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To address the technical problems of target detection and identification of missile-borne radar in the background of strong ground clutter, we propose to combine the high resolution range profile (HRRP) technology and Doppler beam sharpening (DBS) technology to carry out two-dimensional high-resolution imaging of the ground to improve the performance of radar target detection and identification in the presence of clutter. The method uses the linear frequency modulation stepped frequency (LFM-SF) signal as the basic waveform. Firstly, the Doppler effect caused by the platform speed is discussed in detail and corrected. Then, HRRP sequences corresponding to each frame are obtained through distance profile extraction, and aiimuth high resolution is achieved using fast Fourier transform (FFT). Finally, motion compensation is applied to the errors caused by the platform in actual light conditions, completing the two-dimensional resolution of the area within the beam. The processing of measured data has verified the effectiveness and practicality of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In detecting hypersonic vehicles, the radar echo signal is coupled with an intra-pulse Doppler frequency (I-D frequency) component caused by relative motion of a plasma sheath (PSh) and the vehicle, which can induce the phenomenon of a ghost target in a one-dimensional range profile. In order to investigate the I-D frequency generated by the relative motion of a PSh, this study transforms a linear frequency modulated (LFM) signal into a single carrier frequency signal based on echo signal equivalent time delay-dechirp processing and realizes high resolution and fast extraction of the I-D frequency coupled with the frequency-domain echo signal. Furthermore, by relying on the computation of the surface flow field of the RAMC-II Blunt Cone Reentry Vehicle, the coupled I-D frequency in the radar echo signal of a PSh-enveloped target under circumstances of typical altitudes and carrier frequencies is extracted and further investigated, revealing the variation law of I-D frequency. The key findings of this study provide a novel approach for suppressing anomalies in radar detection of PSh-enveloped targets as well as effective detecting and as robust target tracking. [ABSTRACT FROM AUTHOR]

The objective of this paper is to analyze the performance of two possible architectures of tethered satellite systems, used as a platform for a distributed radar sounder. The first architecture consists in a cross-track oriented tethered satellite system, controlled and stabilized by exploiting the aerodynamic forces generated by the interaction with the rarefied atmosphere in Low Earth Orbit. The second architecture involves a tethered satellite system controlled through gyroscopic stabilization, obtained by spinning the system about an axis contained in the orbital plane. After a brief survey of radar sounding techniques, the methodology is introduced for describing the geometry of the systems and their characteristics, the performance of the two architectures are then compared with each other and with the current state of the art. By analyzing the modeled nominal behavior, it is shown that the two proposed architectures can achieve continuous or multiple observations, respectively, at maximum cross-track resolution, during one orbit, minimizing clutter noise. This is a considerable improvement of performance versus a formation flight architecture which can typically achieve only up to four observations per orbit. Finally, the advantages and disadvantages of each architecture are studied, and their possible mission scenarios are discussed. • A Tethered Satellite System is proposed, flying with tether in cross-track direction. • The tether tension is generated by aerodynamic lift forces on plates at its extrema. • Antenna elements distributed along the tether enable radar sounding applications. • Top radar sounding performances are achieved everywhere along the orbit. • An alternative gyroscopic tether system is studied for planets with no atmosphere. [ABSTRACT FROM AUTHOR]

Radar sensors, leveraging the Doppler effect, enable the nonintrusive capture of kinetic and physiological motions while preserving privacy. Deep learning (DL) facilitates radar sensing for healthcare applications such as gait recognition and vital-sign measurement. However, band-dependent patterns, indicating variations in patterns and power scales associated with frequencies in time–frequency representation (TFR), challenge radar sensing applications using DL. Frequency-dependent characteristics and features with lower power scales may be overlooked during representation learning. This paper proposes an Enhanced Band-Dependent Learning framework (E-BDL) comprising an adaptive sub-band filtering module, a representation learning module, and a sub-view contrastive module to fully detect band-dependent features in sub-frequency bands and leverage them for classification. Experimental validation is conducted on two radar datasets, including gait abnormality recognition for Alzheimer's disease (AD) and AD-related dementia (ADRD) risk evaluation and vital-sign monitoring for hemodynamics scenario classification. For hemodynamics scenario classification, E-BDL-ResNet achieves competitive performance in overall accuracy and class-wise evaluations compared to recent methods. For ADRD risk evaluation, the results demonstrate E-BDL-ResNet's superior performance across all candidate models, highlighting its potential as a clinical tool. E-BDL effectively detects salient sub-bands in TFRs, enhancing representation learning and improving the performance and interpretability of DL-based models. [ABSTRACT FROM AUTHOR]

This paper presents a novel, cost-effective technique for estimating the Doppler effect in the time domain using a single pulse and subsequently leveraging the precise Doppler value to recover the radar waveform. The proposed system offers several key advantages over existing techniques, including the ability to calculate the target speed without any frequency ambiguity and the ability to detect a wide range of target speeds. These two features are not available in any existing techniques, including the conventional moving target detection (MTD) processor. To ensure improved accuracy and robust estimation, the system employs ensemble and iterative techniques by recursively and efficiently reducing the Doppler residues from the signal. Furthermore, the proposed system demonstrates effective signal recovery of a well-known bi-phase code shape at low signal-to-noise ratios in just a few iterations. The performance evaluation of the new algorithm demonstrates its practicability and its superiority over traditional radar systems. Implementation on software-defined radio (SDR) reveals that the proposed system excels in Doppler estimation and signal recovery at low SNRs, demonstrating promising results. [ABSTRACT FROM AUTHOR]

Vibration measurements play a critical role in troubleshooting mechanical equipment and assessing the structural integrity of buildings. However, conventional vibration measurement methods rely on contact-based approaches, such as the attachment of accelerometers to the target object, leading to complex equipment deployment. Therefore, non-contact vibration measurement has attracted great attention but has yet to be fully addressed. In this paper, we propose DeepVib, a non-contact vibration measurement system that enables accurate micron-level vibration monitoring. First, we introduce a series of signal processing algorithms to extract the vibration object motion from mmWave reflection signals. Then, we design a deep neural network to effectively suppress noise interference and achieve outputting higher signal-to-noise ratio data. Finally, we eliminate static reflections with geometric-based method to recover the vibrations of the target. The experimental results show that our non-contact measurement method can accurately measure the vibration at the micron level with the average error of vibration frequency less than 0.1%. For the amplitude below 100μm, the median error of estimation is 7.23%. In addition, DeepVib reduces the estimation error of 80th-percentile amplitude by 56.60% compared with the conventional method. [ABSTRACT FROM AUTHOR]

