Target detection, classification, and tracking using a maximum average correlation height and polynomial distance classification correlation filter combination

Abstract

Simultaneous detection and classification of single/multiple identical and dissimilar targets is very important in automatic target recognition applications. A new approach is proposed for this purpose using a combination of maximum average correlation height (MACH) filter and polynomial distance classifier correlation filter (PDCCF). In this technique, full-resolution MACH filters are applied to the input scene, and the regions of interest (ROIs) containing the probable targets are selected from the input scene based on the ROIs with higher-correlation peak values in the correlation output. Then a multiclass PDCCF is applied to these ROIs to identify target types and reject clutters and/or backgrounds. To increase the robustness of the proposed technique, multiple filters are formulated for multiple ranges of target size and/or orientation variations. The simulation results using real-life imagery indicate the effectiveness of the proposed technique for target detection and classification in the presence of distortion, clutter, and other artifacts.