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The sparse-driven radar imaging can obtain the high-resolution images about target scene with the down-sampled data. However, the huge computational complexity of the classical sparse recovery method for the particular situation seriously affects the practicality of the sparse imaging technology. In this paper, this is the first time the quantum algorithms are applied to the image recovery for the radar sparse imaging. Firstly, the radar sparse imaging problem is analyzed and the calculation problem to be solved by quantum algorithms is determined. Then, the corresponding quantum circuit and its parameters are designed to ensure extremely low computational complexity, and the quantum-enhanced reconstruction algorithm for sparse imaging is proposed. Finally, the computational complexity of the proposed method is analyzed, and the simulation experiments with the raw radar data are illustrated to verify the validity of the proposed method., Comment: 5 pages, 3 figures

Multiple radar systems represent an attractive option for target tracking because they can significantly enlarge the area coverage and improve both the probability of trajectory detection and the localization accuracy. The presence of multiple extended targets or weak targets is a challenge for multiple radar systems. Moreover, their performance may be severely deteriorated by regions characterized by a high clutter density. In this article, an algorithm for detection and tracking of multiple targets, extended or weak, based on measurements provided by multiple radars in an environment with heavily cluttered regions, is proposed. The proposed method features three stages. In the first stage, past measurements are exploited to build a spatiotemporal clutter map in each radar; a weight is then assigned to each measurement to assess its significance. In the second stage, a track-before-detect algorithm, based on a weighted 3-D Hough transform, is applied to obtain target tracklets. In the third stage, a low-complexity tracklet association method, exploiting a lion reproduction model, is applied to associate tracklets of the same target. Three experiments are presented to illustrate the effectiveness of the proposed approach. The first experiment is based on synthetic data, the second one is based on actual data from a radar network with two homogeneous air surveillance radars, and the third one is based on actual data from a radar network with four different marine surveillance radars. The results reveal that the proposed method can outperform competing approaches.

The use of Wi-Fi signals for human sensing has gained significant interest over the past decade. Such techniques provide affordable and reliable solutions for healthcare-focused events such as vital sign detection, prevention of falls and long-term monitoring of chronic diseases, among others. Currently, there are two major approaches for Wi-Fi sensing: (1) passive Wi-Fi radar (PWR) which uses well established techniques from bistatic radar, and channel state information (CSI) based wireless sensing (SENS) which exploits human-induced variations in the communication channel between a pair of transmitter and receiver. However, there has not been a comprehensive study to understand and compare the differences in terms of effectiveness and limitations in real-world deployment. In this paper, we present the fundamentals of the two systems with associated methodologies and signal processing. A thorough measurement campaign was carried out to evaluate the human activity detection performance of both systems. Experimental results show that SENS system provides better detection performance in a line-of-sight (LoS) condition, whereas PWR system performs better in a non-LoS (NLoS) setting. Furthermore, based on our findings, we recommend that future Wi-Fi sensing applications should leverage the advantages from both PWR and SENS systems.

Ground-Penetrating Radar (GPR) technology plays an important role in near-surface geophysical survey. Usually, the dataset acquired by higher frequency antenna has higher resolution but limited penetration depth; while the dataset collected by lower one has deeper penetration depth but lower resolution. It is necessary to fuse several different frequency datasets into a composite displayed profile to get a trade-off between penetration depth and resolution. In such case, we propose a novel multi-frequency GPR data fusion method and test it on the data acquired from a limestone quarry in the Taihang Mountains of China. In our algorithm, time-varying weighting factors are calculated by virtues of a sliding time window, i.e. designed with segmented signals. According to this approach, we can achieve better local characteristics of the signal as well as the smoothness of the signal waveform. The results show that the spectrum bandwidth of the fused signal is broadened, and the fusion profile significantly improves the imaging of internal structures for limestone on different scales.

Differential interferometric synthetic aperture radar (DInSAR) has been widely used as one of the most important technologies for determining coseismic deformation. However, DInSAR processing is often perturbed by errors including atmospheric effects and those in topographic models, SAR satellite orbits, and phase unwrapping operation. These nuisance components can degrade the accuracy of the measurements and, therefore, distort the inversion of fault slips especially for moderate earthquakes. We propose in this letter a multitemporal InSAR (MTInSAR) method aiming to accurately determine coseismic deformation. By jointly analyzing a set of preseismic SAR images and one postseismic image, the method allows effective separation of coseismic deformation from topographic and satellite orbital errors deformation based on the distinct spatio-temporal characteristics of the signals. Since the solution is achieved directly from wrapped differential phases, the retrieved deformation is also immune to phase unwrapping errors. The October 6, 2008 Mw 6.3 Dangxiong, China earthquake is studied with the proposed method as an example. The slip inverted, respectively, from the MTInSAR and DInSAR coseismic deformation measurements shows up to 34-cm differences, indicating that the topographic error and inaccurate removal of orbital errors can bias the fault slip inversion.

Recently, a novel design scheme of low-earth-orbit spaceborne mini-synthetic aperture radar (MiniSAR) system is proposed to exploit the integrated transceiver to collect the azimuth periodic block sampling data by using alternated transmitting and receiving operations. Because such collected data are downsampled, the images recovered by the typical matched filtering (MF)-based methods have the problems of obvious azimuth ambiguities, ghosts, and energy dispersion. To find a suitable method for such data, with the help of sparse signal processing technique, we first introduce sparse synthetic aperture radar (SAR) imaging with l₁-norm regularization-based approximated observation method to recover the large-scale considered scene. To further improve the imaging performance, a novel approximated observation unambiguous sparse SAR imaging method via l $_{2,1}$ -norm is proposed. Compared with l₁-norm-based method, the recovered image by the proposed one achieves better imaging quality with reduced azimuth ambiguities and ghosts. Experimental results on simulated and real data validate the proposed method.

