Passive Millimeter Wave Imaging Research Articles

Antenna array layout optimization of collaborative passive millimeter-wave imaging system

Based on the advantages of phased array and synthetic aperture imaging method in passive millimeter-wave imaging,a collaborative passive millimeter-wave imaging system was developed for real-time and high resolution imaging in human security application. In the horizontal orientation,by applying annealing algorithm,different aperture horn antennas were determined for the improvement of the phased array performance,which was generally compared with the maximum main beam effective and most smoothable sidelobe level. In vertical orientation,the synthetic aperture,namely,the sparse antenna array was designed by the criterion of the most uniform redundant baseline,while baseline absence should be avoided. Finally,simulation results reveal that with the optimized antenna array the requirement of increasing singal-to-noise ratio( SNR)in power measurement in the collaborative passive imaging system can be fulfilled.

A PMMW image denoising based on adaptive manifolds and high-dimensional mean median filter

Passive millimeter wave (PMMW) imaging which owes a lot of advantages over infa-red and optical imaging has been widely applied in many fields. But due to the confinement of the antenna aperture size, the acquired PMMW image is often noisy and in low resolution. A novel PMMW image denoising algorithm which combines the high-dimensional mean median filter and the adaptive manifolds is proposed in this paper. Our method firstly applies the weighted traversal algorithm to the input image for generating a high-dimensional image data. To accelerate the algorithm, the PCA based dimension reduction is performed subsequently to extract the main information and. Then the PCA retained high-dimensional data is processed by the proposed mean median filter which computes the mean of pixels near the centered median pixel in each dimension. The adaptive manifolds are generated based on the input image and the PCA retained image data. Finally, the adaptive manifolds filter is used to produce the final response. The experiment results indicate that our method has a good ability in removing the noise with edge preserving and is efficient for both the simulated images and the real PMMW image.

근거리 수동 밀리미터파 이미징 시스템을 이용한 영상 측정과 영상처리

본 논문에서는 근접한 거리의 수동 밀리미터파 이미징 시스템을 설계하고 테스트하였다. 제안한 수동 밀리미터파 이미징 시스템은 안테나와 저잡음 증폭기, 다이오드 검출기로 구성된 94GHz 밀리미터파 이미징 센서와 2축 직교 로봇, 데이터 수집장치, 이미징 프로그램으로 구성된 제어 장치로 되어있다. 금속 공구나 IC 카드, 플라스틱 등의 물체에 대해서 영상을 측정하고, 2축 직교 로봇은 래스터 스캔을 통해서 2차원 영상을 얻을 수 있다. 60초 이내로 20×20 픽셀의 영상을 얻어서 출력하고, 후처리를 위해서 영상을 저장한다. 측정 영상의 해상도 개선을 위해서 여러 가지 보간법을 적용하여 성능을 확인하였다.

Segmentation of Concealed Objects in Passive Millimeter-Wave Images Based on the Gaussian Mixture Model

Passive millimeter wave (PMMW) imaging has become one of the most effective means to detect the objects concealed under clothing. Due to the limitations of the available hardware and the inherent physical properties of PMMW imaging systems, images often exhibit poor contrast and low signal-to-noise ratios. Thus, it is difficult to achieve ideal results by using a general segmentation algorithm. In this paper, an advanced Gaussian Mixture Model (GMM) algorithm for the segmentation of concealed objects in PMMW images is presented. Our work is concerned with the fact that the GMM is a parametric statistical model, which is often used to characterize the statistical behavior of images. Our approach is three-fold: First, we remove the noise from the image using both a notch reject filter and a total variation filter. Next, we use an adaptive parameter initialization GMM algorithm (APIGMM) for simulating the histogram of images. The APIGMM provides an initial number of Gaussian components and start with more appropriate parameter. Bayesian decision is employed to separate the pixels of concealed objects from other areas. At last, the confidence interval (CI) method, alongside local gradient information, is used to extract the concealed objects. The proposed hybrid segmentation approach detects the concealed objects more accurately, even compared to two other state-of-the-art segmentation methods.

SHAPE FEATURE ANALYSIS OF CONCEALED OBJECTS WITH PASSIVE MILLIMETER WAVE IMAGING

Millimeter wave (MMW) imaging has found rapid adoption in security applications such as concealed object detection under clothing. However, the imaging quality is often degraded due to resolution limit and low signal level. This study addresses shape feature analysis following concealed object detection. The object region is extracted by multi-level segmentation. Shape features are composed of several descriptors which are object area, perimeter, major and minor axes of the basic rectangle, rectangularity, compactness, and eccentricity. In the experiments, three objects (gun, hand ax, and plastic bottle containing liquid skin aid) concealed under clothing are captured by the passive MMW imaging system. The extracted shape features are compared with the true features from the object model showing good accuracy.

