Radar Returns Research Articles

EMD-Based Intermittent and Ground Clutter Removal in Radar Windprofilers

An Empirical Mode Decomposition (EMD) based intermittent and ground clutter removal algorithm for Radar Windprofilers is proposed. Intrinsic Mode Functions (IMFs) affected with intermittent clutter are identified and are separated into Amplitude Modulated (AM) and Frequency Modulated (FM) parts. The FM part is multiplied with the envelope of the non-amplitude modulated signal derived from the original amplitude modulated IMF to nullify the effect of intermittent clutter. The IMFs corresponding to ground clutter are identified from the Maximum Entropy Spectral Analysis (MESA) and are rejected prior to signal reconstruction in ground clutter removal. The proposed algorithm is applied on the radar returns of 205 MHz Windprofiler at Advanced Centre for Atmospheric Radar Research (ACARR) of Cochin University of Science and Technology(CUSAT), Cochin, Kerala, India (10.04°N, 76.33°E). The windprofiler signals corrupted with flight and helicopter echoes are retrieved and the ground clutter signatures, which were not removed with existing ground clutter removal methods are removed with the proposed algorithm.

Analysis of a Dynamic Calibration Target for Through-Wall and Through-Rubble Motion Sensing Doppler Radar

Through-wall and through-barrier motion-sensing systems are becoming increasingly important tools to locate humans concealed behind barriers and under rubble. The sensing performance of these systems is best determined with appropriately designed calibration targets, which are ones that can emulate human motion. The effectiveness of various dynamic calibration targets that emulate human respiration, heart rate, and other body motions were analyzed. Moreover, these targets should be amenable to field deployment and not manifest angular or orientation dependences. The three targets examined in this thesis possess spherical polyhedral geometries. Spherical geometries were selected due to their isotropic radar cross-sectional characteristics, which provide for consistent radar returns independent of the orientation of the radar transceiver relative to the test target. The aspect-independence of a sphere allows for more accurate and repeatable calibration of a radar than using a nonspherical calibration artifact. In addition, the radar cross section (RCS) for scattering spheres is well known and can be calculated using far-field approximations. For Doppler radar testing, it is desired to apply these calibration advantages to a dynamically expanding-and-contracting sphere-like device that can emulate motions of the human body. Monostatic RCS simulations at 3.6 GHz were documented for each geometry. The results provide a visual way of representing the effectiveness of each design as a dynamic calibration target for human detection purposes.

An Urban Flooding Index for Unsupervised Inundated Urban Area Detection Using Sentinel-1 Polarimetric SAR Images

Urban flooding causes a variation in radar return from urban areas. However, such variation has not been thoroughly examined for different polarizations because of the lack of polarimetric SAR (PolSAR) images and ground truth data simultaneously collected over flooded urban areas. This condition hinders not only the understanding of the effect mechanism of urban flooding under different polarizations but also the development of advanced methods that could improve the accuracy of inundated urban area detection. Using Sentinel-1 PolSAR and Jilin-1 high-resolution optical images acquired on the same day over flooded urban areas in Golestan, Iran, this study investigated the characteristics and mechanisms of the radar return changes induced by urban flooding under different polarizations and proposed a new method for unsupervised inundated urban area detection. This study found that urban flooding caused a backscattering coefficient increase (BCI) and interferometric coherence decrease (ICD) in VV and VH polarizations. Furthermore, VV polarization was more sensitive to the BCI and ICD than VH polarization. In light of these findings, the ratio between the BCI and ICD was defined as an urban flooding index (UFI), and the UFI in VV polarization was used for the unsupervised detection of flooded urban areas. The overall accuracy, detection accuracy, and false alarm rate attained by the UFI-based method were 96.93%, 91.09%, and 0.95%, respectively. Compared with the conventional unsupervised method based on the ICD and that based on the fusion of backscattering coefficients and interferometric coherences (FBI), the UFI-based method achieved higher overall accuracy. The performance of VV was evaluated and compared to that of VH in the flooded urban area detection using the UFI-, ICD-, and FBI-based methods, respectively. VV polarization produced higher overall accuracy than VH polarization in all the methods, especially in the UFI-based method. By using VV instead of VH polarization, the UFI-based method improved the detection accuracy by 38.16%. These results indicated that the UFI-based method improved flooded urban area detection by synergizing the BCI and ICD in VV polarization.

