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To unlock the full potential of composite materials, reliable measurement methods during and after their manufacturing are required. Established measuring methods are commonly based on ultrasound or thermographic imaging techniques and offer only a limited usability. A promising alternative to the aforementioned methods are millimeter-wave-based systems. It has already been demonstrated that such systems can provide a tomographic representation of composite materials, enabling the detection, localization, and classification of critical defects within the component. The tomographic millimeter-wave imaging system presented here is operating in the W band and is packaged for the operation in a manufacturing environment. For this purpose, it is enclosed by a dustproof housing and can be mounted on an industrial robot, which enables the development of an automated measurement procedure during and after the manufacturing process of composite materials. The direct integration of the measurement system into the manufacturing process allows for early-stage fault detection and classification, which is essential for the production of high-quality, high-performance, and highly reliable composite materials.

In this letter, we propose a method of monitoring driver's movement using a small-sized millimeter-wave radar sensor. First, we install a frequency-modulated continuous wave radar sensor on the steering wheel and collect reflected signals for various driver motions. Because the reflection patterns are different for each motion, we can distinguish different movements of the driver by analyzing the received signal. In our method, we use the spectrogram of the received signal obtained through multiple measurements and apply a window function to extract the radar signal in the form of an image. Then, we use the convolutional neural network architecture to classify the radar signal images. The classification results show that our proposed method can classify four different movements of the driver with an accuracy higher than 80%.

With the rise of high resolution multiple input multiple output (MIMO) systems, radar became an important sensor in the development of Advanced Driver Assistance Systems (ADAS) and autonomous driving applications. Autonomous driving will rely strongly on artificial intelligence. Since most modern classification algorithms are based on neural networks, they require huge amounts of data to perform well, especially in unexpected traffic situations. Radar sensor simulation can potentially produce a great variety of training data for machine learning algorithms, which makes it an important cornerstone in the development of ADAS. Furthermore, with radar simulators, different antenna configurations and various edge cases can be simulated. In this work, a versatile ray tracing toolchain based on the shoot and bouncing rays (SBR) approach is presented. The program is able to simulate complex urban environments including realistic clutter, by utilizing simplistic reflection models. The program does not only produce realistic radar images, but also generates camera-like images using the same materials. Furthermore, this work deals with the adaption of the SBR method to radar sensors with an arbitrary number of transmit (TX)- and receive (RX) antennas, which enables the simulation of large MIMO arrays. A novel performance optimization approach is proposed for large numbers of TX antennas, which reduces the runtime dramatically. The quality of the simulation is verified by measuring a complex and realistic scenario with a high resolution automotive MIMO radar. Also, a study of the effect on quality and runtime is being investigated for various optimization approaches, including the proposed method.

Accurate and robust ego-motion estimation is critical for aiding autonomous vehicle localization in environments devoid of map landmarks or accurate GNSS measurements. RADAR measurements provide unique capabilities for ego-motion estimation while also posing unique challenges. This paper presents the novel real-time RADAR-based ego-motion estimation algorithm, RADARODO (RADAR Odometry). RADARODO is differentiated from similar techniques by two novel attributes. First, it decouples sensor translational and rotational motion by estimating them from Doppler and spatial data, respectively. Secondly, it directly analyzes RADAR images to estimate rotational motion via correspondence-free non-linear optimization. Unlike other methods, RADARODO estimates ego-motion along with its predicted uncertainty from a single RADAR sensor without depending on a lever-arm offset or the zero side-slip assumption. The real-world results demonstrate accurate ego-motion estimates that are particularly suited for integration within a “tightly-coupled” sensor fusion framework.

Frequency modulated continuous wave radar systems are a common component of many driver assistance systems. Modern radar systems are heavily used for fast and reliable surrounding area sensing in both safety and automated driving applications. Recently a lot of of neural networks have been introduced for real time detection and classification of vulnerable road users without the need for traditional radar signal processing. One of these systems is based on the real-time object detection system YOLO (You Only Look Once) applied to the pre-processed radar range-Doppler-angle power spectrum. Therefore, the radar cube data is interpreted as a digital image for the computer-vision based YOLO algorithm. This paper deals with the possibility to increase performance of this algorithm, especially in noisy environments, by adding a denoising autoencoder (DAE) structure to the neural network. The DAE input is the noisy radar sensor data represented as an image. Adapted from computer vision applications, the DAE is trained to reconstruct an uncorrupted representation of the input data and hence reduces the noise effects from the measurement data. Furthermore, the domain adaptation capability of the neural network is researched, using simulated measurement data to improve training.

