Radar Resolution Research Articles

Passive ISAR part II: narrowband imaging

In part II of the passive inverse synthetic radar (P-ISAR) sequence, implications of narrow signal bandwidths on image formation procedures are considered within the previously developed P-ISAR framework of part I. Specifically, the case of single-channel digital television imaging systems is evaluated. For average sized airborne targets illuminated with narrow signal bandwidths, the response is confined to one or two down-range resolution cells, which has significant impact on both inverse synthetic aperture radar (ISAR) resolutions and motion compensation procedures. Simulated results demonstrate that a non-parametric autofocus method, phase gradient algorithm, does not necessarily converge to a properly focused image. Contrast and entropy metrics are also shown to be insufficient for the narrowband imaging case. A parametric Doppler centroid approach is proposed for phase compensation, with resulting imagery closely resembling clairvoyant reconstruction. Finally, the proposed architecture is evaluated in full-scale experiments of commercial air targets, demonstrating the potential for discriminating large and small aircrafts.

CFAR Detection of High Grazing Angle Sea-Clutter Based on KR Distribution

Constant false alarm rate (CFAR) is a traditional method for radar target detection. A lot of relevant researches assume that the sea clutter obeys the K distribution. However, with the improvement of the radar resolution and the increase of the grazing angle, the distribution of sea clutter gradually deviates from the K distribution. Although good fitting results can be obtained through the more complicated sea clutter models such as KA distribution, K+Rayleigh distribution and so on, the computational efficiency of the detection algorithm is reduced obviously. For computational complexities are mainly caused by non-closed expression, A hybrid sea-clutter distribution (KK distribution) with closed-form expression for high-resolution and high grazing angle situation is proposed, which fits the measured data. Inspired by this, a new hybrid sea-clutter distribution (KR distribution) with 4 parameters is proposed. A new method of parameter estimation for KR distribution is given, that is, passingparameter estimation method based on hybrid distribution. The results show that the fitting effect of KR distribution is better than that of both K distribution and KK distribution in case of high resolution and large grazing angle. And the CA-CFAR based on KR distribution of passing-parameter estimation performs relatively better in the aspect of target detection.

Guest Editorial: Passive High Resolution and Imaging Radar

Guest Editorial: Passive High Resolution and Imaging Radar

3D SAR (SYNTHETIC APERTURE RADAR) IMAGING SIMULATION USING JAVA

One of methods in remote sensing is Synthetic Aperture Radar (SAR). When combined with Range DopplerAlgorithm (RDA) can produce smaller radar resolution only by using normal sized antenna placed atplatform. RDA is able to generate much wider aperture �synthetic� antenna, resulting very narrow beamwidthwhen reach earth's ground. By using already established 2D SAR methods in accuracy andprocessing speed this 3D SAR simulation was developed. Simulated on 15 x 15 pixels grayscale targets atdifferent heights, 3D SAR developed on this research can detect object's height accurately. Thissimulation was developed using JAVA as steppingstone in implementing SAR image processing in smallsystem like embedded system or micro computing which normally using C programming language.

Theoretical and Experimental Study on Echo Fluctuation Suppression of a Cirrus Cloud by Millimeter Wave MIMO Radar

The scattering properties of nonspherical particles can be approximately computed by equivalent spherical theory. The scattering properties of ice particles were approximately computed by Rayleigh approximation because the sizes of the ice particles are smaller than the wavelength of millimeter wave radar. Based on the above assumption, the echo fluctuation of moving particles was analyzed by computing the total backscattering field of a cirrus cloud using the classical vector potential technique. The simulation results showed that echo fluctuation influences the accuracy of retrieving the physical parameters of a cloud. To suppress the echo fluctuation of moving ice particles, a video integrator of a millimeter wave cloud radar would be used. However, video integrators lose the rapidly changing information of ice particles and reduce radar range resolution; thus, we propose the pace-diversity technique of MIMO radar to reduce the echo fluctuation, which could be validated by theoretical computation and experimental measurements.

