LSS-RM: Using Multi-Mounted Devices to Construct a Lightweight Site-Survey Radio Map for WiFi Positioning

Abstract

A WiFi-received signal strength index (RSSI) fingerprinting-based indoor positioning system (WiFi-RSSI IPS) is widely studied due to advantages of low cost and high accuracy, especially in a complex indoor environment where performance of the ranging method is limited. The key drawback that limits the large-scale deployment of WiFi-RSSI IPS is time-consuming offline site surveys. To solve this problem, we developed a method using multi-mounted devices to construct a lightweight site-survey radio map (LSS-RM) for WiFi positioning. A smartphone was mounted on the foot (Phone-F) and another on the waist (Phone-W) to scan WiFi-RSSI and simultaneously sample microelectromechanical system inertial measurement-unit (MEMS-IMU) readings, including triaxial accelerometer, gyroscope, and magnetometer measurements. The offline site-survey phase in LSS-RM is a client–server model of a data collection and preprocessing process, and a post calibration process. Reference-point (RP) coordinates were estimated using the pedestrian dead-reckoning algorithm. The heading was calculated with a corner detected by Phone-W and the preassigned site-survey trajectory. Step number and stride length were estimated using Phone-F based on the stance-phase detection algorithm. Finally, the WiFi-RSSI radio map was constructed with the RP coordinates and timestamps of each stance phase. Experimental results show that our LSS-RM method can reduce the time consumption of constructing a WiFi-RSSI radio map from 54 min to 7.6 min compared with the manual site-survey method. The average positioning error was below 2.5 m with three rounds along the preassigned site-survey trajectory. LSS-RM aims to reduce offline site-survey time consumption, which would cut down on manpower. It can be used in the large-scale implementation of WiFi-RSSI IPS, such as shopping malls, hospitals, and parking lots.

Fast Construction of the Radio Map Based on the Improved Low‐Rank Matrix Completion and Recovery Method for an Indoor Positioning System

“…The radio map keeps the mapping relationship between the signal space and location space. In the traditional radio map establishment method, a large number of reference points need to be set in the target area and multiple measurements need to be taken at each reference point to improve the positioning accuracy [10]. This approach consumes a lot of labor and time costs.…”

Editorial for the Special Issue on MEMS Accelerometers

“…Three papers investigate MEMS accelerometer multi-physics modeling [7,8,9,10]. The rest of the papers are focused on the application domains, including environmental monitoring [11] and WiFi positioning [12]. Healthcare monitoring, positioning and daily activity monitoring are discussed in [13,14], while wearable body sensors for patients with gait impairments and the classification of horse gaits for self-coaching are covered in [15,16].…”

“…They achieve a sensitivity of 3.91 × 10 −4 (Vms 2 )/kg over a measurement range of 10 −8 –10 −4 W/kg. Lai M. et al [12] study a large amount of raw data measured by a MEMS accelerometer-based wrist-worn device. This device is used to monitor different levels of physical activities (PAs) for subjects wearing it continuously 24 h a day.…”