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

Lv, Chunfeng; Zhu, Jianping; Zhang, Tao; Pi, Yihai

doi:10.1186/s13638-022-02158-8

Cited by 3 publications

(6 citation statements)

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“…100 Monte Carlo trials are performed for each simulation, and the results are average simulation results for obtaining generating results. The simulation setup is similar to that of MC-MPMC [36], and data transmission is similar to that of [37,38]. Target states and measurements can be presented as Equations ( 35), (36), where xk is the dynamic state vector at time k denoted as…”

Section: Algorithm 2 Gci Fusion Algorithmmentioning

confidence: 99%

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Generalised covariance intersection‐Gamma Gaussian Inverse Wishart‐Poisson multi‐Bernoulli Mixture: An intelligent multiple extended target tracking scheme for mobile aquaculture sensor networks

Lv,

Zhu,

Peng

2024

IET Wireless Sensor Systems

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Poisson multi‐Bernoulli Mixture (PMBM) filter has been known as an available or practical point and multiple extended target tracking (METT) method. The authors present an improved PMBM filter with adaptive detection probability and adaptive newborn distributions, accompanying with an associated distributed fusion strategy for the tracking extended multiple targets. First, the augmented state of unknown and changing target detection probability is assumed as Gamma (GAM) distribution. Second, extended states are described by Inverse Wishart (IW) distribution based on this augmented state, accompanying with dynamic states presented by Gaussian distribution. And then, an adaptive newborn distribution is adopted to describe the newborn targets appearing arbitrarily. Consequently, the closed‐form solutions of the proposed filter can be derived by approximating the intensity of newborn and potential targets to the Gamma Gaussian Inverse Wishart (GGIW) form. Moreover, the fused means that Generalised Covariance Intersection (GCI) is performed in such a large‐scale aquaculture sensor network. Experiments are presented to verify the availability of the GCI‐GGIW‐PMBM method, and comparisons with other METT filters also demonstrate that tracking behaviours are improved largely.

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Section: Algorithm 2 Gci Fusion Algorithmmentioning

confidence: 99%

“…The simulation setup is similar to that of MC-MPMC [36], and data transmission is similar to that of [37,38]. Target states and measurements can be presented as Equations ( 35), (36), where xk is the dynamic state vector at time k denoted as…”

Section: Algorithm 2 Gci Fusion Algorithmmentioning

confidence: 99%

Generalised covariance intersection‐Gamma Gaussian Inverse Wishart‐Poisson multi‐Bernoulli Mixture: An intelligent multiple extended target tracking scheme for mobile aquaculture sensor networks

Lv,

Zhu,

Peng

2024

IET Wireless Sensor Systems

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“…Lv C. et al [ 19 ] proposed a prediction-based object tracking model named Measurement-Compensation-based Mixture-Population-Monte-Carlo (MC-MPMC). This model achieves energy efficiency by using predicted locations to track targets.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network

Dev

Mishra

2023

Sensors

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Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models.

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“…And k H is measurement matrix, and k R is measurement noise. The parameter values can be presented as the values of [29].…”

Section: Distributed Gci Fusionmentioning

confidence: 99%

“…Each result is the average experiment result of 100 Monte Carlo trials. Experiment parameters can be formed as MC-MPMC[29][30].…”

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confidence: 99%

GCI-GGIW-PMBM：An Intelligent Multiple Extended Target Tracking Scheme for Mobile Aquaculture Sensor Networks

Zhu

Peng³

2023

Preprint

Self Cite

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Poisson multi-Bernoulli Mixture (PMBM) filter, accompanying with its improvements, has been known as an available or practical point and extended multi-target tracking algorithm. This paper presents an improved PMBM scheme carried adaptive detection probability and random birth distributions, coupling with an associated distribution fused scheme for tracking extended multiple targets. Firstly, the augmented state of unknown and changing target detection probability is described by Gamma (GAM) distribution. Then, extended target states are described by Inverse Wishart (IW) distribution on account of the augmented state, besides dynamic states of multiple targets presented by Gaussian distribution. And then, an adaptive newborn distribution is adopted to describe the newborn targets appeared arbitrarily. Consequently, the detailed recursion and closed-form solutions of the proposed filter can be obtained through approximating the intensity of newborn and potential targets to Gamma Gaussian Inverse Wishart (GGIW) pattern. Furthermore, a novel distribution fused scheme Generalized Covariance Intersection (GCI), whose associated states and cardinality are fused by anchors abiding by the respective fused weights, is performed in such an extensive aquaculture tracking sensor system with limited computational and processing capacity. Availability and effectiveness of GCI-GGIW-PMBM filter can be verified by comprehensive simulations. Comparisons with other multiple extended target tracking filters also illustrate that tracking behaviors are improved to a great extent, especially energy efficiency is enhanced largely, and tracking accuracy is relatively improved.

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

Cited by 3 publications

References 50 publications

Generalised covariance intersection‐Gamma Gaussian Inverse Wishart‐Poisson multi‐Bernoulli Mixture: An intelligent multiple extended target tracking scheme for mobile aquaculture sensor networks

Generalised covariance intersection‐Gamma Gaussian Inverse Wishart‐Poisson multi‐Bernoulli Mixture: An intelligent multiple extended target tracking scheme for mobile aquaculture sensor networks

Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network

GCI-GGIW-PMBM：An Intelligent Multiple Extended Target Tracking Scheme for Mobile Aquaculture Sensor Networks

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