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International audience; A 3-in-1 depolarizing circular scatterer for the polarimetric radar calibration is proposed in this paper. The proposed scatterer is low cost, compact, planar circuit, and well suited for the radar operating in a compact range, for example, chipless RFID technology. By the virtue of its circular shape, the rear side of the proposed scatterer acts as a metallic disk with strong co polarization backscattered signals. The front side is composed of eight resonant dipoles which make it nondepolarizing and depolarizing scatterers at the inclination of 0° and 45°, respectively. The features of proposed circular scatterer are tested as reference calibration objects for single antenna based polarimetric radar calibration techniques. Two test objects are utilized: a dihedral tilted at 45° and a depolarizing multi resonant planar structure commonly called chipless RFID tag. The performance of proposed circular scatterer is also compared with the standard reference calibration objects: a metallic disk and a dihedral tilted at 22.5°. The performance of proposed circular scatterer is comparable to the standard reference calibration objects for the calibration techniques (namely Type 1 and Type 2). The proposed scatterer is potentially tolerant of displacement up to 1 cm and misalignment equals 2°.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2020-11-06, 資料番号: PA2110067000

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.

This article studies changes in microwave signals of oceanic snowfall in response to the formation of snow-covered sea ice using active and passive coincident data from the radar and radiometer onboard the CloudSat and the global precipitation measurement satellites. Using reanalysis data of liquid and ice water path as well as satellite retrievals of sea ice snow-cover depth, spectral regions are determined over which the snowfall signatures are likely to be obscured or falsely detected. Relying on an a priori database populated with the active-passive coincidences, a Bayesian snowfall retrieval algorithm is presented that links a k-nearest neighbor matching with the inverse Gaussian estimator used in the Goddard profiling algorithm. Without relying on any ancillary data of air temperature, the results demonstrate that over open oceans (sea ice), we can passively retrieve the CloudSat active snowfalls with a true positive rate of 92 (85%) and the root mean squared error of 0.24 (0.15) mm h⁻¹.

Three upcoming Martian missions will deploy a ground-penetrating radar (GPR) to reveal the fine-resolution subsurface structure and dielectric properties of materials beneath the surface. Numerical forward simulations of radar echo using a model of the near-surface structure at the landing site can provide a valuable reference for processing and interpretation of future radar data collected on Mars. In this study, based on the geological information of the Jezero crater, a detailed stratigraphic model of the near-surface structure is derived, which includes several key features, for example, the randomness of the medium, terrain, and cracks. To identify correctly the reflections of subsurface interfaces and fractures from the radar image, a v(z) f-k migration is carried out, the performance of which is evaluated using the GPR data obtained near Antarctic Zhongshan Station since the electrical properties of Antarctic glaciers and Martian materials are to some extent comparable. The results in this work show that compared with common migration algorithm, the v(z) f-k method not only improves the clarity of radar image but also provides the permittivity profiles to infer the composition of the substrate, leading to a better understanding of Martian near-surface geology.

Deep learning is emerging as a powerful tool in scientific applications, such as radar-based convective storm nowcasting. However, it is still a challenge to extend the application of a well-trained deep learning nowcasting model, which demands to incorporate the learned knowledge at a certain location to other locations characterized by different precipitation features. This article designs a transfer learning framework to tackle this problem. A convolutional neural network (CNN)-based nowcasting method is utilized as the benchmark, based on which two transfer learning models are constructed through fine-tune and maximum mean discrepancy (MMD) minimization. The base CNN model is trained using radar data in the source study domain near Beijing, China, whereas the transferred models are applied to the target domain near Guangzhou, China, with only a small amount of data in the target area. The influence of a varying number of target data samples on the nowcasting performance is quantified. The experimental results demonstrate that the deep transfer learning models can improve the nowcasting skills.

The impending spectrum congestion imposed by the emergence of new bandwidth-thirsty applications may be mitigated by the integration of radar and classic communications functionalities in a common system. Furthermore, the merger of a sensing component into wireless communication networks has raised interest in recent years and it may become a compelling design objective for 6G. This article presents the evolution of the hitherto separate radar and communication systems towards their amalgam known as a joint radar and communication (RADCOM) system. Explicitly, we propose to integrate a radio sensing component into 6G. We consider an ultra-dense network (UDN) scenario relying on an active multistatic radar configuration and on cooperation between the access points across the entire coverage area. The technological trends required to reach a feasible integration, the applications anticipated and the open research challenges are identified, with an emphasis on high-accuracy network synchronization. The successful integration of these technologies would facilitate centimeter-level resolution, hence supporting compelling high-resolution applications for next-generation networks, such as robotic cars and industrial assembly lines., 10 pages, 3 figures, accepted for publication in IEEE Vehicular Technology Magazine

In the actual scene of interferometric inverse synthetic aperture radar (InISAR) imaging, the noncooperative targets may make a nonuniform 3-D rotational motion (3-D-RM), which contributes not only to the time-variant Doppler modulation but also to the spatial-variant wave path difference (SVWPD). This, in turn, seriously degrades the 3-D geometry reconstruction accuracy of the targets. Furthermore, it is an enormous challenge to realize InISAR imaging from sparse frequency band and sparse aperture (SFB-SA) signals. This article seeks to address the problems of fine image registration and 2-D joint sparse reconstruction (2-D-JSR) for InISAR imaging with SFB-SA signals. With regard to the maneuvering targets with 3-D-RM, a novel SVWPD signal model is established. Moreover, a new algorithm, named joint wave path difference compensation (JWPDC) algorithm, is developed to perform fine image registration. It can not only combine multiple channels to achieve image registration but also jointly compensate for the non-SVWPD (NSVWPD) and SVWPD. A joint multichannel 2-D-JSR (JMC-2-D-JSR) ISAR imaging algorithm is also proposed according to the SFB-SA signal model to produce high-resolution ISAR images. Underpinned by the Bayesian compressive sensing (BCS) theory, the JMC-2-D-JSR ISAR imaging can be realized by solving a sparsity-driven optimization problem via a modified quasi-Newton solver. Through iterative processing of JMC-2-D-JSR and JWPDC, the high-quality 3-D InISAR images of maneuvering targets with 3-D-RM can be obtained. Extensive experimental results based on both simulated and real data corroborate the effectiveness of the proposed algorithm that outperforms other available InISAR imaging frameworks in 2-D imaging, 3-D imaging, and motion compensation.

