Selection of a clutter rejection algorithm for real-time target detection from an airborne platform

Clutter rejection is often an essential task in applications involving the detection and identification of small targets, making the choice of a clutter rejection algorithm extremely important if such a system is to perform as desired. Many different clutter rejection algorithms have been developed by various groups seeking to address this problem; however, as the performances of the algorithms are often very scenario dependent, selecting an appropriate algorithm for a given application usually requires thorough testing and performance analysis. This paper describes the methodology and results of a study done on clutter rejection algorithms for a system involving a staring IR camera mounted on an airborne platform. The purpose of this system is to detect the use of ordnance on a battlefield and then determine what type of ordnance was used. The clutter rejection algorithm needed to be real-time and possible to implement in hardware. The algorithms chosen for testing included 17 spatial filters and 4 temporal filters, along with two different types of thresholding (spatially fixed and spatially adaptive). Appropriate datasets for testing were created using a combination of real ordnance data taken by the IR camera, and clutter backgrounds from MODIS Airborne Simulator. Several different metrics were chosen to assist in algorithm performance evaluation. The final algorithm selection was based both on computational complexity and algorithm performance.