scholarly journals Noise-Resilient Acoustic Low Energy Beacon for Proximity-Based Indoor Positioning Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1703
Author(s):  
Teodoro Aguilera ◽  
Fernando J. Aranda ◽  
Felipe Parralejo ◽  
Juan D. Gutiérrez ◽  
José A. Moreno ◽  
...  
Keyword(s):  
Pulse Compression ◽  
Computational Cost ◽  
Experimental Tests ◽  
Indoor Positioning ◽  
Low Energy ◽  
Positioning Systems ◽  
Coding Scheme ◽  
Band Noise ◽  
Reliable Detection ◽  
Acoustic Axis

Proximity-Based Indoor Positioning Systems (PIPSs) are a simple to install alternative in large facilities. Besides, these systems have a reduced computational cost on the mobile device of those users who do not continuously demand a high location accuracy. This work presents the design of an Acoustic Low Energy (ALE) beacon based on the emission of inaudible Linear Frequency Modulated (LFM) signals. This coding scheme provides high robustness to in-band noise, thus ensuring a reliable detection of the beacon at a practical range, after pulse compression. A series of experimental tests have been carried out with nine different Android devices to study the system performance. These tests have shown that the ALE beacon can be detected at one meter distance with signal-to-noise ratios as low as −12 dB. The tests have also demonstrated a detection rate above 80% for reception angles up to 50∘ with respect to the beacon’s acoustic axis at the same distance. Finally, a study of the ALE beacon energy consumption has been conducted demonstrating comparable power consumption to commercial Bluetooth Low Energy (BLE) beacons. Besides, the ALE beacon search can save up to 9% more battery of the Android devices than the BLE beacon scanning.

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 A STATISTICAL ANALYSIS ON THE SYSTEM PERFORMANCE OF A BLUETOOTH LOW ENERGY INDOOR POSITIONING SYSTEM IN A 3D ENVIRONMENT

2017 ◽  
Vol IV-2/W4 ◽  
pp. 319-326 ◽  
Author(s):  
G. G. Haagmans ◽  
S. Verhagen ◽  
R. L. Voûte ◽  
E. Verbree
Keyword(s):  
System Performance ◽  
Signal Propagation ◽  
Indoor Positioning ◽  
Low Energy ◽  
Geometric Configuration ◽  
Alternative Methods ◽  
Bluetooth Low Energy ◽  
Positioning Systems ◽  
Definition Of ◽  
A Performance

Since GPS tends to fail for indoor positioning purposes, alternative methods like indoor positioning systems (IPS) based on Bluetooth low energy (BLE) are developing rapidly. Generally, IPS are deployed in environments covered with obstacles such as furniture, walls, people and electronics influencing the signal propagation. The major factor influencing the system performance and to acquire optimal positioning results is the geometry of the beacons. The geometry of the beacons is limited to the available infrastructure that can be deployed (number of beacons, basestations and tags), which leads to the following challenge: Given a limited number of beacons, where should they be placed in a specified indoor environment, such that the geometry contributes to optimal positioning results? This paper aims to propose a statistical model that is able to select the optimal configuration that satisfies the user requirements in terms of precision. The model requires the definition of a chosen 3D space (in our case 7 × 10 × 6 meter), number of beacons, possible user tag locations and a performance threshold (e.g. required precision). For any given set of beacon and receiver locations, the precision, internal- and external reliability can be determined on forehand. As validation, the modeled precision has been compared with observed precision results. The measurements have been performed with an IPS of BlooLoc at a chosen set of user tag locations for a given geometric configuration. Eventually, the model is able to select the optimal geometric configuration out of millions of possible configurations based on a performance threshold (e.g. required precision).

scholarly journals RSS-Fingerprint Dimensionality Reduction for Multiple Service Set Identifier-Based Indoor Positioning Systems

2019 ◽  
Vol 9 (15) ◽  
pp. 3137
Author(s):  
Ahmed Abed ◽  
Ikhlas Abdel-Qader
Keyword(s):  
Dimensionality Reduction ◽  
Multipath Propagation ◽  
Low Cost ◽  
Computational Cost ◽  
Computation Time ◽  
Real Data ◽  
Labor Cost ◽  
Indoor Positioning ◽  
Distinct Advantage ◽  
Positioning Systems

Indoor positioning systems (IPS) have been recently adopted by many researchers for their broad applications in various Internet of Things (IoT) fields such as logistics, health, construction industries, and security. Received Signal Strength (RSS)-based fingerprinting approaches have been widely used for positioning inside buildings because they have a distinct advantage of low cost over other indoor positioning techniques. The signal power RSS is a function of the distance between the Mobile System (MS) and Access Point (AP), which varies due to the multipath propagation phenomenon and human body blockage. Furthermore, fingerprinting approaches have several disadvantages such as labor cost, diversity (in signals and environment), and computational cost. Eliminating redundancy by ruling out non-informative APs not only reduces the computation time, but also improves the performance of IPS. In this article, we propose a dimensionality reduction technique in a multiple service set identifier-based indoor positioning system with Multiple Service Set Identifiers (MSSIDs), which means that each AP can be configured to transmit N signals instead of one signal, to serve different kinds of clients simultaneously. Therefore, we investigated various kinds of approaches for the selection of informative APs such as spatial variance, strongest APs, and random selection. These approaches were tested using two clustering techniques including K-means and Fuzzy C-means. Performance evaluation was focused on two elements, the number of informative APs versus the accuracy of the proposed system. To assess the proposed system, real data was acquired from within the College of Engineering and Applied Sciences (CEAS) at the Western Michigan University (WMU) building. The results exhibit the superiority of fused Multiple Service Set Identifiers (MSSID) performance over the single SSID. Moreover, the results report that the proposed system achieves a positioning accuracy <0.85 m over 3000 m2, with an accumulative density function (CDF) of 88% with a distance error of 2 m.