In remote dynamic hand-gesture recognition, uncertainties in timing and distance of gesture occurrences, coupled with the subtle bodily perturbations induced by arm movements, pose substantial challenges to the accurate extraction of gesture features. In this paper, we propose a lightweight real-time gesture recognition system based on support vector machines. By analyzing the Doppler features of different motion states, a Doppler weighting factor was constructed to suppress bodily micro-motion interference in the range-time spectrum, and achieve foreground extraction of gesture signals concurrently. Furthermore, prior to the extraction of HOG features, we employ Gaussian filtering to suppress abrupt transitions and noise inherent in the gesture signals. This preprocessing significantly enhances the stability of feature extraction. Subsequently, the extracted features are input into an SVM for training and classification. Experimental results demonstrate that, for five distinct gestures exhibited in two different states –– standing and seated –– within a range of 1 to 5 meters, the recognition accuracy reaches 96%. This proves the feasibility of the proposed methodology, and its potential to realize real-time gesture recognition. [ABSTRACT FROM AUTHOR]

In this work, a unique radar hardware is introduced for human-gait micro-Doppler studies. The developed radar sensor operates in C-band microwave frequencies. We share several outdoor experiments at Mersin University facilities to detect and characterize human walking and running movements. In these experiments, various walking and running movements were performed with different people. To examine the Doppler properties of human motion, raw data gathered is transformed onto 2D jointtime-frequency plane. The generation of micro-Doppler signatures in the transformed data is the first step in the extraction of features of the walking/running human motion. It is shown that the directions, durations, range distances as well as torso and limb velocities of walking and running human movements in each experiment are successfully obtained from these micro-Doppler signatures. [ABSTRACT FROM AUTHOR]

This paper presents the results of bats detected with marine radar and their validation with acoustic detectors in the vicinity of a wind turbine with a hub height of 120 m. Bat detectors are widely used by researchers, even though the common acoustic detectors can cover only a relatively small volume. In contrast, radar technology can overcome this shortcoming by offering a large detection volume, fully covering the rotor-swept areas of modern wind turbines. Our study focused on the common noctule bats (Nyctalus noctula). The measurement setup consisted of a portable X-band pulse radar with a modified radar antenna, a clutter shielding fence, and an acoustic bat detector installed in the wind turbine's nacelle. The radar's detection range was evaluated using an analytical simulation model. We developed a methodology based on a strict set of criteria for selecting suitable radar data, acoustic data and identified bat tracks. By applying this methodology, the study data was limited to time intervals with an average duration of 48 s, which is equal to approximately 20 radar images. For these time intervals, 323 bat tracks were identified. The most common bat speed was extracted to be between 9 and 10 m/s, matching the values found in the literature. Of the 323 identified bat tracks passed within 80 m of the acoustic detector, 32% had the potential to be associated with bat calls due to their timing, directionality, and distance to the acoustic bat detector. The remaining 68% passed within the studied radar detection volume but out of the detection volume of the acoustic bat detector. A comparison of recorded radar echoes with the expected simulated values indicated that the in-flight radar cross-section of recorded common noctule bats was mostly between 1.0 and 5.0 cm2, which is consistent with the values found in the literature for similar sized wildlife. [ABSTRACT FROM AUTHOR]

In this study, we present a novel approach for the real-time detection of high-speed moving objects with rapidly changing velocities using a high-resolution millimeter-wave (MMW) radar operating at 94 GHz in the W-band. Our detection methodology leverages continuous wave transmission and heterodyning of the reflected signal from the moving target, enabling the extraction of motion-related attributes such as velocity, position, and physical characteristics of the object. The use of a 94 GHz carrier frequency allows for high-resolution velocity detection with a velocity resolution of 6.38 m/s, achieved using a short integration time of 0.25 ms. This high-frequency operation also results in minimal atmospheric absorption, further enhancing the efficiency and effectiveness of the detection process. The proposed system utilizes cost-effective and less complex equipment, including compact antennas, made possible by the low sampling rate required for processing the intermediate frequency signal. The experimental results demonstrate the successful detection and characterization of high-speed moving objects with high acceleration rates, highlighting the potential of this approach for various scientific, industrial, and safety applications, particularly those involving targets with rapidly changing velocities. The detailed analysis of the micro-Doppler signatures associated with these objects provides valuable insights into their unique motion dynamics, paving the way for improved tracking and classification algorithms in fields such as aerospace research, meteorology, and collision avoidance systems. [ABSTRACT FROM AUTHOR]

With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR's capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material's complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced. [ABSTRACT FROM AUTHOR]

The maritime airborne corner reflector (ACR) is a radar reflector that can measure wind speed in an unknown sea area in real time over a long distance. To improve our understanding of how the ACR works, we investigated the Doppler characteristics of the ACR for the first time from a dynamic perspective. First, we constructed a radar echo signal model of the ACR. Then, we obtained the dynamic Doppler characteristics through pulse Doppler processing and discussed the special phenomenon of Doppler broadening. Finally, we proposed a rectangular window decomposition method to analyze the inner principle of the Doppler broadening phenomenon in more detail. In conclusion, this study provides valuable insights into the Doppler characterization of an ACR from a dynamic viewpoint, which contributes to enriching the basic theory of this equipment. [ABSTRACT FROM AUTHOR]

This paper analyzes the beneficial effects on phase detection arising from the motion of an ac-coupled Doppler radar. Indeed, although the presence of an ac coupling stage suppresses the dc offset after the receiver RF output, due to the coupling capacitor, a high-pass behavior is introduced; the presence of a high-pass behavior leads to signal distortion, particularly for low Doppler frequencies, which are typical in many biomedical or industrial applications. Since the target displacement is usually extracted from the phase history, this effect might, in turn, worsen the overall accuracy of the system. Moreover, if the target alternates stationary and moving time intervals, the phase detection step becomes challenging. Indeed, during the stationary time, the output of the RF front-end shows only noise fluctuations that, in turn, result in uncorrelated phases which might be confused with the real target displacement. This negative effect might be avoided by keeping the radar continuously moving, thus exploiting what is usually considered a state that is negative and worthy of attention. In this contribution, this effect is addressed from a different perspective, and ad hoc experimental case studies are shown to demonstrate the effectiveness of the proposed system. This task has been accomplished through theoretical analysis and related experimental activity. [ABSTRACT FROM AUTHOR]

