Three-dimensional Radar Imaging Research Articles

A Quantitative Precipitation Estimation Method Based on 3D Radar Reflectivity Inputs

Quantitative precipitation estimation (QPE) by radar observation data is a crucial aspect of meteorological forecasting operations. Accurate QPE plays a significant role in mitigating the impact of severe convective weather. Traditional QPE methods mainly employ an exponential Z–R relationship to map the radar reflectivity to precipitation intensity on a point-to-point basis. However, this isolated point-to-point transformation lacks an effective representation of convective systems. Deep learning-based methods can learn the evolution patterns of convective systems from rich historical data. However, current models often rely on 2 km-height CAPPI images, which struggle to capture the complex vertical motions within convective systems. To address this, we propose a novel QPE model: combining the classic extrapolation model ConvLSTM with Unet for an encoder-decoder module assembly. Meanwhile, we utilize three-dimensional radar echo images as inputs and introduce the convolutional block attention module (CBAM) to guide the model to focus on individual cells most likely to trigger intense precipitation, which is symmetrically built on both channel and spatial attention modules. We also employ asymmetry in training using weighted mean squared error to make the model concentrate more on heavy precipitation events which are prone to severe disasters. We conduct experiments using radar data from North China and Eastern China. For precipitation above 1 mm, the proposed model achieves 0.6769 and 0.7910 for CSI and HSS, respectively. The results indicate that compared to other methods, our model significantly enhances precipitation prediction accuracy, with a more pronounced improvement in forecasting accuracy for heavy precipitation events.

Influence of parameter measurement errors on the three-dimensional radar image forming in a distributed radar consisting of a rotating receiving and stationary transmit-receive modules

The purpose of the article is to consider the influence of the accuracy of measuring the rotation parameters of the routing receiving antenna and observation conditions on the formation of 3D radar images using a distributed radar consisting of a rotating receiver, and a fixed stationary transceiver antenna. The article provides a detailed mathematical description of the process of calculating the standard deviation of the height measurement of the observed terrain depending on the next parameters: the slant range, the height of the radar carrier, the radius of rotation of the phase center of the receiving antenna, the vertical distance between the plane of rotation of the phase center of the receiving antenna and the phase center of the transceiver antenna, the phase of the signal at the output of the interferometer, and the angular position of the phase center for the receiving antenna. The impact of errors has been evaluated using mathematical modeling. The presented modeling results show that errors in measuring the rotation parameters and observation conditions lead to errors in the reconstruction of the topography of the observed area. In particular, errors in estimating the signal phase at the interferometer output have been studied; the results showed that the errors in estimating the height of the studied points on the Earth's surface can reach 1 m with signal-to-noise ratio (SNR) of 20 dB and more than 10 m at 5 dB. In addition, with a SNR of 5 dB, it has been observed a distortion of the reconstructed terrain and violation of its contrast. In this case, the standard deviation of the measurement of relief height significantly depends on the angular position of the phase center of the receiving antenna. In this regard, to ensure a minimum error in estimating the height of the earth's surface using the interferometric system implemented by a stationary transceiver module and the circularly rotating receiving module, it is necessary to correctly choose the time corresponding to the angle of rotation of the phase center receiving antenna.

Science results from sixteen years of MRO SHARAD operations

In operation for >16 years to date, the Mars Reconnaissance Orbiter (MRO) Shallow Radar (SHARAD) sounder has acquired data at its nominal 300–450 m along-track and 3-km cross-track resolution covering >55% of the Martian surface, with nearly 100% overlap in coverage at that scale in the polar regions and in a number of smaller mid-latitude areas. While SHARAD data have opened a new window into understanding the interior structures and properties of Martian ices, volcanics, and sedimentary deposits up to a few kilometers in depth, they have also led to new revelations about the deeper interior and the behavior of the planet's ionosphere. Here we summarize the data collected by SHARAD over this time period, the methods used in the analysis of that data, and the resulting scientific findings. The polar data are especially rich, revealing complex structures that comprise up to several dozen reflecting interfaces that extend to depths of 3 km, which inform the evolution of Martian climate in the late Amazonian period. SHARAD observations of mid-latitude lobate debris aprons and other glacier-like landforms detect strong basal reflections and low dielectric loss, confirming that they are ice-rich debris-covered glaciers. In other mid-latitude terrains, SHARAD data demonstrate the presence of widespread ground ices, likely at lower concentrations. SHARAD signals also probe non-icy materials, mapping out stacked lava flows, probing low-density materials thought to be ash-fall deposits, and occasionally penetrating sedimentary deposits, all of which reveal the structures and interior properties diagnostic of emplacement processes. SHARAD signals are impacted by their passage through the Martian ionosphere, revealing variations in time and space of the total electron content linked with the remanent magnetic field. Advanced techniques developed over the course of the mission, which include subband and super-resolution processing, coherent and incoherent summing, and three-dimensional (3D) radar imaging, are enabling new discoveries and extending the utility of the data. For 3D imaging, a cross-track spacing at the nominal 3-km resolution is more than sufficient to achieve good results, but finer spacing of 0.5 km or less significantly improves the spatially interpolated radar images. Recent electromagnetic modeling and a flight test show that SHARAD's signal-to-noise ratio can be greatly improved with a large (∼120°) roll of the spacecraft to reduce interference with the spacecraft body. Both MRO and SHARAD are in remarkably fine working order, and the teams look forward to many more years in which to pursue improvements in coverage density, temporal variability in the ionosphere, and data quality that promise exciting new discoveries at Mars.

