Radar Data Processing Research Articles

Identification and micro-motion parameter estimation of non-cooperative UAV targets

With the wide application of unmanned aerial vehicles (UAV) in industrial production, transportation, and entertainment, it is urgent to identify UAVs in time. Traditional UAV recognition mainly depends on wireless communication, which puts forward high requirements for a communication environment and has no way to deal with non-cooperative targets. Therefore, it is urgent to explore a UAV target recognition scheme based on perception. In this paper, aiming at the time series preprocessing method, a coding-based sequence preprocessing method is proposed. This method effectively improves the effect of the Deep Learning method in the identification task. In order to verify the ability of Deep Learning in radar time series data processing and the effectiveness of the proposed method, the Deep Learning method is used to analyze the radar signal time series of the target to realize the target recognition. Finally,considering the influence of micro-motion factors on UAV targets, the neural network is used to estimate UAV’s micro-motion parameters to enhance the ability of target recognition with the help of micro-motion information.

Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives

Deep learning in remote sensing has received considerable international hype, but it is mostly limited to the evaluation of optical data. Although deep learning has been introduced in synthetic aperture radar (SAR) data processing, despite successful first attempts, its huge potential remains locked. In this article, we provide an introduction to the most relevant deep learning models and concepts, point out possible pitfalls by analyzing special characteristics of SAR data, review the state of the art of deep learning applied to SAR, summarize available benchmarks, and recommend some important future research directions. With this effort, we hope to stimulate more research in this interesting yet underexploited field and to pave the way for the use of deep learning in big SAR data processing workflows.

Multibaseline InSAR Layover Detection Based on Local Frequency and Eigenvalue

Layover detection has long been the focus of attention for interferometric synthetic aperture radar (InSAR) data processing. As for the existing layover detection methods, most of them are applied to single-baseline scenarios. Since multibaseline InSAR provides more image samples, the performance of layover detection can be further improved. In this article, a joint detection method of layover is proposed on the basis of local frequency and eigenvalue of multibaseline InSAR. To obtain a precise local frequency, maximum likelihood estimation is performed for flattened interferometric phase fusion and removing the reference terrain phase is chosen to ensure the proportionality of flattened phase to the baseline length. Adaptive image selection based on coherence is proposed to obtain a distinct eigenvalue. Then, the joint detection method is elaborated on. Based on the results of local frequency estimation and eigenvalue decomposition, precise thresholds are set for joint detection, and joint judgment is made by combining the layover detected by local frequency, the layover detected by eigenvalue, and the thresholds. Finally, the proposed method is validated through comparative experiments on simulated data and real data. Both theoretical analysis and experimental results show the feasibility and superiority of the proposed method.

Retrieval of Ocean Surface Radial Velocities With RADARSAT-2 Along-Track Interferometry

This article provides a detailed presentation of the dual-aperture synthetic aperture radar (SAR) data processing scheme for the retrieval of ocean surface radial velocities.This scheme includes processing of raw SAR data, coregistration of along-track interferometric samples, magnitude and absolute phase calibration, and coherent averaging (multilooking). Several approaches for the absolute phase calibration are provided and compared. Some of the attempted approaches can potentially be used over open ocean (i.e., in scenes that do not contain any land). Main goal of attempting different approaches for the absolute phase calibration was to determine their relative performance, and determine the potential feasibility of some approaches over open ocean. The data processing scheme is applied to a RADARSAT-2 dual-channel MODEX-1 acquisition over a section of the Florida Current. For the dataset used in this study, different absolute phase calibration methods yielded similar radial velocity estimates, with relative mean and RMS differences within approximately 0.1 m/s. Estimates from SAR ATI were also compared to estimates from NASA’s OSCAR dataset. Comparison of visually identified currents showed close spatial overlap between estimates from the two sources. The RMS difference was found to be approximately 0.30 m/s. This difference can be attributed to the physical and temporal differences between the estimates.

