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15 pages, 10096 KiB

Open AccessArticle

The Use of the Reassignment Technique in the Time-Frequency Analysis Applied in VHF-Based Passive Forward Scattering Radar

by Marek Płotka, Karol Abratkiewicz, Mateusz Malanowski, Piotr Samczyński and Krzysztof Kulpa

Sensors 2020, 20(12), 3434; https://doi.org/10.3390/s20123434 - 17 Jun 2020

Cited by 9 | Viewed by 2735

Abstract

This paper presents the application of the time-frequency (TF) reassignment technique in passive forward scattering radar (FSR) using Digital Video Broadcasting – Terrestrial (DVB-T) transmitters of opportunity operating in the Very High Frequency (VHF) band. The validation of the proposed technique was done [...] Read more.

This paper presents the application of the time-frequency (TF) reassignment technique in passive forward scattering radar (FSR) using Digital Video Broadcasting – Terrestrial (DVB-T) transmitters of opportunity operating in the Very High Frequency (VHF) band. The validation of the proposed technique was done using real-life signals collected by the passive radar demonstrator during a measurement campaign. The scenario was chosen to test detection ranges and the capability of estimating the kinematic parameters of a cooperative airborne target in passive FSR geometry. Additionally, in the experiment the possibility of utilizing FSR geometry in foliage penetration conditions taking advantage of the VHF band of a DVB-T illuminator of opportunity was tested. The results presented in this paper show that the concentrated (reassigned) energy distribution of the signal in the TF domain allows a more precise target Doppler rate to be estimated using the Hough transform. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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21 pages, 6204 KiB

Open AccessArticle

A Hybrid SAR/ISAR Approach for Refocusing Maritime Moving Targets with the GF-3 SAR Satellite

by Zhishuo Yan, Yi Zhang and Heng Zhang

Sensors 2020, 20(7), 2037; https://doi.org/10.3390/s20072037 - 4 Apr 2020

Cited by 5 | Viewed by 4001

Abstract

Due to self-motion and sea waves, moving ships are typically defocused in synthetic aperture radar (SAR) images. To focus non-cooperative targets, the inverse SAR (ISAR) technique is commonly used with motion compensation. The hybrid SAR/ISAR approach allows a long coherent processing interval (CPI), [...] Read more.

Due to self-motion and sea waves, moving ships are typically defocused in synthetic aperture radar (SAR) images. To focus non-cooperative targets, the inverse SAR (ISAR) technique is commonly used with motion compensation. The hybrid SAR/ISAR approach allows a long coherent processing interval (CPI), in which SAR targets are processed with ISAR processing, and exploits the advantages of both SAR and ISAR to generate well-focused images of moving targets. In this paper, based on hybrid SAR/ISAR processing, we propose an improved rank-one phase estimation method (IROPE). By using an iterative two-step convergence approach in the IROPE, the proposed method achieves accurate phase error, maintains robustness to noise and performs well in estimating various phase errors. The performance of the proposed method is analyzed by comparing it with other focusing algorithms in terms of processing simulated data and real complex image data acquired by Gaofen-3 (GF-3) in spotlight mode. The results demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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16 pages, 15169 KiB

Open AccessArticle

Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack

by Bruna G. Palm, Dimas I. Alves, Mats I. Pettersson, Viet T. Vu, Renato Machado, Renato J. Cintra, Fábio M. Bayer, Patrik Dammert and Hans Hellsten

Sensors 2020, 20(7), 2008; https://doi.org/10.3390/s20072008 - 3 Apr 2020

Cited by 14 | Viewed by 2898

Abstract

This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false [...] Read more.

This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean; (iii) median; (iv) intensity mean; and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicates that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of

97 %

and a false alarm rate of

0.11 / {km}^{2}

, when considering military vehicles concealed in a forest. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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28 pages, 8177 KiB

Open AccessArticle

A Multi-Scale U-Shaped Convolution Auto-Encoder Based on Pyramid Pooling Module for Object Recognition in Synthetic Aperture Radar Images

by Sirui Tian, Yiyu Lin, Wenyun Gao, Hong Zhang and Chao Wang

Sensors 2020, 20(5), 1533; https://doi.org/10.3390/s20051533 - 10 Mar 2020

Cited by 5 | Viewed by 4333

Abstract

Although unsupervised representation learning (RL) can tackle the performance deterioration caused by limited labeled data in synthetic aperture radar (SAR) object classification, the neglected discriminative detailed information and the ignored distinctive characteristics of SAR images can lead to performance degradation. In this paper, [...] Read more.

