Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Localization from mere connectivity
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Semidefinite programming based algorithms for sensor network localization
ACM Transactions on Sensor Networks (TOSN)
Second-Order Cone Programming Relaxation of Sensor Network Localization
SIAM Journal on Optimization
IEEE Transactions on Signal Processing
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
An Accurate Algebraic Closed-Form Solution for Energy-Based Source Localization
IEEE Transactions on Audio, Speech, and Language Processing
Ranging in a dense multipath environment using an UWB radio link
IEEE Journal on Selected Areas in Communications
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Accurate localization of nodes in a sensor network is a crucial step before the sensor network can be utilized for various applications. This paper proposes a successive estimation method to self-localize the sensor nodes using time of arrival (TOA) measurements, through the aid of a few anchor nodes whose positions are known a priori. The successive nature of the proposed algorithm makes it attractive for distributed computation in a resource constrained environment. The proposed technique uses subsets of TOAs to obtain coarse sensor node estimates. Depending on available computation and transmission resources, the node positions are refined to improve their accuracies. The solution of the proposed node localization algorithm is algebraic and closed-form, which simplifies computation and avoids possible local convergence or divergence problems as in the traditional iterative approaches. A main benefit of the proposed algorithm is that although it is closed-form and performs successive estimation of the node locations, it is able to reach the Cramér-Rao lower bound (CRLB) accuracy under white Gaussian measurement noise with sufficient SNR. This is confirmed by the theoretical analysis and corroborated by simulations.