A Nonlocal Noise Reduction Method Based on Fringe Frequency Compensation for SAR Interferogram

Phase noise reduction is one of the key steps for synthetic aperture radar interferometry data processing. In this article, a novel phase filtering method is proposed. The main innovation and contribution of this research is to 1) incorporate local fringe frequency (LFF) compensation technique into the nonlocal phase filtering method to include more independent and identically distributed samples for filtering; 2) modify the nonlocal phase filter from three aspects: 1) executing nonlocal filtering in the complex domain of the residual phase to avoid gray jumps in phase, 2) adaptively calculating the smoothing parameter based on the LFF and the coherence coefficient, and 3) using the integral image in similarity calculation to improve the efficiency; 3) perform Goldstein filter in high coherence areas to reduce the computation expense. Experiments based on both simulated and real data have shown that the proposed method has achieved a better performance in terms of both noise reduction and edge preservation than some existing phase filtering methods.

A Novel Quality-Guided Two-Dimensional InSAR Phase Unwrapping Method via GAUNet

Phase unwrapping (PU) has always been a critical and challenging step in interferometric synthetic aperture radar (InSAR) data processing. Inspired by existing research, i.e., the PGNet, we propose a novel quality-guided 2-D InSAR PU method via deep learning, and regard PU as a two-stage process. In the first stage, the ambiguity gradient is estimated using the proposed global attention U-Net (GAUNet) architecture, which combines the classic U-Net structure and global attention mechanism. Then, in the second stage, the classical PU framework (e.g., the L1- or L2-norm) is applied as a post-processing operation to retrieve the absolute phase. Since class imbalance is a key factor affecting the estimation of ambiguity gradient, different strategies based on four commonly used quality maps are adopted to deal with the problem. The quality map is not only input as additional information for the guidance of the training process, but also participates in the construction of loss function. As a result, GAUNet can pay more attention to the nonzero ambiguity gradients. By using the number of residues as the evaluation metric, we can choose the optimum strategy for the restoration of the absolute phase. In addition to the simulated interferograms, the proposed method is tested both on a real topographic interferogram exhibiting rugged topography and phase aliasing and a differential interferogram measuring the deformation from MW 6.9 Hawaii earthquake, all yield state-of-art performance when comparing with the widely used traditional 2-D PU methods.

Метод определения направления на цель цифровой антенной решеткой моноимпульсной радиолокационной станции

On the basis of the analysis of the errors of the monopulse method of direction finding of the target of a radar station with a digital antenna array, a group of errors associated with fluctuations of the equisignal direction when scanning the beam of the antenna system, as well as with the non-identical shape of the generated beams of the monopulse group, was separated into a special cluster. This circumstance is especially critical for large-aperture radars with phased array with electric scanning, where the number of formed receiving beams is significant, in addition, the coordinates of the target usually do not coincide with the coordinate planes along which the coordinates are measured. The nonlinearity of this problem requires unique approaches and solutions. The classical formulation of the problem is associated with obtaining an estimate of the radiation vector of the entire antenna array from the known measured vector of complex signals at the output of the antenna array at a fixed time. To solve this problem and determine the decision rule, a neural network approach was used, while the neural network was trained according to the criterion of the minimum of the selected objective function. The substantiation of increasing the accuracy of measurements of angular coordinates in monopulse systems with scanning digital phased antenna arrays based on the use of neural networks in the process of radar data processing without the formation of direction finding characteristics is given. The results of modeling the characteristics of the antenna array (using the example of a flat antenna array with a dimension of 30 by 30 emitters) under the conditions adopted in the model of the antenna array of assumptions when implementing the classical and original methods are shown using examples of the surfaces of the variances of errors in measuring angular coordinates by the monopulse method in a rectangular spatial domain, which showed reduction of error and expansion of the measurement area. In the future, the practical implementation of this approach is assumed using FPGA in the receiving paths of the frequency-dependent part of the radio direction finder.

Neural network processing of radar scanning data array

This article deals with the digital processing of a matrix radar image. The information received from the radar scanner needs to be transformed to enable visual perception. The article describes the main methods of digital processing of matrix data, presents the images transformed by them. The aim of the article was the development of a radar data processing algorithm that identifies the contours and edges of examined objects. The authors propose an algorithm for isolating the geometric structure of the scanned area. The difference between the processing method and the known analogues is based on the nature of the change in the values of the array being processed and consists in the double operation of extracting the gradient of the distribution of values. The software implementation of the algorithm is made in C++ using methods from an open library of computer vision. The efficiency of the algorithm was estimated based on comparison with the algorithms for determining edges based on linear filtering and neural networks. The results of the work can be used to create software for mobile short-range radar devices. Imaging from object boundaries and their edges provides spatial perception of the image by the operator, and free areas are available for rendering additional information. This solution allows you to combine scanning devices and thereby increase the information value of the result.

