Self-calibration of large phased-array antennas for radar

Radar image can provide an important basis for target recognition, which has been a hot area of radar technologies. In vortex electromagnetic radar imaging, it is necessary to transmit multi-mode vortex electromagnetic wave, but pure multi-mode vortex electromagnetic wave is difficult to obtain. Inspired by the generation of vortex electromagnetic waves, a rotating antenna is used to achieve high resolution two-dimensional radar imaging in this paper. The azimuth angle of the target in the range of radar beam can be reconstructed by using the Doppler effect generated by the rotation of the antenna when the radar transmits the linear frequency modulation (LFM) signal, and then the high resolution two-dimensional image of the target can be obtained by combining the one-dimensional range image obtained by the dechirp operation. Compared with the existing algorithms, the proposed algorithm has lower requirements on radar performance, and can generate two-dimensional image of the target under staring condition without transmitting complex signals. Simulation results show that the algorithm is effective and robust.

Inverse synthetic aperture radar (ISAR) imaging has the competence of generating two-dimensional (2-D) high-quality images of remote moving targets, while interferometric ISAR (InISAR) is a technique to reproduce three-dimensional (3-D) high-resolution shapes of targets by employing several ISAR images obtained from different antennas. For the target moving with an invariable acceleration, the Doppler frequency shift of its echoes is proved to be time variant; consequently, the Range-Doppler (RD) algorithm will not work properly for acquiring the clear ISAR image of it. To produce high-resolution ISAR/InISAR image of the maneuvering target, a novel algorithm via the peak extraction technique is proposed. The main contributions made by this paper can be listed as follows: 1) by analyzing ISAR imaging with the RD technique for the target moving with a constant acceleration, it is elaborately demonstrated that each scattering point still corresponds to a unique peak in the defocused ISAR image; 2) the expression of the peak location in the defocused ISAR image corresponding to each scattering point is precisely derived; 3) a novel peak extraction technique is developed for forming a high-resolution radar image of the maneuvering target; 4) due to the discovery that the ISAR images obtained in this way can completely preserve the phase information, a new InISAR imaging method via the peak extraction technique is presented consequently. The correctness of the theoretical derivation and the performance of the new algorithms are illustrated through several simulation experiments.

This paper presents a new Range-Doppler Algorithm based on Fractional Fourier Transform (RDA-FrFT) to obtain High-Resolution (HR) images for targets in radar imaging. The performance of the proposed RDA-FrFT is compared with the classical RDA algorithm, which is based on the Fast Fourier Transform (FFT). A closed-form expression for the range and azimuth compression of the proposed RDA-FrFT is mathematically derived and analyzed from the HR Synthetic Aperture Radar (SAR) imaging point of view. The proposed RDA-FrFT takes its advantage of the property of the FrFT to resolve chirp signals with high precision. Results show that the proposed RDA-FrFT gives low Peak Side-Lobe (PSL) and Integrated Side-Lobe (ISL) levels in range and azimuth directions for detected targets. HR images are obtained using the proposed RDA-FrFT algorithm.

Three-dimensional BP Imaging Algorithm using MIMO System

A photonics-based radar for high-resolution 3D microwave imaging is proposed and demonstrated, in which high range resolution is achieved by employing a wideband linearly frequency modulated signal, and high cross-range resolution in both azimuth and elevation is realized by rotating the target around two orthogonal axes to compose a 2D inverse synthetic aperture. The proposed radar performs photonics-based in-phase/quadrature up-conversion and de-chirp processing in the transmitter and receiver, respectively, which features a compact structure with low-sampling-rate electronics in the receiver. An experiment is carried out. The established radar works in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -band, with bandwidth as large as 8 GHz. Captured echo signals at different rotation angles of the target are processed to a 3D image, in which the voxel values are scattering intensities at different spatial positions.

