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Demonstrating superresolution in radar range estimation using a denoising autoencoder

Robert Czupryniak1,2, Abhishek Chakraborty1,2, Andrew N. Jordan2,3,4,1,*, and John C. Howell2,3,†

  • *Contact author: jordan@chapman.edu
  • †Contact author: johhowell@chapman.edu
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Phys. Rev. Research 8, 033157 – Published 10 August, 2026

DOI: https://doi.org/10.1103/w6m6-n6rk

Abstract

We apply machine learning methods to demonstrate radar range superresolution using a denoising autoencoder trained without supervision. Focusing on the estimation of a single physical parameter, the separation between two scatterers in the subwavelength regime, we constrain the network to a one-dimensional bottleneck layer with its size matched to the parameter dimensionality. We find that the bottleneck layer forms a reproducible, monotonic mapping with the true separation, showing that the network learns a low-dimensional representation directly aligned with the underlying physical parameter. We further show that this representation preserves the Fisher information of the signal, indicating that the network recovers a physically meaningful representation for parameter estimation. We investigate the behavior of the bottleneck layer for the following types of pulses: a traditional sinc pulse, a bandlimited triangle-type pulse, and a theoretically near-optimal pulse created from a spherical Bessel function basis. Our results demonstrate that neural networks can discover physically meaningful compressed representations without explicit supervision, with performance ultimately governed by the information content of the signal.

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Phys. Rev. Research 8, 033157– Published 10 August, 2026
  • Received 21 June 2025
  • Accepted 8 July 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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