Ultra-wideband Synthetic Aperture Radar Research Articles

Lightweight CFARNets for Landmine Detection in Ultrawideband SAR

The high-resolution image obtained by ultrawideband synthetic aperture radar (UWB SAR) includes rich features such as shape and scattering features, which can be utilized for landmine discrimination and detection. Due to the high performance and automatic feature learning ability, deep network-based detection methods have been widely employed in SAR target detection. However, existing deep networks do not consider the target characteristics in SAR images, and their structures are too complicated. Therefore, lightweight deep networks with efficient and interpretable blocks are essential. This work investigates how to utilize the SAR characteristics to design a lightweight deep network. The widely employed constant false alarm rates (CFAR) detector is used as a prototype and transformed into trainable multiple-feature network filters. Based on CFAR filters, we propose a new class of networks called CFARNets which can serve as an alternative to convolutional neural networks (CNNs). Furthermore, a two-stage detection method based on CFARNets is proposed. Compared to prevailing CNNs, the complexity and number of parameters of CFARNets are significantly reduced. The features extracted by CFARNets are interpretable as CFAR filters have definite physical significance. Experimental results show that the proposed CFARNets have comparable detection performance compared to other real-time state-of-the-art detectors but with faster inference speed.

A Fast Two-Dimensional Velocity Estimation Method for Multi-Channel UWB SAR

Moving target detection in ultra-wideband synthetic aperture radar (UWB SAR) has to accumulate target energy with long integration time to improve the probability of target detection. The traditional detection and parameters estimation algorithms usually rely on parameter searching, leading to a heavy computing burden. In this paper, a fast two-dimensional velocity estimation method of moving target for multi-channel UWB SAR is proposed. The method processes the multi-channel data separately to realize the target's coherent accumulation and one-dimensional velocity estimation. Then the multi-channel signals are processed jointly to realize the two-dimensional velocity estimation. Specifically, for each channel, the second-order keystone transform is used to correct the range curvature. The symmetric autocorrelation function in the range-frequency domain is used to accumulate the moving-target energy, achieving the target detection and target's azimuth signal extraction. Next, considering the long integration time, the azimuth signal is processed by the non-uniform cubic phase function (NUCPF) based on the fourth-order phase signal to estimate the relative velocity. The azimuth signal is transformed into the Doppler domain for multi-channel data, and the interferometric signal is calculated. The phase generated by the interferometric signal is related to the radial velocity of the target. Thus, the two-dimensional velocity of the target is obtained. Finally, the effectiveness of the algorithm is verified by simulation experiments.

Evaluation of Common Building Wall in See-Through-Wall Application of Ultra-wideband Synthetic Aperture Radar

This paper presents the reproduced images of various objects from synthetic aperture radar (SAR) technique with ultra-wideband (UWB) signals to evaluate the performance of common building walls in the see-through-wall capability regarding security applications, object along with human detection. The walls were designed from four materials, i.e., drywall, wood, insulation, and brick, which are used in real environments. The images of the objects behind the walls were performed using the commercial UWB radar with 2.2 GHz frequency bandwidth, 2 ns of pulse width, 10 MHz of internal repetition frequency, as well as an image reconstruction algorithm. From the measurement result, the insulated wall degraded the SAR image quality. Consequently, a material that enhance the UWB signal as Frequency Selective Surface should be considered to enable SAR for disaster applications.

Joint Down-Range and Cross-Range RFI Suppression in Ultra-Wideband SAR

Radio frequency interference (RFI) is a critical problem for ultra-wideband synthetic aperture radars (UWB SAR) because the VHF/UHF band used by them is shared by other systems as well. A number of solutions have been proposed over the years. Recently, sparsity and low-rank estimation-based solutions were shown to perform better than traditional methods such as adaptive notch filtering. These algorithms model the SAR signal to be sparse and the RFI to be either sparse or low-ranked in nature and solve an optimization problem to estimate the SAR signal and RFI simultaneously. Algorithms in this class share the common characteristic that the SAR signal sparsity is captured by modeling each data vector as a linear combination of shifted SAR pulses. This data model addresses the structure of SAR signals in the down-range direction, but the inter-aperture cross-range structure, i.e., the fact that SAR signals add coherently across the cross-range, has been completely ignored. In this work, we incorporate this “global” 2-D structure of the SAR data into the RFI mitigation problem. Two algorithms are proposed: 1) (2-D) sparse SAR and sparse RFI estimation and 2) (2-D) sparse SAR and low-rank RFI estimation. The experimental results demonstrate that the 2-D model does a much better job of capturing the sparsity of SAR and the 2-D algorithms consistently perform better than the “local” 1-D algorithms. The level of improvement rises significantly in challenging cases-when the noise level and/or the number of RFI bands increases. Experiments are conducted extensively on simulated data sets as well as real SAR and RFI data sets collected by the U.S. Army Research Laboratory (ARL) to validate the proposed framework.

