Signal modelling based scalable hybrid Wi-Fi indoor positioning system

Location based services (LBS) such as advertising, navigation and social media require a mobile device to be aware of its location anywhere. Global Positioning System (GPS) is accurate outdoors. However, in case of indoor environments, GPS fails to provide a location due to non-line of sight. Even in cases where GPS does manage to get a position fix indoors, it is largely inaccurate due to interference of indoor environment. Wi-Fi based indoor positioning offers best solution indoors, due to wide usage of Wi-Fi for internet access. Wi-Fi based indoor positioning systems are widely based on two techniques, first Lateration which uses distances estimated based on signal properties such as RSS (Received Signal Strength) and second, Fingerprint matching of data collected in offline phase. The accuracy of estimated position using Lateration techniques is lower compared to fingerprinting techniques. However, Fingerprinting techniques require storing a large amount of data and are also computationally intensive. Another drawback of systems based on fingerprinting techniques is that they are not scalable. As the system is scaled up, the database required to be maintained for fingerprinting techniques increases significantly. Lateration techniques also have challenges with coordinate system used in a scaled-up system. This thesis proposes a new scalable positioning system which combines the two techniques and reduces the amount of data to be stored, but also provides accuracy close to fingerprinting techniques. Data collected during the offline/calibration phase is processed by dividing the test area into blocks and then stored for use during online/positioning phase. During positioning phase, processed data is used to identify the block first and then lateration techniques are used to refine the estimated location. The current system reduces the data to be stored by a factor of 20. And the 50th percentile accuracy with this novel system is 4.8m, while fingerprint system accuracy was 2.8m using same data. The significant reduction in database size and lower computational intensity benefits some of the applications like location-based search engines even with slightly lower performance in terms of accuracy

K-Means Fingerprint Clustering for Low-Complexity Floor Estimation in Indoor Mobile Localization

Indoor localization in multi-floor buildings is an important research problem. Finding the correct floor, in a fast and efficient manner, in a shopping mall or an unknown university building can save the users' search time and can enable a myriad of Location Based Services in the future. One of the most widely spread techniques for floor estimation in multi-floor buildings is the fingerprinting-based localization using Received Signal Strength (RSS) measurements coming from indoor networks, such as WLAN and BLE. The clear advantage of RSS-based floor estimation is its ease of implementation on a multitude of mobile devices at the Application Programming Interface (API) level, because RSS values are directly accessible through API interface. However, the downside of a fingerprinting approach, especially for large-scale floor estimation and positioning solutions, is their need to store and transmit a huge amount of fingerprinting data. The problem becomes more severe when the localization is intended to be done on mobile devices which have limited memory, power, and computational resources. An alternative floor estimation method, which has lower complexity and is faster than the fingerprinting is the Weighted Centroid Localization (WCL) method. The trade-off is however paid in terms of a lower accuracy than the one obtained with traditional fingerprinting with Nearest Neighbour (NN) estimates. In this paper a novel K-means-based method for floor estimation via fingerprint clustering of WiFi and various other positioning sensor outputs is introduced. Our method achieves a floor estimation accuracy close to the one with NN fingerprinting, while significantly improves the complexity and the speed of the floor detection algorithm. The decrease in the database size is achieved through storing and transmitting only the cluster heads (CH's) and their corresponding floor labels.Comment: Accepted to IEEE Globecom 2015, Workshop on Localization and Tracking: Indoors, Outdoors and Emerging Network

Low-effort place recognition with WiFi fingerprints using deep learning

Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems. Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization. In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results. Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification. We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification. The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions

Recurrent Neural Networks For Accurate RSSI Indoor Localization

This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the relation among the received signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a weighted average filter is proposed for both input RSSI data and sequential output locations to enhance the accuracy among the temporal fluctuations of RSSI. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of $0.75$ m with $80\%$ of the errors under $1$ m, which outperforms the conventional KNN algorithms and probabilistic algorithms by approximately $30\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, recurrent neuron network (RNN), long shortterm memory (LSTM), fingerprint-based localizatio