Radar Data Processing Research Articles

Training Data Selection and Update Strategies for Airborne Post-Doppler STAP

Space–time adaptive processing (STAP) of multichannel radar data is an established and powerful method for detecting ground moving targets, as well as for estimating their geographical positions and line-of-sight velocities. Crucial steps for practical applications are: 1) the appropriate and automatic selection of the training data and 2) the periodic update of these data to take into account the change of the clutter statistics over space and time. Improper training data and contamination by moving target signals may result in a decreased clutter suppression performance, an incorrect constant false alarm rate threshold, and target cancelation by self-whitening. In this paper, two conventional and two novel methods for training data selection are evaluated and compared using real four-channel X-band radar data acquired with DLR’s airborne sensor F-SAR. In addition, a module for rejecting potential moving target signals and strong scatterers from the training data is proposed and discussed. All methods are evaluated for a conventional post-Doppler (PD) STAP processor and for a particular PD STAP that uses an a priori known road map.

Weather Radar Data Processing and Atmospheric Applications: An Overview of Tools for Monitoring Clouds and Detecting Wind Shear

Rain, snow, and volcanic ash clouds contain particles generated by different physical and chemical processes. When electromagnetic radiation interacts with particle distribution, causing absorption and scattering, the backscattered power enables the retrieval of useful geophysical parameters of particle distribution. This is the measurement principle of microwave weather (meteorological) radar, monostatic remote sensing that can exploit Rayleigh and Mie backscattering to remotely probe atmospheric clouds. This article is an overview of weather radar systems and data processing for rain, snow, and volcanic ash clouds. We describe ground-based weather radars along with other platforms, such as airborne and spaceborne configurations. Signal processing of single-polarization weather radar systems in addition to more advanced schemes, such as those that enable Doppler and dual-polarization capability, are discussed. Additionally, we describe Doppler weather radars with narrow beams, which are used to detect low-level wind shear during rain, microbursts, and gusts. Multifrequency radar for snowfall retrieval is also presented, with an emphases on estimation and classification of the microphysical properties of particles. Finally, future directions for signal processing and applications for microwave weather radar systems are presented.

Echo Model Without Stop-and-Go Approximation for Bistatic SAR With Maneuvers

An echo model plays a pivotal role in the processing of synthetic aperture radar (SAR) data. It has been shown that the widely adopted echo models based on stop-and-go approximation (SAG) are unsuitable for use in many emerging radar systems with high resolution or high-speed platforms. More accurate echo models without SAG have been proposed for some special monostatic scenarios, such as low-Earth-orbit SAR and geosynchronous SAR. However, due to their inherent assumptions, such as linear and constant-speed/orbit-based motion, these conventional echo models cannot be applied to general cases, which involve bistatic configurations and maneuvers. Maneuvers or nonlinear/inconstant-speed motion are very common in cases, such as circular SAR and maneuvering-platform-borne SAR. In this letter, we propose a generalized echo model for bistatic SAR, which accounts for maneuvers of the platforms during pulse propagation. Moreover, we obtain a high-precision closed-form expression of the model by approximating high-order terms of the accurate range history of the receiver. Compared with the conventional models, the proposed echo model can be applied to a much greater number of cases, ranging from slow and constant-speed platforms to fast and maneuvering platforms. Theoretical analysis and backprojection-based image formation simulations confirm the validity of the proposed echo model.

Applying Ground-Penetrating Radar and Microwave Tomography Data Processing in Cultural Heritage: State of the Art and Future Trends

Ground-penetrating radar (GPR) enables inspection of optically opaque media and provides information about buried or otherwise hidden targets. This makes GPR an especially useful tool for monitoring and investigating cultural-heritage (CH) and archeological sites. This article reviews theoretical and practical aspects of GPR for its effective and reliable use in CH applications. Data processing via a linearized microwave tomographic (MWT) approach is our main focus. Theoretical and practical issues related to GPR imaging, such as the mitigation of clutter and artifacts as well as 3D reconstruction of large spatial domains, are addressed in this context. Finally, we explain how GPR was applied in a survey performed at the archeological site of Pompeii, Italy.

