Foliage penetration study using adapted SAR algorithm for munitions

• Main Authors: Moore, Michael R, Waagen, Donald E
• Format: Conference Proceeding
• Language: English
• Published: SPIE 12.04.2021
• ISBN: 9781510642935; 1510642935
• ISSN: 0277-786X
• DOI: 10.1117/12.2584609

Within the realm of automatic target recognition (ATR) using synthetic aperture radar (SAR), significant research has been performed on the e Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The classification results performed on the uncorrupted MSTAR images are typically well above 90% correct, and often approaching 99%. However, in support of operational missions, there is a need to assess the various approaches against a baseline that includes less ideal operating conditions such as foliage penetration (FOPEN). Thus, this paper uses a specialized algorithm that has been proven effective in other settings to assess the effect of a range of increasingly densely spaced pixel amplitude distortions. The results show that once approximately 50% or more of the pixels within the target and shadow region are degraded, the ability to classify the correct target and pose is greatly reduced. Also, as speculated by others, leaving a border of the original clutter appears to yield artificially good classification results in the %50-%90 degraded range before it also rolls off. Finally, when there is no masking, the results are rather sensitive to the chosen confidence level which reinforces the supposition that matches are occurring due to clutter and not just the target.