Design and Implementation Pulse Compression for S-Band Surveillance Radar

The radar air surveillance system consists of 4 main parts, there are antenna, RF front-end, radar signal processing, and radar data processing. Radar signal processing starts from the baseband to IF section. The radar waveform consists of two types of signal, there are continuous wave (CW) radar, and pulse compression radar [1]. Range resolution for a given radar can be significantly improved by using very short pulses. Pulse compression allows us to achieve the average transmitted power of a relatively long pulse, while obtaining the range resolution corresponding to a short pulse. Pulse compression have compression gain. With the same power, pulse compression radar can transmit signal further than CW radar. In the modern radar, waveform is implemented in digital platform. With digital platform, the radar waveform can optimize without develop the new hardware platform.
 Field Programmable Gate Array (FPGA) is the best platform to implemented radar signal processing, because FPGA have ability to work in high speed data rate and parallel processing. In this research, we design radar signal processing from baseband to IF using Xilinx ML-605 Virtex-6 platform which combined with FMC-150 high speed ADC/DAC.

Combining SAR and Optical Earth Observation with Hydraulic Simulation for Flood Mapping and Impact Assessment

Timely mapping, measuring and impact assessment of flood events are crucial for the coordination of flood relief efforts and the elaboration of flood management and risk mitigation plans. However, this task is often challenging and time consuming with traditional land-based techniques. In this study, Sentinel-1 radar and Landsat images were utilized in collaboration with hydraulic modelling to obtain flood characteristics and land use/cover (LULC), and to assess flood impact in agricultural areas. Furthermore, indirect estimation of the recurrence interval of a flood event in a poorly gauged catchment was attempted by combining remote sensing (RS) and hydraulic modelling. To this end, a major flood event that occurred in Sperchios river catchment, in Central Greece, which is characterized by extensive farming activity was used as a case study. The synergistic usage of multitemporal RS products and hydraulic modelling has allowed the estimation of flood characteristics, such as extent, inundation depth, peak discharge, recurrence interval and inundation duration, providing valuable information for flood impact estimation and the future examination of flood hazard in poorly gauged basins. The capabilities of the ESA Sentinel-1 mission, which provides improved spatial and temporal analysis, allowing thus the mapping of the extent and temporal dynamics of flood events more accurately and independently from the weather conditions, were also highlighted. Both radar and optical data processing methods, i.e., thresholding, image differencing and water index calculation, provided similar and satisfactory results. Conclusively, multitemporal RS data and hydraulic modelling, with the selected techniques, can provide timely and useful flood observations during and right after flood disasters, applicable in a large part of the world where instrumental hydrological data are scarce and when an apace survey of the condition and information about temporal dynamics in the influenced region is crucial. However, future missions that will reduce further revisiting times will be valuable in this endeavor.

Optical SAR data processing configuration with simultaneous azimuth and range matching filtering.

The real-time processing of synthetic aperture radar (SAR) data has a high requirement for the processor, which is a difficult problem in SAR real-time processing. With the rapid development of optoelectronic devices, traditional electrical SAR data processing can be converted into optoelectronic processing to improve the processing speed. In this paper, a new type of optical device is proposed to improve the processing speed of SAR data. With the help of a spatial light modulator (SLM), the initial SAR signal and matched filter function are loaded on the input plane and spectrum plane of the 4f system, respectively. Using an optical lens with the function of the Fourier transform, the Fourier transform and inverse Fourier transform of the SAR signal are carried out to realize the fast imaging of SAR. In theory, the processing speed of SAR data is the speed of light. Compared with traditional methods such as the range-Doppler (RD) algorithm, it is no longer necessary to carry out a one-dimensional Fourier transform but to carry out matching filtering for the azimuth and range of the spectrum plane of 4f system at the same time. In this way, it is not necessary to introduce a cylindrical lens, only a spherical lens is needed to realize the Fourier transform imaging of SAR. Finally, a two-dimensional SAR processing optical system is built to obtain the SAR image in real time.

