Stable estimation of a user’s position in a wide range of indoor spaces is difficult. In an indoor localization field, a fingerprint method that compares the received signal strength (RSS) vector with the previously collected signal strength vector in a radio map is mostly applied. However, this method is dependent on the infrastructure around it and does not always provide stable performance owing to the severity of the RSS noise in an indoor environment, which limits the application of this system. In this paper, we propose an indoor positioning system using an accumulated RSS for mobile users. A user spatial RSS pattern is generated using the estimated user trajectory and the accumulated RSS. The user’s trajectory is estimated using pedestrian dead reckoning (PDR) technology. In particular, we proposed a novel concept, namely a heading flag, to estimate a user’s relative heading change in a certain period. The user’s spatial RSS pattern can be generated using this flag even when the user performs various motions while walking. We developed our own Bluetooth low energy (BLE) beacons and installed few beacons on a testbed for real-time positioning. To demonstrate the performance of the proposed system, we conducted a real field test in an office building. An average position estimation result of less than 2 m was obtained using the proposed system. It is expected that a commercial level location-based service (LBS) will be possible using the proposed system.

Among various sensor measurements that are obtained through smartphones, the received signal strength indicator (RSSI) of a radio frequency (RF) signal is widely used to estimate a user’s location. In this study, we propose a novel RF-based simultaneous localization and mapping (SLAM) technology for robust map construction. Typically, building an indoor map using the SLAM requires accurate loop closure detection. However, it is generally difficult to accurately estimate the loop closure using RSSI owing to the large variance of RF RSSI in indoor environments. To address this, we utilize the user trajectory and RSSI sequence to identify the correct loop closure. The RSSI sequence accumulated in consecutive poses generates a unique signal pattern. Therefore, it is possible to accurately estimate the revisiting of a pedestrian to a specific position. To evaluate the performance of the proposed method, we performed a field test in a laboratory building. Moreover, SLAM was performed using only 25% of the received RF measurements to validate that the proposed method can perform accurate SLAM despite a poor RF infrastructure environment. The obtained experimental results verify that a mean error of less than 3 m can be achieved in various test scenarios.

Recently, long tunnels are becoming more prevalent in Korea, and exits are added at certain sections of the tunnels. Thus, a navigation system should correctly guide the user toward the exit; however, adequate guidance is not delivered because the global navigation satellite system (GNSS) signal is not received inside a tunnel. Therefore, we present an accurate position estimation system using a magnetic field for vehicles passing through a tunnel. The position can be accurately estimated using the magnetic sensor of a smartphone with an appropriate attitude estimation and magnetic sensor calibration. Position estimation was realized by attaching the smartphone on the dashboard during navigation and calibrating the sensors using position information from the GNSS and magnetic field database before entering the tunnel. This study used magnetic field sequence data to estimate vehicle positions inside a tunnel. Furthermore, subsequence dynamic time warping was applied to compare the magnetic field data stored in the buffer with the magnetic field database, and the feasibility and performance of the proposed system was reviewed through an experiment in an actual tunnel. The analysis of the position estimation results confirmed that the proposed system could appropriately deliver tunnel navigation.

Some of the shopping malls, airports, hospitals, etc. have underground parking lots where hundreds of vehicles can be parked. However, first-time visitors find it difficult to determine their current location and need to keep moving the vehicle to find an empty parking space. Moreover, they need to remember the parked location, and find a nearby staircase or elevator to move toward the destination. In such a situation, if the user location can be estimated, a new navigation system can be offered, which can assist users. This study presents an underground parking lot navigation system using long-term evolution (LTE) signals. As the proposed system utilizes LTE network signals for which the infrastructure is already installed, no additional infrastructure is required. To estimate the location of the vehicle, the signal strength of the LTE signal is accumulated, and the location of the vehicle is estimated by comparing it with the previously stored database of the LTE received signal strength (RSS). In addition, the acceleration and gyroscope sensors of a smartphone are used to improve the vehicle position estimation performance. The effectiveness of the proposed system is verified by conducting an experiment in a large shopping-mall underground parking lot where approximately 500 vehicles can be parked. From the results of the experiment, an error of less than an average of 10 m was obtained, which shows that seamless navigation is possible using the proposed system even in an environment where GNSS does not function.