Location – Page 5 – BeaconZone Blog

2D vs 3D Bluetooth AoA Direction Finding

Current AoA locators only have antennas in one plane which means they can only provide angles in two (elevation and azimuth) dimensions. A locator therefore sees assets as being somewhere along an imaginary line or ray emanating from the locator.

If the height, perhaps of a worn lanyard, is known and tends to not change much then it’s possible to estimate the 3D location. Obviously, if the person climbs some steps for kneels down then the location becomes less accurate in all dimensions.

The other solution is to use multiple locators to triangulate two or more locator lines. This is more accurate because it doesn’t rely on a known average height and provides the opportunity to use more than two locators to increase accuracy still further.

3D provides the best accuracy. 2D location allows use of fewer locators with the trade off of less accuracy. For example, the four locators in the Minew AoA kit can be placed in different rooms or areas rather than covering an overlapping area. 2D location also has the implicit advantage of supporting more beacons because the locators and subsequent systems are doing less work.

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Bluetooth Direction Finding Antenna Arrays

Bluetooth direction finding Angle of Arrival (AoA) uses multiple antenna in one device that uses the phase difference of signals received at different antenna to determine the angle and hence location of a beacon.

We are seeing a variety of designs but most use printed circuit board patches for antennas for reasons of cost and compactness.

All these designs use a radio frequency switch that switches each antenna, in turn, to just one Bluetooth chip to save cost and complexity. You can see this in some of the designs as tracks leading from each antenna to one chip and then one track from that chip to the Bluetooth system on a chip (SoC). The switch is very fast, of the order of 1 microsecond, to capture the same origin signal across all antennas.

Take care to purchase production-ready hardware. While there are currently many antenna array designs, some are just prototype or reference boards not intended for production. The software accompanying prototype or reference boards also tends to be non-existent and in cases where it does exist, it won’t scale to more than a few beacons.

In practice, a location engine employing AoA radiogoniometry is required to process the radio signals from the Bluetooth SoC. The radio signals are also wirelessly noisy and have to be processed to mitigate reflections, interference and signal spread delays. Additional processing is needed to triangulate the angles from multiple locators. All this isn’t trivial given that the algorithms are computationally expensive and have to be executed extremely quickly to support a large number of beacons.

Minew AoA Kit

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RSSI vs AoA Bluetooth Asset Tracking

In a previous post on iBeacon Microlocation Accuracy we explained how assets can be tracked using Bluetooth Received Signal Strength (RSSI) or Angle of Arrival (AoA). We advised working out what accuracy is needed prior to seeking out an appropriate solution. However, accuracy isn’t always the only consideration and here is a more complete list of factors.

Accuracy

RSSI asset tracking can achieve accuracies of about 1.5m within a shorter range confined space and 5m at the longer distances. RSSI zone-based systems where beacons are found to the nearest gateway, are accurate to the inter-gateway distance that can be of the order of cm. However having such as large gateway density is usually only practical for very small areas.

AoA asset tracking achieves sub-metre accuracy. The accuracy depends most on the distance between the locator and beacon but is also affected by the locator hardware quality, radio signal noise, surfaces causing radio reflections, the accuracy of locator placement and beacon orientation.

Maximum number of beacons

AoA-based asset tracking produces and requires much more data which means the locators and software systems have to deal with more data. The data throughput for both types of system depends on the required minimum latency that in-turn depends on how often the beacons advertise. RSSI-based systems support up to high tens of thousands of beacons while AoA supports thousands of beacons.

Beacon variety and IoT

RSSI-based systems can use any beacon and hence support a large range of sensor beacons that can detect movement (accelerometer), movement (started/stopped moving), button press, temperature, humidity, air pressure, light level, open/closed (magnetic hall effect), proximity (PIR), proximity (cm range), fall detection, smoke, natural gas and water leak.

AoA beacons are more specialised and currently only support limited IoT sensing such as movement (accelerometer) and button press.

Cost

AoA locators, gateways and beacons are more complex and are therefore more costly. AoA also needs more locators/gateways per sq area. Hence, AoA systems are x3 to x4 more expensive than RSSI systems.

Setup effort

The accuracy of AoA requires that locators be more carefully positioned than for RSSI, in particular the site and AoA locator positions need to be carefully measured.

Beaconzone supplies both RSSI and AoA systems. Contact us to determine the best type of system for your needs.

Using Bluetooth Mesh and Space, Time, Frequency Diversity to Improve Locating Accuracy

There’s new research from the Department of Electrical Engineering, University of North Texas, USA on Measurement and Analysis of RSS Using Bluetooth Mesh Network for Localization Applications. The paper explains how received signal strength (RSSI) based solutions have accuracy limitations in radio multipath (radio reflective) environments. It describes a solution that improves accuracy using Bluetooth mesh and Bluetooth channel-based processing.

A system was created that exploits the space, time, and frequency diversities in measurements. Different Bluetooth channels have different fading effects.

Advertising was modified to make it Bluetooth channel-aware to be able to differentiate the fading effects. It was possible to reduce the residual fitting errors in the path loss models by using a space-time-frequency diversity combining scheme.

