Radar Data Processing Research Articles

Adjustment of wind-drift effect for real-time systematic error correction in radar rainfall data

An effective bias correction procedure using gauge measurement is a significant step for radar data processing to reduce the systematic error in hydrological applications. In these bias correction methods, the spatial matching of precipitation patterns between radar and gauge networks is an important premise. However, the wind-drift effect on radar measurement induces an inconsistent spatial relationship between radar and gauge measurements as the raindrops observed by radar do not fall vertically to the ground. Consequently, a rain gauge does not correspond to the radar pixel based on the projected location of the radar beam. In this study, we introduce an adjustment method to incorporate the wind-drift effect into a bias correlation scheme. We first simulate the trajectory of raindrops in the air using downscaled three-dimensional wind data from the weather research and forecasting model (WRF) and calculate the final location of raindrops on the ground. The displacement of rainfall is then estimated and a radar–gauge spatial relationship is reconstructed. Based on this, the local real-time biases of the bin-average radar data were estimated for 12 selected events. Then, the reference mean local gauge rainfall, mean local bias, and adjusted radar rainfall calculated with and without consideration of the wind-drift effect are compared for different events and locations. There are considerable differences for three estimators, indicating that wind drift has a considerable impact on the real-time radar bias correction. Based on these facts, we suggest bias correction schemes based on the spatial correlation between radar and gauge measurements should consider the adjustment of the wind-drift effect and the proposed adjustment method is a promising solution to achieve this.

이동물체 탐지를 위한 레이다 데이터의 거리-도플러 클러스터링 기법

Recently many studies of Radar systems mounted on ground vehicles for autonomous driving, SLAM (Simultaneous localization and mapping) and collision avoidance are reported. In near field, several hits per an object are generated after signal processing of Radar data. Hence, clustering is an essential technique to estimate their shapes and positions precisely. This paper proposes a method of grouping hits in range-doppler domains into clusters which represent each object, according to the pre-defined rules. The rules are based on the perceptual cues to separate hits by object. The morphological connectedness between hits and the characteristics of SNR distribution of hits are adopted as the perceptual cues for clustering. In various simulations for the performance assessment, the proposed method yielded more effective performance than other techniques.

Coherent Processing and Superresolution Technique of Multi-Band Radar Data Based on Fast Sparse Bayesian Learning Algorithm

The coherent processing and superresolution of multi-band radar data from multiple spatially collocated radars is addressed by utilizing a sparse representation technique in this paper. Firstly a parametric model based on geometrical theory of diffraction (GTD) is adopted to construct a redundant dictionary and provide a good match to the scattering mechanism. Then weights of dictionary atoms are computed by using a fast sparse Bayesian learning algorithm. Meanwhile, a multilevel dynamic dictionary is proposed to improve the accuracy and avoid a vast number of computations due to a large dictionary matrix. The final weights and dictionary atoms are adopted to interpolate between and extrapolate outside of the measurement subbands. Furthermore, a procedure for coherent processing is presented to compensate for the lack of mutual coherence among multiband data from multiple spatially collocated radars. The capability and robustness of the proposed method are validated by applying it to the analytical, simulated and static-range data.

Remote sensing of ecosystem health: opportunities, challenges, and future perspectives.

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.

Coregistration based on stochastic parallel gradient descent algorithm for SAR interferometry

The coregistration of complex image pairs is a very important step in interferometric synthetic aperture radar (InSAR) data processing. This article proposes a coregistration method based on the stochastic parallel gradient descent (SPGD) algorithm. Stochastic parallel perturbations are imposed on the translation coefficients of the polynomial coregistration model to make the performance evaluation function converge to a global extremum, which allows the translation coefficients to be obtained, and then the coregistration is achieved after resampling. Data processing of images from Kashgar and Mount Etna show that the proposed method is effective and robust. Furthermore, a series of experiments is designed to evaluate the convergence characteristics of the proposed method, which indicates that it has a stable convergence process and good robustness.

