Handheld Millimeter Wave Imaging System based on a Two-Dimensional Multistatic Sparse Array

Hussung, Raphael; Keil, Andreas; Friederich, Fabian

doi:10.1109/irmmw-thz46771.2020.9370732

Cited by 10 publications

(8 citation statements)

References 4 publications

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“…All testing was carried out on Intel hexa-core i7 CPU with 16 GB RAM and Windows 10 operating system. A total of 542 samples were selected for testing, out of which, 521 samples were generated from the developed physical model whereas the rest 21 images were experimentally generated with the help of a handheld mmW imager [20], based on a multi-static sparse array [21], [22]. Classification reports are generated which lists the precision, recall as well as the f1-score of the predictions.…”

Section: Resultsmentioning

confidence: 99%

Performance Analysis of Classification Algorithms for Millimeter-wave Imaging

Sharma

Hussung

Keil

et al. 2022

2022 16th European Conference on Antennas and Propagation (EuCAP)

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A detailed analysis of the performance of traditional machine learning and deep learning techniques applied on a representative classification problem of millimeter-wave (mmW) images is presented in this paper. The algorithms chosen for this analysis are the k-Nearest Neighbors (KNN), Random Forest (RF) and Convolutional Neural Network (CNN). All algorithms presented here are modeled using 'keras' library inside TensorFlow and 'scikit-learn' module. The dataset for training and testing are generated via a developed near-field coded aperture computational imaging (CI) physical model. The use of a physical model of an imaging system that implements CI techniques instead of an experimental set-up makes the whole dataset generation process facile and less time consuming. The training data, in case of the RF and KNN algorithms, are presented in tabular form whereas for the CNN technique, the synthesized images from the physical model itself are used for training. The models are tested with both synthesized as well as experimental data, generated from the physical model and a mmW handheld imager, respectively. Upon testing, it is observed that the KNN and RF algorithms are able to classify the test samples with accuracies of 82% and 87%, respectively, whereas an accuracy of 90% is observed in case of the CNN classifier. Also, an inference speed test is conducted on all the three algorithms. It was observed that CNN is the fastest to predict classes for all of the test samples with a frame rate of 3.8 ms/sample whereas RF is the slowest, with a frame rate of 65.9 ms/sample. These findings establish the fact that when it comes to image classification, CNN based classifiers perform better than any traditional machine learning algorithms with more accurate and faster predictions, paving the way for various real-time applications such as automatic threat detection.

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Section: Resultsmentioning

confidence: 99%

Performance Analysis of Classification Algorithms for Millimeter-wave Imaging

Sharma

Hussung

Keil

et al. 2022

2022 16th European Conference on Antennas and Propagation (EuCAP)

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“…In addition to the numerical testing to characterize the classification accuracy, in order to demonstrate that the trained classifier model performs accurately with experimental mmW data, mmW images of real threat data are also considered and used as test samples. For this experimental demonstration, a millimetre-wave handheld imager [73] based on a multistatic sparse array [74], [75] is used to image a scissor and a knife, the setup of which is shown in Fig. 8(a).…”

Section: A Classification Resultsmentioning

confidence: 99%

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

et al. 2021

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A millimeter-wave (mmW) classifier system applied to images synthesized from a codedaperture based computational imaging (CI) radar is presented. A developed physical model of a CI system is used to generate the image dataset for the classification algorithm. A convolutional neural network (CNN) is integrated with the physical model and trained using the dataset comprising of synthesized mmW images obtained directly from the developed CI physical model. A k-fold cross validation technique is applied during the training process to validate the classification model. The coded-aperture CI concept enables image reconstruction from a significantly reduced number of back-scattered measurements by facilitating physical layer compression. This physical layer compression can substantially simplify the data acquisition layer of imaging radars, which is realized using only two channels in this article. The integration of the classification algorithm with the CI numerical model is particularly important in enabling the training step to be carried out using relevant system metrics and without the necessity for experimental data. Leveraging the CI numerical model generated data, training step for the classification algorithm is achieved in real-time while also confirming that the numerically trained CI classifier offers high accuracy with both simulated and experimental data. The classifier integrated physical model also enables performance analysis of the classification algorithm to be carried out as a function of key system metrics such as signal-to-noise (SNR) level, ensuring a complete understanding of the classification accuracy under different operating conditions. The trained CI system is tested with synthesized mmW images from the physical model and a classification accuracy of 89% is achieved. The proposed model is also verified using experimental data validating the fidelity of the developed CI integrated classifier system. A classification latency of 3.8 ms per frame is achieved, paving the way for real-time automated threat detection (ATD) for security-screening applications.INDEX TERMS Millimetre-wave, imaging radars, computational imaging, neural networks, image classification, coded-aperture.

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“…For future work, we will speed up the acquisition without reducing the field of view. We intend to apply detector arrays [37] covering the full range of the receiver movement simultaneously.…”

Section: Discussionmentioning

confidence: 99%

A Priori Information and Off-Axis Measurements in Terahertz Tomography

et al. 2023

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Terahertz tomography is a non-contact inspection technique to image objects from multiple angles and reconstruct their 3D volume from intensity and time-of-flight transmission data, without the need for radiation protection measures. Unlike X-rays, terahertz radiation is subject to strong diffraction and refraction when propagating through dielectric materials, which often deteriorate the image reconstruction quality. Our solution to this problem applies ray tracing, considering the beam shape and an a priori model of the sample under investigation to predict the beam paths of the terahertz radiation. We present two reconstruction methods based on the resulting beam path predictions yielding higher image quality. Method 1 filters out beams deviating strongly, thus removing induced artifacts and errors from the reconstruction image. Method 2 employs off-axis measurements that acquire data along the full detection plane and in this way detect even strongly deflected beams. Considering these beams and the information they carry in the reconstruction enhances the image quality. Applying these methods to terahertz tomography, even complicated structures can be imaged. We display the significant enhancements achieved with the two methods by comparing the reconstruction results of different polymeric samples.INDEX TERMS Terahertz radiation, computed tomography, non-destructive testing, a priori information, off-axis measurement, reconstruction algorithms, imaging, time-of-flight, conjugate gradient least square (CGLS).

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Handheld Millimeter Wave Imaging System based on a Two-Dimensional Multistatic Sparse Array

Cited by 10 publications

References 4 publications

Performance Analysis of Classification Algorithms for Millimeter-wave Imaging

Performance Analysis of Classification Algorithms for Millimeter-wave Imaging

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

A Priori Information and Off-Axis Measurements in Terahertz Tomography

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