Change Detection in UWB SAR Images Based on Robust Principal Component Analysis

This paper addresses the use of a data analysis tool, known as robust principal component analysis (RPCA), in the context of change detection (CD) in ultrawideband (UWB) very high-frequency (VHF) synthetic aperture radar (SAR) images. The method considers image pairs of the same scene acquired at different time instants. The CD method aims to maximize the probability of detection (PD) and minimize the false alarm rate (FAR). Such aim fits into a multiobjective optimization problem, since maximizing the probability of detection generally implies an increase in the number of false alarms. In that sense, varying the RPCA regularization parameter leads to PD variation with respect to FAR, which is known as receiver operating characteristic (ROC) curve. To evaluate the proposed method, the CARABAS-II data set was considered. The experimental results show that RPCA via principal component pursuit (PCP) can provide a good trade-off between PD and FAR. A comparison between the results obtained with the proposed method and a classical CD algorithm based on the likelihood ratio test provides the pros and cons of the proposed method.

A Microwave Sensing and Imaging Method for Multiphase Flow Metering of Crude Oil Pipes

This article proposes a microwave sensing and imaging method for multiphase flow monitoring and metering in oil and gas pipes. The ultrawideband (UWB) synthetic aperture radar (SAR) technique is used to create a high-resolution image of the pipe cross section. The image is, then, used to estimate each phase volume by extracting the edge of each phase. Additionally, the generalized impulsization technique is presented and applied to reconstruct a sharp-image, and decrease the error in flow rate estimation. Furthermore, a novel technique to enhance the detectability of weak targets in the images is proposed. Finally, a novel sectional image reconstruction technique is also applied to improve the imaging and metering of the stratified flows. All the proposed techniques are evaluated through experiments. This study demonstrates the ability to accurately estimate the crude oil flow rate with a maximum error of 3.8 ${\%}$ . These results show that UWB SAR is capable of providing a reliable and noninvasive solution for multiphase flow metering.

Clutter Removal of Near-Field UWB SAR Imaging for Pipeline Penetrating Radar

Recently, ultrawideband (UWB) near-field synthetic aperture radar (SAR) imaging has been proposed for pipeline penetrating radar applications thanks to its capability in providing suitable resolution and penetration depth. Because of geometrical restrictions, there are many complicated sources of clutter in the pipe. However, this issue has not been investigated yet. In this article, we investigate some well-known clutter removal algorithms using full-wave simulated data and compare their results considering image quality, signal to clutter ratio and contrast. Among candidate algorithms, two-dimensional singular spectrum analysis (2-D SSA) shows a good potential to improve the signal to clutter ratio. However, basic 2-D SSA produces some artifacts in the image. Therefore, to mitigate this issue, we propose “modified 2-D SSA.” After developing the suitable clutter removal algorithm, we propose a complete algorithm chain for pipeline imaging. An UWB near-field SAR monitoring system including an UWB M-sequence sensor and automatic positioner are implemented and the image of drilled perforations in a concrete pipe mimicking oil well structure as a case study is reconstructed to test the proposed algorithm. Compared to the literature, a comprehensive near-field SAR imaging algorithm including new clutter removal is proposed and its performance is verified by obtaining high-quality images in experimental results.

Extended Subaperture Imaging Method for Airborne Low Frequency Ultrawideband SAR Data.

