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A theoretical sea echo modeling of the floating-platform high-frequency hybrid sky-surface wave radar (HSSWR) is presented. The ionosphere is considered to be a no-tilt reflecting plane moving in the vertical direction. Subsequently, the first-order sea clutter cross section with consideration of the platform sway and yaw motion is derived. Simulations are conducted to explore the influences of the ionospheric movement and the platform oscillation motion on the sea clutter power spectrum. Simulation results indicate that the platform sway motion may induce additional peaks that distribute symmetrically concerning the Bragg peaks, while the platform yaw motion and the ionospheric movement may broaden even split the sea clutter. Experiments are conducted with the newly-developed HSSWR system, of which the receiving arrays are simultaneously deployed on a floating platform and the shoreside. Experimental measurements concerning the frequency shift, Doppler width, and the arrival angle of the E-layer/F-layer ionosphere reflected direct wave are compared and analyzed. The study demonstrates the feasibility of the proposed modeling and provides valuable guidelines for the sea clutter characteristics analysis and the data quality assessments of the floating-platform HSSWR.

Recently, the long coherent accumulation of radar signals becomes relevant in the analysis of Doppler portraits of targets. This interest is related to the desire of researchers to obtain more information about radar purposes. Detailed spectral analysis provides additional information on the parameters of the target and its maneuvers. Some studies published in special articles are being discussed. They analyzed the methods of high resolution for the spectrums of refl ected signals. It is possible to obtain detailed Doppler portraits. The development of the elemental base of the construction of radar devices contributes to the improvement of the processing processes of refl ected signals. The possibilities of highlighting useful signals of small objects against the background of refl ections from the sea surface are considered. The article provides the results of processing signals recorded by radars made by the technology of digital antenna array. The experimental results of the Doppler portraits used a long-term coherent accumulation of signals in the L and X bands. Doppler portraits of ISS signals, Boeing-type passenger aircraft, MiG-29 fi ghter, helicopter are analyzed. The possibility of compensating for changes in the signal phase, which occurs during the radial acceleration of the ISS is shown. This allowed to increase the of signal-to-noise ratio to 6 dВ. Experimental results allow you to observe the maneuver of the target, to measure the acceleration of individual elements of the structure and their evolution. It is proposed to use the method of assessing Doppler portraits in the «radial speed - radial acceleration» coordinate system. The method is useful to use in radar surveillance systems of space objects, in the development of methods for recognizing classes of radar targets and in determining the number of objects in the target group. The development of this method of analysis of signals in prospective RADAR with the digital antenna array will help to solve problems of optimal signal processing, identify the number of targets in the group and maneuver the target, recognize classes of targets by the number of brilliant points and cross-Size targets.

When range ambiguity and range dependence coexist in forward-looking ground moving target indication (GMTI) radar, the traditional space-time adaptive processing (STAP) approaches may fail to work. To cope with this issue, a range-ambiguous clutter suppression approach based on elevation time diverse array (TDA) radar system and azimuth phase coding (APC) technique is presented in this paper. Due to the advantages of frequency scanning, the elevation TDA radar is able to cover all directions by transmitting a single time-shifted waveform. Therefore, a frequency bandpass filter is devised for each single range-bin to achieve the mainlobe echo extraction procedure. In the sequel, the azimuth phase decoding and Doppler filtering are performed to deal with the residual sidelobe ambiguous energy. With two-dimensional filtering, one can extract the unambiguous signals for each range region independently. Then, the traditional clutter compensation and STAP methods are employed to suppress the ground clutter. Finally, the effectiveness of the proposed framework in resolving range ambiguity are verified by the numerical simulation experiments.

Range/Doppler migration and velocity ambiguity are two well-known problems encountered in high-speed moving target detection using linear frequency-modulated continuous wave (LFMCW) automotive radar. To mitigate the problems, we introduce a simple Doppler-Range Processing (DRP) algorithm by first performing Doppler processing via fast Fourier transform (FFT) across slow-time samples, followed by a simple interpolation step, and then range processing via FFT along Doppler migration lines over fast-time samples. The proposed DRP algorithm can achieve full range and velocity resolutions, as well as full coherent integration gains. It attains a computational complexity comparable to that of the conventional 2D-FFT based Range-Doppler Processing (RDP) approach, computationally much more efficient than existing approaches. The proposed DRP algorithm can automatically resolve the velocity ambiguity problems. We analyze its velocity ambiguity mitigation capability in relation to the radar bandwidth and the number of slow-time samples within a Coherent Processing Interval (CPI). The effectiveness and the computational efficiency of the proposed algorithm are demonstrated by numerical examples.

Due to the high‐speed rotors and overlapped propellers, it is still a challenge to extract the micro‐motion parameters of the quadcopter. In this work, the authors establish a radar backscattering model of the quadcopter based on the flight control theory and radar backscattering characteristics. Then, the improved template matching method and peak grouping method are proposed to capture the periodic information for overlapped propellers on a range‐time profile and extract the rotation speeds of rotors. By simulating the nine types of quadcopter movements, the relative error of the rotation speeds is estimated to be lower than 1.4% for each rotor with a signal‐to‐noise ratio of 15 dB.