Obstacle perception based on radar sensor has drawn wide attentions in autonomous driving due to robust performance and low cost. It is significant to utilize fusion, e.g., camera information, to further enhance the radar perception ability. Although much progress has been made, we still observe two problems: First, the spatial alignment among multi-modal data is intractable when involving multiple radar and camera sensors. Second, most existing works are based on object-level fusion, which inevitably has information loss leading to a performance degradation. To this end, we propose a feature-level fusion detection framework based on multiple radars and cameras, termed as the RVDet. We first establish an occupancy grid map by using 4 corner radars and extract radar features in the bird's eye view(BEV). Meantime, the image features of 4 fish-eye cameras are obtained using a pretraining vision detection model. Then, an adaptive projection network is employed to transform all the 4 image features to the BEV domain and integrate them to a dense spatial feature map aligned with the radar feature. Last, the carefully aligned multi-modal feature maps are jointly sent to a deep fusion network to predict final fused detection results. Experiments show that both object detection and positioning performance achieve significant gains by the proposed method in a custom dataset.

International audience; The purpose of this work is to perform co-engineering of an object classification system including both a radar sensor and a software of image processing. We aim at the smallest possible false recognition rate, considering three classes of imaged objects. For this we retain five relevant parameters which impact the recognition performances. We adopt the mixed grey wolf optimizer to provide the best set of parameters.

Simulating realistic radar data has the potential to significantly accelerate the development of data-driven approaches to radar processing. However, it is fraught with difficulty due to the notoriously complex image formation process. Here we propose to learn a radar sensor model capable of synthesising faithful radar observations based on simulated elevation maps. In particular, we adopt an adversarial approach to learning a forward sensor model from unaligned radar examples. In addition, modelling the backward model encourages the output to remain aligned to the world state through a cyclical consistency criterion. The backward model is further constrained to predict elevation maps from real radar data that are grounded by partial measurements obtained from corresponding lidar scans. Both models are trained in a joint optimisation. We demonstrate the efficacy of our approach by evaluating a down-stream segmentation model trained purely on simulated data in a real-world deployment. This achieves performance within four percentage points of the same model trained entirely on real data., 6 pages + 2 references

Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. In this research, we evaluated the performance of synthetically generated snow radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. Our experiments show a very good similarity between real and synthetic snow radar images.

Modular 80- and 160-GHz 4-channel radar sensor platform is introduced based on relaxed single 40 GHz local oscillator source. The first two channels synthesize 2×2 multiple-input multiple-output (MIMO) radar at 80 GHz with on-board planar 8×1 patch arrays for enhanced angular resolution, while the other 2 channels employ 160 GHz system-on-chip (SoC) transceivers with integrated wideband 6-dBi on-chip antennas with air trenches for enhanced range resolution. Bistatic frequency modulated continuous wave (FMCW) and delta-sigma (Δ∑) modulation based frequency division multiplexing (FDM-) MIMO radar techniques are demonstrated. The flexible architecture of the modular radar platform allows also implementing fast-chirp, Doppler and selective distance radar techniques as well as 2-dimensional (2-D) ranging and data communication over radar imaging.

This paper presents an efficient annotation procedure and an application thereof to end-to-end, rich semantic segmentation of the sensed environment using FMCW scanning radar. We advocate radar over the traditional sensors used for this task as it operates at longer ranges and is substantially more robust to adverse weather and illumination conditions. We avoid laborious manual labelling by exploiting the largest radar-focused urban autonomy dataset collected to date, correlating radar scans with RGB cameras and LiDAR sensors, for which semantic segmentation is an already consolidated procedure. The training procedure leverages a state-of-the-art natural image segmentation system which is publicly available and as such, in contrast to previous approaches, allows for the production of copious labels for the radar stream by incorporating four camera and two LiDAR streams. Additionally, the losses are computed taking into account labels to the radar sensor horizon by accumulating LiDAR returns along a pose-chain ahead and behind of the current vehicle position. Finally, we present the network with multi-channel radar scan inputs in order to deal with ephemeral and dynamic scene objects., submitted to IEEE Intelligent Vehicles Symposium (IV) 2020

Radar and lidar, provided by two different range sensors, each has pros and cons of various perception tasks on mobile robots or autonomous driving. In this paper, a Monte Carlo system is used to localize the robot with a rotating radar sensor on 2D lidar maps. We first train a conditional generative adversarial network to transfer raw radar data to lidar data, and achieve reliable radar points from generator. Then an efficient radar odometry is included in the Monte Carlo system. Combining the initial guess from odometry, a measurement model is proposed to match the radar data and prior lidar maps for final 2D positioning. We demonstrate the effectiveness of the proposed localization framework on the public multi-session dataset. The experimental results show that our system can achieve high accuracy for long-term localization in outdoor scenes., Comment: 7 pages, 4 figures. This paper has won the Best Conference Paper Award in 2020 IEEE International Conference on Real-time Computing and Robotics (RCAR). A supplementary video is available at https://youtu.be/wE-DbaCDbsU

