IndoorPositioning – Page 3

Location System Anchor Optimisation

Researchers from Department of Computer Science, University of Jaén, Spain have a new paper on OBLEA: A New Methodology to Optimise Bluetooth Low Energy Anchors in Multi-occupancy Location Systems.

This paper introduces a new methodology called OBLEA, which aims to optimise BLE anchor configurations in indoor settings. It takes into account various BLE variables to enhance flexibility and applicability to different environments. The method uses a data-driven approach, aiming to obtain the best configuration with as few anchors as possible.

The OBLEA method offers a flexible framework for indoor spaces where the occupants are fitted with wrist activity bracelets (beacons) and BLE anchors are set up. The anchors then collect and aggregate data, sending it to a central point (fog node) via MQTT.

A dataset was generated with the maximum number of anchors in the indoor environment, and different configurations were then trained and tested based on this dataset. The best balance between fewer anchors and high accuracy was chosen as the optimal configuration.

This methodology was tested and optimised in a real-world scenario, in a Spanish nursing home in Alcaudete, Jaén. The experiment involved seven inhabitants in four shared double rooms. As a result of this optimisation, the inhabitants could be located in real time with an accuracy of 99.82%, using a method called the K-Nearest-Neighbour algorithm and collating the signal strength (RSSIs) in 30-second time windows.

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Using Beacons to Improve Location of Mobile Robots

There’s new research from King Mongkut’s Institute of Technology Ladkrabang, Thailand on Sensor Fusion of Light Detection and Ranging and iBeacon to Enhance Accuracy of Autonomous Mobile Robot in Hard Disk Drive Clean Room Production Line (pdf).

Mobile robots are broadly divided into automated guided vehicles (AGVs) and autonomous intelligent vehicles (AIVs). AGVs are confined to predetermined paths while AIVs have the flexibility to move in any direction without any infrastructural alterations. Factories often face challenges when it comes to synchronising mobile robots with target machinery. The paper presents a solution to reduce errors in robot localisation and improve parking accuracy.

Adaptive Monte Carlo Localisation (AMCL), a probability-based localisation system which relies on LiDAR and odometry data often misjudges robot positions in environments where the factory production line and room shapes are alike. To mitigate this, a novel landmark-based localisation strategy using iBeacon, a Bluetooth Low Energy (BLE) device, is proposed. This approach aims to provide more accurate localisation of mobile robots, addressing the shortcomings of the AMCL system.

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Indoor Tracking of Individuals with Mild Cognitive Impairment

There’s new research from the USA on Indoor Localization using Bluetooth and Inertial Motion Sensors in Distributed Edge and Cloud Computing Environment (PDF). The paper describes a low-cost, scalable, edge computing system for tracking indoor movements in a large indoor facility. The system uses Bluetooth Low Energy (BLE) and Inertial Measurement Unit sensors (IMU) and is designed to facilitate therapeutic activities for individuals with Mild Cognitive Impairment.

The implementation involved instrumenting a facility with 39 edge computing systems and an on-premise fog server. Subjects carried BLE beacon and IMU sensors on-body. The researchers developed an adaptive trilateration approach that considered the temporal density of hits from the BLE beacon to surrounding edge devices to handle inconsistent coverage of edge devices in large spaces with varying signal strength. They also integrated IMU-based tracking methods using a dead-reckoning technique to improve the system’s accuracy.

The conclusions of the study showed that the proposed system could robustly localise the position of multiple people with an average error of 4 meters across the entire study space, also showing 87% accuracy for room-level localisations. The integration of IMU-based dead-reckoning with Bluetooth-based localisation further enhanced the system’s accuracy.

Using Packet Loss to Infer Location

There’s new research from the University of Illinois titled Packet Reception Probability: Packets That You Can’t Decode Can Help Keep You Safe (pdf). Many existing systems estimate distance using the Receiver Signal Strength Indicator (RSSI) which is negatively impacted by sampling bias and multipath effects. As an alternative, the study uses Packet Reception Probability (PRP) that utilises packet loss to estimate distance.

Localisation is achieved through a Bayesian-PRP approach that also includes an explicit model of multipath. To facilitate straightforward deployment, there’s no need for any modifications to hardware, firmware, or driver-level on standard devices and only minimal training is required.

A variety of devices were used including Bluvision iBeeks, BluFi, a Texas Instrument Packet Sniffer, a laptop, and Android smartphones (Nexus5x). 60 iBeacons were deployed in a library and 38 in a retail store. The Texas Instrument Packet Sniffer, connected to a Windows laptop was used for packet reception from beacons. Android phones were equipped with a purpose-built Android app.

PRP was found to provide metre-level accuracy with just six devices in known locations and 12 training locations. Combining PRP with RSSI was found to be beneficial at short distances up to 2m. Beyond distances of 2m, fusing the two is less effective than using PRP alone because RSSI becomes de-correlated with distance.

More Accurate Beacon Locating Using AI Machine Learning

There’s new research in the Bulletin of Electrical Engineering and Informatics on Bluetooth beacons based indoor positioning in a shopping malls using machine learning. Researchers from Algeria and Italy improved the accuracy of RSSI locating by using AI machine learning techniques. They used extra-trees classifier (ETC) and a k-neighbours classifier to achieve greater than 90% accuracy.

