Compressive sensing based indoor positioning with denosing and filtering in LF space

In this paper, we propose a novel compressive sensing (CS) based indoor positioning approach, which uses the signal strength differentials (SSDs) as location fingerprints (LFs). The target location is regarded as an unknown sparse location vector in the discrete spatial domain. Then it just takes a little number of online noisy SSD measurements for the exact recovery of the sparse location vector by solving an ℓ₁-minimization program. In order to mitigate the influences of large measurements noise on the recovery accuracy, an LF space denosing algorithm is proposed to discriminate the localization contribution rate of every LF according to its SSD variation. Moreover, an LF space filtering strategy is also exploited to lower the high computational complexity of the CS recovery algorithm. Both experimental results and simulations demonstrate that we achieve remarkable improvements on the positioning performance of the CS based approach by using the two proposed algorithms.