Extensions of the CBMeMBer filter for joint detection, tracking, and classification of multiple maneuvering targets

Gao, Lin; Sun, Wen; Wei, Ping

doi:10.1016/j.dsp.2016.05.011

Cited by 20 publications

(6 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If only the position measurements are received, the target class can be determined based on the kinematic state estimation [52]. Assume that the class-dependent model sets are different, the posterior probabilities of target classes can be obtained based on the measurement likelihoods given different motion models [53,54]. In [55], different target classes correspond to different acceleration model sets, and the target class is determined based on the acceleration estimates.…”

Section: Multi-target Jtc Based On Kinematic Estimationmentioning

confidence: 99%

“…The target attribute measurement is introduced in the calculation of the likelihood function to derive the multitarget posterior density. In [17,18], the class information is extended to the target state vector, and an extended classdependent multi-target state distribution is then obtained using a PHD filter. In [19], an approximate sequential PHD filtering method is adopted for multi-sensor multi-target JDTC.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-target joint detection, tracking and classification based on random finite set for aerospace applications

Jing

Leung

2018

View full text Add to dashboard Cite

Multi-target detection, tracking and classification are important problems in aerospace applications, such as reconnaissance, airborne and spaceborne sensing. These problems are correlated but are difficult to be solved simultaneously, especially for systems with multiple sensors. This paper summarized the existing work for multi-target joint detection, tracking and classification based on labeled random finite set. Furthermore, a new algorithm is proposed for multi-sensor multi-target joint detection, tracking and classification problem. A conditional multi-sensor multi-target state estimator is derived, and the optimal solution is then obtained based on the minimum Bayes risk criterion. The numerical simulations are performed, and the results are shown to be more accurate than that of the approximate solutions based on the unlabeled random finite set. It is observed that the labeled random finite set theory provides a good foundation for a joint solution for multi-target detection, tracking and classification.

show abstract

Section: Multi-target Jtc Based On Kinematic Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-target joint detection, tracking and classification based on random finite set for aerospace applications

Jing

Leung

2018

View full text Add to dashboard Cite

show abstract

“…Moreover, knowledge of the target class provides valuable information on the possible kinematic behaviors of the target [8] (e.g., a fighter aircraft can perform sharper maneuvers than a cargo aircraft) which, in turn, can be profitably exploited to improve tracking performance [9]. The recent development of random finite set (RFS) methods has produced several interesting contributions to joint detection, tracking and classification (JDTC) of both a single and multiple targets [10,11,12,13,14,15,16] but all based on a single-sensor system.…”

Section: Introductionmentioning

confidence: 99%

Multi-sensor joint target detection, tracking and classification via Bernoulli filter

Li,

Wei,

Battistelli

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

This paper focuses on joint detection, tracking and classification (JDTC) of a target via multi-sensor fusion. The target can be present or not, can belong to different classes, and depending on its class can behave according to different kinematic modes. Accordingly, it is modeled as a suitably extended Bernoulli random finite set (RFS) uniquely characterized by existence, classification, class-conditioned mode and class&mode-conditioned state probability distributions. By designing suitable centralized and distributed rules for fusing information on target existence, class, mode and state from different sensor nodes, novel centralized and distributed JDTC Bernoulli filters (C-JDTC-BF and D-JDTC-BF), are proposed. The performance of the proposed JDTC-BF approach is evaluated by means of simulation experiments.

show abstract

“…These RFS based trackers are integrated approaches for multi-target joint detection and tracking, and provide the approximated multi-target density with association uncertainty. Compared to traditional approaches [ 20 , 21 , 22 , 23 ], the multi-target joint detection, tracking and classification problem is also solved using multi-model PHD/CPHD [ 1 , 24 , 25 , 26 , 27 , 28 ] and CMeMBer filter [ 29 ]. However, due to track information of the RFS based filters can not be obtained directly, these algorithms only calculate the class-dependent multi-target density without the explicit classification results for each target.…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Joint Detection; Tracking and Classification Based on Marginal GLMB Filter and Belief Function Theory

Liang

Jing

et al. 2020

Sensors

View full text Add to dashboard Cite

This paper proposes a new solution to multi-target joint detection, tracking and classification based on labeled random finite set (RFS) and belief function theory. A class dependent multi-model marginal generalized labeled multi-Bernoulli (MGLMB) filter is developed to analytically calculate the multi-target number, state estimates and model probabilities. In addition, a two-level classifier based on continuous transferable belief model (cTBM) is designed for target classification. To make full use of the kinematic characteristics for classification, both the dynamic modes and states are considered in the classifier, the model dependent class beliefs are computed on the continuous state feature subspace corresponding to different dynamic modes and then fused. As a result that the uncertainty about the classes is well described for decision, the classification results are more reasonable and robust. Moreover, as the estimation and classification problems are jointly solved, the tracking and classification performance are both improved. In the simulation, a scenario contains multi-target with miss detection and dense clutter is used. The performance of multi-target detection, tracking and classification is better than traditional methods based on Bayesian classifier or single model. Simulation results are illustrated to demonstrate the effectiveness and superiority of the proposed algorithm.

show abstract

Extensions of the CBMeMBer filter for joint detection, tracking, and classification of multiple maneuvering targets

Cited by 20 publications

References 32 publications

Multi-target joint detection, tracking and classification based on random finite set for aerospace applications

Multi-target joint detection, tracking and classification based on random finite set for aerospace applications

Multi-sensor joint target detection, tracking and classification via Bernoulli filter

Multi-Target Joint Detection; Tracking and Classification Based on Marginal GLMB Filter and Belief Function Theory

Contact Info

Product

Resources

About