Skip to Main Content
We present a novel approach to the problem of the indoor localization in wireless environments. The main contribution of this paper is fourfold: 1) we show that by projecting the measured signal into a decorrelated signal space, the positioning accuracy is improved, since the cross correlation between each AP is reduced, 2) we demonstrate that this novel approach achieves a more efficient information compaction and provides a better scheme to reduce online computation (the drawback of AP selection techniques is overcome, since we reduce the dimensionality by combing features, and each component in the decorrelated space is the linear combination of all APs; therefore, a more efficient mechanism is provided to utilize information of all APs while reducing the computational complexity), 3) experimental results show that the size of training samples can be greatly reduced in the decorrelated space; that is, fewer human efforts are required for developing the system, and 4) we carry out comparisons between RSS and three classical decorrelated spaces, including Discrete Cosine Transform (DCT), Principal Component Analysis (PCA), and Independent Component Analysis (ICA) in this paper. Two AP selection criteria proposed in the literature, MaxMean and InfoGain are also compared. Testing on a realistic WLAN environment, we find that PCA achieves the best performance on the location fingerprinting task.