Passive THz Radiometer Scanner Object Detection with Adaptive Filtering

Abstract

Passive terahertz radiometer scanner [1] is an emerging type of handheld security inspection device that could overcome some of the shortcomings of current security inspection devices on the market. However, subject to several difficulties such as unstable measurements and ambiguous signal features, to detect hidden objects using this device is challenging. The previous research on this topic was insufficient, and the object detection algorithm was less reliable and lacked scientific verifi-cation. In this paper, we propose a whole new pipeline to address this task. We explore and compare a series of adaptive filtering techniques and propose a customized Kalman filter to extract the signal features that describe hidden objects. Then, we adopt two machine learning methods on the filtered signal to detect the hidden objects. Experiment shows that the proposed pipeline can achieve over 85 % accuracy, which hugely outperforms the old methods.