High-resolution and wide-swath synthetic aperture radar (SAR) imaging can be achieved by the azimuth multichannel system. The minimum variance distortionless response (MVDR) beamformer can be utilized to suppress azimuth ambiguities. However, the presence of channel phase errors significantly deteriorates the performance of the azimuth multichannel SAR system. Instead of employing subspace techniques, this letter proposes a robust channel phase error calibration algorithm via maximizing the MVDR beamformer output power. Compared with the conventional subspace-based calibration methods, there is no redundancy of channels required to estimate the subspaces in the proposed algorithm. Also, the proposed algorithm is relatively robust, because it avoids the subspace swap phenomenon, which probably takes place at low signal-to-noise ratios for the subspace techniques. Moreover, the proposed method has the advantage of estimating the channel phase errors without covariance matrix decomposition, which reduces the computation load. The simulation experiments and the real data processing validate the effectiveness of the proposed calibration method.

The vortex electromagnetic (EM) wave carrying orbital angular momentum (OAM) offers a new degree of freedom for radar techniques, although to date its application to synthetic aperture radar (SAR) imaging has not yet been widely reported. In this letter, a SAR imaging model based on vortex EM waves is derived. Ku-band vehicle-borne vortex EM waves SAR experiment using phased uniform circular array antennas is reported for the first time. A detailed comparison with conventional plane EM waves SAR imaging is given in terms of signal-to-noise ratio and azimuth resolution. The experimental results demonstrate the effectiveness of the proposed imaging model, and indicate that vortex EM waves can be exploited in SAR imaging with higher azimuth resolution than plane EM waves. OAM, which is completely independent of frequency and polarization, is a new degree of freedom in SAR imaging. This work shows the new applications of vortex EM waves, which paves the way for the development of novel SAR systems.

Radar imaging of noncooperative targets is an interesting application of all-weather high-resolution coherent radars. However, these images are usually blurred when using the standard range-Doppler algorithm, if a long coherent processing interval (CPI) is used, and motion compensation techniques are hence necessary to improve imaging quality. If the CPI is reduced enough, target scatterers do not migrate of resolution cells and their corresponding Doppler frequencies are constant. Hence, for a short CPI, motion compensation is not longer necessary, but Doppler resolution gets degraded. In that case, superresolution algorithms may be applied. Here, we compare the superresolution-based focusing techniques with motion compensation-based methods. Our conclusion is that imaging quality after employing the superresolution approaches is not improved and, consequently, the use of motion compensation-based approaches to focus the radar images cannot be circumvented. Simulated and real data from high-resolution radars have been used to make the comparisons.

A scheme of high-resolution inverse synthetic aperture radar (ISAR) imaging based on photonic receiving is demonstrated. In the scheme, the linear frequency modulated (LFM) pulse echoes with 8 GHz bandwidth at the center frequency of 36 GHz are directly sampled with the photonic analog-to-digital converter (PADC). The ISAR images of complex targets can be constructed without detection range swath limitation due to the fidelity of the sampled results. The images of two pyramids demonstrate that the two-dimension (2D) resolution is 3.3 cm × 1.9 cm. Furthermore, the automatic target recognition (ATR) is employed based on the high-resolution experimental dataset under the assistance of deep learning. Despite of the small training dataset containing only 50 samples for each model, the ATR accuracy of three complex targets is still validated to be 95% on a test dataset with the equal number of samples.

A novel biologically-inspired target detection method based on saliency analysis for synthetic aperture radar (SAR) imagery

A novel algorithm for detection and classification of 3D objects in high-resolution microwave radar images for security screening purposes is proposed. To detect and automatically recognize concealed objects a computer vision approach is applied. A three-step detection procedure based on SIFT, Histogram thresholding and matched filtering is developed for 3D image processing. This novel procedure has proved to be capable of detecting and classifying a set of objects which are likely to be carried by an individual at an airport security checkpoint.

