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FMCW Radar Sensing for Indoor Drones Using Learned Representations
Robotics · Submitted 6 Jan 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We show with real radar data that, utilizing our learned representations, we match the performance of conventional radar processing techniques and that our model can be trained on different input modalities such as raw ADC samples of only two consecutively transmitted chirps.
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The abstract, sentence by sentence
Frequency-modulated continuous-wave (FMCW) radar is a promising sensor technology for indoor drones as it provides range, angular as well as Doppler-velocity information about obstacles in the environment. Recently, deep learning approaches have been proposed for processing FMCW data, outperforming traditional detection techniques on range-Doppler or range-azimuth maps. However, these techniques come at a cost; for each novel task a deep neural network architecture has to be trained on high-dimensional input data, stressing both data bandwidth and processing budget. In this paper, we investigate unsupervised learning techniques that generate low-dimensional representations from FMCW radar data, and evaluate to what extent these representations can be reused for multiple downstream tasks. To this end, we introduce a novel dataset of raw radar ADC data recorded from a radar mounted on a flying drone platform in an indoor environment, together with ground truth detection targets. We show with real radar data that, utilizing our learned representations, we match the performance of conventional radar processing techniques and that our model can be trained on different input modalities such as raw ADC samples of only two consecutively transmitted chirps.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Frequency-modulated continuous-wave (FMCW) radar is a promising sensor technology for indoor drones as it provides range, angular as well as Doppler-velocity information about obstacles in the environment. | background 1.00 | 0.05 | 0.02 | asserted 0.84 |
| s1 | Recently, deep learning approaches have been proposed for processing FMCW data, outperforming traditional detection techniques on range-Doppler or range-azimuth maps. | background 1.00 | 0.04 | 0.02 | asserted 0.99 |
| s2 | However, these techniques come at a cost; for each novel task a deep neural network architecture has to be trained on high-dimensional input data, stressing both data bandwidth and processing budget. | gap 0.47 | 0.05 | 0.03 | asserted 1.00 |
| s3 | In this paper, we investigate unsupervised learning techniques that generate low-dimensional representations from FMCW radar data, and evaluate to what extent these representations can be reused for multiple downstream tasks. | contribution 0.97 | 0.61 | 0.09 | unclear 0.53 |
| s4 | To this end, we introduce a novel dataset of raw radar ADC data recorded from a radar mounted on a flying drone platform in an indoor environment, together with ground truth detection targets. | contribution 0.76 | 0.97 | 0.03 | asserted 0.98 |
| s5 | We show with real radar data that, utilizing our learned representations, we match the performance of conventional radar processing techniques and that our model can be trained on different input modalities such as raw ADC samples of only two consecutively transmitted chirps. | result 1.00 | 0.97 | 0.81 | asserted 1.00 |
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Original abstract
Frequency-modulated continuous-wave (FMCW) radar is a promising sensor technology for indoor drones as it provides range, angular as well as Doppler-velocity information about obstacles in the environment. Recently, deep learning approaches have been proposed for processing FMCW data, outperforming traditional detection techniques on range-Doppler or range-azimuth maps. However, these techniques come at a cost; for each novel task a deep neural network architecture has to be trained on high-dimensional input data, stressing both data bandwidth and processing budget. In this paper, we investigate unsupervised learning techniques that generate low-dimensional representations from FMCW radar data, and evaluate to what extent these representations can be reused for multiple downstream tasks. To this end, we introduce a novel dataset of raw radar ADC data recorded from a radar mounted on a flying drone platform in an indoor environment, together with ground truth detection targets. We show with real radar data that, utilizing our learned representations, we match the performance of conventional radar processing techniques and that our model can be trained on different input modalities such as raw ADC samples of only two consecutively transmitted chirps.
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Patel et al. [ref] similarly utilize a convolutional neural network for object classification in a supervised manner, while Wheeler et al. [ref] proposed a VAE architecture fine-tuned to range-azimuth maps of autonomous driving data to learn an accurate radar sensor model.
Deep Stochastic Radar Models · Related work · 0.93
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