Fast-time and slow-time processing of the pseudorandom amplitude-phase-shift keyed signals

The two-dimensional raw data structure is used for modern pulse-Doppler radars. Fast-time and slow-time processing of radar return signals is performed. The matched filter compresses each received pulse in fast time. The FFT-based spectral processing of the compressed pulses is then performed in slow time. The two-dimensional structure of raw data has specific features in radars with the transmission and reception of pseudorandom amplitude-phase-shift keyed (APSK) signals to a common aerial. It is formed when the coherent processing interval of the APSK signal is divided into subintervals. The article describes the fast-time and slow-time processing of the APSK signal subintervals. The structure of the signal in the subintervals is also analyzed. The choice of the subinterval duration is discussed. The possible energy losses during the processing of the reflected signals are estimated. The results of the processing modeling of the additive sum of APSK signals with different Doppler frequencies are presented.

Challenges in Observation-Based Mapping of Daily Precipitation across the Conterminous United States

AbstractThere is a great need for gridded daily precipitation datasets to support a wide variety of disciplines in science and industry. Production of such datasets faces many challenges, from station data ingest to gridded dataset distribution. The quality of the dataset is directly related to its information content, and each step in the production process provides an opportunity to maximize that content. The first opportunity is maximizing station density from a variety of sources and assuring high quality through intensive screening, including manual review. To accommodate varying data latency times, the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) Climate Group releases eight versions of a day’s precipitation grid, from 24 h after day’s end to 6 months of elapsed time. The second opportunity is to distribute the station data to a grid using methods that add information and minimize the smoothing effect of interpolation. We use two competing methods, one that utilizes the information in long-term precipitation climatologies, and the other using weather radar return patterns. Last, maintaining consistency among different time scales (monthly vs daily) affords the opportunity to exploit information available at each scale. Maintaining temporal consistency over longer time scales is at cross purposes with maximizing information content. We therefore produce two datasets, one that maximizes data sources and a second that includes only networks with long-term stations and no radar (a short-term data source). Further work is under way to improve station metadata, refine interpolation methods by producing climatologies targeted to specific storm conditions, and employ higher-resolution radar products.

Alternatives to Liquid Water Beneath the South Polar Ice Cap of Mars

AbstractRadar sounding of Mars’ south polar ice cap has revealed portions of its base with stronger radar returns than its surface. These have been used to estimate high real permittivity values consistent with liquid water. However, the thermal, chemical, and geological conditions required to sustain such water bodies are challenging to achieve in the contemporary Martian environment. Three recent papers address this conflict. Khuller and Plaut (2021, https://doi.org/10.1029/2021GL093631) find similar radar signatures across the ice cap, expanding the range conditions associated with water candidates. Bierson et al. (2021, https://doi.org/10.1029/2021GL093880) show that, for non‐zero conductivity, a wider range of materials could produce the observed echoes. Similarly, Smith et al., (2021, https://doi.org/10.1029/2021GL093618) use modeling, laboratory tests, and remote sensing data to propose smectite deposits as their specific source. While not incompatible with bodies of liquid brines, these papers provide potential alternative sources for the bright radar observations.

