Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
The bits and flops of the n-hop multilateration primitive for node localization problems
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Location errors in wireless embedded sensor networks: sources, models, and effects on applications
ACM SIGMOBILE Mobile Computing and Communications Review
GPS-free Positioning in Mobile Ad Hoc Networks
Cluster Computing
Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks
ATEC '02 Proceedings of the General Track of the annual conference on USENIX Annual Technical Conference
Recursive Position Estimation in Sensor Networks
ICNP '01 Proceedings of the Ninth International Conference on Network Protocols
An Analysis of Error Inducing Parameters in Multihop Sensor Node Localization
IEEE Transactions on Mobile Computing
Wireless Information Networks (Wiley Series in Telecommunications and Signal Processing)
Wireless Information Networks (Wiley Series in Telecommunications and Signal Processing)
Wideband radio propagation modeling for indoor geolocation applications
IEEE Communications Magazine
Indoor geolocation science and technology
IEEE Communications Magazine
IEEE Communications Magazine
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Recently, node localization for sensor networks has attracted considerable attention for military application. Despite the recent proposals, the relationship between the channel behavior and the performance analysis of cooperative algorithms has not been addressed. The assumptions about the statistics of the ranging error used in the literature are either too general or overly optimistic. Additionally when sensors collaborate to localize each other, there is no attempt to characterize the error in the position of the nodes. This lack of error propagation-awareness can degrade the performance of an algorithm and create divergence problems. In this paper we first introduce detailed modeling of channel propagation in indoor environments in the form of novel empirical path loss (PL) and distance measurement error (DME) models developed from the results of UWB channel measurements. Then we integrate these models in developing error propagation aware (EPA) precise cooperative localization algorithm that tracks the extent of the position error in each sensor node and its overall effect on subsequent multi-laterations. Finally we compare the algorithm against the Cramer-Rao Lower Bound (CRLB).