In this paper, we discuss the use of radar sensor to localize an airborne platform using a proposed rotation-invariant template matching algorithm based on data fusion. If a digital elevation model (DEM) under the platform is measured by radar, it can be used to estimate the position of the platform through comparing a pre-recorded DEM of large area through template matching. We implemented four different rotation-invariant template-matching algorithms and compared their performances from the perspective of position estimation accuracy and computational complexity. Based on the investigations, we chose a Fourier transform-based method that utilizes the characteristic of magnitude invariance with the shift of the original data. To improve the position estimation by providing more information, the gradients of DEM were derived. To combine the total error cost from the original DEM and its gradient, we employ an adaptive weighting inspired by the information theory. A higher weight is given to data that have more information. The information is measured as entropy, which is the inverse of the probability that the error cost is the minimum out of all possible error costs in the template-matching process. Through the suggested adaptive weighting, the position estimation accuracy has been increased by 16.04% compared with the uniform weighting. Compared with only using a DEM, the use of gradients of DEM improves the accuracy by 55.37%.

Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network.

This paper presents a technological demonstrator with traffic applications, which carries out three typical intelligent transportation system (ITS) tasks: traffic flow measurement, speed estimation, and vehicles classification. The proposed system is a frequency-modulated continuous wave radar working at 24 GHz, with side-looking installation. Two signal-processing modes, 1-D and 2-D, are introduced as detection scheme. Unlike other existing commercial systems, this demonstrator makes use of phase information from target echoes in order to perform clutter cancellation and target speed measurement. To this end, two different speed-estimating methods are proposed and compared by means of experimental results obtained from a complex scenario.

Studies have demonstrated the usefulness of micro-Doppler signatures for classifying dynamic radar targets such as humans, helicopters, and wind turbines. However, these classification works are based on the assumption that the propagation channel consists of only a single moving target. When multiple targets move simultaneously in the channel, the micro-Dopplers, in their radar backscatter, superimpose thereby distorting the signatures. In this paper, we propose a method to detect multiple targets that move simultaneously in the propagation channel. We first model the micro-Doppler radar signatures of different movers using dictionary learning techniques. Then, we use a sparse coding algorithm to separate the aggregate radar backscatter signal from multiple targets into their individual components. We demonstrate that the disaggregated signals are useful for accurately detecting multiple targets.

[EN] Multiple-input multiple-output (MIMO) radars have been shown to improve target detection for surveillance applications thanks to their proven high-performance properties. In this paper, the design, implementation, and results of a complete 3-D imaging frequency-modulated continuous-wave MIMO radar demonstrator are presented. The radar sensor working frequency range spans between 16 and 17 GHz, and the proposed solution is based on a 24-transmitter and 24-receiver MIMO radar architecture, implemented by timedivision multiplexing of the transmit signals. A modular approach based on conventional low-cost printed circuit boards is used for the transmit and receive systems. Using digital beamforming algorithms and radar processing techniques on the received signals, a high-resolution 3-D sensing of the range, azimuth, and elevation can be calculated. With the current antenna configuration, an angular resolution of 2.9° can be reached. Furthermore, by taking advantage of the 1-GHz bandwidth of the system, a range resolution of 0.5 m is achieved. The radio-frequency front-end, digital system and radar signal processing units are here presented. The medium-range surveillance potential and the high-resolution capabilities of the MIMO radar are proved with results in the form of radar images captured from the field measurements.

The first- and second-order high-frequency radar cross sections of the ocean surface are derived for an antenna on a floating platform. In this analysis, simulations are conducted for a more complicated platform motion than appear in earlier work and comparisons are made to model outputs for a fixed antenna. Results show that motion-induced peaks appear symmetrically in the Doppler frequency and have less energy in the second-order radar cross section than those in the first-order radar cross section. The magnitude and width of the Bragg peaks are seen to decrease and broaden, respectively, as compared to the case for a fixed antenna. The platform motion modulates the radar signals as a frequency modulator, and the modulation indices are related to the amplitudes of the platform motion. With a larger amplitude of platform motion, more energy is transferred from the Bragg peaks to the motion-induced peaks, and more motion-induced peaks need to be considered.

In this paper, the feasibility of a bistatic passive system for maritime surveillance and marine navigation is considered based on the communication satellite constellation Inmarsat as a source of illuminating signals. A power budget analysis is detailed for Inmarsat signals, considering both the reference and the radar channels. The effect of sea clutter is also examined. It is shown that relatively poor range resolution can be improved if the available communication channels are combined to improve the bandwidth, a process that also improves the sensitivity. For the first time, detection and preliminary bistatic range-Doppler tracking are presented for a representative maritime target.