Oils floating on the sea surface can be observed as ``dark'' patches on radar images since the backscattered signals from the contaminated area are reduced in two dominant ways. First, oil slicks could damp short gravity and capillary waves on the sea surface responsible for backscattering energy. Second, the oil-covered sea surface permittivity decreases significantly if the oil film is sufficiently thick or mixed with seawater, i.e., oil emulsion. In this article, the geometry of the oil-covered sea surface is accounted for by the damping of sea waves, which is described by the model of local balance (MLB) combined with the sea wave spectrum. The radar backscattering is predicted by the advanced integral equation method (AIEM) model. The reflection coefficients are calculated based on a layered-medium model to analyze the impact of oil thickness and emulsions on the radar scattering. Numerical simulations demonstrate that: 1) the sensitivity to oil thickness and water content of the oil spills increases when the radar frequency increases; 2) the backscattering signals exhibit a nonlinear behavior with respect to oil thickness; and 3) high wind speed can generally narrow the difference between the radar backscattering from the clean and oil-covered sea surface, while the incidence angle has little effect. Numerical simulations are then compared with the multifrequency synthetic aperture radar observations acquired during the Gulf of Mexico Deepwater Horizon (DWH) oil spill accident and the 2011 Norwegian Clean Seas Association for Operating Companies (NOFO) oil-on-water exercise. Comparison results show that it is possible to estimate the oil thickness at reasonably good accuracy.

Deconvolution technique can be utilized in the forward-looking radar (FLR). However, the forward-looking imaging performance degenerates greatly due to the effect of high-speed movement of the platform. In this article, an efficient Bayesian forward-looking superresolution imaging algorithm based on Doppler deconvolution in expanded beam space is proposed. First, the Doppler phase information caused by the high-speed platform is fully exploited and the Doppler matrix is integrated with the antenna pattern. The Doppler convolution model of the echo signal for forward-looking is derived in this article. Then, the Doppler phase information is adopted to perform the Doppler deconvolution. Moreover, an expanded beam space is constructed to enhance the sparsity of the imaging scene. The complex Gaussian distribution and the Laplace distribution have been used to model the distribution characteristics of noise and targets in the imaging scene, respectively. Finally, based on the Bayesian framework, the forward-looking imaging problem is converted into the convex optimization problem. The performance assessment based on simulated and experimental data, also in comparison to the conventional real beam, truncated singular value decomposition (TSVD), iterative adaptive approach (IAA) methods, has demonstrated the effectiveness of our proposed algorithm under high-speed platform scenarios.

Microwave imaging with large rotating angle and sparse sampling is an attractive approach to obtain the high-resolution target image with reduced radar resource. However, the popular imaging methods, e.g., Range-Doppler (RD), back projection (BP), and sparse recovery (SR), are difficult to deal with large rotating angle and sparse sampling simultaneously. In recent years, deep learning (DL) has been widely studied and been successfully used to handle the problems in computer vision. However, since most existing DL networks are put forward for the real visual image and a large amount of data is essential for network training, DL cannot be directly used to process the complex and sparse target echo for microwave imaging. In this article, a new learning imaging framework is proposed and a model-driven learning imaging network (MDLI-Net) is built for high-resolution microwave imaging with large rotating angle and sparse sampling. In the proposed framework, the electromagnetic scattering model is used to generate the training data efficiently, and the sparse microwave imaging theory is applied to guide the design of the deep imaging network. By inputting the 2-D sparse complex-valued target echo, the trained MDLI-Net can output the high-resolution and focused target image efficiently. The effectiveness of the proposed learning imaging method is validated by experiment results with both simulated and real data.

We present a generalized power-law roughness spectrum to account for the spatial anisotropy effects on the radar scattering of a rough surface, where both the correlation anisotropy and the scaling anisotropy are accounted for. The spatial anisotropy is essential to correctly interpret the radar scattering from an agriculture field where both plow and sow are practiced. We investigate the dependence of the backscattering coefficient on the correlation anisotropy and the scaling anisotropy through a model simulation. A drastic change in backscattering strength is observed due to the anisotropy. The correlation anisotropy and the scaling anisotropy generate similar backscattering angular behavior, implying that in the context of spatial anisotropy, merely using correlation length in scattering modeling is insufficient. Equivalently, the correlation length retrieved from the backscattering coefficients perhaps is not unique. Fair use of the generalized anisotropic power-law roughness spectrum in conjunction with the scattering model is illustrated by comparing the backscattering coefficients with experimental measurements. However, the anisotropy complicates the roughness description in terms of surface parameters retrieval because we can generate similar backscattering angular patterns by combining different correlation anisotropy and scaling anisotropy. When the soil moisture is of primary interest, a more suitable radar observation geometry to minimize the spatial anisotropy influence is desirable.

The space-time adaptive processing (STAP) technique has achieved good clutter suppression performance for a side-looking airborne radar; however, it suffers from severe performance degradation in nonside-looking airborne radar. This is because the clutter distribution varies considerably with range. The 3D STAP can provide better performance compared with the traditional STAP methods in such a nonstationary clutter environment, but it requires high computational complexity and sample support. In this letter, the characteristic of azimuth-elevation-Doppler 3-D beam pattern for the planar array is explored, and a novel reduced-dimension scheme combined with this characteristic is proposed. We further developed three basic reduced-dimension structures according to this scheme. Furthermore, we also prove that the first and third structures are just the direct extension of the traditional 2-D generalized multibeam and joint-domain localized methods. The second one is an original structure that performs local reduced-dimension processing with three orthogonal planar windows. Numerical results verify that the proposed structures have significant advantages in reducing computational load and training sample numbers compared with existing 3D STAP methods. In particular, the second structure shows the best comprehensive performance in the bad samples environment.

