scholarly journals Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2671
Author(s):  
Yifang Shi ◽  
Jee Woong Choi ◽  
Lei Xu ◽  
Hyung June Kim ◽  
Ihsan Ullah ◽  
...  
Keyword(s):  
Measurement Accuracy ◽  
Tracking System ◽  
Sampling Interval ◽  
Fusion Center ◽  
Forward Prediction ◽  
Distributed Target ◽  
Calculated Probability ◽  
Track Management ◽  
Internal Processing ◽  
Distributed Tracking

In the multiple asynchronous bearing-only (BO) sensors tracking system, there usually exist two main challenges: (1) the presence of clutter measurements and the target misdetection due to imperfect sensing; (2) the out-of-sequence (OOS) arrival of locally transmitted information due to diverse sensor sampling interval or internal processing time or uncertain communication delay. This paper simultaneously addresses the two problems by proposing a novel distributed tracking architecture consisting of the local tracking and central fusion. To get rid of the kinematic state unobservability problem in local tracking for a single BO sensor scenario, we propose a novel local integrated probabilistic data association (LIPDA) method for target measurement state tracking. The proposed approach enables eliminating most of the clutter measurement disturbance with increased target measurement accuracy. In the central tracking, the fusion center uses the proposed distributed IPDA-forward prediction fusion and decorrelation (DIPDA-FPFD) approach to sequentially fuse the OOS information transmitted by each BO sensor. The track management is carried out at local sensor level and also at the fusion center by using the recursively calculated probability of target existence as a track quality measure. The efficiency of the proposed methodology was validated by intensive numerical experiments.

scholarly journals An Adaptive Density-Based Fuzzy Clustering Track Association for Distributed Tracking System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 135972-135981
Author(s):  
Mousa Nazari ◽  
Saeid Pashazadeh ◽  
Leyli Mohammad-Khanli
Keyword(s):  
Fuzzy Clustering ◽  
Tracking System ◽  
Track Association ◽  
Distributed Tracking

Linear Multi-Target Integrated Probabilistic Data Association Filter With Automatic Track Management for Autonomous Vehicles

2018 ◽  
Author(s):  
Andinet Hunde ◽  
Beshah Ayalew
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Data Association ◽  
Tracking Algorithm ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Address Data ◽  
Multi Target Tracking ◽  
Track Management ◽  
Track Initiation

Target tracking in public traffic calls for a tracking system with automated track initiation and termination facilities in a randomly evolving driving environment. In addition, the key problem of data association needs to be handled effectively considering the limitations in the computational resources onboard an autonomous car. In this paper, we discuss a multi-target tracking system that addresses target birth/initiation and death/termination processes with automatic track management feature. The tracking system is based on Linear Multi-target Integrated Probabilistic Data Association Filter (LMIPDAF), which is adapted to specifically include algorithms that handle track initiation and termination, clutter density estimation and track management. The performance of the proposed tracking algorithm is compared to other single and multi-target tracking schemes and is shown to have acceptable tracking error. It is further illustrated through multiple traffic simulations that the computational requirement of the tracking algorithm is less than that of optimal multi-target tracking algorithms that explicitly address data association uncertainties.

scholarly journals Closed-Loop Estimation for Randomly Sampled Measurements in Target Tracking System

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jin Xue-bo ◽  
Lian Xiao-feng ◽  
Su Ting-li ◽  
Shi Yan ◽  
Miao Bei-bei
Keyword(s):  
Target Tracking ◽  
Dynamic Model ◽  
Closed Loop ◽  
Tracking System ◽  
Estimation Method ◽  
Dynamic Parameter ◽  
Sampling Interval ◽  
Recursive Estimation ◽  
Irregular Sampling ◽  
Sampled Measurements

Many tracking applications need to deal with the randomly sampled measurements, for which the traditional recursive estimation method may fail. Moreover, getting the accurate dynamic model of the target becomes more difficult. Therefore, it is necessary to update the dynamic model with the real-time information of the tracking system. This paper provides a solution for the target tracking system with randomly sampling measurement. Here, the irregular sampling interval is transformed to a time-varying parameter by calculating the matrix exponential, and the dynamic parameter is estimated by the online estimated state with Yule-Walker method, which is called the closed-loop estimation. The convergence condition of the closed-loop estimation is proved. Simulations and experiments show that the closed-loop estimation method can obtain good estimation performance, even with very high irregular rate of sampling interval, and the developed model has a strong advantage for the long trajectory tracking comparing the other models.

scholarly journals Distributed Tracking System for Patients with Cognitive Impairments

2011 ◽  
pp. 49-56 ◽  
Author(s):  
Xabier Laiseca ◽  
Eduardo Castillejo ◽  
Pablo Orduña ◽  
Aitor Gómez-Goiri ◽  
Diego López-de-Ipiña ◽  
...  
Keyword(s):  
Tracking System ◽  
Cognitive Impairments ◽  
Distributed Tracking

scholarly journals Distributed Particle Flow Filter for Target Tracking in Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Junjie Wang ◽  
Lingling Zhao ◽  
Xiaohong Su
Keyword(s):  
Sensor Networks ◽  
Target Tracking ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Particle Flow ◽  
Consensus Protocol ◽  
Distributed Target ◽  
Two Phases ◽  
Limited Sensing ◽  
Distributed Tracking