Factorized discriminative conditional variational auto-encoder for radar HRRP target recognition

Abstract In this paper, we propose a conditional generative model for radar high resolution range profile (HRRP) target recognition to learn the discriminative representations and sufficiently encode the observed feature variability by taking the multi-layer perception (MLP) as the sufficient statistics of posterior approximation distribution, thus offering the potential to improve the overall recognition performance. Considering the target-aspect sensitivity of HRRP, the model is regularized through reconstructing the average profiles. Then we introduce three-way weight tensors for MLPs to capture the multiplicative interactions between label information and HRRP samples, which are then further factorized to effectively reduce model parameters. The extensive experimental results on the measured HRRP data demonstrate that the proposed algorithm achieves the promising target recognition and reconstruction performance.

PDF-Euclidean Distance-Based Adaptive Waveform Selection for Maximizing Radar Practical Resolution

This paper focuses on the issue of adaptive waveform selection to optimize the radar resolvability of closely located targets, which is significant in radar detection and estimation. The well-known ambiguity function-based radar resolution only takes transmitted waveform into consideration, which cannot reflect the achievable resolution of a real radar system. In this paper, we consider radar practical resolution based on a geometric metric, Euclidean Distance between probability density functions (PDF-ED), which is defined as square difference between the probability density functions of radar measurements. The PDF-ED takes the PDF's envelope curve into account and specifies the essential difference between the two PDFs in terms of information geometry. Thus the radar practical resolution based on this conveniently characterizes the effect of waveform parameter, target state and measurement model, etc. and offers a statistical way to assess radar sensing capability for a given application. Accordingly, an adaptive waveform selection criterion aiming to maximize the practical resolution is proposed. The experimental simulations verify its effectiveness in decreasing the probability of error when distinguishing two targets.

Micromotion Feature Extraction Based on Phase-Derived Range and Velocity Measurement

Micromotion features of a target contain unique structural information and motion information about the target, which can be used as an important basis for target recognition. One of the technical bottlenecks for micromotion feature extraction is achieving a fine characterization of the target motion state. With the high-precision range and velocity measurement, this paper uses the phase-derived method to reconstruct the motion state of the target and realize accurate translational compensation for the target. At the same time, the high range resolution of wideband radar is used to effectively extract target scattering points with micromotion from the perspective of the one-dimensional high range resolution profile and the two-dimensional inverse synthetic aperture radar (ISAR) image. On this basis, reliable micromotion feature extraction can be achieved by using the slow variability of the target motion and eliminating the translational or rotational information of the target. The performance of the high-precision range and velocity measurement and the feasibility of the micromotion feature extraction based on phase-derived range and velocity measurement are verified by simulation results. Then, the effectiveness of the proposed algorithm is further verified by carrying out a steel ball ejection experiment and a civil aviation aircraft experiment.

Radar Target Recognition Based on Feature Pyramid Fusion Lightweight CNN

In order to improve the accuracy and robustness of radar target recognition under low SNR conditions, a novel radar high range resolution profile (HRRP) target recognition method based on feature pyramid fusion lightweight CNN is proposed in this paper. The proposed method combines the multi-scale space theory with a deep convolutional neural network. Because of the local connection characteristic of convolutional kernel, feature extracted by CNN mainly focus on the local information of the target. To make full use of both the local and global information in the target HRRP, multi-scale representation of the HRRP with different Gaussian kernels is introduced to construct the multi-channel input of the model. The generalization performance is improved by reducing the parameters of the proposed model with a depthwise separable convolution feature extraction block. Simultaneously, feature pyramid fusion is adapted to take full advantage of the features extracted by each block, which effectively improves the stability of the model and the training efficiency. The experimental results show that the multi-scale representation of the HRRP contributes to robust feature extraction. Meanwhile, the proposed feature pyramid fusion lightweight CNN can effectively prevent over-fitting and improve the stability of the model.

