The major problem of Wi-Fi fingerprint-based positioning technology is the signal strength fingerprint database creation and maintenance. The significant temporal variation of received signal strength (RSS) is the main factor responsible for the positioning error. A probabilistic approach can be used, but the RSS distribution is required. The Gaussian distribution or an empirically-derived distribution (histogram) is typically used. However, these distributions are either not always correct or require a large amount of data for each reference point. Double peaks of the RSS distribution have been observed in experiments at some reference points. In this paper a new algorithm based on an improved double-peak Gaussian distribution is proposed. Kurtosis testing is used to decide if this new distribution, or the normal Gaussian distribution, should be applied. Test results show that the proposed algorithm can significantly improve the positioning accuracy, as well as reduce the workload of the off-line data training phase.

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Independent component analysis (ICA), instead of the traditional discrete cosine transform (DCT), is often used to project log Mel spectrum in robust speech feature extraction. The paper proposed using symmetric orthogonalization in ICA for projecting log Mel spectrum into a new feature space as a substitute in extracting speech features to solve the problem of cumulative error and unequal weights that deflation orthogonalization brings, so as to improve the robustness of speech recognition systems, and increase the efficiency of estimation at the same time. Furthermore, the paper studied the nonlinearities of the objective function in ICA and their coefficients, tested them in all kinds of environments, finding that they influenced the recognition rate greatly in speech recognition systems, and applied a new coefficient in the proposed method. Experiments based on HMM and Aurora-2 speech corpus suggested that the new method was superior to deflation-based ICA and MFCC.

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Dilution of precision (DOP) is a value that can describe the effect on the relationship between measurement error and position determination error. DOP of a positioning system using time of arrival (TOA) such as GPS has been well researched. Some researchers have also investigated the DOP of the angle of arrival (AOA) system. However, the DOP of the AOA and TOA combined system has been rarely studied. As more AOA based systems appear in the market, the AOA/TOA combined system is likely to be used widely in the future. This paper investigates the DOP of a positioning system combining AOA and TOA. The concept of DOP is briefly introduced, and the DOP of AOA and of TOA is discussed separately. A unified formula to calculate DOP is derived for a positioning system using AOA and/or TOA measurements. The simulation shows that the DOP value is associated with the size of the deployment of the base stations and the ratio between the standard deviations of TOA and AOA measurements. If the ratio is fixed, when the size approaches infinity, the DOP approaches that of a positioning system using only TOA measurement. On the contrary, when the size approaches infinitely small, the DOP approaches that of a positioning system using only AOA measurement. An Ultra-Wide band positioning system was used to conduct experiments. The results show that the unified formula can be used to guide the deployment of base stations of the AOA and/or TOA based positioning system. INDEX TERMS Angle of arrival, Dilution of precision, Time of arrival.