Although unsupervised representation learning (RL) can tackle the performance deterioration caused by limited labeled data in synthetic aperture radar (SAR) object classification, the neglected discriminative detailed information and the ignored distinctive characteristics of SAR images can lead to performance degradation. In this paper, an unsupervised multi-scale convolution auto-encoder (MSCAE) was proposed which can simultaneously obtain the global features and local characteristics of targets with its U-shaped architecture and pyramid pooling modules (PPMs). The compact depth-wise separable convolution and the deconvolution counterpart were devised to decrease the trainable parameters. The PPM and the multi-scale feature learning scheme were designed to learn multi-scale features. Prior knowledge of SAR speckle was also embedded in the model. The reconstruction loss of the MSCAE was measured by the structural similarity index metric (SSIM) of the reconstructed data and the images filtered by the improved Lee sigma filter. A speckle suppression restriction was also added in the objective function to guarantee that the speckle suppression procedure would take place in the feature learning stage. Experimental results with the MSTAR dataset under the standard operating condition and several extended operating conditions demonstrated the effectiveness of the proposed model in SAR object classification tasks. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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20 pages, 1036 KiB

Open AccessArticle

Compressive Sensing-Based Bandwidth Stitching for Multichannel Microwave Radars

by Paul Berry, Ngoc Hung Nguyen and Hai-Tan Tran

Sensors 2020, 20(3), 665; https://doi.org/10.3390/s20030665 - 24 Jan 2020

Cited by 10 | Viewed by 2407

Abstract

The problem of obtaining high range resolution (HRR) profiles for non-cooperative target recognition by coherently combining data from narrowband radars was investigated using sparse reconstruction techniques. If the radars concerned operate within different frequency bands, then this process increases the overall effective bandwidth [...] Read more.

The problem of obtaining high range resolution (HRR) profiles for non-cooperative target recognition by coherently combining data from narrowband radars was investigated using sparse reconstruction techniques. If the radars concerned operate within different frequency bands, then this process increases the overall effective bandwidth and consequently enhances resolution. The case of unknown range offsets occurring between the radars’ range profiles due to incorrect temporal and spatial synchronisation between the radars was considered, and the use of both pruned orthogonal matching pursuit and refined

l_{1}

-norm regularisation solvers was explored to estimate the offsets between the radars’ channels so as to attain the necessary coherence for combining their data. The proposed techniques were demonstrated and compared using simulated radar data. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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22 pages, 19455 KiB

Open AccessArticle

On the Slow-Time k-Space and its Augmentation in Doppler Radar Tomography

by Hai-Tan Tran, Emma Heading and Brian W.-H. Ng

Sensors 2020, 20(2), 513; https://doi.org/10.3390/s20020513 - 16 Jan 2020

Cited by 4 | Viewed by 3199

Abstract

Doppler Radar Tomography (DRT) relies on spatial diversity from rotational motion of a target rather than spectral diversity from wide bandwidth signals. The slow-time k-space is a novel form of the spatial frequency space generated by the relative rotational motion of a [...] Read more.

Doppler Radar Tomography (DRT) relies on spatial diversity from rotational motion of a target rather than spectral diversity from wide bandwidth signals. The slow-time k-space is a novel form of the spatial frequency space generated by the relative rotational motion of a target at a single radar frequency, which can be exploited for high-resolution target imaging by a narrowband radar with Doppler tomographic signal processing. This paper builds on a previously published work and demonstrates, with real experimental data, a unique and interesting characteristic of the slow-time k-space: it can be augmented and significantly enhance imaging resolution by signal processing. High resolution can reveal finer details in the image, providing more information to identify unknown targets detected by the radar. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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18 pages, 2584 KiB

Open AccessArticle

Compressive Sensing for Tomographic Imaging of a Target with a Narrowband Bistatic Radar

by Ngoc Hung Nguyen, Paul Berry and Hai-Tan Tran

Sensors 2019, 19(24), 5515; https://doi.org/10.3390/s19245515 - 13 Dec 2019

Cited by 3 | Viewed by 2446

Abstract

This paper introduces a new approach to bistatic radar tomographic imaging based on the concept of compressive sensing and sparse reconstruction. The field of compressive sensing has established a mathematical framework which guarantees sparse solutions for under-determined linear inverse problems. In this paper, [...] Read more.