An iterative shrinkage threshold method for radar angular super-resolution

This paper proposes a fast iterative shrinkage threshold (FIST) method for improving radar angular resolution. Based on radar signal processing theory, the implementation of angular super-resolution is equivalent to restoring the radar's target angular information without changing radar's work system. In this method we first establish a convex quadratic programming model by orthogonalising the antenna pattern matrix, which transforms the radar angular super-resolution problem into a constrained optimisation problem. Consequently, the restored angular information can be regarded as the optimal solution of a convex quadratic programming model. Then, the IST algorithm is employed by modifying the residual at each iteration to find this optimal solution of the model. The advantage of this method is to overcome the shortcoming of ringing effect at a low signal to noise ratio (SNR) situation, and the ill-posed problem existing in those classical super-resolution methods is addressed effectively. Simulations further confirm our theoretical discussion, and manifest that a desirable resolution performance is gained and comparisons of signal to restoration error ratio (SRER) provide an amazing result that our method is superior to other methods in terms of efficiency while SNR is less than 20 dB.

HRRP target recognition based on kernel joint discriminant analysis

With the improvement of radar resolution, the dimension of the high resolution range profile (HRRP) has increased. In order to solve the small sample problem caused by the increase of HRRP dimension, an algorithm based on kernel joint discriminant analysis (KJDA) is proposed. Compared with the traditional feature extraction methods, KJDA possesses stronger discriminative ability in the kernel feature space. K-nearest neighbor (KNN) and kernel support vector machine (KSVM) are applied as feature classifiers to verify the classification effect. Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality, and improve target recognition performance.

Comparative Evaluation of Snowfall Retrievals from the CloudSat W-band Radar Using Ground-Based Weather Radars

AbstractInstantaneous liquid-equivalent snowfall rates S retrieved from CloudSat W-band cloud radar reflectivity Ze measurements are compared to estimates of S from operational Weather Surveillance Radar-1988 Doppler (WSR-88D) systems when the CloudSat satellite overflew the ground-based radar sites during spatially extensive nimbostratus snowfall events. For these comparisons, the ground-based radar measurements are interpolated to closely match in space and time spaceborne radar resolution volumes above ground clutter, thus avoiding uncertainties in deriving near-surface snowfall rates from measurements aloft by both radar types. Although typical uncertainties of both ground-based and spaceborne snowfall-rate retrieval approaches are quite high, the results from the standard optimal estimation CloudSat 2C-SNOW-PROFILE algorithm are on average in good agreement with the WSR-88D default snowfall algorithm results with correlation coefficients being around 0.8–0.85. The CloudSat standard optimal estimation snowfall-rate products are also shown to be in satisfactory agreement with retrievals from several simple W-band Ze–S relations suggested earlier. The snowfall rate and snow/ice water content (IWC) parameters from the CloudSat 2C-SNOW-PROFILE algorithm are highly interdependent. A tight relation between S and IWC is apparently introduced through the ice particle fall velocity assumption that is made in the reflectivity-based snowfall retrieval algorithm. This suggests that ice sedimentation rate estimates can also be deduced from applications of numerous empirical IWC–reflectivity relations derived previously for different cloud conditions when appropriate assumptions about fall velocities are made. Intercomparisons between different CloudSat snow/ice water content products indicated significant discrepancies in IWC values from different standard CloudSat retrieval algorithms.

New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR Images

The fine resolution of synthetic aperture radar (SAR) images enables the rapid detection of severely damaged areas in the case of natural disasters. Developing an optimal model for detecting damage in multitemporal SAR intensity images has been a focus of research. Recent studies have shown that computing changes over a moving window that clusters neighboring pixels is effective in identifying damaged buildings. Unfortunately, classifying tsunami-induced building damage into detailed damage classes remains a challenge. The purpose of this paper is to present a novel multiclass classification model that considers a high-dimensional feature space derived from several sizes of pixel windows and to provide guidance on how to define a multiclass classification scheme for detecting tsunami-induced damage. The proposed model uses a support vector machine (SVM) to determine the parameters of the discriminant function. The generalization ability of the model was tested on the field survey of the 2011 Great East Japan Earthquake and Tsunami and on a pair of TerraSAR-X images. The results show that the combination of different sizes of pixel windows has better performance for multiclass classification using SAR images. In addition, we discuss the limitations and potential use of multiclass building damage classification based on performance and various classification schemes. Notably, our findings suggest that the detectable classes for tsunami damage appear to differ from the detectable classes for earthquake damage. For earthquake damage, it is well known that a lower damage grade can rarely be distinguished in SAR images. However, such a damage grade is apparently easy to identify from tsunami-induced damage grades in SAR images. Taking this characteristic into consideration, we have successfully defined a detectable three-class classification scheme.