Due to the requirements of portability and omni-directivity, the planar bow-tie antenna is widely used in impulse through-wall radar (ITWR). When the planar bow-tie antenna is used to radiate the ultrawide bandwidth (UWB) signal, the ringing phenomenon of the transmitted signal would be serious, which can damage the quality of radar imaging. The previous solutions for this problem are the usage of various hardware loadings; however, those loadings could cause signal energy loss and reduce the signal gain. Alternatively, this letter studies a deconvolution-technique-based signal processing approach to suppress the signal ringing. Because the proposed approach does not require any hardware loadings on the antenna, it can help improve the signal-to-noise ratio (SNR) and significantly reduce the energy loss of signal. The effectivity of this signal processing approach is verified by the radar detecting experiments.

Recent deep learning (DL) detectors adopted by radar or sonar (RS) are normally trained with transfer learning, where the typical workflow is to pretrain a convolutional neural network (CNN) on external large-scale classification datasets (e.g., ImageNet) as the backbone and then finetune the entire detector on detection datasets. Though transfer learning could effectively avoid overfitting, transferred models are usually redundant and might not generalize well on RS datasets. To achieve high generalization and to eliminate the dependence on transfer learning, a self-trained target detection method is established by including Automatic Deep Learning (AutoDL) to design optimal detectors. This self-trained target detection consists of three stages. First, a derived classification dataset (DCD) consisting of image blocks of targets and backgrounds is derived from detection datasets. Then, a memory-efficient Differentiable Architecture Search algorithm with flexible search space and large inputs (FL-DARTS), which is characterized by its predefined multistride convolutions, poolings, and unique super-structure, is proposed to automatically design and self-train optimal CNNs on DCDs. Finally, self-trained AutoDL detectors are implemented with the automatic backbone designed by FL-DARTS. We evaluated three self-trained AutoDL detectors on the public SAR ship detection dataset (SSDD) and the self-made sonar common target detection dataset (SCTD). The experiments show that while the number of parameters of automatic backbones designed for SSDD and SCTD are only 11.8% and 15.2% of that of ResNet50, self-trained AutoDL detectors implemented with automatic backbones significantly outperform their transfer learning detectors and achieve state-of-the-art detection precisions and high detection speeds. Data, codes are publicly available.

This article addresses the detection problem of radar targets embedded in nonhomogeneous and non-Gaussian sea clutter. Nonhomogeneity leads to insufficiency of secondary data for estimating the clutter speckle covariance matrix, and non-Gaussianity causes sea clutter to become spiky. In this article, the persymmetry of the clutter covariance matrix is adopted to alleviate the requirement of secondary data, and the prior distribution of clutter texture is exploited to tackle the clutter non-Gaussianity. Based on such clutter knowledge, three adaptive detectors are proposed according to the principles of the generalized likelihood ratio test, the Wald test, and the Rao test. It is proven that three detectors ensure constant false alarm rate (CFAR) properties with respect to both the clutter speckle covariance matrix and the clutter power mean. Simulation experiments show that three detectors outperform their competitors.

In radar forward-looking super-resolution imaging, improving the azimuth resolution while acquiring the contour information of the target has significant research value. In this letter, an approach based on the balanced Tikhonov and total variation (TV) deconvolution is proposed for radar forward-looking super-resolution imaging. We combine the Tikhonov regularization and TV regularization to construct the objective function and resolve the respective cost function using the alternating direction method of multipliers (ADMM). In each iteration, the gradient function of the target scattering coefficient is used as the adaptive weighted parameter to control automatically the weighting between the penalty terms from TV and the Tikhonov regularization. For the target with a sharper outline, the proportion of TV regularization penalty terms is increased; for the target with a smoother outline, the proportion of penalty term from the Tikhonov regularization is enhanced. The simulation and experimental results are considered to show the effectiveness of the proposed method. Compared with traditional super-resolution imaging methods, the proposed approach has superior outline retention capacity.

Frequency-modulated continuous-wave (FMCW) radar is known for distance and velocity measurements of close and slow targets, i.e., targets with round-trip delays much shorter and Doppler periods much longer than the chirp repetition period. Solutions for distant targets have been presented for advanced FMCW radar architectures. Here, we analyze how a conventional homodyne I-channel FMCW radar can be used for a multitarget setting without the restrictions of close and slow targets. In a long-range setting, each target appears at two different ranges with opposite velocities. We develop a pure data processing solution to resolve this ambiguity and validate this approach through simulations and experimental results with a long-range moving test target. The work is motivated by our goal to use an off-the-shelf, low-power, and low-cost 24-GHz I-channel FMCW Doppler radar for continuous observation of volcanic eruptions and pyroclastic flows. These targets are moving at high speed and, for safety reasons, can only be observed from a greater distance, requiring the high-velocity long-range solution.

Convective precipitation nowcasting remains challenging due to the fast change in convective weather. Radar images are the most important data source in nowcasting research area. This study proposes a radar data-based U-Net model for precipitation nowcasting. The nowcasting problem is first transformed into an image-to-image translation problem in deep learning under the U-Net architecture, which is based on convolutional neural networks (CNNs). The input of the model is five consecutive radar images; the output is the predicted radar reflectivity image. The model consists of three operations: upsampling, downsampling, and skip connection. Three methods, U-Net, TREC, and TrajGRU, are used for comparison in the experiments. The experimental results show that both deep learning methods outperform the TREC method, and the CNN-based U-Net can achieve almost the same performance as TrajGRU which is a recurrent neural network (RNN)-based model. With the advantages that U-Net is simple, efficient, easy to understand, and customize, this result shows the great potential of CNN-based models in addressing time-series applications.

Different from conventional radar systems, for the single-photon radar, a single-photon detector acts as the receiver to detect the echo signal with only a few photons, which can be used to achieve ultrahigh sensitivity for long-distance target detection. In this letter, the heterodyne single-photon radar at terahertz frequencies is studied, for the first time. First, the working principle of the developed heterodyne single-photon radar is presented, and the target echo signal is derived. Subsequently, the estimation method of the target distance and the Doppler frequency is proposed, and the performance is analyzed by probability density function (PDF) derivation. Simulation results show good agreement with the theoretical analysis. This work can pave the way to the development of a new radar paradigm.