This paper introduces a new approach to bistatic radar tomographic imaging based on the concept of compressive sensing and sparse reconstruction. The field of compressive sensing has established a mathematical framework which guarantees sparse solutions for under-determined linear inverse problems. In this paper, we present a new formulation for the bistatic radar tomography problem based on sparse inversion, moving away from the conventional k-space tomography approach. The proposed sparse inversion approach allows high-quality images of the target to be obtained from limited narrowband radar data. In particular, we exploit the use of the parameter-refined orthogonal matching pursuit (PROMP) algorithm to obtain a sparse solution for the sparse-based tomography formulation. A key important feature of the PROMP algorithm is that it is capable of tackling the dictionary mismatch problem arising from off-grid scatterers by perturbing the dictionary atoms and allowing them to go off the grid. Performance evaluation studies involving both simulated and real data are presented to demonstrate the performance advantage of the proposed sparsity-based tomography method over the conventional k-space tomography method. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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33 pages, 66091 KiB

Open AccessArticle

Geometrical Matching of SAR and Optical Images Utilizing ASIFT Features for SAR-based Navigation Aided Systems

by Jakub Markiewicz, Karol Abratkiewicz, Artur Gromek, Wojciech Ostrowski, Piotr Samczyński and Damian Gromek

Sensors 2019, 19(24), 5500; https://doi.org/10.3390/s19245500 - 12 Dec 2019

Cited by 18 | Viewed by 4554

Abstract

This article presents a new approach to the estimation of shift and rotation between two images from different kinds of imaging sensors. The first of the image is an orthophotomap that is created using optical sensors with georeference information. The second one is [...] Read more.

This article presents a new approach to the estimation of shift and rotation between two images from different kinds of imaging sensors. The first of the image is an orthophotomap that is created using optical sensors with georeference information. The second one is created utilizing a Synthetic Aperture Radar (SAR) sensor.The proposed solution can be mounted on a flying platform, and, during the flight, the obtained SAR images are compared with the reference optical images, and thus it is possible to calculate the shift and rotation between these two images and then the direct georeferencing error. Since both images have georeference information, it is possible to calculate the navigation correction in cases when the drift of the calculated trajectory is expected. The method can be used in platforms where there is no satellite navigation signal and the trajectory is calculated on the basis of an inertial navigation system, which is characterized by a significant error. The proposed method of estimating the navigation error utilizing Affine Scale-Invariant Feature Transform (ASIFT) and Structure from Motion (SfM) is described, and techniques for improving the quality of SAR imaging using despeckling filters are presented. The methodology was tested and verified using real-life SAR images. Differences between the results obtained for a few selected despeckling methods were compared and commented on. Deep investigation of the nature of the SAR imaging technique and noise creation character allows new algorithms to be developed, which can be implemented on flying platforms to support existing navigation systems in which trajectory error occurs. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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20 pages, 1622 KiB

Open AccessArticle

Research of a Radar Imaging Algorithm Based on High Pulse Repetition Random Frequency Hopping Synthetic Wideband Waveform

by Songhua He and Xiaotian Wu

Sensors 2019, 19(24), 5424; https://doi.org/10.3390/s19245424 - 9 Dec 2019

Cited by 3 | Viewed by 2802

Abstract

Aiming at the imaging algorithm of high-pulse-repetition random-frequency-hopping synthetic wideband radar on a supersonic/hypersonic aircraft platform, this study established an echo simulation model of target and clutter, analyzed the special range-Doppler coupling effect and its influence on imaging, and proposes a method of [...] Read more.

Aiming at the imaging algorithm of high-pulse-repetition random-frequency-hopping synthetic wideband radar on a supersonic/hypersonic aircraft platform, this study established an echo simulation model of target and clutter, analyzed the special range-Doppler coupling effect and its influence on imaging, and proposes a method of imaging with pipeline-parallel processing based on generalized 2D matched-filtering and Doppler pre-processing. In the method, Doppler-beam-sharpening was advanced to be performed with the pulse compression process in each frame, and the special range-Doppler coupling effect caused by high dynamic motion of platform and random frequency hopping in bandwidth synthesis was well suppressed; several modes of random frequency hopping were designed and the pipeline-parallel image processing algorithm was optimized for each mode. Theoretical analysis and simulation results show that the proposed imaging method can effectively avoid the divergence of 2D range-Doppler images in the range direction, and can meet the requirements of real-time imaging. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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12 pages, 1611 KiB