Design of Quasi-Optical Lens Antenna for W-Band Short Range Passive Millimeter-Wave Imaging

A quasi-optical dielectric lens used for W-band focal plane array passive imaging has been developed. The imaging system requires the lens to form beam spot with 3 dB width less than 35 mm at distance of 3500 mm. The powerful optical design software ZEMAX was utilized to design the contours of the lens, and numerical method based on ray tracing and Huygens’ Principle was processed to verify the design result. Measurement result shows that the 3 dB width of the beam spot formed by the lens is 34 mm at distance of 3460 mm, and the beam pattern on imaging plane are equally arranged and the intensity decreases only 0.55 dB while the object lateral deviation increases to 300 mm.

The Application of Improved PSO Algorithm in PMMW Image OSTU Threshold Segmentation

The study of passive millimeter-wave (PMMW) image with low resolution and high noise is a research hotspot recently. Therefore, 2D OSTU threshold segmentation method is introduced into PMMW image processing. However, traditional 2D OSTU method has gorgeous calculation and low operation efficiency. As a result, Multi-Period Particle Swarm Optimization (MP-PSO) algorithm is proposed to achieve the goal of fast segmentation. Experiments show that MP-PSO can not only avoid premature convergence of PSO and improve the accuracy of the search, but also be more accurate and efficient. It is convenient for PMMW image real-time processing.

Development of passive millimeter wave imaging for concealed weapon detection indoors

ABSTRACTThe passive millimeter wave (PMMW) imaging is often successful for outdoors as radiation of natural sky (cold source) provides the required contrast for imaging. When this method is used indoors, to detect weapons hidden inside the human body they fail detection due to the contrast between the target and subject being not obvious from lack of sky radiation. An active illumination although provides the desired contrast for the image recognition, but cause the image “blind” and “flicker” resulting in poor quality of imaging and target identification. This article describes a PMMW imaging security systems suitable for indoor usage. This system includes a large‐diameter parabolic antenna for convergence of target radiation, a front end array consisting of 9 94 GHz imaging radiometer, eight Ka‐band source with discrete frequency and discrete radiation direction to radiating harmonic illumination, and provide affordable energy “like noise” radiation, avoiding image's glint under coherent radiation. The results obtained from experiments on indoor detection of concealed weapon in human body validate the theory of proposed imaging systems, its feasibility, and practical utility. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:1701–1706, 2014

PASSIVE MILLIMETER WAVE IMAGE DENOISING BASED ON ADAPTIVE MANIFOLDS

Since the characters of poor inherent resolution and low signal-to-noise limit the application of the passive millimeter wave (PMMW) image, it is particularly important to improve the resolution and denoise the PMMW image. In this paper, the adaptive manifolds flltering algorithm based on non-local means (AM-NLM) is illustrated in detail. And an improved version of AM-NLM flltering algorithm is proposed for processing the PMMW image. The proposed algorithm flrstly applies the AM-NLM flltering to obtain the basic denoised PMMW image. Then the image enhancement based on Laplacian of Gaussian operator is performed to enhance the edge of the target in PMMW image. Finally, the hard-threshold flltering with difierent thresholds is adopted to fllter each dimension to achieve the flnal flltering response. Experimental results have shown that the proposed PMMW flltering algorithm has better and more satisfactory performance compared to AM-NLM, both in subjective visual efiect and objective image quality metric. Additionally, our proposed algorithm is also available for real PMMW images.

Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization

A semiblind image deconvolution algorithm with spatially adaptive total variation (SATV) regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish flat areas from edges. Meanwhile, the split Bregman method is used to optimize the proposed SATV model. The proposed algorithm integrates the spatial constraint and parametric blur-kernel and thus effectively reduces the noise in flat regions and preserves the edge information. Comparative results on simulated images and real passive millimeter-wave (PMMW) images are reported.

Parametric blind deconvolution for passive millimeter wave images with framelet regularization

A poor inherent resolution capability of the passive millimeter-wave (PMMW) imaging becomes a problem in many applications. This paper proposes a parametric blind deconvolution approach for improving the resolution of PMMW images with low signal-to-noise ratio. Image deconvolution is a challenging ill-posed inverse problem when only partial knowledge of the point spread function (PSF) is available, therefore, regularization techniques need to be used. To restore high quality PMMW image, framelet based regularization constraint is incorporated into parametric blind PMMW image deconvolution framework, which is modeled as an alternative optimization problem about the image and the PSF. The PSF is modeled as a parametric form to restrict the PSF solution space. Furthermore, the split Bregman iteration is used to solve the resulting minimization problem. Comparative experimental results on simulated and real PMMW images show that the proposed method can effectively suppress noise, reduce artifacts, and improve the spatial resolution.

