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Accurate and Low-cost Indoor Location Estimation Using Kernels

Original Signal Space. Original Location Space. AP3. AP2. Time t: (-47dB ,-36dB ,-62dB ). Where am I ?. Feature Signal Space. Feature Location Space. AP1. Accurate and Low-cost Indoor Location Estimation Using Kernels. J.F. Pan, J.T. Kwok, Q. Yang and Y.Q. Chen, IJCAI 2005.

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Accurate and Low-cost Indoor Location Estimation Using Kernels

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  1. Original Signal Space Original Location Space AP3 AP2 Time t: (-47dB,-36dB,-62dB) Where am I ? Feature Signal Space Feature Location Space AP1 Accurate and Low-cost Indoor Location Estimation Using Kernels J.F. Pan, J.T. Kwok, Q. Yang and Y.Q. Chen, IJCAI 2005 Problem A user with a mobile device walks in an indoor wireless environment (Covered by WiFi signal) • Motivation • Similar signals may not necessarily be nearby locations, or vice versa • Maximize correlation between signal and location under feature transformation Accuracy (Error in 3.0m) Radio Map Methodology Kernel Canonical Correlation Analysis • Proposed by [D.R Hardoon et al. 2004] • Two non-linearCanonical Vectors Wx&Wy Wx = X α Wy = Y β • K is the kernel Φ: x → Φ(x) K (x, z) = <Φ(x), Φ(z) > • Maximize the correlation of projections Experiment Results Reduce Calibration Effort • LE-KCCA 91.6% • SVM 87.8% • MLE 86.1% • RADAR 78.8% Outperform the others using 10-15 samples from each location

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