Open AccessCommunication

New Concept of Combined Microwave Delay Lines for Noise Radar-Based Remote Sensors

by Zenon Szczepaniak and Waldemar Susek

Sensors 2019, 19(22), 4842; https://doi.org/10.3390/s19224842 - 6 Nov 2019

Cited by 2 | Viewed by 2806

Abstract

Delay lines with a tunable length are used in a number of applications in the field of microwave techniques. The digitally-controlled analogue wideband delay line is particularly useful in noise radar applications as a precise detector of movement. In order to perform coherent [...] Read more.

Delay lines with a tunable length are used in a number of applications in the field of microwave techniques. The digitally-controlled analogue wideband delay line is particularly useful in noise radar applications as a precise detector of movement. In order to perform coherent reception in the noise radar, a delay line with a variable delay value is required. To address this issue, this paper comprises a new concept of a digitally-controlled delay line with a set of fine distance gates. In the paper, a solution for micro-movement detection is proposed, which is based on direct signal processing in the time domain with the use of a microwave analogue correlator. This concept assumes the use of a microwave analogue tapped delay line structure. It was found that the optimal solution for a noise radar with an analogue signal correlator is a combined delay line consisting of switched reference sections, a tapped delay line, and a precision phase shifter. The combined delay line presented in this paper is dedicated to serving as the adjustable reference delay for a noise radar intended for the detection of micro-movement. The paper contains the calculation results and delay line implementation for a given example. The new structure of the analogue tapped delay line with the calculation of optimal parameters is also presented. The precise detector of movement can be successfully used for the remote sensing of human vital signs (especially through-the-wall), e.g., breathing and heart beating, with the simultaneous determination of position. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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12 pages, 6413 KiB

Open AccessArticle

A MIMO-SAR Tomography Algorithm Based on Fully-Polarimetric Data

by Lingyu Kong and Xiaojian Xu

Sensors 2019, 19(22), 4839; https://doi.org/10.3390/s19224839 - 6 Nov 2019

Cited by 5 | Viewed by 3080

Abstract

A fully-polarimetric unitary multiple signal classification (UMUSIC) tomography algorithm is proposed, which can be used for acquiring high-resolution three-dimensional (3D) imagery, in a polarimetric multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with a small number of baselines. In terms of the elevation resolution, UMUSIC [...] Read more.

A fully-polarimetric unitary multiple signal classification (UMUSIC) tomography algorithm is proposed, which can be used for acquiring high-resolution three-dimensional (3D) imagery, in a polarimetric multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with a small number of baselines. In terms of the elevation resolution, UMUSIC provides an improvement over standard MUSIC by utilizing the conjugate of the complex sample data and converting the complex covariance matrix into a real matrix. The combination of UMUSIC and fully-polarimetric data permits a further reduction of the noise of the sample covariance matrix, which is obtained through pixel averaging of multiple two-dimensional (2D) images. Considering the consistency of four polarizations, this algorithm not only makes scattering centers have the same estimated height in four polarizations, but it also improves the estimation accuracy. Simulation results show that this algorithm outperforms the popular distributed compressed sensing (DCS). Image processing of measured data of an aircraft model using a multiple-input multiple-output synthetic aperture radar (MIMO-SAR) with six baselines is presented to validate the proposed algorithm. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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20 pages, 2479 KiB

Open AccessArticle

Two-Dimensional Augmented State–Space Approach with Applications to Sparse Representation of Radar Signatures

by Kejiang Wu and Xiaojian Xu

Sensors 2019, 19(21), 4631; https://doi.org/10.3390/s19214631 - 24 Oct 2019

Cited by 4 | Viewed by 2566

Abstract

In this work, we focus on sparse representation of two-dimensional (2-D) radar signatures for man-made targets. Based on the damped exponential (DE) model, a 2-D augmented state–space approach (ASSA) is proposed to estimate the parameters of scattering centers on complex man-made targets, i.e., [...] Read more.

