Wireless localization using time difference of arrival in narrow-band multipath systems
- C. R. Comsa, J. H. Luo, A. Haimovich, S. Schwartz
- Proc. of International Symposium on Signals…
Due to the low cost and capabilities of sensors, wireless sensor networks (WSNs) are promising for military and civilian surveillance of people and vehicles. One important aspect of surveillance is target localization. A location can be estimated by collecting and analyzing sensing data on signal strength, time of arrival, time difference of arrival, or angle of arrival. However, this data is subject to measurement noise and sensitive to environmental conditions, so its location estimates can be inaccurate. In this paper, we add a novel process to further improve localization accuracy after the initial location estimates are obtained from some existing algorithm. Our idea is to exploit the consistency of the spatial-temporal relationships of targets we track. Spatial relationships are the relative target locations in a group and temporal ∗Corresponding address: Department of Computer Science, Texas State University, San Marcos, TX 78666, United States Email addresses: email@example.com (Xiao Chen), firstname.lastname@example.org (Neil C. Rowe), email@example.com (Jie Wu), firstname.lastname@example.org (Kaiqi Xiong) Preprint submitted to Journal of Parallel and Distributed Computing April 9, 2012 relationships are the locations of a target at different times. We first develop algorithms that improve location estimates using spatial and temporal relationships of targets separately, and then together. We prove mathematically that our methods improve localization accuracy. Furthermore, we relax the condition that targets should strictly keep their relative positions in the group and also show that perfect time synchronization is not required. Simulations were also conducted to test the algorithms. They used initial target location estimates from existing signal-strength and time-of-arrival algorithms and implemented our own algorithms. The results confirmed improved localization accuracy, especially in the combined algorithms. Since our algorithms use the features of targets and not the underlying WSNs, they can be built on any localization algorithm whose results are not satisfactory.