Signature adaptive target detection and threshold selection for constant false alarm rate

Abstract

A constant false alarm rate (CFAR) detection algorithm and a threshold selection algorithm are adapted and developed for use in multiband-image small-target detection. While it is often difficult to predict the spectral signatures of targets, the shape of the target may be known. This detection algorithm exploits geometric target features and spectral differences between the target and the surrounding area. The detection algorithm is derived from a general statistical model of the data with most emphasis on the background. The utility of CFAR algorithms is that the selection of a detection threshold can be made independently of image intensity. However, varied applications of the algorithms show that detection values are dependent on the scene adherence to the model. Achieving a CFAR in applications is very difficult. The threshold for a desired number of false alarms fluctuates with differing backgrounds. By appropriately mapping observations to the model, an automatic threshold selection algorithm is shown. Combining the CFAR-detection algorithm with the threshold selection algorithm produces a reliable constant false alarm rate.