Tagged: direction finding

Near Field Localization with Machine Learning and 7 Coherent RTL-SDRs

Thanks to Laakso Mikko and Risto Wichman researchers at the Department of Signal Processing and Acoustics in Aalto University, Finland for submitting news that their recent paper titled "Near-field localization using machine learning: an empirical study" is available on IEEE Xplore. (To access the paper you need an IEEE subscription, but we see no harm in letting individuals know that they can search for the DOI on sci-hub to get it for free).

The work described in the paper uses 7 RTL-SDR dongles with their clocks connected together. Combined with noise source calibration, this results in a coherent SDR. They then train a Deep Neural Network to perform near field localization using an antenna array. If you are interested, we have out own 5-channel coherent SDR called "KrakenSDR" which will soon be released for crowd funding. The abstract reads:

Estimation methods for passive near-field localization have been studied to an appreciable extent in signal processing research. Such localization methods find use in various applications, for instance in medical imaging. However, methods based on the standard near-field signal model can be inaccurate in real-world applications, due to deficiencies of the model itself and hardware imperfections. It is expected that deep neural network (DNN) based estimation methods trained on the nonideal sensor array signals could outperform the model-driven alternatives. In this work, a DNN based estimator is trained and validated on a set of real world measured data. The series of measurements was conducted with an inexpensive custom built multichannel software-defined radio (SDR) receiver, which makes the nonidealities more prominent. The results show that a DNN based localization estimator clearly outperforms the compared model-driven method.

The paper notes that the code used in the experiments is open source and available on GitHub.

If you're interested, we also posted about Laakso's previous work on beamforming with a phase coherent 21-channel RTL-SDR array back in February.

Examples of MUSIC pseudospectra. The units are [m] for range r on the vertical axis and degrees for θ on the horizontal axis. Red crosses mark the true location and black circles the NFLOPnet estimated location.

Sparse Array Beamforming with a Phase Coherent 21-Channel RTL-SDR Array

Thank you to Laakso Mikko a PhD student at Aalto University School of Electrical Engineering for submitting news about his research group's latest paper involving a 21-channel phase coherent RTL-SDR system. Laakso writes that he an his colleagues have built a (massive) multichannel receiver array from RTL-SDRs to use in low-budget research. The paper presented at EUSIPCO2020 can be found at IEEE, and for free on their research portal (direct pdf link). The code is also entirely open source and available on GitHub.

Phase coherent SDRs enable interesting applications such as radio direction finding (RDF), passive radar and beam forming.

We introduce a modular and affordable coherent multichannel software-defined radio (SDR) receiver and demonstrate its performance by direction-of-arrival (DOA) estimation on signals collected from a 7 X 3 element uniform rectangular array antenna, comparing the results between the full and sparse arrays. Sparse sensor arrays can reach the resolution of a fully populated array with reduced number of elements, which relaxes the required structural complexity of e.g. antenna arrays. Moreover, sparse arrays facilitate significant cost reduction since fewer expensive RF-IF front ends are needed. Results from the collected data set are analyzed with Multiple Signal Classification (MUSIC) DOA estimator. Generally, the sparse array estimates agree with the full array.

Mikko notes that his next paper on applying deep neural nets to the problem of near-field localization will be presented at this years VTC2021 conference, so we are looking forward to that paper too. 

21 element array connected to a 21-input phase coherent RTL-SDR array

KerberosSDR with DF-Aggregator Direction Finding Attempt

Back in October we first posted about the release of DF-Aggregator, a program by Corey (ckoval7) which can be used to receive and plot data from multiple KerberosSDR direction finding units. 

If you weren't already aware KerberosSDR is our 4-channel phase coherent capable RTL-SDR unit that we previously crowdfunded back in 2018. With a 4-channel phase coherent RTL-SDR interesting applications like radio direction finding (RDF), passive radar and beam forming become possible. It can also be used as four separate RTL-SDRs for multichannel monitoring.

In one of his latest DragonOS videos, Aaron has been testing out DF-Aggregator. In his test he had two vehicles driving around each with a KerberosSDR and antenna array, with both using a mobile data connection to send data to a remote PC running DF-Aggregator. The results were successful, with the team being able to determine the location of a broadcast FM transmitter to within a few meters after a short drive.

