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Sensor and detection algorithm-based clutter metrics

机译:基于传感器和检测算法的杂波指标

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Abstract: Clutter metrics are important image measures for evaluating the expected performance of sensors and detection algorithms. Typically, clutter metrics attempt to measure the degree to which background objects resemble targets. That is, the more target-like objects or attributes in the background the higher the clutter level. However, it is critically important that the characteristics of the sensor systems and the detection algorithms be included in any measure of clutter. For example, clutter to a coarse resolution sensor coupled with a pulse thresholding detection algorithm is not necessarily clutter to a second generation FLIR with a man in the loop. Using present state- of-the-art first and second order clutter metrics and respective performance studies, a new class of sensor/algorithm clutter metrics will be derived which explicitly use characteristics of the sensor and detection algorithms. A methodology will be presented for deriving sensor/algorithm dependent clutter metric coefficients and algorithms for a broad class of systems.!9
机译:摘要:杂乱度量是用于评估传感器和检测算法的预期性能的重要图像度量。通常,混乱度量会尝试测量背景对象与目标相似的程度。也就是说,背景中类似目标的对象或属性越多,杂波级别就越高。但是,至关重要的是,传感器系统的特性和检测算法应包含在任何杂波措施中。例如,杂乱无章的粗分辨率传感器与脉冲阈值检测算法相结合,并不一定杂乱无章。使用当前最新的一阶和二阶杂波指标以及各自的性能研究,将得出一类新的传感器/算法杂波指标,这些指标明确使用了传感器和检测算法的特性。将介绍一种方法,用于为广泛的系统推导与传感器/算法有关的杂波度量系数和算法。9

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