This article presents similarities and differences in clutter detection between electronic scans and mechanical scans with a cylindrical polarimetric-phased array radar (CPPAR). Theoretical explanations of clutter features in electronic scans that are different from those in mechanical scans are explored and verified by observations with the CPPAR. Clutter detection results with the CPPAR, based on the copolar correlation coefficient, dual-scan cross-correlation coefficient, power ratio, and their combinations in the electronic scan and mechanical scan modes are presented and compared.

Oil spills in the Arctic are becoming more likely as shipping traffic increases in response to climate-related sea ice loss. To improve oil spill detection capability, we used a controlled mesocosm to analyze the multi-polarized C-band backscatter response of oil in newly-formed sea ice. Artificial sea ice was grown in two cylindrical tubs at the University of Manitoba’s Sea-ice Environmental Research Facility. The sea ice physical characteristics, including surface roughness, thickness, temperature, and salinity, were measured before and after oil injection below the ice sheet. Time-series C-band radar backscatter measurements detected the differences in the sea ice evolution and oil migration to the sea ice surface in the oil-contaminated tub, which was compared to uncontaminated ice in a control tub. Immediately prior to the presence of oil on the ice surface, the co-polarized backscatter increased by 13 dB local maximum, while the cross-polarized backscatter decreased by 9 dB. Ice physical properties suggest that the local backscatter maximum and minimum, which occurred immediately before oil migrated onto the surface, were related to a combination of brine and oil upward migration. The findings of this work provide a baseline data interpretation for oil detection in the Arctic Ocean using current and future C-band multi-polarization radar satellites.

Ultrawideband (UWB) multiple-input-multiple-output (MIMO) radar is widely used for through-wall imaging (TWI) due to its excellent penetrability and large aperture. Multichannels in the MIMO radar system are usually time-division multiplexing based on microwave switches to reduce the complexity of the system in engineering. The switching process of the channel will bring time delay, which cannot be ignored in the TWI of the moving target. The switching time delay will cause the defocus and position shift of the TWI of the moving target. This letter proposes a motion compensation method based on multiframe data fusion (MFDF) used for correcting the echo of the through-wall moving target. A geometric model is established in the proposed method through the echo of the current frame and the next frame, and the compensated signal is obtained through the geometric solution. The proposed method is compared with before compensation and the traditional single-channel motion compensation algorithm (SCMCA) through simulation and experimental data verification. The visual images and quantitative results show that the proposed motion compensation method can obtain a good focus image of the through-wall moving target and reduce the positioning error.

In this letter, a model of wide-area distribution multistatic T^M-R^N (M independent transmitters and N independent receivers) sky-wave over-the-horizon radar (WDMS-OTHR) is investigated. A localization algorithm that utilizes the hill-climbing algorithm based on the weighted least squares (HC-WLS) method for this model is also proposed. The localization algorithm mainly includes three stages. First, the geodetic coordinate of station position is transformed into the spatial rectangular coordinate. Second, the initial position is estimated by WLS that forms a pseudolinear equation from the measurements of the time of arrival (TOA) and introduces nuisance variables as additional unknowns. Third, the spatial rectangular coordinate of the initial position is transformed into the geodetic coordinate, and the initial position is optimized by HC that performs eight neighborhood searches around the initial position and adopts multiple iteration searches until the optimal position is obtained. Numerical simulations demonstrate that the positioning accuracy of the proposed algorithm is better than WLS.

Global navigation satellite system reflectometry (GNSS-R) altimetry has great potential to provide high spatial-temporal resolution sea surface heights (SSHs) at low cost. Interferometric phase measurements between direct and reflected signals can be used for altimetry retrieval to achieve high-precision solutions. The motions of the medium Earth orbit (MEO) satellites cause interferometric phase change rapidly, which would increase the probability of occurrence of the phase unwrapping errors than the case of the geosynchronous Earth orbit (GEO) satellite. In order to overcome this problem, we propose a coastal GNSS-R altimetry algorithm using the signals from Quasi-Zenith Satellite System (QZSS) GEO satellite. Precise SSH variations can be achieved using the interferometric phase measurements without ambiguity fixed. We also perform coastal experiments on a trestle using a specialized GNSS-R setup to verify our algorithm. It is composed of an intermediate frequency (IF) data collector and two antennas. The up-looking antenna is used to receive direct signals, while the down-looking antenna receives the signals reflected from the sea surface. Raw IF data sampled at 62 MHz are collected and processed to derive interferometric carrier phase delay measurements using a self-developed software-defined receiver. Approximately 7 h of reflector heights are retrieved at 1-min intervals and the solutions are evaluated via comparison with measurements provided by a 26-GHz altimetry radar located near the GNSS-R setups. The results show that the root mean square error (RMSE) of sea level estimation is about 1.4 cm by using the QZSS GEO data.

Millimetre-wave (mmWave) radar is increasing in popularity for human activity recognition, due to its advantages of high resolution, non-intrusive nature and suitability for various environments. In this paper, we present a novel human posture estimation system using mmWave radars. The system detects people with arbitrary postures in indoor environments at close distances (within two metres), and estimates the posture by localising the key joints. We use two mmWave radars to capture the scene and a neural network model to estimate the posture. The neural network model consists of a part detector that estimates the subject’s joint positions, and a spatial model that learns the correlation between the joints. A temporal correlation step is introduced to further refine the estimate when in real-time operation. The system can provide an accurate posture estimate of the person in real-time at 20 fps, with a mean localisation error of 12.2 cm and an average precision of 71.3%.

Multipolarizations of micro-Doppler signature (MDS) were combined to classify the elevation angle of a drone by a convolutional neural network (CNN). We classified the drone's elevation angle based on the MDS, which depends on the elevation angle of the drone. To enhance the classification accuracy, we utilized micro-Doppler from multiple polarized radar signals. We utilized and analyzed four different polarizations for the receiver, namely, vertical/horizontal/right-handed circular polarization (RHCP)/left-handed circular polarization (LHCP), while the polarization for the transmitter was vertical. The four receivers with different polarizations were fully synchronized with Tx for accurate MDS measurement for each polarization. The radar data received from the multiple polarizations were combined for the classification algorithm based on CNN. The classification rate of the elevation angles of a drone was improved from 84.745% to 97.9% compared to a single polarization by using multiple polarizations.