scholarly journals Impact of Beacon-Dependent Parameters on Bluetooth Low Energy Indoor Positioning Accuracy

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1223 ◽  
Author(s):  
Gabriel de Blasio ◽  
Alexis Quesada-Arencibia ◽  
José Carlos Rodríguez-Rodríguez ◽  
Carmelo R. García ◽  
Roberto Moreno-Díaz Jr.
Keyword(s):  
Data Collection ◽  
Automatic System ◽  
Indoor Positioning ◽  
Low Energy ◽  
Transmission Power ◽  
Energy Technology ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Ubiquitous Systems ◽  
The Impact

Blue Low Energy technology is playing an important role nowadays in ubiquitous systems, being the beacons a key element. The configuration of parameters related to the beacons, such as their transmission power or their advertising interval should be studied in order to build fingerprinting indoor positioning systems based on this technology as accurate as possible. In this work, we study the impact and the interplay of those parameters in static indoor positioning as well as the orientation effect in the calibration phase. To reduce the time of data collection, a semi-automatic system is introduced.

scholarly journals Beacon-Related Param of Bluetooth Low Energy: Development of a Semi-Automatic System to Study Their Impact on Indoor Positioning Systems

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3087 ◽  
Author(s):  
de Blasio ◽  
Rodríguez-Rodríguez ◽  
García ◽  
Quesada-Arencibia
Keyword(s):  
Data Collection ◽  
Indoor Positioning ◽  
Low Energy ◽  
Positioning Accuracy ◽  
Automatic Data ◽  
Bluetooth Low Energy ◽  
Positioning Systems ◽  
Main Challenge ◽  
Automatic Data Collection ◽  
On Chip

Indoor positioning systems (IPS) are used to locate people or objects in environments where the global positioning system (GPS) fails. The commitment to make bluetooth low energy (BLE) technology the leader in IPS and their applications is clear: Since 2009, the Bluetooth Special Interest Group (SIG) has released several improved versions. BLE offers many advantages for IPS, e.g., their emitters or beacons are easily deployable, have low power consumption, give a high positioning accuracy and can provide advanced services to users. Fingerprinting is a popular indoor positioning algorithm that is based on the received signal strength (RSS); however, its main drawbacks are that data collection is a time-consuming and labor-intensive process and its main challenge is that positioning accuracy is affected by various factors. The purpose of this work was to develop a semi-automatic data collection support system in a BLE fingerprinting-based IPS to: (1) Streamline and shorten the data collection process, (2) carry out impact studies by protocol and channel on the static positioning accuracy related to configuration param of the beacons, such as transmission power (Tx) and the advertising interval (A), and their number and geometric distribution. With two types of systems-on-chip (SoCs) integrated in Bluetooth 5 beacons and in two different environments, our results showed that on average in the three BLE advertising channels, the configuration of the highest Tx (+4 dBm) in the beacons produced the best accuracy results. However, the lowest Tx (−20 dBm) did not worsen them excessively (only 11.8%). In addition, in both scenarios, when lowering the density of beacons by around 42.7%–50%, the error increase was only around 8%–9.2%.

scholarly journals Indoor positioning utilizing bluetooth low energy (BLE) RSSI on LoRa system

2021 ◽  
Vol 23 (2) ◽  
pp. 927
Author(s):  
Kavetha Suseenthiran ◽  
Abd Shukur Ja'afar ◽  
Ku Wei Heng ◽  
Mohamad Zoinol Abidin Abd Aziz ◽  
Azmi Awang Md Isa ◽  
...  
Keyword(s):  
Particle Filter ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Low Energy ◽  
Cumulative Distribution ◽  
Receive Signal Strength Indicator ◽  
Positioning System ◽  
Coverage Problem ◽  
Bluetooth Low Energy ◽  
Positioning Systems

Indoor positioning systems has become popular in this era where it is a network of devices used to locate people or object especially in indoor environment instead of satellite-based positioning. The satellite-based positioning global positioning system (GPS) signal is affected and loss incurred by the wall of the building causes the GPS lack of precision which leads to large positioning error. As a solution to the indoor area coverage problem, an indoor positioning based on bluetooth low energy (BLE) and long range (LoRa) system utilising the receive signal strength indicator (RSSI) is proposed, designed and tested. In this project, the prototype of indoor positioning system is built using node MCU ESP 32, LoRa nodes and BLE beacons. The node MCU ESP 32 will collect RSSI data from each BLE beacons that deployed at decided position around the area. Then, linear regression algorithm will be used in distance estimation. Next, particle filteris implemented to overcome the multipath fading effect and the trilateration technique is applied to estimate the user’s location. The estimated location is compared to the actual position to analyze the root mean square error (RMSE) and cumulative distribution function (CDF). Based on the experiment result, implementing the particle filter reduces the error of location accuracy. The particle filter achieves accuracy with 90% of the time the location error is lower than 2.6 meters.

Sign in / Sign up

Export Citation Format

Share Document