Differs from conventional phased-array, frequency diverse array (FDA) employs a frequency increment across the array elements and thus produces a range-angle-dependent beampattern. This additional range dependence finds applications in target detection/localization, high-resolution synthetic aperture radar (SAR) imaging, and SAR deceptive jamming. To capture the essence behind those applications, we analyze the range, angle, and Doppler resolution capabilities of FDA radar signal via ambiguity function. As conventional ambiguity function is defined for a single-input single-output (SISO) signal, we extend it to the generalized ambiguity function for FDA radar signal, parameterized with the range, angle, and Doppler shifts of a target. Both theoretical analysis and numerical results show that the FDA radar with a large frequency increment has potentials in high-resolution ranging but will suffer from additional range ambiguity within the range bin and Doppler ambiguity. Moreover, the use of frequency increment brings FDA radar a virtual transmit aperture but decreases the directional gain.

The impulsive noise originated from regional lightning discharges can dramatically degrade the performance of skywave over-the-horizon radar (OTHR) target detection with generally 10-20-dB increase of noise level in the Doppler domain. As a result, it is necessary to excise impulsive noise in corrupted data while preserving the useful echo signal. Traditional techniques first locate the impulsive noise in slow-time data with a high-pass finite-duration impulse response (FIR) notch filter and then restore the contaminated samples by neighboring good data. However, the effect of impulsive noise is propagated into the subsequent filtered residuals, which will mislead the impulsive noise detection. Instead of the conventional detection-interpolation methods, we propose a novel impulsive noise excision approach based on robust smoothing. The proposed method can robustly smooth the corrupted data and determine the position of impulsive noise with a preset threshold against the residuals of corrupted data subtracting the smoothed signal. The contaminated data at last are replaced by the smoothed signal. Experimental results have demonstrated the effectiveness of our proposed method.

This letter proposes a new method for focusing ground maneuvering targets and estimating the motion parameters with a synthetic aperture radar (SAR) system. In this method, the Hough transform is applied to estimate the cross-track velocity from the slope of the range walk (RW) trajectory, and the RW and Doppler centroid shift are compensated. The second-order Keystone transform is performed to correct the additional range curve caused by the along-track velocity and cross-track acceleration. Then, we adopt the adaptive polynomial Fourier transform to estimate the second- and third-order Doppler parameters from a 1-D parameter interval, and the corresponding motion parameters are calculated. Finally, the moving target is well focused after the motion parameters compensation because the second- and third-order Doppler parameters are efficiently eliminated. Both the simulated and real data processing results are presented to demonstrate the validity of the proposed algorithm.

This paper deals with the problem of adaptive moving target detection, embedded in homogeneous Gaussian noise with unknown covariance matrix, for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar operating in interference-dominant environment. Unlike traditional adaptive moving target detectors that need training data to estimate the jamming covariance matrix (JCM), we present the Rao and Wald test based adaptive detector, which requires no training data. Furthermore, we propose a two-stage approach to obtain maximum likelihood estimate (MLE) of the joint range, angle and Doppler, respectively. The corresponding signal-to-jamming-plus-noise ratio (SJNR) is derived to evaluate the FDA-MIMO radar performance. Simulation results show that the proposed detector outperforms the generalized likelihood ratio test (GLRT).

The broadening first-order sea clutters caused by the signals from different directions with various radial current velocities create severe disturbance for target detection using high-frequency surface wave radar (HFSWR). Conventional sea clutter suppression methods tend to remove the sea clutter and target signals when they are mixed in the Doppler spectrum. Based on the characteristics of the target signal and sea clutter in spatial-temporal domain, a new first-order sea clutter suppression method for HFSWR using orthogonal projection is proposed. The proposed method uses the data from multichannels and slow-time domain at the adjacent range cell to construct a covariance matrix, which can be used to obtain the sea clutter subspace by eigendecomposition. Later, original signals are projected onto the sea clutter subspace. Finally, subtract the component of the original signals in the sea clutter subspace from the original signals to achieve the suppression of sea clutter by retaining the target signals. The simulation and experimental results for a single target and multiple targets cases indicate that the proposed method can suppress the first-order sea clutter effectively, which enhances the target detection capacity in the sea clutter zone for HFSWR.

For radar modeling and simulation of sea clutter, there are a number of key characteristics used to describe the observed features. These include the mean backscatter power, amplitude statistics, spatial and long-time temporal correlation, and the Doppler spectrum. Regarding the latter, a common modeling approach is to use the mean Doppler characteristics (width and center point) to relate to the sea conditions. However, this does not capture the time- and range-varying fluctuations of the Doppler spectra, which are important for assessing the behavior of coherent detection schemes. The evolving Doppler spectrum model offers a way to model these variations and can be described with a moderate number of parameters. Relating these to the observed geometry, environment and radar characteristics are important to make the models accessible. Previous work has characterized X-band radar sea clutter over a range of sea conditions and azimuth and grazing angles. In this article, new models are presented that capture variations with both the radar resolved area and integration time. This enables the evolved Doppler spectra model to be used over a much wider range of applications.