Precise inland surface altimetry (PISA) with nadir specular echoes from Sentinel-3: Algorithm and performance assessment

In the recent years satellite radar altimetry has evolved from pulse-limited low resolution mode (LRM) to a synthetic aperture radar (SAR) high resolution mode. The SAR mode focusses and coherently sums all radar return echoes within the 2-s time window the target surface area is in the antenna beam. In principle the SAR processing improves along-track resolution. Land contamination has been a major concern for inland waters altimetry and SAR can reduce land interference. This paper shows that the physics of specular echoes from smooth inland waters leads to a very different approach which we call precise inland surface altimetry (PISA). PISA uses only echoes within the specular “flash” period, which is approximately the time the satellite nadir crosses over the water body. The processing is four orders of magnitude less than SAR. Land interference is negligible because specular water echoes are usually >50 dB greater than land. Sentinel-3 SRAL dataset on the salar de Uyuni (Bolivia) is used to evaluate PISA ranging precision. During inundation (wet months), echoes are at the theoretical maximum radar cross section (RCS), σ = 129 dBsm, and ranging precision is ~1 mm. In dry months the echoes are quasi-specular, with σ = 70–100 dBsm, and ranging precision is ~1 cm. The precision assessment is made with variate-differences, with pass-to-pass repeatability, and by comparison with GPS measurements. In addition to the salar de Uyuni analysis we gathered σ statistics on five millions Sentinel-3 SRAL Ku-band altimeter bursts (one burst = 64 contiguous echoes) from 52 passes of Sentinel-3A track 167 over South America. We illustrate specular and quasi-specular waters on lakes, a river, and a fjord. Ranging precision is similar to Uyuni, in the 1 mm-1 cm range. Water surface slopes of 1–3 cm/km are detected. We propose a simple rule-set to distinguish specular waveforms (σ >100 dBsm, sidelobes (with Hamming window) of −37 dB or lower) and quasi-specular (σ >70 dBsm, sidelobes lower than −20 dB), and non-specular (sidelobes> − 20 dB). PISA is appropriate to specular and quasi-specular echoes.

The Impact of Reflectivity Gradients on the Performance of Range-Oversampling Processing

AbstractRange-oversampling processing is a technique that can be used to lower the variance of radar-variable estimates, reduce radar update times, or a mixture of both. There are two main assumptions for using range-oversampling processing: accurate knowledge of the range correlation and uniform reflectivity in the radar resolution volume. The first assumption has been addressed in previous research; this work focuses on the uniform reflectivity assumption. Earlier research shows that significant reflectivity gradients can occur in storms; we utilized those results to develop realistic simulations of radar returns that include effects of reflectivity gradients in range. An important consideration when using range-oversampling processing is the resulting change in the range weighting function. The range weighting function can change for different types of range-oversampling processing and some techniques, such as adaptive pseudowhitening, can lead to different range weighting functions at each range gate. To quantify the possible effects of differing range weighting functions in the presence of reflectivity gradients, we developed simulations to examine varying types of range-oversampling processing with two receiver filters: a matched receiver filter and a wider-bandwidth receiver filter (as recommended for use with range oversampling). Simulation results show that differences in range weighting functions are the only contributor to differences in radar reflectivity measurements. Results from real weather data demonstrate that the reflectivity gradients that occur in typical severe storms do not cause significant changes in reflectivity measurements, and the benefits from range-oversampling processing outweigh the possible isolated effects from large reflectivity gradients.

Strong Modulation of Short Wind Waves and Ka-Band Radar Return Due to Internal Waves in the Presence of Surface Films. Theory and Experiment

Strong variability of Ka-band radar backscattering from short wind waves on the surface of water covered with surfactant films in the presence of internal waves (IW) was studied in wave tank experiments. It has been demonstrated that modulation of Ka-band radar return due to IW strongly depends on the relationship between the phase velocity of IW and the velocity of drifting surfactant films. An effect of the strong increase in surfactant concentration was revealed in convergent zones, associated with IW orbital velocities in the presence of a “resonance” surface steady current, the velocity of which was close to the IW phase velocity. A phenomenological model of suppression and modulations in the spectrum of small-scale wind waves due to films and IW was elaborated. It has been shown that backscatter modulation could not be explained by the modulation of free (linear) millimeter-scale Bragg waves, but was associated with the modulation of bound (parasitic) capillary ripples generated by longer, cm–dm-scale waves—a “cascade” modulation mechanism. Theoretical analysis based on the developed model was found to be consistent with experiments. Field observations which qualitatively illustrated the effect of strong modulation of Ka-band radar backscatter due to IW in the presence of resonance drift of surfactant films are presented.

