This paper proposes a novel approach to 3-D microwave imaging using dynamic metasurface antennas in a multistatic configuration. By introducing a panel-to-panel model and a preprocessing technique, raw measurements are converted into the space-frequency domain for efficient data acquisition and reconstruction. Adapting the range migration algorithm in this work enables fast Fourier-based image reconstruction. Simulation results showcase the effectiveness of the proposed method, highlighting its potential for real-world applications.
Deep learning methodologies are extensively applied in addressing two-dimensional (2D) and three-dimensional (3D) computer vision challenges, encompassing tasks like object detection, super-resolution (SR), and classification. Radar imagery, however, contends with lower resolution compared to optical counterparts, posing a formidable obstacle in developing accurate computer vision models, particularly classifiers. This limitation stems from the absence of high-frequency details within radar imagery, complicating precise predictions by classifier models. Common strategies to mitigate this issue involve training on expansive datasets or employing more complex models, potentially susceptible to overfitting. However, generating sizeable datasets, especially for radar imagery, is challenging. Presenting an innovative solution, this study integrates a Convolutional Neural Network (CNN)-driven SR model with a classifier framework to enhance radar classification accuracy. The SR model is trained to upscale low-resolution millimetre-wave (mmW) images to high-resolution (HR) counterparts. These enhanced images serve as inputs for the classifier, distinguishing between threat and non-threat entities. Training data for the dual CNN layers is generated utilising a numerical model simulating a near-field coded-aperture computational imaging (CI) system. Evaluation of the resulting dual CNN model with simulated data yields a remarkable classification accuracy of 95%, accompanied by rapid inference time (0.193 seconds), rendering it suitable for real-time threat classification applications. Further validation with experimentally generated reconstruction data attests to the model’s robustness, achieving a classification accuracy of 94%. This integrated approach presents a promising solution for enhancing radar imagery analysis accuracy, offering substantial implications for real-world threat detection scenarios.
In recent years, dynamic metasurface antennas (DMAs) have been proposed as an efficient alternative platform for computational imaging, which can drastically simplify the hardware architecture. In this paper, we first mathematically describe the existing solution to be able to convert raw measurements obtained by a DMA in the frequency-space domain into raw data on Fourier bases. Next, an optimization problem based on compressive sensing theory is defined, through which only a limited share of the total frequency/spatial data will be needed. The converted/retrieved data are used to reconstruct the image in the Fourier domain. The performance of the corresponding image reconstruction techniques (with/without Stolt interpolation operation) is evaluated in terms of the quality of the reconstructed image (both visually and quantitatively) and computational time with computer simulations.
In this paper, first, the structure of a linear sparse periodic array for two-dimensional scanning is described. Then, based on its characteristics, an algorithm is presented for fast image reconstruction of the scene in a near-field (NF) multistatic terahertz imaging scenario. Although the basis of this algorithm is developed in the Fourier domain, it is compatible with the non-uniform structure of the array and also takes into account the phase deviations caused by multistatic imaging in NF. The performance of the proposed approach is evaluated with numerical data obtained from electromagnetic simulations in FEKO as well as experimental data. The results are discussed in terms of computational time on the central processing unit and graphics processing unit as well as the quality of the reconstructed image.
An approach to designing multiple waveforms in a multiple-input multiple-output (MIMO) system is presented so that the full capacity of the transmitting and receiving antennas can be utilized at the same time. On the transmitter-side, the antenna elements are classified into different groups according to their specific signal. On the receive-side, we use a multi-resolution analysis to retrieve the signals of each channel. Due to the superior characteristics of the FMCW signal, especially in terms of sampling, in the proposed approach, an FMCW radar is considered. To adapt the introduced system to multistatic near-field imaging, we use more accurate models than the effective phase center principle. This contributes to the successful reconstruction of the scene image by efficient Fourier-based image reconstruction methods. The performance of the proposed approach is confirmed by numerical simulations.
Computational millimetre-wave (mmW) imaging and machine learning have followed parallel tracks since their inception. Recent developments in computational imaging (CI) have significantly improved the imaging capabilities of mmW imaging systems. Machine learning algorithms have also gained huge popularity among researchers in the recent past with several approaches being investigated to make use of them in imaging systems. One such algorithm, image classifier, has gained significant traction in applications such as security screening and traffic surveillance. In this article, we present the first steps towards a machine learning integrated CI physical model for image classification at mmW frequencies. The dataset used for training CI system is generated using the developed single-pixel CI forward-model, eliminating the need for traditional raster-scanning based imaging techniques.
KEYWORDS: Radar, Receivers, Radar signal processing, Antennas, Signal processing, Information security, Data acquisition, Compressed sensing, Complex systems, Coded apertures
Radar systems for direction of arrival (DoA) estimation have been the subject of significant research with applications ranging from security to channel sounding and automotive radars. Conventional DoA retrieval techniques rely on an array based system architecture as the receiving unit, typically synthesized at the Nyquist limit. This classical array based approach makes it necessary to collect the received radar signals from multiple channels, and process it using DoA estimation algorithms to retrieve the DoA information of incoming far-field sources. A challenge with this multi-pixel approach is that, as the operating frequency is increased, the number of antennas (and hence the number of data acquisition channels) also increases. This can result in a rather complex system architecture at the receiver unit, especially at millimetre-wave and submillimetre-wave frequencies. As an enabling technology for the compressing sensing paradigm, a single-pixel based coded aperture can substantially simplify the physical hardware layer for DoA estimation. A significant advantage of this technique is that the received data from the source is compressed into a single channel, circumventing the necessity to have array-based multiple channels to retrieve the DoA information. In this work, we present a passive compressive sensing radar technique for DoA estimation using a single-frequency, dynamically reconfigurable wave-chaotic metasurface antenna as a receiver. We demonstrate that using spatiotemporarily incoherent measurement modes generated by the coded programmable metasurface aperture to encode and compress source generated far-field incident waves into a single channel, we can retrieve high fidelity DoA patterns from compressed measurements.
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