Edge sensing with micro-power pulse-Doppler radars is an emergent domain in monitoring and surveillance with several smart city applications. Existing solutions for the clutter versus multi-source radar classification task are limited in terms of either accuracy or efficiency, and in some cases, struggle with a tradeoff between false alarms and recall of sources. We find that this problem can be resolved by learning the classifier across multiple time-scales. We propose a multi-scale, cascaded recurrent neural network architecture, MSC-RNN, composed of an efficient multi-instance learning (MIL) Recurrent Neural Network (RNN) for clutter discrimination at a lower tier and a more complex RNN classifier for source classification at the upper tier. By controlling the invocation of the upper RNN with the help of the lower tier conditionally, MSC-RNN achieves an overall accuracy of 0.972. Our approach holistically improves the accuracy and per-class recalls over machine learning models suitable for radar inferencing. Notably, we outperform cross-domain handcrafted feature engineering with purely time-domain deep feature learning, while also being up to ∼3× more efficient than a competitive solution.

中文翻译：

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一种尺寸不适合所有人

使用微功率脉冲多普勒雷达进行边缘传感是监测和监视多个智能城市应用的新兴领域。杂波与多源雷达分类任务的现有解决方案在准确性或效率方面都受到限制，并且在某些情况下，难以在误报和源召回之间进行权衡。我们发现这个问题可以通过跨多个时间尺度学习分类器来解决。我们提出了一种多尺度、级联循环神经网络架构 MSC-RNN，它由用于低层杂波识别的高效多实例学习 (MIL) 循环神经网络 (RNN) 和用于源分类的更复杂的 RNN 分类器组成在上层。通过有条件地在下层的帮助下控制上层RNN的调用，MSC-RNN 的总体准确度为 0.972。我们的方法全面提高了适用于雷达推理的机器学习模型的准确性和每类召回率。值得注意的是，我们通过纯时域深度特征学习优于跨域手工特征工程，同时效率也比竞争解决方案高 3 倍。