X-band radar observations with a transmitting frequency of 9.4 GHz were conducted for over 10 h at the mouth of Tenryu River, Japan, to estimate surface velocity distributions during floods in 2010 and 2020 and analyse changes in the flow field caused by dredging in 2014. X-band radar is an imaging radar that can collect backscatter of floating objects flowing downstream with the flood flow every 2 s. The observation area extends approximately 1000 m × 1000 m. Particle image velocimetry is applied to the image sequences to guess the surface flow pattern. Flow direction in the main river channel changed smoothly, while surface flow speeds ranged from 1 to 3 m s–1 depending on the discharge. Water levels were computed using the estimated surface flow velocities, river cross-section from survey data, and observed discharge for indirect validation: it agreed within an error of 0.3 m with the observed water level. This paper highlights radar's potential for estimating surface velocity distribution during floods and harsh weather conditions. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent and connected vehicles, the experimental road test for the advanced driving assistance system (ADAS) is dramatically increasing around the world. Considering its high cost and hazardous situations, simulation test based on a dummy model is becoming a promising way for ADAS road test practice to reduce the experiment expanses. This study proposed a methodology for the evaluation of the performance of human and dummies with distinct designed materials based on the data extracted from the Doppler effect of millimeter-wave radar. Echo data of 8 different angles from 0 to 360 degrees, with the an interval of 45 degrees, at the same distance between the test object and the signal source is collected. Meanwhile, the echo energy is collected for correlation modeling and analysis among groups. By evaluating the performance of humans and dummies via statistical analysis, a close correlation was found which results verified the substitutability of the dummy for the ADAS experiment test. The correlation coefficient between human and dummies ranges from 0.75 to 0.93. The support vector machine (SVM) model was developed and fitted to predict the echo energy in diverse environments. The mean average error (MAE) is 5.42–11.42 in the training and testing datasets while root mean square error (RMSE) is 0.43–0.90. The methods developed in the study can simulate the real ADAS road test environment and support future experimental research. [ABSTRACT FROM AUTHOR]

The traditional algorithms for generating 3D human point clouds often face challenges in dealing with issues such as phantom targets and target classification caused by electromagnetic multipath effects, resulting in a lack of accuracy in the generated point clouds and requiring manual labeling of the position of the human body. To address these problems, this paper proposes an adaptive method for generating 3D human point clouds based on 4D millimeter-wave radar (Self-Adaptive mPoint, SA-mPoint). This method estimates the rough human point cloud by considering micro-motion and respiration characteristics while combining the echo dynamic with static information. Furthermore, it enhances the density of point cloud generation. It reduces interference from multipath noise through multi-frame dynamic fusion and an adaptive density-based clustering algorithm based on the center points of humans. The effectiveness of the SA-mPoint algorithm is verified through experiments conducted using the TI Millimeter Wave Cascade Imaging Radar Radio Frequency Evaluation Module 77G 4D cascade radar to collect challenging raw data consisting of single-target and multi-target human poses in an open classroom setting. Experimental results demonstrate that the proposed algorithm achieves an average accuracy rate of 97.94% for generating point clouds. Compared to the popular TI-mPoint algorithm, it generates a higher number of point clouds on average (increased by 87.94%), improves the average accuracy rate for generating point clouds (increased by 78.3%), and reduces the running time on average (reduced by 11.41%). This approach exhibits high practicality and promising application prospects. [ABSTRACT FROM AUTHOR]

The Gaussian noise model and estimators based on least squares (LS) are widely used in target localisation with distributed multiple‐input multiple‐output (MIMO) radar because of their computational efficiency. However, the accuracy of existing LS‐based target localisation algorithms deteriorates sharply in the presence of outliers in the measurements. Thus, a robust solution is developed based on the lp ${l}_{p}$‐norm minimisation criterion and iteratively reweighted least squares (IRLS) for locating a moving target with impulse noise using the angle of arrival (AOA), time delay (TD), and Doppler shift (DS) measurements. First, the AOA, TD, and DS measurement noise models are developed based on the α‐stable distribution. Then, the localisation problem is transformed into an lp ${l}_{p}$‐norm minimisation problem by linearising the AOA, TD, and DS measurement equations. Finally, the lp ${l}_{p}$‐norm minimisation problem is solved using an IRLS method to obtain the target position and estimate the velocity. Moreover, the optimum of the norm order (p) and the Cramér–Rao lower bound for the target position and velocity estimation are derived under α‐stable distributed measurement noise. The simulation results demonstrate that the developed algorithm offers higher accurascy and robustness than the existing ones in the presence of measurement outliers. [ABSTRACT FROM AUTHOR]

The heart is an important organ that maintains human life activities, and its movement reflects its health status. Utilizing electromagnetic waves as a sensing tool, radar sensors enable noncontact measurement of cardiac motion, offering advantages over conventional contact-based methods in terms of comfort, hygiene, and efficiency. In this study, the high-precision displacement detection algorithm of radar is applied to measure cardiac motion. Experimental is conducted using a single out-channel frequency modulated continuous wave (FMCW) radar operating in the ISM frequency band with a center frequency of 24 GHz and a bandwidth of 150 MHz. Since the detection signal is influenced by both respiratory and heartbeat movements, it is necessary to eliminate the respiratory signal from the measurement signal. Firstly, the harmonic composition of the respiratory signal is analyzed, and a method is proposed to calculate the parameters of the respiratory waveform by comparing the respiratory waveform coverage area with the area of the circumscribed rectangle. This allows for determining the number of respiratory harmonics, assisting in determining whether respiratory harmonics overlap with the frequency range of the heartbeat signal. Subsequently, a more accurate cardiac motion waveform is extracted. A reference basis is provided for extracting cardiac health information from radar measurement waveforms by analyzing the corresponding relationship between certain extreme points of the waveform and characteristic positions of the electrocardiogram (ECG) signal. This is achieved by eliminating the fundamental frequency component of the heartbeat waveform to emphasize other spectral components present in the heartbeat signal and comparing the heartbeat waveform, the heartbeat waveform with the fundamental frequency removed, and the heartbeat velocity waveform with synchronized ECG signals. [ABSTRACT FROM AUTHOR]

Abstract Hand gesture recognition based on micro‐Doppler (MD) radar has garnered considerable attention from researchers as a potential method for human–computer interaction (HCI). However, two significant challenges, that is, obvious differences in MD map size and lots of redundant information contained in the MD maps, are encountered. Here, a multi‐scale graph‐based hand gesture recognition framework is proposed. First, a MD graph representation method is developed, which is adapted to arbitrary‐sized frames and enables to map the key features in a sparse manner. Then, the multi‐scale information from MD graph is fully extracted for hand gesture recognition. Experimental results show that the proposed framework achieves a state‐of‐the‐art accuracy of 96.73% on the four‐class radar hand gesture dataset, while reducing up to 99% of the redundant information in the MD maps. This framework requires only a little amount of memory storage with good hand gesture recognition capability, demonstrating its high potential in the HCI field.