Indoor Passive Millimeter-Wave Imaging for Concealed Object Detection

Passive millimeter-wave (PMMW) imaging is an effective technique for concealed objects detection. A PMMW imaging system optimized for corresponding with indoor surroundings is developed and tested. The metal is deposited on the surface of carbon fiber by chemical plating process and the temperature contrast between the human body and concealed objects can be improved by adding the metal deposited carbon fiber to concealed objects. An image enhancement algorithm is proposed on the basis of the combination of wavelet transformation and top-hit transformation, and the experiment results demonstrate that the algorithm proposed is highly efficient for the indoor passive millimeter-wave images. Furthermore, Lucy-Richardson iterative, restricted least square algorithm and user-defined palliative module filtering are combined to process the detecting images, and then the edge detection can be effectively realized using Canny arithmetic operator.

Parametric semi-blind deconvolution algorithm with Huber–Markov regularization for passive millimeter-wave images

Passive millimeter-wave (PMMW) images often suffer common problems of noise and blurring. A new method is proposed to estimate the instrument response function (IRF) and desired image simultaneously. The proposed variational model integrates the adaptive weight data term, image smooth term, and IRF smooth term. The major novelty of this work is that Huber–Markov regularization is adopted for PMMW image restoration, which can preserve structural details as well as suppress noise effectively. The IRF is parametrically formulated as a Gaussian-shaped function based on experimental measurements through the utilized PMMW imaging system. The alternation minimization iterative method is applied to achieve the IRF width and desired image. Comparative experimental results with some real PMMW images reveal that the proposed approach can effectively suppress noise, reduce ringing artifacts, and improve the spatial resolution.

Compressive Blind Image Deconvolution

We propose a novel blind image deconvolution (BID) regularization framework for compressive sensing (CS) based imaging systems capturing blurred images. The proposed framework relies on a constrained optimization technique, which is solved by a sequence of unconstrained sub-problems, and allows the incorporation of existing CS reconstruction algorithms in compressive BID problems. As an example, a non-convex lp quasi-norm with is employed as a regularization term for the image, while a simultaneous auto-regressive regularization term is selected for the blur. Nevertheless, the proposed approach is very general and it can be easily adapted to other state-of-the-art BID schemes that utilize different, application specific, image/blur regularization terms. Experimental results, obtained with simulations using blurred synthetic images and real passive millimeter-wave images, show the feasibility of the proposed method and its advantages over existing approaches.

Passive millimeter wave synthetic aperture imaging system

Due to some problems (such as abnormal time consuming, low spatial resolution and large system volume) existing in the traditional passive millimeter wave (PMMW) imaging system, the application of traditional imaging system based on real aperture incoherent technology is limited on many occasion (security/medical check, etc). These defects can be overcame by replacing the traditional bulky imaging systems with two dimensional antenna arrays and electronic processors. This paper discusses preliminary design work for a demonstrator of a near range millimeter wave synthetic aperture passive imaging system. In the synthetic aperture system developed in this paper, the theory of near range synthetic aperture imaging technology is discussed, then a simple 2-d PMMW imaging prototype is designed for obtaining high-resolution PWMM images on the basis of this theory. Finally, we verify the feasibility of the system by numerical simulation. The experimental result demonstrates the superior performance in improving the spatial resolution of PMMW images.

INITIAL RESULTS OF A PASSIVE MILLIMETER-WAVE IMAGER USED FOR CONCEALED WEAPON DETECTION BHU-2D-U

A passive millimeter-wave imager BHU-2D-U has been developed by Beihang University. This imager is designed for detecting concealed weapons on human body. It is a subsequent model of BHU- 2D, which is also developed by the same group. In this paper, the improvements of BHU-2D-U over BHU-2D are introduced. Firstly, BHU-2D-U is used for whole body scan, which is difierent from BHU- 2D. Thus, the fleld of view (FOV) of the new imager is enlarged and a new antenna array type is adopted. Secondly, the enlarged FOV requires smaller antenna spacing and compact receiver arrays. In order to reduce the volume of the receivers, I/Q demodulators operating in double side band mode are adopted because it does not need the image-reject fllter, which is usually a bulky one. Thirdly, the quantity of the correlators increases because the number of receiving elements is doubled. To cope with the increase of the correlator quantity, a multiplexing technique is used in the FPGA internal layout of the correlator array unit. After a brief introduction, the system design and conflguration is illustrated in detail. Finally, imaging experiments on a person with concealed weapon are conducted, by which the design and image reconstruction algorithms are verifled. To conclude, initial results of BHU-2D-U have proved that the improvements are efiective.