Influence of the wind turbine blade model on echo's Doppler characteristics

Wind turbines have serious electromagnetic interference to nearby radar stations. Accurately obtaining the Doppler characteristics of wind turbine's radar echo is the key technology and precondition to solve this problem. Based on the point scattering model of the wind turbines, the expression of the echo of the wind turbine blade scattering point model which is located at any point in the space is given, and the Doppler characteristic of the wind turbine blade echo is analysed by combining the existing integral model, which proves that the existing integral model is a special case of the scattering point model. Based on the radar resolution and Rayleigh resolution criteria, the scattering point spacing of the wind turbine blade is given. The simulation model is established according to the actual size of the wind turbines. After the analysis and comparison of the scattering point model and the integral wind turbine blade model, the simulation results of the wind turbine radar Doppler results are consistent with the theoretical analysis, which is of great significance to obtain the Doppler characteristics of the wind turbine blades.

Detecting bird movements with L‐band avian radar and S‐band dual‐polarization Doppler weather radar

AbstractMarine surveillance radars with X‐band (3‐cm) and S‐band (10‐cm) wavelengths not only detect birds but also record return from insects, rain, ground objects and rough seas that often make discrimination of echoes from birds difficult. We compare the density of bird tracks recorded by an L‐band (ca. 23‐cm wavelength) avian radar at the Dallas/Fort Worth International Airport that does not detect insects and rain with the amount of mean bird reflectivity in resolution cells of a dual‐polarization S‐band Doppler weather surveillance radar. Radar data files of 1.5° scans gathered near midnight from January through May for the years 2014, 2015 and 2016 were processed by filtering radar resolution cells to retain only those with backscatter differential phase values characteristic of birds flying more or less perpendicular to the radar beam. In general, increases and decreases in the density of bird movements were correlated significantly between the two radars, but the low R2 values confirm that results obtained from one radar cannot be used to make quantitative estimates for the other. A decrease in the mean biomass index of migrating birds from January through May, and the difference in coverage volume and resolution between the two radars likely account for the low R2 values. Each type of radar has advantages and provides important information on bird movements at different spatial scales.

Sub-Nyquist SAR Based on Pseudo-Random Time-Space Modulation.

Sub-Nyquist sampling technology can ease the conflict between high resolution and wide swath in a synthetic aperture radar (SAR) system. However, the existing sub-Nyquist SAR imposes a constraint on the type of the observed scene and can only reconstruct the scene with small sparsity (i.e., number of significant coefficients). The information channel model of microwave imaging radar based on information theory, in which scene, echo, and the mapping relation between the two correspond to information source, sink, and channel, is built, and noisy-channel coding theorem explains the reason for the aforementioned under this model. To allow the wider application of sub-Nyquist SAR, this paper proposes sub-Nyquist SAR based on pseudo-random space-time modulation. This modulation is the spatial and temporal phase modulation to the traditional SAR raw data and can increase the mutual information of information source and sink so that the scenes with large sparsity can be reconstructed. Simulations of scenes with different sparsity, e.g., an ocean with several ships and urban scenes, were run to verify the validity of our proposed method, and the results show that the scenes with large sparsity can be successfully reconstructed.

Statistical Recognition Methods Based on Complex HRRP

The radar high range resolution echo (radar target complex echo signal) is the sum of the projection vectors of the scattering points of the target detected by high resolution radar in the radar line of sight, and it contains the rich geometrical structure information of the target in that direction. At present, most of the statistical recognition work based on radar high-resolution range echo is using the amplitude information (i.e. High Range Resolution Profile, HRRP) obtained after high range resolution echo of radar target, which will result in limited recognition performance of the recognition system. In fact, the phase information of radar target high range resolution echo is same as amplitude information, and it also reflects some geometrical structure information of radar target, which is valuable to improve the recognition performance of radar target recognition system. In this paper, the complex HRRP was studied. Complex Probabilistic Principal Component Analysis and Complex Adaptive Gaussian Classifier models were used for statistical modeling, and the simulation results were given.