Ground-penetrating radar (GPR) is a well-known geophysical electromagnetic method used to detect the underground facilities such as landmines, pipelines, and cavities. In general, the clutter presented in GPR B-scan image obscures the underground objects, thus damaging the performance of the underground object detection algorithm. In this letter, we proposed a new clutter suppression method based on robust autoencoder (RAE). The proposed algorithm decomposes a GPR B-scan image into its low-rank and sparse components. The low-rank component catches the clutter, whereas the sparse component captures the underground object responses. The commonly used clutter removal algorithms, mean subtraction (MS), singular value decomposition (SVD), robust principal component analysis (RPCA), and morphological component analysis (MCA), are compared with the proposed algorithm on both the numerical simulated data and real GPR data. The visual and quantitative results demonstrate the effectiveness of the proposed RAE-based algorithm over the widely used state-of-the-art clutter removal algorithms.

Accurate acquisition of human micro-Doppler (m-D) frequencies remains a challenging task, which can help us to identify the target of interest and provide valuable information about its motion dynamics. In this letter, a novel theoretical method to estimate the m-D frequencies of human specific scattering parts from continuous-wave radar echo is proposed with a united application of a modified Viterbi algorithm and a nonlinear prediction technology. Although the proposed algorithm achieves a limited improvement in the estimation accuracy and produces better human m-D frequency curves, this algorithm suppresses the frequency ambiguity effectively. The employed search method enhances upon traditional ones and improves the efficiency of finding the optimal paths considerably. Experimental results demonstrate the superior performance of the proposed method.

Nonline-of-sight (NLOS) radar imaging is a novel technique that can inverse the scattering characteristics of targets in the NLOS area, which has been one of the hot pots of radar imaging field. However, the existing NLOS radar mainly focuses on 1-D or 2-D imaging, which inevitably suffers from the geometric loss of real 3-D scenes, and its applications are restricted in the urban environment. In this article, we propose an NLOS radar 3-D imaging model and method for looking around corner (LAC) situation by multi-input-multioutput (MIMO) millimeter-wave (mmW) array antennas. In this scheme, first, the model of NLOS radar 3-D imaging with mmW MIMO antennas is established and the multipath scattering of targets with this model is analyzed. Then, the theoretical resolution of LAC 3-D imaging is derived and discussed. Second, exploiting the three bounces of LAC and extraction of linear structure, an effective imaging algorithm with mirror projection theory and Radon transform, dubbed as mirror symmetry backprojection (MSBP), is proposed for 3-D image focusing. Moreover, to suppress the uncertainties of phase caused by both LAC and system error, the minimum entropy principle is introduced to MSBP. Finally, an NLOS 3-D imaging system with 77-GHz mmW MIMO radio frequency module and 2-D rails is developed. Different types of targets, such as metal balls and ornaments, are tested in LAC. The results demonstrate that our NLOS technique can not only provide a high-quality 3-D focusing of the hidden targets but also extract positions of targets without prior knowledge of the NLOS area.

Traditional space-time (ST) adaptive processing (STAP) theory is based on the assumption of narrowband or ``zero-bandwidth,'' where the decorrelation within the ST snapshot is ignored. However, with radar bandwidths increasing, this assumption becomes invalid due to the deteriorated decorrelation of the received signals within the ST snapshot. The decorrelation directly causes the dispersion of the received signals in both spatial and temporal domains, leading to the spreading of the clutter spectrum in the 2-D frequency (Doppler-spatial frequency) domain. With the spreading of the clutter spectrum, the clutter suppression notch in the traditional STAP filters is widened, resulting in a relative poor ability to detect slow-moving targets. In this article, we focus on the clutter suppression for wideband radar STAP. A generalized signal model of the ground clutter is first established for the wideband array radar. Using this outcome, we analyze the influence of bandwidth on the characteristics of the ground clutter and quantitatively describe the 2-D spreading of the ground clutter on the Doppler-spatial frequency plane. Moreover, the model of clutter covariance matrix for wideband STAP (W-STAP) is established. Finally, a 2-D keystone transform (KT) algorithm, referred to as ST KT (ST-KT), is proposed to eliminate the spreading of the ground clutter in the 2-D frequency domain caused by increasing bandwidths. Simulation results are employed to validate the theoretical analysis and verify the overperformance of the ST-KT based W-STAP method in terms of the output signal-to-clutter-plus-noise ratio (SCNR) of moving targets.

Aerosol extinction coefficient (AEC) is a crucial factor of aerosol optical properties that have an important influence on the atmospheric process. As a combination of laser technology and radar technology, LiDAR is a powerful remote sensing technology for aerosol measurement. However, many uncertainties still exist in LiDAR data retrieval because frequently used retrieval methods need some assumptions or complex numerical operations for retrieving AEC, which may cause large deviation. In this letter, combining the advantages and disadvantages of Mie scattering LiDAR and high spectral resolution LiDAR (HSRL), a new inversion model based on deep belief network (DBN) is proposed to predict AEC, which can effectively avoid the uncertainty caused by many assumptions and improve the detection accuracy of Mie scattering LiDAR. The experiment results indicate that the trained DBN model is robust and satisfactory and provides a competitive solution for predicting AEC of Mie scattering LiDAR. In addition, this letter reveals that deep learning method has huge potential for LiDAR data retrieval.

