scholarly journals A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position

2021 ◽  
Vol 11 (17) ◽  
pp. 8170
Author(s):  
Shenglei Xu ◽  
Yunjia Wang ◽  
Meng Sun ◽  
Minghao Si ◽  
Hongji Cao
Keyword(s):  
Real Time ◽  
High Precision ◽  
Dead Reckoning ◽  
Integrated System ◽  
Gross Error ◽  
True Position ◽  
Time Position ◽  
Heading Angle ◽  
The Mean ◽  
Pedestrian Dead Reckoning

Indoor position technologies have attracted the attention of many researchers. To provide a real-time indoor position system with high precision and stability is necessary under many circumstances. In a real-time position scenario, gross errors of the Bluetooth low energy (BLE) fingerprint method are more easily occurring and the heading angle of the pedestrian will drift without acceleration and magnetic field compensation. A real-time BLE/pedestrian dead-reckoning (PDR) integrated system by using an improved robust filter has been proposed. In the PDR method, the improved Mahony complementary filter based on the pedestrian motion states is adopted to estimate the heading angle reducing the drift error. Then, an improved robust filter is utilized to detect and restrain the gross error of the BLE fingerprint method. The robust filter detected the gross error at different granularity by constructing a robust vector changing the observation covariance matrix of the extended Kalman filter (EKF) adaptively when the application is running. Several experiments are conducted in the true position scenario. The mean position accuracy obtained by the proposed method in the experiment is 0.844 m and RMSE is 0.74 m. Compared with the classic EKF, these two values are increased by 38% and 18%, respectively. The results show that the improved filter can avoid the gross error in the BLE method and provide high precision and scalability in indoor position service.

scholarly journals Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4565
Author(s):  
Cedric De Cock ◽  
Wout Joseph ◽  
Luc Martens ◽  
Jens Trogh ◽  
David Plets
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Clustering Algorithm ◽  
Dead Reckoning ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Batch Mode ◽  
Access Points ◽  
Median Position ◽  
Pedestrian Dead Reckoning

We present a smartphone-based indoor localisation system, able to track pedestrians over multiple floors. The system uses Pedestrian Dead Reckoning (PDR), which exploits data from the smartphone’s inertial measurement unit to estimate the trajectory. The PDR output is matched to a scaled floor plan and fused with model-based WiFi received signal strength fingerprinting by a Backtracking Particle Filter (BPF). We proposed a new Viterbi-based floor detection algorithm, which fuses data from the smartphone’s accelerometer, barometer and WiFi RSS measurements to detect stairs and elevator usage and to estimate the correct floor number. We also proposed a clustering algorithm on top of the BPF to solve multimodality, a known problem with particle filters. The proposed system relies on only a few pre-existing access points, whereas most systems assume or require the presence of a dedicated localisation infrastructure. In most public buildings and offices, access points are often available at smaller densities than used for localisation. Our system was extensively tested in a real office environment with seven 41 m × 27 m floors, each of which had two WiFi access points. Our system was evaluated in real-time and batch mode, since the system was able to correct past states. The clustering algorithm reduced the median position error by 17% in real-time and 13% in batch mode, while the floor detection algorithm achieved a 99.1% and 99.7% floor number accuracy in real-time and batch mode, respectively.

Real time indoor localization integrating a model based pedestrian dead reckoning on smartphone and BLE beacons

2017 ◽  
Vol 10 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Lucio Ciabattoni ◽  
Gabriele Foresi ◽  
Andrea Monteriù ◽  
Lucia Pepa ◽  
Daniele Proietti Pagnotta ◽  
...  
Keyword(s):  
Real Time ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Model Based ◽  
Pedestrian Dead Reckoning

scholarly journals PEDESTRIAN DEAD RECKONING USING SMARTPHONES SENSORS: AN EFFICIENT INDOOR POSITIONING SYSTEM IN COMPLEX BUILDINGS OF SMART CITIES

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 905-912
Author(s):  
E. Saadatzadeh ◽  
A. Chehreghan ◽  
R. Ali Abbaspour
Keyword(s):  
Decision Tree ◽  
Smart Cities ◽  
Dead Reckoning ◽  
Principal Component ◽  
Indoor Positioning ◽  
Sensor Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
The Mean ◽  
Pedestrian Dead Reckoning