The system was demonstrated using ESP32 BLE modules.

The system significantly reduced the residual linear regression fitting errors in path loss models. It was able to more accurately use RSSI to measure the distance between the transmitter and receiver. The researchers demonstrated it’s possible to implement the proposed multi-receiver configuration and the diversity combining scheme using commercial off-the-shelf standard BLE devices.

Probabilistic vs Neural Network iBeacon Positioning

There’s new research by ITMO University, Russia on the Implementation of Indoor Positioning Methods: Virtual Hospital Case. The paper describes how positioning can be used to discover typical pathways, queues and bottlenecks in healthcare scenarios. The researchers implemented and compared two ways to mitigate noise in Bluetooth beacon RSSI data.

The probabilistic and neural network methods both use past recorded data to compare with new data. This is known as fingerprinting. The neural network method is less complex when there’s need to scale to locating many objects. The researchers tested the methods at the outpatient department of the cardio medical unit of Almazov National Medical Research Centre.

Comparison of the methods showed they give approximately the same error of between 0.96m and 2.11m. However, the neural network-based approach significantly increased performance.

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.

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

Bluetooth (BLE) vs Ultra-Wideband (UWB) for Locating

We previously mentioned how cost, battery life and second sourcing are the main advantages of Bluetooth over Ultra-Wideband (UWB). An additional, rarely mentioned, advantage is scalability.

Servers that process Bluetooth or Ultra-Wideband support a particular maximum throughout. The rate at which updates reach systems depends on the number of assets, how often they report and the area covered (number of gateways/locators). Each update needs to be processed and compared with very recent updates from other gateways/locators to determine an asset’s position.

For Bluetooth, updates tend to be of the order of 2 to 10 seconds but in some scenarios can be 30 seconds or more for stock checking where assets rarely move. Motion triggered beacons can be used to provide variable update periods depending on an asset’s movement patterns. This allows Bluetooth to support high 10s of thousands of assets without overloading the server.

For Ultra-Wideband, refresh rates tend to be of the order of hundreds of milliseconds (ms) thus stressing the system with more updates/sec. This is why most Ultra-Wideband systems support of the order of single digit thousands of assets and/or smaller areas. More frequent advertising is also the reason why the tags use a lot of battery power.

How does all this change with the new Bluetooth 5.1 direction finding standard? The standard was published in January 2019 but solutions have been slow to come to the market. The products that have so far appeared all have shortcomings that mean we can’t yet recommend them to our customers. Aside from this, in evaluating these products we are seeing compromises compared to traditional Bluetooth locating using received signal strength (RSSI).

Bluetooth 5.1 direction finding needs more complex hardware that, at least in current implementations, are reporting much more often. The server has to do complex processing to convert phase differences to angles and angles to positions thus supporting fewer updates/sec. Bluetooth direction finding is looking more like UWB in that cost, scalability and battery life are sacrificed for increased accuracy. Direction finding locators are currently x6 to x10 more costly than existing Bluetooth/WiFi gateways. Beacon battery life is reduced due to the more frequent and longer advertising. We are seeing Bluetooth 5.1 direction finding being somewhere between traditional Bluetooth RSSI-based locating and Ultra-Wideband in terms of flexibility vs accuracy.

Despite these intrinsic compromises, Bluetooth direction finding is set to provide strong competition to UWB for high accuracy applications. We are already seeing UWB providers seeking to diversify into Bluetooth to provide lower cost, longer battery life and greater scalability.

Using Multi Bluetooth iBeacon Trilateration For Increased Accuracy

There’s a new paper from the journal Telkomnika Telecommunication, Computing, Electronics and Control on Smartphone indoor positioning based on enhanced BLE beacon multi-lateration (pdf). The paper by Ngoc-Son Duong of Vietnam National University describes a relatively simple method to improve location accuracy.

The paper starts by describing trilateration and the author voices the opinion that another method, fingerprinting, requires a lot of effort and isn’t feasible for practical implementation.

The new method makes use of the fact that accuracy is usually good when the received signal strength (RSSI) is -70 dBm or better. The use of more beacons and basing calculations on ‘reliable circles’ of higher signal strength, when available, provides for more accuracy.

The data is also filtered using a Kalman filter to reduce signal noise by about 37%.

Read about Determining Location Using Bluetooth Beacons

Bluetooth Location Services Infographic

Indoor Navigation Using Bluetooth LE

There’s a new article from the Icontech International Journal of Surveys, Engineering, Technology on Indoor Position Routing (IPR) and Data Monitor Using Bluetooth Low Energy Technology by researchers at the Hasan Kalyoncu University, Institute of Science, Electrical & Electronics Engineering, Gaziantep, Turkey.

This article is different because it considers navigation as opposed to just locating. It explains the advantages of Bluetooth LE over WiFi and also compares with RFID:

Trilateration, Received Signal Strength Indicator (RSSI) and Decibel-milliwatts (dBmW) are explained and how these fit into locating position.

The article describes a system created for navigation that uses iBeacon sensor nodes, an Android device and app.

Read Determining Location Using Bluetooth Beacons

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