533 research outputs found
Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation
We introduce an interpretable deep learning approach for direction of arrival
(DOA) estimation with a single snapshot. Classical subspace-based methods like
MUSIC and ESPRIT use spatial smoothing on uniform linear arrays for single
snapshot DOA estimation but face drawbacks in reduced array aperture and
inapplicability to sparse arrays. Single-snapshot methods such as compressive
sensing and iterative adaptation approach (IAA) encounter challenges with high
computational costs and slow convergence, hampering real-time use. Recent deep
learning DOA methods offer promising accuracy and speed. However, the practical
deployment of deep networks is hindered by their black-box nature. To address
this, we propose a deep-MPDR network translating minimum power distortionless
response (MPDR)-type beamformer into deep learning, enhancing generalization
and efficiency. Comprehensive experiments conducted using both simulated and
real-world datasets substantiate its dominance in terms of inference time and
accuracy in comparison to conventional methods. Moreover, it excels in terms of
efficiency, generalizability, and interpretability when contrasted with other
deep learning DOA estimation networks.Comment: 10 pages, 10 figure
Super-Resolution Radar Imaging with Sparse Arrays Using a Deep Neural Network Trained with Enhanced Virtual Data
This paper introduces a method based on a deep neural network (DNN) that is
perfectly capable of processing radar data from extremely thinned radar
apertures. The proposed DNN processing can provide both aliasing-free radar
imaging and super-resolution. The results are validated by measuring the
detection performance on realistic simulation data and by evaluating the
Point-Spread-function (PSF) and the target-separation performance on measured
point-like targets. Also, a qualitative evaluation of a typical automotive
scene is conducted. It is shown that this approach can outperform
state-of-the-art subspace algorithms and also other existing machine learning
solutions. The presented results suggest that machine learning approaches
trained with sufficiently sophisticated virtual input data are a very promising
alternative to compressed sensing and subspace approaches in radar signal
processing. The key to this performance is that the DNN is trained using
realistic simulation data that perfectly mimic a given sparse antenna radar
array hardware as the input. As ground truth, ultra-high resolution data from
an enhanced virtual radar are simulated. Contrary to other work, the DNN
utilizes the complete radar cube and not only the antenna channel information
at certain range-Doppler detections. After training, the proposed DNN is
capable of sidelobe- and ambiguity-free imaging. It simultaneously delivers
nearly the same resolution and image quality as would be achieved with a fully
occupied array.Comment: 15 pages, 12 figures, Accepted to IEEE Journal of Microwave
Coherent, super resolved radar beamforming using self-supervised learning
High resolution automotive radar sensors are required in order to meet the
high bar of autonomous vehicles needs and regulations. However, current radar
systems are limited in their angular resolution causing a technological gap. An
industry and academic trend to improve angular resolution by increasing the
number of physical channels, also increases system complexity, requires
sensitive calibration processes, lowers robustness to hardware malfunctions and
drives higher costs. We offer an alternative approach, named Radar signal
Reconstruction using Self Supervision (R2-S2), which significantly improves the
angular resolution of a given radar array without increasing the number of
physical channels. R2-S2 is a family of algorithms which use a Deep Neural
Network (DNN) with complex range-Doppler radar data as input and trained in a
self-supervised method using a loss function which operates in multiple data
representation spaces. Improvement of 4x in angular resolution was demonstrated
using a real-world dataset collected in urban and highway environments during
clear and rainy weather conditions.Comment: 28 pages 10 figure
Localization and tracking of electronic devices with their unintended emissions
The precise localization and tracking of electronic devices via their unintended emissions has a broad range of commercial and security applications. Active stimulation of the receivers of such devices with a known signal generates very low power unintended emissions. This dissertation presents localization and tracking of multiple devices using both simulation and experimental data in the form of five papers.
First the localization of multiple emitting devices through active stimulation under multipath fading with a Smooth MUSIC based scheme in the near field region is presented. Spatial smoothing helps to separate the correlated sources and the multipath fading and results confirm improved accuracy. A cost effective near-field localization method is proposed next to locate multiple correlated unintended emitting devices under colored noise conditions using two well separated antenna arrays since colored noise in the environment degrades the subspace-based localization techniques.
Subsequently, in order to track moving sources, a near-field scheme by using array output is introduced to monitor direction of arrival (DOA) and the distance between the antenna array and the moving source. The array output, which is a nonlinear function of DOA and distance information, is employed in the Extended Kalman Filter (EKF). In order to show the near- and far-field effect on estimation accuracy, computer simulation results are included for localization and tracking techniques.
Finally, an L-shaped array is constructed and a suite of schemes are introduced for localization and tracking of such devices in the three-dimensional environment. Experimental results for localization and tracking of unintended emissions from single and multiple devices in the near-field environment of an antenna array are demonstrated --Abstract, page iv
Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars
A novel framework to enhance the angular resolution of automotive radars is
proposed. An approach to enlarge the antenna aperture using artificial neural
networks is developed using a self-supervised learning scheme. Data from a high
angular resolution radar, i.e., a radar with a large antenna aperture, is used
to train a deep neural network to extrapolate the antenna element's response.
Afterward, the trained network is used to enhance the angular resolution of
compact, low-cost radars. One million scenarios are simulated in a Monte-Carlo
fashion, varying the number of targets, their Radar Cross Section (RCS), and
location to evaluate the method's performance. Finally, the method is tested in
real automotive data collected outdoors with a commercial radar system. A
significant increase in the ability to resolve targets is demonstrated, which
can translate to more accurate and faster responses from the planning and
decision making system of the vehicle.Comment: Under revision at IEEE Transactions on Vehicular Technolog
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