  Indoor localization is of particular interest due to its immense practical
applications. However, the rich multipath and high penetration loss of indoor
wireless signal propagation make this task arduous. Though recently studied
fingerprint-based techniques can handle the multipath effects, the sensitivity
of the localization performance to channel fluctuation is a drawback. To
address the latter challenge, we adopt an artificial multi-layer neural network
(MNN) to learn the complex channel impulse responses (CIRs) as fingerprint
measurements. However, the performance of the location classification using MNN
critically depends on the correlation among the training data. Therefore, we
design two different decorrelation filters that preprocess the training data
for discriminative learning. The first one is a linear whitening filter
combined with the principal component analysis (PCA), which forces the
covariance matrix of different feature dimensions to be identity. The other
filter is a nonlinear quantizer that is optimized to minimize the distortion
incurred by the quantization. Numerical results using indoor channel models
illustrate the significant improvement of the proposed decorrelation MNN (DMNN)
compared to other benchmarks.