Distributed inverse synthetic aperture radar (ISAR) exploits the data acquired by multiple radar sensors carried by multiple platforms working in formation to increase the cross range resolution with respect to the value achievable by single platform systems. In this frame, the paper addresses the problem of the estimation of the ship dynamics, i.e., yaw, pitch, and roll rotation motions, exploiting the signals collected by such multiplatform radar imaging systems providing angular diversity in order to enable the focusing of the distributed ISAR images. Specifically, in this work a multi-angle formation of sensors is considered and the corresponding maximum likelihood estimator and Cramer Rao bound are derived. Then, a new Doppler matching based technique is proposed as a suboptimal approach exploiting only the linear component of the phase of the received signals. The performance analysis proves the effectiveness of the proposed techniques to separately estimate the horizontal, radial, and vertical components of the rotation vector, therefore making possible both the focusing and accurate cross-range scaling of the distributed ISAR products (as well as of the low-resolution ISAR images regarding the different sensors) and, as a further advantage, providing knowledge of the orientation of the corresponding image projection planes. The analysis of experimental multisensor datasets confirms the feasibility of the proposed techniques.

Interferometric synthetic aperture radar (InSAR) methods provide high-resolution maps of surface deformation applicable to many scientific, engineering, and management studies. Despite its utility, the specialized skills and computer resources required for InSAR analysis remain as barriers for truly widespread use of the technique. Reduction of radar scenes to maps of temporal deformation evolution requires not only detailed metadata describing the exact radar and surface acquisition geometries, but also a software package that can combine these for the specific scenes of interest. Furthermore, the range-Doppler reference frame and radar coordinate system itself are confusing, so that many users find it hard to incorporate even useful products in their customary analyses. Finally, the sheer data volume needed for interferogram time series makes InSAR analysis challenging for many analysis systems. We show here that it is possible to deliver radar data products to users that address all of these difficulties, so that the data acquired by large, modern satellite systems are ready to use in more natural coordinates, without requiring further processing, and in as small volume as possible.

A new 94-GHz frequency-modulated continuous wave (FMCW) Doppler radar–radiometer system [Jülich Observatory for Cloud Evolution (JOYCE) Radar–94 GHz (JOYRAD-94)] is presented that is suitable for long-term continuous observations of cloud and precipitation processes. New features of the system include an optimally beam-matched radar–radiometer; a vertical resolution of up to 5 m with sensitivities down to −62 dBZ at 100-m distance; adjustable measurement configurations within the vertical column to account for different observational requirements; an automatic regulation of the transmitter power to avoid receiver saturation; and a high-powered blowing system that prevents hydrometeors from adhering to the radome. JOYRAD-94 has been calibrated with an uncertainty of 0.5 dB that was assessed by observing a metal sphere in the radar’s far field and by comparing radar reflectivities to a collocated 35-GHz radar. The calibrations of the radar receiver and the radiometric receiver are performed via a two-point calibration with liquid nitrogen. The passive channel at 89 GHz is particularly useful for deriving an estimate of the liquid water path (LWP). The developed retrieval shows that the LWP can be retrieved with an RMS uncertainty (not including potential calibration offsets) of about ±15 g m−2 when constraining the integrated water vapor from an external source with an uncertainty of ±2 kg m−2. Finally, a dealiasing method [dual-radar dealiasing method (DRDM)] for FMCW Doppler spectra is introduced that combines measurements of two collocated radars with different measurement setups. The DRDM ensures high range resolution with a wide unambiguous Doppler velocity range.

A compact-array high-frequency surface wave radar equipped with two crossed-loop/monopole receiving antennas has been established for vessel detection. Using two compact antennas of the same design, this system can obtain two extremely similar sets of radar range-Doppler spectra over the same period. To detect vessel targets efficiently, the spectra of two antennas are enhanced by performing a principle component analysis. A wavelet-based approach is then applied to suppress clutter and reduce noise. The signal-to-noise ratios and signal-to-clutter ratios of the echoes are thus improved. Finally, an adaptive threshold is used to extract targets. The real radar data detection results are compared with Automatic Identification System data as well as those from the conventional ordered-statistic constant false alarm rate method. The feasibility and the validity of method proposed here are thus demonstrated.

Ionospheric sounding is a technique that provides real-time data on high-frequency ionospheric-dependent radio propagation. This letter presents a Universal Software Radio Peripheral-based ionospheric sounding radar, which relies on a basic system consisting of a synchronized transmitter and receiver. The radar has the advantages of miniaturization, modularization, low power, and low cost. The three most significant features of the radar system are that it is software-defined and universal platform-based and that it has low transmitting power. This novel software-defined vertical-incidence radar system can probe the ionosphere and obtain real-time plasma parameters according to the simulation. Ionograms that directly express probe results are generated by MATLAB after data processing and simulation. Successful development of such an ionospheric sounding software radar will allow universalization and miniaturization of an ionosonde radar system. This letter introduces the implementation of the novel ionospheric sounding radar.