On the Processing of Very High Resolution Spaceborne SAR Data

This paper addresses several important aspects that need to be considered for the processing of spaceborne synthetic aperture radar (SAR) data with resolutions in the decimeter range. In particular, it will be shown how the motion of the satellite during the transmission/reception of the chirp signal and the effect of the troposphere deteriorate the impulse response function if not properly considered. Further aspects that have been investigated include the curved orbit, the array pattern for electronically steered antennas, and several considerations within the processing itself. For each aspect, a solution is proposed, and the complete focusing methodology is expounded and validated using simulated point targets and staring spotlight data acquired by TerraSAR-X with 16-cm azimuth resolution and 300-MHz range bandwidth.

New analytical imaging algorithm for general case airborne bistatic SAR

This paper focuses on the general case (GC) airborne bistatic synthetic aperture radar (SAR) data processing, and a new analytical imaging algorithm based on the extended Loffeld's bistatic formula (ELBF) is proposed. According to the bistatic SAR geometry, the track decoupling formulas that convert the bistatic geometry to the receiver-referenced geometry in a concise way are derived firstly. Then phase terms of ELBF are decomposed into two independent phase terms as the range phase term and the azimuth phase term in a new way. To get the focusing result, the bistatic deformation (BD) term is compensated in the two-dimensional (2-D) frequency domain, and the space-variances of the range phase term and the azimuth phase term are eliminated by chirp scaling (CS) and chirp z-transform (CZT), respectively. The effectiveness of the proposed algorithm is verified by the simulation results.

The Design and Implementation of CW Radar Signal Processing

This paper study continuous wave radar data processing algorithm for low altitude detection, it is applied to continuous wave low altitude search radar data terminal, it can realize real-time correlation and track processing for the data of target echo and false alarm false alarm filtering, to get the real targets of track parameters. Finally, through the realization of FPGA, the experimental results achieve the precision of the system.

Processing of 3D Weather Radar Data with Application for Assimilation in the NWP Model

Abstract The paper is focused on the processing of 3D weather radar data to minimize the impact of a number of errors from different sources, both meteorological and non-meteorological. The data is also quantitatively characterized in terms of its quality. A set of dedicated algorithms based on analysis of the reflectivity field pattern is described. All the developed algorithms were tested on data from the Polish radar network POLRAD. Quality control plays a key role in avoiding the introduction of incorrect information into applications using radar data. One of the quality control methods is radar data assimilation in numerical weather prediction models to estimate initial conditions of the atmosphere. The study shows an experiment with quality controlled radar data assimilation in the COAMPS model using the ensemble Kalman filter technique. The analysis proved the potential of radar data for such applications; however, further investigations will be indispensable.

Review of methods to combat clutter in radar systems

The article provides an overview of existing methods of combating a clutter considering modern domestic and foreign sources. The authors examined the methods of combating a clutter in transmission path, antenna path, and intermediate frequency path as well as in blocks of primary and secondary radar data processing. The paper presents statistical and spectral methods.

Study on Ground Penetrating Radar Signal Denoising Method Based on Surfacelet Transform and 3D Context Model

The ground penetrating radar and radar wave propagation in the subsurface environment is very complex. All kinds of noise and clutter interference is very serious, and detection echo data is a variety of with clutter. Therefore, the key techniques of data processing is to suppress clutter processing of ground penetrating radar record data. Surfacelet transform can efficiently capture and represent local surface singularities with different sizes. In order to improve the reliability of 3D ground penetrating radar detection results and accuracy, this paper presents a three-dimensional ground penetrating radar signal denoising method based on Surfacelet transform. Using Surfacelet transform and 3D context model for ground penetrating radar (GPR) analog signal to denoising, the noise in the case of low signal noise ratio (SNR) still can obtain a better result, and the simulations prove the effectiveness of the method.