The subaperture processing is one of the essential strategies for low frequency ultrawideband synthetic aperture radar (LF UWB SAR) imaging, especially for the real-time LF UWB SAR imaging because it can improve the parallelization of the imaging algorithm. However, due to the longer synthetic aperture of LF UWB SAR, the traditional subaperture imaging encounters an azimuth ambiguities problem, which severely degrades the focused quality of the imaging results. In this paper, the reason for the presence of azimuth ambiguities in the LF UWB SAR subaperture imaging and its influence on image quality is first analyzed in theory. Then, an extended subaperture imaging method based on the extension of subaperture length before Range Cell Migration Correction (RCMC) was proposed. By lengthening the subaperture length, the azimuth ambiguities are effectively eliminated. Finally, the extended part of subaperture is wiped off before the azimuth compression (AC), and the LF UWB SAR image of high focused quality is obtained. The correctness of the theory analysis and the effectiveness of the proposed method have been validated through simulated and real LF UWB SAR data.

Classifying Multichannel UWB SAR Imagery via Tensor Sparsity Learning Techniques

Using low-frequency (UHF to L-band) ultrawideband synthetic aperture radar (SAR) technology for detecting buried and obscured targets, e.g., bomb or mine, has been successfully demonstrated recently. Despite promising recent progress, a significant open challenge is to distinguish obscured targets from other (natural and manmade) clutter sources in the scene. The problem becomes exacerbated in the presence of noisy responses from rough ground surfaces. In this paper, we present three novel sparsity-driven techniques, which not only exploit the subtle features of raw captured data, but also take advantage of the polarization diversity and the aspect angle dependence information from multichannel SAR data. First, the traditional sparse representation-based classification is generalized to exploit shared information of classes and various sparsity structures of tensor coefficients for multichannel data. Corresponding tensor dictionary learning models are consequently proposed to enhance classification accuracy. Finally, a new tensor sparsity model is proposed to model responses from multiple consecutive looks of objects, which is a unique characteristic of the data set we consider. Extensive experimental results on a high-fidelity electromagnetic simulated data set and radar data collected from the U.S. Army Research Laboratory side-looking SAR demonstrate the advantages of proposed tensor sparsity models.

Comparison of the Rayleigh and K-Distributions for Application in Incoherent Change Detection

The aim of this letter is to compare two incoherent change-detection algorithms for target detection in low-frequency ultrawideband (UWB) synthetic aperture radar (SAR) images. The considered UWB SAR operates in the frequency range from 20 to 90 MHz. Both approaches employ a likelihood ratio test according to the Neyman–Pearson criterion. First, the bivariate Rayleigh probability distribution is used to implement the likelihood ratio test function. This distribution is well known and has been used for change-detection algorithms in low-frequency UWB SAR with good results. Aiming to minimize the false alarm rate and taking into consideration that low-frequency UWB SAR images have high resolution compared to the transmitted wavelength, the second approach implements the test by using a bivariate K-distribution. This distribution has scale and shape parameters that can be used to adjust it to the data. No filter is applied to the data set images, and the results show that with a good statistical model, it is not needed to rely on filtering the data to decrease the number of false alarms. Therefore, we can have a better tradeoff between resolution and detection performance.

Monostatic and Bistatic Low Frequency Ultrawideband SAR Imaging Experiment

Low frequency ultrawideband (LF UWB) synthetic aperture radar (SAR) has lately become of a particular interest to SAR community. Monostatic and bistatic LF UWB SAR system has the well foliage penetrating capability, high-resolution imaging and providing the increased information. In 2015, a monostatic and bistatic LF UWB SAR imaging experiment was conducted. In this experiment, the monostatic and bistatic data were collected simultaneously by operating a moving vehicle-based radar in the SAR mode, in conjunction with a stationary ground-based receiver. The aim was to investigate the imaging property of the bistatic LF UWB SAR system. The one pulse per second (1 PPS) signal in combination with the global position system (GPS) disciplined 100 MHz oscillator from the GPS receivers was used to implement the time and frequency synchronization in this SAR system. The bistatic SAR image was obtained by the subaperture spectrum-equilibrium method integrated with the fast factorized back projection (FFBP) algorithm. Bistatic experiment results are show to prove the validity of the bistatic LF UWB SAR imaging experiment.

