scholarly journals Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning

2021 ◽  
Vol 11 (15) ◽  
pp. 6805
Author(s):  
Khaoula Mannay ◽  
Jesús Ureña ◽  
Álvaro Hernández ◽  
José M. Villadangos ◽  
Mohsen Machhout ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Experimental Tests ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Smart Devices ◽  
Final Position ◽  
Positioning Systems ◽  
Local Positioning System ◽  
Tightly Coupled ◽  
Position Estimate

Indoor positioning systems have become a feasible solution for the current development of multiple location-based services and applications. They often consist of deploying a certain set of beacons in the environment to create a coverage volume, wherein some receivers, such as robots, drones or smart devices, can move while estimating their own position. Their final accuracy and performance mainly depend on several factors: the workspace size and its nature, the technologies involved (Wi-Fi, ultrasound, light, RF), etc. This work evaluates a 3D ultrasonic local positioning system (3D-ULPS) based on three independent ULPSs installed at specific positions to cover almost all the workspace and position mobile ultrasonic receivers in the environment. Because the proposal deals with numerous ultrasonic emitters, it is possible to determine different time differences of arrival (TDOA) between them and the receiver. In that context, the selection of a suitable fusion method to merge all this information into a final position estimate is a key aspect of the proposal. A linear Kalman filter (LKF) and an adaptive Kalman filter (AKF) are proposed in that regard for a loosely coupled approach, where the positions obtained from each ULPS are merged together. On the other hand, as a tightly coupled method, an extended Kalman filter (EKF) is also applied to merge the raw measurements from all the ULPSs into a final position estimate. Simulations and experimental tests were carried out and validated both approaches, thus providing average errors in the centimetre range for the EKF version, in contrast to errors up to the meter range from the independent (not merged) ULPSs.

scholarly journals INDOOR POSITIONING FOR SMART DEVICES BASED ON SENSOR FUSION WITH PARTICLE FILTER: LOCALIZATION AND MAP UPDATING

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 259-266
Author(s):  
Y. Yang ◽  
C. Toth
Keyword(s):  
Particle Filter ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Smart Devices ◽  
Visual Localization ◽  
Positioning Systems ◽  
Indoor Location ◽  
Map Updating ◽  
Smart Mobile ◽  
New Sensors

Abstract. With every new generation of smart devices, new sensors are introduced, such as depth camera or UWB sensors. Combined with the rapidly growing number of smart mobile devices, indoor positioning systems (IPS) have seen increasing interest due to numerous indoor location-based services (ILBS) and mobile applications at large. Wi-Fi Received Signal Strength (RSS) based fingerprinting positioning (WF) techniques are popularly used in many IPS as the widespread deployment of IEEE 802.11 WLAN (Wi-Fi) networks, as this technique requires no line-of-sight to the access points (APs), and it is easy to extract Wi-Fi signal from 802.11 networks with smart devices. However, WF techniques have problems with fingerprint variance, i.e., fluctuation of the sensed signal, and efficient map updating due to the frequently changing environment. To address these problems, we propose a novel framework of IPS which uses particle filter to fuse WF and state-of-the-art CNN-based visual localization method to better adapt to changing indoor environment. The suggested system was tested with real-world crowdsourced data collected by multiple devices in an office hallway. The experimental results demonstrate that the system can achieve robust localization at a 0.3~1.5 m mean error (ME) accuracy, and map updating with a 79% correction rate.

scholarly journals Attitude and Heading Estimation for Indoor Positioning Based on the Adaptive Cubature Kalman Filter

Micromachines ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 79
Author(s):  
Jijun Geng ◽  
Linyuan Xia ◽  
Dongjin Wu
Keyword(s):  
Kalman Filter ◽  
Angular Rate ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Statistical Characteristics ◽  
Fading Memory ◽  
Limited Memory ◽  
Cubature Kalman Filter ◽  
The Mean ◽  
Heading Estimation

The demands for indoor positioning in location-based services (LBS) and applications grow rapidly. It is beneficial for indoor positioning to combine attitude and heading information. Accurate attitude and heading estimation based on magnetic, angular rate, and gravity (MARG) sensors of micro-electro-mechanical systems (MEMS) has received increasing attention due to its high availability and independence. This paper proposes a quaternion-based adaptive cubature Kalman filter (ACKF) algorithm to estimate the attitude and heading based on smart phone-embedded MARG sensors. In this algorithm, the fading memory weighted method and the limited memory weighted method are used to adaptively correct the statistical characteristics of the nonlinear system and reduce the estimation bias of the filter. The latest step data is used as the memory window data of the limited memory weighted method. Moreover, for restraining the divergence, the filter innovation sequence is used to rectify the noise covariance measurements and system. Besides, an adaptive factor based on prediction residual construction is used to overcome the filter model error and the influence of abnormal disturbance. In the static test, compared with the Sage-Husa cubature Kalman filter (SHCKF), cubature Kalman filter (CKF), and extended Kalman filter (EKF), the mean absolute errors (MAE) of the heading pitch and roll calculated by the proposed algorithm decreased by 4–18%, 14–29%, and 61–77% respectively. In the dynamic test, compared with the above three filters, the MAE of the heading reduced by 1–8%, 2–18%, and 2–21%, and the mean of location errors decreased by 9–22%, 19–31%, and 32–54% respectively by using the proposed algorithm for three participants. Generally, the proposed algorithm can effectively improve the accuracy of heading. Moreover, it can also improve the accuracy of attitude under quasistatic conditions.