Fast three-dimensional image reconstruction algorithm for layered medium target with array radar system

High-resolution near-field three-dimensional (3D) radar imaging techniques with millimeter and terahertz wave have played an increasingly significant role in applications including hidden object detection and nondestructive testing. Currently, imaging methods suffer from low efficiency when imaging defects in stratified media. This is caused by the inadaptability of the fast Fourier transform in the frequency domain due to the alteration of wave numbers in different media. We propose an imaging method based on the nonuniform fast Fourier transform to overcome the problem. Numerical simulations and experiments were carried out to verify our idea. Results show that our proposed method can greatly improve the imaging efficiency while maintaining the imaging quality. This work is of great significance for future development of nondestructive testing technology based on millimeter wave/terahertz imaging.

Multi-Objective Classification of Three-Dimensional Imaging Radar Point Clouds: Support Vector Machine and PointNet

Multi-Objective Classification of Three-Dimensional Imaging Radar Point Clouds: Support Vector Machine and PointNet

Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN

High-quality three-dimensional (3-D) radar imaging is one of the challenging problems in radar imaging enhancement. The existing sparsity regularizations are limited to the heavy computational burden and time-consuming iteration operation. Compared with the conventional sparsity regularizations, the super-resolution (SR) imaging methods based on convolution neural network (CNN) can promote imaging time and achieve more accuracy. However, they are confined to 2-D space and model training under small dataset is not competently considered. To solve these problem, a fast and high-quality 3-D terahertz radar imaging method based on lightweight super-resolution CNN (SR-CNN) is proposed in this paper. First, an original 3-D radar echo model is presented and the expected SR model is derived by the given imaging geometry. Second, the SR imaging method based on lightweight SR-CNN is proposed to improve the image quality and speed up the imaging time. Furthermore, the resolution characteristics among spectrum estimation, sparsity regularization and SR-CNN are analyzed by the point spread function (PSF). Finally, electromagnetic computation simulations are carried out to validate the effectiveness of the proposed method in terms of image quality. The robustness against noise and the stability under small are demonstrate by ablation experiments.

Doppler Velocity Enhanced Range Migration Algorithm for High Resolution and Noise-Robust Three-Dimensional Radar Imaging

Millimeter-wave and microwave short-range radar are among the most promising environmental sensing methods, with applications to situations that are otherwise optically invisible. This paper presents a Doppler velocity-enhanced range migration algorithm (RMA) that achieves both high spatial resolution and low-complexity three-dimensional (3-D) radar imaging. In this method, we address the problem of human body recognition with motion using the assumption that the motion of different human parts generates distinct micro-Doppler variations in the radar data. These variations could not only separate the RMA images with different Doppler velocities in a coherent integration-based radar imaging process, but enhances a noise reduction effect by decomposing in the Doppler velocity space. To achieve a low complexity in 3-D imaging, we incorporate the RMA method and Doppler velocity-based data decomposition. The results of numerical and experimental tests on a realistic human phantom with a walking motion demonstrate that our method provides more informative and highly separated 3-D images, even in much low SNR situation.

Identifying airport runway pavement diseases using complex signal analysis in GPR post-processing

Non-destructive detection of pavement diseases on airport runways is of great importance for airport maintenance and ensuring the normal use of airport runways. In this study, a full coverage scan of an airport pavement was conducted using the MALA three-dimensional ground penetrating radar (GPR). Based on complex signal analysis technology, the Hilbert transform were used to comprehensively interpret the anomalous features of the scanned images at specific disease location. The theoretical void volume of pavement diseases was calculated using MATLAB image processing and Origin software. The analysis showed that the main airport pavement diseases were loose layers and small holes, which were more comprehensively revealed by three-dimensional radar images. The three instantaneous parameter spectrums were used to verify and optimize the initial results obtained with the Reflexw post-processing software. The theoretical void volume was calculated using the pixel equivalent method. The void value of typical diseases was 49.38 m3, which was approximately 2.97 times of the calculated value with the rSlicer post-processing technique. This proposed technique can provide a reference for the exact recognition of disease types and the corresponding calculation of grouting reinforcements while overcoming the shortcomings of the single parameter method with multi-parameter analysis.