Abstract. This paper proposes an indoor positioning method using Pedestrian Dead Reckoning (PDR) based on the detection of the mode of the user’s smartphone. In the first step, to determine the mode of carrying the smartphone (Holding, Calling, Swinging) by suitably formed feature vectors based on sensor data, three classification algorithms (Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN)) are evaluated. From the classification algorithm perspective, the decision tree algorithm had the best performance in terms of processing time and classification. Secondly, to determine the user position, the step detection is performed by defining the upper threshold and time threshold for Acceleration norm values. The orientation component is obtained by combining accelerometer, magnetometer, and gyroscope data using Complementary Filtering and Principal Component Analysis based on Global Acceleration (PCA-GA) methods. The mean standard deviation along the direct path for the three modes of carrying (Holding, Calling, and Swinging) were obtained 6.22, 6.82, and 14.68 degrees, respectively. Localization experiments were performed on 3 modes of carrying a smartphone in a rectangular geometry path. The mean final error of positioning from ordinary walking for the three modes of holding (Calling, Holding, Swinging) were obtained 2.11, 2.34, and 4.5 m, respectively.

scholarly journals An Indoor Localization Method for Pedestrians Base on Combined UWB/PDR/Floor Map

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2578 ◽  
Author(s):  
Fei Liu ◽  
Jian Wang ◽  
Jixian Zhang ◽  
Houzeng Han
Keyword(s):  
Indoor Localization ◽  
Dead Reckoning ◽  
Ultra Wideband ◽  
Kalman Filter Algorithm ◽  
Heading Angle ◽  
The Difference ◽  
Position Problem ◽  
Map Data ◽  
Pathological Parameter ◽  
Pedestrian Dead Reckoning

This paper propose a scheme for indoor pedestrian location, based on UWB (Ultra Wideband)/PDR (Pedestrian Dead Reckoning) and Floor Map data. Firstly, a robust algorithm that uses Tukey weight factor and a pathological parameter for UWB positioning is proposed. The ill-conditioned position problem is solved for a scene where UWB anchors are placed on the same elevation of a narrow corridor. Secondly, a heading angle-computed strategy of PDR is put forward. According to the UWB positioning results, the location of pedestrians is mapped to the Floor Map, and 16 possible azimuth directions with 22.5° interval in this position are designed virtually. Compared to the heading angle of PDR, the center direction of the nearest interval is adopted as the heading. However, if the difference between the head angles of PDR and the nearest map direction is less than five degrees, the heading angle of PDR is regarded as the moving heading. Thirdly, an EKF (Extended Kalman Filter) algorithm is suggested for UWB/PDR/Floor Map fusion. By utilizing the positioning results of UWB, PDR, and the possible heading angle of Floor Map, high precision positioning results are acquired. Finally, two experimental scenarios are designed in a narrow corridor and computer room at a university. The accuracy of pedestrian positioning when all the data are available is verified in the first scenario; the positioning accuracy of a situation where part of UWB is unlock is verified in the second scenario. The results show that the proposed scheme can reliably achieve decimeter-level positioning.

scholarly journals RTK/Pseudolite/LAHDE/IMU-PDR Integrated Pedestrian Navigation System for Urban and Indoor Environments

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1791
Author(s):  
Ruihui Zhu ◽  
Yunjia Wang ◽  
Hongji Cao ◽  
Baoguo Yu ◽  
Xingli Gan ◽  
...  
Keyword(s):  
Real Time ◽  
High Precision ◽  
Navigation System ◽  
Carrier Phase ◽  
Inertial Measurement Unit ◽  
Dead Reckoning ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Inertial Measurement ◽  
Pedestrian Navigation

