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Selection of a Clutter Rejection Algorithm or Real-Time Target Detection from an Airborne Platform

机译:从机载平台选择杂波抑制算法或实时目标检测

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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 describves 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 hte use of ordnance on a battlefield and then determien 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 adn 4 temporal filters, along with two different types of thresholding (spatially fixed and spatially adaptive). Appropriate datasets for testng were crated 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 evalaution. The final algorithm selection was based both on computational complexity and algorithm performance.
机译:在涉及检测和识别小目标的应用中,杂波抑制通常是一项必不可少的任务,因此,如果要根据需要执行此操作,则杂波抑制算法的选择就显得尤为重要。为了解决这个问题,各个小组开发了许多不同的杂波抑制算法。但是,由于算法的性能通常与场景有关,因此为给定应用选择合适的算法通常需要进行全面的测试和性能分析。本文介绍了针对杂波抑制算法的研究方法和研究结果,该算法针对的系统涉及一个安装在机载平台上的凝视红外摄像机。该系统的目的是检测战场上军械的使用情况,然后确定使用了哪种军械。杂波抑制算法需要实时并且可以在硬件中实现。测试所选择的算法包括17个空间滤波器和4个时间滤波器,以及两种不同类型的阈值(空间固定和空间自适应)。使用红外摄像机拍摄的真实军械数据和来自MODIS机载模拟器的混乱背景的组合来创建适当的测试数据集。选择了几种不同的指标来辅助算法性能的评估。最终的算法选择基于计算复杂度和算法性能。

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