Multiobject Tracking for Generic Observation Model Using Labeled Random Finite Sets

Li, Suqi; Yi, Wei; Hoseinnezhad, Reza; Wang, Bailu; Kong, Lingjiang

doi:10.1109/tsp.2017.2764864

Cited by 47 publications

(22 citation statements)

References 43 publications

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“…Recently, in a series of works, the notion of labeled random finite set (RFS) was introduced to address object trajectories and their uniqueness [22]- [28]. Vo et al [22], [23] proposed a particular class of labeled multi-object densities called generalized labeled multi-Bernoulli (GLMB) densities.…”

Section: Introductionmentioning

confidence: 99%

Robust Distributed Fusion With Labeled Random Finite Sets

Hoseinnezhad

et al. 2018

IEEE Trans. Signal Process.

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112

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Abstract-This paper considers the problem of the distributed fusion of multi-object posteriors in the labeled random finite set filtering framework, using Generalized Covariance Intersection (GCI) method. Our analysis shows that GCI fusion with labeled multi-object densities strongly relies on label consistencies between local multi-object posteriors at different sensor nodes, and hence suffers from a severe performance degradation when perfect label consistencies are violated. Moreover, we mathematically analyze this phenomenon from the perspective of Principle of Minimum Discrimination Information and the so called yesobject probability. Inspired by the analysis, we propose a novel and general solution for the distributed fusion with labeled multi-object densities that is robust to label inconsistencies between sensors. Specifically, the labeled multi-object posteriors are firstly marginalized to their unlabeled posteriors which are then fused using GCI method. We also introduce a principled method to construct the labeled fused density and produce tracks formally. Based on the developed theoretical framework, we present tractable algorithms for the family of generalized labeled multi-Bernoulli (GLMB) filters including δ-GLMB, marginalized δ-GLMB and labeled multi-Bernoulli filters. The robustness and efficiency of the proposed distributed fusion algorithm are demonstrated in challenging tracking scenarios via numerical experiments.

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Section: Introductionmentioning

confidence: 99%

Robust Distributed Fusion With Labeled Random Finite Sets

Hoseinnezhad

et al. 2018

IEEE Trans. Signal Process.

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112

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“…The performance of our S-LMB-GOM filter is evaluated with OSPA metric [30] (c = 100 m, p = 1) and compared with G-LMB-GOM filter over 100 Monte Carlo trials. We should point out that there are many papers [20][21][22][23][24][25] based on the Bayes filter to solve the TBD problem of radar, but the G-LMB-GOM filter [26] is the new method which adapts to inseparable likelihood and reduces the computational complexity compared with the method in Reference [20]. Therefore, we choose the G-LMB-GOM filter for comparison.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…As a typical application of the nonstandard observation model, the TBD algorithm of radar targets [20][21][22][23][24][25] works with the raw radar data directly, and detects and tracks the targets jointly, so it is suitable for application in a low signal-to-noise ratio (SNR) scenario. The observation model for radar sensor is called a generic observation model (GOM) [20,26]. This is because the measurements affected by different target point spread functions may overlap.…”

Section: Introductionmentioning

confidence: 99%

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Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets

2019

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A labeled multi-Bernoulli (LMB) filter is presented to jointly detect and track radar targets. A relevant LMB filter is recently proposed by Rathnayake which assumes that the measurements of different targets do not overlap, leading to the favorable separable likelihood assumption. However, new or close tracks often violate the assumption and lead to a bias in the cardinality estimate. To address this problem, a one-to-one association method between measurements and tracks is proposed. In our method, any target only corresponds to its associated measurements and different tracks have little mutual interference. In addition, an approximate method for calculating the point spread function of radar is developed to improve the computational efficiency of likelihood function. The simulation under low signal-to-noise ratio scenario with closely spaced targets have demonstrated the effectiveness and efficiency of the proposed algorithm.

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“…Principled approximations of the δ-GLMB filter that preserve the key statistical properties of the full multitarget density were also proposed to further reduce the numerical complexity, which includes the labeled multi-Bernoulli (LMB) filter [20] and the marginalized δ-GLMB filter [21]. The δ-GLMB filter has inspired much work, such as multitarget tracking with merged measurements [22], extended target [23], and generic observation model [24]. More recently, unlabeled solutions that also output targets trajectories using the so-called sets of trajectories are also presented [25], [26].…”

Section: Introductionmentioning

confidence: 99%

Multitarget Tracking Using One Time Step Lagged Delta-Generalized Labeled Multi-Bernoulli Smoothing

Liang

et al. 2020

IEEE Access

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Aiming at improving the tracking performance of the delta-generalized labeled multi-Bernoulli (δ-GLMB) filter, we present a one time step lagged δ-GLMB smoother in this work, which also inherently outputs targets trajectories and differs from the Probability hypothesis density (PHD), Multi-Bernoulli (MB), and Cardinalized probability hypothesis density (CPHD) smoothers that are incapable of generating target trajectories directly. Under the standard multitarget measurement likelihood and state transition kernel, we show that a δ-GLMB distributed multitarget filtering density would result in a same distributed one time step lagged multitarget smoothing density. An efficient implementation of the proposed smoothing algorithm using the standard ranked assignment technique is also given. Numerical results show that the proposed smoother is capable of tracking a time-varying number of targets, in the presence of measurement origin uncertainty, target detection uncertainty, and clutter, and show that the proposed smoother outperforms the δ-GLMB filter, and the PHD, MB, and CPHD smoothers of the same time lag on both the estimates of target number and state and it also outperforms the LMB and the approximated δ-GLMB smoothers of the same time lag on target number estimate. INDEX TERMS Delta-generalized labeled multi-Bernoulli, multitarget tracking, random finite set, smoothing.

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Multiobject Tracking for Generic Observation Model Using Labeled Random Finite Sets

Cited by 47 publications

References 43 publications

Robust Distributed Fusion With Labeled Random Finite Sets

Robust Distributed Fusion With Labeled Random Finite Sets

Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets

Multitarget Tracking Using One Time Step Lagged Delta-Generalized Labeled Multi-Bernoulli Smoothing

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