There has been significant research devoted to the development of distributed microwave wireless systems in recent years. The progression from large, single-platform wireless systems to collections of smaller, coordinated systems on separate platforms enables significant benefits for radar, remote sensing, communications, and other applications. The ultimate level of coordination between platforms is at the wavelength level, where separate platforms operate as a coherent distributed system. Wireless coherent distributed systems operate in essence as distributed phased arrays, and the signal gains that can be achieved scale proportionally to the number of transmitters squared multiplied by the number of receivers, providing potentially dramatic increases in wireless system capabilities enabled by increasing the number of nodes in the array. Coordinating the operations of nodes in a distributed array requires accurate control of the relative electrical states of the nodes. The basic challenge is the synchronization and stability of the relative phases of the signals transmitted or received. Generally, such control requires wireless frequency synchronization, phase calibration, and time alignment. For radar operations, phase control also requires high-accuracy knowledge of the relative positions of the nodes in the array to support beamforming. Various technologies have been developed in recent years to address the coordination challenges for closed-loop applications, such as distributed communications, and more recently, there has been growing interest in new technologies for open-loop applications, such as radar and remote sensing. This article presents an overview of distributed phased arrays, the principal challenges involved in their coordination, and recent research progress addressing these challenges.

The NOAA Wide Swath Radar Altimeter (WSRA) and its processing are described. The WSRA provides real-time measurements of sea surface significant wave height and directional wave spectra during flights in hurricanes and other environments. The characteristics of near nadir scattering from the sea surface and the resulting distortion of the wave topography measured by the WSRA are discussed, as well as the simulation which generated a matrix to correct the directional wave spectra produced from the WSRA wave topography.

Dual function radar communications (DFRC) systems are attractive technologies for autonomous vehicles, which utilize electromagnetic waves to constantly sense the environment while simultaneously communicating with neighbouring devices. An emerging approach to implement DFRC systems is to embed information in radar waveforms via index modulation (IM). Implementation of DFRC schemes in vehicular systems gives rise to strict constraints in terms of cost, power efficiency, and hardware complexity. In this paper, we extend IM-based DFRC systems to utilize sparse arrays and frequency modulated continuous waveforms (FMCWs), which are popular in automotive radar for their simplicity and low hardware complexity. The proposed FMCW-based radar-communications system (FRaC) operates at reduced cost and complexity by transmitting with a reduced number of radio frequency modules, combined with narrowband FMCW signalling. This is achieved via array sparsification in transmission, formulating a virtual multiple-input multiple-output array by combining the signals in one coherent processing interval, in which the narrowband waveforms are transmitted in a randomized manner. Performance analysis and numerical results show that the proposed radar scheme achieves similar resolution performance compared with a wideband radar system operating with a large receive aperture, while requiring less hardware overhead. For the communications subsystem, FRaC achieves higher rates and improved error rates compared to dual-function signalling based on conventional phase modulation., 16 pages

A multiphase continuous-wave (MPCW) Doppler radar with a multiarc circle fitting (MACF) algorithm is proposed for improving the accuracy of small periodic displacement measurements. In the proposed radar, local oscillator (LO) signals with different phases are incident to multiple I/Q mixers, to simultaneously generate multiarcs for small periodic motions. The multiarcs obtained from the synchronized data are used in the circle fitting method to provide dc offset compensation, for accurate phase demodulation. The proposed MACF algorithm, based on the Levenberg–Marquardt method, can more precisely compensate for the dc offset by combining multiarcs generated from the proposed radar. A 5.8-GHz MPCW Doppler radar module with four-phased LO signals is implemented as a demonstration. The small periodic displacement of a metal plate on a single-axis linear stage is measured using the proposed radar module and algorithm. Compared with conventional CW Doppler radar using a single arc, measurement results using the proposed radar show that the root-mean-square error (RMSE) can be effectively decreased in the periodic motion from 1 to 2 mm, at distances from 30 to 60 cm. The RMSE is reduced by more than 33% for a peak-to-peak displacement of 1 mm at a distance of 60 cm.

In this article, we propose a multichannel sea clutter model in a spaceborne early warning radar system and analyze the influence of the sea clutter motion characteristics on the space-time adaptive processing (STAP) performance. To establish a multichannel sea clutter model, the 3-D Gerstner wave model is applied to construct the sea surface. Then the Pierson–Moskowitz wave spectrum and the stereo wave observation project (SWOP) directional spectrum are combined to describe the amplitude distribution of waves in different frequencies and directions. At the same time, the two-scale model is applied to obtain the specific backscattering coefficients of sea clutter at different time and positions. In addition, breaking waves are added in sea clutter returns with the form of false targets. Finally, the space-time distribution characteristics of sea clutter in a spaceborne multichannel array system and the influences of sea clutter under different wind speeds and directions on STAP performance are analyzed based on the simulation processing results. Processing results of some real-measured radar data are also exhibited to verify the theoretical analyses.