A novel insect orientation extraction method is proposed based on the target polarization scattering matrix (PSM) estimation, which is applicable for traditional vertical-looking insect radar with noncoherent reception as well as the coherent radar. The insect echo signal at different polarization directions on the radar polarization plane is usually acquired by means of rotating linearly polarized antenna. In this letter, the insect echo signal is first used to accurately estimate insect PSM by an iterative algorithm based on the second-order polynomial approximation. Meanwhile, the Cramer–Rao lower bound is also analyzed to test the estimation performance. Next, based on the assumption that the target orientation is consistent with the dominant eigenvector, the insect orientation is extracted from the estimated PSM. Finally, both theoretical simulations and real experimental data are used to validate the effectiveness and feasibility of our proposed method, which can achieve good orientation estimation accuracy at low signal-to-noise ratio.

Distance measurement tasks in micromachine tools need to be performed with micrometer accuracy. For such tasks, frequency-modulated continuous-wave (FMCW) radars with a combination of frequency and phase evaluations are a good choice. However, the accuracy cannot be indefinitely increased as there are constraints on the target size and placement imposed by the limited space inside micromachines. This paper investigates the influence of target geometry and position on the accuracy of its range estimation using a W-band FMCW radar with a bandwidth of 25 GHz. A relation between target geometry and accuracy is established through the Cramér-Rao lower bound (CRLB). Based on the measurements of different targets, an optimal shape and size is proposed, which provides an average accuracy in the single digit micrometer range. Furthermore, the influence of different bandwidths on the accuracy is investigated. It is also demonstrated how the CRLB can be used to optimize the size of a target, when a certain accuracy is needed. In addition, antenna field regions are analyzed for suitable target placements. Finally, the radar system is implemented in a machine tool and measurements with accuracies in the micrometer range are carried out.

During the World War II (WWII), radar was invented as an all-day, all-weather, long-range sensor. Over the past 75 years or so, radar has acted as the clairvoyance and clairaudience of humans and has had wide applications [1]–[19] in both defense and civilian fields, e.g., surveillance, reconnaissance, fire control, border monitoring, collision avoidance, and traffic control. Along with radar development, radar signal processing (RSP) always plays an important role. In 1943, North [4] pointed out that the matched filter should be the foundation of radar to detect targets from echoes generated by the known transmitting waveform. In 1950, Woodward and Davies [5] introduced the Bayesian statistics theory into the RSP field and proposed the constant false alarm ratio (CFAR) rule for target detection. Based on matched filtering and the CFAR rule, the embryonic framework of classic RSP has been formed and used thus far. In 1953, Woodward [6] further proposed an ambiguity function as a useful tool to evaluate the range and Doppler discrimination abilities of a transmitting waveform, which paves the foundation for radar waveform design. Furthermore, to deal with the tradeoff between large transmitting energy and high range resolution, the early unmodulated rectangular waveform has been replaced by more waveforms with a large time-bandwidth product (TBP), such as linear frequency modulation (LFM) and phase-coded waveforms. These waveforms with large TBP have been widely used to realize the range compression via matched filter.

An explicit expression for stochastic processes describing the dual-polarization weather radar echoes is presented. The probability distributions of the proposed model are defined in terms of the point values assumed by polarimetric and Doppler variables in the relevant radar sampling volume. The statistical properties of the model are discussed in order to verify its faithfulness as representative of real radar signals. The discussion considers the most general situation, i.e., it is not related to specific hydrometeor distributions or beam filling conditions.

The Hainan coherent scatter phased array radar (HCOPAR) is one of the most important radio systems of the Chinese Meridian Space Weather Monitoring Project (Meridian Project). The radar is located at Fuke station (19.5°N, 109.1°E, dip latitude 8.1°N), Hainan island, China, to observe the field-aligned irregularities in ionospheric $E$ - and $F$ -regions. It is operated with a peak power of 54 kW and uses the Barker codes for good sensitivity. Its antenna array is composed of $18 \times 4$ five-element Yagi antennas and is used for both transmitting and receiving. The Yagi antennas are arranged in a rectangular grid 100 m long and 20 m wide, covering an area of 2000 m2. This antenna arrangement forms seven radar beams from −22.5° to 22.5° in azimuth with an angle step of 7.5°. The central beam points to due geographic north. A brief description of the radar system, data processing, and signal characteristics is also provided. The intensity, Doppler velocities, and spectral widths of the quasi-periodic echoes measured in the nighttime of June 23, 2013 are presented as typical data of type II irregularities in the $E$ -region. The data recorded by the HCOPAR in the fan-beam mode on September 15, 2013 are displayed as the typical $F$ -region plasma bubbles.