High-frequency surface wave radar (HFSWR) can be effectively used to detect ships in the exclusive economic zone. However, the ship signal is concealed and interfered with various clutter and background noise in the Doppler spectrum. In this letter, a range-Doppler (RD) image-based novel ship detection algorithm is proposed by exploiting spatial-frequency information and a unique feature fusion based on the analysis of variance. The algorithm subsumes three successive stages: Stage I--the plausible region of interest is captured, Stage II--the features from different sources are fused into one generalized feature space, and Stage III--an extreme learning machine-based classifier is utilized to localize the ships. Experimental results on challenging HFSWR-RD datasets demonstrate that the proposed algorithm has a competitive performance over other ship detection algorithms.

The rotating phased array radar (RPAR) has the potential to improve the capabilities of the current U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) operational network and can be more affordable than other candidate phased array radar (PAR) architectures that have been evaluated to replace the WSR-88D. Considering the demanding functional requirements for the future U.S. weather surveillance radar, it is expected that several advanced RPAR scanning techniques will need to be applied simultaneously to achieve them. In this letter, we present the integration of two such RPAR scanning techniques: motion-compensated steering (MCS) and distributed beams (DBs). MCS exploits beam agility to mitigate beam smearing, while DB exploits digital beamforming to reduce the scan time or the standard deviation (SD) of estimates. The integration of these techniques is demonstrated with the National Severe Storms Laboratory's (NSSL) dual-polarization advanced technology demonstrator (ATD) radar system. Results show that these techniques can be used simultaneously to enhance azimuthal resolution and reduce the SD of estimates without impacting data quality if certain obtainable tradeoff considerations are incorporated in the radar design process.

With the development of the radar sensing technology, hand gesture sensing and recognition has attracted much attention. This letter adopts a frequency-modulated continuous wave (FMCW) radar to achieve short-range hand gesture sensing and recognition. Specifically, the range, Doppler, and angle parameters of hand gestures are measured by fast Fourier transformation (FFT) and multiple signal classification (MUSIC) algorithm, respectively. The mixup (MP) algorithm combined with augmentation (AU) algorithm using a weight factor is applied to expand the hand gesture data. Then, a complementary multidimensional feature fusion network-based hand gesture recognition (CMFF-HGR) is designed to extract the features and achieve HGR. Finally, a series of experiments are carried out to verify the effectiveness of the proposed approach, and the results show that the recognition accuracy is higher than the existing alternatives with low computational complexity.

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

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

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

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

The Doppler shift of microwave radar signal is determined by the relative motion between radar and ocean surface, including contributions from movement of the radar platform, wind-wave induced motion (phase velocity of the resonant Bragg waves and orbital motions of ocean waves that are longer than the Bragg waves) in addition to the ocean surface current. One of the main challenges in ocean surface current retrieval is the accurate estimation of the wind-wave induced Doppler shift, which needs to be accurately removed from the total Doppler shift to determine the surface current. In this study, we investigate the effects of different wave directional spectra on estimates of the wind-wave induced Doppler shift using a Modulation Transfer Function (MTF)-based and time-independent Doppler model. Our results show that the wind-wave induced Doppler shift estimates are highly dependent on the selection of wave spectra and the directional spreading function. This study also confirms the importance of ocean wave spectra parameterization on Doppler estimation of the ocean current. We find that Apel’s spectra model is a more consistent fit to the observations, but that the optimal spreading function is wind speed dependent.

To address the problem of Doppler ambiguity in low pulse repetition frequency (PRF) radar, we propose the use of frequency diverse array multiple-input multiple-output (FDA-MIMO) radar to detect high-speed moving targets. This method does not require the radar to transmit multiple-PRF pulses. The possible Doppler ambiguity is resolved by exploiting the multicarrier characteristics of FDA-MIMO radar. The effectiveness of the proposed method is verified by simulation results.

Traditional range-instantaneous Doppler (RID) methods for maneuvering target imaging suffer from the problems of low resolution and poor noise suppression. We propose a new super-resolution inverse synthetic aperture radar (ISAR) imaging method based on deep-learning-assisted time-frequency analysis (TFA). Our deep neural network resembles the basic structure of a U-net with two additional convolutional-upsampling layers and l₁-norm loss function for super-resolution generation and noise suppression. The neural network is trained in advance to learn the mapping function between the low-resolution time-frequency spectrum inputs and their high-resolution references. Then, the linear TFA assisted by the pretrained network is integrated into the RID-based ISAR imaging system and is found to achieve sharply focused and denoised target image with super-resolution. Both the simulated and real radar data are used to evaluate the performance of the proposed method. Numerical experimental results demonstrate the superiority of the proposed ISAR imaging method over traditional ones.

High speed, high maneuverability, and weak space-moving targets (SMTs) are major threats for space-borne radar (SBR) systems. First, the severe range migration (RM) and Doppler extension are induced by complex relative motion between the radar platform and non-cooperative moving targets, making moving target detection (MTD), and parameter estimation particularly difficult. Apart from this challenge, Doppler ambiguity and Doppler aliasing arise from the limited pulse repetition frequency (PRF) of a SBR system to ensure an adequate coverage rate, which may make the existing MTD algorithms deteriorate dramatically. To address these issues, we focus on the detection of high speed and high maneuverability targets based on the modified keystone transform matched filtering (MKTMF), whose Doppler frequency exceeds PRF as well as spans multiple PRFs. The proposed method is suitable for the weak MTD under a low signal-to-noise ratio (SNR) case since the nonlinear operation is not involved. Finally, some numerical results and real data results are provided to validate the superiority of the proposed method.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2020-11-27, 資料番号: PA2120067000

