A Bank of Decentralized Extended Information Filters for Target Tracking in Event-Triggered WSNs

2020 ◽  
Vol 50 (9) ◽  
pp. 3281-3289
Author(s):  
Xusheng Yang ◽  
Wen-An Zhang ◽  
Li Yu
Keyword(s):  
Target Tracking ◽  
Information Filters ◽  
Event Triggered

Distributed event-triggered target tracking under cyber attacks

2021 ◽  
Author(s):  
Ahmadreza Jenabzadeh ◽  
Behrouz Safarinejadian ◽  
Yu Lu ◽  
Weidong Zhang
Keyword(s):  
Target Tracking ◽  
Cyber Attacks ◽  
Event Triggered

Event-triggered Kalman-consensus filter for two-target tracking sensor networks

2017 ◽  
Vol 71 ◽  
pp. 103-111 ◽  
Author(s):  
Housheng Su ◽  
Zhenghao Li ◽  
Yanyan Ye
Keyword(s):  
Sensor Networks ◽  
Target Tracking ◽  
Consensus Filter ◽  
Event Triggered

scholarly journals Event-Triggered Model Predictive Control for target tracking with angles-only measurements

2017 ◽  
Vol 50 (1) ◽  
pp. 15959-15964 ◽  
Author(s):  
Nicolas Merlinge ◽  
Héléne Piet-Lahanier ◽  
Karim Dahia
Keyword(s):  
Model Predictive Control ◽  
Target Tracking ◽  
Predictive Control ◽  
Event Triggered

Distributed energy-efficient target tracking algorithm based on event-triggered strategy for sensor networks

2019 ◽  
Vol 13 (10) ◽  
pp. 1564-1570 ◽  
Author(s):  
Ya Zhang ◽  
Lingling Zhang ◽  
Lishuang Du ◽  
Cheng-Lin Liu ◽  
Yang-Yang Chen
Keyword(s):  
Sensor Networks ◽  
Target Tracking ◽  
Energy Efficient ◽  
Tracking Algorithm ◽  
Distributed Energy ◽  
Event Triggered

scholarly journals Coordinated Target Tracking by Distributed Unscented Information Filter in Sensor Networks with Measurement Constraints

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yintao Wang ◽  
Junbing Li ◽  
Qi Sun
Keyword(s):  
Sensor Networks ◽  
Target Tracking ◽  
Sensor Node ◽  
Second Phase ◽  
Consistent Estimate ◽  
Consensus Protocol ◽  
Time Step ◽  
Sigma Point ◽  
Information Filter ◽  
Information Filters

Tracking a target in a cluttered environment is a representative application of sensor networks. In this paper, we develop a distributed approach to estimate the motion states of a target using noisy measurements. Our method consists of two parts. In first phase, using the unscented sigma-point transformation techniques and information filter framework, a class of algorithms denoted as unscented information filters was developed to estimate the states of a target to be tracked. These techniques exhibit robustness and accuracy of sigma-point filters for nonlinear dynamic inference while being as easily fused as the information filters. In the second phase, we proposed a novel consensus protocol which allows each sensor node to find a consistent estimate of the value of the target. Under this protocol, the final estimate of the value of the target at each time step is iteratively updated only by fusing the neighbors’ measurements when one sensor node is out of the measurement scope of the target. Performance of the distributed unscented information filter is demonstrated and discussed on a target tracking task.

Sign in / Sign up

Export Citation Format

Share Document