A smartphone app was used to receive beacon RSSI and send it to an indoor positioning system’s data collection module. RSSI data was also filtered by a data processing module to limit the error range. KNN, RFC, extra trees classifiers (ETC), SVM, gradient boosting classifiers (GBC) and decision trees (DT) algorithms were evaluated.

The ETC model gave the best accuracy. ETC is an algorithm that uses a group of decision trees to classify data. It is similar to a random forest classifier but uses a different method to construct the decision trees. ETC fits a number of randomised decision trees on sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. ETC is a good choice for applications where accuracy is important but the data is noisy and where computational efficiency is important.

Advantages of Real Time Location Systems (RTLS)

RTLS systems are used to track the location of objects or people, tagged with Bluetooth beacons, in real time. Some of the advantages of using a RTLS include:

Improved efficiency: RTLS systems allow organisations to track the location of assets or personnel in real time, which can help improve the efficiency of operations. For example, a RTLS system can be used to track the location of equipment in a warehouse, allowing workers to quickly locate and retrieve items when needed.
Enhanced safety: RTLS systems can also be used to improve safety in a variety of settings. For example, a RTLS system could be used to track the location of workers in a construction site, allowing supervisors to quickly respond to any safety incidents.
Increased visibility: RTLS systems provide organisations with real-time visibility into the location of assets or personnel, which can help with decision making and resource allocation. For example, a RTLS system can be used to track the location of vehicles on a site, allowing managers to optimise routes and reduce fuel consumption.
Improved asset utilisation: RTLS systems can help organisations to better utilise their assets, by providing real-time information about their location and availability. For example, a RTLS system could be used to track the location of equipment in a hospital, allowing better matching of demand with supply.

Overall, the main advantage of using a RTLS system is that it provides organisations with real-time information about the location of assets or personnel, which can help them to improve efficiency, enhance safety, and better utilise their resources.

Read about BeaconRTLS

Integrating Beacons into Existing Systems

There are three main ways beacons can be integrated into existing systems:

1. Using Smartphone Apps

Beacons are usually stationary. Apps on users’ smartphone use the standard Bluetooth iOS and Android APIs to detect beacons and send information to your cloud or servers, typically via HTTP(S).

2. Using Ethernet/WiFi Gateways

Beacons are using moving. Gateways in fixed positions detect beacons and send information to your cloud or servers, typically via HTTP(S) or MQTT.

3. Using an Intermediate Platform Such as a Real Time Location System (RTLS)

This is a variant on #2 in that gateways send information to a system such as BeaconRTLS™ or PrecisionRTLS™. These systems have HTTP(S) APIs that can be used by your cloud or servers.

More information:
What are beacons?
Beacons for the Internet of Things (IoT)

If you need more project specific help we also offer consultancy and feasibility studies.

Implementing Bluetooth AoA Using Software Defined Radio (SDR)

There’s new research from Poznan University of Technology, Poland on Angle of arrival estimation in a multi-antenna software defined radio system: impact of hardware and radio environment.

The researchers implemented Software Defined Radio (SDR), on an inexpensive USRP B210, using the Root Multiple Signal Classification (Root-MUSIC) algorithm to provide Bluetooth AoA. Consideration was given to errors caused by the hardware and the radio environment.

Hardware errors were mainly synchronization errors. The accuracy of the AoA was affected by the degree of multipath propagation and filtering was found to improved accuracy. An implementation with two antennas and the Root-MUSIC AoA algorithm was able to achieve less than 10m estimation error in most environments.

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Introduction to Bluetooth Direction Finding

The Bluetooth SIG, the owner of Bluetooth standards, has a useful video introduction to Bluetooth® Location Services and High-Accuracy Direction Finding. It’s the 4th video from Embedded World 2020. Strangely, you need to view direct from the Bluetooth SIG site because this video isn’t available direct from Vimeo.

Martin Woolley, Senior Developer Relations Manager, provides a high level overview and explains how direction finding differs to positioning using RSSI signal strength. He describes how Bluetooth Angle of Arrival (AoA) and Angle of Departure (AoD) make use of multiple angles to provide accurate location.

Martin dives deeper into direction finding theory and phase sampling. He explains how Bluetooth uses Frequency Shift Keying (FSK) of the radio carrier signal that necessitates use of a Constant Tone Extension (CTE) to enable direction finding. It’s explained how Bluetooth Controller IQ sampling fits into the Bluetooth stack.

View G2 AoA Gateway Kit

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Fingerprinting Positioning Using Multiple Advertising Slots

There’s interesting research from Spain on Multi-Slot BLE Raw Database for Accurate Positioning in Mixed Indoor/Outdoor Environments. It looks into fingerprinting with beacons simultaneously advertising six slots rather than one slot.

Fingerprinting is where you first measure the signal levels at various known points and then later compare new data with the old to work out the position. This is usually performed with one signal from each beacon. The researchers increased this to six signals to attempt to improve positional accuracy.

Tests were performed at the campus of the University of Extremadura in Badajoz in the Physics and Mathematics buildings and also outside. Beacons were set up to transmit four slots using the Eddystone protocol and two using the iBeacon protocol. Different transmit powers were used for each slot. Measurements were performed using three different smartphones with a custom developed Android application. The resultant data is available on Zonodo.

The researchers compared a simple Nearest Neighbours algorithm (NN) using all the slots, the one slot with the highest transmission power and the average of all slots from the same beacon. The results showed that using all the slots or just one per beacon gives similar results for accuracy, floor, and Tag ID recognition. Results using the averaged values increased the accuracy by 10%.