A novel particle filter for extended target tracking with random hypersurface model

Zhang, Xing; Yan, Zhibin; Chen, Yunqi; Yuan, Ye

doi:10.1016/j.amc.2022.127081

Cited by 12 publications

(13 citation statements)

References 15 publications

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“…where k r is the ellipse center position, k A is the positive definite matrix used to describe the shape of the ellipse, including the long axis, short axis, and direction angle of the ellipse, and the positive definite matrix k A is Cholesky decomposed to simplify the calculation [22]:…”

Section: A Elliptic Rhm Definitionmentioning

confidence: 99%

“…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%

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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: A Elliptic Rhm Definitionmentioning

confidence: 99%

Section:  mentioning

confidence: 99%

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

2023

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“…Then, UKF is used to identify parameters and the physical membership function is used to fuse local models and estimate final states. After simplifying the existing approximate likelihood function where the distribution of the scaling factor is approximated by Gaussian one, a feasible weighting scheme is obtained and a novel particle filtering algorithm (NPFA) is proposed in [35]. A maneuvering target tracking scheme under measurement origin uncertainties is derived based on the approximation and propagation of the target state posterior distribution by combining Bayesian decision theory and suitable hypothesis merging procedures in [36], in which very low levels of track loss rate are obtained even for scenarios with high false alarm probability and trajectories with a high degree of maneuverability.…”

Section: Related Workmentioning

confidence: 99%

An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

Zhu

Zhang

et al. 2022

J Wireless Com Network

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Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (measurement compensation-based mixture population Monte Carlo) which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee sampling diversity. And also, extra measurements or observations generating method is introduced to compensate missed prediction locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can be generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed.

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“…Then UKF is used to identify parameters and physical membership function is used to fuse local models and estimate final states. After simplifying existing approximate likelihood function where the distribution of the scaling factor is approximated by Gaussian one, a feasible weighting scheme is obtained and a novel particle filtering algorithm (NPFA) is proposed in [29]. A maneuvering target tracking scheme under measurement origin uncertainties is derived based on the approximation and propagation of the target state posterior distribution by combining Bayesian decision theory and suitable hypothesis merging procedures in [30], in which very low levels of track loss rate is obtained even for scenarios with high false alarm probability and trajectories with high degree of maneuverability.…”

Section: Related Workmentioning

confidence: 99%

An Improved Target Tracking Scheme based on MC-MPMC Method for Mobile Wireless Sensor Networks

Zhu

Zhang

2022

Preprint

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Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (Measurement Compensation based Mixture Population Monte Carlo) method which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee the sample diversity. And also, extra measurements or observations generating method is introduced to compensate missing predict locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, the HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed.

show abstract

A novel particle filter for extended target tracking with random hypersurface model

Cited by 12 publications

References 15 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

An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

An Improved Target Tracking Scheme based on MC-MPMC Method for Mobile Wireless Sensor Networks

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