With the development of the Internet of things, radar-based human activity recognition is becoming more and more important, because they play an indispensable role in fields such as safety and health monitoring. In this work, a novel network named 1-D dense attention neural network (1-D-DAN) is proposed for the radar-based human activity recognition. In the proposed network, a novel attention mechanism network structure specifically designed for radar spectrogram is proposed, equipping 1-D convolutional network with attention mechanism. With the x-axis of the spectrogram represents time and the y-axis represents frequency, the proposed attention mechanism includes two branches: 1) time attention branch and 2) frequency attention branch. Moreover, a dense attention operation that can make full use of features in the network is also introduced in the proposed attention mechanism. Experimental results show that compared with the state-of-the-art methods, our proposed 1-D-DAN achieves the highest accuracy in human activity recognition with the lowest computational complexity.

State-of-the-art weather forecasting systems depend on a variety of data collected by airborne, orbiting, and ground sensors. Regional CubeSat constellations have the potential to improve hurricane forecasting by collecting sensor data over data-starved oceanic regions. Even in regions where strong terrestrial sensor networks exist, constellation sensor data can help reduce forecasting model errors. To this end, the paper considers the problem of designing a low-Earth orbit CubeSat constellation that meets given resolution requirements over a region of interest. We propose a novel optimization framework that utilizes the concept of satellite coverage maps to determine the number of satellites and constellation pattern. Numerical simulations are presented for asymmetric constellation design that can provide sensor data over important geographical regions within a specified repeated time-window.

Spaceborne synthetic aperture radar (SAR) real-time imaging is especially important for disaster emergencies and real-time monitoring applications with highly desired real-time requirements. Therefore, the continuous improvement of real-time imaging efficiency is an important development trend. At present, traditional real-time imaging algorithms based on constant pulse repetition frequency (PRF) have low accuracy when processing spaceborne SAR data with nonlinear trajectory. For this problem, the existing methods usually introduce some complex signal processing steps, such as scaling or interpolation processing, to improve the accuracy of the real-time imaging, but this will reduce its efficiency. Therefore, this article proposes a new real-time imaging algorithm based on variable PRF for nonlinear trajectory spaceborne SAR. By introducing the variable PRF, the proposed algorithm is equivalent to complete the complex signal processing steps in the radar signal transmission stage, which can greatly improve the efficiency of real-time imaging. Simulation experiments verify the effectiveness of the algorithm.

In this letter, we propose a dual-temporal dual-channel (DTDC) algorithm for soil moisture retrieval by using time-series observations from the Sentinel-1 C-band synthetic aperture radar. This algorithm utilizes the ancillary information of vegetation water content derived from optical images and assumes no variation on the surface roughness during the two consecutive radar measurements. Therefore, with the DTDC backscatter observations, four equations could be established using forward models, while three unknowns (the two consecutive soil moisture values and one roughness parameter) could be solved simultaneously by minimizing a cost function. The algorithm was tested with a series of Sentinel-1 dual-channel (VV + VH) data over croplands (sugarcane and cassava) of Northeast Thailand with an upscaling resolution of 1 km. Results show that the proposed algorithm could well capture the temporal change of soil moisture with root-mean-square errors within 0.06 m³/m³ when ignoring days with precipitation, and could achieve a similar spatial pattern of soil moisture as detected from the Soil Moisture Active Passive mission, indicating the Sentinel-1 might be a proper tool for agricultural water management.

In inverse synthetic aperture radar (ISAR) imaging for a uniformly moving rigid-body target, a finely focused ISAR image can be obtained by using the conventional range-Doppler algorithm. However, the ISAR image quality may significantly deteriorate when the time-vary Doppler phases in virtue of target maneuvering motions are present, such as an airplane with nonuniformly rotation and a ship with fluctuation. This has become a challenging task, especially under nonhigh signal-to-noise ratio (SNR) environment. In this article, a novel ISAR imaging algorithm for a maneuvering target with moderate reflection intensity is proposed. After motion compensation, the radar echo signal in a range cell is modeled as a multicomponent cubic phase signal (CPS), in which the chirp rate and the quadratic chirp rate are two important physical quantities that may determine the target ISAR focusing quality. Based on a symmetrical instantaneous autocorrelation function, the received CPSs are transformed into the time and lag-time plane, and then a 2-D coherent integration can be realized after the generalized time-scaled transform and 1-D maximization. This forms a high-quality ISAR image. The effectiveness and superiority of the proposed algorithm are validated by the ISAR imaging results of simulated and real measured data.

Sea-Surface small target detection is challenging for maritime radar. Unfortunately, conventional detection methods are often limited to complex marine environment and low signal-to-clutter ratio (SCR). This article presents a multi-frame detection approach for sea-surface small target by using deep convolutional neural network. The moving targets can be reconstructed and detected from the sequential Range-Doppler (RD) spectra. A two-step detection framework is proposed, where the intra-frame and the inter-frame detection is achieved by using the differences of features and inter-frame correlations between moving target and sea clutter, respectively. The proposed approach has been verified on both the simulated and real sea-surface small targets, which shows better detection performance than the conventional multi-frame detection algorithms. Additionally, this approach exhibits acceptable generalization ability.

The accumulation rate is a key parameter in computing the mass balance of glaciers and ice sheets to estimate sea level rise. A shallow-layers-detection ice sounding radar (SLDISR) is developed to measure the accumulation rate and shape of near-surface internal layers with high resolution. With a transmitting frequency from 500 to 2000 MHz, this frequency-modulated continuous wave (FMCW) radar provides a range resolution of about 16 cm in free space by using a Hanning window and a penetrating depth about 150 m under polar ice. The spectral analysis and coherent integration techniques are used to obtain a high processing gain and to improve the signal-to-noise ratio of the system. A phase-locked loop with wideband yttrium iron garnet (YIG) oscillator is applied to generate a sweeping chirp signal as an input source for the transmitter. A stable, low-frequency reference chirp signal is generated with a direct digital synthesizer (DDS) integrated in field-programmable gate array (FPGA). To reduce the high-speed requirement to the analog-to-digital converter (ADC), dechirp technology is adopted at the RF section of the receiver. The implementation of the digital unit is based on an FPGA chip. The designed radar has been successfully deployed in Antarctica during the 31st Chinese Antarctic Research Expedition (CHINARE 31) and CHINARE 33, mainly over the East Antarctic Ice Sheet (EAIS). The echograms indicate the effectiveness of the radar system on detecting clear internal reflecting horizons (IRHs) over ice sheets.