To investigate the limits of building detection from very high-resolution (VHR) synthetic aperture radar (SAR) images, a new method, based on statistical and structural information fusion, is proposed in this paper. The proposed method contains two stages: First, using order statistics constant false alarm rate (OS-CFAR) and power ratio (PR) detectors, a set of detections are made. These detections have different statistical properties, compared to the other objects, and these properties are selected for discriminating buildings from clutters. Second, the morphological analysis is used for increasing the precision of the detection. In this stage, segments, which have the most similarities to buildings in terms of shape and size, are extracted via various structural elements (SEs). The final result is obtained by fusing the two sets of detections. The experimental results on the four real VHR SAR images show that the proposed method has a high detection rate (DR) and low false alarm rate (FAR).

The application of high-resolution synthetic aperture radar (SAR) images from aerial and satellite sensors challenges the researchers for new and effective interpretation tools. Although most of the main axes in the road network may be detected by a skilled human observer looking for dark or bright linear structures, automatic detection remains a difficult task. In this paper, we proposed a new simple approach to extract main road network in high-resolution SAR images automatically. The approach is based on three steps. The first step is pre-filtering the initial SAR images by using a two-way thresholding process in order to discard uninteresting parts of image. In the second step, the results of the first step are input and use the Hough transform to identify the roads respectively. The last step is based on a feature fusion technique. The road networks detected are combined with the fusion operators. We show the improved results from some examples. It is proved that this approach is effective for the straight highways in high-resolution images

We report improvements in the scanning speed and standoff range of an ultra-wide bandwidth terahertz (THz) imaging radar for person-borne concealed object detection. Fast beam scanning of the single-transceiver radar is accomplished by rapidly deflecting a flat, light-weight subreflector in a confocal Gregorian optical geometry. With RF back-end improvements also implemented, the radar imaging rate has increased by a factor of about 30 compared to that achieved previously in a 4 m standoff prototype instrument. In addition, a new 100 cm diameter ellipsoidal aluminum reflector yields beam spot diameters of approximately 1 cm over a 50&times;50 cm field of view at a range of 25 m, although some aberrations are observed that probably arise from misaligned optics. Through-clothes images of concealed pipes at 25 m range, acquired in 5 seconds, are presented, and the impact of reduced signal-to-noise from an even faster frame rate is analyzed. These results inform the requirements for eventually achieving sub-second or video-rate THz radar imaging.

Advanced driver assistance systems (ADASs) and autonomous vehicles rely on different types of sensors, such as camera, radar, ultrasonic, and LiDAR to sense the surrounding environment. Compared with the other types of sensors, millimeter-wave automotive radar has advantages in terms of cost and reliability under bad weather conditions (e.g., snow, rain, fog) and doesn't suffer from light condition variations (e.g., darkness). Typical radar devices used in today's commercial vehicles with ADAS features produce sparse point clouds in low angular resolution with a limited number of antennas. In this paper, we present a machine learning-aided signal processing chain to suppress the radar imaging blur effect introduced by the phase migration in time-division multiplexing multi-input multi-output (MIMO) radar, to generate low-level high-resolution radar bird's eye view (BEV) spectra with rich object's features. Compared with radar point clouds, there is no information loss in radar BEV spectra. We then propose a Temporal-fusion, Distance tolerant single stage object detection Network, termed as, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TDRadarNet</i> , and an enhanced version, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TDRadarNet+</i> to robustly detect vehicles in both long and short ranges on radar BEVs. We introduce a first-of-its-kind multi-model dataset, containing 14,800 frames of high-resolution low-level radar BEV spectra with synchronized stereo cameras RGB images and three-dimensional (3D) LiDAR point clouds. Our dataset achieves 0.39 m range resolution and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.2^\circ$</tex-math></inline-formula> degree azimuth angular resolution with 100 meters maximum detectable range. Moreover, we create a sub-dataset, the Doppler Unfolding dataset, containing 244,140 beam vectors extracted from the 3D radar data cube. With extensive testing and evaluation, we demonstrate that our Doppler unfolding network achieves 93.46% Doppler unfolding accuracy. Compared to YOLOv7, a state-of-the-art image-based object detection network, TDRadarNet achieves a 70.3% Average Precision (AP) for vehicle detection, demonstrating a 21.0% improvement; TDRadarNet+ achieves a 73.9% AP, showing a 24.6% improvement in performance.