The latest developments in radar mission concepts suggest that subdaily synthetic aperture radar will become available in the next decades. The goal of this study was to demonstrate the potential value of subdaily spaceborne radar for monitoring vegetation water dynamics, which is essential to understand the role of vegetation in the climate system. In particular, we aimed to quantify fluctuations of internal and surface canopy water (SCW) and understand their effect on subdaily patterns of L-band backscatter. An intensive field campaign was conducted in north-central Florida, USA, in 2018. A truck-mounted polarimetric L-band scatterometer was used to scan a sweet corn field multiple times per day, from sowing to harvest. SCW (dew, interception), soil moisture, and plant and soil hydraulics were monitored every 15 min. In addition, regular destructive sampling was conducted to measure seasonal and diurnal variations of internal vegetation water content. The results showed that backscatter was sensitive to both transient rainfall interception events, and slower daily cycles of internal canopy water and dew. On late-season days without rainfall, maximum diurnal backscatter variations of >2 dB due to internal and SCW were observed in all polarizations. These results demonstrate a potentially valuable application for the next generation of spaceborne radar missions.

Modern millimeter-wave automotive radars are employed to keep a safe distance between vehicles and reduce the collision risk when driving. Meanwhile, an on-board radar module is supposed to operate in the line-of-sight condition, which limits its sensing capabilities in intersections with obstructed visibility. Therefore, this paper investigates the scheme with passive reflector, enabling the automotive radar to detect an approaching vehicle in the non-line-of-sight (blind) urban intersection. First, extensive radar measurements of the backscattering power are carried out with the in-house assembled millimeter-wave radar equipment. Next, the measured data is employed to calibrate an accurate analytical model, deduced and described in this paper. Finally, the analytical models are deployed to define the optimal parameters of the radar scheme in the particular geometry of the selected intersection scenario. Specifically, it is found that the optimal angular orientation of the reflector is 43.5, while the 20 m curvature radius shows better performance compared to a flat reflector. Specifically, the curved convex shape increases scattering power by 20 dB in the shadow region and, thus, improves the detection probability of the vehicle, approaching the blind intersection. publishedVersion

Breathing rate monitoring is a must for hospitalized patients with the current coronavirus disease 2019 (COVID-19). We review in this paper recent implementations of breathing monitoring techniques, where both contact and remote approaches are presented. It is known that with non-contact monitoring, the patient is not tied to an instrument, which improves patients' comfort and enhances the accuracy of extracted breathing activity, since the distress generated by a contact device is avoided. Remote breathing monitoring allows screening people infected with COVID-19 by detecting abnormal respiratory patterns. However, non-contact methods show some disadvantages such as the higher set-up complexity compared to contact ones. On the other hand, many reported contact methods are mainly implemented using discrete components. While, numerous integrated solutions have been reported for non-contact techniques, such as continuous wave (CW) Doppler radar and ultrawideband (UWB) pulsed radar. These radar chips are discussed and their measured performances are summarized and compared.

This paper is concerned with the distributed ${H} _{\infty }$ -consensus filtering problem on attitude tracking over a radar filter network subject to switching topology and random packet dropouts occurring in the data transmission from both the Sun sensor and the filters. Since ground-based radars cannot directly measure the satellite attitude, a Sun sensor is deployed at the satellite side and its measurements are transmitted to radar filters through different network communication channels while suffering from random packet dropouts with different probabilities. In the radar filter network, each radar filter receives data not only from the Sun sensor but also from its local neighboring radar filters in accordance with a switching network topology. A delicate distributed ${H} _ {\infty}$ -consensus filtering algorithm, which incorporates the effects of switching network topology and random packet dropouts, is adopted to estimate attitude and attitude-rate. The algorithm guarantees ${H} _ {\infty }$ -consensus attenuation performance for the estimation deviations among radar filters, and the robustness against the switching network topology and packet dropouts for the radar filter network. The illustrative examples are given to verify the effectiveness of the proposed distributed ${H} _ {\infty}$ -consensus filtering algorithm.

Joint communication and radar/radio sensing (JCAS), also known as dual-function radar communications , enables the integration of communication and radio sensing into one system, sharing a single transmitted signal. The perceptive mobile network (PMN) is a natural evolution of JCAS from simple point-to-point links to a mobile/cellular network with integrated radio-sensing capability. In this article, we present a system architecture that unifies three types of sensing, investigate the required modifications to existing mobile networks, and exemplify the signals applicable to sensing. We also provide a review to stimulate research problems and potential solutions, including mutual information, joint design and optimization for waveform and antenna grouping, clutter suppression, sensing parameter estimation and pattern recognition, and networked sensing under the cellular topology.

Recent publications have investigated and demonstrated new digital modulation techniques that allow realization of orthogonal frequency division multiplexing (OFDM) radar with relatively low hardware effort. This is especially interesting as the digitally defined signals offer additional freedom for multiplexing schemes of multiple-input multiple-output (MIMO) radar sensors. MIMO radar sensors rely on strictly orthogonal signals to distinguish between multiple transmitters (TX). To span a virtual array with a maximum number of antenna positions, a novel method that combines orthogonal signals with strongly correlated signals generated by space-time coding (STC) is introduced. This leads to virtual transmit antenna positions that are used to improve the unambiguous angular area of an otherwise ambiguous MIMO antenna array. The proposed method is validated with an integrated OFDM radar at millimeter-wave frequencies.

This article is devoted to the problem of multitarget tracking in the maritime environment using Bernoulli track-before-detect (TBD) filtering. Detection and tracking of weak targets in sea clutter using high-resolution airborne radar is notoriously challenging compared to classical target tracking problems. Furthermore, the measured amplitude in range–azimuth cells that are in the vicinity of closely spaced (interacting) targets represents a superposition of individual target contributions, demanding a special joint treatment. This article proposes a novel solution for multitarget tracking using Bernoulli TBD for maritime radar and covers cases when targets are both far apart and closely spaced. Comparisons with existing TBD algorithms are used to verify the efficacy of the proposed Bernoulli TBD algorithm with realistic sea clutter simulations used to create challenging scenarios with variable target strengths.

