Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 60, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2022.3169642
Keywords
Radar; Azimuth; Radar antennas; Chirp; Location awareness; Radar signal processing; Radar detection; Deep learning (DL); frequency-modulated continuous-wave (FMCW) radar; millimeter-wave (mmWave) radar; object localization; radar signal processing
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This study proposes a deep learning-based approach for localizing small moving objects with a single millimeter-wave frequency-modulated continuous-wave radar. By combining classical transforms with neural networks, the method achieves accurate predictions of object locations and improves the performance over state-of-the-art approaches.
We propose a deep learning-based approach to localizing a small moving object with a single millimeter-wave frequency-modulated continuous-wave (FMCW) radar. The main challenge that foils conventional localization techniques, such as 3-D fast Fourier transform (3-D-FFT), Pisarenko method, multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance technique (ESPRIT), Capon's method, and Burg's method, is the low signal-to-noise ratio of the reflected signal from millimeter-sized objects. Our key idea is to combine useful but noisy features from classical transforms [e.g., fast Fourier transform (FFT)] with neural networks that can refine and interpret those features into range and angle estimates by training on a large dataset of examples. Importantly, our networks were designed to be translation-equivariant, which enables accurate predictions of unseen object locations and improves the range and azimuth root mean square error (RMSE) scores by 34%-46% and 41%-60%, respectively, over state-of-the-art approaches. This pilot study establishes a new baseline for small-object tracking using FMCW and can enable tracking of small animals, such as ants inside the colony for behavior studies. Our first FMCW-small-object dataset and the source code are publicly available on https://github.com/shikuzen/RA-CNN.
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