Using a microwave or millimetre wave radar system, this study aims to achieve highly accurate Doppler velocity estimation and radar imaging that would be suitable for various remote‐sensing sensors, such as self‐driving, surveillance, or security applications. In particular, micro‐Doppler signatures are one of the most promising approaches for human recognition; however, the traditional limitations of both velocity and temporal resolution need to be addressed. The weighted kernel density (WKD) algorithm using super‐resolution Doppler velocity estimation has been one solution. Using WKD, this study incorporates the range points migration (RPM) method of radar imaging to enhance accuracy in both Doppler velocity and radar imaging using an iterative data selection scheme. Furthermore, to obtain more informative Doppler‐associated RPM‐based images using less data by exploiting a unique feature of WKD and RPM methods, this study introduces the image integration approach along the slow‐time profile. In this framework, bi‐directional data processing between Doppler velocity and imaging analysis is achieved. At each pulse hit sequence, both numerical and experimental tests demonstrate that the proposed method yields a more accurate Doppler‐associated radar image compared with the methods in previous studies.

As a new imaging framework, the rotating synthetic aperture radar (ROSAR) derives a synthetic aperture through antenna rotation instead of traditional linear platform motion. The rotational synthetic aperture is sensitive to high-frequency vibrations aboard helicopters. The vibration causes severe phase error in signal echoes and imaging degradation. Unlike traditional synthetic aperture radar (SAR) imaging where range-Doppler algorithm (RDA) maybe applied for autofocus to improve the imaging performance, vibration phase errors cannot be estimated via conventional imaging algorithms due to severe range and azimuth couplings. A new ROSAR imaging procedure is proposed to compensate the phase error resulting from high-frequency vibrations of helicopters. A vibration model of ROSAR signal echo is established. The double Doppler keystone transform (DDKT) is adopted to correct the range cell migration (RCM) induced by slant range history and vibration errors. Analytical echo expression with range-independent vibration phase error is also derived in greater details. The focused image can then be obtained via classical autofocus algorithms. The effectiveness of the proposed technique is sufficiently demonstrated through simulation studies.

Radio frequency interference (RFI) has become a growing concern for weather radar, distorting radar variable estimation. By simultaneously or alternately transmitting the horizontal and vertical polarized waves, polarimetric weather radar can be referred to as SHV radar or AHV radar. The SHV radar can mimic the AHV radar by discarding either H- or V-channel measurements, which leads to an alternating scheme. In this research, the real RFI measurements from an operational C-band SHV radar are used to characterize the RFI temporal, spectral, and polarimetric features. Then, the RFI is simulated to quantify the performance of the object-orientated spectral polarimetric (OBSPol) filter in RFI mitigation. The OBSPol filter has been previously proposed by the authors to mitigate the narrowband clutter (both stationary and moving) and noise. This work extends the application of the filter to remove the RFI for SHV radar. Specifically, by taking advantage of the low copolar correlation of the RFI signal measured in AHV radar, the RFI mitigation method is designed, and its effectiveness is proven by qualitative and quantitative analyses. In particular, in the case of RFI overlapped to weather echoes in the time domain, the RFI can be mitigated, also when the duty cycle of the RFI is high. However, this work does not provide a full evaluation of the RFI mitigation performance on all radar data outputs but a proof of concept to show the effectiveness of the proposed filter for RFI mitigation.

This article defines Doppler steering laws for different bi- and multistatic acquisition geometries for synthetic aperture radar (SAR). Central swath line (CSL) and transmit swath line (TSL) acquisition geometries are introduced. Bistatic zero Doppler steering (BZDS) is defined. Multistatic acquisitions are classified into dedicated and supplementary types. Numerical approaches for the calculation of yaw, pitch, and roll angles are described providing zero Doppler, constant Doppler, and/or full Tx-Rx beam footprint overlap. The appendix provides an approximated analytical approach to calculate yaw and pitch angles to achieve a desired swath line.

As a new attempt to reject clutter in High Frequency Passive Bistatic Radar (HFPBR), the Cycle-Consistent Adversarial Network (CycleGAN)-based image domain clutter suppression algorithm works successfully by treating the clutter rejection problem as an image-to-image migration between the range-doppler spectrograms without and with clutter suppression. However, this algorithm also has some disadvantages such as too many calculation parameters, difficult model training and low clutter suppression rate. In this paper, a novel clutter suppression method based on an improved DenseNet CycleGAN (D-CycleGAN) is proposed. In this method, the ResNet block of the generator in the original CycleGAN network is replaced with the DenseNet block, to better extract clutter features. To better evaluate the clutter suppression performance of the deep learning network-based graph-relational method, a new index suitable for HF passive radar is introduced. Furthermore, the influence of the number of Dense blocks on the performance of the algorithm is also discussed. Finally, the performance of the proposed method is tested and verified on the measured data, compared with the traditional clutter suppression method and other popular image migration algorithms, such as Pix2pix. The results show that the D-CycleGAN-based method has better clutter suppression performance, stronger generalization ability, and better retained radar targets simultaneously.

