Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Solving Euclidean Distance Matrix Completion Problems Via Semidefinite Programming
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
On the minimum node degree and connectivity of a wireless multihop network
Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing
Convex Optimization
Theory of semidefinite programming for sensor network localization
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Distributed weighted-multidimensional scaling for node localization in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Semidefinite programming based algorithms for sensor network localization
ACM Transactions on Sensor Networks (TOSN)
A Theory of Network Localization
IEEE Transactions on Mobile Computing
Wireless sensor network localization techniques
Computer Networks: The International Journal of Computer and Telecommunications Networking
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
Parameter estimation problems with singular information matrices
IEEE Transactions on Signal Processing
Exact and Approximate Solutions of Source Localization Problems
IEEE Transactions on Signal Processing
Network Localization with Biased Range Measurements
IEEE Transactions on Wireless Communications
Understanding and solving flip-ambiguity in network localization via semidefinite programming
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
RSSI-based relative localisation for mobile robots
Ad Hoc Networks
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We propose a robust non-parametric strategy to weight scarce and imperfect ranging information, which is shown to significantly improve the accuracy of distance-based network localization algorithms. The proposed weights have a dispersion component, which captures the effect of noise under the assumption of bias-free samples, and a penalty component, which quantifies the risk of the latter assumption and penalizes it proportionally. The dispersion weights result from the application of small-scale statistics with confidence bounds optimized under a maximum entropy criterion that mathematizes the empirical concept of reliability commonly found in related literature. In turn, the penalty weights are derived from the relationship between the risk incurred by the bias-free assumption and the geometry of 3-node cliques, established by statistical-geometry. The performance of the distance-based network localization algorithm employing the proposed dispersion-penalty weights is compared against the Cramér-Rao lower bound (CRLB) and to equivalent algorithms employing alternative weights. The comparison reveals that, amongst the alternatives, the network localization algorithm with the proposed weights performs best and closest to an unbiased estimator.