Improving performance of indoor localization using compressive sensing and normal hedge algorithm

Abstract

Accurate indoor localization technologies are currently in high demand in wireless sensor networks, which strongly drive the development of various wireless applications including healthcare monitoring, patient tracking and endoscopic capsule localization. The precise position determination requires exact estimation of the time varying characteristics of wireless channels. In this paper, we address this issue and propose a three-phased scheme, which employs an optimal single stage TDOA/FDOA/AOA indoor localization based on spatial sparsity. The first contribution is to formulate the received unknown signals from the emitter as a compressive sensing problem. Then, we solve an ℓ1 minimization problem to localize the emitter's position. To combat the nonstationary behavior of wireless channels between sensor nodes, the results of our proposed localization algorithm are finally fused using a novel fusion method based on the adaptive normal hedge algorithm. To improve the accuracy of the estimated location, an optimal set of weighed coefficients are derived through introducing a new loss function. Monte Carlo simulation results show that the accuracy of the proposed localization framework is superior compared to the existing indoor localization schemes in low SNR regimes.