Robustness in Fingerprinting-Based Indoor Positioning Systems

2018 ◽  
pp. 88-141 ◽  
Author(s):  
Shih-Hau Fang
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Signal Strength ◽  
Histogram Equalization ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Location Estimation ◽  
Location Based Services ◽  
Indoor Environments ◽  
Positioning Systems

Indoor positioning systems have received increasing attention for supporting location-based services in indoor environments. Received signal strength (RSS), mostly utilized in Wi-Fi fingerprinting systems, is known to be unreliable due to two reasons: orientation mismatch and variations in hardware. This chapter introduces an approach based on histogram equalization to compensate for orientation mismatch in robust Wi-Fi localization. The proposed method involves converting the temporal-spatial radio signal strength into a reference function (i.e., equalizing the histogram). This chapter also introduces an enhanced positioning feature, which is called delta-fused principal strength, to enhance the robustness of Wi-Fi localization against the problem of heterogeneous hardware. This algorithm computes the pairwise delta RSS and then integrates with RSS using principal component analysis. The proposed methods effectively and efficiently improve the robustness of location estimation in the presence of mismatch orientation and hardware variations, respectively.

scholarly journals Easily-Deployable Acoustic Local Positioning System Based on Auto-Calibrated Wireless Beacons

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1385 ◽  
Author(s):  
José Moreno ◽  
Fernando Álvarez ◽  
Teodoro Aguilera ◽  
José Paredes
Keyword(s):  
Spread Spectrum ◽  
Low Cost ◽  
Experimental Tests ◽  
Real Time System ◽  
Positioning Systems ◽  
Local Positioning System ◽  
Update Rate ◽  
Positioning Algorithm ◽  
Node Position

Self-calibrated Acoustic Local Positioning Systems (ALPS) generally require a high consumption of hardware and software resources to obtain the user’s position at an acceptable update rate. To address this limitation, this work proposes a self-calibrated ALPS based on a software/hardware co-design approach. This working architecture allows for efficient communications, signal processing tasks, and the running of the positioning algorithm on low-cost devices. This fact also enables the real-time system operation. The proposed system is composed of a minimum of four RF-synchronized active acoustic beacons, which emit spread-spectrum modulated signals to position an unlimited number of receiver nodes. Each receiver node estimates the beacons’ position by means of an auto-calibration process and then computes its own position by means of a 3D multilateration algorithm. A set of experimental tests has been carried out where the feasibility of the proposed system is demonstrated. In these experiments, accuracies below 0.1 m are obtained in the determination of the receptor node position with respect to the set of previously-calibrated beacons.

scholarly journals Indoor Positioning on Disparate Commercial Smartphones Using Wi-Fi Access Points Coverage Area

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4351 ◽  
Author(s):  
Ashraf ◽  
Hur ◽  
Park
Keyword(s):  
Human Mobility ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Indoor Environments ◽  
Coverage Area ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Access Points ◽  
Intersection Area ◽  
The Impact

The applications of location-based services require precise location information of a user both indoors and outdoors. Global positioning system’s reduced accuracy for indoor environments necessitated the initiation of Indoor Positioning Systems (IPSs). However, the development of an IPS which can determine the user’s position with heterogeneous smartphones in the same fashion is a challenging problem. The performance of Wi-Fi fingerprinting-based IPSs is degraded by many factors including shadowing, absorption, and interference caused by obstacles, human mobility, and body loss. Moreover, the use of various smartphones and different orientations of the very same smartphone can limit its positioning accuracy as well. As Wi-Fi fingerprinting is based on Received Signal Strength (RSS) vector, it is prone to dynamic intrinsic limitations of radio propagation, including changes over time, and far away locations having similar RSS vector. This article presents a Wi-Fi fingerprinting approach that exploits Wi-Fi Access Points (APs) coverage area and does not utilize the RSS vector. Using the concepts of APs coverage area uniqueness and coverage area overlap, the proposed approach calculates the user’s current position with the help of APs’ intersection area. The experimental results demonstrate that the device dependency can be mitigated by making the fingerprinting database with the proposed approach. The experiments performed at a public place proves that positioning accuracy can also be increased because the proposed approach performs well in dynamic environments with human mobility. The impact of human body loss is studied as well.

scholarly journals LOCATE-US: Indoor Positioning for Mobile Devices Using Encoded Ultrasonic Signals, Inertial Sensors and Graph-Matching

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1950
Author(s):  
David Gualda ◽  
María Carmen Pérez-Rubio ◽  
Jesús Ureña ◽  
Sergio Pérez-Bachiller ◽  
José Manuel Villadangos ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Acoustic Waves ◽  
Graph Matching ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Global Navigation Satellite Systems ◽  
Positioning Systems