Pulse compression has enabled a new generation of low-cost and compact spaceborne weather radar systems. To successfully utilize pulse compression techniques for cloud and precipitation applications, the effects of Doppler-range migration must be considered during the design and operation of the radar. Pulse compression for spaceborne weather applications introduces additional interdependence between the radar system and the operations when compared with traditional pulsed radar systems, primarily as a result of the large platform velocities. Pulse compression signals for weather radar can be simulated with high fidelity to predict and optimize the radar’s performance. In this article, we evaluate the pulse compression performance of RainCube, a Ka-band precipitation radar in a CubeSat, through analysis and comparison of observations and radar simulations. Through these comparisons, design and operational considerations for pulse compression weather radar are discussed. This work shows that the optimal pointing angle for RainCube to achieve the finest vertical resolution is not at nadir, but when pointing forward approximately 2.25°, in the direction of the spacecraft’s orbit.

Emerging 5G millimeter-wave (mm-wave) networks use electronic beamforming and beamsteering and support signal bandwidths on the order of hundreds of MHz. Given these characteristics, opportunities exist to develop 3-D sensing applications that leverage 5G mm-wave communications infrastructure. In this context, this work introduces a signal processing pipeline that can: 1) accurately extract the Time of Flight (ToF) of reflected orthogonal frequency division multiplexing (OFDM) communications signals and 2) enhance range resolution by coherently aggregating the reflection information from separate frequency bands. In combination with precise beamsteering, the proposed signal processing techniques enable high-resolution 3-D radar imaging without affecting communications protocols . An experimental system demonstrating this concept has been implemented and is described. This system consists of two software-defined phased array radios (SDPARs), one configured as a prototype 5G base station TX, and one as an auxiliary prototype 5G RX. Each SDPAR primarily consists of a Si-based 28-GHz, 64-element, phased array transceiver module and software-defined radio. Simulation and benchmark results show that our coherent bandwidth stitching enables accurate OFDM-based ranging with 15-cm resolution. Measurement results show 3-D radar images of indoor scenes with 2° angular and 15-cm ranging resolution, created by processing reflected 5G-like communication waveforms at 28 GHz. The produced 3-D radar images effectively depict the location of objects in the scene, and these locations are in close agreement with the ground truth.

We consider the problem of corrupted radar super-resolution, a generalization of compressed radar super-resolution in which one aims to recover the continuous-valued delay-Doppler pairs of moving objects from a collection of corrupted and noisy measurements. The received signal in this type consists of contributions from objects, outlier and noise. While this problem is ill-posed in general, tractable recovery is possible when both the number of objects and corrupted measurements are limited. In this brief, we propose an atomic norm optimization in order to find the delay-Doppler pairs and the outlier signal. The objective function of our optimization problem encourages both sparsity in the continuous delay-Doppler domain and the sparsity of perturbations in the outlier signal. We show that the associated problem can be solved via semidefinite programming (SDP). Moreover, we provide a reasonably fast and efficient algorithm for solving this SDP based on the alternating method of multipliers (ADMM). Simulations results verify the superior performance of our proposed problem and the high accuracy and low computational complexity of our algorithm.

Estimating the signal transmitted by a noncooperating radar is of great use in radar countermeasures. In this article, we propose to improve the noise resilience of the radar signal estimation by constraining the estimate to be in a low-dimensional signal space. In particular, we focus on estimating nonlinear frequency modulated radar signals that have good target resolution characteristics. A flexible 3-D odd polynomial frequency signal model is introduced as the low-dimensional signal space to compute accurate estimates at low signal-to-noise ratios (SNRs) with small number of intercepted signals. The quality of the low-dimensional signal estimates is compared with that of the estimate from the unconstrained high-dimensional space computed using principal component analysis estimator, which is the method in predominant use in the multisensor scenario. It is shown that signal estimation through low-dimensional signal spaces yields more accurate estimates at low SNRs with a smaller number of sensors compared to searching in the unconstrained high-dimensional signal space.

Within the autonomous driving community, millimeter-wave frequency-modulated continuous-wave (FMCW) radars are not used to their fullest potential. Classical, hand-designed target detection algorithms are applied in the signal processing chain and the rich contextual information is discarded. This early discarding of information limits what can be applied in algorithms further downstream. In contrast with object detection in camera images, radar has thus been unable to benefit fully from data-driven methods. This work seeks to bridge this gap by providing the community with a diverse, minimally processed FMCW radar dataset that is not only RGB-D (color and depth) aligned but also synchronized with inertial measurement unit (IMU) measurements in the presence of ego-motion. Moreover, having time-synchronized measurements allow for verification, automated or assisted labelling of the radar data, and opens the door for novel methods of fusing the data from a variety of sensors. We present a system that could be built with accessible, off-the-shelf components within a $1000 budget and an accompanying dataset consisting of diverse scenes spanning indoor, urban and highway driving. Finally, we demonstrated the ability to go beyond classical radar object detection with our dataset with a classification accuracy of 85.1% using the low-level radar signals captured by our system, supporting our argument that there is value in retaining the information discarded by current radar pipelines.

This article presents a robust hand-gesture recognition method via multidimensional feature representation and learning specifically designed for commercial frequency-modulated continuous wave (FMCW) multi-input multi-output (MIMO) millimeter-wave radar. First, the optimal configuration of the radar system parameters for the hand-gesture recognition scenario is investigated and a standard procedure to determine the system configuration is given. Then a moving scattering center model is proposed to represent the 3-D point cloud in the range–Doppler (RD)-angular multidimensional feature space. A scattering point detection and tracking algorithm is presented based on a set of motion constraints in terms of position, velocity, and acceleration. It is derived from the space-time continuity of a nonrigid target. Finally, a lightweight multichannel convolutional neural network (CNN) is designed to learn and classify multidimensional gesture features including radial RD and tangential azimuth–elevation. Extensive experiments are carried out with the developed system and a large data set is obtained to train and test the classifier. The results show that the proposed gesture recognition method can effectively distinguish gestures that are easily confused in the RD domain and achieve robust performances under various conditions.

