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Deep temporal detection - A machine learning approach to multiple-dwell target detection

机译：深度时间检测-一种用于多停留目标检测的机器学习方法

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摘要

Detecting small targets, such as an Unmanned Aerial Vehicle (UAV) in high clutter and non-homogeneous environments is challenging for a radar system. Traditional Constant False Alarm Rate (CFAR) detectors have suboptimal performance in many scenarios. In this paper, we attempt a new approach to radar detection, based on machine learning, to increase the $P_{D}$ while retaining a low $F_{FA}$. We propose two approaches, using a Convolutional Neural Network (CNN) on the range-Doppler images and stacking multiple range-Doppler images as layers, called the Temporal CNN detector. The models are trained and tested solely on measured radar data by using the estimated position and velocity from a collaborative target UAV. It is shown that training a model based solely on measured data is achievable and performance metrics calculated from the testing data shows that both models outperform the Cell-Averaging Constant False Alarm Rate (CA-CFAR) by having higher $P_{D}$ with the same $P_{FA}$. The current test results indicate that the temporal CNN is able to increase the detection distance close to 30%, while retaining the same $P_{FA}$ as the CA-CFAR.
机译：对于雷达系统而言，在高杂波和非均匀环境中检测小型目标（例如无人飞行器（UAV））是一项挑战。传统的恒定误报率（CFAR）检测器在许多情况下性能都不理想。在本文中，我们尝试基于机器学习的雷达检测新方法，以增加 $ P_ {D} $ < / tex> 同时保持低位 $ F_ {FA} $ < / tex> 。我们提出了两种方法，即在距离多普勒图像上使用卷积神经网络（CNN），并将多个距离多普勒图像堆叠为层，称为时间CNN检测器。通过使用来自协作目标无人机的估计位置和速度，仅在测得的雷达数据上对模型进行训练和测试。结果表明，仅基于测量数据就可以训练模型，并且根据测试数据计算出的性能指标表明，这两种模型的信噪比均高于固定平均虚警率（CA-CFAR）。 $ P_ {D} $ < / tex> 一样的 $ P_ {FA} $ < / tex> 。当前的测试结果表明，时域CNN能够将检测距离增加到接近30％，同时保持不变 $ P_ {FA} $ < / tex> 作为CA-CFAR。

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