Gaussian Mixture Model Parameter Optimization in Range Points Migration-Based Three-Dimensional Radar Imaging

Millimeter-wave (MMW) imaging radar is one of the major environmental sensors in recent self-driving or driver assistance systems and provides environmentally robust sensing even in optically challenging conditions. As a promising three-dimensional (3-D) radar imaging technique, this study focuses on the range points migration (RPM) method, which has advantages in terms of accuracy and low complexity. In the original RPM algorithm, the parameters are manually or empirically determined by considering the sensor configuration and target shape or distance. To address this limitation, we introduced a novel parameter optimization scheme based on the Gaussian mixture model (GMM) and the expectation maximization (EM) algorithm. In addition, we used the k-space decomposition-based parameter determination scheme to determine target shape-dependent parameter selection. The results, assuming the human body imaging scenario, showed that our proposed method retains a highly accurate target image without requiring empirical parameter selection.

Photoelectric Sensor Circuit and Image Segmentation Method for Radar System

In this exploration, based on the principle and system parameters of laser three-dimensional (3D) radar imaging technology, the corresponding photoelectric sensor circuit scheme is formulated. The sense circuit of avalanche photon diode (APD) converts the signal through the transresistance amplifier circuit. Then, LMH6629 is selected as a precision amplifier with low input noise voltage and low input error current. The capacitance is used as a compensation element to compensate the phase. For the power supply scheme, choosing the mode of switching power supply and LDO to work together can improve the efficiency of power supply and reduce the output of current ripple. At the same time, semantic segmentation is carried out for the obtained photoelectric images. Based on the traditional spatial pyramid pooling algorithm, the fusion of mean intersection over union and cross information entropy loss function is introduced to improve the weight of local image region. In the experiment, Multisim software is used to simulate the circuit. The APD reverse bias voltage is set to 90 V, and the multiplication coefficient is 98.7. The feedback resistance, bandwidth, phase compensation capacitance and other parameters are further calculated. It is found that there is obvious self-excited phenomenon in the output waveform of the transresistance amplifier without phase compensation capacitor. When the feedback capacitance reaches 0.8 pF, the oscillation phenomenon is obviously reduced; further calculation shows that the bandwidth of transresistance amplifier is 230 MHz, and the noise of APD power supply is mainly caused by BUCK switching power supply switch when the bottom noise of oscilloscope is ignored. However, the noise is suppressed under the action of the back-end LDO device; after the loss function is introduced, the contour of the photoelectric image is preserved completely, and then the more accurate segmentation results are obtained.

Microwave Photonic MIMO Radar for Short-Range 3D Imaging

In the fields of radar imaging and detection, the use of optical components appears as an attractive solution to overcome limitations of current technologies. This paper focuses on the development of a new class of microwave-photonic radar imaging systems enabling volumetric image reconstruction. These operations are made possible using a multiple-input multiple-output antenna array architecture combined with the conception of a dedicated optical multiplexing technology to limit the complexity of the whole reception chain. Particular emphasis is placed on the development of mathematical formalisms specifically adapted to these microwave-photonic radar architectures, making it possible to model the measurements carried out and to reconstruct three-dimensional radar images. These solutions are validated by the reconstruction of complex scenes with a multiple-input multiple-output radar made of up to 16 transmitters and 16 receivers.

Evaluation of Standoff Multistatic 3-D Radar Imaging at 300 GHz

In the past decades, monostatic security screening radars in the millimeter and submillimeter wave bands have progressed into feasible threat detection prototypes. Although these systems have been proven to be successful in revealing concealed weapons in static and controlled environments, future security checkpoints demand high detection probabilities even with moving subjects. In this regard, monostatic systems can only generate images from a single viewpoint, omitting the detection of person-borne threats in nonviewed areas of the body. In addition, the vanishing and glint caused by specular reflection as the person moves through the checkpoint are still unsolved problems. In this article, a proof-of-concept implementation of a multistatic architecture to bypass these issues for a 300 GHz three-dimensional imaging radar is evaluated. The approach is based on the distribution of separated transmitters to provide enough angular diversity to maintain target detection in at least one of the resulting images. Measured results from various viewpoints are presented to show the usefulness of such a configuration with imaging scenarios of common threats.