In this work, we focus on sparse representation of two-dimensional (2-D) radar signatures for man-made targets. Based on the damped exponential (DE) model, a 2-D augmented state–space approach (ASSA) is proposed to estimate the parameters of scattering centers on complex man-made targets, i.e., the complex amplitudes and the poles in down-range and aspect dimensions. An augmented state–space approach is developed for pole estimation of down-range dimension. Multiple-range search strategy, which applies one-dimensional (1-D) state–space approach (SSA) to the 1-D data for each down-range cell, is used to alleviate the pole-pairing problem occurring in previous algorithms. Effectiveness of the proposed approach is verified by the numerical and measured inverse synthetic aperture radar (ISAR) data. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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15 pages, 3364 KiB

Open AccessArticle

Azimuth Phase Center Adaptive Adjustment upon Reception for High-Resolution Wide-Swath Imaging

by Wei Xu, Jialuo Hu, Pingping Huang, Weixian Tan and Yifan Dong

Sensors 2019, 19(19), 4277; https://doi.org/10.3390/s19194277 - 2 Oct 2019

Cited by 5 | Viewed by 2460

Abstract

A spaceborne azimuth multichannel synthetic aperture radar (SAR) system can effectively realize high resolution wide swath (HRWS) imaging. However, the performance of this system is restricted by its two inherent defects. Firstly, non-uniform sampling is generated if the pulse repetition frequency (PRF) deviates [...] Read more.

A spaceborne azimuth multichannel synthetic aperture radar (SAR) system can effectively realize high resolution wide swath (HRWS) imaging. However, the performance of this system is restricted by its two inherent defects. Firstly, non-uniform sampling is generated if the pulse repetition frequency (PRF) deviates from the optimum value. Secondly, multichannel systems are very sensitive to channel errors, which are difficult to completely eliminate. In this paper, we propose a novel receive antenna architecture with an azimuth phase center adaptive adjustment which adjusts the phase center position of each sub-aperture to improve multichannel SAR system performance. On one hand, the optimum value of the PRF can be adaptively adjusted within a certain range by adjusting receiving phase centers to obtain uniform azimuth sampling. On the other hand, false targets introduced by residual channel errors after azimuth multichannel error compensation can be further suppressed. The effectiveness of the proposed method to compensate for non-uniform sampling and suppress false targets is verified by simulation experiments. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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25 pages, 7958 KiB

Open AccessArticle

Noise Suppression for GPR Data Based on SVD of Window-Length-Optimized Hankel Matrix

by Wei Xue, Yan Luo, Yue Yang and Yujin Huang

Sensors 2019, 19(17), 3807; https://doi.org/10.3390/s19173807 - 3 Sep 2019

Cited by 14 | Viewed by 4383

Abstract

Ground-penetrating radar (GPR) is an effective tool for subsurface detection. Due to the influence of the environment and equipment, the echoes of GPR contain significant noise. In order to suppress noise for GPR data, a method based on singular value decomposition (SVD) of [...] Read more.

Ground-penetrating radar (GPR) is an effective tool for subsurface detection. Due to the influence of the environment and equipment, the echoes of GPR contain significant noise. In order to suppress noise for GPR data, a method based on singular value decomposition (SVD) of a window-length-optimized Hankel matrix is proposed in this paper. First, SVD is applied to decompose the Hankel matrix of the original data, and the fourth root of the fourth central moment of singular values is used to optimize the window length of the Hankel matrix. Then, the difference spectrum of singular values is used to construct a threshold, which is used to distinguish between components of effective signals and components of noise. Finally, the Hankel matrix is reconstructed with singular values corresponding to effective signals to suppress noise, and the denoised data are recovered from the reconstructed Hankel matrix. The effectiveness of the proposed method is verified with both synthetic and field measurements. The experimental results show that the proposed method can effectively improve noise removal performance under different detection scenarios. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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12 pages, 1555 KiB

Open AccessArticle

Focusing Bistatic Forward-Looking Synthetic Aperture Radar Based on an Improved Hyperbolic Range Model and a Modified Omega-K Algorithm

by Chenchen Wang, Weimin Su, Hong Gu and Jianchao Yang

Sensors 2019, 19(17), 3792; https://doi.org/10.3390/s19173792 - 1 Sep 2019

Cited by 2 | Viewed by 3155

Abstract

For parallel bistatic forward-looking synthetic aperture radar (SAR) imaging, the instantaneous slant range is a double-square-root expression due to the separate transmitter-receiver system form. The hyperbolic approximation provides a feasible solution to convert the dual square-root expression into a single-square-root expression. However, some [...] Read more.

