Global map matching using BLE beacons for indoor route and stay estimation

Abstract

Recently, location service using Bluetooth Low-Energy (BLE) beacon is gaining popularity. There also exist researches that estimate the route of the user from the location estimation results, visualize, and analyze it. In the conventional route estimation method based on the BLE beacon, after estimating the all locations from the radio field strength of the BLE, the route is estimated from the sequence of the estimated locations. Therefore, one of the causes of deterioration in accuracy could be the fact that the estimation was repeated twice. Therefore, in this paper, we propose a novel estimation method of the user's route and stay using BLE beacons. Specifically, we apply the idea of global map matching, used in the field of GPS, to route estimation based on BLE beacons. Thus, the global optimum route in beacon network can be estimated from the radio field intensities of the BLE beacon directly. We associate a beacon network with path network to estimate the route and stay of users. We installed approximately 1,600 BLE beacon transmitters in all the classrooms and corridors of Nagoya Institute of Technology, and confirmed the effectiveness of the proposed method by experiments.

Applying Movement Constraints to BLE RSSI-Based Indoor Positioning for Extracting Valid Semantic Trajectories

“…Similar to Reference [9], we do not assume that all detailed characteristics or spatiotemporal constraints of the BLE beacons in the indoor environment are known. This case is different from previous works [5,19,20] where a position was directly related to a beacon. According to Reference [9], the learning-based method cleans the RFID of the trajectory data to an RFID observation at a time.…”

“…Indoor positioning yields the position of a current user from given several measurements. Some techniques utilize inertial sensors [4,17] and RSSI-based sensors [4,5,7,8,13,17] in an indoor environment. However, most of them work on 2D exact positions, such as particle filter [17], kNN [7,8], and reinforcement learning [13].…”

“…Another approach [3] directly infers the room where a user resides by performing classification using convolutional neural network on transformed images that consider BLE signals and positions as feature. However, only a few methods [13,17] along with the HMM method [4,5] consider the trajectory as the output of indoor positioning. Not dealing with this issue leads to invalid trajectory, as the consecutive positioning result may be far apart.…”

“…Indoor trajectories [1] support these services for several applications, such as people behavior analytics, movement patterns extraction, next-to-visit recommendations, and hotspot detection. Positioning devices, such as Wi-Fi [2], BLE (Bluetooth Low Energy) [3][4][5][6][7][8], and RFID (radio frequency identification) [9], provide the trajectories using indoor positioning techniques.…”

“…Then, the methods estimate the current user position using the knowledge from the reference set. Some popular indoor positioning techniques are the Hidden Markov Model (HMM) [4,5,[9][10][11][12], k-nearest neighbors (kNN) [6][7][8], and Deep Neural Networks (DNN) [3,13]. Those methods (HMM, kNN, and DNN) utilize machine learning as their core.…”