Performance assessment of SVM-based classification techniques for the detection of artificial debondings within pavement structures from stepped-frequency A-scan radar data

Owing to traffic and weather conditions, pavement structures may suffer from horizontal and vertical cracks that shorten the overall lifetime of roadways. In this paper, we focus on the detection of the horizontal subsurface cracks (debondings) occurring between the first two pavement layers from Stepped-frequency Radar (SFR) data. The processing of radar data requires some refined signal analysis to detect constructive interference between overlapping echoes. It is performed from timely data by a supervised machine learning method namely, Support Vector Machines (SVM) and compared to the conventional reference method, namely, the Amplitude Ratio Test (ART), which is routinely performed at the operational level. Besides, the straightforward application of SVM on raw data is compared to the solution using physical signal features (global and local) that reduces the computational burden. Performance assessment and comparison of the processing methods are conducted on data collected on a specific test-site with three different types of artificial debondings.

Processing of Synthetic Aperture Radar Data Using Frequency Modulated Signals

The main objective of Synthetic Aperture Radar (SAR) processing is converting the raw acquired data into pixels in the final processed image. The signal is spread in the range and azimuth directions, and the signal migrates to other range cells. Range Doppler Algorithm (RDA) is the most widely applied algorithm in SAR data processing. It performs range compression followed by azimuth compression to form the final reconstructed image. Unfortunately, SAR images suffer from many imperfections that will be studied and analyzed in this paper. Firstly, range compression introduces sidelobes that reduce the Signal-to-Noise Ratio (SNR). For this case, a comparison between three configurations of frequency modulated signals is presented. Moreover, the channel effect that causes degradations in the received signals is investigated and regularized de-convolution is used to reduce this effect. Finally, the random noise that causes a pixel-to-pixel variation and appears as a granular noise in the image will be reduced by applying a median filter to the reconstructed image.

DeepMTT: A deep learning maneuvering target-tracking algorithm based on bidirectional LSTM network

In the field of radar data processing, traditional maneuvering target-tracking algorithms assume that target movements can be modeled by pre-defined multiple mathematical models. However, the changeable and uncertain maneuvering movements cannot be timely and precisely modeled because it is difficult to obtain sufficient information to pre-define multiple models before tracking. To solve this problem, we propose a deep learning maneuvering target-tracking (DeepMTT) algorithm based on a DeepMTT network, which can quickly track maneuvering targets once it has been well trained by abundant off-line trajectory data from existent maneuvering targets. To this end, we first build a LArge-Scale Trajectory (LAST) database to offer abundant off-line trajectory data for network training. Second, the DeepMTT algorithm is developed to track the maneuvering targets using a DeepMTT network, which consists of three bidirectional long short-term memory layers, a filtering layer, a maxout layer and a linear output layer. The simulation results verify that our DeepMTT algorithm outperforms other state-of-the-art maneuvering target-tracking algorithms.

Impact of the Altitudinal Gradients of Precipitation on the Radar QPE Bias in the French Alps

In the French Alps the quality of the radar Quantitative Precipitation Estimation (QPE) is limited by the topography and the vertical structure of precipitation. A previous study realized in all the French Alps, has shown a general bias between values of the national radar QPE composite and the rain gauge measurements: a radar QPE over-estimation at low altitude (+20% at 200 m a.s.l.), and an increasing underestimation at high altitudes (until −40% at 2100 m a.s.l.). This trend has been linked to altitudinal gradients of precipitation observed at ground level. This paper analyzes relative altitudinal gradients of precipitation estimated with rain gauges measurements in 2016 for three massifs around Grenoble, and for different temporal accumulations (yearly, seasonal, monthly, daily). Comparisons of radar and rain gauge accumulations confirm the bias previously observed. The parts of the current radar data processing affecting the bias value are pointed out. The analysis shows a coherency between the relative gradient values estimated at the different temporal accumulations. Vertical profiles of precipitation detected by a research radar installed at the bottom of the valley also show how the wide horizontal variability of precipitation inside the valley can affect the gradient estimation.