Year-long, broad-band, microwave backscatter observations of an alpine meadow over the Tibetan Plateau with a ground-based scatterometer

Abstract. A ground-based scatterometer was installed on an alpine meadow over the Tibetan Plateau to study the soil moisture and temperature dynamics of the top soil layer and air–soil interface during the period August 2017–August 2018. The deployed system measured the amplitude and phase of the ground surface radar return at hourly and half-hourly intervals over 1–10 GHz in the four linear polarization combinations (vv, hh, hv, vh). In this paper we describe the developed scatterometer system, gathered datasets, retrieval method for the backscattering coefficient (σ0), and results of σ0. The system was installed on a 5 m high tower and designed using only commercially available components: a vector network analyser (VNA), four coaxial cables, and two dual-polarization broad-band gain horn antennas at a fixed position and orientation. We provide a detailed description on how to retrieve the backscattering coefficients for all four linear polarization combinations σpq0, where p is the received and q the transmitted polarization (v or h), for this specific scatterometer design. To account for the particular effects caused by wide antenna radiation patterns (G) at lower frequencies, σ0 was calculated using the narrow-beam approximation combined with a mapping of the function G2/R4 over the ground surface. (R is the distance between antennas and the infinitesimal patches of ground surface.) This approach allowed for a proper derivation of footprint positions and areas, as well as incidence angle ranges. The frequency averaging technique was used to reduce the effects of fading on the σpq0 uncertainty. Absolute calibration of the scatterometer was achieved with measurements of a rectangular metal plate and rotated dihedral metal reflectors as reference targets. In the retrieved time series of σpq0 for L-band (1.5–1.75 GHz), S-band (2.5–3.0 GHz), C-band (4.5–5.0 GHz), and X-band (9.0–10.0 GHz), we observed characteristic changes or features that can be attributed to seasonal or diurnal changes in the soil: for example a fully frozen top soil, diurnal freeze–thaw changes in the top soil, emerging vegetation in spring, and drying of soil. Our preliminary analysis of the collected σpq0 time-series dataset demonstrates that it contains valuable information on water and energy exchange directly below the air–soil interface – information which is difficult to quantify, at that particular position, with in situ measurement techniques alone. Availability of backscattering data for multiple frequency bands (raw radar return and retrieved σpq0) allows for studying scattering effects at different depths within the soil and vegetation canopy during the spring and summer periods. Hence further investigation of this scatterometer dataset provides an opportunity to gain new insights in hydrometeorological processes, such as freezing and thawing, and how these can be monitored with multi-frequency scatterometer observations. The dataset is available via https://doi.org/10.17026/dans-zfb-qegy (Hofste et al., 2021). Software code for processing the data and retrieving σpq0 via the method presented in this paper can be found under https://doi.org/10.17026/dans-xyf-fmkk (Hofste, 2021).

Insights into wind turbine reflectivity and radar cross-section (RCS) and their variability using X-band weather radar observations

