Millimeter-wave Imaging Research Articles

An Efficient MMW 3-D Imaging Algorithm for Near-Field MIMO-SAR With Nonuniform Transmitting Array

In this letter, an efficient millimeter-wave (MMW) frequency-domain imaging algorithm is presented for 3-D object reconstruction of the near-field multiple-input–multiple-output synthetic aperture radar (MIMO-SAR) with nonuniform transmitting array. For each single-input–multiple-output (SIMO) case of the MIMO-SAR setup, the precise spectral echo expression is derived by spherical-wave decomposition and fast Fourier transform (FFT) operations. To obtain the linear phase in range direction, the multiorder approximation theory is employed to expand the coupling phases, and thus the target subimage can be reconstructed by utilizing phase compensation, central wavenumber approximation, and inverse FFT. Finally, the fully focused 3-D image for MIMO setup will be realized through accumulating all the SIMO subimages. Compared with the existing representative algorithms, our method avoids complicated interpolation and achieves high-quality fast imaging with only matrix multiplication and a small amount of iteration operations. The experimental results validate the effectiveness of the proposed algorithm.

Efficient Wavenumber Domain Processing for Near-Field Imaging With Polyline Arrays

We propose an efficient wavenumber domain approach for polyline array-based millimeter-wave imaging. The polyline multiple-input–multiple-output (MIMO) array provides more uniform illumination of the target than the planar MIMO array while enjoying the planar array’s easy manufacturing. However, no existing efficient imaging algorithms deal with such an array topology. This article presents an approach that first implements a polyline-to-arc transformation on the polyline array, accompanied by a phase calibration method to maintain the transformation precision. The wavenumber domain technique is then employed to perform image reconstruction. This processing enables the transformation of the polyline array scheme into a monostatic cylindrical aperture, for which efficient imaging algorithms exist. The imaging performance and computational efficiency of the proposed approach are demonstrated with both simulations and experiments.

Millimeter-Wave Imaging System Based on Direct-Conversion Focal-Plane Array Receiver.

A new approach to millimeter-wave imaging was suggested and experimentally studied. This approach can be considered as the evolution of the well-established focal-plane array (FPA) millimeter-wave imaging. The significant difference is the use of a direct-conversion array receiver, instead of the direct-detection array receiver, along with the frequency-modulated continuous-wave (FMCW) radar technique. The sensitivity of the direct-conversion receiver is several orders higher than the sensitivity of the direct-detection one, which allows us to increase the maximum imaging range by more than one order of magnitude. The additional advantage of the direct-conversion technique is the opportunity to obtain information about the range to an object. The realization of the direct-conversion FPA imaging system was made possible due to original sensitive simple-designed receiving elements based on low-barrier Mott diodes. The suggested imaging method’s main characteristics, which include the achievable angular and range resolution and the achievable maximum imaging range, were studied. A maximum range of up to 100 m was experimentally determined. A 94 GHz 8 × 8 imaging system was developed for demonstration purposes and studied in detail. The suggested technique is assumed to be useful for creating a long-range millimeter-wave camera, in particular, for robotic systems that operate in poor environmental conditions.

Multi-Source Aggregation Transformer for Concealed Object Detection in Millimeter-Wave Images

The active millimeter wave scanner has been widely used for detecting objects concealed underneath a person’s clothing in the field of security inspection and anti-terrorism. However, the active millimeter wave (AMMW) images always suffer from low signal-noise ratio, motion blur, and small size objects, making it challenging to detect concealed objects efficiently and accurately. The scanner usually captures a sequence of images in different views around a human body at once, while the existing algorithms only utilize the single image without considering the relationships among images. In this paper, we design a multi-source aggregation transformer (MATR) with two different attention mechanisms to model spatial correlations within an image and contextual interactions across images. Specifically, a self-attention module is introduced to encode local relationships between the region proposals in each image, while a cross-attention mechanism is built to focus on modeling the cross-correlations between different images. Besides, to handle the problem of small objects in size and suppress the noise in AMMW images, we present a selective context module (SCM). It designs a dynamic selection mechanism to enhance the high-resolution feature with spatial details and make it more distinguishable from the noisy background. Experiments on two AMMW image datasets demonstrate that the proposed methods lead to a remarkable improvement compared to previous state-of-the-art and will benefit the concealed object detection in practice.