For parallel bistatic forward-looking synthetic aperture radar (SAR) imaging, the instantaneous slant range is a double-square-root expression due to the separate transmitter-receiver system form. The hyperbolic approximation provides a feasible solution to convert the dual square-root expression into a single-square-root expression. However, some high-order terms of the range Taylor expansion have not been considered during the slant range approximation procedure in existing methods, and therefore, inaccurate phase compensation occurs. To obtain a more accurate compensation result, an improved hyperbolic approximation range form with high-order terms is proposed. Then, a modified omega-K algorithm based on the new slant range form is adopted for parallel bistatic forward-looking SAR imaging. Several simulation results validate the effectiveness of the proposed imaging algorithm. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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13 pages, 6369 KiB

Open AccessFeature PaperArticle

Target Doppler Rate Estimation Based on the Complex Phase of STFT in Passive Forward Scattering Radar

by Karol Abratkiewicz, Piotr Krysik, Zbigniew Gajo and Piotr Samczyński

Sensors 2019, 19(16), 3627; https://doi.org/10.3390/s19163627 - 20 Aug 2019

Cited by 11 | Viewed by 4203

Abstract

This article presents a novel approach to the estimation of motion parameters of objects in passive forward scattering radars (PFSR). In such systems, most frequency modulated signals which are used have parameters that depend on the geometry of a radar scene and an [...] Read more.

This article presents a novel approach to the estimation of motion parameters of objects in passive forward scattering radars (PFSR). In such systems, most frequency modulated signals which are used have parameters that depend on the geometry of a radar scene and an object’s motion. Worth noting is that in bistatic (or multistatic) radars forward scattering geometry is present thus in this case only Doppler measurements are available while the range measurement is unambiguous. In this article the modulation factor, also called the Doppler rate, was determined based on the chirp rate (equivalent Doppler rate) estimation concept in the time-frequency (TF) domain. This approach utilizes the idea of the complex phase of the short-time Fourier transform (STFT) and its modification known from the literature. Mathematical dependencies were implemented and verified and the simulation results were described. The accuracy of the considered estimators were also verified using the Cramer-Rao lower bound (CRLB) to which simulated data for the considered estimators was compared. The proposed method was validated using a real-life signal collected from a radar operating in PFSR geometry. The Doppler rate provided by a car crossing the baseline between the receiver and the GSM transmitter was estimated. Finally, the concept of using CR estimation, which in the case of PFSR can be understood as Doppler rate, was confirmed on the basis of both simulated and real-life data. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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17 pages, 24052 KiB

Open AccessArticle

Microwave Staring Correlated Imaging Based on Unsteady Aerostat Platform

by Zheng Jiang, Yuanyue Guo, Jie Deng, Weidong Chen and Dongjin Wang

Sensors 2019, 19(12), 2825; https://doi.org/10.3390/s19122825 - 24 Jun 2019

Cited by 4 | Viewed by 3541

Abstract

Microwave staring correlated imaging (MSCI), with the technical capability of high-resolution imaging on relatively stationary targets, is a promising approach for remote sensing. For the purpose of continuous observation of a fixed key area, a tethered floating aerostat is often used as the [...] Read more.

Microwave staring correlated imaging (MSCI), with the technical capability of high-resolution imaging on relatively stationary targets, is a promising approach for remote sensing. For the purpose of continuous observation of a fixed key area, a tethered floating aerostat is often used as the carrying platform for MSCI radar system; however, its non-cooperative random motion of the platform caused by winds and its unbalance will result in blurred imaging, and even in imaging failure. This paper presents a method that takes into account the instabilities of the platform, combined with an adaptive variable suspension (AVS) and a position and orientation system (POS), which can automatically control the antenna beam orientation to the target area and measure dynamically the position and attitude of the stochastic radiation radar array, respectively. By analyzing the motion feature of aerostat platform, the motion model of the radar array is established, then its real-time position vector and attitude angles of each antenna can be represented; meanwhile the selection matrix of beam coverage is introduced to indicate the dynamic illumination of the radar antenna beam in the overall imaging area. Due to the low-speed discrete POS data, a curve-fitting algorithm can be used to estimate its accurate position vector and attitude of each antenna at each high-speed sampling time during the imaging period. Finally, the MSCI model based on the unsteady aerostat platform is set up. In the simulations, the proposed scheme is validated such that under the influence of different unstable platform movements, a better imaging performance can be achieved compared with the conventional MSCI method. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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12 pages, 318 KiB

Open AccessArticle

Target Localization Using Double-Sided Bistatic Range Measurements in Distributed MIMO Radar Systems

by Hyuksoo Shin and Wonzoo Chung

Sensors 2019, 19(11), 2524; https://doi.org/10.3390/s19112524 - 2 Jun 2019

Cited by 6 | Viewed by 2736

Abstract

We develop a novel approach improving existing target localization algorithms for distributed multiple-input multiple-output (MIMO) radars based on bistatic range measurements (BRMs). In the proposed algorithms, we estimate the target position with auxiliary parameters consisting of both the target–transmitter distances and the target–receiver [...] Read more.

