Analysis of the effects of fixed pattern noise on a fully adaptive matched filter

Fully adaptive matched filters typically can suppress clutter to the level of the sensor fixed pattern noise. A fully adaptive filter assumes that the clutter is a wide-sense stationary process which can be modeled by a constant mean and unknown covariance function. Fixed pattern noise within a data sequence is unknown and tends to be a non-stationary process. As a result fixed pattern noise is minimally affected by fully adaptive filters. The signal processing philosophy for detecting unresolved targets is to enhance the target signal based on the sensor point spread function. When sensor fixed pattern noise exists, the signal from a point target can be significantly different from the sensor point spread function and can result in a loss in SCR. This SCR loss can make weak targets undetectable. This paper describes the effect of a fully adaptive filter on fixed pattern noise manifested as channel dependent bias and gain errors. Spectral analysis which quantifies the impact of these errors is presented. Experimental results on synthetic data and on real data from an infrared scanning sensor with channel dependent fixed pattern noise are given.