One of the fundamental technical challenges of any new spaceborne vegetation remote sensing mission is the determination of what sensor(s) to place onboard and what, if any, overlapping modes of operation they will employ as each onboard sensor adds significant cost to the overall mission. In this article, the remote sensing of forest parameters using multimodal remote sensing is presented. In particular, polarimetric radar, Light Detection And Ranging (LiDAR), and near-IR passive optical sensing platforms are employed in conjunction with physics-based models. These models are used to accurately estimate forest aboveground biomass as well as canopy height in homogeneous areas. It is shown that this proposed method is capable of achieving high accuracy estimates while using minimal ancillary data in the estimation process. We present a method to combine measured data sets with our geometric and electromagnetic sensor models to develop a forest parameter estimation algorithm that fuses multimodal remote sensing technologies with a minimal amount of ground information and yields an accurate estimate of forest structure including dry biomass and canopy height with rms errors of 1.6 kg/m2 and 1.68 m respectively.

Sensor registration is an important problem that must be considered when attempting to perform any kind of data fusion in multimodal, multisensor target tracking. In this multiple target tracking (MTT) application, any inaccuracies in the registration can lead to false tracks being created, and tracks of true targets being stopped prematurely. This article introduces a method for simultaneously tracking multiple targets in a surveillance region and estimating appropriate sensor registration parameters so that sensor fusion can be performed accurately. The proposed method is based around particle belief propagation (BP), a recent but highly efficient framework for tracking multiple targets. The proposed method also uses a hierarchical model which allows for multiple processes to be linked and interact with one another. We present a comprehensive set of simulations and results using differing, asynchronous sensor setups, and compare with a random finite set (RFS) approach, namely the sequential Monte Carlo (SMC)-probability hypothesis density (PHD) filter. The results show the proposed method is 17% more accurate than the RFS approach on average.

For onshore high-frequency surface-wave radar (HFSWR), target parameter estimation focuses mainly on distance and velocity demodulation, then on azimuth determination. However, these parameters are difficult to solve accurately for shipborne HFSWR targets due to additional modulation on the echo signal introduced by the forward and six-degree-of-freedom (6-DOF) movement of the platform, causing target points spread and shift in radar Doppler spectra. To overcome this difficulty, the influence of platform motion on target detection is mathematically analyzed in terms of echo signal processing, and then theoretical equations are derived to correct the bias in measurements of the target’s state and features. Furthermore, to meet the requirement of shipborne HFSWR installed in limited space, the direction of arrival (DOA) estimation of irregular radar arrays with unequal intervals and arbitrary numbers of antennas is also analyzed. With the derived formulas, the parameters of the shipborne HFSWR target, including the range, radial velocity, and azimuth, can be accurately estimated with the help of inertial navigation system (INS) data. Moreover, both the simulation and the field experiment results validate the theoretical analysis and the derived equations.

This manuscript was accepted for publication on IEEE Transactions on Geoscience and Remote Sensing.Abstract: In radar polarimetry, incoherent target decompositiontechniques help extract scattering information from polarimetricSAR data. This is achieved either by fitting appropriate scattering models or by optimizing the received wave intensitythrough the diagonalization of the coherency (or covariance)matrix. As such, the received wave information depends onthe received antenna configuration. Additionally, a polarimetricdescriptor that is independent of the received antenna configuration might provide additional information which is missed by the individual elements of the coherency matrix. This implies that existing target characterization techniques might neglect this information. In this regard, we suitably utilize the 2D and 3D Barakat degree of polarization which is independent of the received antenna configuration to obtain distinct polarimetric information for target characterization. In this study, we introduce new roll-invariant scattering-type parameters for both full-polarimetric (FP) and compact-polarimetric (CP) SAR data. These new parameters jointly use the information of the 2D and 3D Barakat degree of polarization and the elements of the coherency (or covariance) matrix. We use these new scattering type parameters, which provide equivalent information as the Cloude alpha for FP SAR data and the ellipticity parameter chi for CP SAR data, to characterize various targets adequately. Additionally, we appropriately utilize these new scattering-type parameters to obtain unique non-model based three-component scattering power decomposition techniques. We obtain the even-bounce, and the odd-bounce scattering powers by modulating the total polarized power by a proper geometrical factor derived using the new scattering-type parameters for FP and CP SAR data. The diffused scattering power is obtained as the depolarized fraction of the total power. Moreover, due to the nature of its formulation, the decomposition scattering powers are nonnegative and roll-invariant while the total power is conserved. The proposed method is both qualitatively and quantitatively assessed utilizing the L-band ALOS-2 and C-band Radarsat-2 FP and the associated simulated CP SAR data.

We present the characteristics of dual-polarized weather radar variables as observed by a planar phased-array radar (PPAR) operating at X-band. The characteristics are governed by the projection of the polarizations radiated by the array into the canonical spherical coordinates defining the measurement geometry of ground-based weather radars. The direction-dependent gain and phase of the radiating elements, the orientation of the radiated polarizations, and the mechanical tilting of the array all contribute to the projection, which results in biased observations compared with those of a mechanically scanned weather radar employing a reflector antenna. We decompose the projection into components that can be separated and customized for various PPAR configurations, including consideration of possible cross-polarized radiation by the elements and the effect of a wet radome covering the array face. These are represented by matrices whose product comprises the total projection. We apply this methodology to an X-band PPAR using in-place measurements of precipitation to obtain the properties of the radiation pattern. We then obtain the resulting biases of common weather radar polarimetric variables. We show that the biases can be represented as a product of the intrinsic variables with projection- and target-dependent corrections. The projection-dependent corrections depend only upon elements of the projection matrix, while the target-dependent corrections also depend upon the intrinsic differential reflectivity and copolar or cross-polar correlation coefficients. We find that the projection-dependent corrections are sufficient to achieve acceptable bias over most of the scan range.

