Adaptive whitening filters for small target detection

This paper studies the performance of the two dimensional least mean square adaptive filter as a prewhitening filter for detection systems. In two dimensional infrared sensor data, the clutter is correlated and much wider in spatial extent than the signal of interest. The two dimensional adaptive filter can be trained to adapt and predict the clutter, thereby enabling the error channel output to contain the signal of interest in white noise. Performance of the adaptive prewhitener, in terms of local signal to clutter ratio's(LSCR) and the gain obtained is described. The gain in LSCR due to this augmenting filter, is shown to depend on the statistics of the background clutter, in particular on the local mean. It is shown that, as the amount of color in the background clutter increases, the performance of the conventional matched filter performance degrades much more than the performance of a detector based on the augmenting prewhitener.

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