Heat-Assisted Detection and Ranging (HADAR)

Overview

Thermal radiation, an omnipresent phenomenon characterized by electromagnetic wave emission from objects above absolute zero, has consistently intrigued scientific exploration throughout history and profoundly influences various technological applications. Traditionally, the primary utilization of thermal radiation has been limited to fields such as lighting, cooling, and energy harvesting. However, the true potential of thermal radiation extends far beyond these energy-oriented applications. Every object imprints a unique signature within its emitted thermal radiation. These signatures, distinguished by their wide-ranging spectral and polarimetric characteristics, represent a rich information source about the emitting objects. The goal of my research is to offer novel prospective and platforms to expand our perception and utilization of the spectral and polarimetric attributes of thermal radiation. It seeks to augment the conventional understanding of thermal radiation as merely an energy source, underlining its immense potential as an information carrier.

Heat-assisted detection and ranging

a, Fully passive HADAR makes use of heat signals, as opposed to active sonar, radar, LiDAR and quasi-passive cameras. Atmospherical transmittance window (white area) and temperature of the scene determine the working wavelength of HADAR. b, HADAR takes thermal photon streams as input, records hyperspectral-imaging heat cubes, addresses the ghosting effect through TeX decomposition and generates TeX vision for improved detection and ranging. c, TeX vision demonstrated on our HADAR database and outdoor experiments clearly shows that HADAR sees textures through the darkness with comprehensive understanding of the scene.

Machine perception uses advanced sensors to collect information about the surrounding scene for situational awareness. State-of-the-art machine perception using active sonar, radar and LiDAR to enhance camera vision faces difficulties when the number of intelligent agents scales up. Exploiting omnipresent heat signal could be a new frontier for scalable perception. However, objects and their environment constantly emit and scatter thermal radiation, leading to textureless images famously known as the ‘ghosting effect’. Thermal vision thus has no specificity limited by information loss, whereas thermal ranging—crucial for navigation—has been elusive even when combined with artificial intelligence (AI). In this work, we propose and experimentally demonstrate heat-assisted detection and ranging (HADAR) overcoming this open challenge of ghosting and benchmark it against AI-enhanced thermal sensing. HADAR not only sees texture and depth through the darkness as if it were day but also perceives decluttered physical attributes beyond RGB or thermal vision, paving the way to fully passive and physics-aware machine perception. We develop HADAR estimation theory and address its photonic shot-noise limits depicting information-theoretic bounds to HADAR-based AI performance. HADAR ranging at night beats thermal ranging and shows an accuracy comparable with RGB stereovision in daylight. Our automated HADAR thermography reaches the Cramér–Rao bound on temperature accuracy, beating existing thermography techniques. Our work leads to a disruptive technology that can accelerate the Fourth Industrial Revolution (Industry 4.0) with HADAR-based autonomous navigation and human–robot social interactions.

Reference:

Heat-Assisted Heat Assisted Detection and Ranging, Nature, 619, pages 743-748, 2023

Bao, F., Wang, X., Sureshbabu S., Sreekumar, G., Yang, L., Aggarwal, V., Boddeti, V., and Jacob, Z.

(Journal Cover; Highlighted by Nature, Science, Nature Photonics, Optics & Photonics News etc.)

Spinning metasurface stack for spectro-polarimetric thermal imaging

Long-wave infrared (LWIR) spectro-polarimetric thermal imaging. (a) Room-temperature blackbody radiation (shown in red) and atmospheric transmission spectrum (shown as a shaded area). The LWIR spectral region is crucial for thermal imaging due to its peaked room-temperature thermal radiation spectrum and the atmospheric transparency window. (b)–(d) Conventional methods for spectral imaging, such as using (b) a mosaic sensor, (c) a filter wheel, or (d) interferometry, either pose limitations or are infeasible for LWIR thermal imaging. (e) In this study, we propose a new approach for spectro-polarimetric thermal imaging, achieved by combining large-area spinning metasurfaces and compressive sensing reconstruction algorithms.

Spectro-polarimetric imaging in the long-wave infrared (LWIR) region plays a crucial role in applications from night vision and machine perception to trace gas sensing and thermography. However, the current generation of spectro-polarimetric LWIR imagers suffers from limitations in size, spectral resolution, and field of view (FOV). While meta-optics-based strategies for spectro-polarimetric imaging have been explored in the visible spectrum, their potential for thermal imaging remains largely unexplored. In this work, we introduce an approach for spectro-polarimetric decomposition by combining large-area stacked meta-optical devices with advanced computational imaging algorithms. The co-design of a stack of spinning dispersive metasurfaces along with compressive sensing and dictionary learning algorithms allows simultaneous spectral and polarimetric resolution without the need for bulky filter wheels or interferometers. Our spinning-metasurface-based spectro-polarimetric stack is compact (<10×10×10cm) and robust, and it offers a wide field of view (20.5°). We show that the spectral resolving power of our system substantially enhances performance in machine learning tasks such as material classification, a challenge for conventional panchromatic thermal cameras. Our approach represents a significant advance in the field of thermal imaging for a wide range of applications including heat-assisted detection and ranging (HADAR).

Spectro-polarimetric thermal imaging results. (a) An optical image of the “PURDUE” imaging target that is constructed from titanium letters on a glass substrate (75mm×50mm). Inset, a zoomed-in optical microscope image of the micro-structures in the letters, which generate distinctive spectral and polarimetric signatures. (b)–(e) Reconstructed spectra of four representative pixels (corresponding to the letter “R”, “U”, “E”, and the glass substrate, respectively) compared with the ground truth spectra measured by a Fourier-transform infrared spectrometer. (f) Reconstructed spectral frames at 6 representative wavelengths. The contrast between different frames demonstrates that the system can effectively reveal the LWIR spectral properties of various materials and structures. (g), (h) Degree-of-linear-polarization and angle-of-linear-polarization frames. Distinctive polarimetric signatures can be observed for each letter in the images. (i) Simulated spectral reconstruction results. The ground truth spectra (solid lines) are Gaussian peaks with 0.6 µm FWHM centered at different wavelengths (8.5 µm, 9.3 µm, 10.2 µm, 11 µm, 11.8 µm, 12.7 µm, and 13.5 µm). The reconstructed spectra (dotted lines) show good agreements with the ground truth

Reference

Spinning Metasurface Stack for Spectro-polarimetric Thermal Imaging, Optica 11, 1, 73 - 80, 2024,

Wang, X., Yang, Z., Bao, F., Sentz, T., and Jacob, Z.

(Featured in Laser Focus World, Optics & Photonics News etc.)

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