Experiment of Azimuth-invariant Bistatic UHF UWB SAR

Bistatic ultrahigh frequency ultrawideband synthetic aperture radar (UHF UWB SAR) has the well ability of the penetrating the foliage, high-resolution imaging and providing the increased information. In the paper, an imaging experiment of the azimuth-invariant bistatic UHF UWB SAR is described and the result is proposed. In August 2015, an along-track bistatic UHF UWB SAR experiment was conducted in China, and the raw data was collected. In this bistatic SAR system, the transmitter and receiver are both carried by a vehicle and separated by an invariable distance. The aim was to investigate the imaging property of the bistatic UHF UWB SAR system. Bistatic image was obtained using the subaperture spectrum-equilibrium method integrated with the fast factorized back projection algorithm (FFBPA). Experiment results prove the validity of the bistatic UHF UWB SAR experiment.

Phase-Coded Stepped Frequency Linear Frequency Modulated Waveform Synthesis Technique for Low Altitude Ultra-Wideband Synthetic Aperture Radar

This paper discusses a new waveform synthesis technique particularly suitable for low altitude ultra-wideband (UWB) synthetic aperture radar (SAR) systems. The proposed technique synthesizes a phase-coded UWB chirp by transmitting a burst of coherent phase-coded sub-band chirp pulses and combining the collected echoes using a special digital signal processing (DSP) algorithm. A lab prototype is built for the feasibility study and validation of the proposed technique. From the results, it is clearly shown that 1) the proposed technique can synthesize UWB chirp pulses; 2) the proposed DSP algorithm can combine the collected sub-band echoes into an echo of a wider bandwidth; and 3) the proposed technique can improve the range resolution of the SAR system without altering much of its system design. The main advantage of the proposed technique is that it can improve the range resolution of an existing SAR system without the need to increase the baseband bandwidth of the system.

GLRT-Based Mitigation of Partial Discharge Effect on Detection of Radial Deformation of Transformer HV Winding Using SAR Imaging Method

Online and on time detection of defects leads to technical and economic merits for networks. Recently, an online method has been introduced for the radial deformation detection of transformer high-voltage winding using electromagnetic waves called an ultrawideband synthetic aperture radar (SAR) imaging method. The basis of this method is comparison of sound and deformed images of transformer windings to detect the radial deformation. Partial discharge (PD), as another defect in the transformers, emits ultrahigh-frequency (UHF) signals that contain 0.3 to 3 GHz frequencies. PD detection by using UHF antennas has been tested on transformers. If an SAR imaging method can be applied in UHF band, a set of antennas can be used for the detection of both defects and get more information about transformer condition. In this paper, a feasibility study into the application of UHF band in an SAR imaging method is carried out. It is shown that PD, during a UHF SAR imaging process, leads to image distortion and wrong decision about deformation condition. In order to achieve correct imaging, a UHF stepped-frequency imaging method is tested for the detection of radial deformation and generalized likelihood ratio test (GLRT) is adapted and applied to solve the problem of the PD effect on imaging process. In addition, the effects of GLRT parameter variations on the problem are investigated in this paper.

Efficient and Robust RFI Extraction Via Sparse Recovery

This paper presents a simple adaptive framework for robust separation and extraction of multiple sources of radio-frequency interference (RFI) from raw ultra-wideband (UWB) radar signals in challenging bandwidth management environments. RFI sources pose critical challenges for UWB systems since 1) RFI often occupies a wide range of the radar’s operating frequency spectrum; 2) RFI might have significant power; and 3) RFI signals are difficult to predict and model due to the nonstationary nature as well as the complexity of various communication devices. Our proposed framework involves an initial RFI estimation step that operates directly on already contaminated radar signals to identify RFI-dominant frequency sub-bands. This vital prior information is then taken into account to construct an adaptive RFI dictionary with various sinusoidal patterns covering the aforementioned RFI-contaminated frequency spectrum. Finally, we employ a sparsity-driven optimization strategy to separate and then extract RFI from the received radar signals. Our method can be implemented as a denoising preprocessing stage for raw radar signals prior to image formation and other follow-up tasks such as target detection and classification. Recovery results from extensive simulated data sets as well as real-world signals collected by the U.S. Army Research Laboratory (ARL) UWB synthetic aperture radar (SAR) systems illustrate the robustness and effectiveness of our proposed framework.

