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Passive millimeter-wave (PMMW) imaging systems are widely used in concealed weapon detection due to their passive nature. Improving imaging resolution according to target distance is an essential technique in these systems, and it plays a vital role in detecting concealed weapons. Hence, a variable focal length optical configuration has been applied to a PMMW imaging system to enhance imaging resolution depending on the target distance. In this way, it is possible to set the system for variable target distances. The system parameters, aside from the effective focal length and the target distances, were kept constant throughout the tests. Experiments were carried out taking into account the detection of concealed weapons up to 45 m in indoor environments, and the results were presented. The experiments validate that it is possible to improve the resolution of PMMW images using the optical setup with a variable focal length. As a consequence, this research will contribute to the development of PMMW imaging systems against suicide bombers in the near future to detect concealed weapons at long target ranges.

Passive millimeter-wave imaging systems (PMWIS) are employed for detecting concealed objects by mapping millimeter waves emitted from materials or living tissues. The emitted waves are measured by a receiver or radiometers without employing external wave sources. In this paper, a new super-heterodyne receiver front-end of a PMWIS at 35 GHz with an even order band pass filter is simulated and implemented. The receiver has a suitable temperature resolution for the use of hidden object imaging, is integrated and lightweight assembled on one-layer board. It has a bandwidth of 1.5 GHz, a noise figure (NF) of 2.2, and a temperature resolution of 0.126 K. The even order filter is implemented based on the substrate-integrated waveguide (SIW) technology, with Chebyshev response. The filters are designed at the central frequency of 35 GHz with the bandwidth of 1.5 GHz and one of them has controllable transmission zeroes. The filters are made by printed circuit board technology, employing SIW as resonators, having a high-quality factor of 23.33. Additionally, a triple-stage radio frequency (RF) low noise amplifier has been implemented having the specification of: RF 34.25–35.75 GHz; bandwidth 1.5 GHz; gain >60 dB; NF <2.3 dB, which are better indexes compared to some other works.

This paper presents a Ka band eight-channel integrated packaged phased array receiver front-end for a passive millimeter-wave imaging system. Since multiple receiving channels are integrated in a given package, the mutual coupling issue affecting the channel will deteriorate imaging quality. Therefore, in this study, the influence of channel mutual coupling on the system array pattern and amplitude phase error is analyzed, and the design requirements are proposed according to the results. During the design implementation, the coupling paths are discussed, and passive circuits in the path are modeled and designed to reduce the level of channel mutual coupling and spatial radiation. Finally, an accurate coupling measurement method for a multi-channel integrated phased array receiver is proposed. The receiver front-end achieves a 28~31 dB single channel gain, a 3.6 dB noise figure, less than −47 dB of channel mutual coupling. Furthermore, the array pattern of the two-dimensional 1024 channel system composed of the front end of the receiver is consistent with the simulation, and the receiver’s performance is verified by a human-body-imaging experiment. The proposed coupling analysis, design, and measurement methods are also applicable to other multi-channel integrated packaged devices.

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.

Post-processing techniques for polarimetric passive millimeter wave (MMW) imagery are proposed to display imaging information comprehensively. Initially an image fusion method based on two-scale decomposition is proposed to realize polarimetric passive imagery fusion. The fusion rules are separately designed for base layer and detail layer to reconstruct weight maps. Then an improved technique for displaying polarization information through color is proposed to present polarization features simultaneously with unpolarized imagery. Experimental results demonstrate that the proposed post-processing techniques are capable of presenting more informative imagery.

Passive millimeter-wave (MMW) and TeraHertz (THz) imaging systems have become increasingly popular in recent years due to their cost-effectiveness and non-invasive characteristics compared to active systems, prompting a surge in research interest. Evaluating the quality of reconstructed images used in these systems is essential for revealing the fine details. General image quality metrics such as the structural similarity index (SSIM) and the peak signal-to-noise ratio (PSNR) require a reference image in order to compare the reconstructed image. However, there is a notable gap in the literature regarding the evaluation of reconstruction or deconvolution algorithms with a reference image in the passive MMW/THz bands. This study proposes a reference image generation technique for passive MMW/THz imaging systems using an infrared imaging system that shares a similar physical background. Then, passive MMW/THz images were evaluated using the reference images at varying target distances and spatial resolutions. Besides these, the assessment of passive MMW/THz images with the SSIM and PSNR metrics after the reconstruction algorithms were performed. The metrics SSIM and PSNR, are inadequate in the evaluation of reconstruction algorithms alone in terms of concealed object (CO) detection. Because of this reason, the contrast level (CL) method was proposed to address the application-based shortcomings of PSNR and SSIM metrics. Hence, the image quality metric, CL, indicates that the Richardson–Lucy (RL) algorithm yielded superior results in variable optical configurations and target distances with the aid of CL metric. Finally, contrast enhancement techniques were developed in order to increase the contrast level of the CO. As a result, the introduction of these novel methods—the reference image generation technique using an infrared imaging system in passive MMW/THz bands, the evaluation of the reconstructed images with the application-based CL metric, and contrast enhancement techniques for single-band or multi-band imaging methods—holds the potential for the development of innovative techniques. These advancements may contribute to the creation of new applications within the passive MMW/THz bands, particularly focusing on the improvement of detection methods in the future.