Millimeter-wave imaging and near-field spectroscopy for burn wound assessment

Abstract Diagnostic applications for skin in the microwave range have developed significantly in recent years, due the non-invasiveness of these applications and their ability to assess tissue water content. Despite their capabilities, however, there is still no appropriate clinically applicable microwave tool for the assessment of burn wounds. A common practice is the visual inspection and evaluation of burns by the doctor, which is a challenging task even for experienced medical professionals. An incorrect assessment can have far-reaching consequences, such as unnecessary surgery or surgery that is necessary but omitted. In this paper, two different approaches of millimeter-wave burn wound assessment are presented: millimeter-wave imaging and near-field spectroscopy. For imaging, a MIMO sparse array was used to assess ex vivo burns on porcine skin in the frequency range of 70–80 GHz. With a resonant millimeter-wave near-field probe, reflective spectroscopy at individual sites of an ex vivo burn on porcine skin in the frequency range of 75–110 GHz was performed. The results showed individual advantages and drawbacks for both approaches, with surprising benefits of the spectroscopic method. Nevertheless, both approaches were shown to be suitable for clinical usage in diagnosing burns.

Millimeter-Wave Radar and Vision Fusion Target Detection Algorithm Based on an Extended Network

The need for a vehicle to perceive information about the external environmental as an independent intelligent individual has grown with the progress of intelligent driving from primary driver assistance to high-level autonomous driving. The ability of a common independent sensing unit to sense the external environment is limited by the sensor’s own characteristics and algorithm level. Hence, a common independent sensing unit fails to obtain comprehensive sensing information independently under conditions such as rain, fog, and night. Accordingly, an extended network-based fusion target detection algorithm for millimeter-wave radar and vision fusion is proposed in this work by combining the complementary perceptual performance of in-vehicle sensing elements, cost effectiveness, and maturity of independent detection technologies. Feature-level fusion is first used in this work according to the analysis of technical routes of the millimeter-wave radar and vision fusion. Training and test evaluation of the algorithm are carried out on the nuScenes dataset and test data from a homemade data acquisition platform. An extended investigation on the RetinaNet one-stage target detection algorithm based on the VGG-16+FPN backbone detection network is then conducted in this work to introduce millimeter-wave radar images as auxiliary information for visual image target detection. We use two-channel radar and three-channel visual images as inputs of the fusion network. We also propose an extended VGG-16 network applicable to millimeter-wave radar and visual fusion and an extended feature pyramid network. Test results showed that the mAP of the proposed network improves by 2.9% and the small target accuracy is enhanced by 18.73% compared with those of the reference network for pure visual image target detection. This finding verified the detection capability and algorithmic feasibility of the proposed extended fusion target detection network for visually insensitive targets.

Resolution Enhancement for Millimeter-Wave Radar ROI Image with Bayesian Compressive Sensing.

For millimeter-wave (MMW) imaging security systems, the image resolution promisingly determines the performance of suspicious target detection and recognition. Conventional synthetic aperture radar (SAR) imaging algorithms only provide limited resolution in active MMW imaging, which is limited by the system. In terms of enhancing the resolution of a region of interest (ROI) image containing suspicious targets, super-resolution (SR) imaging is adopted via Bayesian compressive sensing (BCS) implemented by fast Fourier transform (FFT). The spatial sparsity of MMW ROI images is well exploited with BCS to achieve resolution enhancement without computational cost. Both simulated and measured experiments confirm that the proposed scheme effectively improves the resolution of ROI images.

