The Effect Of Background Clutter On The TA Performance Of IR Imaging Systems

This paper discusses how the background clutter in the scene affects the target acquisition performance of human vision and machine vision based IR imaging systems with typical methods for quantifying background clutter as the research object respectively. To model the target acquisition capability of an IR imaging system operated by a human, one should take into account how the clutter in the scene affects the human performance. Based on the human's propensity to focus on edges and its adaptability to scale, we present a global clutter metric, a local clutter metric and a target metric. Using them we propose a new method for modifying classical models of human target acquisition. The modified model takes account of the background clutter effects without markedly increasing the complexity of the classic model. In terms of machine vision based systems, background clutter greatly reduces the effective SIR, thereby increasing the false alarm rate or decreasing the probability of detection. At the same time, more and more complex algorithms are adopted in the detection procedure to improve SIR and increase probability of detection. The detection performance in a sense of probability of detection is highly related to the complexity of the background clutter and the applied algorithm. Based on the Butterworth model for quantifying power spectral density (PSD), we present a robust performance evaluation technique with emphasis on the quantitative description of the background clutter and the detection algorithms.