Long-Time Coherent Integration Algorithm for Radar Maneuvering Weak Target With Acceleration Rate

In this paper, we propose a long-time coherent integration method for a maneuvering target with the first-, second-, and third-order range migrations, and the complex Doppler frequency migration. In this method, after range compression, the echo signal is first transformed into the range-frequency and Doppler domain based on series reversion, and then an azimuth matched filtering procedure is implemented in the 2-D frequency domain. It can eliminate the coupling effects between range and azimuth jointly caused by the radial velocity, radial acceleration, and radial acceleration rate of a moving target. Due to the linear transform property, the proposed method can work well under low signal-to-clutter and noise ratio. In addition, the Doppler ambiguities, when target azimuth spectrum either is within a pulse repetition frequency (PRF) or spans over neighboring PRF bands, can be well solved. Both simulated and real synthetic aperture radar data processing results are provided to validate the effectiveness of the proposed algorithm.

Data Processing and Interpretation of Antarctic Ice-Penetrating Radar Based on Variational Mode Decomposition

In the Arctic and Antarctic scientific expeditions, ice-penetrating radar is an effective method for studying the bedrock under the ice sheet and ice information within the ice sheet. Because of the low conductivity of ice and the relatively uniform composition of ice sheets in the polar regions, ice-penetrating radar is able to obtain deeper and more abundant data than other geophysical methods. However, it is still necessary to suppress the noise in radar data to obtain more accurate and plentiful effective information. In this paper, the entirely non-recursive Variational Mode Decomposition (VMD) is applied to the data noise reduction of ice-penetrating radar. VMD is a decomposition method of adaptive and quasi-orthogonal signals, which decomposes airborne radar data into multiple frequency-limited quasi-orthogonal Intrinsic Mode Functions (IMFs). The IMFs containing noise are then removed according to the information distribution in the IMF’s components and the remaining IMFs are reconstructed. This paper employs this method to process the real ice-penetrating radar data, which effectively eliminates the interference noise in the data, improves the signal-to-noise ratio and obtains the clearer inner layer structure of ice. It is verified that the method can be applied to the noise reduction processing of polar ice-penetrating radar data very well, which provides a better basis for data interpretation. At last, we present fine ice structure within the ice sheet based on VMD denoised radar profile.

Residual Motion Error Correction with Backprojection Multisquint Algorithm for Airborne Synthetic Aperture Radar Interferometry.

For airborne interferometric synthetic aperture radar (InSAR) data processing, it is essential to achieve precise motion compensation to obtain high-quality digital elevation models (DEMs). In this paper, a novel InSAR motion compensation method is developed, which combines the backprojection (BP) focusing and the multisquint (MSQ) technique. The algorithm is two-fold. For SAR image focusing, BP algorithm is applied to fully use the navigation information. Additionally, an explicit mathematical expression of residual motion error (RME) in the BP image is derived, which paves a way to integrating the MSQ algorithm in the azimuth spatial wavenumber domain for a refined RME correction. It is revealed that the proposed backprojection multisquint (BP-MSQ) algorithm exploits the motion error correction advantages of BP and MSQ simultaneously, which leads to significant improvements of InSAR image quality. Simulation and real data experiments are employed to illustrate the effectiveness of the proposed algorithm.

Slope‐compensated interferogram filter with ESPRIT for adaptive frequency estimation

Interferometric phase filtering represents an indispensable step in interferometric synthetic aperture radar (InSAR) data processing. However, conventional filtering methods fail to make a balance between the noise elimination and phase preserving in strong noise environments or dense fringe regions. This in turn leads to an inaccurate interferometric phase. To overcome this problem, the authors introduce the concept of slope-compensated filter based on the local adaptive frequency estimation. This method uses the estimation of signal parameters via rotational invariance techniques (ESPRITs), based on the singular value decomposition of the correlation matrix and generalised eigenvalue solution for providing an accurate local fringe frequency. Meanwhile, the size of the estimation window is adaptively determined by the coherence coefficient, and the judgement and modification of the invalid frequency estimation are utilised to further improve the accuracy of the local fringe frequency. Moreover, the authors consider a two-stage with the back projection technique to further eliminate the noise residual and improve the filtering performance. The simulated and TerraSAR-X add-on for Digital Elevation Measurements (TanDEM) data sets are used to verify the effectiveness of the proposed method. The experimental results show that this method is capable of efficiently reducing the noise level as well as minimising the loss of the signal, outperforming other conventional methods.

