Robust CPHD Fusion for Distributed Multitarget Tracking Using Asynchronous Sensors

Yu, Benru; Li, Tiancheng; Ge, Shaojia; Gu, Hong

doi:10.1109/jsen.2021.3128226

Cited by 21 publications

(7 citation statements)

References 61 publications

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“…The correlation of Gaussian components uses the distance-correlation method [38] to calculate the distance between the Gaussian mean m i s,k and m j s ,k of the Gaussian components from different sensors s ∈ S(≤ t) after communication on the sensor s:…”

Section: Gaussian Mixture Methods For Distance-correlated Phdmentioning

confidence: 99%

“…Yu et al proposed a recursive arithmetic averaging method on time to reduce the communication delay for multi-sensor fusion systems with different sampling rates. They proposed an extended method of multi-sensor CPHD filters to adapt to the environment of an unknown clutter rate and unknown detection probability [38]. The multi-sensor network structure based on the PHD filter [39][40][41] has been proposed and used for asynchronous sensors with different sampling rates.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Target Tracking AA Fusion Method for Asynchronous Multi-Sensor Networks

Wang,

Zhang,

Zheng

et al. 2023

Sensors

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Aiming at the problem of asynchronous multi-target tracking, this paper studies the AA fusion optimization problem of multi-sensor networks. Firstly, each sensor node runs a PHD filter, and the measurement information obtained from different sensor nodes in the fusion interval is flood communicated into composite measurement information. The Gaussian component representing the same target is associated with a subset by distance correlation. Then, the Bayesian Cramér–Rao Lower Bound of the asynchronous multi-target-tracking error, including radar node selection, is derived by combining the composite measurement information representing the same target. On this basis, a multi-sensor-network-optimization model for asynchronous multi-target tracking is established. That is, to minimize the asynchronous multi-target-tracking error as the optimization objective, the adaptive optimization design of the selection method of the sensor nodes in the sensor network is carried out, and the sequential quadratic programming (SQP) algorithm is used to select the most suitable sensor nodes for the AA fusion of the Gaussian components representing the same target. The simulation results show that compared with the existing algorithms, the proposed algorithm can effectively improve the asynchronous multi-target-tracking accuracy of multi-sensor networks.

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Section: Gaussian Mixture Methods For Distance-correlated Phdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Target Tracking AA Fusion Method for Asynchronous Multi-Sensor Networks

Wang,

Zhang,

Zheng

et al. 2023

Sensors

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show abstract

“…The CPHD filter [ 29 , 30 , 31 ] merges the propagation intensity function and cardinality distribution (which represents the probability distribution of the target number) by considering the clutter RFS as an independent and identically distributed (i.i.d.) process.…”

Section: Research Backgroundmentioning

confidence: 99%

An Efficient Implementation Method for Distributed Fusion in Sensor Networks Based on CPHD Filters

Wang,

Chen

2023

Sensors

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A highly efficient implementation method for distributed fusion in sensor networks based on CPHD filters is proposed to address the issues of unknown cross-covariance fusion estimation and long fusion times in multi-sensor distributed fusion. This method can effectively and efficiently fuse multi-node information in multi-target tracking applications. Discrete gamma cardinalized probability hypothesis density (DG-CPHD) can effectively reduce the computational burden while ensuring computational accuracy similar to that of CPHD filters. Parallel inverse covariance intersection (PICI) can effectively avoid solving high-dimensional weight coefficient convex optimization problems, reduce the computational burden, and efficiently implement filtering fusion strategies. The effectiveness of the algorithm is demonstrated through simulation results, which indicate that PICI-GM-DG-CPHD can substantially reduce the computational time compared to other algorithms and is more suitable for distributed sensor fusion.

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“…N distributed multi-sensor multi-target tracking (DMMT) in sensor networks, most sensors are distributed fusion architectures, and commonly used random finite set frameworks are suitable for the distributed fusion of multiple sensors. The commonly used filtering forms include probability hypothesis density (PHD) [1][2][3][4], cardinalized probability hy-pothesis density (CPHD) [5][6][7][8], labeled multi-Bernoulli (LMB) [9][10] (e-mail: wangliucclg1995@163.com;Zl1980@cust.edu.cn;wangtong@cust.edu. cn; chenguifenfcclg@163.com) [11][12], as well as optimized and improved filtering algorithms [13].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Outlier Selection via GM-CPHD Fusion for Multitarget Tracking Using Sensors With Different Fields of View

Wang,

Chen,

Zhang

et al. 2024

IEEE Sensors J.

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In response to the issue of different perspectives affecting fusion accuracy in distributed multi-sensor multi-target tracking (DMMT), this study investigates the fusion strategies for multi-target tracking with different field-of-view sensors. We explore the impacts of different fields of view (FoVs) on the multi-sensor Geometric Average/Arithmetic Average (GA/AA), generalized covariance intersection (GCI) fusion strategies, and differential perspective boundary segmentation using examples. A Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) fusion method for selecting random outliers in multi-target tracking based on limited perspective sensors is proposed. The boundary is segmented according to different perspectives, and the posterior intensity function is decomposed into multiple sub-intensities using Stochastic Outlier Selection(SOS) clustering. The distribution of the number of targets in the corresponding region is characterized using a multi Bernoulli reconstruction cardinal distribution. The complexity of the validation algorithm is sim-ilar to that of CPHD filtering. A simulation verifies the robustness and effectiveness of this method. By calculating OSPA(Optimal Sub-Pattern Assignment) and algorithm runtime, it can be verified that:the algorithm is more robust based on the reduction in detection probability and the increase in the clutter rate.

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Robust CPHD Fusion for Distributed Multitarget Tracking Using Asynchronous Sensors

Cited by 21 publications

References 61 publications

Multi-Target Tracking AA Fusion Method for Asynchronous Multi-Sensor Networks

Multi-Target Tracking AA Fusion Method for Asynchronous Multi-Sensor Networks

An Efficient Implementation Method for Distributed Fusion in Sensor Networks Based on CPHD Filters

Stochastic Outlier Selection via GM-CPHD Fusion for Multitarget Tracking Using Sensors With Different Fields of View

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