In this paper, a dual-frequency continuous wave radar is proposed to achieve both localization and activity/ gesture recognition simultaneously. Specifically, features of different movements will be classified by the activity and gesture recognition network (AGRNet) which is a lightweight network based on MobileNet. The data that are recognized corresponding to walking will be used for moving target localization by comparing the phase difference in the Doppler domain between dual frequencies. In addition, a segmentation method is proposed to effectively segment continuous signals into individual time-periods corresponding to different motions by detecting the boundaries of signal changing. The experimental results show that the proposed method accomplishes the classification accuracy over 91% with 8 motion classes with a localization accuracy in the centimeter level.

In multitarget scenarios, the masking of small targets by large targets nearby may severely deteriorate radar detectability due to the range and Doppler sidelobes. In this article, a 2-D joint iterative adaptive filtering (2-D JIAF) method is proposed, by adopting the reiterative minimum mean square error (RMMSE) criterion to the outputs of a 2-D matched filter. The main advantages of the proposed method over the state-of-the-art (SOTA) methods, including modified adaptive multipulse compression and iterative adaptive approach, are twofold: 1) it is able to suppress the sidelobes in both range and Doppler dimensions and thus obtain an improved range-Doppler image; 2) the computational complexity is significantly reduced by adopting a small processing window in both range and Doppler dimensions. The derivation of 2-D JIAF is detailed and an efficient two-stage implementation is outlined. The performance of 2-D JIAF is validated with simulation results over a wide range of scenarios and compared with two SOTA approaches. The impacts of different parameters, including the number of iterations and the choice of the size of the processing window, are also extensively studied with Monte Carlo trials.

The sub-Doppler absorption and fluorescence spectra at the frequencies of D1- and D2-line transitions in Cs atomic vapor, enclosed in a nanocell (NC) with a thickness L ~ 400 nm, have been investigated experimentally at a near-room temperature. It is shown that a decrease in the NC temperature to 35°C causes narrowing of the absorption lines to values smaller than the Doppler width by a factor of 6, due to which the strong spectral broadening induced by laser field can be measured. The high intensity threshold for optical pumping (which limits the fluorescence intensity in centimeter-length cells) made it possible to record fluorescence spectra (using laser power of several milliwatts) at a temperature of 20°C; thus, a frequency marker of transitions in the range of D1,2 lines can be developed based on a room-temperature NC. A comparison with a widely used marker based on saturated absorption is performed.

A multiple-input multiple-output (MIMO) radarwith widely separated antennas on moving platforms suffers from the effects of platform deviations on the target parameter estimation since the trajectories of the moving platforms are sensitive to environmental factors, such as strong wind. This article addresses the joint estimation of multiple target positions and velocities as well as radar system deviations to minimize the impact of platform deviations. The proposed algorithm can also be regarded as a self-calibration technique. First, the grid search dimensions for parameter estimation are reduced via a generalized maximum likelihood (GML) algorithm. Second, the adaptive gradient (AdaGrad) method is used to implement the GML estimation for multitarget echo delays and Doppler shifts. Finally, to address the nonlinear estimation problem of interest, the iterative least squares method is used to estimate the multiple target positions and velocities as well as radar system deviations based on the estimated delays and Doppler shifts. Prior information, such as target positions or the probability distribution of the echo coefficient, is not needed in the proposed method. The parameter identifiability is also derived in this article. Numerical simulations show that compared to a method without the estimation of system deviations, the proposed method is more efficient with respect to the derived Cramer–Rao bound.

Consensus criteria for tremor classification in Parkinson’s disease (PD) patients are clinically important for automatically evaluating the PD rating scale. Wearable sensing tools with direct contact measurements can obtain physiological signals to monitor tremor symptoms. Then, machine learning algorithms (MLAs) can train the frequency-based parameters and motion features to accurately measure PD-related tremors. Noncontact measurement with customized computer information devices can also digitalize the digitized handwritten patterns with the bespoke movements that a hand makes for identifying tremor classes. However, wearable sensors need a set of multiple electrodes to be placed on a patient’s body to acquire biosignals, and the setup does not allow continuous measurement and limits the patient’s motion range. The handwritten patterns of noncontact-based methods need frequency-domain and linearization transformations. In addition, feature extraction methods and MLAs are limited in complex computations and adaptive applications. Hence, in this work, a noncontact measurement with an array X-band microwave (10 GHz) Doppler-based linear quantizer is designed to continuously measure upper limb movements for tremor class scaling. To overcome the complex computations, time-domain parametric features, including zero crossing (ZC), Willison amplitude (WAMP), and waveform length (WL) indexes, are used to extract the physical changes in the oscillation frequencies, amplitudes, and directions of tremor signals for scaling upper limb tremor (ULT) levels. In the experiments involving 10 subjects, the proposed noncontact bioradar sensor could quantify asymmetrical and irregular oscillations with a positive correlation (mean R2 > 0.85) between the three indexes (ZC, WAMP, WL) and various oscillation frequencies. The linear relationship quantizer could predict the ULT levels from 0 Hz to 8 Hz for PD patients (typical tremor frequency: 4–6 Hz). It could also map the three indexes into colored visual representation for computerized visual analysis.

