Advanced real-time indoor tracking based on the Viterbi algorithm and semantic data

A real-time indoor tracking system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy, that is, the environment of the object that is being tracked and a motion model. The starting point is a fingerprinting technique for which an advanced network planner is used to automatically construct the radio map, avoiding a time consuming measurement campaign. The developed algorithm was verified with simulations and with experiments in a building-wide testbed for sensor experiments, where a median accuracy below 2 m was obtained. Compared to a reference algorithm without Viterbi or semantic data, the results indicated a significant improvement: the mean accuracy and standard deviation improved by, respectively, 26.1% and 65.3%. Thereafter a sensitivity analysis was conducted to estimate the influence of node density, grid size, memory usage, and semantic data on the performance

Advanced indoor localisation based on the Viterbi algorithm and semantic data

In this work a real-time indoor localisation system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy: i.e., the environment of the object that is being tracked and an adjustable maximum speed. The developed algorithm was verified by simulations and with experiments in a building-wide testbed for sensor and WiFi experiments. Compared to a reference algorithm without Viterbi or semantic data, the results indicated a significant improvement: the mean accuracy and standard deviation improved by respectively 26.4% and 63.9%

Enhanced indoor location tracking through body shadowing compensation

This paper presents a radio frequency (RF)-based location tracking system that improves its performance by eliminating the shadowing caused by the human body of the user being tracked. The presence of such a user will influence the RF signal paths between a body-worn node and the receiving nodes. This influence will vary with the user's location and orientation and, as a result, will deteriorate the performance regarding location tracking. By using multiple mobile nodes, placed on different parts of a human body, we exploit the fact that the combination of multiple measured signal strengths will show less variation caused by the user's body. Another method is to compensate explicitly for the influence of the body by using the user's orientation toward the fixed infrastructure nodes. Both approaches can be independently combined and reduce the influence caused by body shadowing, hereby improving the tracking accuracy. The overall system performance is extensively verified on a building-wide testbed for sensor experiments. The results show a significant improvement in tracking accuracy. The total improvement in mean accuracy is 38.1% when using three mobile nodes instead of one and simultaneously compensating for the user's orientation

Joint received signal strength, angle-of-arrival, and time-of-flight positioning

This paper presents a software positioning framework that is able to jointly use measured values of three parameters: the received signal strength, the angle-of-arrival, and the time-of-flight of the wireless signals. Based on experimentally determined measurement accuracies of these three parameters, results of a realistic simulation scenario are presented. It is shown that for the given configuration, angle-of-arrival and received signal strength measurements benefit from a hybrid system that combines both. Thanks to their higher accuracy, time-of-flight systems perform significantly better, and obtain less added value from a combination with the other two parameters

A comparison of human body compensation models for RSSI based localization and tracking

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An unsupervised learning technique to optimize radio maps for indoor localization

A major burden of signal strength-based fingerprinting for indoor positioning is the generation and maintenance of a radio map, also known as a fingerprint database. Model-based radio maps are generated much faster than measurement-based radio maps but are generally not accurate enough. This work proposes a method to automatically construct and optimize a model-based radio map. The method is based on unsupervised learning, where random walks, for which the ground truth locations are unknown, serve as input for the optimization, along with a floor plan and a location tracking algorithm. No measurement campaign or site survey, which are labor-intensive and time-consuming, or inertial sensor measurements, which are often not available and consume additional power, are needed for this approach. Experiments in a large office building, covering over 1100 m(2), resulted in median accuracies of up to 2.07 m, or a relative improvement of 28.6% with only 15 min of unlabeled training data

Improved tracking by mitigating the influence of the human body

This paper presents a location tracking system that improves its performance by mitigating the influence caused by the human body of the user being tracked. The presence of such a user will influence the signal path between a body-worn mobile device and a receiving node. This influence will vary with the user's location and orientation and, as a result, the performance will deteriorate. By making use of the user's orientation towards the fixed infrastructure nodes, the influence of the body can be explicitly compensated, hereby improving the tracking accuracy. The overall system performance is extensively verified with experiments on a building-wide testbed. Compensating for this human body shadowing results in a relative improvement of 23.6%