Statistical Analysis Of Median Subtraction Filtering With Application To Point Target Detection In Infrared Backgrounds

Abstract

The nonstationarity of infrared interference backgrounds which prevents the implementation of the usual optimum linear filtering techniques makes clutter suppression signal processing for point target detection in infrared surveillance systems a challenging and difficult problem. Hence, more robust filtering schemes are sought which will perform well in structured backgrounds where the underlying probability distribution defining that structure is not well known or characterized. This paper investigates a promising candidate spatial filter for point-like feature detection in infrared systems. The technique, known as median subtraction filtering, is a robust, nonlinear, order statistic type filter which exhibits highpass filter characteristics without the usual ringing associated with linear highpass filters. A quantitative analysis of the statistical properties of the median subtraction filter is presented, including analytic expressions for the output distribution of the filter (thus analytic expressions for the probability of detection and probability of false alarm), its autocorrelation function and spectral density function. Performance results of a signal processing simulation comparing a median subtraction filter with an adaptive linear filter of the LMS type using actual infrared video as input are also included.