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.

Read about BluetoothLocationEngine™

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

Read about BluetoothLocationEngine™

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%.

Bluetooth for Locating

The Bluetooth SIG, the organisation that produces Bluetooth standards, has a recent post The Myths & Facts About Bluetooth Technology as a Positioning Radio. It talks about the location services in general and how they have evolved over time. It explains how Bluetooth helps solve key enterprise pain points to save tens to hundreds of billions of dollars globally through enhanced operational efficiencies, increased worker safety, and loss prevention.

In manufacturing facilities, billions of dollars are lost through unplanned downtime thanks to being unable to locate assets, tools, and equipment. In warehouses, RTLS can help automate the tracking of assets, such as pallets, which is becoming more essential with the ever-increasing size, complexity, and amount of assets stored

Despite the gains thus far, this only represents as small proportion of the opportunity because only a very small percentage of the potential addressable market in the enterprise is using RTLS.

The article continues with a summary of the myths we covered in a previous post.

ABI Research expects that will be a 2.5x increase in total Bluetooth RTLS deployments over the next five years, with the fastest growing segments being healthcare, warehouse and logistics, manufacturing and smart building.

Detecting Proximity Using Bluetooth Beacons in Museums

There’s new research by the Institute of Information Science and Technologies, Pisa, Italy on Detecting Proximity with Bluetooth Low Energy Beacons for Cultural Heritage. The paper starts by describing alternative technologies including Ultra-wideband (UWB), Near Field Communication (NFC) and vision.

The RE.S.I.STO project allows media on the medieval town of Pisa to be accessible via smartphones and tablets. The system is implemented using the React Native Javascript Framework to allow cross-platform aps to be created on iOS and Android.

Beacons are attached to exhibits and the paper compares two proximity detection algorithms, a ‘Distance-based Proximity Technique’ and a ‘Threshold-based Proximity Technique’. The paper describes stress, stability and calibration testing of the system.

RSSI time series of 5 tags

The researchers found a strong variation of RSSI value for different tags that they say is caused by the varying channel (frequency) used by Bluetooth LE as well as environmental issues such as obstacles, fading and signal reflections.

The system was able to successfully detect the correct artwork with an accuracy up 95% using the Distance-based Proximity Technique.

Read about Determining Location Using Bluetooth Beacons

Learn About Indoor Positioning

There’s a recent paper Review of Indoor Positioning: Radio Wave Technology that provides a great overview of indoor positioning technologies.

From a hardware perspective it covers, RFID, UWB, Bluetooth, ZigBee, IR, WiFi, ultrasonic and hybrid systems. There’s a useful comparison table of the various technologies:

The paper describes methods of using radio signals to determine position such as RSSI ranging, trilateration, angle of arrival (AOA), round trip time of flight (RTOF), phase of arrival (POA) and time of arrival (TOA).

Trilateration

It also describes methods such as fingerprint localization.

A Comparison of Beacon Locating Methods in a Retail Store

There’s a recent paper by researchers at the Department of Management Science and Technology, Athens University of Economics and Business on An Ensemble Filter for Indoor Positioning in a Retail Store Using Bluetooth Low Energy Beacons.

The paper starts with an overview of indoor positioning techniques including trilateration, fingerprinting, dead reckoning and AI machine learning. It also provides a synposis of different technologies such as RFID, WiFi and Bluetooth.

The paper explains that while fingerprinting is widely used, it faces limitations when used in dynamically changing environments. Fingerprinting requires ongoing maintenance and updating of the reference fingerprinting map that’s manually intensive and time-consuming. Fingerprinting also requires a large number of beacon reference points to perform accurate locating.

