A Bayesian Approach to Multiple Target Detection and Tracking

Morelande, Mark R.; Kreucher, Chris; Kastella, Keith

doi:10.1109/tsp.2006.889470

Cited by 138 publications

(91 citation statements)

References 32 publications

(69 reference statements)

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“…The PFs belonging to the second group assume posterior independence between well-separated targets rather than between individual targets. The representative works are the joint optimal importance density (JOID) method in [191]. The adaptive systems approach of [192] applies the idea of joint sampling to subsets of the measurement record.…”

Section: Bayesian Approachesmentioning

confidence: 99%

“…The JMPD method can also be derived using the mathematics of RFS and expressed in the FISST framework [106]. In [191] and [196], the JMPD is found by the usual Bayesian filtering recursion involving evaluation of the Chapman-Kolmogorov equation followed by the multiplication of the result by the likelihood. This simplifies the calculation of the posterior in comparison with the RFS framework mainly due to the use of vector integrals rather than set integrals.…”

Section: Bayesian Approachesmentioning

confidence: 99%

“…PFs provide a recursive stochastic grid approximation to the exact solution of Bayesian state estimation problems. Based on [191,196] considers targets of varying number. A model for the time evolution of the extended multitarget state is developed that allows the rates at which targets arrive and depart to vary both spatially and temporally.…”

Section: Bayesian Approachesmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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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.

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Section: Bayesian Approachesmentioning

confidence: 99%

Section: Bayesian Approachesmentioning

confidence: 99%

Section: Bayesian Approachesmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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“…This section reviews the Joint Multitarget Probability Density (JMPD) and its Particle Filter (PF) implementation [1]- [3]. The JMPD is a single probabilistic entity that captures all of the uncertainty about a surveillance region.…”

Section: The Joint Multitarget Probability Density (Jmpd)mentioning

confidence: 99%

Network sensor management for tracking and localization

Hero

Kreucher

2007

2007 10th International Conference on Information Fusion

Self Cite

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Abstract-This paper addresses the problem of sensor management for a large network of agile sensors. Sensor management refers to the process of dynamically retasking agile sensors in response to an evolving environment. Sensors may be agile in a variety of ways, e.g., the ability to reposition, point an antenna, choose sensing mode, or waveform. The goal of sensor management in a large network is to choose actions for individual sensors dynamically so as to maximize overall network utility.Sensor management in the multiplatform setting is a challenging problem for several reasons. First, the state space required to characterize an environment is typically of very high dimension and poorly represented by a parametric form. Second, the network must simultaneously address a number of competing goals. Third, the number of potential taskings grows exponentially with the number of sensors. Finally, in low communication environments, decentralized methods are required.The approach we present addresses these challenges through a novel combination of particle filtering for nonparametric density estimation, information theory for comparing actions, and physicomimetics for computational tractability. The efficacy of the method is illustrated in a realistic surveillance application by simulation, where an unknown number of ground targets are to be detected and tracked by a network of mobile sensors.

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“…Since the JMPD is a high-dimensional entity that can not be computed in closed form, particle filters (PFs) have been used to approximate the JMPD in realistic scenarios involving tracking multiple targets [11]. While the particle filter based JMPD approach is theoretically sound, it demands intense computation, with a huge number of particles required to explore different dimensional statespaces for target detection.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scan Mixture Particle Filter for Joint Detection and Tracking of Multiple Targets

Liu¹,

Han²,

Yao³

et al. 2013

PIER B

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Abstract-In this paper, a novel algorithm named multi-scan mixture particle filter is proposed for joint detection and tracking for a varying number of targets. The posterior distribution of multiple target state in a single-target state space is a multi-mode distribution with each mode corresponding to either a target or clutter. A general global posterior distribution is adopted in this work, which consists of existing components and new components. The new components are generated at each time step to capture the new modes due to newly appeared targets or clutter. In order to distinguish targets from clutter, multiple scan information is incorporated. The history of each component's associate weights is stored in a multi-scan sliding window, which is used to judge whether the component is from a target or clutter. Moreover, a novel sampling method which combines the likelihood sampling and prior sampling is proposed to draw particles from the desired parts of the state space at each time step. From the simulation results, it could be seen that the proposed algorithm can effectively detect the appearance/disappearance of the targets as well as track the existing target.

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A Bayesian Approach to Multiple Target Detection and Tracking

Cited by 138 publications

References 32 publications

A Survey of Motion-Based Multitarget Tracking Methods

A Survey of Motion-Based Multitarget Tracking Methods

Network sensor management for tracking and localization

A Multi-Scan Mixture Particle Filter for Joint Detection and Tracking of Multiple Targets

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