Abstract. The increasing need of renewable energy fosters the expansion of wind turbine sites for power production throughout Europe with manifold effects, both on the positive and negative side. The latter concerns, among others, radar observations in the proximity of wind turbine (WT) sites. With the aim of better understanding the effects of large, moving scatterers like wind turbines on radar returns, MeteoSwiss performed two dedicated measurement campaigns with a mobile X-band Doppler polarimetric weather radar (METEOR 50DX) in the northeastern part of Switzerland in March 2019 and March 2020. Based on the usage of an X-band radar system, the performed campaigns are up to now unique. The main goal was to quantify the effects of wind turbines on the observed radar moments, to retrieve the radar cross-section (RCS) of the turbines themselves and to investigate the conditions leading to the occurrence of the largest RCS. Dedicated scan strategies, consisting of PPI (plan position indicator), RHI (range–height indicator) and fixed-pointing modes, were defined and used for observing a wind park consisting of three large wind turbines. During both campaigns, measurements were taken in 24/7 operation. The highest measured maxima of horizontal reflectivity (ZH) and RCS reached 78.5 dBZ and 44.1 dBsm, respectively. A wind turbine orientation (yawing) stratified statistical analysis shows no clear correlation with the received maximum returns. However, the median values and 99th percentiles of ZH show different enhancements for specific relative orientations. Some of them remain still for Doppler-filtered data, supporting the importance of the moving parts of the wind turbine for the radar returns. Further, we show, based on investigating correlations and an OLS (ordinary least square) model analysis, that the fast-changing rotor blade angle (pitch) is a key parameter, which strongly contributes to the variability in the observed returns.

Survey of Transverse Range Fire Scars in 10Years of UAVSAR Polarimetry.

Because cross‐polarized radar returns are highly associated with volume scatter, radar polarimetry returns tend to show strong evidence of wildfire scars and recovery in forest and chaparral. We focus on the polarimetry images from UAVSAR (PolSAR) line SanAnd_08525, which covers a roughly 20 km wide swath over the Transverse Range including parts of the Santa Monica, San Gabriel and San Bernardino Mountains. We select images from four acquisition dates from October 2009 to September 2020, very roughly 4 years apart. These are compared to fire perimeters from the national Geospatial Multi‐Agency Coordination and NIFC databases for years 2003–2020, which shows the areas affected by the major fires (west to east) Springs2013, Woolsey2018, Topanga2005, LaTuna2017, Station2009, BlueCut2016, Pilot2016, Slide2007, Butler2007, and many smaller fires. PolSAR images are shown to be helpful in identifying types and boundaries of fire, 50‐meter scale details of vegetation loss, and variability of vegetation recovery in post‐fire years.

Estimation of Complex High-Resolution Range Profiles of Ships by Sparse Recovery Iterative Minimization Method

It is always an important problem to recover sparse signals from observations corrupted by Gaussian noise and has been extensively investigated. In high-resolution maritime surveillance radars working at scan mode, ship classification, and recognition need to recover high-resolution range profiles (HRRPs) of ships from radar returns of several pulses with severe range sidelobe effect. Multipulse synthetic data by the aid of ship Doppler information are complex sparse signals corrupted by non-Gaussian correlated interference. In this article, a sparse recovery via iterative minimization (SRIM) method is proposed to estimate complex HRRPs of ships from multipulse synthetic data. The SRIM method adapts the non-Gaussianity nature of the interference in the multipulse synthetic data and ship complex HRRPs are modeled by the random sequences of the biparametric generalized Gaussian distributions (GGDs) (0 < <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> ≤ 1). In the SRIM method, the parameters of the GGD model are iteratively searched by the minimal criterion of the Kolmogorov–Smirnov distance of the residue and the interference model. The SRIM method is compared with the recent linear-programming-based method and the classic sparse learning via iterative minimization (SLIM) method by using simulated and measured radar data and the results show that the SRIM method obtains the better performance.

An Automatic Target Detection Method Based on Multidirection Dictionary Learning for HFSWR

Target detection in a high-frequency surface-wave radar (HFSWR) system is a challenging task because radar returns are strongly polluted by various sources of interference. To address the detection problem, this letter presents an automatic detection method based on multidirection dictionary learning for HFSWR. First, we perform clutter identification and statistical analysis to become aware of the time-varying clutter environment. The analysis of real data shows that the clutter in an HFSWR system has spatial correlations and geometric directions. Second, motivated by this information, we design a multidirection dictionary learning-based constant false alarm rate (MDDL-CFAR) detector in which the spatial and geometric direction information is well represented by multidirectional dictionaries. The MDDL-CFAR can simultaneously learn dictionaries and estimate clutter statistics to adaptively set the detection threshold. Experimental results on HFSWR data sets demonstrate the effectiveness of the proposed detection method.