Sequential detection of target trajectory tracking loss using the decision statistics of pips

A sequential algorithm of detection of the tracking loss of target trajectory based on the cumulative sums method has been obtained using the values of decision statistics of pips obtained during the primary processing of radar data. In this case the probabilistic data combining method is used for the estimation of moving target parameters at small values of the signal-to-noise ratio. The obtained algorithm was analyzed by using the statistical simulation of a model case of the target tracking with the data of surveillance radar measuring the range and radial velocity of target. The description of decision statistics of target and noise pips involved the use of the Rice and exponential distribution laws, respectively.

Modeling Watershed Response in Semiarid Regions With High-Resolution Synthetic Aperture Radars

In this paper, we propose a methodology devoted to exploit the outstanding characteristics of COSMO-SkyMed for monitoring water bodies in semiarid countries at a scale never experienced before. The proposed approach, based on appropriate registration, calibration, and processing of synthetic aperture radar (SAR) data, allows outperforming the previously available methods for monitoring small reservoirs, mainly carried out with optical data, and severely limited by the presence of cloud coverage, which is a frequent condition in wet season. A tool has been developed for computing the water volumes retained in small reservoirs based on SAR-derived digital elevation model. These data have been used to derive a relationship between storage volumes and surface areas that can be used when bathymetric information is unavailable. Due to the lack of direct measures of river's discharge, the time evolution of water volumes retained at reservoirs has been used to validate a simple rainfall-runoff hydrological model that can provide useful recommendation for the management of small reservoirs. Operational scenarios concerning the improvement in the efficiency of reservoirs management and the estimation of their impact on downstream area point out the applicative outcomes of the proposed method.

Performance Evaluation of Frequency-Domain Algorithms for Chirped Low Frequency UWB SAR Data Processing

This paper studies the performance of the frequency-domain algorithms (FDAs) for low-frequency ultra-wideband synthetic aperture radar (UWB SAR) data processing. First, a generalized theoretical derivation of the FDAs is presented from the viewpoint of SAR signal processing. The derivation not only provides a deeper understanding to the imaging principle of the extended Omega-K algorithm (EOKA), but also makes it compatible and comparable with the other FDAs. Second, the performance comparison on different FDAs is made based on theoretical analysis, simulation and experimental data. The comparison results show that the Omega-K algorithm (ωKA) has the highest imaging precision in the ideal case (i.e, no motion error), but its application is limited by the poor ability of compensating motion errors. In contrast, the EOKA and nonlinear chirp scaling algorithm (NCSA) have excellent performance on dealing with the motion error, but they can only be applied under specific preconditions. Besides, as cetner frequency gets lower, the fractional bandwidth and integration angle get larger, the imaging precision of NCSA greatly decreases, while the ωKA and EOKA still keep high precision.

Long-term diagnostics of precipitation estimates and the development of radar hardware monitoring within a radar product data quality management system

Quality is key to ensuring that the potential offered by weather radar networks is realized. To achieve optimum quality, a comprehensive radar data quality management system, designed to monitor the end-to-end radar data processing chain and evaluate product quality, is being developed at the UK Met Office. Three contrasting elements of this system are described: monitoring of key radar hardware performance indicators; generation of long-term integrations of radar products; and monitoring of radar reflectivity factor using synthesized observations from numerical weather prediction model fields. Examples of each component are presented and ways in which the different types of monitoring information have been used to both identify issues with the radar product data quality and help formulate solutions are given.Editor Z.W. Kundzewicz; Guest editor R.J. MooreCitation Harrison, D., Georgiou, S., Gaussiat, N., and Curtis, A., 2013. Long-term diagnostics of precipitation estimates and the development of radar hardware monitoring within a radar product data quality management system. Hydrological Sciences Journal, 59 (7), 1327–1342. http://dx.doi.org/10.1080/02626667.2013.841316

