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Adaptive spatial-temporal filtering methods for clutter removal and target tracking

机译：自适应时空滤波方法用于杂波去除和目标跟踪

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摘要

In space-based infrared (IR) ballistic missile defense sensor systems, cluttered backgrounds are typically much more intense than the equivalent sensor noise or the targets being detected. Therefore, the development of efficient clutter removal and target preservation/enhancement algorithms is of crucial importance. To meet customer requirements, the advanced clutter rejection algorithms should provide more than 20 dB improvement in detection sensitivity. We propose an adaptive parametric spatial-temporal filtering technique together with the jitter compensation (scene stabilization). The results of simulations and processing of real data show that the developed adaptive spatial-temporal clutter suppression (CLS) algorithms allow for efficient clutter rejection in all tested situations. Proposed algorithms completely remove heavy clutter in the presence of substantial jitter and do not require expensive subpixel jitter stabilizers. In contrast, spatial-only filters and temporal differencing methods can be used only for weak and relatively correlated clutter. A stand-alone simulator was developed to demonstrate capabilities and performance of various algorithmic approaches. Simulations model various geometries, resolutions, illuminations, and meteorological conditions for space-based IR staring sensor systems.
机译：在天基红外（IR）弹道导弹防御传感器系统中，杂乱的背景通常比等效的传感器噪声或被检测到的目标要强烈得多。因此，开发有效的杂波去除和目标保持/增强算法至关重要。为了满足客户的需求，先进的杂波抑制算法应将检测灵敏度提高20 dB以上。我们提出了一种自适应参数时空滤波技术以及抖动补偿（场景稳定）功能。真实数据的仿真和处理结果表明，已开发的自适应时空杂波抑制（CLS）算法可在所有测试情况下有效地抑制杂波。所提出的算法在存在明显抖动的情况下完全消除了严重的杂波，并且不需要昂贵的子像素抖动稳定器。相反，仅空间滤波器和时间差分方法只能用于较弱且相对相关的杂波。开发了一个独立的模拟器来演示各种算法方法的功能和性能。仿真为空基红外凝视传感器系统建模各种几何形状，分辨率，照明和气象条件。

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