The processing of synthetic aperture radar (SAR) echoes collected during radar antenna beam steering, such as in the staring spotlight, sliding spotlight, and TOPS operating modes, requires additional efforts due to the limited pulse repetition frequency (PRF). Subaperture processing is mostly used in these cases, but the complexity arising from subaperture dividing and recombination is preferably avoided. The two-step approach (TSA) is an elegant solution to full-aperture processing. Nevertheless, for high-resolution airborne and spaceborne SAR processing, the TSA is still faced with a number of difficulties, which does not occur, however, in subaperture processing. For instance, the high range bandwidth induces Doppler spectrum aliasing during the spectral analysis (SPECAN) stage of the original TSA. Moreover, the Doppler spectrum obtained in TSA, when inverse Fourier transformed back to the slow time domain, might also suffer from aliasing, which precludes the estimation and correction of the unknown residual motion error or tropospheric disturbance. In this article, we extend the TSA to address these problems and present a full-aperture processing framework to precisely focus the high-resolution airborne and spaceborne SAR data. Simulation of X-band spaceborne SAR echoes with transmission signal bandwidth of 3.6 GHz and coherent integration angle of 12.5° has validated the extended TSA (ETSA). Meanwhile, experimental X-band airborne SAR data with the same range and azimuth resolution are also processed using the ETSA, where the conventional autofocusing techniques have been smoothly incorporated.

In this article, we review and compare different methods for the localization of an illuminator of opportunity for passive radar systems employing cooperative targets. For assessment and comparison, we use numerical simulations and real-world radar measurements. To achieve reliable operation under real-world conditions, a correct and robust association of the bistatic radar target observations with ground truth information is necessary. We introduce a novel solution to this issue with an efficient preprocessing algorithm based on a planar approximation.

Most of the investigations on the micro-Doppler (MD) effect caused by a small unmanned aerial vehicle (UAV) have been conducted using joint time–frequency (JTF) images rather than the Doppler spectrum. On the other hand, several researchers utilized the Doppler spectrum instead of JTF images to observe the MD signature of a small UAV, and found the relationship between the spectral distribution of a small UAV and its physical specifications. However, the studies using the Doppler spectrum still lack concrete and theoretical foundations of the MD effects of a small UAV, focusing mainly on phenomena identified by measurement data. In this article, we establish the theoretical foundation connecting the MD signatures and motion dynamics of small UAVs based on the Doppler spectrum, and analyze their spectral distribution using simulations and measured data. In addition, experimental analysis is conducted using the data measured from various types of small UAVs considering the translational motion and aspect change. In contrast to already existing investigations, we completely explain and predict the changes on the Doppler spectrum relative to the physical specifications of a small UAV (e.g., blade length and rotor rotation rate). In particular, we show that the Doppler spectrum, compared to the JTF images, is a considerably simple and useful tool for analyzing the MD effects of small flying UAVs. The analysis results reveal that the MD features obtained from the measured echoes of small UAVs have considerable potential for detection and classification of small UAVs.

This paper is focused on the detection and resolution of Unmanned Aerial Vehicles (UAVs) via a multi-channel phased-array radar with emphasis on the challenging situation where multiple targets are unresolved in the range-Doppler domain and closely spaced in angle. An adaptive processor is devised which, after triggering a detection in a given range-Doppler cell, implements a binary decision about the presence of a single or multiple targets. In the former situation, the plain monopulse algorithm is used to estimate the target azimuth whereas, in the latter case, a super-resolution technique is activated to get the angular parameters of the UAVs. The performance of the signal processor is assessed both on simulated and on measured data (endowed by ground-truth) collected by a 12-channel phased-array radar operating in the C-band. The results highlight the capabilities of the architecture to detect the presence of targets, to discriminate the single from the multiple targets scenario, and to resolve the UAVs in angle domain when they are unresolved in range and Doppler.

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

In order to achieve remote voice acquisition, a 9.75-GHz microwave Doppler radar with a high-sensitivity coherent homodyne demodulator is used to detect voice signals. However, the combined noise sources, including background noise and shock noise, are included in the Doppler audio radar-captured voice, which seriously damages the quality of speech. In the current work, a multi-stage enhancement method is used to enhance Doppler audio radar-captured voice. The first stage is dictionary learning, which uses the K-singular value decomposition algorithm to train speech and noise dictionaries. The noise training data come from the Doppler audio radar-captured shock noise signals under the influence of different relative motion modulations. The second stage generates a position mask of the shock noise. The third stage is the removal of background noise and shock noise. The removal of background noise adopts our proposed constrained low-rank sparse matrix decomposition algorithm based on power spectral density. The final speech spectrum with shock noise removed is obtained through sparse restoration using the LARC algorithm. The experimental results show that the 9.75-GHz Doppler audio radar can effectively acquire human voice signals, and the proposed multi-stage enhancement method can effectively remove the background noise and shock noise in the radar speech, thereby improving the quality of the audio radar voice. The obtained results imply the potential to implement practical Doppler audio radar systems for human voice signal acquisition.