Hand gesture recognition based on micro‐Doppler (MD) radar has garnered considerable attention from researchers as a potential method for human–computer interaction (HCI). However, two significant challenges, that is, obvious differences in MD map size and lots of redundant information contained in the MD maps, are encountered. Here, a multi‐scale graph‐based hand gesture recognition framework is proposed. First, a MD graph representation method is developed, which is adapted to arbitrary‐sized frames and enables to map the key features in a sparse manner. Then, the multi‐scale information from MD graph is fully extracted for hand gesture recognition. Experimental results show that the proposed framework achieves a state‐of‐the‐art accuracy of 96.73% on the four‐class radar hand gesture dataset, while reducing up to 99% of the redundant information in the MD maps. This framework requires only a little amount of memory storage with good hand gesture recognition capability, demonstrating its high potential in the HCI field. [ABSTRACT FROM AUTHOR]

The GNSS-S radar utilizes the signals of a global navigation satellite system (GNSS) to carry out target detection. Due to the very low power of GNSS signals, long-term accumulation is needed to improve the gain of the echo signals. However, when it is used for moving object detection, the random movement of the target will cause residual Doppler frequency after the echoes are correlated and compressed through the direct signal. The residual Doppler frequency will cause two problems: on the one hand, the signal coherence will deteriorate, affecting the coherent accumulation gain; on the other hand, the amplitude of the signal after compression will decrease due to the sensitivity of GNSS signals to Doppler frequency. Therefore, how to increase the signal amplitude and eliminate the phase fluctuation caused by the Doppler frequency shift in the GNSS echoes of moving targets is an important issue for GNSS-S radar to detect moving targets. This paper proposes a dual-frequency GNSS echo enhancement method that uses the dual-frequency signals transmitted by the GNSS satellites to enhance and regularize the target echo. First, the phase relationship model of the GNSS dual-frequency echo is constructed, and the phase difference is made to the compressed dual-frequency echo signal to obtain the differential phase without fluctuation; then, the amplitudes of the dual-frequency echo signals are added together; and finally, a new signal with enhanced amplitude and consistent phase is constructed by using the dual-frequency additive amplitude and differential phase, and the long-term coherent accumulation of the signal is carried out, which can improve the processing gain of the weak echo signal of the moving target. The simulation and field experiments show that this method makes full use of the energy of the GNSS dual-frequency signal and eliminates the phase fluctuation in the echo signal of the moving target so that the compressed signal energy remains consistent in the slow-time dimension. After long-term coherent accumulation, the echo SNR was greatly improved, which enabled the detection of two high-speed cars by GNSS-S radar in the experiment. [ABSTRACT FROM AUTHOR]

The Multiple Input Multiple Output (MIMO) radar, as a new type of radar, emits orthogonal waveforms, which provide it with waveform diversity characteristics, leading to increased degrees of freedom and improved target detection performance. However, it also poses challenges such as difficulty in meeting higher data demand, separating waveforms, and suppressing the multidimensional sidelobes (range sidelobes, Doppler sidelobes, and angle sidelobes) of targets. Phase-coded signals are frequently employed as orthogonal transmission signals in the MIMO radar. However, these signals exhibit poor Doppler sensitivity, and the intra-pulse Doppler frequency shift can have an impact on the effectiveness of the matching filtering process. To address the aforementioned concerns, this paper presents a novel approach called the Space–Time–Range Joint Adaptive Focusing and Detection (STRJAFD) method. The proposed method utilizes the Mean Square Error (MSE) criterion and integrates spatial, temporal, and waveform dimensions to achieve efficient adaptive focusing and detection of targets. The experimental results demonstrate that the proposed method outperforms conventional cascaded adaptive methods in effectively addressing the matching mismatch issue caused by Doppler frequency shift, achieving super-resolution focusing, possessing better suppression effects on three-dimensional sidelobes and clutter, and exhibiting better detection performance in low signal-to-clutter ratio and low signal-to-noise ratio environments. Furthermore, STRJAFD is unaffected by coherent sources and demands less data. [ABSTRACT FROM AUTHOR]

Millimeter-wave FMCW radar has been widely used in joint range-velocity estimation of multiple targets. However, most existing algorithms are unable to estimate the range-velocity information with high accuracy simultaneously and fail to discriminate the targets with either closely spaced ranges or closely spaced velocities in the 2D range-Doppler spectrum. In order to deal with these problems, this paper proposes a 2D-Unitary ESPRIT-based joint range and velocity estimation algorithm of multiple targets for FMCW radar. Firstly, The 1D-IF signal is constructed into a 2D virtual array signal, the virtual array signals are preprocessed by a 2D-spatial smoothing technique to generate a new matrix signal. Then, according to the 2D-Unitary ESPRIT algorithm, the 2D real-valued information of the target parameters is obtained from this matrix signal, and then a new complex-value matrix is constructed. Finally, the eigenvalue decomposition of this new complex-value matrix is performed, and the range-velocity estimates of multiple targets are, respectively, calculated from the real and imaginary parts of the eigenvalues, and paired automatically. The simulation results illustrate that the proposed algorithm not only provides highly accurate range-velocity estimates but also has high-resolution performance and achieves automatic pairing of the range-velocity estimates in multi-target scenarios, thus effectively improving the multi-target joint range and velocity estimation performance of FMCW radar. [ABSTRACT FROM AUTHOR]

This research aims to propose a comprehensive simulation and implementation methodology for LFM (Linear Frequency Modulated) Radar Signal Processing and its application, using digital signal processing techniques on the DSP Starter Kit (DSK) 6713 board. The motivation behind this study is to develop control software based on MATLAB R14 and SIMULINK to model various system software tasks, including detection, A/D conversion, Fast Fourier Transform (FFT), modulation, accumulation, decision-making, and target detection. The simulations are categorized into two groups: ideal beat frequency and parameterized beat frequency. We introduce several important terminologies for consideration, including pulse compression, SNR, matched filter, Doppler effect, and more. The use of real-time data exchange (RTDX) will facilitate the generation of input data and enable real-time calculations for outputs, leading to the creation of machine code for the DSP chip. This process aims to ensure data verification calculations and enhance the credibility and performance of the proposed methodology. By conducting thorough simulations, verification, and practical testing, the study demonstrates the satisfactory credibility and performance of the developed method. Using this research, we aim to contribute to the advancement of LFM Radar Signal Processing and enable its efficient implementation using digital signal processing techniques on the DSP Starter Kit (DSK) 6713 board. [ABSTRACT FROM AUTHOR]

