Distributed GGIW-CPHD-Based Extended Target Tracking Over a Sensor Network

Li, Guchong; Li, Gang; He, You

doi:10.1109/lsp.2022.3158589

Cited by 22 publications

(13 citation statements)

References 22 publications

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“…a and b are the long and short axes of the ellipse,  is the rotation angle of the ellipse, representing the angle between the long axis of the ellipse and the positive semi-axis of the x-axis, [0, 2 ]   ;  is the coordinate parameter; k e is the unit vectors from polar to Cartesian coordinates [22]. Substituting (10) into (8), we obtained an equation for the ellipse RHM in terms of measurement.…”

Section:  mentioning

confidence: 99%

“…Accordingly, Mahler implemented a probabilistic hypothesis density (PHD) filter [8] based on the finite set statistics theory (FISST) [9] to handle this problem. Examples of highly sophisticated FISST algorithms are the Cardinalized PHD (CPHD) filter, the Cardinal Balanced Multi-target Multi-Bernoulli (CBMeMBer) filter, and the generalized label multi-Bernoulli (GLMB) filter [10][11][12]. Consequently, their study tracked an unknown number of multiple targets in Poisson false alarms, missed detections, and target emergence, disappearance, and generation.…”

Section: Introductionmentioning

confidence: 99%

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RHM-δ-GLMB Tracking Algorithm on the Focal Plane for UAV Cluster Targets

2023

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This paper offers a new tracking algorithm for clustered targets of focal plane unmanned aerial vehicles (UAVs) by integrating the random hypersurface model (RHM) model into the δ-generalized label multi-Bernoulli (δ-GLMB) filter to guarantee a robust UAV cluster target tracking on such focal planes. We first investigated the infrared imaging features of UAV cluster targets by emphasizing the target imaging size and dispersion. Next, we designed an elliptical RHM-based measurement model to map the measurement, state parameters, scaling factors, and errors to pseudo-measurement "0", followed by establishing a pseudo-measurement equation to reflect the target's extended shape size. Then, in order to enhance measurement estimation accuracy, the RHM model was implemented with the δ-GLMB filter and Gamma-Gaussian mixture, which can perform a real-time estimate of the targets' centroid motion state and extended state. We also used the grid-based fast density-based spatial clustering of applications with noise (DBSCAN) segmentation algorithm to overcome the distance-based segmentation method restrictions for infrared radiation (IR) measurement data and diminish the algorithm's complexity even more in the measurement update. Extensive simulations demonstrated that this algorithm outperformed existing matching filtering algorithms in the target centroid motion and extended states. Our investigations also revealed that the algorithm was less vulnerable to clutter and more adaptive, making it more straightforward to accomplish reliable tracking of UAV cluster targets.

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Section:  mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RHM-δ-GLMB Tracking Algorithm on the Focal Plane for UAV Cluster Targets

2023

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“…Recently, the most wide used RFS based MTT algorithms include probability hypothesis density (PHD) filter [25], Gaussian mixture PHD (GM‐PHD) filter [26], cardinality probability hypothesis density (CPHD) filter [27], and sequential Monte Carlo PHD (SMC‐PHD) filter [28]. Due to the multi‐integrals in the update step of PHD algorithm, it is hard to obtain the estimation results in time.…”

Section: Introductionmentioning

confidence: 99%

“…The accurate estimate results of number and state of targets can be obtained based on the local neighboring interactions. Compared with the traditional MTT algorithms, such as [18, 19, 27], the estimate results of the proposed algorithm are robust to the cluster disturbances due to the design of Gauss components pruning and consensus fusion protocol. (2)The stability of the DGMPHD algorithm is analyzed. Based on the stochastic process theory, the sufficient conditions for the boundedness of the state estimation error are given in a general form.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the stochastic process theory, the sufficient conditions for the boundedness of the state estimation error are given in a general form. Considering the target dynamic model and sensor measurement model, a lot of MTT algorithms have been proposed, such as [25–28], but few of them analyzed the stability of the algorithm. (3)Most of traditional MTT algorithms only consider the simple target dynamic model, such as [27, 28, 30 32, 34]. The designed DGMPHD algorithm is also suitable for multiple maneuvering targets, which is illustrated in the simulation section.…”

Section: Introductionmentioning

confidence: 99%

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Distributed robust cooperative tracking against multiple maneuvering targets based on random finite set

Zhang

Dong

et al. 2022

IET Control Theory & Appl

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This paper considers the distributed multi-target tracking problem with mobile sensor network. Based on the random finite set and consensus methods, a distributed multi-target tracking algorithm is designed by local filtering stage and consensus fusion stage. In the local filtering stage, the state and measurement information of multi-target are described by random finite set. Each node in the sensor network obtains the state estimates of multi-target individually by applying the probability hypothesis density filtering algorithm. To reduce the computational complexity, the Jacobian matrix and Hessian matrix are used to calculate the priori probability density. With the local neighboring interactions, the estimate results of multi-target system are obtained after the consensus fusion stage. Besides, the stochastic boundedness of the estimation error of the distributed multi-target tracking algorithm is proved by constructing a stochastic process. In the final, a simulation example is given to verify the multi-target tracking performance of the proposed algorithm.

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Multisensor multitarget distributed fusion for discrepant fields of view

Jin,

Lu,

2024

Digital Signal Processing

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Distributed GGIW-CPHD-Based Extended Target Tracking Over a Sensor Network

Cited by 22 publications

References 22 publications

RHM-δ-GLMB Tracking Algorithm on the Focal Plane for UAV Cluster Targets

RHM-δ-GLMB Tracking Algorithm on the Focal Plane for UAV Cluster Targets

Distributed robust cooperative tracking against multiple maneuvering targets based on random finite set

Multisensor multitarget distributed fusion for discrepant fields of view

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