Tensor Representation for Three-Dimensional Radar Target Imaging With Sparsely Sampled Data

Three-dimensional (3-D) radar imaging can provide additional information along elevation dimension about the target with respect to the conventional 2-D radar imaging, but usually requires a huge amount of data collected over 3-D frequency-azimuth-elevation space, which motivates us to perform 3-D imaging by using sparsely sampled data. Traditional compressive sensing (CS) based 3-D imaging methods with sparse data convert the 3-D data into a long vector, and then complete the sensing and recovery steps. This 1-D vectorized model, however, faces challenges of high computational complexity and huge memory usage and may not be viable in real applications. In this article, we solve the 3-D sparse imaging problem efficiently in a tensor way. For this aim, we firstly derive the 3-D imaging model from a tensor perspective under some assumptions. Then we review three kinds of sparse data sampling schemes that are common on the existing 3-D compressive radar imaging applications. Subsequently, with the help of prior information hidden in the radar signal, i.e., sparsity and low-rank property, we propose efficient image reconstruction algorithms for different sampling schemes to produce 3-D images with sidelobes and artifacts suppressed significantly. Finally, extensive experiments based on simulated and real-measured datasets are carried out. Results show that the proposed methods can effectively generate competitive images with small reconstruction error even when the data sampling ratio is low, which confirm the validity of proposed methods.

A Multi-Perspective 3D Reconstruction Method with Single Perspective Instantaneous Target Attitude Estimation

Due to the limited information of two-dimensional (2D) radar images, the study of three-dimensional (3D) radar image reconstruction has received significant attention. However, the target attitude obtained by the existing 3D reconstruction methods is unknown. In addition, using a single perspective, one can only get 3D reconstruction result of a simple target. For a complex target, due to occlusion and scattering characteristics, 3D reconstruction information obtained from a single perspective is limited. To tackle the above two problems, this paper proposes a new method for multi-perspective 3D reconstruction and single perspective instantaneous target attitude estimation. This method consists of three steps. First, the result of 3D reconstruction with unknown attitude is obtained by the traditional matrix factorization method. Then, in order to obtain the attitude of a target 3D reconstruction, additional constraints are added to the projection vectors which are computed from the matrix factorization method. Finally, the information from different perspectives are merged into a single layer information according to certain rules. After the information fusion, a multi-perspective 3D reconstruction structure with better visibility and more information is obtained. Simulation results have proved the effectiveness and robustness of the proposed method.

Recent progress on advanced infrared photodetectors

Modern infrared detector technology has a history of nearly eighty years. Since the first PbS photodiode was put into use during the World War II, infrared detectors have achieved significant progress, even the third-generation infrared systems have been proposed. In the past decades, the traditional infrared detectors represented by HgCdTe, InSb and InGaAs have been widely applied in military, remote sensing, communication, bioscience, and space exploration. However, the increasing applications demand higher performance infrared detectors. Especially in recent years, the intelligent infrared detection technique was strongly demanded in many high-tech fields such as artificial intelligence, virtual reality systems and smart city. Therefore, the fabricating of infrared detection systems with smaller size, lighter weight, lower power, higher performance and lower price has become an urgent task. At present, the infrared photodetectors are in an age of rapid change, and many new type of advanced infrared photodetectors come to the fore quickly. For the purpose of summarizing these detectors, they are reviewed covering four parts: microstructure coupled infrared detector, infrared detector based on band engineering, new type of low-dimensional material infrared detector, and new directions for traditional infrared detectors. In the infrared detection systems, these photodetectors can be fully used for their prominent performance. The microstructure coupled infrared detector can improve chip integration with high quantum efficiency. Precise design of band structure will raise the operating temperature for mid and long wavelenth infrared photodetectors. Owing to the unique structures and physical properties, low-dimensional material infrared photodetectors have shown their potential application value in flexibility and room temperature detection systems. The ability of avalanche photodetector to detect the extremely weak signal makes it possible using in the frontier science such as quantum private communication and three-dimensional radar imaging systems. The device based on hot electron effect provides a new idea for far infrared detection. The barrier detectors will reduce the manufacturing cost of traditional materials and the design is also very illuminating for other new materials. In this review, firstly we present the history of infrared photodetectors in short. Then the mechanism and achievements of the advanced infrared photodetectors are introduced in detail. Finally, the opportunities and challenges of infrared detection are summarized and predicted.