Vertical-looking radar (VLR) is a significant milestone in the development of insect radars with the capability of detecting the behavior of migratory insects and their biological parameters. In current VLRs, high-speed continuous sampling and long-time integration can barely be performed simultaneously, leading to a low detection probability for tiny insects (weight < 10 mg). Based on the large amount of data acquired by our developed high-range resolution insect radar, the insect echo signals and vertical motion characteristics are initially analyzed and demonstrate that the linear-motion mode is dominant in insect migration; also, the echo signal power of most insects follows the gamma distribution. Based on these characteristics, a long-time integration and detection method for detecting migratory insects, especially tiny targets from echo signals that often dip below the noise level, is proposed. The radial target velocity is also measured as one of the output parameters. The theoretical derivation and optimal choice of detection thresholds are also presented. Simulation and experimental results demonstrate that the proposed method exhibits better insect detection performance and effectively increases the detection range compared with conventional methods. In addition, the measured target velocity can be directly applied to current continuous-sampling VLRs for the ascent and descent rate analysis. Many typical insect migration phenomena have been detected effectively utilizing our developed VLR, and the measured ascent and descent rates of insects agree well with typical take-off, cruising, and landing behaviors. This is the first reported successful VLR application on take-off and landing behaviors of migratory tiny and dense insects.

Signal fluctuations at the receiving antenna have been studied from decades by the radar community, especially to understand the decorrelation of the scattering in radar interferometry. This was done assuming uncorrelated point-like scatterers, leading to a simple model for the geometric decorrelation. In this case, the scattering is certainly incoherent. The quasi-specular reflections gathered under the illumination of signals of opportunity can exhibit significant temporal fluctuations. They are related to the statistical features of the surface roughness and can be observed even in almost flat regions, where a predominant coherent reflection could be expected. The presence of gentle undulations, however, i.e., those showed by surfaces having variations of the profiles comparable with the wavelength over the vertical scale, but much longer over the horizontal one, can determine transition regions where the scattering is neither coherent nor completely incoherent. In these conditions, the nature of the fluctuations of the scattering is not well understood and it requires additional studies. A discussion about the dominance of coherent or incoherent reflection in the Global Navigation Satellite System Reflectometry (GNSS-R) community is presently ongoing. To describe the nature of the scattering, and to understand the decorrelation of the near-specular components in GNSS-R, we propose a numerical study of the field collected by a moving airborne receiver based on the Kirchhoff approximation. Our study demonstrates that the near-specular scattering collected over representative natural landscapes by a GNSS-R receiver is partially coherent and essentially incoherent in most cases. Its correlation time is a function of the roughness parameters, namely standard deviation and correlation length, as well as of the system parameters (i.e., spatial resolution and height). The analysis can provide useful information for the interpretation of GNSS data, which present intrinsic variability that can significantly affect the retrieval of the relevant bio-geophysical parameters.

The maximum unambiguous Doppler in classical saw-tooth frequency-modulated continuous wave (FMCW) radar is inherently limited by the sweep repetition frequency (SRF). In this letter, an unambiguous Doppler extension method for FMCW radar is proposed, based on the combination of Poisson disk sampling (PDS) and sparse recovery. The PDS is adopted in the slow time domain such that there are random intervals between adjacent transmitted sweeps and no overlaps. Taking advantages of the excellent spectrum antialiasing performance of PDS, the maximum unambiguous Doppler of FMCW radar can be extended without reducing the range resolution. Then, the iterative soft thresholding-like (IST-like) algorithm is utilized to reconstruct the accurate range-Doppler spectrum. Compared with the existing unambiguous Doppler extension methods, the proposed method performs better in multitarget and low signal-to-noise ratio (SNR) scenarios. The effectiveness of the proposed method is verified by the experiments on real FMCW radar data.

It is known that the challenge for detection of small targets on the sea surface is the low signal-to-clutter ratio (SCR). In particular, for low grazing angles and high sea states, wave shading and sea spikes make the small target detection even more difficult. This letter proposed a novel phase-feature detector of small targets in sea clutter. Three phase features, which correspond to different radar scattering mechanisms between the target and sea surface, are extracted in the phase domain of radar echoes, namely, the number of phase crossing zero points, the maximum value of the phase difference probability density function, and the decorrelation time of the phase difference. A detector with a controllable false alarm rate (FAR) based on phase features is constructed using the fast convex hull learning algorithm. Experimental results on measured databases demonstrate that the proposed phase-feature detector attains better performance than the existing tri-feature detectors.

This research aims at the robust and high-resolution reconstruction of asteroid's interior structure using lander-to-orbiter radar system. In recent years, the full waveform inversion (FWI) has been suggested as a potential method to asteroid tomography. Due to the limitation of computing capacity, FWI is usually performed by local rather than global optimization method, which makes it suffer from local minima problem especially when the signal lacks low-frequency components. To ensure the global convergence, FWI requires the initial model be accurate enough to avoid the local minima. But in practice, the low-frequency components are naturally absent in the remote radar signal as the limitation of bandwidth, and the prior information of asteroid is usually not sufficient to build an accurate initial model, which leads that using conventional FWI directly may not be capable to obtain a robust reconstruction. Considering the above problems, envelope inversion (EI) which works on the baseband signal and therefore, can recover the long-wavelength structure of asteroid is proposed as a supplementary to FWI. Initial model dependence and noise sensitivity of FWI and EI in asteroid tomography are analyzed based on 2-D numerical experiments. The EI + FWI combination constrained by total variation regularization shows the characteristics of good independence on the initial model and high imaging resolution. Based on EI + FWI strategy, a series of 3-D numerical experiments are conducted to test the influence of orbital measurement density and landing site on the tomography.