DragonOS Focal KerberosSDR x2 Mobile w/ DF-Aggregator Direction Finding Attempt 2 (Better Results)

DF Aggregator: New Software for Networked Radio Direction Finding with KerberosSDR

Over on GitHub Corey (ckoval7) has released a new open source radio direction program called "DF Aggregator". This software is able to receive bearings and locations from multiple remotely networked KerberosSDRs, and display them on a map.

If you weren't already aware KerberosSDR is our 4-channel phase coherent capable RTL-SDR unit that we previously crowdfunded back in 2018. With a 4-channel phase coherent RTL-SDR interesting applications like radio direction finding (RDF), passive radar and beam forming become possible. It can also be used as four separate RTL-SDRs for multichannel monitoring.

A single KerberosSDR combined with an antenna array is able to determine a bearing towards a signal source. By using multiple KerberosSDR units spread over a large area it is possible to triangulate the location of a transmitter and display it on a map. Corey's software uses a modified branch of our open source KerberosSDR code in order to generate a modified XML page that the mapping software polls for updated data. Some instructions on it's use are available on our forums and on the GitHub.

The image below shows three KerberosSDR stations on the map, and two transmitter locations that have been triangulated using the bearings from the three distributed KerberosSDR units. 

Alternative direction finding mapping software includes our Android App (mostly for mobile vehicular use), and RDF Mapper with our adapter code.

DF Aggregator: KerberosSDR Direction Finding Mapping Software

A 3D Printed Automatically Adjusting Linear Antenna Array for KerberosSDR Radio Direction Finding

Over on GitLab Josh Conway has released a design for an automatically adjusting antenna array which can be used with radio direction finding capable SDRs like our KerberosSDR. KerberosSDR is a SDR consisting of four RTL-SDRs connected to the same oscillator, a USB hub, a built in noise source and calibration hardware which allows software to use the four RTL-SDRs coherently. Coherent operation of SDRs enables interesting applications such as radio direction finding, passive radar and beam forming.  

With coherent antenna array based direction finding, the optimal spacing between the antenna elements is proportional to the wavelength of the frequency being received. If you want to do RF direction finding on different frequencies, either multiple antenna arrays with different element spacings, or manually adjusting the antenna array with each frequency change is required.

Josh's design automates this problem with an antenna array that can adjust the spacing automatically. The design puts the antennas on an extending pantograph arm whose length is controlled via a threaded rod connected to a stepper motor. An Arduino microcontroller controls the stepper, thus allowing the spacing to be adjusted automatically. 

A Pantograph Antenna Array for Direction Finding

A full description of the build is provided in the document on GitLab titled "provisional_patent_application.pdf". From Twitter it appears that Josh (@CrankyLinuxUser) was unable to secure a patent for this design, so he has released the design for free under AGLP3. Most of the parts are 3D printed, and the CAD stl files all appear to be available on the GitLab. The Arduino microcontroller firmware is also available.

Thank you to Josh for releasing this design!

Pantograph Antenna Array for Direction Finding

KerberosSDR Currently On Sale – Ends Midnight Sunday 1 Nov

KerberosSDR is currently on sale for US$150 over on the Othernet store

Sale ends 1 NOV SUNDAY MIDNIGHT (PT)!

If you weren't aware KerberosSDR is our 4-channel phase coherent capable RTL-SDR unit that we previously crowdfunded back in 2018. With a 4-channel phase coherent RTL-SDR interesting applications like radio direction finding (RDF), passive radar and beam forming become possible. It can also be used as 4 separate RTL-SDRs for multichannel monitoring.

In previous posts we've shown some interesting experiments performed with the KerberosSDR. For example:

We note that V2 of our KerberosSDR demo software is also on the way but a little delayed. We are aiming to release a beta around the end of the year, or early next year at the latest. The new software will have better handling of bursty intermittent signals, and paves the way for new developments coming in 2021 such as combined passive radar direction finding.