Contactless Fall Detection Using Time-Frequency Analysis and Convolutional Neural Networks

Automatic detection of a falling person based on noncontact sensing is a challenging problem with applications in smart homes for elderly care. In this article, we propose a radar-based fall detection technique based on time-frequency analysis and convolutional neural networks. The time-frequency analysis is performed by applying the short-time Fourier transform to each radar return signal. The resulting spectrograms are converted into binary images, which are fed into the convolutional neural network. The network is trained using labeled examples of fall and nonfall activities. Our method employs high-level feature learning, which distinguishes it from previously studied methods that use heuristic feature extraction. The performance of the proposed method is evaluated by conducting several experiments on a set of radar return signals. We show that our method distinguishes falls from nonfalls with 98.37% precision and 97.82% specificity, while maintaining a low false-alarm rate, which is superior to existing methods. We also show that our proposed method is robust in that it successfully distinguishes falls from nonfalls when trained on subjects in one room, but tested on different subjects in a different room. In the proposed convolutional neural network, the hierarchical features extracted from the radar return signals are the key to understand the fundamental composition of human activities and determine whether or not a fall has occurred during human daily activities. Our method may be extended to other radar-based applications such as apnea detection and gesture detection.

Improved Static Hand Gesture Classification on Deep Convolutional Neural Networks Using Novel Sterile Training Technique

In this paper, we investigate novel data collection and training techniques towards improving classification accuracy of non-moving (static) hand gestures using a convolutional neural network (CNN) and frequency-modulated-continuous-wave (FMCW) millimeter-wave (mmWave) radars. Recently, non-contact hand pose and static gesture recognition have received considerable attention in many applications ranging from human-computer interaction (HCI), augmented/virtual reality (AR/VR), and even therapeutic range of motion for medical applications. While most current solutions rely on optical or depth cameras, these methods require ideal lighting and temperature conditions. mmWave radar devices have recently emerged as a promising alternative offering low-cost system-on-chip sensors whose output signals contain precise spatial information even in non-ideal imaging conditions. Additionally, deep convolutional neural networks have been employed extensively in image recognition by learning both feature extraction and classification simultaneously. However, little work has been done towards static gesture recognition using mmWave radars and CNNs due to the difficulty involved in extracting meaningful features from the radar return signal, and the results are inferior compared with dynamic gesture classification. This article presents an efficient data collection approach and a novel technique for deep CNN training by introducing “sterile” images which aid in distinguishing distinct features among the static gestures and subsequently improve the classification accuracy. Applying the proposed data collection and training methods yields an increase in classification rate of static hand gestures from 85% to 93% and 90% to 95% for range and range-angle profiles, respectively.

Radar Maneuvering Target Motion Parameter Estimation Based on Hough Transform and Polynomial Chirplet Transform

This paper considers the problem of estimating the motion parameters of a low-observable high-speed maneuvering target with high-order motions, where the target’s complex motions will result in the range migration (RM) and Doppler frequency migration (DFM) effects within the observation time interval, which will significantly affect the radar performance. Based on the Hough transform (HT) and the polynomial Chirplet transform (PCT), a novel method for the motion parameter estimation, which we coin as HPCT, is proposed in this paper. It first employs the trajectory feature extracted from the image of range profiles by the HT to correct the first-order RM and resolve the Doppler ambiguity. Then, it compensates the residual range curvature and the DFM with the motion parameters estimated from the time-frequency ridge, which is extracted from the highly concentrated time-frequency representation (TFR) generated by the PCT. After that, the energy of radar returns can be coherently integrated along the target’s moving trajectory. In addition, we evaluate the parameter estimation performance, integration performance and computational cost of the proposed method via theoretical analysis or numerical experiments. The results show that in terms of the anti-noise ability, cross-term interference avoidance, measurement accuracy and computational complexity, the HPCT is superior to some common methods for the motion parameter estimation. Finally, we extend it to the scenario of multiple targets with different motion orders.

