Minace filter infrared target tracking, recognition, and rejection tests with aspect view, depression angle, and scale variations

摘要

We examine the sensitivity of minimum noise and correlation energy (MINACE) filters to three different types of distortion variations (aspect view, depression angle, and scale) that are typically present in infrared (IR) imagery used for automatic target recognition (ATR) and tracking applications. Prior DIF (distortion-invariant filter) ATR work has addressed at most two simultaneous variations - aspect view and depression angle variations for SAR data, and aspect view and thermal state variations for IR data. No prior Minace ATR work has addressed scale variations. In our tests, we consider all three simultaneous variations - aspect view, depression angle, and scale. This is new. Our goal is to determine if one Minace filter per object can handle full 360° aspect view variations and can handle small depression angle variations, and to determine the range of scales that one Minace filter per object can handle after training on data at one or more scales. This determines when new Minace filters are needed in an image closing sequence. In all cases, shifts of the target test inputs are considered. We use our autoMinace algorithm that automates selection of the Minace filter parameter c and the training set images to be included in the filter. We also consider rejection of unseen confuser objects and clutter. No confuser, clutter, or test set data are present in the training or the validation set. We present test results using both real and CAD IR data.