Building Detection in Very High Resolution SAR Images Via Sparse Representation Over Learned Dictionaries

The spatial resolution of synthetic aperture radar (SAR) images has significantly increased in the past few years. As a result, very high resolution images are available for interpretation of the urban area. Buildings are one of the most important parts of the urban area. In this paper, we propose a novel method for building detection from SAR images. First, a set of primary detections, using double bounce, layover, and shadow features, are made. These features are extracted using the order statistics constant false alarm rate method and power ratio detector. The detectors parameters are adjusted in such a way that all bright and dark areas are well extracted. Second, the K-singular value decomposition method is used to learn two dictionaries for discrimination of buildings from clutters. The main idea is to utilize recognition techniques in detection algorithms in order to reduce the number of the false alarms. Gray level statistical, textural, and shape features are used to form the dictionaries. In this step, a secondary set of detections are available, and the final detections are made by the fusion of these two sets. The effectiveness of the algorithm is evaluated by two different TerraSAR-X images from a densely built-up area. The results show that the proposed method is an effective way to detect building through single SAR images.

Quaternary coastal plains as reservoir analogs: Wave-dominated sand-body heterogeneity from outcrop and ground-penetrating radar, central Santos Basin, southeast Brazil

Sub-seismic sand-body characterization depends largely on understanding the heterogeneities observed in outcrops of equivalent depositional settings to subsurface reservoirs (reservoir analogs). Outcrop information, however, rarely covers the whole depositional system. Ground-penetrating radar (GPR), on the other hand, is a powerful tool for the high-resolution visualization of sand-rich deposits in the shallow subsurface, allowing for the description of their internal architecture, relationships with depositional systems, and its larger scale stratigraphic evolution. The internal character of radar patterns can be interpreted with respect to facies seen in exposures, leading to the prediction of heterogeneities below radar resolution. In this paper we use GPR and outcrops to describe the heterogeneity of Quaternary coastal-plain deposits from central Santos Basin (Paraná state, southern Brazil) where Pleistocene and Holocene, wave-dominated regressive barriers and transgressive lagoons and estuaries were formed under the influence of Quaternary sea level and climatic changes. Sedimentary facies reveal the predominance of sand deposited under the influence of tidal processes, fair-weather and storm wave processes. Radarfacies indicate a tendency of progradation of beach-face reflectors over subtidal radarfacies within two regressive successions separated by a regional unconformity. Interpretation of GPR resulted in the definition of 13 radarfacies arranged in three associations reflecting major depositional systems: (1) strandplains formed by prograding beach/dune-ridge sets over subtidal deposits, (2) spit-inlets, with progradation of beach faces over subtidal deposits and longshore accretion of spits, and (3) paleolagoons/paleoestuaries. Sedimentary facies indicate that strandplains and spit-inlets are predominantly sandy, while a muddy composition is expected in paleolagoons/paleoestuaries. The distribution and evolution of these systems were controlled by base-level oscillations and/or sediment supply that resulted in the formation of two regressive barriers separated by a wave ravinement surface configuring two parasequences with a wide variety of inter- and intra-sand body heterogeneities. This arrangement imprints a relatively high degree of compartmentalization within deposits that are traditionally considered continuous and homogeneous. Therefore, the Quaternary coastal plain of Paraná can be used as an analog for compartmentalized coastal reservoirs.

Plane segmentation and fitting method of point clouds based on improved density clustering algorithm for laser radar

Plane segmentation and fitting method of point clouds based on improved density clustering algorithm is put forward. We proposed the plane segmentation and fitting framework, which comprises of four steps: coordinate transformation, filtering, coarse segmentation, fine segmentation, plane fitting. The global coordinates of laser radar are deduced. Abnormal points are removed using statistical filtering based on Gaussian distribution. After filtering, Point clouds are segmented roughly adopting improved density clustering algorithm with proposed threshold, which is originally related to the resolution of laser radar. The point clouds are segmented furthermore with normal vector, which could make up for shortcomings, which are over-segmentation and under-segmentation. Finally planes are fitted with normal vector and centroid point. The laser radar was designed, and plane segmentations and fitting were carried out. The experimental results show that it is effective and automatic for plane segmentation with proposed method.