A stepped-frequency radar that allows for adaptability in the radiated spectrum while maintaining high-resolution radar imagery has been developed. The spectrally agile frequency-incrementing reconfigurable (SAFIRE) radar system is a vehicle-mounted, ground-penetrating radar that is capable of producing high-resolution radar imagery for the detection of obscured targets (either buried or concealed surface targets). It can be easily transitioned between forward- and side-looking orientations. The SAFIRE system is capable of precisely excising subbands within its operating bandwidth, thus making the system “spectrally agile.” It is also highly reconfigurable thereby allowing for on-the-fly adjustment of many of the system parameters. The spectrally agile and reconfigurable aspects of the SAFIRE radar together with its enhanced IF processing scheme represent a novel contribution to the state of the art. This paper discusses the system design, implementation, and performance characteristics, and also presents preliminary high-resolution imagery.

The rising population of space debris poses an enormous threat to all space vehicles, including space shuttles and other spacecraft with operators aboard; and their detection, tracking, and identification are of great importance. Using the spinning motion and the translational motion component that is parallel to its major axis, we have established a 3-D spiral synthetic aperture radar (SAR) imaging geometry and its corresponding signal model for space debris in this letter. Spatial offsets and coupling in the along-track and altitude directions, caused by the spiral motion of the radar, are compensated in the frequency domain. With these compensation procedures, the spiral SAR is simplified as a cylindrical scan mode. Then, a 3-D wavenumber domain algorithm is proposed to realize coherent imaging. The real data processing results are presented to demonstrate the effectiveness of the proposed algorithm for imaging space debris.

In a synthetic aperture radar (SAR) system, the radar platform may move with a curved trajectory due to the existence of vertical velocity and acceleration, which may result in the failure of the conventional imaging methods. In order to deal with this problem, this paper proposes an improved wavenumber-domain imaging algorithm for high-resolution-high-squint SAR with a curved trajectory. It mainly includes three aspects. First, a modified equivalent range model for a curved trajectory is derived. Second, an improved wavenumber domain imaging algorithm based on the proposed range model is analyzed in detail. Finally, the imaging distortion caused by vertical velocity and acceleration is corrected via geometry and inverse projection. Simulated results and Ku-band real SAR data processing are used to validate the proposed model and imaging algorithm.

When a pulse radar signal is implemented in a range-limited anechoic chamber for radar measurement, the transmitted and reflected signal will be coupled at the receiver. To solve this problem and equivalently simulate the whole process of pulse radar measurement in an anechoic chamber, the interrupted transmitting and receiving method is proposed in this letter based on interrupted sampling. The constraints of the transmitting and receiving parameters are deduced with the sizes of the anechoic chamber and target. The pulse compression of the proposed method is performed. Then, the window function is applied to extract the main peaks after pulse compression. Both the simulation and experimental results are provided to demonstrate the effectiveness of the proposed method in overcoming the coupling between the transmitted and reflected pulse signals.

Automatic target recognition (ATR) is the process where computer algorithms are used to detect and classify objects or regions of interest in sensor data [1]. ATR algorithms have been developed for a broad range of sensors, including electro-optic (EO), infrared (IR), and microwave sensors (radar). A key advantage of microwave radar is its utility for all weather and time of day compared with other types of sensors. One limitation of many radar systems' ATR performance is the poor spatial resolution in the range and cross-range dimensions. While the range resolution of the microwave radar system can be straightforwardly improved by increasing the system's radio frequency bandwidth, superior cross-range (or azimuth) resolution requires an increase in antenna size. In order to achieve resolution similar to EO or IR systems, an enormous antenna must be used for transmission and reception, hindering microwave radar system's general applicability to ATR problems. However, by operating a synthetic aperture radar (SAR), the large antenna requirement can be overcome, and very fine cross-range resolution can be obtained with a modest "real" aperture antenna. Indeed, SAR has been used to generate high-resolution 2-D or 3-D object images at a variety of different microwave frequencies. In particular, wideangle SAR images can contain important features of an object from a diverse set of observation angles collected by a radar system orbiting around a target or scene. These feature-rich wide-angle SAR images are useful for ATR due to their high resolution and comprehensive coverage of the target. Interference, jamming, or data dropouts, however, are commonplace in practical SAR environments, and result in an incomplete data set. These missing and corrupted data cause substantial degradation in the generated SAR imagery, hampering their utility for subsequent ATR processing especially when data-independent image formation algorithms are used. A prime example of these difficulties is operating a SAR system in the very high frequency (VHF) or ultrahigh frequency (UHF) bands, where the spectrum is frequently crowded [2].

Range estimation is an important issue of radar system, and its performance is mainly limited by the transmitting bandwidth. To improve the range resolution, the existing approaches often either occupied wider bandwidth or employed some non-linear techniques which cause high complexity. The wider the transmitting bandwidth has not only the more the network resource takes but also the easier the radio frequency interference (RFI) is introduced. The authors propose a new radar system by utilising jointly the compressive sensing (CS) and the proposed virtual expanding technique, referred to as the compressing sensing and virtual expanding (CSVE) radar system. The proposed CSVE radar system not only provides high-range resolution with low cost by saving transmitting bandwidth but also is robust to the bad effect of the RFI by utilising a pre-detection phase at the transmitter and a detection phase at the receiver. Simulation results validate the superiority of the proposed CSVE radar system over both the high-resolution techniques such as Capon, multiple signal classification and matched filter, and the CS step-frequency radar system.