The paper presents the results of experimental studies of aperiodic and quasi-periodic disturbances in the ionosphere that were observed under the influence of powerful radiation from the SURA facility on the ionospheric plasma in quiet and magnetically disturbed conditions. A vertical Doppler sounding radar located at a distance of ~960 km from the heating facility was used for diagnostics. The recorded aperiodic disturbances with a delay of ~25 min are most likely associated with the generation of a shock-wave disturbance related to atmospheric gravity waves. Quasiperiodic disturbances of 12–15 min had a period equal to the period of the heating cycle. In other cases, the period of disturbances was equal to the period of natural oscillations of the air in the atmosphere. The speed of quasi-periodic disturbances was 300–400 m/s. The effects of the artificial generation of plasma turbulence, the joint action of solar terminator and powerful radiation, and the resonance effects associated with the impact of radio waves with a frequency equal to the fourth harmonic of the electron gyrofrequency on the ionosphere are found. The role of magnetic activity in the Earth–atmosphere–ionosphere–magnetosphere system, which determined the nature of disturbances in the ionosphere, is noted.

A new compact static wind imaging interferometer, called the Birefringent Doppler Wind Imaging Interferometer (BIDWIN), has been developed for the purpose of observing upper atmospheric winds using suitably isolated airglow emissions. The instrument combines a field-widened birefringent delay plate placed between two crossed Wollaston prisms with an imaging system, waveplates and polarizers to produce four fixed 90∘ phase-stepped images of the interference fringes conjugate to the scene of interest. A four-point algorithm is used to extract line-of-sight Doppler wind measurements across the image of the scene. The arrangement provides a similar throughput to that of a field-widened Michelson interferometer; however, the interferometric component of BIDWIN is smaller, simpler to assemble and less complicated to operate. Consequently, the instrument provides a compact, lightweight and robust alternative that can be constructed and operated with lower cost. In this paper, the instrument concept is presented, and the design and optimization of a prototype version of the instrument are discussed. Characterization of the lab prototype is presented, and the performance of the instrument is examined by applying the instrument to measure a low-velocity two-dimensional Doppler wind field with a high precision (5 m s−1) in the lab.

Recent progress in radar development enable high-resolution radar sensors to resolve multiple detections for an extended object. One of the main challenges in radar-based object detection and tracking is the handling of an uncertain measurement model problem dealing with wandering dominant scatter points on the extended object surface depending on the relative orientation and motion. This phenomenon needs to be considered for near extended object tracking in critical driving situations, e.g., for safety applications, which require fast and accurate target state estimation in high dynamic scenarios. This paper presents a method to detect rotating wheels of approaching targets. Using the position and velocity of detected wheels as input for a tracking procedure, instead of target detections, relaxes the uncertain measurement model problem. A generic radar signal processing method to detect the position and bulk velocities of rotating wheels for approaching target vehicles is presented. The method evaluates the micro-Doppler signals, generated by rotating wheels, of close targets. These wheel hypotheses serve as input for a subsequent sequential Monte Carlo tracking framework. The presented methods are evaluated on real experimental data where a target vehicle is used to reconstruct safety-critical dynamic evading maneuvers.

Purpose: The ionospheric channel is widely used for the communication, radio navigation, radar, direction finding, radio astronomy, and remote radio probing systems. The radio channel parameters are characterized by nonstationarity due to the dynamic processes in the ionosphere, and therefore their study is one of the topical problems of space radio physics and earth-space radio physics of geospace. This work aims at presenting the results of synthesis of temporal variations in the Doppler spectra obtained by the Doppler probing of the ionosphere at vertical and quasi-vertical incidence. Design/methotology/approach: One of the most effective methods of ionosphere research is the Doppler sounding technique. It has a high time resolution (about 10 s), a Doppler shift resolution (0.01–0.1 Hz), and the accuracy of Doppler shift measurements (~0.01 Hz) that permits monitoring the variations in the ionospheric electron density (10–4–10–3) or the study of the ionospheric plasma motion with the speed of 0.1-1 m/s and greater. The solution of the inverse radio physical problem, consisting in determination of the ionosphere parameters, often means solving the direct radio physical problem. In the Doppler sounding technique, it belongs with the construction of variations in Doppler spectra and comparing them with the Doppler spectra measurements. Findings: For the radio wave ordinary component, three echoes being produced by three rays are observed. Influence of the geomagnetic fi eld and large horizontal gradients in the electron density of δ≥10 % give rise to complex ray structures with caustic surfaces. The ionospheric disturbances traveling along the magnetic meridian form the skip zones. The longitudinal and transverse displacement of the ray reflection point attains a few tens of kilometers along the vil. Haidary to vil. Hrakove quasi-vertical radiowave propagation path, for which the great circle range is 50 km. For the vertical incidence, the signal azimuth at the receiver coincides with the traveling ionospheric disturbance azimuth. The synthesis of temporal variations in the HF Doppler spectra has been made and compared with the temporal variations in the Doppler spectra recorded with the V. N. Karazin Kharkiv National University radar. The estimate of δ=15 % obtained confirms the existence of large horizontal gradients in electron density. Conclusions: Temporal variations in Doppler spectra and in azimuth have been calculated for the vertical and quasi-vertical incidence with allowance for large horizontal gradients of the electron density caused by traveling ionospheric disturbances. Key words: ionosphere, Doppler sounding at oblique incidence, synthesis of temporal variations in HF Doppler spectra, traveling ionospheric disturbances, electron density

The problem of the radar search-detection-classification of objects on the ground moving with random changes of the direction of the velocity vector is considered. A method for controlling the orientation of the radar beam, which maximizes the monitoring bandwidth on the Earth‘s surface, while providing the maximum possible resolution on it and the absence of “dead zones” to the left and right of the direction of movement of the objects, usually arising from the Doppler selection of the moving objects, is proposed. A computer model is created that allows us to study the dependence of the probabilities of detecting and classifying objects on the speed of their movement under various scenarios of monitoring by a massive numerical experiment. The advantages of the proposed method in comparison with traditional ones are proved by the modeling methods.