Coupling Effects Analysis and Suppression in a Highly Integrated Ka-Band Receiver Front-End MMIC for a Passive Millimeter-Wave Imager System

This paper presents the coupling effects analysis and suppression of a highly integrated receiver front-end MMIC for a passive millimeter-wave imager system. The receiver MMIC consists of a low-noise amplifier, double-balanced image-reject mixer, frequency quadrupler, and analog phase shifter. In order to integrate these devices into a compact single chip without affecting the core performance, coupling problems need to be solved. We analyze the influence of coupling effects on the image rejection ratio, and propose corresponding solutions for three different coupling paths. (1) The coupling in the LO-RF path of the mixer is solved by designing a double-balanced mixer with high isolation characteristics. (2) The coupling between the LO chain and the LNA from space and dielectric is suppressed by optimizing the two main transmission lines spacing and adding isolation vias. (3) The coupling caused by the line crossing is restrained by designing a differential line crossover structure. The design and implementation of the MMIC are based on 0.15 µm GaAs pHEMT process. The receiver chip has 6.1~8.7 dB conversion gain in 32~36 GHz, less than 3.5 dB of noise figure, and more than 35 dB of image rejection ratio. The measurement results show that the receiver MMIC is especially suitable for high-sensitivity passive millimeter-wave imaging systems.

MilliNet: Applied Deep Learning Technique for Millimeter-Wave Based Object Detection and Classification

ABSTRACT Navigation and surveillance are crucial aspects of various fields such as the transportation, defence, and aeronautical industries. Imaging the terrain and the surrounding environment is a key part of a good navigation system. Some of the most widely used imaging techniques are visible light and Light Imaging Detection and Ranging (LiDAR) imaging, which covers two- and three-dimensional imaging. These imaging techniques face difficulties in adverse weather and atmospheric conditions, so there is a need for stronger radiation formation. Moreover, the data collected by radars, which emit these radiations cannot be used directly due to their disorganized characteristic. This article introduces the use of millimeter-wave imaging for the purpose of navigation and surveillance. Millimeter-wave images are captured by radar and fed to a novel neural network architecture, known as MilliNet, which is trained using LiDAR data (in point clouds) and can be used to classify and segment millimeter-wave imagery input involving features such as permutation and symmetric invariance. This provides a three-dimensional image in adverse weather conditions and conveniently uses that data for object detection and classification. The results obtained after testing the model on a dataset have been validated for different conditions and objects.

Millimeter-wave breast cancer imaging by means of a dual-step approach combining radar and tomographic techniques: preliminary results.

Breast cancer is one of the most diagnosed forms of cancer among women worldwide. However, the survival rate is very high when the tumor is diagnosed early. The search for diagnostic techniques increasingly able to detect lesions of the order of a few millimeters and to overcome the limitations of current diagnostic techniques (e.g., the X-ray mammography, currently used as standard for screening campaigns) is always active. Among the main emerging techniques, microwave and millimeter-wave imaging systems have been proposed, using either radar or tomographic approaches. In this paper, a novel dual-step millimeter-wave imaging which combines the advantages of tomographic and radar approaches is proposed. The goal of this work is to reconstruct the dielectric profile of suspicious regions by exploiting the morphological information from the radar maps as a priori information within quantitative tomographic techniques. Promising preliminary dielectric reconstruction results against simulated data are shown in both single- and dual-target scenarios, in which high-density healthy and tumor tissues are present. The reconstruction results were compared to the dielectric characteristics of human breast exvivo tissues used in the simulated models. The proposed dual-step approach allows to distinguish the nature of the targets also in the most challenging case represented by the co-presence of high-density healthy tissues and a malignant lesion, thus paving the way for a deeper investigation of this approach in experimental scenarios. Clinical Relevance-The proposed dual-step approach in the millimeter-wave regime allows to improve the reliability of the diagnostic technique, increasing its specificity.