Specifying parameters of high-speed targets as range and velocity in radar systems operating with phase-modulated signals is a problematic process because of the serious effect of high Doppler frequency on these types of waveforms. In the existence of a high-speed target, its Doppler frequency will be very high such that it totally changes the phase sequence of the received signal. As a result, the matched filter output will be distorted. Therefore, estimating target parameters will be impossible. In this letter, a new method is proposed for detecting and estimating velocity of high speed targets using phase-modulated signals. The suggested technique methodology depends on compensating the target high-Doppler frequency for signal recovery using a customized waveform shape. Moreover, the Doppler frequency value is estimated without ambiguity from one pulse, and without the need for a range-Doppler module. As such, the implementation of the proposed method is simple and inexpensive. The performance evaluation of the new method shows its superiority in compensation and estimation compared to other techniques when examined under higher Doppler frequency and noise compared to the conventional radar. [ABSTRACT FROM AUTHOR]

The Doppler frequency of the echo signal exhibits time-varying and high-order characteristics due to the high-speed relative motion between the satellites in spaceborne inverse synthetic aperture radar (ISAR) imaging of space target, even during a short coherent processing interval (such as 1 s). Therefore, the selection of the optimal imaging time interval (OITI) is crucial to obtain a well-focused image. The existing methods do not consider the fact that the OITIs of scatterers on the target can also vary significantly. In this letter, an OITI selection method for spaceborne ISAR imaging of space targets based on the time–frequency analysis is proposed. First, the differences in optimal imaging instants (OIIs) of scatterers are analyzed. Then, based on the combination of the Margenau–Hill–spectrogram (MHS) and the S-method, the time–frequency curves can be obtained after extracting the time–frequency ridge and polynomial fitting. The OII of the entire target can be estimated after eliminating the differences in the OIIs by multiplying the constructed third-order phase terms with the signal. Finally, a more suitable OITI can be obtained because of the elimination of the differences in the OIIs. The effectiveness and advancement of the proposed method are verified by simulation and real data results. [ABSTRACT FROM AUTHOR]

In this letter, the clutter range dependence issue caused by crab angle is investigated in an airborne multichannel radar system. A new method is proposed to accomplish the crab angle estimation and the nonstationary error compensation. First, from the center-biased and spectrum-broadened characteristics of the clutter space-time spectrum in the case of nonneglected crab angle, a 1-D cost function based on space-time spectrum broadening degree is constructed to achieve the accurate crab angle estimation. Then, the clutter nonstationary errors can be effectively compensated in the post-Dopplerdomain,significantly improving the subsequent space-time adaptive processing (STAP) performance. Simulation results are presented to validate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The range-ambiguous clutter in space-based early warning radar is more severe compared with that in airborne radar due to the faster movement velocity. Meanwhile, the clutter angle-Doppler characteristics vary with range owing to Earth’s rotation. As a result, the mainbeam clutter returned from different ambiguous ranges occupies most of the Doppler spectrum, which leads to the degradation of the traditional space–time adaptive processing (STAP) performance in canceling clutter. Though frequency diverse array (FDA) based on multiple-input multiple-output (MIMO) can provide the ability to identify the range-ambiguous clutter, it cannot perform well for space-based early warning radar because of the finite available vertical elements. In this letter, a novel STAP method, which utilizes the element-pulse coding (EPC) and FDA technique to mitigate the range-ambiguous clutter, is proposed. First, EPC is used to prewhiten the most range-ambiguous clutter via coding and decoding in horizontal elements and coherent pulses. Then the interval in the vertical frequency of the residual range-ambiguous clutter is expanded by FDA, and thus the targets and clutter from the vertical mainlobe are easily extracted using a spatial filter with a few vertical elements. Finally, the extracted clutter can be effectively suppressed through the traditional STAP method. Simulation results are provided to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Coast-transmit ship-receive (CTSR) bistatic high-frequency surface wave radar (HFSWR) can fully exploit the flexibility of moving platforms and the anti-interference advantages of bistatic radar. However, the platform motion can cause the spread of target echo in the frequency spectrum and reduction of amplitude, which is not conducive to detection. In this letter, a motion compensation method using a direct wave signal is proposed for CTSR bistatic HFSWR. First, the frequency shift characteristics of direct wave signal and target echo are analyzed by simulation. The simulation results show that the platform motion has a similar modulation form on both target echo and direct wave signal, and their difference is mainly caused by different azimuths. In addition, the direct wave signal is only affected by the platform motion, so it can be used to obtain the platform motion effect. Then, the proposed method uses a direct wave signal as the reference information to estimate and eliminate the phase modulation caused by the platform motion. At last, the effectiveness of the method is verified by the experiment based on simulated and measured attitude data. [ABSTRACT FROM AUTHOR]

The radon Fourier transform (RFT)-based coherent integration is an important target detection method. However, the computational cost of parameter searching and blind-speed sidelobe (BSSL) remain the main challenges in actual RFT applications. In this letter, we propose a Whale optimization algorithm-based RFT (WOA-RFT) to accelerate the parameter searching process and improve BSSL suppression performance. First, the discrete RFT signal model is derived; WOA-RFT is then proposed to speed up the RFT and suppress BSSL. The simulation results demonstrate that our proposed method has a better performance in terms of parameter estimation accuracy, BSSL suppression capability, and computational cost, compared to some existing methods. [ABSTRACT FROM AUTHOR]

The backscattering of electromagnetic waves incident on a rotating metallic wind turbine (WT) is analyzed by using the Physical Optics method. The model developed is general and allows the computation of the spectral Doppler shift of the backscattered waves. All the parameters involved are taken into account, relative to incident wave direction, wind horizontal direction, WT geometric and electromagnetic properties. Numerical computations are carried out for various cases and presented relative to a search radar. [ABSTRACT FROM AUTHOR]

Digital radar waveforms such as orthogonal frequency-division multiplexing (OFDM) often have the disadvantage that they require high sampling rates if fine range resolutions have to be achieved. The frequency comb OFDM radar scheme offers a possibility to overcome this drawback and to improve the range resolution without increasing the sampling rate. Simultaneously, the high unambiguous velocity, which is one of the advantages of digital radar waveforms, is retained and due to the simple generation of orthogonal transmit signals, it is well suited for multiple-input multiple-output (MIMO) applications. To prove all these features of the frequency comb OFDM radar scheme, a suitable 4 × 4 MIMO demonstrator including frequency comb generation as well as up- and downconversion with these combs has been set up. Its functionality has been validated with real measurements in an anechoic chamber in conjunction with a radar target simulator to emulate very high velocities. [ABSTRACT FROM AUTHOR]