Through-wall human sensing is of high interest for both military and nonmilitary applications, including counter-terrorism and law enforcement purposes, particularly in the modern, highly urbanized environment. Conventional active radar sensors possess good performance, however, at the expense of high cost and complexity of the transmitter/receiver design. This article demonstrates the effectiveness of a passive through-wall human sensing technique using opportunistic WiFi signals. With a simple software-defined radio receiver and advanced signal processing techniques, the position of indoor WiFi access point can be accurately localized, and its signal can be exploited to clearly detect the Doppler/micro-Doppler of human motions inside the room. Compared to the results in the current literature, this article demonstrates better detection sensitivity and capability to sense not only major motions such as walking and waving hand, but also very small human motions such as keyboard typing and breathing. In addition, an iterative adaptive approach is applied to effectively improve the Doppler resolution of WiFi passive radar. These enhanced sensitivities greatly expand the potential of WiFi passive radar for through-wall sensing applications.

Winds measured by lidar from the Aeolus satellite are compared with winds measured by two ground-based radars – MARA in Antarctica (70.77∘ S, 11.73∘ E) and ESRAD (67.88∘ N, 21.10∘ E) in Arctic Sweden – for the period 1 July–31 December 2019. Aeolus is a demonstrator mission to test whether winds measured by Doppler lidar from space can have sufficient accuracy to contribute to improved weather forecasting. A comprehensive programme of calibration and validation has been undertaken following the satellite launch in 2018, but, so far, direct comparison with independent measurements from the Arctic or Antarctic regions have not been made. The comparison covers heights from the low troposphere to just above the tropopause. Results for each radar site are presented separately for Rayleigh (clear) winds, Mie (cloudy) winds, sunlit (“summer”) and non-sunlit (“winter”) seasons, and ascending and descending satellite tracks. Horizontally projected line-of-sight (HLOS) winds from Aeolus, reprocessed using baseline 2B10, for passes within 100 km of the radar sites, are compared with HLOS winds calculated from 1 h averaged radar horizontal wind components. The agreement in most data subsets is very good, with no evidence of significant biases ( m s−1). Possible biases are identified for two subsets (about −2 m s−1 for the Rayleigh winds for the descending passes at MARA and about 2 m s−1 for the Mie winds for the ascending passes at ESRAD, both in winter), but these are only marginally significant. A robust significant bias of about 7 m s−1 is found for the Mie winds for the ascending tracks at MARA in summer. There is also some evidence for increased random error (by about 1 m s−1) for the Aeolus Mie winds at MARA in summer compared to winter. This might be related to the presence of sunlight scatter over the whole of Antarctica as Aeolus transits across it during summer.

Fast chirp FMCW MIMO radar with inter-chirp coding provides high emission power by utilizing simultaneous antennas transmissions. However, the coding produces high side-lobes in the Doppler and angle dimensions of the radar's ambiguity function. The high side-lobes may cause miss-detection due to masking between targets that are at similar range and have large received power difference, as is often the case in automotive scenarios. In this paper, we develop a novel code optimization method that attenuates the side-lobes of the radar's ambiguity function and hence improves the probability of detection in the case of multiple targets. We formulate an analytical expression for the ambiguity function side-lobes, which takes into account that the targets range difference migrates during the detection interval, and that the migration is proportional to the targets Doppler frequency difference. We then develop an optimization algorithm that finds the code that minimizes the ambiguity function side-lobes average energy. The optimized code attenuates the ambiguity function side-lobes at the Doppler frequencies that are near the main lobe frequency. These frequencies correspond to targets with small speed difference, for which the range difference between the targets does not change significantly during the detection interval, and hence overlapping in range can cause severe interference. We demonstrate by simulation that the optimized code achieves an improvement in the ambiguity function side-lobes attenuation and an improvement in the probability of detection compared to reference codes.

A single-input and single-output (SISO) frequency-modulated continuous wave (FMCW) radar architecture is proposed and in vivo demonstrated for remote 2-D localization (range and angular information) and vital signs monitoring of multiple subjects. The radar sensor integrates two frequency scanning antennas which allow angular separation and enable determining a 2-D map (range versus angle) of a room environment from which people can be distinguished from objects and clutter. After technical tests to validate the functionality of the proposed architecture and data processing algorithm, a practical setup was successfully demonstrated to locate human volunteers, at different absolute distances and orientations, and to retrieve their respiratory and heart rate information. Experimental results show that this radar sensor can monitor accurately the vital signs of multiple subjects within typical room settings, reporting maximum mean absolute errors of 0.747 breaths-per-minute and 2.645 beats-per-minute, respectively, for respiration rate and heartbeat. Practical applications arise for Internet of Things (IoT), ambient-assisted living, healthcare, geriatric and quarantine medicine, automotive, rescue and security purposes.