A Phase Slicing 2-D Phase Unwrapping Method Using the L1-Norm

2-D phase unwrapping (PU) is one of the key steps in the processing of interferometric synthetic aperture radar (InSAR) data. PU in large-gradient areas has always been a fundamental problem. The phase continuity assumption makes it difficult to obtain ideal PU results in large-gradient areas. To address this issue, in this letter, we propose a phase slicing 2-D PU method using the L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> -norm that can effectively reduce the phase gradient interval. First, the study area is analyzed using an external digital elevation model (DEM) and interferometric phase information to obtain the number of phase slices and assisted phases. Then, we cut the interferometric phase to obtain the sliced interferometric phase and use the single-baseline (SB) L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> -norm PU method to unwrap the sliced interferometric phase. Finally, the unwrapped phase is recovered by summing the sliced unwrapped phases and assisted phases. Experiments are conducted with simulated and TanDEM-X InSAR data sets. Compared with the representative SB-PU method, the proposed method can effectively avoid the problem of large gradients in PU.

Research on Anti-collision Method of Trackless Equipment in Underground Mine

In trackless transport of underground mines, accidents are much more frequent and serious due to the lack of effective safety warning measures. In this paper, a method of anti-collision for trackless equipment based on lidar was described. The background of the technology was recommended and the characteristics of roadway and data processing method were introduced. The method of processing radar data was proposed and a distance model was built, so the distance judgment method was provided. Finally, the method was applied on a Load-Haul-Dump (LHD) and it was proved that this method had obvious effectiveness and wide application for unmanned LHD in deep mining.

Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR

Stable and efficient ground moving target tracking and refocusing is a hard task in synthetic aperture radar (SAR) data processing. Since shadows in video-SAR indicate the actual positions of moving targets at different moments without any displacement, shadow-based methods provide a new approach for ground moving target processing. This paper constructs a novel framework to refocus ground moving targets by using shadows in video-SAR. To this end, an automatic-registered SAR video is first obtained using the video-SAR back-projection (v-BP) algorithm. The shadows of multiple moving targets are then tracked using a learning-based tracker, and the moving targets are ultimately refocused via a proposed moving target back-projection (m-BP) algorithm. With this framework, we can perform detecting, tracking, imaging for multiple moving targets integratedly, which significantly improves the ability of moving-target surveillance for SAR systems. Furthermore, a detailed explanation of the shadow of a moving target is presented herein. We find that the shadow of ground moving targets is affected by a target’s size, radar pitch angle, carrier frequency, synthetic aperture time, etc. With an elaborate system design, we can obtain a clear shadow of moving targets even in X or C band. By numerical experiments, we find that a deep network, such as SiamFc, can easily track shadows and precisely estimate the trajectories that meet the accuracy requirement of the trajectories for m-BP.

Algorithm for processing and interpretation of ground-penetrating radar data in the study of permafrost geocryological structures

Relevance. Ground ice is one of unstable components of the cryolithozone and occupies 400–600 km3 in total. Changes in the conditions of heat transfer of ground ice under the influence of external or internal factors can lead to the formation of thermokarst sinkholes, solifluction mudslides, cryogenic landslides and other dangerous cryogenic phenomena in the frozen rock mass. Various methods are used to study underground ice. You can distinguish the method of GPR. Its advantages are efficiency and high detail of measurements. However, the process of identifying underground ice based on the results of processing and interpreting ground-penetrating radar data is currently not formalized in the form of an algorithm. Purpose of the work. Rapid mapping of geocryological structures requires the development of an appropriate algorithm (sequence of formalized procedures) for the processing and interpretation of ground penetrating radar data. Methodology of the algorithm is based on the analysis of characteristics of GPR traces, the use of fast Fourier transform to calculate the spectra of GPR traces and further analysis of the results. Results of the study. The developed algorithm will make it possible to quickly study the structural features of the frozen rock massif with the release of layer ice. The algorithm was tested on the results of physical and computer modeling of ground-penetrating radar measurements of rock mass with ice included. Conclusions. The use of algorithms for processing and interpretation of ground-penetrating radar data for the operational mapping of geocryological structures of frozen rock massifs makes it possible to reasonably and rationally apply a set of measures to prevent the occurrence of unfavorable geotechnical processes or to reduce economic losses when negative geocryological processes are activated.

