter is tested on a surveillance scenario involving two ground-based Doppler radars in a challenging environment with significant measurement noise, limited observability, missed detections, false alarms, and no a priori knowledge about the number and the initial states of the objects in the scene. The tracking algorithm shows good performance in initiating tracks from object-generated observations and in maintaining track custody throughout the scenario, even when the objects are outside of the sensors' fields of view, despite the challenging conditions of the surveillance scenario.Nomenclature α y = probability of existence of previously detected target with observation path y cH = probability of existence of hypothesis H H = hypothesis, i.e., subset of compatible tracks in Y (H, n) = multitarget configuration, describing a possible composition of population X H tjt−1 ; H t = set of all hypotheses, propagated by the filter for distinguishable and independent stochastic populations (DISP) lz; x = likelihood of association between target with state x and observation with state z P tjt−1 , P t = law of whole population of targets, propagated by the distinguishable and independent stochastic populations filter P a = law of population of appearing targets p d x = probability of detection of target with state x p fa z = probability that observation with state z is false alarm p y = probability distribution of previously detected target with obs. path y p ϕ = probability distribution of each yet-to-be-detected target wH; n = joint probability of existence of targets in configuration (H, n) X = population of targets X = target state space X = target state space augmented with empty state ψ x = target state (e.g., position and velocity coordinates) Y tjt−1 , Y t = set of all observation paths, propagated by distinguishable and independent stochastic populations filter y = observation path or track characterized by said o...

show abstract

<title>Tracking multiple targets using a particle filter representation of the joint multitarget probability density</title>

Cited by 40 publications

References 7 publications

A Bayesian Approach to Multiple Target Detection and Tracking

A Bayesian Approach to Multiple Target Detection and Tracking

A Survey of Motion-Based Multitarget Tracking Methods

Novel Multi-Object Filtering Approach for Space Situational Awareness

Contact Info

Product

Resources

About