The paper presents the results of the project which examines the level of accuracy that can be achieved in precision indoor positioning using a new improved Zigbee network fingerprint method. This method can provide more accurate positioning by filtering algorithm for improving the fingerprint Zigbee database accuracy. In our experiment the following two steps have been completed. Firstly, we filtered out the interference data generated in the fingerprint database source data acquisition process what improved the accuracy of establishing the fingerprint database. Next, the nearest algorithm, the weighted nearest algorithm and Bayesian algorithm were used to calculate pedestrian’s location, and then the results are compared and analyzed. As a result, the average error with the improved fingerprint database is less than or equal to 0.81 m in a long distance range. The accuracy of the results got much better compared with the results without filtering. A newly developed ZigBee system by us can be applied to the location based services in a bigger space inside the buildings (distances up to 40 m) or in the underground mines.

Recent developments enable to access raw Global Navigation Satellite System (GNSS) measurements of mobile phones. Initially, researchers using signals gathered by mobile phones for high accuracy surveying were not successful in ambiguity fixing. Nowadays, GNSS chips, which are built in the latest smartphones, deliver code and primarily carrier phase observations available for detailed analysis in post-processing applications. Therefore, we decided to check the performance of carrier phase ambiguity fixing and positioning accuracy results of the latest Huawei P30 pro smartphone equipped with a dual-frequency GNSS receiver. We collected 3 h of raw static data in separate sessions at a known point location. For two sessions, the mobile phone was mounted vertically and for the third one—horizontally. At the same time, a high-class geodetic receiver was used for L1 and L5 signal comparison purposes. The carrier phase measurements were processed using commercial post-processing software with reference to the closest base station observations located 4 km away. Additionally, 1 h sessions were divided into 10, 15, 20 and 30 min separate sub-sessions to check the accuracy of the surveying results in fast static mode. According to the post-processing results, we were able to fix all L1 ambiguities based on Global Positioning System (GPS)-only satellite constellation. In comparison to the fixed reference point position, all three 1 h static session results were at centimeters level of accuracy (1–4 cm). For fast static surveying mode, the best results were obtained for 20 and 30 min sessions, where average accuracy was also at centimeters level.

The paper presents the results of the project which examines the level of accuracy that can be achieved in precision indoor positioning by using a pedestrian dead reckoning (PDR) method. This project is focused on estimating the position using step detection technique based on foot-mounted IMU. The approach is sensor-fusion by using accelerometers, gyroscopes and magnetometers after initial alignment is completed. By estimating and compensating the drift errors in each step, the proposed method can reduce errors during the footsteps. There is an advantage of the step detection combined with ZUPT and ZARU for calculating the actual position, distance travelled and estimating the IMU sensors’ inherent accumulated error by EKF. Based on the above discussion, all algorithms are derived in detail in the paper. Several tests with an Xsens IMU device have been performed in order to evaluate the performance of the proposed method. The final results show that the dead reckoning positioning average position error did not exceed 0.88 m (0.2% to 1.73% of the total traveled distance – normally ranges from 0.3% to 10%), what is very promising for future handheld indoor navigation systems that can be used in large office buildings, malls, museums, hospitals, etc.

Purpose: In this study, an indoor sensor information fusion positioning system of the quadrotor unmanned aerial vehicle (UAV) was investigated to solve the problem of unstable indoor flight positioning.Design/methodology/approach: The presented system was built on Light Detection and Ranging (LiDAR), Inertial Measurement Unit (IMU) and LiDAR-Lite devices. Based on this, one can obtain the aircraft's current attitude and the position vector relative to the target and control the attitudes and positions of the UAV to reach the specified target positions. While building a UAV positioning model relative to the target for indoor positioning scenarios under limited Global Navigation Satellite Systems (GNSS), the system detects the environment through the NVIDIA Jetson TX2 (Transmit Data) peripheral sensor, obtains the current attitude and the position vector of the UAV, packs the data in the format and delivers it to the flight controller. Then the flight controller controls the UAV by calculating the posture to reach the specified target position.Findings: The authors used two systems in the experiment. The first is the proposed UAV, and the other is the Vicon system, our reference system for comparison purposes. Vicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.Originality/value: Vicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.

This paper presents numerical analyzes of code differential GPS positioning with the use of two Huawei P30 Pro mobile phones. Code observations on L1 and L5 frequencies were chosen for DGPS positioning analysis. For project purposes, we additionally used one high-class geodetic GNSS receiver (Javad Alpha) acting as a reference station. Smartphones were placed at the same distance of 0.5 m from the reference receiver. Such a close distance was specially planned by the authors in order to achieve identical observation conditions. Thus, it was possible to compare the DGPS positioning accuracy using the same satellites and the P(L1) and P(L5) code only, for single observation epochs and for sequential DGPS adjustment. Additionally, the precision of observations of the second differences in the observations P(L1) and P(L5) was analyzed. In general, the use of the P(L5) code to derive DGPS positions has made it possible to significantly increase the accuracy with respect to the positions derived using the P(L1) code. Average errors of horizontal and vertical coordinates were about 60–80% lower for the DGPS solution using the P(L5) code than using the P(L1) code. Based on the simulated statistical analyses, an accuracy of about 0.4 m (3D) with 16 satellites may be obtained using a smartphone with P(L5) code. An accuracy of about 0.3 m (3D) can be achieved with 26 satellites.