Facilitating radar precipitation data processing, assessment and analysis: a GIS-compatible python approach

Abstract A review of existing tools for radar data processing revealed a lack of open source software for automated processing, assessment and analysis of weather radar composites. The ArcGIS-compatible Python package radproc attempts to reduce this gap. Radproc provides an automated raw data processing workflow for nationwide, freely available German weather radar climatology (RADKLIM) and operational (RADOLAN) composite products. Raw data are converted into a uniform HDF5 file structure used by radproc's analysis and data quality assessment functions. This enables transferability of the developed analysis and export functionality to other gridded or point-scale precipitation data. Thus, radproc can be extended by additional import routines to support any other German or non-German precipitation dataset. Analysis methods include temporal aggregations, detection of heavy rainfall and an automated processing of rain gauge point data into the same HDF5 format for comparison to gridded radar data. A set of functions for data exchange with ArcGIS allows for visualisation and further geospatial analysis. The application on a 17-year time series of hourly RADKLIM data showed that radproc greatly facilitates radar data processing and analysis by avoiding manual programming work and helps to lower the barrier for non-specialists to work with these novel radar climatology datasets.

Optimization of Secondary Surveillance Radar Data Processing

Optimization of Secondary Surveillance Radar Data Processing

Systematic preparation and processing of interferometric synthetic aperture radar data for monitoring linear transportation infrastructure

We propose a methodology for the systematic preparation and processing of interferometric synthetic aperture radar (InSAR) data for monitoring linear transportation infrastructure subject to geohazards. The methodology is applied to two RADARSAT-2 Spotlight synthetic aperture radar datasets, and three case studies in Cornwall, Eastern Ontario, Canada, are examined. An InSAR processing sequence was established and 19 SLA24 and 15 SLA74 images were used to create time-series deformation maps spanning from March 2015 to September 2016. The noise floors were ±1.5 and ±1.0 cm, for the SLA24 and SLA74 datasets, respectively. Phase unwrapping errors, atmospheric path delay, and the limited number of images were identified as the largest contributors to measurement uncertainty, which was of the same order as the ground deformation field. To improve coherence and utility of the radar images for monitoring the effects of geohazards on infrastructure, it is recommended that imagery acquisitions consider the use of small incidence angles with moderate image resolution and 6- to 12-day revisit periods.

Security Imaging for Multi-Target Screening Based on Adaptive Scene Segmentation With Terahertz Radar

In this paper, a terahertz (THz) radar security imaging method for multi-target screening is proposed. This imaging system is constituted of several transceivers to get multi-angle echo data. Through aperture synthesis and target detection processing of the multi-angle radar echo data, the multi-target scene can be segmented into several subscenes with each single target inside. In order to fast reconstruct the images of subscenes without the hypothesis of plane wave decomposition, which can cause severe defocus and distortion at the edge of THz radar near-field imaging results, wavenumber domain algorithm is modified for parallel processing of all subscenes data with the center locations of subscenes. Furthermore, convolution integral in slow time is used to speed up the processing of subscenes imaging with fast Fourier transform. Simulation results are analyzed and compared to demonstrate the performance and effectiveness of our method. Moreover, verifiable experiments are designed for proof-of-concept, where imaging scenes are rotating along with a fixed speed turntable. The experimental data are transmitted and received by a stationary THz radar, which operates at 340 GHz with the bandwidth of 28.8 GHz and the output peak power of 5 mW.

