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Tri-feature-based detection of floating small targets in sea clutter

机译：基于三特征的海杂波漂浮小目标检测

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摘要

It is always a challenging problem for marine surface surveillance radar to detect sea-surface floating small targets. Conventional detectors using incoherent integration and adaptive clutter suppression have low detection probabilities for such targets with weak returns and unobservable Doppler shifts. In this paper, three features of a received vector at a resolution cell ?? the relative amplitude, relative Doppler peak height, and relative entropy of the Doppler amplitude spectrum ?? are exploited to give returns with targets from sea clutter. Real datasets show that each feature alone has some discriminability, and the three features jointly exhibit strong discriminability. Due to diversity of targets in practice, it is impossible to get features of returns with all kinds of targets. We recast detection of sea-surface floating small targets as a one-class anomaly detection problem in the 3D feature space. A fast convexhull learning algorithm is proposed to learn the decision region of the clutter pattern from feature vectors of clutter-only observations. As a result, a tri-feature-based detector is developed. The experiment results for the IPIX datasets show that the proposed detector at an observation time of several seconds attains better detection performance than several existing detectors.
机译：对于海洋表面监视雷达来说，检测海面漂浮的小目标始终是一个具有挑战性的问题。使用非相干积分和自适应杂波抑制的常规探测器对于具有弱返回和无法观察到的多普勒频移的目标具有较低的探测概率。在本文中，在分辨单元??上接收到的矢量的三个特征？多普勒振幅谱的相对振幅，相对多普勒峰值高度和相对熵可以利用海杂物给目标提供回报。实际数据集显示，每个特征本身都具有一定的可辨别性，并且三个特征共同展现出强大的可辨别性。由于实际中目标的多样性，不可能获得具有各种目标的收益特征。我们将对海面漂浮小目标的检测重铸为3D特征空间中的一类异常检测问题。提出了一种快速凸包学习算法，用于从仅杂波观测的特征向量中学习杂波模式的决策区域。结果，开发了基于三特征的检测器。 IPIX数据集的实验结果表明，所提出的探测器在几秒钟的观察时间内比现有的几种探测器具有更好的探测性能。

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