NEAR FIELD IMAGE RECONSTRUCTION ALGORITHM FOR PASSIVE MILLIMETER-WAVE IMAGER BHU-2D-U

A passive millimeter-wave imager BHU-2D-U based on synthetic aperture interferometric radiometer (SAIR) technique has been developed by Beihang University. The imager is designed for detecting concealed weapons on human body and operated under the near-field condition of the antenna array, thus the conventional Fourier imaging theory does not apply. In this paper, an accurate numerical image reconstruction algorithm using regularization theory is proposed. By means of adding a prior information of desired brightness temperature image, the influences of measurement noise and focusing error on the reconstructed image have been reduced. Numerical simulations and experiments on BHU-2D-U have been performed to verify the superiorities of the proposed algorithm over the corrected Fourier method and the Moore-Penrose pseudo inverse method. The results demonstrate that the proposed method is an advantageous imaging algorithm for near-field millimeter-wave SAIR.

LINEAR POLARIZATION SUM IMAGING IN PASSIVE MILLIMETER-WAVE IMAGING SYSTEM FOR TARGET RECOGNITION

In passive millimeter-wave imaging systems used indoors, the radiometric temperature contrast is barely enough for coarse object detection, being usually insu-cient for recognition due to the absence of cold sky. The image contrast results from a combination of emissivity and re∞ectivity which are dependent on the dielectric constant of objects, the angle of incidence, and the polarization direction. To improve the capability of target recognition, we proposed the linear polarization sum imaging method which is based on the combination of the difierent polarization images for increasing the intensity contrast between the target area and the background area. In order to capture the linear polarization sum images of a metal sphere, a metal and a ceramic cup, we designed W-band quasi-optical imaging system which can generate the polarization dependent images by manually changing the linear polarization direction of its radiometer receiver from 0 to …=2 by the step size of …=8. The theoretical and experimental results of the linear polarization sum imaging show that it is capable for achieving good image quality enough to recognize the target.

A PASSIVE MILLIMETER-WAVE IMAGER USED FOR CONCEALED WEAPON DETECTION

An 8mm-band passive millimeter-wave imager BHU-2D has been developed by Beihang University. This imager is designed for detecting concealed weapons on human body. The imager adopts two-dimensional synthetic aperture interferometric radiometer (SAIR) technique which avoids the radiation risk to human body. Compared with scanning technique, SAIR technique could obtain images of a larger fleld of view (FOV) while achieving high imaging rate, which are necessary for security monitoring. In this paper, the imaging principle of SAIR is stated flrstly. Secondly, background cancellation method designed for BHU-2D is interpreted. The technique is used to reduce the complexity as well as the dimensional requirements of the receiving elements. Thirdly, system conflguration is illustrated in detail. Then external point source calibration method is introduced and discussed speciflcally for security check applications. Finally, imaging experiments on a person with concealed weapon are conducted by which the design and image calibration algorithms are verifled. To conclude, experimental results prove that BHU-2D could be employed in security check applications.

A BiCMOS W-Band 2×2 Focal-Plane Array With On-Chip Antenna

This paper presents a W-band 2 × 2 focal-plane array (FPA) for passive millimeter-wave imaging in a standard 0.18 μm SiGe BiCMOS process (fT/fmax = 200/180 GHz). The FPA incorporates four Dicke-type receivers representing four imaging pixels. Each receiver employs the direct-conversion architecture consisting of an on-chip slot folded dipole antenna, an SPDT switch, a low noise amplifier, a single-balanced mixer, an injection-locked frequency tripler (ILFT), an IF variable gain amplifier, a power detector, an active bandpass filter and a synchronous demodulator. The LO signal is generated by a shared Ka-band PLL and distributed symmetrically to four local ILFTs. The measured LO phase noise is -93 dBc/Hz at 1 MHz offset from the 96 GHz carrier. This imaging receiver (without antenna) achieves a measured average responsivity and noise equivalent power of 285 MV/W and 8.1 fW/Hz1/2, respectively, across the 86-106 GHz bandwidth, which results a calculated NETD of 0.48 K with a 30 ms integration time. The system NETD increases to 3 K with on-chip antenna due to its low efficiency at W-band. MMW images have been generated in transmission mode. This work demonstrates the highest integration level of any silicon-based systems in the 94 GHz imaging band.