Toward a maritime surveillance objective, many ship detection and tracking algorithms have been investigated but are faced with poor performance in practical ocean environments. Compact high-frequency (HF) radar has also faced critical issues due to its long coherent processing interval and varying response from its orthogonal antenna structure. Hence, a simulator based on compact HF radar is proposed in this letter to provide a guideline for effective assessment of ship detection and tracking algorithms while considering these practical issues. To validate the proposed simulator, the simulator generated data has been compared with real data obtained by the compact HF radar sites.

Based on the narrowband radar, an imaging method for space targets is proposed in this letter, which is named as single-range interferometric imaging. First, the theory of space target echo signal interferometric processing in narrowband radar is explained. Then, through conducting short-time Fourier transform on the echo signal received by three antennas, the curves on time-frequency plane correspond to different scatterers are effectively extracted and separated and the interferometric phase of different scatterers is obtained. Finally, 2-D imaging for space target is realized. Compared to existing methods, only a single multiantenna radar is needed to obtain the 2-D image of target with accurate scaling result. The simulation results under different occasions have confirmed the effectiveness of the proposed method.

This letter presents a two-tone radar sensor system that can concurrently detect both absolute distance and relative movement. In contrast to the traditional two-tone radar, the proposed radar sensor can measure both distance and vibration. Moreover, it is more accurate in predicting the phase shift by leveraging the accuracy of nonlinear phase demodulation in the digital domain. Unlike the frequency-modulated continuous wave radar, the proposed radar system has simpler system architecture and does not require broadband signals. With a tone difference of 40MHz, the radar system can detect a distance up to 1.875m without ambiguity. Experiments show that the proposed radar system was able to not only capture the delaying movement of a Newton's cradle but to measure the distance with mm accuracy as well. It successfully detected the gesture motion of virtual slider, making it promising for sensing action gestures for human-computer interaction.

Developments in microwave and millimeter-wave systems have enabled remote sensing techniques traditionally used in long-range applications to be employed in the relatively closer range applications of detection and classification of human presence and measurement of human properties. This paper reviews the techniques used in microwave remote sensing of humans and discusses prominent examples of experimental systems from the literature. Radar techniques have been developed for measuring the time-varying scattering returns from the various parts of the human body during motion, which are used for human presence detection and the classification of human activities. The effects of breathing and heartbeat are also detectable using Doppler radar techniques, which is applicable to medical applications as well as search-and-rescue and security. Recent developments in distributed radars have enabled the direct measurement of the angular velocity of moving people, and passive systems have also been developed which provide a complementary detection mode that can aid in detection and classification of humans.

The analysis and correction of the bias occurring in weather radar polarimetric measurements is a challenging problem. Polarization coupling due to the cross-polar radiation pattern of the radar’s antenna is known to be responsible for errors in the estimation of the polarimetric covariance matrix, and consequently in the hydrometeor classification and quantification, either when using the ATSR or the SHV method. An alternative method for Doppler and polarimetric measurements based on transmitting three different polarizations (3-PolD) has been proven to provide accurate polarimetric covariance matrix estimates without making any hypothesis about the target polarimetric response or its Doppler spectrum. This method does not reduce the Doppler capabilities or the unambiguous range of the radar despite alternately transmitting 3-PolD. These characteristics have encouraged evaluating the polarimetric parameter biases due to cross-polar radiation when this method is used. Biases are calculated considering reflector antenna systems as well as phased-array antenna systems. The results show that this method may guarantee a tolerable bias level even with a poor co- to cross-polar antenna pattern ratio.

Systems based on (sub-)millimeter-wave technology demand compact transceivers to be commercialized. We have developed a compact front-end for a three-dimensional imaging homodyne radar at 300 GHz where a commercial subharmonic mixer acts both as a transmitter and as a receiver, due to the finite isolation between the local oscillator and radio frequency ports of the mixer. This allows the radar electronics compactness and a reduction in power consumption and cost without sacrificing imaging quality, which is the fundamental figure of merit for this application.