Micro-Doppler (m-D) analysis is the most effective mechanism for detecting rotating targets or components; however, it fails when the target rotation plane is perpendicular to the radar line of sight (LOS). The vortex electromagnetic wave (VEMW) provides a unconventional structure of wavefront phase modulation on the cross-plane of the radar LOS, on which the radial m-D vanishes while the rotational Doppler (RD) appears. In the absence of the position of rotation center, this paper focuses on the micromotion parameters estimation based on RD effect for rotating target, and then proposes an estimation procedure, referred to as the two-step method. The micromotion parameters of the rotating target include the rotation attitude, the rotation radius and the position of the rotation center while the latter is coupled to the former two. Firstly, the micromotion parameters are roughly estimated based on the RD curve parameters obtained from the time-frequency (TF) spectrum of the received signal. Secondly, the maximum likelihood estimation (MLE) is used to accurately estimate the micromotion parameters. In addition, the Cramér–Rao bound (CRB) of parameter estimation is derived. The simulation studies the influencing factors of estimation performance and verifies that the proposed estimation method can provide excellent estimation accuracy of the micromotion parameters. [ABSTRACT FROM AUTHOR]

Radar technologies reserve a large latent capacity in dealing with human motion recognition (HMR). For the problem that it is challenging to quickly and accurately classify various complex motions, an HMR algorithm combining the attention mechanism and convolution neural network (ECA-CNN) using vital signs is proposed. Firstly, the original radar signal is obtained from human chest wall displacement. Chirp-Z Transform (CZT) algorithm is adopted to refine and amplify the narrow band spectrum region of interest in the global spectrum of the signal, and accurate information on the specific band is extracted. Secondly, six time-domain features were extractedfor the neural network. Finally, an ECA-CNN is designed to improve classification accuracy, with a small size, fast speed, and high accuracy of 98%. This method can improve the classification accuracy and efficiency of the network to a large extent. Besides, the size of this network is 100 kb, which is convenient to integrate into the embedded devices. [ABSTRACT FROM AUTHOR]

This paper is concerned with the measurement outlier-resistant mobile robot localization problem by using multiple Doppler-azimuth radars under round-robin protocol (R-RP). In the considered robot localization system, multiple Doppler-azimuth radars are equipped on the robot platform to produce the measurement including the Doppler frequency shift and the azimuth. In order to assuage communication link congestion, the R-RP is used. For mitigating the influence of outliers, a time-varying state estimator is constructed which contains a saturation function with variable saturation levels. This paper aims at seeking out a practicable yet effective solution to the addressed robot localization problem by devising the constructed estimator which can assure that, over a finite horizon, the localization error satisfies the given H ∞ performance index. By constructing an appropriate Lyapunov function, the sufficient condition, which can guarantee the localization error to fulfill the given H ∞ performance, is established. Then, by resorting to the solution to a set of linear matrix inequalities, the constructed estimator can be devised. In the light of the estimator design strategy proposed in this paper, the corresponding robot localization algorithm is developed. At last, some simulations are conducted to testify the usefulness of the developed robot localization algorithm. • The measurement outlier and the round-robin protocol are considered simultaneously for the robot localization problem for the first time. • A discrete state estimator containing a saturation function with the variable saturation level is constructed. • By using Lyapunov theory and the RLMI method, the estimator is designed. • The corresponding measurement outlier-resistant robot localization algorithm is given. [ABSTRACT FROM AUTHOR]

The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission is a joint effort by the European Space Agency (ESA) and the Japanese Aerospace Exploration Agency (JAXA). The EarthCARE mission features the first spaceborne 94 GHz cloud-profiling radar (CPR) with Doppler capability. The raw CPR observations and auxiliary information are used as input to three Level-2 (L2) algorithms: (1) C-APC: Antenna Pointing Characterization; (2) C-FMR: CPR feature mask and reflectivity; (3) C-CD: Corrected CPR Doppler Measurements. These algorithms apply quality control and corrections to the CPR primary measurements and derive important geophysical variables, such as hydrometeor locations, and best estimates of particle sedimentation fall velocities. The C-APC algorithm uses natural targets to introduce any corrections needed to the CPR raw Doppler velocities due to the CPR antenna pointing. The C-FMR product provides the feature mask based on only-reflectivity CPR measurements and quality-controlled radar-reflectivity profiles corrected for gaseous attenuation at 94 GHz. In addition, C-FMR provides best estimates of the path-integrated attenuation (PIA) and flags identifying the presence of multiple scattering in the CPR observations. Finally, the C-CD product provides the quality-controlled, bias-corrected mean Doppler velocity estimates (Doppler measurements corrected for antenna mispointing, non-uniform beam filling and velocity folding). In addition, the best estimate of the particle sedimentation velocity is estimated using a novel technique. [ABSTRACT FROM AUTHOR]

A method for time‐domain motion compensation of high frequency (HF) radar signals for the case of a floating transmitter and fixed receiver is proposed when the motion parameters (including the amplitude and angular frequency of the motion) are not known a priori. In this study, the floating platform is assumed to follow a single‐frequency motion model. Additionally, instead of trying to estimate platform motion parameters from the received motion‐contaminated Doppler spectrum, which is proportional to the observed radar cross‐section of the ocean surface from the floating platform, the motion parameters from the autocorrelation function of the received electric field are estimated, which is related to the received radar cross‐section by application of an inverse temporal Fourier transform. By comparing the locations of the zeros of the autocorrelation function for the fixed antenna case with that for an antenna on a floating platform and finding the zeros associated with the platform motion, the motion parameters are estimated. These parameters are matched with actual motion parameter values, from which the motion‐compensated Doppler spectrum is recovered from the Doppler spectrum of the antenna on a floating platform. [ABSTRACT FROM AUTHOR]

This paper proposes a system that will guard infrastructures against incoming threats from drones by detecting it with the use of a radar device-based detection scheme. The database acquired, named Real Doppler RAD-DAR (Radar with Digital Array Receiver) is constructed by a Microwave and Radar Group. The radar used uses a Frequency Modulated Continuous Wave (FMCW) on an 8.75 GHz based frequency band with a BWmaxof 500 MHz. The proposed Convolutional Neural Network (CNN), CNN-32DC is varied with different number of filters, combination layers, and number of feature extraction blocks, the preference that will give the most accurate result was selected and compared with different machine learning and classification learning algorithms gained an accuracy that exceeds other networks with less processing time.

