The active badge location system
ACM Transactions on Information Systems (TOIS)
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
The anatomy of a context-aware application
Wireless Networks - Selected Papers from Mobicom'99
The smart floor: a mechanism for natural user identification and tracking
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Distributed localization in wireless sensor networks: a quantitative comparison
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks
Tracking moving devices with the cricket location system
Proceedings of the 2nd international conference on Mobile systems, applications, and services
A soft computing approach to localization in wireless sensor networks
Expert Systems with Applications: An International Journal
Real-time localization of an UAV using Kalman filter and a Wireless Sensor Network
Journal of Intelligent and Robotic Systems
An anti-collision algorithm for localization of multiple chirp-spread-spectrum nodes
Expert Systems with Applications: An International Journal
Enhanced ALOHA algorithm for chirp spread spectrum positioning
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Hi-index | 12.05 |
In this paper, we propose an algorithm for acquiring enhanced position coordinates when using the chirp spread spectrum (CSS) specified in IEEE 802.15.4a. Because the measured distances are generally perturbed by measurement noise, the extended Kalman filter (EKF) can be applied to trilateration to treat the variation in the measured distances due to the noise. In practice, the average value of many measured distances obtained using the CSS is not equal to the true distance, because the noise in the measurement has a nonzero mean; that is, a nonzero average error exists. Because the EKF is only suitable for a case with zero mean noise, the average error needs to be eliminated in advance. For this purpose, we propose a minimization criterion that determines weighting parameters. The average error is reduced by multiplying measured distances by the weighting parameters. To verify the performance of the proposed method, we conduct experiments for the CSS-based positioning of given targets, and compare the results for the EKF with and without the proposed algorithm. From the results, we can find that the coordinates estimated by the EKF with the proposed algorithm are more accurate.