An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Estimation of nominal direction of arrival and angular spread using an array of sensors
Signal Processing - Special issue on subspace methods, part I: array signal processing and subspace computations
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
IEEE Transactions on Mobile Computing
PERCOM '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications
Robust estimator for non-line-of-sight error mitigation in indoor localization
EURASIP Journal on Applied Signal Processing
Bearing estimation for a distributed source: modeling, inherentaccuracy limitations and algorithms
IEEE Transactions on Signal Processing
Energy-based sensor network source localization via projection onto convex sets
IEEE Transactions on Signal Processing
On the optimal performance of collaborative position location
IEEE Transactions on Wireless Communications
IEEE 802.15.4a CSS-based mobile object locating system using sequential Monte Carlo method
Computer Communications
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We investigate localization of a transmitting node using angle of arrival (AoA) measurements made at a geographically dispersed network of cooperating receivers with known locations. A low-complexity sequential algorithm for updating the source location estimates under line-of-sight (LOS) environments is developed. This serves as a building block for an algorithm that suppresses outliers arising due to multipath scattering and reflection in non-line-of-sight (NLOS) scenarios. Maximal likelihood (ML) location estimation requires exhaustive testing of estimates from all possible subsets of measurements. We avoid this by utilizing a randomized algorithm that approaches the ML performance at a complexity that is only quadratic in the number of measurements. The localization error is proportional in the AoA error variance and coverage area, and can be reduced by an increase in the number of estimates with a strong LOS component.