This article addresses the problem of radar pulse compression. First, a matched filter at the receiver is assumed and a method to design a transmit waveform characterized by an autocorrelation function with a narrow mainlobe and low sidelobes is proposed. After that, a mismatched filter is considered and a method to design the optimum linear filter receiver is proposed. In both cases, the pursued objectives are minimum sidelobe energy and peak level through a strategy based on quadratic and nonlinear optimization. In this article, we focus particularly on the case of ground weather radars, where the problem of pulse compression is particularly challenging.

This article presents a new universal radar target simulator (RTS) principle based on multirate sampling. The proposed concept enables a realistic emulation of moving targets independently of the radar type and signal and is suited for all common modulation types, such as frequency-modulated continuous-wave (FMCW), chirp sequence (CS), orthogonal frequency division multiplexing (OFDM), and pseudorandom impulse sequences. This versatile applicability is achieved by delaying the radar signal with a time-variant delay that is changed continuously over time. Analog systems use mixing to simulate moving targets, which is not precise. Standard digital radio frequency memory (DRFM) systems with discrete programmable time delays, in contrast, typically drop or delay samples to simulate moving targets. This time-delay discretization, however, introduces discontinuities to waveforms and therefore creates spurious signals. These distortions particularly affect processing in the Doppler dimension. To overcome this disadvantage, the proposed multirate system simulates continuous target movement and the Doppler effect of real targets by using different sample rates for an analog-to-digital converter (ADC) and digital-to-analog converter (DAC). The resulting Doppler phase variation equals that of a real moving target exactly. With this multirate RTS, even slow-moving real-world targets can be mimicked accurately. The attractive properties of the proposed RTS concept are verified by real-world measurements with a 77-GHz demonstration system.

This paper studies the spectrum allocation problem between spectrum service providers (SSPs) and terminals equipped with orthogonal frequency division multiplexing (OFDM) integrated radar and communication (IRC) systems. In particular, IRC-equipped terminals such as autonomous vehicles need to buy spectrum for their radar functions, e.g., sensing and detecting distant vehicles, and communication functions, e.g., transmitting sensing data to road-side units. The terminals determine their spectrum demands from the SSPs subject to their IRC performance requirements, while the SSPs compete with each other on the service prices to attract terminals. Taking into account the complicated interactions, a hierarchical Stackelberg game is proposed to reconcile the spectrum demand and service price, where the SSPs are the leaders and the terminals are the followers. Due to the spectrum constraints of the SSPs, we model the lower-layer subgame among the terminals as a generalized Nash equilibrium problem. An iterative searching algorithm is then developed that guarantees the convergence to the Stackelberg equilibrium. Numerical results demonstrate the effectiveness of our proposed scheme in terms of social welfare compared to baseline schemes.

In this paper, we consider the multi-frame integration (including intra-frame integration and inter-frame integration) problem for the detection of weak moving targets, where range walk effect (RWE) occurs within the integration time. A novel integration method is proposed to achieve the accumulation of a target's multi-frame radar echoes. More specifically, the proposed integration method contains two main steps, where the modified Radon Fourier transform (RFT) is firstly used to realize the intra-frame coherent integration for every frame of echo signals. After that, the RFT-domain integration algorithm is presented to achieve the inter-frame coherent integration based on the characteristics of RFT outputs, during which the accumulation of target energy distributed in multiple frames could be realized. The explicit expressions and analysis for various performance measures of the proposed method, such as integration output response, probability of detection, input-output signal-to-noise ratio (SNR), and blind speed sidelobe (BSSL) response are derived and given. Simulation experiments are given to demonstrate the effectiveness of the proposed method.

The effects of sea surface temperature (SST) on ocean radar backscattering are investigated under both the neutral and nonneutral atmospheric conditions for the applications of wind retrieval. The impact factors are parameterized as functions of SST. The SST effects on the variations in ocean scattering and wind retrieval are evaluated using an analytic model which combines the KHCC03 spectrum and the second-order small slope approximation (SSA-II) model. Under the neutral condition, we present the following new insights at three commonly used bands: 1) the seawater permittivity accounts for a dominant effect of SST at the L-band. The SST effects induce a wind underestimation of 0.3 m/s over cold seawater and a wind overestimation of 0.24 m/s over warm seawater at the L-band and a wind speed of 8 m/s. The seawater viscosity plays a significant role in the SST effects on ocean scattering at the C- and Ku-bands, while its variation induced by SST has insignificant effects on L-band scattering; 2) for the C-band, the SST-induced wind retrieval error can be neglected at a medium wind speed due to the neutralization of the effects of various factors on surface roughness. Yet, the SST effects are not negligible at low and high wind speeds; and 3) both the dielectric and dynamic factors play significant roles in the SST effects on ocean scattering at the Ku-band. Under the nonneutral condition, the simulation results show that the air–sea interaction governs the SST effects on ocean scattering and wind velocity variations. Other than the air–sea interaction, the wind retrieval errors induced by other SST-related factors are negligible at the L- and C-bands.

While a number of signal structures have been proposed for radar, frequency modulation (FM) remains the most common in practice because it is well-suited to high-power transmitters, which tend to introduce significant distortion to other waveform classes. That said, various forms of coding provide useful parameterizations for which a variety of optimization methods can be readily applied to accomplish different operational goals. To that end, the polyphase-coded FM (PCFM) implementation was previously devised as a means to bridge this gap between optimizable parameters and physically realizable waveforms. However, the original method employed to optimize PCFM waveforms involved a piecewise greedy search that, while relatively effective, was rather slow and cumbersome. Here, the continuous nature of this framework is leveraged to formulate a gradient-based optimization approach that updates all parameters simultaneously and can be efficiently performed using fast Fourier transforms, thus facilitating a general design methodology for practical waveforms that is directly extensible to myriad waveform-diverse arrangements. Results include a large number of optimization assessments to discern performance trends in aggregate and detailed analysis of specific cases, as well as both loopback and free-space experimental measurements to demonstrate practical efficacy.

