Smartphone based indoor positioning has greatly helped people in finding their positions in complex and unfamiliar buildings. One popular positioning method is by utilizing indoor magnetic field, because this feature is stable and infrastructure-free. In this method, the magnetometer embedded on the smartphone measures indoor magnetic field and queries its position. However, the environments of the magnetometer are rather harsh. This harshness mainly consists of coarse-grained hard/soft-iron calibrations and sensor electronic noise. The two kinds of interferences decrease the position distinguishability of the magnetic field. Therefore, it is important to extract location features from magnetic fields to reduce these interferences. This paper analyzes the main interference sources of the magnetometer embedded on the smartphone. In addition, we present a feature distinguishability measurement technique to evaluate the performance of different feature extraction methods. Experiments revealed that selected fingerprints will improve position distinguishability.

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This paper describes the relationships between the surface wave attenuation properties and the electromagnetic parameters of radar absorbing materials (RAMs). In order to conveniently obtain the attenuation constant of TM surface waves over a wide frequency range, the simplified dispersion equations in thin absorbing materials were firstly deduced. The validity of the proposed method was proved by comparing with the classical dispersion equations. Subsequently, the attenuation constants were calculated separately for the absorbing layers with hypothetical relative permittivity and permeability. It is found that the surface wave attenuation properties can be strongly tuned by the permeability of RAM. Meanwhile, the permittivity should be appropriate so as to maintain high cutoff frequency. The present work provides specific methods and designs to improve the attenuation performances of radar absorbing materials.