A Compact Broadband Analog Complex Correlator with High Correlation Efficiency for Passive Millimeter-Wave Imaging System

In this paper, the design, fabrication, and measurement of a compact broadband (4–8 GHz) analog complex correlator for a passive millimeter-wave imaging system are presented. To achieve high sensitivity and high integration of the imaging system, the wideband and miniaturization of the correlator are required. The correlator achieves wide bandwidth by using the add-and-square method, which is composed of a six-port circuit and a detection circuit. In order to realize the miniaturization of the correlator, the six-port circuit is realized on the chip base on the 0.15-μm gallium arsenide (GaAs) process. The influence of mismatch of the detection circuit that employs zero-bias Schottky diodes on the correlator is also analyzed to guide the design of the correlator. The measurement results of the designed chips and detector are consistent with the simulation result. Finally, a Sweep-frequency test is applied to the designed correlator, and the measurement results show that, within the frequency range of 4–8 GHz, the correlation amplitude fluctuation is less than 1.9 dB and the correlation efficiency is larger than 99%, which reveal that the correlator is suited for interferometric passive millimeter-wave imaging applications.

A Near-Field Imaging Algorithm Based on Angular Spectrum Theory for Synthetic Aperture Interferometric Radiometer

For the near-field synthetic aperture interferometric radiometer (SAIR), the Fourier transform relationship between the visibility function and the near-field brightness temperature (BT) distribution is not valid. It is a challenging task for near-field SAIR imaging to realize very close-range accurate imaging with a large field of view (FOV). This article presents a new SAIR near-field imaging algorithm based on the angular spectrum theory to realize the passive millimeter-wave (PMMW) imaging, called synthetic-angular-spectrum imaging (SASI) algorithm. This SASI algorithm mainly addresses data samples of a 4-D visibility function acquired from planar arrays. First, we invert 4-D visibility samples into the angular spectrum domain via the Fourier transformation. The obtained angular spectrum data cannot be directly used to estimate the near-field BT distribution. Second, a dedicated phase factor compensation is adopted for handling this problem. The dimension-reducing accumulation is employed to generate the synthetic angular spectrum (SAS) of near-field BT distribution. Finally, we reconstruct the BT image by using the generated SAS data. Simulation and experiment results show that the presented SASI algorithm has the superiority on image reconstruction quality, especially for the off-axis regions, compared with the existing near-field SAIR imaging methods.

Angle-Insensitive Human Motion and Posture Recognition Based on 4D Imaging Radar and Deep Learning Classifiers

The need for technologies for Human Activity Recognition (HAR) in home environments is becoming more and more urgent because of the aging population worldwide. Radar-based HAR is typically using micro-Doppler signatures as one of the main data representations, in conjunction with classification algorithms often inspired from deep learning methods. One of the limitations of this approach is the challenging classification of movements at unfavorable aspect angles (i.e., close to 90°) and of static postures in between continuous sequences of activities. To address this problem, a hierarchical processing and classification pipeline is proposed to fully exploit all the information available from millimeter-wave (mm-wave) 4D imaging radars, specifically the azimuth and elevation information in conjunction to the more conventional range, Doppler, received power, and time features. The proposed pipeline uses the two complementary data representations of Point Cloud (PC) and spectrogram, and its performance is validated using an experimental dataset with 6 activities performed by 8 participants. The results show good performance of the proposed pipeline compared with alternative baseline approaches in the literature, and the effect of key parameters such as the amount of training data, signal-to-noise levels, and virtual aperture size is investigated. Leave-one-subject-out test is also applied to study the impact of body characteristics on the generalizability of the trained classifiers.