The researchers looked into positioning within a two floor (grocery) retail store. Retail stores are of of the more challenging environments as there are shoppers moving about that can affect indoor localisation

Several indoor positioning techniques were considered including fingerprinting and trilateration. The researchers implemented fingerprinting and compared it to seven established classifiers. The random forest algorithm worked the best and inspired the authors to build an ensemble classification filter with lower absolute mean and root mean squared errors.

iBeacon Deployment Parameters for Locating

Researchers from the The Hong Kong Polytechnic University have a new paper on Performance Evaluation of iBeacon Deployment for Location-Based Services in Physical Learning Spaces (pdf) that tests environmental and deployment factors, indoors and outdoors, related to using ibeacons for locating. It provides recommendations for iBeacon deployment in terms of location, density, transmission interval, fingerprint space interval and collection time.

iBeacon deployment

The paper provides a great introduction to positioning using beacon received signal strength (RSSI). It describes trilateration and fingerprinting methods for determining location.

Key insights are:

  • High temperature, strong wind and blocking by pedestrians degraded the signal strength.
  • Pedestrians traffic blocking the line of sight caused the most signal attenuation and variation.
  • High air temperature caused significant increase of packet loss that affected the RSSI.
  • Strong wind reduced the signal strength but didn’t affect the stability of signals.
  • Trees and nearby vehicle traffic didn’t have any negative effects on signals.
  • Lower error rates were observed when beacons were deployed on the ceiling as opposed to on the wall.
  • Positioning accuracy improved with ceiling placement due to the reduction of obstructions.
  • If ceilings are too high or ceiling deployment is impracticable wall mounted iBeacons should be placed as high as possible.
  • For fingerprinting, sample at 2m grid intervals for 6s to 10s at each point. Avoid having too many beacons as this won’t improve the positioning accuracy. A transmission interval of 100ms is detrimental to the positioning accuracy. 417ms is better.
  • For fingerprinting, positioning accuracy varies greatly according to the what is in the room.

The paper mentions that beacon UUID, major and minor are used to uniquely identify beacons. While this is true in the context of detecting using apps, most locating systems use gateways. Gateways use the Bluetooth MAC address to uniquely identify beacons and the advertising type, iBeacon, Eddystone or other, is irrelevant. Using gateways as receivers is also a solution to the problem of variability in receiving capability across smartphones.

The study only considered one beacon type and two receiving smartphones. At Beaconzone, we recommend experimenting with the actual hardware in the actual environment as, being wireless radio, optimum settings and can vary considerably.

Read about location accuracy

Read about Using Beacons, iBeacons for Real-time Locating Systems (RTLS)

System for Searching Factory Stock

A common problem in factories is manual searching for stock for input to manufacturing. Stock is usually stored in boxes or pallets and can be in one of many rooms, warehouses or might already be somewhere on the factory floor. A large amount of stock arrives and leaves every day leading to logistical challenges keeping up with the whereabouts of goods. Timely delivery of components or sub-assemblies is critical to ensure smooth flowing of production and making best use of factory resources.

Manual paper-based processes are extremely inefficient and prone to human error. Old fashioned RFID or barcodes are also susceptible to error because data is only as up to date as the last scan and a recent scan might not have occurred.

Bluetooth is an ideal technology for solving this problem because it provides real-time location. We previously wrote about the advantages of using beacons in industry and how Bluetooth is suitable for use on the electrically noisy factory floor.

We offer multiple solutions for tracking stock and can adapt them to your exact needs, for example integrating with your existing systems. Once you have a tracking system in place you can use it for extra purposes such as locating jobs/work orders, monitoring machine/people capacity and providing for location based instruction/tasks. Sensing open/closed, on/off and quantities such as temperature and vibration enables diagnostics, monitoring and prognostics.

Read about Asset and Pallet Tracking for Manufacturers

FIND Framework for Internal Navigation and Discovery

FIND is an open source indoor locating system for home automation, indoor local positioning and passive tracking. It uses your smartphone or laptop to pinpoint your position in your home or office with a location precision of below 10 sq ft.

FIND uses scanning of WiFi and Bluetooth:

FIND compiles these different signals can be compiled into a fingerprint which can be used to uniquely classify the current location of that device

Read the documentation, the FAQ and source code on GitHub.