Towards an improvement of GPR-based detection of pipes and leaks in water distribution networks

Ground-penetrating radar (GPR) is a non-destructive tool that can be used to help detecting water leaks in water distribution networks. This study, which was undertaken in the framework of the SENSPORT project (Walloon Region, Belgium), aims at improving GPR-based detection of pipes and leaks in water distribution networks using advanced, integrated radar data processing and visualization strategies. The proposed method includes a physically-based near-field antenna effects removal approach and reflection detection and segmentation algorithms. It also involves a quantitative estimation of the medium properties using full-wave inversion. Finally, a specific human-computer interface allowing the end-user to visualize buried utilities and 2-D/3-D processed data on mobile devices is proposed. We successfully validated the general methodology through a laboratory experiment with near-field measurements performed at different times over a leaky pipe buried in a sandbox. This integrated tool appears to be promising to help detecting and monitoring water leaks. Future research will focus on more complex real cases.Index Terms—Full-wave inversion (FWI), ground-penetrating radar (GPR), human-computer interaction, subsurface imaging, water leaks detection.

Effective Processing of Radar Data for Bridge Damage Detection

This paper presents the practical results of the evaluation of the data obtained by using ground‐based radar interferometer during measurements carried out on bridge structures. Due to the nature of the objects studied, the authors proposed a comprehensive method of data analysis, which identifies whether the passage of the vehicle did not damage the bridge. The effective use of vehicles as a source of bridge excitation allowed us to first develop a method for determining the damping parameters resistant to potentially occurring beating frequencies. As a result, it is possible to determine these subsets of data registered with radar, for which it is possible to assume compliance with linear systems. This type of data, often omitted in other works, forms the basis for the second important element of the research—an algorithm based on the ARMA model supporting defect detection. The optimization of the performed calculations, in particular the proposed optimal ARMA model order, the method of fault identification based on the DSF parameter, or fault identification based on a nonmetrical Cook’s distance leads to a robust and scalable method. The method’s low computational complexity allows for implementation in real‐time solutions. In addition, the distribution of errors and the sensitivity of classifiers based on the DSF parameter and Cook’s distances leaving them will enable the automation of the classification process using machine learning. The proposed method is universal; in particular, it can be used for radar interferometry methods because it is resistant to potentially variable environmental conditions.

Assessment of Ground-Reference Data and Validation of the H-SAF Precipitation Products in Brazil

The uncertainties associated with rainfall estimates comprise various measurement scales: from rain gauges and ground-based radars to the satellite rainfall retrievals. The quality of satellite rainfall products has improved significantly in recent decades; however, such algorithms require validation studies using observational rainfall data. For this reason, this study aims to apply the H-SAF consolidated radar data processing to the X-band radar used in the CHUVA campaigns and apply the well established H-SAF validation procedure to these data and verify the quality of EUMETSAT H-SAF operational passive microwave precipitation products in two regions of Brazil (Vale do Paraíba and Manaus). These products are based on two rainfall retrieval algorithms: the physically based Bayesian Cloud Dynamics and Radiation Database (CDRD algorithm) for SSMI/S sensors and the Passive microwave Neural network Precipitation Retrieval algorithm (PNPR) for cross-track scanning radiometers (AMSU-A/AMSU-B/MHS sensors) and for the ATMS sensor. These algorithms, optimized for Europe, Africa and the Southern Atlantic region, provide estimates for the MSG full disk area. Firstly, the radar data was treated with an overall quality index which includes corrections for different error sources like ground clutter, range distance, rain-induced attenuation, among others. Different polarimetric and non-polarimetric QPE algorithms have been tested and the Vulpiani algorithm (hereafter, R q 2 V u 15 ) presents the best precipitation retrievals when compared with independent rain gauges. Regarding the results from satellite-based algorithms, generally, all rainfall retrievals tend to detect a larger precipitation area than the ground-based radar and overestimate intense rain rates for the Manaus region. Such behavior is related to the fact that the environmental and meteorological conditions of the Amazon region are not well represented in the algorithms. Differently, for the Vale do Paraíba region, the precipitation patterns were well detected and the estimates are in accordance with the reference as indicated by the low mean bias values.

Software for the Linesman Radar Data Processing System

Software for the Linesman Radar Data Processing System