This research investigates the use of a Binary Phase Shift Key (BPSK) sequence derived from the 192-bit key Advanced Encryption Standard (AES-192) algorithm for radar signal modulation to mitigate Doppler and range ambiguities. The AES-192 BPSK sequence has a non-periodic nature resulting in a single large and narrow main lobe in the matched filter response but also produces undesired periodic side lobes that can be mitigated through the use of a CLEAN algorithm. The performance of the AES-192 BPSK sequence is compared to an Ipatov–Barker Hybrid BPSK code, which effectively extends the maximum unambiguous range but has some limitations in terms of signal processing requirements. The AES-192 based BPSK sequence has the advantage of having no maximum unambiguous range limit, and when the pulse location within the Pulse Repetition Interval (PRI) is randomized, the upper limit on the maximum unambiguous Doppler frequency shift is greatly extended. [ABSTRACT FROM AUTHOR]

The Doppler shift of microwave radar sea surface echoes serves as the foundation for sea surface current field retrieval; it includes the shift caused by satellite platform motion, ocean waves, and sea surface currents. The Doppler shift caused by ocean waves is known as the wave-induced Doppler velocity (UWD), and its removal is critical for the accurate retrieval of sea surface current fields. The low-incidence Ka-band real-aperture radar rotary scan regime has the capability of directly observing wide-swath two-dimensional current fields, but as a new regime to be further explored and validated, simulation and analysis of UWD in this regime have a significant influence on the hardware design and currently observed applications of this satellite system in its conceptual stage. In this study, we simulated and investigated the impacts of radar parameters and sea-state conditions on the UWD obtained from small-incidence-angle Ka-band rotational scanning radar data and verified the simulation results with the classical analytical solution of average specular scattering point velocity. Simulation results indicate that the change in the azimuth direction of platform observation affects UWD accuracy. Accuracy is the lowest when the antenna is in a vertical side-view. The UWD increases slowly with the incidence angle. Ocean waves are insensitive to polarization in the case of small-incidence-angle specular scattering. The increase in wind speed and the development of wind waves result in a substantial increase in UWD. We classified swell by wavelength and wave height and found that UWD increases with swell size, especially the contribution of swell height to UWD, which is more significant. The contribution of the swell to UWD is smaller than that of wind waves to UWD. Furthermore, the existence of sea surface currents changes the contribution of ocean waves to UWD, and the contribution weakens with increasing wind speed and increases with wind wave development. [ABSTRACT FROM AUTHOR]

The growing number of civil applications in which Unmanned Aerial Vehicles (UAVs) are involved can create many concerns for airspace security and surveillance. Gathering as much information as possible about a drone can be crucial to apply proper countermeasures if a potentially dangerous situation is detected. Of course, the presence of a UAV can be detected by radar, but it is possible to extend the system capabilities to obtain additional information. For example, in the case in which the UAV is equipped with propellers, the radar-measured rotational speed could be important information to classify the type of UAV or to reveal if it is carrying some possibly harmful payload. In addition, the rotational speed measured through radar could be used for different purposes, such as to detect a drone manumission, to estimate its maximum payload, or for predictive maintenance of the drone. Measuring the propellers' rotational speed with radar systems is a critical task, as the Doppler generated by the rotation can be very high, and it is very difficult to find commercial radar systems in the market able to handle such a high Doppler. Another problem is caused by the typically very small Radar Cross-Section (RCS) of the propellers, which makes their detection even more difficult. In the literature, common detection techniques are based on the measurement of the Doppler effect produced by the propellers to derive their rotational speed, but due to the very limited capabilities of commercial sensors, this approach can be applied only at very low values of the rotational speed. In this work, a different approach based on a Frequency-Modulated Continuous Wave (FMCW) radar is proposed, which exploits the vibration of the UAV generated by the rotation of the propellers. The phenomenon and how the sensor can detect it will be presented, which is joined with a performance analysis comparing different estimation techniques for the indirect measurement of the propellers' speed to evaluate the potential benefits of the proposed approach. [ABSTRACT FROM AUTHOR]

Near real-time ship monitoring is crucial for ensuring safety and security at sea. Established ship monitoring systems are the automatic identification system (AIS) and marine radars. However, not all ships are committed to carry an AIS transponder and the marine radars suffer from limited visibility. For these reasons, airborne radars can be used as an additional and supportive sensor for ship monitoring, especially on the open sea. State-of-the-art algorithms for ship detection in radar imagery are based on constant false alarm rate (CFAR). Such algorithms are pixel-based and therefore it can be challenging in practice to achieve near real-time detection. This letter presents two object-oriented ship detectors based on the faster region-based convolutional neural network (R-CNN). The first detector operates in time domain and the second detector operates in Doppler domain of airborne Range-Compressed (RC) radar data patches. The Faster R-CNN models are trained on thousands of real X-band airborne RC radar data patches containing several ship signals. The robustness of the proposed object-oriented ship detectors is tested on multiple scenarios, showing high recall performance of the models even in very dense multitarget scenarios in the complex inshore environment of the North Sea. [ABSTRACT FROM AUTHOR]

In this article, a stepped-carrier orthogonal frequency-division multiplexing (OFDM) system with a nonlinear hopping pattern is developed for joint radar and communication (RadCom). In the proposed OFDM-based RadCom system, the carrier frequencies of OFDM symbols for multiple RadCom nodes are orthogonally switched to avoid interference while also maximizing the signal-to-radar interference ratio (SRIR). Differently from conventional linear stepped-carrier OFDM radar that does not consider the communication performance, we first develop a subband allocation method that considers both the radar and the communication performance for multiple RadCom nodes. Channel state information (CSI) of multiple nodes is shared with a centralized server, which then computes the subband allocation strategy that gives the maximum SRIR. Furthermore, a distributed subband allocation strategy based on stateless Q-learning is also proposed, which is suitable when the RadCom nodes do not share their CSI. We also develop the radar signal processing algorithm for range and velocity estimation when the stepped-carrier OFDM waveforms are transmitted through multiple subbands with a nonlinear hopping pattern. Through computer simulations, we confirm that the proposed stepped-carrier OFDM with the nonlinear hopping pattern exhibits a higher achievable sum rate than conventional linear stepped OFDM while not sacrificing the radar performance of target parameter estimation. [ABSTRACT FROM AUTHOR]

In order to quantitatively evaluate the influence of the aberration of the antenna radiation pattern of airborne radar on clutter feature, the range‐azimuth two dimensional (2D) model of the airborne radar clutter is established. Then the theoretic modelling and simulation analysis are proposed in three aspects, including the Doppler, power spectrum and space‐time 2D characteristics of the clutter. Finally, the processing of moving target indication, pulse Doppler and space‐time adaptive processing are used to suppress the clutter of the echo signals before and after the aberration. The results of the simulations indicate the aberration would elevate the sidelobe level, extend the power spectrum and bend the space‐time trajectory of the clutter Doppler spectra. And the residual clutter would be elevated nearly 5 dB after STAP processing. Then the pattern preserving and fast time cancel combined space‐time adaptive processing method is proposed, which can obtain narrow clutter notch and large improvement factor, and effectively suppress clutter. The modelling and quantitative analysis study have important theoretical and engineering significance for the detection capability of the airborne radar installed on the fuselage of the aircraft platform, the tactical and technical performance index, and improving the signal processing ability of airborne radar. [ABSTRACT FROM AUTHOR]

