An important goal of indoor positioning systems is to improve positioning accuracy as well as reduce power consumption. In this paper, we propose an indoor positioning method based on the received signal strength (RSS) fingerprint. The proposed method used a certain criterion to select fixed access points (FPs) in an offline phase instead of an online phase for location estimation. Principal component analysis (PCA) was applied to reduce the features of the RSS measurements but retain the most information possible for establishing the positioning model. Then, a kernel-based ridge regression method was used to obtain the nonlinear relationship between the principal components of the RSS measures and the position of the target. We thoroughly investigated the performance of the proposed method in realistic wireless local area network (WLAN) and wireless sensor network (WSN) indoor environments and made comparisons with recently developed methods. The experimental results indicated that the proposed method was less dependent on the density of the reference points and had higher positioning accuracy than the commonly used positioning methods, and it adapts to different application environments.

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A novel, to the best of our knowledge, and simple heterodyne interferometer that uses spatially separated input beams to minimize the influence of the periodic nonlinearity is constructed. A custom designed polarizing beam displacer is used to split the input beams to parallel outputs with orthogonal polarizations, which provides a balanced path and completely symmetric structure for the interferometer. This novel optical setup suppresses the nonlinearity caused by the frequency and polarization mixing, and the very simple optical structure makes the interferometer less susceptible to environmental turbulence with potential use in many sensor applications. Experiments have confirmed that the interferometer maintains sub-nanometer nonlinearities in the laboratory environment.