The Role of Micro Breaking of Small-Scale Wind Waves in Radar Backscattering from Sea Surface

The study of the microwave scattering mechanisms of the sea surface is extremely important for the development of radar sensing methods. Some time ago, Bragg (resonance) scattering of electromagnetic waves from the sea surface was proposed as the main mechanism of radar backscattering at moderate incidence angles of microwaves. However, it has been recently confirmed that Bragg scattering is often unable to correctly explain observational data and that some other physical mechanisms should be taken into consideration. The newly introduced additional scattering mechanism was characterized as non-polarized, or non-Bragg scattering, from quasi-specular facets appearing due to breaking wave crests, the latter usually occurring in moderate and strong winds. In this paper, it was determined experimentally that such non-polarized radar backscattering appeared not only for rough sea conditions in which wave crests strongly break and “white caps” occur, but also at very low wind velocities close to their threshold values for the wave generation process. The experiments were performed using two polarized Doppler radars. The experiments demonstrated that a polarization ratio, which characterizes relative contributions of non-polarized and Bragg components to the total backscatter, changed slightly with wind velocity and wind direction. Detailed analysis of radar Doppler shifts revealed two types of scatterers responsible for the non-polarized component. One type of scatterer, moving with the velocities of decimeter-scale wind waves, determined radar backscattering at low winds. We identified these scatterers as “microbreakers” and related them to nonlinear features in the profile of decimeter-scale waves, like bulges, toes and parasitic capillary ripples. The scatterers of the second type were associated with strong breaking, moved with the phase velocities of meter-scale breaking waves and appeared at moderate winds additionally to the “microbreakers”. Along with strong breakers, the impact of microbreaking in non-polarized backscattering at moderate winds remained significant; specifically the microbreakers were found to be responsible for about half of the non-polarized component of the radar return. The presence of surfactant films on the sea surface led to a significant suppression of the small-scale non-Bragg scattering and practically did not change the non-Bragg scatterer speed. This effect was explained by the fact that the nonlinear structures associated with dm-scale waves were strongly reduced in the presence of a film due to the cascade mechanism, even if the reduction of the amplitude of dm waves was weak. At the same time, the velocities of non-Bragg scatterers remained practically the same as in non-slick areas since the phase velocity of dm waves was not affected by the film.

High Accuracy Adaptive Microwave Ranging Using SNR-Based Perception for Coherent Distributed Antenna Arrays

We present a high accuracy, adaptive cooperative internode ranging system for coherent distributed antenna arrays. Coherent distributed antenna arrays are networks of wireless systems coordinated at the level of the wavelength of the wireless signal to support distributed beamforming. Enabling coherent operation between separate mobile nodes at microwave frequencies requires accurate and robust knowledge of the relative positions of the nodes in the array in the presence of changing conditions. We present an adaptive microwave ranging system operating at 4 GHz that is capable of maintaining a designated ranging accuracy in the presence of changing signal-to-noise ratio (SNR) conditions. The system is based on a novel perception process that evaluates the SNR of the radar returns and estimates the accuracy of the internode ranging measurement; the output of this process feeds into a controller which determines the changes necessary in the waveform to maintain the desired accuracy. Experimental results show the ability to maintain ranging accuracy of 5 mm, which enables 90% of the ideal distributed coherent gain at frequencies up to 4 GHz.

Extraction of Global and Local Micro-Doppler Signature Features From FMCW Radar Returns for UAV Detection

In this article, an unmanned aerial vehicles detection system is proposed for an X-band ground-based surveillance frequency modulated continuous wave Doppler radar. Two novel features are directly extracted from the Doppler processing results without a time-frequency analysis. Experimental results on measured radar echo signals show that our system consistently outperforms the state of the art in terms of detection accuracy and computational efficiency.