This paper presents an evaluation of real-time kinematic (RTK)/Pseudolite/landmarks assistance heuristic drift elimination (LAHDE)/inertial measurement unit-based personal dead reckoning systems (IMU-PDR) integrated pedestrian navigation system for urban and indoor environments. Real-time kinematic (RTK) technique is widely used for high-precision positioning and can provide periodic correction to inertial measurement unit (IMU)-based personal dead reckoning systems (PDR) outdoors. However, indoors, where global positioning system (GPS) signals are not available, RTK fails to achieve high-precision positioning. Pseudolite can provide satellite-like navigation signals for user receivers to achieve positioning in indoor environments. However, there are some problems in pseudolite positioning field, such as complex multipath effect in indoor environments and integer ambiguity of carrier phase. In order to avoid the limitation of these factors, a local search method based on carrier phase difference with the assistance of IMU-PDR is proposed in this paper, which can achieve higher positioning accuracy. Besides, heuristic drift elimination algorithm with the assistance of manmade landmarks (LAHDE) is introduced to eliminate the accumulated error in headings derived by IMU-PDR in indoor corridors. An algorithm verification system was developed to carry out real experiments in a cooperation scene. Results show that, although the proposed pedestrian navigation system has to use human behavior to switch the positioning algorithm according to different scenarios, it is still effective in controlling the IMU-PDR drift error in multiscenarios including outdoor, indoor corridor, and indoor room for different people.

scholarly journals Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors

2020 ◽  
Vol 10 (6) ◽  
pp. 2003 ◽  
Author(s):  
Liu Liu ◽  
Bofeng Li ◽  
Ling Yang ◽  
Tianxia Liu
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Reasonable Accuracy ◽  
Positioning Accuracy ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Smartphone Sensors ◽  
Pedestrian Dead Reckoning

For localization in daily life, low-cost indoor positioning systems should provide real-time locations with a reasonable accuracy. Considering the flexibility of deployment and low price of iBeacon technique, we develop a real-time fusion workflow to improve localization accuracy of smartphone. First, we propose an iBeacon-based method by integrating a trilateration algorithm with a specific fingerprinting method to resist RSS fluctuations, and obtain accurate locations as the baseline result. Second, as turns are pivotal for positioning, we segment pedestrian trajectories according to turns. Then, we apply a Kalman filter (KF) to heading measurements in each segment, which improves the locations derived by pedestrian dead reckoning (PDR). Finally, we devise another KF to fuse the iBeacon-based approach with the PDR to overcome orientation noises. We implemented this fusion workflow in an Android smartphone and conducted real-time experiments in a building floor. Two different routes with sharp turns were selected. The positioning accuracy of the iBeacon-based method is RMSE 2.75 m. When the smartphone is held steadily, the fusion positioning tests result in RMSE of 2.39 and 2.22 m for the two routes. In addition, the other tests with orientation noises can still result in RMSE of 3.48 and 3.66 m. These results demonstrate our fusion workflow can improve the accuracy of iBeacon positioning and alleviate the influence of PDR drifting.

scholarly journals Real-Time Pedestrian Tracking Terminal Based on Adaptive Zero Velocity Update

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3808
Author(s):  
Ran Wei ◽  
Hongda Xu ◽  
Mingkun Yang ◽  
Xinguo Yu ◽  
Zhuoling Xiao ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
Inertial Measurement Unit ◽  
Dead Reckoning ◽  
Measurement Unit ◽  
Tracking Accuracy ◽  
Zero Velocity ◽  
And Performance ◽  
Pedestrian Dead Reckoning ◽  
Motion Types

In the field of pedestrian dead reckoning (PDR), the zero velocity update (ZUPT) method with an inertial measurement unit (IMU) is a mature technology to calibrate dead reckoning. However, due to the complex walking modes of different individuals, it is essential and challenging to determine the ZUPT conditions, which has a direct and significant influence on the tracking accuracy. In this research, we adopted an adaptive zero velocity update (AZUPT) method based on convolution neural networks to classify the ZUPT conditions. The AZUPT model was robust regardless of the different motion types of various individuals. AZUPT was then implemented on the Zynq-7000 SoC platform to work in real time to validate its computational efficiency and performance superiority. Extensive real-world experiments were conducted by 60 different individuals in three different scenarios. It was demonstrated that the proposed system could work equally well in different environments, making it portable for PDR to be widely performed in various real-world situations.

Sign in / Sign up

Export Citation Format

Share Document