Indoor Positioning System Based on Bluetooth Low Energy Technology and a Nature-Inspired Optimization Algorithm

A BLE based turnkey indoor positioning system for mobility assessment in aging-in-place settings

Indoor positioning systems (IPS) can be used to measure mobility at home, which is an important indicator for health and wellbeing. In this work, we designed and developed a Bluetooth Low Energy (BLE) based IPS that identifies individual users; does not require floorplans; and allows the end-users to perform on-site install/setup. Additionally, a dynamic calibration process is implemented to learn room boundaries based on the distribution of the BLE signal strength. The functionality and performance of IPS system were validated in two residential home settings. Raw and filtered relative signal strength indicators (RSSI) and variability of RSSI were measured during testing. Room detection was determined by comparing a user input location (ground truth) with the IPS detected location for over 300 positions. The IPS produced a 96% accuracy of correctly detecting room location when using RSSI and the additional motion sensors. The use of PIR motion and ultrasonic sensors information provided improved validity when compared with existing indoor positioning systems. The ease of use and modular design of this IPS makes it a good choice for implementation in larger scale smart healthcare monitoring systems.

Fast 50 Hz Updated Static Infrared Positioning System Based on Triangulation Method

One of the important issues being explored in Industry 4.0 is collaborative mobile robots. This collaboration requires precise navigation systems, especially indoor navigation systems where GNSS (Global Navigation Satellite System) cannot be used. To enable the precise localization of robots, different variations of navigation systems are being developed, mainly based on trilateration and triangulation methods. Triangulation systems are distinguished by the fact that they allow for the precise determination of an object's orientation, which is important for mobile robots. An important feature of positioning systems is the frequency of position updates based on measurements. For most systems, it is 10-20 Hz. In our work, we propose a high-speed 50 Hz positioning system based on the triangulation method with infrared transmitters and receivers. In addition, our system is completely static, i.e., it has no moving/rotating measurement sensors, which makes it more resistant to disturbances (caused by vibrations, wear and tear of components, etc.). In this paper, we describe the principle of the system as well as its design. Finally, we present tests of the built system, which show a beacon bearing accuracy of Δφ = 0.51°, which corresponds to a positioning accuracy of ΔR = 6.55 cm, with a position update frequency of fupdate = 50 Hz.

CSI-based sliding window fingerprinting method tailored for a signal blocking environment in VLP systems

In visible light indoor positioning systems, the localization performance of the received signal strength (RSS)-based fingerprinting algorithm would drop dramatically due to the occlusion of the line-of-sight (LOS) signal caused by randomly moving people or objects. A sliding window fingerprinting (SWF) algorithm based on channel state information (CSI) is put forward to enhance the accuracy and robustness of indoor positioning in this work. The core idea behind SWF is to combine CSI with sliding matching. The sliding window is used to match the received CSI and the fingerprints in the database twice to obtain the optimal matching value and reduce the interference caused by the lack of the LOS signal. On this premise, in order to reflect the different contributions of various paths in CSI to the calculation of match values, a weighted sliding window fingerprinting (W-SWF) is also proposed for the purpose of further improving the accuracy of fingerprint matching. A 4 m × 4 m × 3 m indoor multipath scene with four LEDs is established to evaluate the positioning performance. The simulation results reveal that the mean errors of the proposed method are 0.20 cm and 1.43 cm respectively when the LOS signal of 1 or 2 LEDs is blocked. Compared with the traditional RSS algorithm, the weighted k-nearest neighbor (WKNN) algorithm, and the adaptive residual weighted k-nearest neighbor (ARWKNN) algorithm, the SWF algorithm achieves over 90% improvement in terms of mean error and root mean square error (RMSE).

A Context-Aware Smartphone-Based 3D Indoor Positioning Using Pedestrian Dead Reckoning

The rise in location-based service (LBS) applications has increased the need for indoor positioning. Various methods are available for indoor positioning, among which pedestrian dead reckoning (PDR) requires no infrastructure. However, with this method, cumulative error increases over time. Moreover, the robustness of the PDR positioning depends on different pedestrian activities, walking speeds and pedestrian characteristics. This paper proposes the adaptive PDR method to overcome these problems by recognizing various phone-carrying modes, including texting, calling and swinging, as well as different pedestrian activities, including ascending and descending stairs and walking. Different walking speeds are also distinguished. By detecting changes in speed during walking, PDR positioning remains accurate and robust despite speed variations. Each motion state is also studied separately based on gender. Using the proposed classification approach consisting of SVM and DTree algorithms, different motion states and walking speeds are identified with an overall accuracy of 97.03% for women and 97.67% for men. The step detection and step length estimation model parameters are also adjusted based on each walking speed, gender and motion state. The relative error values of distance estimation of the proposed method for texting, calling and swinging are 0.87%, 0.66% and 0.92% for women and 1.14%, 0.92% and 0.76% for men, respectively. Accelerometer, gyroscope and magnetometer data are integrated with a GDA filter for heading estimation. Furthermore, pressure sensor measurements are used to detect surface transmission between different floors of a building. Finally, for three phone-carrying modes, including texting, calling and swinging, the mean absolute positioning errors of the proposed method on a trajectory of 159.2 m in a multi-story building are, respectively, 1.28 m, 0.98 m and 1.29 m for women and 1.26 m, 1.17 m and 1.25 m for men.