Distributed fusion with PHD filter for multi-target tracking in asynchronous radar system

Li, Guchong; Yi, Wei; Jiang, Meng; Kong, Lingjiang

doi:10.1109/radar.2017.7944432

Cited by 21 publications

(15 citation statements)

References 12 publications

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“…The trajectory of each object is determined at the beginning of radar scanning; then, measurement information from the sensors is used to update the initially established target track. When measurement data (echo) from the sensor enters the target's tracking gate, the echo is called a valid measurement (or valid back) wave [28][29][30][31]. Even with only one target, there may be multiple valid measurements due to clutter interference.…”

Section: Target Tracking Backgroundmentioning

confidence: 99%

An Improved UAV-PHD Filter-Based Trajectory Tracking Algorithm for Multi-UAVs in Future 5G IoT Scenarios

Tang

Hong

et al. 2019

Electronics

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The 5G cellular network is expected to provide core service platform for the expanded Internet of Things (IoT) by supporting enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low latency communications (URLLC). Unmanned aerial vehicles (UAVs), also known as drones, provide civil, commercial, and government services in various fields. Particularly in a 5G IoT scenario, UAV-aided network communications will fulfill an increasingly important role and will require the tracking of multiple UAV targets. As UAVs move quickly, maintaining the stability of the communication connection in 5G will be a challenge. Therefore, it is necessary to track the trajectory of UAVs. At present, the GM-PHD filter has a problem that the new target intensity must be known, and it cannot obtain the moving target trajectory and the influence of the clutter is likely to cause false alarm. A UAV-PHD filter is proposed in this work to improve the traditional GM-PHD filter by applying machine learning to the emergency detection and trajectory tracking of UAV targets. An out-of-sight detection algorithm for multiple UAVs is then presented to improve tracking performance. The method is assessed by simulation using MATLAB, and OSPA distance is utilized as an evaluation indicator. The simulation results illustrate that the proposed method can be applied to the tracking of multiple UAV targets in future 5G-IoT scenarios, and the performance is superior to the traditional GM-PHD filter.

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Section: Target Tracking Backgroundmentioning

confidence: 99%

An Improved UAV-PHD Filter-Based Trajectory Tracking Algorithm for Multi-UAVs in Future 5G IoT Scenarios

Tang

Hong

et al. 2019

Electronics

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“…We assume that each target evolves and generates observations independently of one another, the clutter is independent of target-originated measurements, and the clutter and predicted multitarget RFS follow a Poisson distribution. Let v k (•) denotes the multitarget posterior density intensity, γ k (x) denotes the intensity of the birth RFS k at time k, β k|k−1 (• | ς) denotes the intensity of the RFS B k|k−1 (ς ) spawned at time k by a target with previous state ς , κ k (z) denotes the intensity of clutter RFS K k at time k, then the posterior intensity can be propagated by the PHD recursion: (11) According to GMM theory and GMPHD algorithm, the Equations (12) and (13) could be substituted by Equations (10) and 11:…”

Section: Gmphd Filter Algorithmmentioning

confidence: 99%

“…Random Finite Set (RFS) [8]. In addition, these approaches have been well developed and widely applied in such as sonar [9], [10], radar [11], biology [12], autonomous vehicle [13], [14], automotive recognition [15], and sensor network [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Asynchronous Multisonar Data Integration Approach Used to Detect Sparse Targets

et al. 2019

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Due to the advantage of the distributed multisensor detection system which makes sonar detect further and more accurate estimation of the target state, distributed multisensor data fusion algorithms are widely applied to the sonar detection system. However, on the one hand, the most asynchronous algorithms focus on how to convert asynchronous data fusion into synchronous data fusion. On the other hand, sonar detection system suffers from more serious asynchronous data problems (such as more serious random delay and packet loss defaults) than radar and other fields. Therefore, the traditional asynchronous fusion method has some limitations. When the targets are sparse, this paper proposed a novel asynchronous multisonar data integration approach, in which the Gaussian mixture probability hypothesis density (GMPHD) filter is used to filter clutter for local sonar sensor. Then, the Gaussian mixture model (GMM) algorithm is used to model asynchronous data over a period of time. Finally, all local sonar detection data are integrated into a surveillance region image to help to detect the target. Several simulation tests and a sea test are presented in this paper to test this approach performance.

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“…The GCI fusion amounts to computing the density that minimizes the sum of the information gains (Kullback-Leibler divergences [7,19], KLD) from local posteriors, thus avoiding the problem of double-counting of common information [25]. In the past years, any distributed RFS filters based on the GCI fusion rule have been proposed [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Distributed multi-sensor multi-view fusion based on generalized covariance intersection

Battistelli

et al. 2020

Signal Processing

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Distributed multi-target tracking (DMTT) is addressed for sensors having different fields of view (FoVs). The proposed approach is based on the idea of fusing the posterior Probability Hypotheses Densities (PHDs) generated by the sensors on the basis of the local measurements. An efficient and robust distributed fusion algorithm combining the Generalized Covariance Intersection (GCI) rule with a suitable Clustering Algorithm (CA) is proposed. The CA is used to decompose each posterior PHD into well-separated components (clusters). For the commonly detected targets, an efficient parallelized GCI fusion strategy is proposed and analyzed in terms of L 1 error. For the remaining targets, a suitable compensation strategy is adopted so as to counteract the GCI sensitivity to independent detections while reducing the occurrence of false targets. Detailed implementation steps using a Gaussian Mixture (GM) representation of the PHDs are provided. Numerical experiments clearly confirms the effectiveness of the proposed CA-GCI fusion algorithm.

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Distributed fusion with PHD filter for multi-target tracking in asynchronous radar system

Cited by 21 publications

References 12 publications

An Improved UAV-PHD Filter-Based Trajectory Tracking Algorithm for Multi-UAVs in Future 5G IoT Scenarios

An Improved UAV-PHD Filter-Based Trajectory Tracking Algorithm for Multi-UAVs in Future 5G IoT Scenarios

Asynchronous Multisonar Data Integration Approach Used to Detect Sparse Targets

Distributed multi-sensor multi-view fusion based on generalized covariance intersection

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