In urban contexts, the increasing density of electronic devices equipped with Global Navigation Satellite System (GNSS) receivers and complementary positioning technologies is attracting research and development efforts devoted to an improvement of the quality of life towards the smart city paradigm. Vehicular and pedestrian positioning and navigation capabilities are among the major drivers for innovation in this process. Ultra-low-cost electronics such as smartphones and Internet of Things (IoT) sensors aim at providing accurate and reliable positioning solutions through a set of promising solutions. Among these, snapshot positioning allows to remotely perform the post-processing of GNSS signals in IoT sensor networks while Wi-Fi™ ranging and cooperative positioning provide auxiliary anchors of opportunity to enhance indoor/outdoor positioning capabilities. This paper presents an innovative platform to perform a centralised testing and assessment of such positioning and navigation technologies along with a set of results obtained in the context of the European project HANSEL, by relying on current network technologies and infrastructures (i.e., Wi-Fi™ and cellular connectivity).
WiFi Round Trip Time (RTT) unlocks meter level accuracies in user terminal positions where no other navigation systems, such as Global Navigation Satellite Systems (GNSS), are able to (e.g., indoors). However, little has been done so far to obtain a scalable and automated system that computes the position of the WiFi Access Points (WAP) using RTT and that is able to estimate, in addition to the position, the hardware biases that offset the WiFi ranging measurements. These biases have a direct impact on the ultimate position accuracy of the terminals. This work proposes a method in which the computation of the WiFi Access Points positions and hardware biases (i.e., products) can be estimated based on the ranges and position fixes provided by user terminals (i.e., inverse positioning) and details how this can be improved if raw GNSS measurements (pseudoranges and carrier phase) are also available in the terminal. The data setup used to obtain a performance assessment was configured in a benign scenario (open sky with no obstructions) in order to obtain an upper boundary on the positioning error that can be achieved with the proposed method. Under these conditions, accuracies better than 1.5 m were achieved for the WAP position and hardware bias. The proposed method is suitable to be implemented in an automated manner, without having to rely on dedicated campaigns to survey 802.11mc-compliant WAPs. This paper offers a technique to automatically estimate both mild-indoor WAP products (where terminals have both Wi-Fi RTT and GNSS coverage) and deep-indoor WAP (with no GNSS coverage where the terminals obtain their position exclusively from previously estimated mild-indoor WAPs).
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