Matrix application for multi-radar processing of radar data arrays

In the processing of dynamically changing data, for example, radar data (RD), a crucial part is made by the representation of various data sets containing information about routes and signs of air objects. In the practical implementation of the computational process, it previously seemed natural that RD processing in data arrays was carried out by the elementwise search method. However, the representation of data arrays in the form of matrices and the use of matrix math allow optimal calculations to be formed during tertiary processing. Forming matrices and working with them requires a significant computational resource, so the authors can assume that a certain gain in calculation time may be achieved if there is a large amount of data in the arrays, at least several thousand messages. The article shows the sequences of the most frequently repeated operations of tertiary network processing, such as searching for and replacing an array element. The simulation results show that the processing efficiency (relative reduction of processing time and saving of computing resources) with the use of matrices, in comparison with elementwise search and replacement, increases in proportion to the number of messages received by the information processing device. The most significant gain is observed when processing several thousand messages (array elements). Thus, the use of matrices and the mathematical apparatus of matrix math for processing arrays of dynamically changing data can reduce processing time and save computational resources. The proposed matrix method of organizing calculations can also find its place in the modeling of complex information systems.

Flood Mapping Using Relevance Vector Machine and SAR Data: A Case Study from Aqqala, Iran

The use of satellite imagery to monitor flood areas is essential to determine the damage and prevent related problems in the future. This paper examines thresholding and unsupervised classification for flood mapping using Sentinel-1 SAR image. Thresholding helps us to determine over-detection and under-detection regions in the flooded areas, and so, gamma distribution is used to select the thresholds. Also, the relevance vector machine (RVM) and the object-based classification method have been used for classification. The RVM algorithm obtained better results with overall accuracy = 0.89 and k = 0.95, while for the object-based classification method, these values were 0.87 and 0.91, respectively. According to the results, over- and under-detection occurred in flat areas and man-made structures, respectively. The results demonstrate a great potential of radar imagery for operational detection and delimitation of water in flood risk areas. The automation of satellite radar data processing operation has been tested, and it shows a potential for optimising the system of monitoring and early detection of flood risk.

Neural Networks to Increase Range Resolution of FMCW Radar

Deep learning offers interesting tools for radar data processing. This letter presents a signal processing method using a neural network to increase the range resolution of frequency-modulated continuous wave radar. The duration of the beat signal can be increased in the time domain for a better frequency resolution during conventional spectral estimation. This technique allows the radar bandwidth limitation to be exceeded and improves the sensing performance. A methodology is proposed as well as experimental results on real radar signal.

Millimeter wave radar data of people walking.

This dataset contains complex signals coming from a mmWave FMCW radar system. Signals were acquired during a measurement campaign taken indoor and aimed to assess people's different ways of walking. Measurement setup and devices are described.The dataset consists of the acquisitions of six different types of activities, performed by 29 subjects who repeat each activity several times. Therefore, the dataset contains multiple different experiments for each activity, for a total of 231 acquisitions. The subjects walk without any constraint or do not follow any pattern, thus making this dataset useful not only for human gait recognition but also to evaluate the performance of different radar data processing algorithms.

Interlink between lightning activity of hailstorms and their radar characteristics

The time course and the relationship of radar and lightning characteristics of the powerful hail clouds of the North Caucasus are considered. The studies were conducted in 2016-2018 at the automated meteorological radar complex ASU-MRL, which provided simultaneous acquisition and processing of radar data from the MRL-5 and 4-point network of LS-8000 lightning detection sensors, visualization of thunderstorm discharges on the background of the radar reflectivity map, as well as measurement complex of radar and thunderstorm cloud characteristics, including their maximum radar reflectivity Zmax, heights, volumes and water content of clouds at different reflectivity values, frequency of intracloud lightning discharges (VHF), “cloud-to-ground” discharges with positive (LF+) and negative polarity (LF-), current and duration of discharges. An analysis of the results showed that lightning discharges begin in the developing hail clouds of the North Caucasus when their maximum radar reflectivity reaches Zmax ≥ 40 dBZ, the frequency of discharges increases with increasing reflectivity, integral water content and enhancing the process of precipitation formation. The highest thunderstorm activity is noted at Zmax≈ 70-75 dBZ. Most low-frequency discharges of LF- and LF+ types are observed on the windward flank in the zone of intensive precipitation formation, and the majority of high-frequency VHF discharges are on their leeward side. Seeding of hail clouds with glaciogenic reagents with anti-hail rockets also leads to an increase in the frequency of discharges of all types.