Simulation Of Clutter Rejection Signal Processing For Mid-Infrared Surveillance Systems

With most wide field of view surveillance systems, extensive clutter rejection signal processing is needed to suppress false alarms. At long range the objects sought appear to be point sources, and spatial discrimination can be used to reject extended sources (clutter) and to ensure a low false alarm rate. In this work a variety of one-dimensional signal processing techniques have been evaluated using high resolution (0.15 mrad) noise data gathered with a scanning IR data collection sensor operating in the 4.0 - 4.8 pm spectral band. The recorded noise contains segments with strong clutter from back-lit clouds and segments from uniformly radiant sky with no clutter. These segments were separated in the simulation. Two ordinary bandpass filters and a spatial filter matched by a least-mean-square (LMS) technique to the detector output from a point source were evaluated. A digital computer was used to pass the noise data through algorithms representing the filters and through two types of threshold algorithms, one having a fixed threshold, the other a threshold that adapts to changes in the noise level. Except in one case, the LMS filter performed better than the bandpass filters with both threshold algorithms. No significant degradation in the performance of an adaptive threshold sensor (151 samples long) was observed due to the use of an LMS filter (7 samples long) at a false alarm rate of one in 1.6 x l0 pixels. A one-dimensional LMS filter and adaptive threshold sensor were shown to be an effective clutter rejection combination at least for sensors with an NEI exceeding 1 x 10-13 W/cm2.