Calibration of Mst Radar Using Radio Source Virgo-A With Digital Receiver

MST Radar has been established to study the dynamics of atmospheric processes. In this paper, a method, by recording the noise temperature of a radio source during its transit over the radar, to find errors in antenna beam orientation is discussed. The source used for Radar at Gadanki is Virgo-A (3C274) which has appropriate right ascension and declination to be able to pass through the antenna array beam. This paper outlines the Indian MST Radar signal and data processing methods, method of offline data processing, VIRGO transit recording methodology with the help of digital receiver. Keywords: MST, Yagi antenna, signal processing, Concept of Interferometry, Virgo-A

Validation of High Spatial Resolution Wave Data From Envisat RA-2 Altimeter in the Gulf of Cádiz

Extending the applications of satellite altimetry to the coastal zone requires validated, quality controlled data. We present a validation study in the Gulf of Cadiz (SW Iberian Peninsula), an area of relevant social, economic, and strategic importance. We compare against in-situ data seven years (Dec. 2002-Jan. 2010) of significant wave height (SWH) measurements by the Envisat RA-2 altimeter from two sources: 1) standard geophysical data records (GDR, 1 Hz) and 2) the processor developed in the COASTALT (development of radar altimetry data processing in the coastal zone) project (18 Hz). The comparison is made along two Envisat passes (one descending, i.e., north to south, one ascending). For the descending pass (land to ocean transition) the COASTALT processor improves SWH retrieval in the coastal fringe. In particular, the COASTALT SWH product displays accuracies in the sub-coastal strip (12-20 km from the coastline) of a very similar magnitude to those further offshore, representing a clear improvement over GDR. The bias and standard deviation of the difference with regard to the in-situ measurements in the coastal fringe is about 60% and 40% lower, respectively, when COASTALT data are used instead of the standard GDR product. For the ascending pass, the differences in the ocean to land transition are less marked, probably due to the altimeter keeping a good lock on the sea surface until relatively close proximity to the coastline. This validation case shows that this new coastal-oriented product gets closer to the shoreline than before, while also making available higher-resolution along-track estimates.

A Method for Spaceborne SAR Geolocation Based on Continuously Moving Geometry

③ (Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China) Abstract: Geolocation is a very important step in Synthetic Aperture Radar (SAR) data processing. The precision of geolocation severely affects the applications of SAR images. This paper analyzes the influences on SAR geolocation caused by the traditional stop-go approximation, and establishes the range-Doppler equations for the real continuously moving configuration. It also provides a simplified way of solving the equations. Simulations and geolocation experiments on real SAR data of the Beijing area validate the proposed method and show the correctness of the analysis.

NASA Computational Case Study: SAR Data Processing--Ground-Range Projection

Radar technology is used extensively by NASA for remote sensing of the Earth and other planetary bodies. In this case study, we learn about different computational concepts for processing radar data. In particular, we learn how to correct a slanted radar image by projecting it on the surface that was sensed by a radar instrument. The Web extra contains a Matlab data format file, as mentioned in the article.

Maximizing stochastic robustness of static resource allocations in a periodic sensor driven cluster

This research investigates the problem of robust static resource allocation for distributed computing systems operating under imposed Quality of Service (QoS) constraints. Often, such systems are expected to function in an environment where uncertainty in system parameters is common. In such an environment, the amount of processing required to complete a task may fluctuate substantially. Determining a resource allocation that accounts for this uncertainty—in a way that can provide a probability that a given level of QoS is achieved—is an important area of research. We have designed novel techniques for maximizing the probability that a given level of QoS is achieved. These techniques feature a unique application of both path relinking and local search within a Genetic Algorithm. In addition, we define a new methodology for finding resource allocations that are guaranteed to have a non-zero probability of addressing the timing constraints of the system. We demonstrate the use of this methodology within two unique steady-state genetic algorithms designed to maximize the robustness of resource allocations. The performance results for our techniques are presented for a simulated environment that models a heterogeneous cluster-based radar data processing center.