A long-standing goal in the terrestrial remote sensing community has been the development of a root-zone soil moisture (RZSM) product based on microwave radar or radiometry observations. For example, the National Aeronautics and Space Administration's (NASA's) Airborne Microwave Observatory of Subcanopy and Surface (AirMOSS) mission between 2012 and 2015 served as an airborne testbed for the development of RZSM estimates acquired from a 420-440 MHz (P-band) radar. Results from the AirMOSS mission suggest that P-band backscatter can be inverted to provide a direct measurement of 0- to 40-cm RZSM. However, whether AirMOSS RZSM is inherently more robust than indirect RZSM estimates including soil MERGE (SMERGE) and Soil Moisture Active Passive Level 4 (SMAP L4) is not clear. SMERGE and SMAP L4 leverage surface (2-5 cm depth), higher-frequency microwave observations (C- and L-bands) via data fusion (DFus) and data assimilation (DA) techniques, respectively. Therefore, the comparisons of these products are warranted and were based on bias, root mean square error (RMSE), and unbiased root mean standard deviation (ubRMSD) metrics. Comparisons also were made against in situ observations providing additional context. The seven AirMOSS sites within the contiguous United States (CONUS) were examined. Overall, direct AirMOSS RZSM measurements, based on the original retrieval algorithms, did not outperform indirect SMERGE and SMAP L4 RZSM estimates. These results provide insight into the viability of achieving enhanced RZSM accuracy via deployment of a spaceborne P-band radar.

In this letter, we consider a multi-input multi-output (MIMO) dual-functional radar and communication (DFRC) system, where a co-designed waveform is optimized for dual purposes of detecting multiple targets and serving several single-antenna users. In particular, two novel waveform design optimization problems based on constructive interference (CI) are proposed, where a constraint of waveform similarity error (WSE) and a joint radar-communication objective function are imposed under a Pareto optimization framework, by taking into account different transmit power constraints. To tackle the proposed optimization problems, we propose specifically tailored convex optimization techniques by using manifold algorithms. Numerical results show that the CI-based methods outperform the existing least-squares (LS) based methods, in terms of the symbol error rate (SER), without the need of knowing the exact constellation power.

In this study, we analyzed low-frequency drone-borne ground-penetrating radar (GPR) and full-wave inversion for soil electrical conductivity mapping. Indeed, in the lowest GPR frequency ranges, the soil surface reflexion coefficient depends more on the soil electrical conductivity than on its permittivity. Numerical experiments were conducted within the frequency range 15–45 MHz to analyze parameter sensitivities, the well-posedness of the inverse problem as well as the depth of sensitivity. The results show that the soil surface reflexion is significantly more sensitive to the soil electrical conductivity than the soil permittivity. Therefore, the conductivity can be retrieved using full-wave inversion within this frequency range, with a characterization depth varying from 0.5 to 1 m, depending on the soil properties. Yet, the permittivity also affects the results and should be accounted for in the inversion strategy. Field measurements were performed using low-frequency drone-borne radar with a 5-m half-wave dipole antenna, and electromagnetic induction (EMI) measurements with different depth sensitivities were conducted for comparison. Kriging interpolation was used to get maps from measurement points. The soil conductivity maps obtained by the proposed GPR and EMI are compliant in terms of absolute values and spatial patterns. This study demonstrated the capacity of low-frequency drone-borne GPR for fast, field-scale soil electrical conductivity mapping.

This letter studies the estimation of structured clutter covariance matrix (CCM) for space-time adaptive processing (STAP)-based airborne multiin multiout (MIMO) radar with limited training data. The Kronecker-product-expansion structure of the CCM is considered, where each term involves two Kronecker product operators. By exploiting the low-rankness of two permutations of the CCM, we propose a novel estimator based on least squares penalized by two nuclear norms. The estimator is also extended by considering the linear structures of the CCM. We examine the structure of the proposed solution and demonstrate its superior performance through simulation studies.

Lava tubes are buried channels that transport thermally insulated lava. Nowadays, lava tubes on the Moon are believed to be empty and thus indicated as potential habitats for humankind. In recent years, several studies investigated possible lava tube locations, considering the gravity anomaly distribution and surficial volcanic features. This article proposes a novel and unsupervised method to map candidate buried empty lava tubes in radar sounder data (radargrams) and extract their physical properties. The approach relies on a model that describes the geometrical and electromagnetic (EM) properties of lava tubes in radargrams. According to this model, reflections in radargrams are automatically detected and analyzed with a fuzzy system to identify those associated with lava tube boundaries and reject the others. The fuzzy rules consider the EM and geometrical properties of lava tubes, and thus, their appearance in radargrams. The proposed method can address the complex task of identifying candidate lava tubes on a large number of radargrams in an automatic, fast, and objective way. The final decision on candidate lava tubes should be taken in postprocessing by expert planetologists. The proposed method is tested on both a real and a simulated data set of radargrams acquired on the Moon by the Lunar Radar Sounder (LRS). Identified candidate lava tubes are processed to extract geometrical parameters, such as the depth and the thickness of the crust (roof).

This letter proposes a Doppler through-wall radar (TWR) positioning method based on the Hough transform of the extended Bezier model. First, the Hough transform based on the extended Bezier model is used to extract the interested target components from received echoes and estimate their instantaneous frequency (IF). Next, the real-time position of the target is estimated according to the Doppler processing. To improve the adaptability of the algorithm to different target trajectories, especially to solve the problem of asymmetric curve fitting, the proposed extended Bezier model can approach the practical IF curve by dynamically adjusting two parameters, which can not only keep the relatively low computational complexity but also significantly improve the positioning accuracy. Moreover, the proposed algorithm effectively solves the problem of ``frequency ambiguity.'' The experimental results demonstrate and validate that the proposed algorithm is effective in the field of human sensing applications of Doppler radar.