Errata: Spatial resolution analysis of low frequency ultrawidebeam–ultrawideband synthetic aperture radar based on wavenumber domain support of echo data

Errata: Spatial resolution analysis of low frequency ultrawidebeam–ultrawideband synthetic aperture radar based on wavenumber domain support of echo data

Spatial resolution analysis of low frequency ultrawidebeam–ultrawideband synthetic aperture radar based on wavenumber domain support of echo data

Compared with the traditional narrowbeam–narrowband (NB) synthetic aperture radar (SAR), the large fractional signal bandwidth and wide azimuth beamwidth of the low frequency (LF) ultrawidebeam–ultrawideband (UWB) SAR induce serious coupling between the azimuth and range wave numbers of the echo data, which might influence the behavior of the spatial resolution in the SAR imaging. The spatial resolution considering the system wave number coupling and radar squint angle for the LF UWB SAR system is proposed based on the wave number domain support (WDS) of the echo data. First, the SAR imaging geometry is built and then the spatial wave number of the echo data for the LF UWB SAR is analyzed. Second, based on the WDS of the echo data, the spatial resolution of the LF UWB SAR is derived. Moreover, the narrowing/broadening factor defined as the ratio of the proposed spatial resolution to the traditional spatial resolution is presented, which indicates the effects of the fractional bandwidth and associated integration angle as well as the squint angle on the spatial resolution. Finally, simulation results are given to prove the correctness of the theory analysis and validity of the proposed spatial resolution.

ISAR imaging of high-speed moving targets in short-range using impulse radar

In order to improve the measurement accuracy of weapons, ultra-wideband (UWB) inverse synthetic aperture radar (ISAR) imaging of high-speed moving targets in short-range is studied. In the scheme, because the positions of scatters vary with the radar line-of-sight (LOS) angle when the high-speed target is passing through the closest point of the approach, the concept of equivalent scattering point (ESP) is proposed. The Hough transform is employed to implement ESP motion parameter estimation, while the target translational motion is compensated by using the ESP time delay, and the angle spanned by the target is estimated by utilizing geometric relationship of ESP motion. Finally, the two-dimensional image of the target is restructured by using the near field time-domain back-projection algorithm. Simulation results confirm that the proposed method is efficient.

Modified nonlinear chirp scaling algorithm for one-stationary bistatic low frequency ultrawideband synthetic aperture radar imaging

We outline a modified nonlinear chirp scaling algorithm (NLCSA) for one-stationary bistatic synthetic aperture radar (BiSAR) using low-frequency (LF) and an ultrawideband (UWB) signal. The existing NLCSAs for one-stationary BiSAR will be invalidated in an LF and UWB application because the higher-order phase error (HOPE) due to the Taylor series approximation cannot be ignored in this case. We first analyze the effect of the HOPE on bistatic LF UWB SAR imaging theoretically. Then, a modified method based on the higher-order phase compensation (HOPC) and block processing is proposed to deal with the HOPE. Finally, the simulation results demonstrate the correctness of the theory analyses, and the validity of the proposed algorithm is presented.

Ultrawideband Synthetic Aperture Radar for Respiratory Motion Detection

This paper describes a method for detecting human respiratory motion with respiration-rate estimation using ultrawideband (UWB) synthetic aperture radar (SAR). In addition, positions of the breathing humans can be spatially resolved. The coherence of the SAR data is used to derive the generalized coherence factor (GCF), the generalized incoherence factor (GICF), and the filter-bank-based GCF (FBGCF) for the detection and estimation. The coherence is decreased by motion of the image object, and the GCF and GICF are used to detect the position of the moving object. Furthermore, since the spectral shift of SAR data varies with motion, the FBGCF can be used to determine the respiration rate. The efficacy of the proposed method was tested by constructing a UWB SAR system with a 1.5-GHz center frequency and a 1-GHz bandwidth. Through-wall SAR data of objects with various motions were acquired and analyzed. Moving objects were successively detected with a spectral resolution of 0.1 Hz, and using the GICF achieved a rejection ratio of 38 dB between stationary and moving objects. These results indicate that the FBGCF can be used for respiration-rate estimation.