September 14, 2021

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.

February 3, 2021

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.