Indoor positioning remains a challenge and, despite much research and development carried out in the last decade, there is still no standard as with the Global Navigation Satellite Systems (GNSS) outdoors. This paper presents an indoor positioning system called LOCATE-US with adjustable granularity for use with commercial mobile devices, such as smartphones or tablets. LOCATE-US is privacy-oriented and allows every device to compute its own position by fusing ultrasonic, inertial sensor measurements and map information. Ultrasonic Local Positioning Systems (U-LPS) based on encoded signals are placed in critical zones that require an accuracy below a few decimeters to correct the accumulated drift errors of the inertial measurements. These systems are well suited to work at room level as walls confine acoustic waves inside. To avoid audible artifacts, the U-LPS emission is set at 41.67 kHz, and an ultrasonic acquisition module with reduced dimensions is attached to the mobile device through the USB port to capture signals. Processing in the mobile device involves an improved Time Differences of Arrival (TDOA) estimation that is fused with the measurements from an external inertial sensor to obtain real-time location and trajectory display at a 10 Hz rate. Graph-matching has also been included, considering available prior knowledge about the navigation scenario. This kind of device is an adequate platform for Location-Based Services (LBS), enabling applications such as augmented reality, guiding applications, or people monitoring and assistance. The system architecture can easily incorporate new sensors in the future, such as UWB, RFiD or others.

Virtual Reality Using Smart-Devices in Educational Frameworks

2020 ◽  
pp. 629-641
Author(s):  
Isidro Navarro ◽  
Oriol de Reina ◽  
David Fonseca ◽  
Macarena Gómez ◽  
Álvaro Ferrer
Keyword(s):  
Virtual Reality ◽  
Indoor Positioning ◽  
Qualitative Approach ◽  
Smart Devices ◽  
Spatial Skills ◽  
Positioning Systems ◽  
Indoor Location ◽  
Wearable Technologies ◽  
Visual Technologies

The main aim of this study is to improve the understanding of historical buildings through the use of advanced visual technologies. The main innovative features of the project are focused on the use of mobile and wearable technologies, the indoor location, and their mixed assessment an educational project. We will use smartphones, virtual reality and indoor positioning systems. Both the devices and the users' experience will be assessed with a quantitative and a qualitative approach. The proposal seeks to complement, the real experience of visiting an emblematic space (our case study: the Casa Batlló Museum, 1904-1906, Antonio Gaudí, Barcelona, Spain), in order to improve the spatial skills of architecture students and general visitors of this type of architectural landmarks.

scholarly journals Characterization of an Ultrasonic Local Positioning System for 3D Measurements

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2794 ◽  
Author(s):  
Khaoula Mannay ◽  
Jesús Ureña ◽  
Álvaro Hernández ◽  
Mohsen Machhout ◽  
Taoufik Aguili
Keyword(s):  
Low Cost ◽  
Likelihood Estimation ◽  
Indoor Positioning ◽  
Global Navigation Satellite Systems ◽  
Positioning System ◽  
Fusion Algorithm ◽  
Coverage Area ◽  
Positioning Systems ◽  
Indoor Location ◽  
Local Positioning System

Indoor location and positioning systems (ILPS) are used to locate and track people, as well as mobile and/or connected targets, such as robots or smartphones, not only inside buildings with a lack of global navigation satellite systems (GNSS) signals but also in constrained outdoor situations with reduced coverage. Indoor positioning applications and their interest are growing in certain environments, such as commercial centers, airports, hospitals or factories. Several sensory technologies have already been applied to indoor positioning systems, where ultrasounds are a common solution due to its low cost and simplicity. This work proposes a 3D ultrasonic local positioning system (ULPS), based on a set of three asynchronous ultrasonic beacon units, capable of transmitting coded signals independently, and on a 3D mobile receiver prototype. The proposal is based on the aforementioned beacon unit, which consists of five ultrasonic transmitters oriented towards the same coverage area and has already been proven in 2D positioning by applying hyperbolic trilateration. Since there are three beacon units available, the final position is obtained by merging the partial results from each unit, implementing a minimum likelihood estimation (MLE) fusion algorithm. The approach has been characterized, and experimentally verified, trying to maximize the coverage zone, at least for typical sizes in most common public rooms and halls. The proposal has achieved a positioning accuracy below decimeters for 90% of the cases in the zone where the three ultrasonic beacon units are available, whereas these accuracies can degrade above decimeters according to whether the coverage from one or more beacon units is missing. The experimental workspace covers a large volume, where tests have been carried out at points placed in two different horizontal planes.

scholarly journals Indoor Positioning Tightly Coupled Wi-Fi FTM Ranging and PDR Based on the Extended Kalman Filter for Smartphones

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 49671-49684 ◽  
Author(s):  
Meng Sun ◽  
Yunjia Wang ◽  
Shenglei Xu ◽  
Hongxia Qi ◽  
Xianxian Hu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Indoor Positioning ◽  
Tightly Coupled
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