We propose, in this paper, a fully distributed tracking algorithm based on particle flow filter over sensor networks based on the max-consensus. The presented distributed particle flow filter is particularly suitable for the sensor network with limited sensing range and consists of two phases: the estimation phase and consensus phase. The local estimation results are obtained via particle flow filter in the estimation phase; then the sensor nodes agree on the best estimation based on max-consensus protocol in the consensus phase. Numerical simulations and comparisons with other distributed target tracking algorithms are carried out to show the effectiveness and feasibility of our approach.

scholarly journals Improving pupil diameter measurement accuracy in a remote eye tracking system

2019 ◽  
Vol 63 (1) ◽  
pp. 49-53
Author(s):  
Joseph T Coyne ◽  
Noelle Brown ◽  
Cyrus K. Foroughi ◽  
Ciara M Sibley
Keyword(s):  
Eye Tracking ◽  
Measurement Accuracy ◽  
Pupil Diameter ◽  
Tracking System ◽  
Reference Value ◽  
Cognitive Tasks ◽  
Diameter Measurement ◽  
Average Difference ◽  
Fixed Size ◽  
System Data

Pupil diameter (PD) has been used to track changes in mental effort across a broad range of cognitive tasks for over 60 years. PD is often measured from remote eye tracking systems, which all have the same limitation: the lack of a known reference value to convert the pixels captured within the systems to millimeters. Researchers frequently normalize their data within an individual to overcome this issue, however recent studies have found individual differences in resting PDs. This paper investigated the use of a fiduciary marker of a fixed size and an individual’s interpupillary distance, as known reference values. Both techniques substantially improved the accuracy of PD data compared to the unadjusted system data. Further, the average difference between both techniques and the uncorrected pupil diameter was just under .4mm, which is approximately the equivalent of most studies finding differences in cognitive load.

scholarly journals Laser Ranging-Assisted Binocular Visual Sensor Tracking System

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 688 ◽  
Author(s):  
Qilong Wang ◽  
Yu Zhang ◽  
Weichao Shi ◽  
Meng Nie
Keyword(s):  
Target Tracking ◽  
Binocular Vision ◽  
Measurement Accuracy ◽  
Mechanical Performance ◽  
Optical Axis ◽  
Tracking System ◽  
Laser Ranging ◽  
Visual Sensor ◽  
Vision Sensors ◽  
Federated Filtering

Aimed at improving the low measurement accuracy of the binocular vision sensor along the optical axis in the process of target tracking, we proposed a method for auxiliary correction using a laser-ranging sensor in this paper. In the process of system measurement, limited to the mechanical performance of the two-dimensional turntable, the measurement value of a laser-ranging sensor is lagged. In this paper, the lag information is updated directly to solve the time delay. Moreover, in order to give full play to the advantages of binocular vision sensors and laser-ranging sensors in target tracking, federated filtering is used to improve the information utilization and measurement accuracy and to solve the estimated correlation. The experimental results show that the real-time and measurement accuracy of the laser ranging-assisted binocular visual-tracking system is improved by the direct update algorithm and the federal filtering algorithm. The results of this paper are significant for binocular vision sensors and laser-ranging sensors in engineering applications involving target tracking systems.

scholarly journals Dynamic Track Management in MHT for Pedestrian Tracking Using Laser Range Finder

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Abdul Hadi Abd Rahman ◽  
Hairi Zamzuri ◽  
Saiful Amri Mazlan ◽  
Mohd Azizi Abdul Rahman ◽  
Yoshio Yamamoto ◽  
...  
Keyword(s):  
Real Time ◽  
Autonomous Navigation ◽  
Tracking System ◽  
Computational Cost ◽  
Laser Range Finder ◽  
Tracking Problem ◽  
Range Finder ◽  
Pedestrian Tracking ◽  
Laser Range ◽  
Track Management

Real time pedestrian tracking could be one of the important features for autonomous navigation. Laser Range Finder (LRF) produces accurate pedestrian data but a problem occurs when a pedestrian is represented by multiple clusters which affect the overall tracking process. Multiple Hypothesis Tracking (MHT) is a proven method to solve tracking problem but suffers a large computational cost. In this paper, a multilevel clustering of LRF data is proposed to improve the accuracy of a tracking system by adding another clustering level after the feature extraction process. A Dynamic Track Management (DTM) is introduced in MHT with multiple motion models to perform a track creation, association, and deletion. The experimental results from real time implementation prove that the proposed multiclustering is capable of producing a better performance with less computational complexity for a track management process. The proposed Dynamic Track Management is able to solve the tracking problem with lower computation time when dealing with occlusion, crossed track, and track deletion.

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