The KerberosSDR: 4x Tuner Coherent Capable RTL-SDR
The KerberosSDR: 4x Tuner Coherent Capable RTL-SDR
KerberosSDR Android App for Direction Finding
KerberosSDR Android App for Direction Finding
KerberosSDR Passive Radar Display Peak Hold
An Example of KerberosSDR Passive Radar Display Peak Hold Displaying Aircraft and Road Tracks

 

DragonOS: KerberosSDR Bearing Server Setup with RDFMapper

DragonOS is a ready to use Linux OS image that includes many SDR programs preinstalled and ready to use. The creator Aaron also runs a YouTube channel that has multiple tutorial videos demonstrating software built into DragonOS.

In a recent video Aaron has provided a two part tutorial showing how to set up and use KerberosSDR with the RDFMapper software on DragonOS. This allows you to network multiple KerberosSDR units together and display each units radio bearing on the same map. Two or more bearings crossing can be used to determine the location of a transmitter. In the future Aaron will use this setup to have multiple mobile and fixed  KerberosSDR units connected together via Zero Tier. Aaron writes:

In this first video I show how to install software to control the KerberosSDR – A 4-Channel Phase Coherent RTL-SDR for Passive Radar, Direction Finding and more onto DragonOS Focal (Lubuntu 20.04 based). A fork of the main code is required due to some changes in dependencies and packages. This fork is only meant for or at least tested on Ubuntu, Kubuntu, and Lubuntu 20.04.

I also show some issues you may experience due to poor quality USB cables, insufficient power, and/or issues with USB ports being used to power the KerberosSDR or connect to it.

In this second video I show how to install and use RDFMapper with the KerberosSDR software and Android App. I also cover some common problems I've experienced with the current KerberosSDR Android App.

Recommended to watch the first video if you are planning to run the KerberosSDR on a PC or a SBC like the Raspberry Pi. This video and setup procedure can be adapted to use the Raspberry Pi/Android App instead of a PC. 

I plan to make a couple more videos on this topic. By the end, it should be possible to have multiple KerberosSDR stations, both mobile and stationary, linked to one instance of RDFMapper over Zero Tier all simultaneously performing direction finding on one frequency.

KerberosSDR is our 4-channel phase coherent capable RTL-SDR unit that we previously successfully crowdfunded back in 2018.  With a 4-channel phase coherent RTL-SDR interesting applications like radio direction findingpassive radar and beam forming become possible. It can also be used as 4 separate RTL-SDRs for multichannel monitoring. KerberosSDR is currently in stock and available on the Othernet store.

DragonOS Focal KerberosSDR setup (20.04 fork, x86_64 Laptop) part 1

DragonOS Focal KerberosSDR w/ Bearing Server setup (RDFMapper, Android App, x86_64 Laptop) part 2

KerberosSDR Tracking a Drone Carrying an FM Beacon

KerberosSDR is our 4-channel phase coherent capable RTL-SDR unit that we previously successfully crowdfunded back in 2018.  With a 4-channel phase coherent RTL-SDR interesting applications like radio direction findingpassive radar and beam forming become possible. It can also be used as 4 separate RTL-SDRs for multichannel monitoring. KerberosSDR is currently in stock and available on the Othernet store.

Recently Zuokun Li et al from the University of East China Normal University published an open access conference paper that documents their results at using a KerberosSDR to track a drone. As typical drone control frequencies at 2.4 GHz are outside the range of the RTL-SDRs used on the KerberosSDR, they carried a 446 MHz FM beacon on the drone.

In their experiment they set up both circular and linear antenna arrays for the KerberosSDR, then flew the drone in front of the antenna array while recording the bearings calculated by the KerberosSDR system. The results showed that the KerberosSDR was able to successfully track the drone's bearing with either antenna array, however the linear array produced more accurate results as expected.

We note that a linear array cannot differentiate if an object is in front or behind the array. However, if this knowledge is known it can be used instead of a circular array to get more accurate bearings that are less affected by multipath.

If you're interested in this, you might also like our articles on using a KerberosSDR to track a weather balloon, to locate a P25 transmitter, or our Android app in car demos

The KerberosSDR + Drone Setup
Results from the drones at three locations.