Near Field 3-D Millimeter-Wave SAR Image Enhancement and Detection with Application of Antenna Pattern Compensation

In this paper, a novel near-field high-resolution image focusing technique is proposed. With the emergence of Millimeter-wave (mmWave) devices, near-field synthetic aperture radar (SAR) imaging is widely used in automotive-mounted SAR imaging, UAV imaging, concealed threat detection, etc. Current research is mainly confined to the laboratory environment, thus ignoring the adverse effects of the non-ideal experimental environment on imaging and subsequent detection in real scenarios. To address this problem, we propose an optimized Back-Projection Algorithm (BPA) that considers the loss path of signal propagation among space by converting the amplitude factor in the echo model into a beam-weighting. The proposed algorithm is an image focusing algorithm for arbitrary and irregular arrays, and effectively mitigates sparse array imaging ghosts. We apply the 3DRIED dataset to construct image datasets for target detection, comparing the kappa coefficients of the proposed scheme with those obtained from classic BPA and Range Migration Algorithm (RMA) with amplitude loss compensation. The results show that the proposed algorithm attains a high-fidelity image reconstruction focus.

Incoherent Point Spread Function Estimation and Multipoint Deconvolution for Active Incoherent Millimeter-Wave Imaging

We present an approach to image deconvolution in active incoherent millimeter-wave (AIM) imaging. While traditional incoherent imaging systems capture thermal radiation from the scene, AIM imaging uses the transmission of noise signals to increase the signal-to-noise ratio (SNR) while maintaining the necessary space–time incoherence required for image reconstruction. Images formed by any imaging technique are corrupted by the impulse response, or point spread function (PSF), of the imaging system; however, the degradation of the image due to the PSF can be alleviated significantly via deconvolution if the PSF is characterized. Typically, estimation of the PSF is challenging due to spatial undersampling and the impact of noise. In this work, we experimentally demonstrate an approach to estimate the PSF of AIM imaging systems by averaging PSF estimates from multiple point targets using a shift-and-add method in a 38-GHz imaging system. We demonstrate image deconvolution using the CLEAN algorithm to reconstruct a scene with multiple targets.

Spatiotemporal Analysis of Electromagnetic Field Coherence in Complex Media

We study the coherence in time and space of electromagnetic fields propagated through complex media. Whether for localization, imaging, or telecommunication, the development of dedicated numerical techniques is generally based on the exploitation of simplified models considering either coherent or diffuse fields. The optimization of such applications in conditions of partial coherence can therefore be particularly challenging, requiring the development of hybrid algorithms adaptable to prior knowledge on the processed fields. The objective of this work is to provide numerical techniques for decomposing an electromagnetic field into subspaces that can then be filtered according to their level of spatial and temporal coherence. In contrast to the studies carried out on space-space transfer matrices notably used for the calculation of Wigner-Smith operators, these decompositions are carried out on space-time matrices in order to facilitate the study of temporal dispersion. The theory is developed for illustrative purposes using experimental results from a leaky resonant system, but seem to be applicable to any scattering and reverberating media capable of transforming localized and coherent excitations into complex and diffuse distributions. To conclude this work, the proposed technique is exploited to improve image reconstruction in a millimeter-wave computational imaging demonstration. In the studied context and from a more general perspective, we propose a technique to select the most suitable subspaces for each application operating under conditions of partial coherence, whether these correspond in the most extreme cases to ballistic paths or to diffuse fields.

An Applied Frequency Scaling Algorithm Based on Local Stretch Factor for Near-Field Miniature Millimeter-Wave Radar Imaging

The frequency scaling algorithm (FSA) is a popular imaging algorithm for dechirped SAR data. To obtain a large azimuth detection area, the miniature millimeter-wave (mmW) linear-frequency-modulated continuous-wave (LFMCW) surveillance radar requires a wider azimuth beamwidth, which leads to additional range frequency aliasing in FSA. Because of the adoption of the dechirp-on-receive technique, the sampling frequency is much smaller than the range bandwidth during near-field imaging, which further aggravates the aliasing effects. The target cannot be well focused, and it makes the weak targets submerged in the background. To acquire high-quality SAR images, an improved FSA using the local stretch operation is proposed. The aliasing bandwidth properties introduced by the FS operation and the desired objective range cell migration (RCM) factor are used in this proposed local-stretch FSA (LSFSA). The initial RCM factor is adjusted by the stretch operation to eliminate the frequency aliasing to a certain level without increasing the computing load. The LSFSA is suitable for solving the problem of range frequency aliasing in near-field side-looking SAR and high squint SAR with wide azimuth beamwidth. The proposed method is validated using reasonable simulations and convincing experiments.

