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Efficient Localization Using Different Mean Offset Models In Gaussian Processesстатья
- Авторы: Panyov A., Golovan A.A., Smirnov A.S., Kosyanchuk V.V.
- Сборник: Proceeding of the 5th IEEE International Conference on Indoor Positioning Indoor Navigation
- Год издания: 2014
- Место издания: IEEE Xplore [Piscataway, N.J.], United States
- DOI: 10.1109/IPIN.2014.7275504
- Аннотация: Indoor positioning using wireless signal strength has become an area of highly active research. Many papers prior to this one have demonstrated how Gaussian processes can be used to generate a likelihood model for signal strength measurements. One advantage of Gaussian processes is the ability to efficiently calibrate devices by using SLAM technique. However, Gaussian process is, by default, a zero mean process, which doesn't reflect the true nature of signal propagation. In many works, algorithms are modified to use a constant, non-zero mean offset. There is also a modification using a simple mean offset model where signal strength decreases linearly with the distance from the access point. In this paper, a log-distance radio propagation model as a mean offset model for Gaussian processes is proposed. This model was chosen since many works have demonstrated its good correlation to experimental results. Amongst the three models described, the log-distance model provides the highest accuracy. Also a comparison was made with the k-nearest neighbor method and probabilistic histogram approach, showing the superiority of methods using Gaussian processes. All algorithms were optimized which made it possible to perform all calculations on a mobile phone in real time.
- Добавил в систему: Смирнов Алексей Сергеевич