The passive millimeter wave (PMMW) imaging sensor can generate images using the passive detection of the natural millimeter wave radiation from a scene. Despite the advantages of PMMW images in detecting concealed objects under clothing, they have lower resolution and fewer details than visible images. This paper proposes a new method to fuse PMMW and visible images to highlight concealed objects on the human body while preserving the details of the visible images. In this method, the PMMW image is initially segmented into three binary images, target, foreground, and background, utilizing an innovative segmentation algorithm that incorporates histogram-based thresholding and the generation of a saliency map image. Subsequently, the visible and PMMW images are individually decomposed into base and detail subbands using the new Gradient Transform (GT). Then, by individually fusing the base and detail subbands of the PMMW and visible images using innovative L2-norm weighting criteria, the fused image’s base and detail subbands are produced. Based on these criteria, between the two corresponding subbands of the input images, the subband with more detail contributes more to the final fused subband. Finally, the fused image is generated by applying the inverse GT to the newly generated fused subbands. Experimental results demonstrate a notable enhancement in terms of evaluation criteria like Q A B / F and MI , surpassing the most recent algorithms in this field.

In this paper, a high-performance detection algorithm of concealed forbidden objects on human body is presented based on deep neural networks (DNN) and complementary advantages of passive millimeter wave imagery (PMMWI) and visible imagery (VI). With well capacity of penetrability, PMMWI can effectively reveal suspected forbidden objects concealed on human body without harm of ionizing radiation compared with conventional X-ray methods. However, due to its current limited imaging capability, the resolution of PMMWI is still unsatisfactory and easy to result in false alarms. Therefore, by complementarity of superiorities, VI is employed to overcome the deficiency of confusions. In this way, massive image samples of PMMWI and VI are simultaneously acquired and manually annotated as necessary training datasets to carry out deep learning on DNN models so as to achieve high-performance human body profile segmentation on both PMMWI and VI. Then, high-precision region registration of human body profiles is implemented between PMMWI and VI to localize and confirm high confident suspected targets and remove false alarm regions as well. According to the principle of synthetic integration and global optimization, a high performance detection algorithm system is constructed, analyzed, and assessed. A series of comprehensive experiment results demonstrate the outstanding performance of our proposed detection algorithm.

Optical imaging in disease diagnosis and treatment benefits from high spatiotemporal resolution and the availability of numerous optical agents. However, many optical imaging probes are cleared by the reticuloendothelial system, which can lead to probe accumulation in the liver and spleen and hence organ toxicity. By contrast, renal-clearable optical agents (RCOAs) are rapidly excreted from the body via the kidneys, undergoing minimal metabolism. In this Review, we discuss the design principles of RCOAs, with a focus on imaging and disease theranostics (the combination of diagnosis and therapy). Renal excretion of RCOAs makes them intrinsically suitable for targeted kidney imaging, including passive monitoring of the glomerular filtration rate and detection of early kidney injury biomarkers. The pharmacokinetics of RCOAs can further be tailored to prolong their circulation in the blood, allowing deep tumour penetration and high-contrast tumour imaging. Finally, we discuss intraoperative image-guided surgery and optical urinalysis, and suggest future applications of RCOAs. Intravenously injected renal-clearable optical agents (RCOAs) are rapidly cleared by the kidneys, allowing disease diagnosis and treatment by optical imaging, while avoiding unwanted tissue accumulation and adverse effects. This Review discusses the design of RCOAs for kidney imaging, kidney injury detection, cancer theranostics, intraoperative image-guided surgery and optical urinalysis.

Multiple illuminators of opportunity (IOs) and a large rotation angle are often required for current passive radar imaging techniques. However, a large rotation angle demands a long observation time, which cannot be implemented for actual passive radar system. To overcome this disadvantage, this paper proposes a super-resolution passive radar imaging framework with a sparsity-inducing compressed sensing (CS) technique, which allows for fewer IOs and a smaller rotation angle. In the proposed imaging framework, the sparsity-based passive radar imaging is modeled mathematically, and the spatial frequencies and amplitudes of different scatterers on the target are recovered by the log-sum penalty function-based CS reconstruction algorithm. In doing so, a super-resolution passive radar imagery is obtained by the frequency searching approach. Simulation results not only validate that the proposed method outperforms existing super-resolution algorithms, such as ESPRIT and RELAX, especially in the cases with low signal-to-noise ratio (SNR) and limited number of measurements, but also have shown that our proposed method can perform robust reconstruction no matter if the target is on grid or not.