Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks
Se-Young Kang, TaeHyun Kim, Wonzoo Chung
Abstract
We present a target localization method using an approximated error covariance matrix based weighted least squares (WLS) solution, which integrates received signal strength (RSS) and angle of arrival (AOA) data for wireless sensor networks. We approximated linear WLS errors via second-order Taylor approximation, and further approximated the error covariance matrix using a least-squares solution and the variance in measurement noise over the sensor nodes. The algorithm does not require any prior knowledge of the true target position or noise variance. Simulations validated the superior performance of our new method.
Topics & Concepts
RSSAlgorithmCovariance matrixCovarianceNoise (video)Wireless sensor networkPosition (finance)MathematicsAngle of arrivalComputer scienceVariance (accounting)Least-squares function approximationStatisticsArtificial intelligenceTelecommunicationsEconomicsBusinessAccountingAntenna (radio)Image (mathematics)FinanceComputer networkEstimatorOperating systemIndoor and Outdoor Localization TechnologiesTarget Tracking and Data Fusion in Sensor NetworksDistributed Sensor Networks and Detection Algorithms