In addition to traditional linear Doppler shift, the angular Doppler shift in vortex electromagnetic wave (VEMW) radar systems carrying orbital angular momentum (OAM) can provide more accurate target identification micro-motion parameters, especially the detailed features perpendicular to the radar line-of-sight (LOS) direction. In this paper, a micro-motion feature extraction method for a spinning target with multiple scattering points based on VEMW radar is proposed. First, a multi-scattering-point spinning target detection model using vortex radar is established, and the mathematical mechanism of echo signal flash shift in time-frequency (TF) domain is deduced. Then, linear Doppler shift is eliminated by interference processing with opposite dual-mode VEMW. Subsequently, the shift in TF flicker is focused on the reference zero frequency by the iterative phase compensation method, and the number of scattering points is estimated according to the focusing effect. After this, through the constructed compensation phase, the angular Doppler shift is separated, then the angular velocity, rotation radiusand initial phase of the target are estimated. Theoretical and simulation results verify the effectiveness of the proposed method, and more accurate rotation parameters can be obtained in the case of multiple scattering points using the VEMW radar system. [ABSTRACT FROM AUTHOR]

The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named unambiguous Doppler-based forward-looking multiple-input multiple-output (MIMO) radar beam sharpening scan (UDFMBSC) is proposed, based on the combination of MIMO processing and DBS. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data. [ABSTRACT FROM AUTHOR]

A ${k}$ -space and Doppler velocity decomposition based on an accurate millimeter wave (mmW) 3-D target localization method is presented, using the range points’ migration (RPM) method to provide multi-information associated with point clouds. The incoherent method, known as RPM, has a number of advantages over coherent localization, including avoiding a false response due to phase uncertainty or the necessity for highly accurate phase calibration in multiple arrays. However, various concerns must be addressed to retain the benefit of high-frequency mmW radar. In order to bring some benefits of high-frequency mmW radar into RPM scheme, this study introduces the ${k}$ -space and Doppler velocity decomposition schemes for multiple objects moving at different speeds into the RPM scheme, where a new weighted term is introduced. In addition, the RPM point cloud is incorporated with the incoherent Doppler velocity estimation method known as the weighted kernel density (WKD) method, which provides a multifunctional 3-D localization. The mmW radar experiment in the 79-GHz band demonstrates that our proposed method achieves accurate 3-D Doppler-associated localization, even with a small aperture array. [ABSTRACT FROM AUTHOR]

Speeding is one of the leading causes of traffic crashes worldwide. Radar velocimeter is widely used in the capture monitoring of road overspeed violations, which can effectively reduce the probability of traffic accidents and protect people's life and property safety to the greatest extent. As a new type of radar velocimeter, multitarget radar velocimeter (MTRV) can monitor the speed of more than two vehicles at the same time. However, the verification method and device of MTRV's performance need to be studied. In order to solve the problem of performance verification for MTRV, a verification device based on echo signal simulation technology is developed in this paper. The measurement mechanism of MTRV with different performance including velocity, distance, and angle is first introduced. Then, a verification method based on the echo signal simulation technology is proposed. The verification device can receive the emission signal of MTRV and process the signal by echo simulation technology, including target generation, Doppler frequency shift, time delay, and angel control, and targets are simulated with nominal velocity, distance, and angle value. The processed echo signal with simulated nominal parameter values is reflected to the MTRV. After the echo signal is received and processed by MTRV, the measurement values of simulated velocity, distance, and angle for targets are obtained. Comparing the measured values of the MTRV with the simulated nominal values of the verification device, the measurement error of MTRV is obtained. The verification device of MTRV is realized to verify the accuracy and reliability of the MTRV measurement results. The simulated velocity range of the verification device is up to (-300~300) km/h, and the simulated distance range of the verification device is up to (10~45) m when the simulated incident angle range was within the range of (-60~60)°. The simulation target generation for the two targets of the device is also verified. And the maximum permissible error (MPE) of the simulated velocity was ±0.05 km/h, the MPE of simulated distance is ±0.3 m, and the MPE of simulated angle is ±0.2°. Finally, the verification and uncertainty evaluation results of the MTRV sample validated the effectiveness and feasibility of the proposed verification method and the developed verification device of MTRV. [ABSTRACT FROM AUTHOR]

As a noncontact and noninvasive sensor, radar offers attractive modality for human motion recognition in many fields, such as surveillance, smart homes, and assisted living. As a dynamic process, a comprehensive multidomain analysis in stream is crucial for human motion recognition. In this article, a novel spatial–temporal convolution long short-term memory (ST-ConvLSTM) network using dynamic range-Doppler frames (DRDF) based on portable frequency-modulated continuous-wave (FMCW) radar is proposed to address this problem. First, DRDF is introduced to characterize different human motions in time, range, Doppler, and radar cross section (RCS) domains with a time-sequenced range-Doppler maps. Then, ConvLSTM cell is investigated to extract continuous 2-D image features by combining both convolution operation and LSTM cell. Furthermore, a spatial attention module is utilized to emphasize the spatial distribution of human motion in the range-Doppler domain, while a temporal attention module is designed to obtain optimal temporal weights considering behavior continuity. Finally, an extensive radar dataset from 16 volunteers demonstrated its feasibility and superiority in the classification of six typical daily human motions. The contributions of attention modules and its robustness facing individual diversity are also investigated and discussed. [ABSTRACT FROM AUTHOR]

Conventional radars repeat the transmission of the same waveform after a predefined interval of time called pulse-repetition-interval (PRI). This technique helps estimate the range and Doppler shift of the targets and suppress clutter. However, in dual-function radar communication (DFRC), a different symbol waveform is transmitted after each PRI. Depending on the number of targets, radar receiver output yields several peaks representing different targets’ ranges. Each peak comes with its side-lobes called range-side-lobes (RSL). In DFRC, due to different symbol waveform transmission, peaks and RSLs do not remain coherent, making Doppler shift estimation and clutter suppression challenging tasks. In most of the available literature, iterative receive filters have been designed for DFRC to minimize RSLs and achieve coherent output for different waveforms. However, the proposed receive filter does not guarantee coherent output for more than two waveforms. In contrast, we proposed two novel closed-form algorithms to design receive filters for DFRC that guarantee coherent output response for several waveforms and suppress RSLs. Simulation results demonstrate that the proposed receivers achieve full coherency, and the RSLs are significantly lower than the conventional method. Furthermore, the advantage of achieving coherent output response is shown in target detection and bit-error-rate improvement. [ABSTRACT FROM AUTHOR]