🤖 AI Summary
This study addresses the significant degradation in ranging accuracy in passive long-wave infrared (LWIR) systems caused by downwelling sky-reflected radiance, particularly when the temperature contrast between the target and its background is small. To mitigate this interference, the work introduces— for the first time—the use of ozone absorption features and proposes two novel approaches: a closed-form quad-spectral method based on four narrowband measurements, and a hyperspectral method that jointly retrieves range, temperature, and emissivity from hyperspectral data. Experimental results demonstrate that without correction, ranging errors exceed 100 meters; the quad-spectral method reduces this error to 6.8 meters, while the hyperspectral approach further improves accuracy to 1.2 meters, substantially outperforming existing techniques.
📝 Abstract
Passive long-wave infrared (LWIR) absorption-based ranging relies on atmospheric absorption to estimate distances to objects from their emitted thermal radiation. First demonstrated decades ago for objects much hotter than the air and recently extended to scenes with low temperature variations, this ranging has depended on reflected radiance being negligible. Downwelling radiance is especially problematic, sometimes causing large inaccuracies. In two new ranging methods, we use characteristic features from ozone absorption to estimate the contribution of reflected downwelling radiance. The quadspectral method gives a simple closed-form range estimate from four narrowband measurements, two at a water vapor absorption line and two at an ozone absorption line. The hyperspectral method uses a broader spectral range to improve accuracy while also providing estimates of temperature, emissivity profiles, and contributions of downwelling from a collection of zenith angles. Experimental results demonstrate improved ranging accuracy, in one case reducing error from over 100 m when reflected light is not modeled to 6.8 m with the quadspectral method and 1.2 m with the hyperspectral method.