Three-dimensional radar imaging of structures and craters in the Martian polar caps

Over the last decade, observations acquired by the Shallow Radar (SHARAD) sounder on individual passes of the Mars Reconnaissance Orbiter have revealed the internal structure of the Martian polar caps and provided new insights into the formation of the icy layers within and their relationship to climate. However, a complete picture of the cap interiors has been hampered by interfering reflections from off-nadir surface features and signal losses associated with sloping structures and scattering. Foss et al. (The Leading Edge 36, 43–57, 2017, https://doi.org/10.1190/tle36010043.1) addressed these limitations by assembling three-dimensional data volumes of SHARAD observations from thousands of orbital passes over each polar region and applying geometric corrections simultaneously. The radar volumes provide unprecedented views of subsurface features, readily imaging structures previously inferred from time-intensive manual analysis of single-orbit data (e.g., trough-bounding surfaces, a buried chasma, and a basal unit in the north, massive carbon-dioxide ice deposits and discontinuous layered sequences in the south). Our new mapping of the carbon-dioxide deposits yields a volume of 16,500 km3, 11% larger than the prior estimate. In addition, the radar volumes newly reveal other structures, including what appear to be buried impact craters with no surface expression. Our first assessment of 21 apparent craters at the base of the north polar layered deposits suggests a Hesperian age for the substrate, consistent with that of the surrounding plains as determined from statistics of surface cratering rates. Planned mapping of similar features throughout both polar volumes may provide new constraints on the age of the icy layered deposits. The radar volumes also provide new topographic data between the highest latitudes observed by the Mars Orbiter Laser Altimeter and those observed by SHARAD. In general, mapping of features in these radar volumes is placing new constraints on the nature and evolution of the polar deposits and associated climate changes.

Time‐varying three‐dimensional interferometric imaging for space rotating targets with stepped‐frequency chirp signal

Three-dimensional (3D) radar imaging can provide abundant information about space targets, thus playing a significant role in space target recognition, measurement and cataloguing. In this study, a time-varying interferometric 3D imaging method for space rotating targets is proposed based on stepped-frequency chirp signal. In this study, with L-shaped three-antenna configuration, the interferometric signal model is first set up and high-resolution range profile (HRRP) series of the three antennas are obtained. Then through interferometric processing of HRRP series on different interferometric planes, the instantaneous spatial positions in the azimuth and pitching directions of each target scatterer are reconstructed. Combining with the instantaneous positions in range direction extracted from HRRP series, the time-varying 3D image of target can be reconstructed accordingly. Compared to existing 3D imaging method for space rotating targets, the proposed method can obtain real time-varying 3D image of target with one multi-antenna set radar. Simulation results verify the validity and robustness of the proposed method.

Three-dimensional terrain elevation in airborne interferometric frequency-domain radar

This paper is devoted to the method for a three-dimensional radar image restoration of terrain elevations using airborne two antenna interferometric frequency-domain radar. A method’s main feature is the parallel obtaining of two-dimensional frequencydomain spectra both of radar terrain echo and its derivative for next synthesizing. The architecture of such interferometric radar is proposed, and math equations for one’s performance calculation are presented. The numerical algorithm for the terrain elevations calculation over an arbitrary horizontal plane is developed.

Ground-Based 3D Radar Imaging of Trees Using a 2D Synthetic Aperture

Motivated by the desire to gain insight into the details of conventional airborne synthetic aperture radar (SAR) imaging of trees, a ground-based SAR system designed for short-range three-dimensional (3D) radar imaging is developed using a two-dimensional (2D) synthetic aperture. The heart of the system is a compact linear frequency modulation-continuous wave (LFM-CW) radar, a custom two-dimensional scan mechanism, and a three-dimensional time-domain backprojection algorithm that generates three-dimensional backscatter images at an over-sampled resolution of 10 cm by 10 cm by 10 cm. The backprojection algorithm is formulated directly in spatial coordinates. A new method for estimating and compensating for signal attenuation within the canopy is used that exploits the backprojection image formation approach. Several three-dimensional C-band backscatter images of different individual trees of multiple species are generated from data collected for trees both in isolation and near buildings. The trees imaged in this study are about 10 m in height. The transformation of the three-dimensional images to airborne SAR images is described and a sample result provided.

3D POSITION MEASUREMENT SIMULATION OF AIR TARGET'S SCATTERRERS IN INTERFEROMETRIC ISAR

If one introduces an additional antenna spaced apart in height in inverse synthetic aperture radar (ISAR) it is possible to carry out an interferometric measurement of the scatterers’ heights. The theoretical relation for estimating the accuracy of the scatterer height measurement is obtained. A simulation model for obtaining the three-dimensional radar images (RI) of air objects in interferometric ISAR was developed. The comparison of the scatterer height measurements accuracy by theoretical relation and the results of simulation at different SNRs are performed