A highly miniaturized and commercially available millimeter wave (mmw) radar sensor working in the frequency range between 121 and 127 GHz is presented in this paper. It can be used for distance measurements with an accuracy in the single-digit micrometer range. The sensor is based on the frequency modulated continuous wave (CW) radar principle; however, CW measurements are also possible due to its versatile design. An overview of the existing mmw radar sensors is given and the integrated radar sensor is shown in detail. The radio frequency part of the radar, which is implemented in SiGe technology, is described followed by the packaging concept. The radar circuitry on chip as well as the external antennas is completely integrated into an 8 mm $\times \,\, 8$ mm quad flat no leads package that is mounted on a low-cost baseband board. The packaging concept and the complete baseband hardware are explained in detail. A two-step approach is used for the radar signal evaluation: a coarse determination of the target position by the evaluation of the beat frequency combined with an additional determination of the phase of the signal. This leads to an accuracy within a single-digit micrometer range. The measurement results prove that an accuracy of better than $\pm 6~\mu \text{m}$ can be achieved with the sensor over a measurement distance of 35 mm.

Future weather radar systems will need to provide rapid updates within a flexible multifunctional overall radar network. This naturally leads to the use of electronically scanned phased array antennas. However, the traditional multifaced planar antenna approaches suffer from having radiation patterns that are variant in both beam shape and polarization as a function of electronic scan angle; even with practically challenging angle-dependent polarization correction, this places limitations on how accurately weather can be measured. A cylindrical array with commutated beams, on the other hand, can theoretically provide patterns that are invariant with respect to azimuth scanning with very pure polarizations. This paper summarizes recent measurements of the cylindrical polarimetric phased array radar demonstrator, a system designed to explore the benefits and limitations of a cylindrical array approach to these future weather radar applications.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2016-12-30, 資料番号: PA1720012000

Mobile radar platforms designed for observation of severe local storms have consistently pushed the boundaries of spatial and temporal resolution in order to allow for detailed analysis of storm structure and evolution. Digital beamforming, or radar imaging, is a technique that is similar in nature to a photograwphic camera, where data samples from different spaces at the same range are collected simultaneously. This allows for rapid volumetric update rates compared to radars that scan with a single narrow beam. The Atmospheric Imaging Radar (AIR) is a mobile X-band (3.14-cm wavelength) imaging weather radar that transmits a vertical, 20° fan beam and uses a 36-element receive array to form instantaneous range–height indicators (RHIs) with a native beamwidth of 1° × 1°. Rotation in azimuth allows for 20° × 90° volumetric updates in under 6 s, while advanced pulse compression techniques achieve 37.5-m range resolution. The AIR has been operational since 2012 and has collected data on tornadoes and supercells at ranges as close as 6 km, resulting in high spatial and temporal resolution observations of severe local storms. The use of atmospheric imaging is exploited to detail rapidly evolving phenomena that are difficult to observe with traditional scanning weather radars.

This study proposes a modeling process of equivalent terrains to reduce the computational load and time of a full- wave electromagnetic (EM) simulation. To verify the suitability of the proposed process, an original terrain model with a size of 3 m × 3 m is equivalently quantized based on the minimum range resolution of a radar, and the radar image of the quantized model is compared with that of the original model. The results confirm that the simulation time can be reduced from 407 hours to 162 hours without a significant distortion of the radar images, and an average estimation error of the quantized model (20.4 mm) is similar to that of the original model (20.3 mm).

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2017-03-07, 資料番号: PA1720013000

This paper proposes a new scheme for improving the classification performance of inverse synthetic aperture radar (ISAR) images. The proposed scheme utilizes the trace transform to gather abundant information from an ISAR image, regardless of the spatial distribution of the target response in the ISAR image. In simulations using ISAR images with various spatial distributions, the proposed scheme substantially improved the classification performance compared with existing methods, which are highly vulnerable to spatial variation.

Noise radars possess several advantageous properties, including low probability of detection and identification, especially when working in continuous-wave mode. One of the main drawbacks of such radars is the occurrence of the masking effect, when a weak target echo is masked by the sidelobes of a strong target echo. The sidelobes of the ambiguity function emerge on the level of the time–bandwidth product below the main peak and are spread in the entire range–Doppler plane. While most approaches to mitigate the masking effect focus on the processing of the received signal, it is possible to move the computation burden to the waveform design phase. This paper describes a filter-based method of creating noise-like waveforms that have very low sidelobes in the area of certain range and Doppler shifts. Both theoretical analysis and measurement results are presented and compared with the lattice filter method of masking effect cancellation.

Objectives: The aim of this work was to perform experimental studies of hybrid laser radar as an intermediate option between the classical scanning pulsed laser radar and a system built in accordance with a 3D-Flash LADAR technology. Methods: The studies were performed on the ground optical track using modern equipment for the registration of measurement results. Modern methods of mathematical modeling were applied for data processing. Findings: Hybrid laser radar provided location frame frequency significantly greater than the frequency of the classical pulse radar, without the use of a matrix photodetector required for 3D-Flash LADAR. Frame frequency of 28 frames per second was obtained in the experimental settings with the view field of 20×20°. A signal/noise ratio was also determined imitating maximal operating range of 2 km on the ground track or of 5 km in space. Improvements: Hybrid laser radar can be used in robotic systems as a 3D technical vision system.