Median filtering for target detection in an airborne threat warning system

Detection of point targets and blurred point targets in midwave infrared imagery is difficult because few assumptions can be made concerning the characteristics of the background. In this thesis, real time spatial prefiltering algorithms that facilitate the detection of such targets in an airborne threat warning system are investigated. The objective of prefiltering is to pass target signals unattenuated while rejecting background and noise. The use of unsharp masking with median filter masking operators is recommended. Experiments involving simulated imagery are described, and the performance of median filter unsharp masking is found to be superior to that of the Laplacian filter, the linear point detection filter, and unsharp masking with a mean filter mask.

A primary difficulty in implementing real time median filters is the design of a mechanism for extracting local order statistics from the input. By performing a space-time transformation on a standard selection network, a practical sorting architecture for this purpose is developed. A complete hardware median filter unsharp masking design with a throughput of 25.6 million bits per second is presented and recommended for use in the airborne threat warning system.