Active Millimeter Wave Imaging Using Leaky-Wave Focusing Antenna

In this article, an active millimeter-wave imaging system using two rectangular waveguide leaky-wave focusing antennas deployed in a quasi-monostatic transmitter and receiver pair is proposed. The broadband design method of the focusing antenna is presented, where the phase constant is derived from the radiation direction of a rectangular waveguide leaky-wave antenna. To eliminate the effect of mutual coupling between the transmitting and receiving antennas and scattering from the surrounding environment, the scattered field from the object is obtained by subtracting the incident field from the total field. The 1-D detection of conducting sphere, conducting and dielectric cylinders, and a conducting plate is performed by experiment to validate the imaging capability of the proposed system. The 2-D imaging of a conducting cylinder in free space and positioned in front of a human body phantom representing the human body is finally performed to simulate the practical application of the system.

Development of an efficient approach for detection and measurement of crack length in ceramic tile manufacturing using millimeter-wave imaging

The detection of crack and their measurement in the packed ceramic tiles is an important procedure for quality monitoring and testing in the tile manufacturing industry. In the investigation of undercover targets high-definition (HD) cameras or surface crack detection methods like eddy current testing, etc. fail but millimeter-wave imaging can be useful. In our experimental work, cracked tiles are covered with the packaging cardboard sheet, and the unknown crack is detected using Millimeter-Wave Imaging. In this paper, an adaptive and statistical approach is proposed for crack detection and a computer-aided non-invasive technique is developed to measure a crack length. After critically analyzing various available signal and image processing techniques, a line enhancement known as Frangi filtering is selected for crack detection. In the current scenario; the decision parameter for the Frangi filter known as the Frangi scale ratio is required to be fixed manually, to minimize the selection error automation of this process is done using a Genetic Algorithm (GA). In literature, the focus is limited to only the detection of cracks in the tile, but the estimation of the crack length is also essential. The possible use of image morphing techniques for the measurements of the crack length is explored. It is observed that the conventional Hough transform fails to measure the length of the individual crack segment, hence the pixel connectivity enforcement for hole filling and local intensity distribution equalization is proposed to extract the approximate coefficients belonging to the crack segment. The raw data collected previously from our laboratory is used and processed using the proposed methodology, the obtained results are satisfactory and very encouraging as compared to earlier results reported in the literature.

Fast and accurate concealed dangerous object detection for millimeter-wave images

Millimeter-wave (MMW) imaging is a touch-free method for security inspection at railway stations and airports. However, automatic detection of dangerous objects in MMW images is challenging; the characteristics of images are remarkable with variable appearances of an object, different contrasts, and lower resolutions. We propose a fast and accurate concealed dangerous object detection method called MMW image Detection (MMWDet). MMWDet comprises two stages: single-image detection and multi-angle image detection. In the single-image detection stage, we design a more robust feature fusion relationship between adjacent feature maps in a feature pyramid network to achieve a more robust feature representation of an object. Meanwhile, we employ localization confidence rather than category confidence in the classification branch to address the negative sample interference in the training process. Then, multi-angle image detection is proposed to reduce the false and missed detection rate based on a single image by refining the detection results of multi-angle images, which maps and filters the prediction boxes frame by frame based on matching pairs of adjacent image feature points. We collected a MMW dataset that included 38k images. Extensive experiments on the MMW dataset demonstrate the effectiveness and improved performance of the proposed method compared with advanced general detection methods.