We develop a novel approach improving existing target localization algorithms for distributed multiple-input multiple-output (MIMO) radars based on bistatic range measurements (BRMs). In the proposed algorithms, we estimate the target position with auxiliary parameters consisting of both the target–transmitter distances and the target–receiver distances (hence, “double-sided”) in contrast to the existing BRM methods. Furthermore, we apply the double-sided approach to multistage BRM methods. Performance improvements were demonstrated via simulations and a limited theoretical analysis was attempted for the ideal two-dimensional case. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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17 pages, 3076 KiB

Open AccessArticle

Strip-Mode Microwave Staring Correlated Imaging with Self-Calibration of Gain–Phase Errors

by Rui Xia, Yuanyue Guo, Weidong Chen and Dongjin Wang

Sensors 2019, 19(5), 1079; https://doi.org/10.3390/s19051079 - 3 Mar 2019

Cited by 2 | Viewed by 2815

Abstract

Microwave staring correlated imaging (MSCI) can realize super resolution imaging without the limit of relative motion with the target. However, gain–phase errors generally exist in the multi-transmitter array, which results in imaging model mismatch and degrades the imaging performance considerably. In order to [...] Read more.

Microwave staring correlated imaging (MSCI) can realize super resolution imaging without the limit of relative motion with the target. However, gain–phase errors generally exist in the multi-transmitter array, which results in imaging model mismatch and degrades the imaging performance considerably. In order to solve the problem of MSCI with gain–phase error in a large scene, a method of MSCI with strip-mode self-calibration of gain–phase errors is proposed. The method divides the whole imaging scene into multiple imaging strips, then the strip target scattering coefficient and the gain–phase errors are combined into a multi-parameter optimization problem that can be solved by alternate iteration, and the error estimation results of the previous strip can be carried into the next strip as the initial value. All strips are processed in multiple rounds, and the gain–phase error estimation results of the last strip can be taken as the initial value and substituted into the first strip for the correlated processing of the next round. Finally, the whole imaging in a large scene can be achieved by multi-strip image splicing. Numerical simulations validate its potential advantages to shorten the imaging time dramatically and improve the imaging and gain–phase error estimation performance. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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Review

Jump to: Research

19 pages, 8135 KiB

Open AccessReview

Compressed Sensing Radar Imaging: Fundamentals, Challenges, and Advances

by Jungang Yang, Tian Jin, Chao Xiao and Xiaotao Huang

Sensors 2019, 19(14), 3100; https://doi.org/10.3390/s19143100 - 13 Jul 2019

Cited by 35 | Viewed by 6632

Abstract

In recent years, sparsity-driven regularization and compressed sensing (CS)-based radar imaging methods have attracted significant attention. This paper provides an introduction to the fundamental concepts of this area. In addition, we will describe both sparsity-driven regularization and CS-based radar imaging methods, along with [...] Read more.

In recent years, sparsity-driven regularization and compressed sensing (CS)-based radar imaging methods have attracted significant attention. This paper provides an introduction to the fundamental concepts of this area. In addition, we will describe both sparsity-driven regularization and CS-based radar imaging methods, along with other approaches in a unified mathematical framework. This will provide readers with a systematic overview of radar imaging theories and methods from a clear mathematical viewpoint. The methods presented in this paper include the minimum variance unbiased estimation, least squares (LS) estimation, Bayesian maximum a posteriori (MAP) estimation, matched filtering, regularization, and CS reconstruction. The characteristics of these methods and their connections are also analyzed. Sparsity-driven regularization and CS based radar imaging methods represent an active research area; there are still many unsolved or open problems, such as the sampling scheme, computational complexity, sparse representation, influence of clutter, and model error compensation. We will summarize the challenges as well as recent advances related to these issues. Full article

(This article belongs to the Special Issue Recent Advancements in Radar Imaging and Sensing Technology)

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