Fast Fourier transform (FFT) is one of the fundamental signal processing algorithms widely used in radar applications. The Radon–Fourier transform (RFT) can be seen as an FFT generalization that can overcome some of its limitations. This work derives three spectral RFT (SRFT) based approaches to address major challenges of the multiple-input multiple-output automotive radars. First, two SRFT-based approaches are derived to increase maximal target detection range by mitigation of target migration in range and direction of arrival, jointly, and by multidwell integration processing, which increases the radar coherent integration time without compromising its detection update rate. Next, SRFT-based approach is proposed to address the cluster-to-track association problem that arises in multiple distributed target tracking scenarios that characterize automotive radar operation in dense urban environments.

Publisher Copyright: IEEE Copyright: Copyright 2021 Elsevier B.V., All rights reserved. The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. Harnessing the power of multilinear algebra through tensor analysis in wireless signal processing, channel modeling, and parametric channel estimation provides greater flexibility in the choice of constraints on data properties and permits extraction of more general latent data components than matrix-based methods.Tensor analysis has also found applications in Multiple-Input Multiple-Output (MIMO) radar because of its ability to exploit the inherent higher-dimensional signal structures therein. In this paper, we provide a broad overview of tensor analysis in wireless communications and MIMO radar. More specifically, we cover topics including basic tensor operations, common tensor decompositions via canonical polyadic and Tucker factorization models, wireless communications applications ranging from blind symbol recovery to channel parameter estimation, and transmit beamspace design and target parameter estimation in MIMO radar.

Radar sounder surface imaging is a rather unexplored approach to the analysis of planetary bodies. While a radar sounder is an instrument specifically designed for subsurface investigations, a particular set of power measurements (denoted as off-nadir surface echoes) can be exploited together with an external digital elevation model to produce images of the investigated surface at meters wavelength. The use of the off-nadir data may also reveal the presence of previously undetected subsurface features. In this article, we present a method for producing surface roughness images by high-frequency (HF) radar sounder data. The study of surface roughness in the HF band is particularly useful for both geologic studies and landing-zone reconnaissance as it is evaluated at meters to hundreds of meters horizontal scale. The proposed method combines off-nadir data of the Shallow Radar Sounder (SHARAD) with the Mars Orbiter Laser Altimeter (MOLA) digital elevation model. The produced roughness images at 20 MHz (15-m wavelength) of the Martian surface provide higher coverage and resolution of the surface roughness characterization at a 10–100-m horizontal scale than previous SHARAD work. By comparing the experimental roughness image with the one obtained by radar backscattering simulations, it is possible to identify subsurface features. In our experiments, we were able to produce a bidimensional image of a previously undetected large buried crater (10 km $\times12$ km) located in the Nili Fossae. This finding opens up new possibilities in exploiting radar sounding data for better detecting shallow subsurface features.

A nonlinear trajectory of a radar platform in synthetic aperture radar (SAR) may lead to severe coupling between the range and the azimuth, which may make the ambiguity function (AF) analysis complicated. The numerical algorithm-based AF analysis may be computationally expensive, while the existing analytical algorithm-based AF analysis may cause large errors because it does not consider the coupling between the range and the azimuth. By observing that the singular value decomposition (SVD) is good to deal with the coupling problem, in this article, we propose an effective AF analysis based on SVD. The key idea is to first use a small amount of sampling points for SVD of the coupled term in the AF and then the decoupled vectors are fitted to high-order polynomials for the analytical AF calculation. It converts the double integral into the product of two single integrals in the calculation. From the proposed SVD-based AF analysis, three parameters, namely, 3-dB resolution, peak sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR), are then effectively computed. The simulated results verify the good performance of the proposed SVD-based AF analysis.

IEEE In this paper, we investigate the network utility maximization problem in a dual-functional radar-communication multi-unmanned aerial vehicle (multi-UAV) network where multiple UAVs serve a group of communication users and cooperatively sense the target simultaneously. To balance the communication and sensing performance, we formulate a joint UAV location, user association, and UAV transmission power control problem to maximize the total network utility under the constraint of localization accuracy. We then propose a computationally practical method to solve this NP-hard problem by decomposing it into three sub-problems, i.e., UAV location optimization, user association and transmission power control. Three mechanisms are then introduced to solve the three sub-problems based on spectral clustering, coalition game, and successive convex approximation, respectively. The spectral clustering result provides an initial solution for user association. Based on the three mechanisms, an overall algorithm is proposed to iteratively solve the whole problem. We demonstrate that the proposed algorithm improves the minimum user data rate significantly, as well as the fairness of the network. Moreover, the proposed algorithm increases the network utility with a lower power consumption and similar localization accuracy, compared to conventional techniques.

Subbanding technique has been applied in wideband (WB) digital array radar systems to address the challenge of high-speed signal processing and WB jamming cancellation. This article reveals that the well-established subband decomposition by polyphase subband filtering could still leave jamming decorrelation post subbanding, which would deteriorate the jamming cancellation performance. A novel implementation of the subbanding processor is then proposed by isolating digital down conversion incorporated in the subbanding operation and performing it at baseband explicitly. This method provides flexibility to process segments of the digitized spectrum, which accommodates radar receivers to contemporary requirements of frequency agility. This article proceeds to propose a joint jamming cancellation scheme for use in the complicated real-world electronic warfare scenario including both mainlobe and sidelobe jamming. The jamming cancellation performance is evaluated by simulation with a detailed investigation on jamming decorrelation mitigation and subbanding implementation consideration. More results are shown by testing the cancellation scheme on data collected with an experimental airborne WB digital array radar in a multijamming scenario toward sea surface targets.

