The accuracy of smartphone-based positioning systems using WiFi usually suffers from ranging errors caused by nonline-of-sight (NLOS) conditions. Previous research usually exploits several distribution features from a long time series (hundreds of samples) of WiFi received signal strength (RSS) or WiFi round-trip time (RTT) to achieve a high identification accuracy. However, the long time series or large sample size attributes to high power and time consumption in data collection for both training and testing. This will also undoubtedly be detrimental to user experience as the waiting time for getting enough samples is quite long. Therefore, this paper proposes three new real-time NLOS/LOS identification methods for smartphone-based indoor positioning systems using WiFi RSS and RTT distance measurement (RDM). Based on our extensive analysis of RSS and RDM dispersion features, three machine learning algorithms were chosen and developed to separate the samples for NLOS/LOS conditions. Experiments show that our best method achieves a discrimination accuracy of over 96% with a sample size of 10. Considering the theoretically shortest WiFi ranging interval of 100ms of the RTT-enabled smartphones, our algorithm is able to provide the shortest latency of 1s to get the testing result among all of the state-of-art methods.
The smartphone magnetometer has been used in many indoor positioning systems to provide location information, such as orientation, user trajectory construction, and magnetic field-based fingerprint. However, suffering from magnetic disturbance, the magnetometer measurements are vulnerable to interference from metal infrastructures, electrical equipment, and other electronic devices in complex indoor environments. This paper extracts and explores the statistical features of the smartphone magnetometer measurements. Extensive experiments in various conditions show that the covariance and the magnitude difference can help detect the magnetic disturbance. Based on this, two unsupervised learning-based methods using Gaussian Mixture Model and k-means are developed to explore the two features mentioned above in magnetic disturbance detection. Experimental results demonstrate that the two proposed approaches have superior detection accuracy, which is 5% to 20% higher than the widely adopted vector selection methods in the literature.
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