Poor signal-to-noise ratios (SNRs) and low spatial resolutions have impeded low-cost pyroelectric infrared (PIR) sensors from many intelligent applications for thermal target detection/recognition. This article presents a cognitive signal conditioning and modulation learning framework for PIR sensing with the following two innovations to solve these problems: 1) a reconfigurable signal conditioning circuit design to achieve high SNRs and 2) an optimal sensor mask design to achieve high recognition performance. By using a programmable system on chip, the PIR signal amplifier gain and filter bandwidth can be adjusted automatically according to working conditions. Based on the modeling between PIR physics and thermal images, sensor masks can be optimized through training convolution neural networks with large thermal image datasets for feature extraction of specific thermal targets. The experimental results verify the improved performance of PIR sensors in various working conditions and applications by using the developed reconfigurable circuit and application-specific masks.

中文翻译：

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实现认知热释电红外传感：从可重构信号调节到传感器掩模设计


较差的信噪比 (SNR) 和低空间分辨率阻碍了低成本热释电红外 (PIR) 传感器在许多用于热目标检测/识别的智能应用中的应用。本文提出了一种用于 PIR 传感的认知信号调节和调制学习框架，通过以下两项创新来解决这些问题：1）可重构信号调节电路设计，以实现高信噪比；2）最佳传感器掩模设计，以实现高识别性能。通过使用可编程片上系统，可以根据工作条件自动调整PIR信号放大器增益和滤波器带宽。基于 PIR 物理和热图像之间的建模，可以通过使用大型热图像数据集训练卷积神经网络来优化传感器掩模，以提取特定热目标的特征。实验结果验证了通过使用开发的可重构电路和特定应用掩模，PIR 传感器在各种工作条件和应用中性能的提高。