Traveling waves can be supported by long, smooth surfaces under horizontal polarization, such as the wings or bodies of aircraft. A 3-D scattering center (SC) model for traveling waves is proposed in this work. The equivalent location of the SC and the dependence function of scattering amplitude on aspect angles is derived based on traveling wave antenna theory and modified according to real scattering characteristics. Chamber-measured data and full-wave numerical results of a series of geometries are presented to verify the validity of this model in imaging simulation. We find that the equivalent position of this SC is located along a circular arc. In consideration of its polarization characteristics and inverse synthetic aperture radar (ISAR) image signatures, the proposed SC model is promising for used in the model-based automatic target recognition (ATR) to distinguish traveling waves from other scattering components.

Tapering an active array antenna system on transmit lowers the sidelobe level, but it adversely affects equivalent isotropically radiated power (EIRP) by reducing both power and gain. In this article, we analyze this effect. First, we propose a new taper efficiency coefficient, which allows us to assess the effect of tapering on EIRP (in contrast to the standard taper efficiency that describes the effect of tapering on gain only). Then, we present an optimization method to compute the taper weights that maximize the defined efficiency for an assumed bound on the sidelobe level. The obtained optimal weights can be used not only to taper an antenna array but also as a benchmark for other tapers. Therefore, we analyze several known tapers and compare their transmit efficiencies. As a result, we find that for some combinations of sidelobe level and array length, the proposed optimal taper provides substantially better efficiency than Dolph–Chebyshev or Taylor tapers.

In this article, a hybrid radar configuration for automotive radar applications is proposed, with the system architecture, design, and measured results reported. The hybrid radar system consists of a vertically distributed phased transmitting array (PTA) and a horizontally aligned digital beamforming (DBF) receiving array, thereby enabling 2-D beam scanning with a reduced channel number. In contrast to time-division multiplexing (TDM) multiple-input multiple-output (MIMO) radars, in which only a single transmitter is active at a time in the TDM MIMO radar configuration due to the working principle of switching between different transmitting elements, here, all the phased array transmitters of the proposed radar work simultaneously. The demonstrated system realizes a higher transmitted power and signal-to-noise ratio, provided that the equivalent isotropically radiated power (EIRP) is not limited and the radiated power of each transmitter is equal in these two radar configurations. In addition, only 1-D DBF is required for the proposed hybrid radar because beamforming in another dimension is accomplished by the PTA, thus alleviating the baseband complexity and signal processing burden. As a proof-of-concept example for system validation, a hybrid radar prototype is fabricated and measured at around 24 GHz, without considering any specific EIRP limitations in this band, thus confirming the validity of the proposed concept.

Randomized stepped-frequency (RSF) radar, which transmits random-frequency pulses in a coherent processing interval (CPI), has an excellent electronic counter-countermeasures (ECCM) performance and the ability to synthesize wide-frequency band with a narrow bandwidth receiver. However, due to the use of different frequency pulses, RSF waveform is incompatible with the conventional moving-target-detection (MTD) technique, which makes clutter suppression a difficult problem for RSF radar and limits its application. In this article, the effect of clutter on the coherent processing for RSF radar is first analyzed. The result shows that coherent processing can improve the signal-to-clutter ratio on target Doppler channel by $N$ (number of pulses in one CPI) times, which suppresses weak clutter scatterers to a large extent whilst strong clutter scatterers would still be left influential. Focusing on dominant clutter scatterers, the adaptive range-Doppler clutter suppression algorithm, which is based on designing clutter suppression filters with two-dimensional property to obtain the clear high-range-resolution profile of moving target in clutter environment, is then proposed for RSF radar. The proposed algorithm, which takes into account clutter Doppler extension, can be used for RSF radar coherent processing in clutter environment.

In this article, we propose new waveform design procedures and classification schemes to improve target classification performance nonadaptively in radar systems. The new designs and schemes are all inspired by 2-class and multiclass Fisher discriminant analysis. The proposed system does not require as much computational capability as adaptive waveform design systems while also overcoming: 1) angular uncertainty in classifying high fidelity targets and 2) drops in performance experienced by nonadaptive systems when classification is extended to more than two targets. The waveform design procedure is based on an optimization problem to find the waveform that maximizes the objective function inspired by Fisher analysis under constant energy constraint. We also derive two closed-form solutions for the optimization problems under certain conditions for the 2-class and multiclass cases. All the methods are tested using synthetic and real data to show the performance of the proposed methods against the average Mahalanobis distance (AMD) nonadaptive waveform design and classifier.

Amplitude comparison monopulse systems encounter the target resolution problem when multiple targets occupy the same range-azimuth cell. To solve this problem, a new angle estimation approach is proposed. The traditional range-cell-based signal processing is improved by modeling the signal in the frequency domain. Thus, the range straddling loss and the range migration problem can be avoided. The closely spaced targets in the same range-azimuth cell can be well resolved by the jointly maximum likelihood estimation of the range and velocity of the targets. Then, the angle estimates are extracted by using the monopulse ratio technique with the estimated parameters of the resolved targets in the two channels. Furthermore, the RELAX algorithm is utilized to reduce the computational burden of the proposed approach. The performance of the proposed estimator is evaluated using Monte Carlo simulation.

This paper deals with the multiple targets localization problem via multi-channel ultra-wideband (UWB) imaging radar non-line-of-sight (NLOS) signal processing. A novel matching-based radar imaging algorithm is proposed to obtain the positions of multiple targets in the L-shaped corner scenario with complex multipath ghost signals. Firstly, a multipath propagation model for the multiple targets scenario is established. Then the positions of the actual multipath ghosts are extracted from the radar image, and the candidate targets corresponding to these multipath ghosts are derived. Secondly, the ellipse-cross-localization method is proposed to obtain the positions of the candidate multipath ghosts, followed by two defined matching factors to measure the similarity between actual and candidate multipath ghosts. According to the similarity, decision rules are designed to determine the actual targets. Compared with the localization algorithm based on one-dimensional range profile, the proposed algorithm can effectively cope with the cases of multiple targets, even in the cases of rough walls and noise. Finally, simulations and experimental data are used to validate the effectiveness of the proposed algorithm.