Next Article in Journal
Agricultural Robotics for Field Operations
Next Article in Special Issue
Robust Control for the Detection Threshold of CFAR Process in Cluttered Environments
Previous Article in Journal
Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building
Open AccessArticle

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

1
School of Automation, Hangzhou Dianzi University, Xiasha Higher Education Zone, 2rd Street, Hangzhou 310018, China
2
Department of Marine Science and Convergence Engineering, Hanyang University, Ansan 15588, Korea
3
R&D Center, Shenzhen XuanQi Intelligent Technology Co., Ltd., Shenzhen 518000, China
4
Department of Electronic Systems Engineering, Hanyang University, Ansan 15588, Korea
5
Department of Electrical Engineering, COMSATS University, Abbottabad Campus, Abbottabad 22060, Pakistan
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(9), 2671; https://doi.org/10.3390/s20092671
Received: 21 March 2020 / Revised: 23 April 2020 / Accepted: 1 May 2020 / Published: 7 May 2020
(This article belongs to the Special Issue Multi-Sensor Fusion for Object Detection and 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. View Full-Text
Keywords: multiple asynchronous BO sensors tracking; track management; OOS information; distributed tracking; LIPDA; DIPDA-FPFD multiple asynchronous BO sensors tracking; track management; OOS information; distributed tracking; LIPDA; DIPDA-FPFD
Show Figures

Figure 1

MDPI and ACS Style

Shi, Y.; Choi, J.W.; Xu, L.; Kim, H.J.; Ullah, I.; Khan, U. Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors. Sensors 2020, 20, 2671. https://doi.org/10.3390/s20092671

AMA Style

Shi Y, Choi JW, Xu L, Kim HJ, Ullah I, Khan U. Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors. Sensors. 2020; 20(9):2671. https://doi.org/10.3390/s20092671

Chicago/Turabian Style

Shi, Yifang; Choi, Jee W.; Xu, Lei; Kim, Hyung J.; Ullah, Ihsan; Khan, Uzair. 2020. "Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors" Sensors 20, no. 9: 2671. https://doi.org/10.3390/s20092671

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop