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Frequency-modulated continuous wave (FMCW) synthetic aperture radar (SAR) system is widely used for earth observation and space detection, where short revisits time at low cost are desirable, or small size is required. Therefore a FMCW SAR raw signal simulator is required to quantitatively support the design of a SAR mission operating in the FMCW mode, to help mission planning, test processing algorithms and analyse jamming and noises. Time-domain SAR raw signal simulation can be easily conceived, but it is extremely time and memory consuming. In this study, an efficient FMCW SAR raw signal simulation approach for extended scene is proposed based on chirp-Z transform (CZT). This is the first time that a FMCW SAR simulator is proposed in the two-dimensional frequency domain. By taking advantage of fast Fourier transform, the proposed approach highly reduces the computational load with respect to the time domain approach. Furthermore, CZT algorithm can accurately simulate the range-variation of the range cell migration term, which guarantees the precision of the proposed approach. Wavenumber domain algorithm is applied to focus the conceived raw signal. Finally, point targets, the electromagnetic imaging and real FMCW image are used to validate the proposed simulation approach. [ABSTRACT FROM AUTHOR]

For inverse synthetic aperture radar (ISAR) imaging of manoeuvring target, the received signal in a range bin is very complicated, which will degrade the azimuth focusing quality greatly. In this study, the author characterises the received signal as multi-component cubic phase signal, and a new approach of parameters estimation for the cubic phase signal is presented. Combined with the range-instantaneous-Doppler and range-instantaneous-chirp-rate algorithms, the high-quality instantaneous ISAR images can be obtained. The results of simulated and real data demonstrate the validity of the new method proposed. [ABSTRACT FROM AUTHOR]

To obtain sufficient resolution, the operating band of synthetic aperture radar (SAR) must extend over several tens of megahertz. Consequently, SAR reception occurs across frequency bands allocated for other broadcasting stations and SAR often suffers radio frequency interference (RFI). This study proposes a novel approach to suppress RFI in SAR. The authors combine the classical spectral analysis method singular spectrum analysis, with an efficient subspace tracking method fast approximated power iteration (FAPI) which has the most economical computational cost among state-of-the-art algorithms. Furthermore, they supply a gap of FAPI so that it can achieve rank tracking based on eigenvalues distribution of the covariance matrix of the observed SAR record and in that way can detect the number of RFIs along the SAR azimuth time. The proposed algorithm is a time domain processing method and performs on SAR range records one-by-one. Finally, well-focused SAR imagery is obtained from the 'decontaminated' SAR data using classical SAR imaging algorithms. The proposed algorithm could be applied in complicated interference circumstances no matter the styles of RFI. Imaging results of artificial and real data have proved the validity and robustness of this algorithm. [ABSTRACT FROM AUTHOR]

Power lines are an important target type in topographic mapping, as well as in post-disaster and post-strike damage detection. A method of detecting power lines from polarimetric synthetic aperture radar (POLSAR) data using the coherence between the co- and cross-polarisation is proposed based on the azimuth symmetry of the clutter. First, the scattering of power lines is analysed from the aspects of radar cross-section and polarimetric response. Next, the covariance and coherence between the co- and cross-polarisation of power lines and clutter are characterised, and a constant false-alarm rate detector based on the coherence statistics is proposed. In order to obtain a high accuracy, the coherence is estimated in the Hough domain, and the proposed method is improved by considering the sign of the polarimetric orientation angle and using only the real part of the coherence. In addition, the coherence in the special case of azimuth symmetrical power lines is estimated using rotated scattering matrix. Finally, the proposed detector is demonstrated using both simulated and real P-band POLSAR data. [ABSTRACT FROM AUTHOR]

Automatic detection of disjoint regions in synthetic aperture radar (SAR) images for applications requiring localisation and identification of objects is complicated by the nature of the speckle. A multi-level ratio of exponential weighted averages (MROEWA) together with a fast algorithm for optimal threshold selection is proposed for SAR segmentation. A non-parametric and unsupervised principle using the grey level histogram is utilised for producing the regions that are homogenous. An optimal threshold is automatically selected by maximising the separability of the classes in grey level by incorporating a simple search strategy. Experimental results on both synthetic and real images verify the effectiveness of the proposed method. It is validated that the proposed method outperforms greatly by reducing the number of arithmetic operations required for computing the optimal threshold. [ABSTRACT FROM AUTHOR]

Spotlight synthetic aperture radar (SAR) is an effective way to obtain finer azimuth resolution than the achievement in strip-map mode with the same physical antenna. In general, for spotlight SAR imaging, high azimuth resolution requires long synthetic aperture length. However, in practical airborne applications, because of the inevitable atmospheric turbulence during the long flight trajectory, more complicated motion error is induced that severely degrades the focusing quality of the SAR image. It makes motion compensation (MOCO) for high-resolution spotlight (HR-Spotlight) SAR imagery more difficult than that for other SAR systems. To tackle the HR-Spotlight SAR data contaminated by the complicated motion error, a novel MOCO algorithm based on entropy minimisation is proposed, which is named by minimum entropy MOCO (ME-MOCO). In this approach, by fitting a polynomial to the motion error, the entropy of a focused image is utilised as the optimisation function of the polynomial coefficients. Attributed to damped Newton method, a modified strategy is designed, which results in that the data-driven ME-MOCO algorithm estimates high order polynomial parameters accurately and efficiently. Besides, the proposed algorithm is efficient for the capability of exploiting the fast Fourier transform through the processing chain. Real data experiments and comparisons demonstrate the effectiveness and superiority of the proposal. [ABSTRACT FROM AUTHOR]

In this study, a two-dimensional (2D) sinc interpolation method is proposed to carry out the geometric correction in highly squint synthetic aperture radar (SAR). The point spread function (PSF) has 2D non-perpendicular sidelobes in highly squint SAR. This kind of sidelobes may result in the folded skewed spectrum. The conventional interpolation methods cannot do the geometric correction for slant range images with the folded skewed spectrum. A new 2D sinc kernel is designed according to PSF's non-perpendicular sidelobes, in order to circumvent the problem mentioned above. The new kernel has the same sidelobe direction as the PSF. And then two coefficients are introduced to obtain ground range images with large pixel spacing, which can avoid under-sampling and eliminate the resolution's space-variation. The proposed method is verified by computer simulation and SAR real data processing. [ABSTRACT FROM AUTHOR]

A novel algorithm is proposed for detecting and estimating vibrating targets in synthetic aperture radar (SAR) data based on a pulse-repetition-interval (PRI) transform. Azimuthal signals of vibrating targets can be modelled as sinusoidal frequency-modulated (SFM) ones. The algorithm utilises the resemblance between the Doppler spectrum of vibrating-target SFM signals (or ghost image) and a pulse train, and applies to the spectrum the PRI transform originally used for estimating PRIs of pulse trains. The algorithm can detect SAR vibrating targets under moderate signal-to-noise/clutter ratios, and is also capable of accurately estimating the vibration frequencies even if there are multiple targets in a single range cell. The algorithm proposed has been successfully applied to both simulated and quasi-real data, and compared with that of the autocorrelation method, showing its superiority. [ABSTRACT FROM AUTHOR]

An eigenvector method for maximum-likelihood estimation (MLE) of phase error has better algorithmic performance than phase gradient autofocus (PGA), which is implemented by the simultaneous processing of multiple-pulse vectors of the range-compressed data. However, this method requires eigendecomposition of the sample covariance matrix, which is a computationally expensive task and also limits the real-time application. In order to overcome such difficulty, this study proposes a novel autofocus algorithm using the projection approximation subspace tracking (PAST) approach. With this methodology, the computational cost can be reduced effectively to the level of PGA via avoiding the procedures of covariance matrix estimation and eigendecomposition. Monte Carlo tests and real synthetic aperture radar (SAR) data validate that although undergoing performance loss compare with the original multiple-pulse MLE algorithm, the new approach outperforms the mostly used PGA. [ABSTRACT FROM AUTHOR]

Automatic target classification under all conditions is a key challenge for modern radar and sonar systems. Echolocating nectar feeding bats are able to detect and select flowers of bat-pollinated plants even in highly cluttered environments. It is thought that these flowers have evolved to ease classification by bats, and that their echo-acoustic signatures contain critical information that aids the bat in choosing the most suitable flowers. In investigating the features of these flowers that aid the bats search for nectar, the strategy underpinning the task of classification of static targets by bats may be understood and this may additionally offer lessons for radar and sonar systems. Here, we analyse a real set of data containing high range resolution profiles of unpollinated corollas of Cobaea scandens, which is a flower of the type that is pollinated by bats. These were collected by transmitting a synthetic wideband linear chirp with an acoustic radar capable of very high range resolution. Classification performance of a k-NN classifier and a Naïve Bayesian classifier is assessed using information available in both the time and frequency domains. This facilitates quantification of the differences in these echoes because of the flower wilting process and lack of physical parts. [ABSTRACT FROM AUTHOR]

A novel technique is presented for the ship detection and ship speed estimation based on the azimuth scanning mode of synthetic aperture radar. The improved Doppler beam sharpening imaging algorithm is used to produce a sequence of individual sub-images of ships in the same scan region from different aspect angles with a high revisit rate. These sub-images have an inherent property that they are formed at different discretely delayed times. The azimuth resolutions of the sub-images formed in different scan angles are discussed. The cross-correlation function of two specific sub-images, which have the same azimuth resolution, is then produced. The radar cross-section of ships is higher than the surrounding sea clutter, so that their sub-images possess a higher degree of coherence than the uncorrelated random images of the surrounding sea surface. Moreover, the speed vector of moving ships can be estimated based on the shift of the correlation peak. The processing results show the effectiveness of the algorithms. [ABSTRACT FROM AUTHOR]

For synthetic aperture radar (SAR) images, traditional edge detection methods can hardly extract the complete and true edges since they are sensitive to noise. In this study, the authors propose an edge detection method based on bionic vision in non-subsampled contourlet transform (NSCT) domain for SAR images, which makes use of the characteristics of NSCT (e.g. multiscale, multidirection edge expression and outstanding edge location) and the mechanisms of strengthening and positioning the image edge by fixational eye movements. First, source images are decomposed in the NSCT domain. The minimum mean squared error (MMSE) filter in the NSCT domain is adopted to reduce speckle noise of SAR images. Movement, difference and competition are applied on each subband to obtain original multiscale and multidirection subband edges. Lipschitz regularity and non-maximum suppression are also adopted to effectively remove noise points. Finally, hysteresis thresholding with adaptive thresholds is introduced to avoid streaking and trial-and-error, and multiscale edge fusion is used to reduce the false alarm rate. Edge extraction results of simulated and real SAR images demonstrate that the proposed method is better than other edge detection methods based on Canny operator, ratio of averages (ROA) operator, ratio of exponentially weighted averages (ROEWA) operator, wavelet modulus maximum and curvelet. [ABSTRACT FROM AUTHOR]

Bistatic frequency-modulated continuous wave (FMCW) synthetic aperture radar (SAR) mounts the radar transmitter and receiver on separate platforms, which offers considerable capabilities, reliability and flexibility in designing FMCW SAR missions. Moreover, the spatial separation achieves better isolation between transmission and reception channels compared with the monostatic FMCW SAR where the two separate dedicated antennas are fixed on one platform. In this study, a bistatic FMCW SAR signal model is proposed to formulate the bistatic slant range history in bistatic FMCW configuration. Based on the preceding model, an approach is presented to deal with the double-square-root (DSR) term, and thus an accurate FMCW bistatic point target reference spectrum (BPTRS) is derived. Besides accurately addressing the signal characteristics of bistatic FMCW SAR, the proposed spectrum also significantly simplifies the signal processing of bistatic FMCW SAR, which will really push the applications of bistatic FMCW SAR in remote sensing fields. Furthermore, based on the proposed BPTRS, an extended inverse chirp-Z transform (EICZT) algorithm is proposed to process the bistatic FMCW SAR data by introducing a perturbation function to deal with the range-variance of second- and third-order range-azimuth coupling terms. Two simulation experiments are carried out to verify the accuracy of this novel formulation and highlight the performance of the proposed focusing approach. [ABSTRACT FROM AUTHOR]

The authors propose an advanced synthetic aperture radar (SAR) image formation framework based on iterative inversion algorithms that approximately solve a regularised least squares problem. The framework provides improved image reconstructions, compared to the standard methods, in certain imaging scenarios, for example when the SAR data are under-sampled. Iterative algorithms also allow prior information to be used to solve additional problems such as the correction of unknown phase errors in the SAR data. However, for an iterative inversion framework to be feasible, fast algorithms for the generative model and its adjoint must be available. The authors demonstrate how fast, N2 log2 N complexity, (re/back)-projection algorithms can be used as accurate approximations for the generative model and its adjoint, without the limiting geometric approximations of other N2 log2 N methods, for example, the polar format algorithm. Experimental results demonstrate the effectiveness of their framework using publicly available SAR datasets. [ABSTRACT FROM AUTHOR]

The problem of target classification using inverse synthetic aperture radar (ISAR) images is studied under conditions of mass data processing, sparse scattering centre distribution, image deterioration and variation with the radar imaging view, all of which make target classification difficult. In this study, the authors propose a novel method based on combination of the feature space and the visual perception theory to achieve an accurate and robust classification of ISAR images. In order to make full use of local spatial structure information for classification, the local non-negative matrix factorisation (LNMF) is employed to construct an initial feature space, which is then optimised to calculate more discriminable feature projection vectors of each target. The approaches including speckle noise and stripes suppression, centroid and scale normalisation, LNMF, feature space optimisation with the maximum intersubject variation and minimum intrasubject variation and feature projection vectors calculation are detailed. Finally, the classification is performed with a k neighbours classifier. ISAR images used are obtained by range-Doppler imaging method with radar echoes of aircraft models generated by RadBase. Simulation results show a significant improvement on recognition accuracy and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

The problem of how best to maximise the ratio of mean target intensity to mean background intensity for slowly moving targets in sets of multi-channel synthetic aperture radar images is discussed for highly non-Gaussian background clutter. The problem is formulated as a direct maximisation of target-to-clutter ratio thus giving a true maximisation of that ratio. Complex-valued weights derived using generalised eigensystem theory are used to maximise the ratio of quadratic forms representing the mean intensity of the target and background derived from their coherence matrices. For two to four channels it is shown that when the target is highly coherent an optimum steering vector is a discrete Fourier transform. For more than two channels it is shown that the optimal solution is only valid within a subspace of the whole parameter space defined by the correlation parameters of the background clutter. Images from a publically released ground moving target indicator dataset are filtered using the results for three channels. The method outperform a standard space-time adaptive processing algorithm in suppressing the stationary background urban clutter image intensity relative to the image intensity because of a known slowly moving ground vehicle. Moreover, the steering vector is much simpler to implement. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) systems are used to form high-resolution images from radar backscatter signals. The fractional Fourier transform (FrFT), which is a generalised form of the well-known Fourier transform, has opened up the possibility of a new range of potentially promising and useful applications that involve the use and detection of chirp signals that include pattern recognition and SAR imaging. In this study a time variant problem associated with the use of the FrFT for SAR processing is addressed and a new algorithm is presented that resolves this problem. Two new FrFT-based SAR processing algorithms are presented namely the FrRDA and the eFrCSA that are shown to improve the well-established range-Doppler and chirp-scaling algorithms for SAR processing. The performance of the algorithms are assessed using simulated and real Radarsat-1 data sets. The results confirm that the FrFT-based SAR processing methods provide enhanced resolution yielding both lower side lobes effects and improved target detection. [ABSTRACT FROM AUTHOR]

Non-cooperative moving targets typically appear defocused in synthetic aperture radar (SAR) images as they are non-stationary during the coherent processing interval. Typically, the problem of refocusing moving targets in SAR scenes is addressed as a problem of the target's motion estimation and compensation. In this study, the problem is addressed as a problem of non-cooperative target imaging and the authors propose a solution based on the use of inverse synthetic aperture radar (ISAR) processing to solve this problem. Taking into consideration the advantages of SAR processing, the problem is tackled starting from formed SAR images, where non-cooperative targets images are firstly detected, then backprojected to the received data domain and reprocessed as ISAR data. The effectiveness of the proposed method is then tested on Cosmo-Skymed spotlight SAR data of maritime targets. [ABSTRACT FROM AUTHOR]

This study proposes a method that improves the robustness of independent component analysis (ICA) by adding outlier rejection rule for solving synthetic aperture radar (SAR) image analysis problems. Since the noise in SAR images is multiplicative, the applicability of ICA is seriously reduced. The proposed robust approach includes three procedures. After a pre-processing stage of principal component analysis, the authors remove outliers by applying outlier rejection rule for multivariate data. Then the ICA method is applied on the clean data set. Its applications in SAR are discussed. The results show the potential usage of this robust approach in SAR image processing problems. [ABSTRACT FROM AUTHOR]

A novel approach for inverse synthetic aperture radar (ISAR) imaging of three-dimensional (3-D) rotation target is presented in this study. It is based on the assumption that the manoeuverability is not too severe, and the received signal in a range bin can be characterised as multi-component linear frequency modulated (LFM) signal. The two-order match Fourier transform (TMFT) is used to estimate the parameters of the LFM signal, and the asymptotic statistical performance is analysed simultaneously. Combined with the Range-instantaneous-Doppler (RID) algorithm, the high-quality instantaneous ISAR images can be obtained. The results of simulated and real data demonstrate the effectiveness of the method proposed. [ABSTRACT FROM AUTHOR]

A target detection algorithm is developed based on a supervised learning technique that maximises the margin between two classes, that is, the target class and the non-target class. Specifically, the proposed target detection algorithm consists of (i) image differencing, (ii) maximum-margin classifier, and (iii) diversity combining. The image differencing is to enhance and highlight the targets so that the targets are more distinguishable from the background. The maximum-margin classifier is based on a recently developed feature weighting technique called Iterative RELIEF; the objective of the maximum-margin classifier is to achieve robustness against uncertainties and clutter. The diversity combining utilises multiple images to further improve the performance of detection, and hence it is a type of multi-pass change detection. The authors evaluate the performance of the proposed detection algorithm, using the CARABAS-II synthetic aperture radar (SAR) image data and the experimental results demonstrate superior performance of the proposed algorithm, compared to the benchmark algorithm. [ABSTRACT FROM AUTHOR]

This study focuses on the application of H/α decomposition to original compact polarimetric (CP) synthetic aperture radar (SAR) data. In general, the authors can extract the physical scattering mechanisms (PSMs) of targets by using pseudo fully polarimetric (FP) data that are approximately reconstructed from the original CP data. In this study, the H/α decomposition is extended to the original CP data to avoid large data volume and heavy computational burden during the reconstruction of pseudo FP data, while escaping from the possible information loss caused by the reconstruction process. The H/α plane is modified and re-plotted out to discriminate different PSMs from CP data. Two CP modes are considered in the study: the π/4 mode and the dual circular polarimetric (DCP) mode. The study shows that under the DCP mode, the modified H/α decomposition performs well to discriminate different PSMs, whereas it collapses in the π/4 mode. The feasibility and effectiveness of the new H/α decomposition for CP modes are analysed and verified by experiments with measured airborne data. [ABSTRACT FROM AUTHOR]

To achieve high-resolution two dimension images, synthetic aperture radar (SAR) with ultra wide-band faces considerably technical challenges such as long data collection time, huge amount of data storage and high hardware complexity. In these years, several imaging modalities based on compressive sensing (CS) have been proposed which can provide high-resolution images using significantly reduced number of samples. However, the CS-based methods are sensitive to noise and clutter. In this study, a new imaging modality based on Bayesian compressive sensing (BCS) is proposed along with a novel compressed sampling scheme. Clutter, which the previous CS-based methods not considered, is also included in this study. This new imaging scheme requires minor change to traditional system and allows both range and azimuth compressed sampling. Also, the Bayesian formalism accounts for additive noise encountered in the compressed measurement process. Experiments are carried out with noisy and cluttered imaging scenes to verify the new imaging scheme. The results indicate that the Bayesian formalism can provide a sharp and sparse image absence of side-lobes, which is the common problem in conventional imaging methods and has fewer artifacts compared with the previous version of CS-based methods. [ABSTRACT FROM AUTHOR]

All kinds of approaches have been proposed to implement the pixel fusion for remote-sensing images that must be strictly registered in advance. Errors introduced by finite precision registration would influence the performance of fusion process. Moreover, it is not easy to achieve high precision registration on synthetic aperture radar (SAR) images. Thus it is significative to evaluate the performance of diverse fusion approaches under different precision registration, especially in low precision condition. In this study, several current image fusion methods are applied to execute the pixel fusion on real SAR images under different precision registration, and the influence of registration error on their respective performance is tested. The experimental outcomes indicate that the contrast pyramid and wavelet transform fusion methods have shown the better performance than the other compared methods. [ABSTRACT FROM AUTHOR]

In order to rescale an inverse synthetic aperture radar image into a homogeneous range cross-range domain, a novel method is proposed to estimate rotation velocity (RV) by exploiting the phase slope difference of two prominent scatterers on a complex image. Firstly, two prominent scatterers are automatically extracted in a range-Doppler image via watershed method. Subsequently, the signal obtained by the two scatterers is divided into two equal segments, each of which provides an estimation of the phase slope via phase unwrapping and the least-square fitting. Finally, the target RV can be estimated based on the difference of the two phase slopes. The proposed method can be implemented automatically and is more computationally efficient than the traditional methods. Finally, the results based on simulation data and real data are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A method for low-frequency ultra-wideband synthetic aperture radar (SAR) imaging of ground moving targets is proposed in this study. This method can deal with large range migration of the moving target. A first-order keystone transform is used to correct range walk (first-order range migration), and range curvature (second-order range migration) is compensated by using a second-order keystone transform. Both the first-order and second-order keystone transform parameters are independent of the motion parameters, so that this method can correct the range migration for all targets without knowing their velocity. The advantage of this method is that it can correct range curvature for all targets in one processing step, so that it is simple to implement. Simulation and experimental SAR data processing results are shown to demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Scene segmentation and semantic labelling are important for analysing and understanding synthetic aperture radar (SAR) images. In this study, the authors propose an effective and efficient labelling method for SAR images with conditional random fields on a region adjacency graph (CRF-RAG). More precisely, for an SAR image, a region adjacency graph (RAG) representation is firstly built on an initially over-segmentation of the image. Subsequently, a conditional random field (CRF) model is established over the RAG instead of over pixels. To train and infer the CRF-RAG model, a fast max-margin training strategy and the graph cut optimisation method are finally employed. As the CRF model is based on RAG, the computation complexity of the model can be reduced significantly. Compared to the Markov random field (MRF) model on RAG, the proposed CRF-RAG model is more efficient to incorporate different measures of SAR images, such as scattering intensity, texture and image context, into a unified model. Experiments on the TerraSAR-X imagery achieve promising results with modest computation cost, which validates the generality and flexibility of the proposed method. [ABSTRACT FROM AUTHOR]

Small unmanned aircraft or other aircraft that operate at short range are likely to need to operate at steep look-down angles. Imaging at high grazing angle causes range-layover which affects synthetic aperture radar (SAR) images in the down-range direction, but not in cross-range. By making a number of images at different look angles relative to the target, the 3-D position of individual scatterers on the target can be calculated, as long as they remain visible over an appreciable angular extent. The range-layover to height mapping algorithm is presented here for forming 3-D images from high grazing angle SAR data. This algorithm only requires data collected from a single antenna and a single pass of the target to form a 3-D image. [ABSTRACT FROM AUTHOR]

The use of synthetic aperture radar (SAR) for moving target imaging has recently attracted a great deal of interest. The ability to obtain focused images of moving targets makes it possible to maximise the use of existing single-channel SAR systems, without upgrading to more complex and expensive multi-channel systems. In this study, a novel technique is presented for moving target imaging utilising a single-channel SAR operating in Spotlight mode. First, the second-order keystone transform is applied to remove range curvature. Next, a non-linear phase correction is applied to correct the remaining range walk. Finally, the nominally quadratic phase in azimuth is estimated and corrected to provide focused imagery. An experimental result is presented to demonstrate the performance of this approach. [ABSTRACT FROM AUTHOR]

The high range resolution profile (HRRP) can be reconstructed by combining frequency bands coherently for inverse synthetic aperture radar (ISAR) imaging of a moving target in stepped-frequency (SF) radar systems. However, the range migration and phase errors among sub-pulses in a pulse train caused by the target translational motion results in difficulty for HRRP synthesis of SF sub-pulses. In order to compensate the translational motion for SF ISAR imaging, a novel method based on adjacent pulses correlation integration and coherent processing is proposed in this study. Firstly, the radial motion parameters can be estimated by the adjacent pulses correlation integration for the range echo envelope in a pulse train. Owing to strong coherence between two adjacent pulses, the correlation integration achieves high gain of signal-to-noise ratio. It is robust to noise ensuring precise estimation of translational motion. Secondly, after compensating the phase errors, the HRRP can be reconstructed by the combination of frequency bands coherently and the inverse Fourier transform. Finally, a nice ISAR image of a moving target can be obtained by conventional range-Doppler algorithm (RDA). In the end, simulated and real data are used to demonstrate the good performance of the method proposed in this study. [ABSTRACT FROM AUTHOR]

In this study, three decision fusion strategies in synthetic aperture radar (SAR) image target recognition for classification of ground vehicles in the Moving and Stationary Target Acquisition and Recognition public release database are proposed. These strategies include multi-view decision fusion strategy, multi-feature decision fusion strategy and multi-classifier decision fusion strategy. In multi-view decision fusion strategy, each view implements its recognition process individually and provides only its decision from the classifier to the fuser. In multi-feature decision fusion strategy, a variety of feature extraction algorithms are used to extract features from an image separately and each of the extracted feature vectors is submitted to a classifier. The decisions from the classifiers are then provided to the fuser. In multi-classifier decision fusion strategy, different classifiers are used to classify a feature vector. Each classifier executes its classification process individually and then provides its decision to the fuser. The fuser combines decisions to produce a single fused final decision in these three strategies. The three proposed strategies are evaluated and verified with experiments. Experimental results indicate that there are significant target recognition performance benefits in the probability of correct classification, when the proposed decision fusion strategies are properly applied in SAR image target recognition. [ABSTRACT FROM AUTHOR]

The two-dimensional (2D) geometry of extended objects is modelled as a discrete constellation of a small number of scattering centres. Each scattering centre is characterised by its 2D location and extension, as well as by its observability from aspect angles spanning a wide angular interval. The considered input information of the modelling consists of high range resolution (HRR) data sets, which are measured by a network of scanning surveillance radars from a limited set of aspect angles of the object. During the short time-on-target of the antennas within each radar scan, inverse synthetic aperture radar (ISAR) imaging is performed. The Expectation-Maximisation (ER) algorithm is applied in order to fit each ISAR image to a Gaussian mixture model (GMM), pertaining to the aspect angle applicable to the respective radar. A merging algorithm for clustering all the partial GMMs into a unique 2D model of the extended object is designed. Cluster validation criteria are applied for correct alignment of the multi-radar multi-scan object model estimates. A Mixture of Gaussians classifier (MOGC), which is based on representation of each object by the estimated 2D model, is discussed. Results from application of the algorithms to two measured HRR radar data sets are presented. [ABSTRACT FROM AUTHOR]

Micro-motion targets are gaining an increased interest from the synthetic aperture radar (SAR) community. Their SAR returns can be modelled as sinusoidal frequency-modulated signals in the noise/clutter environment. A detector based on the generalised likelihood ratio test (GLRT) is proposed to detect such targets using SAR raw signals instead of SAR imagery. The GLRT detector entails searching three parameters for the maximum of a statistic, followed by comparison of the maximum with a threshold determined by the constant false alarm rate. It can also provide estimation of the micro-motion parameters. The detection performance is derived theoretically and demonstrated by both simulated and quasi-real data, showing favourable detection ability under low signal-to-noise/clutter ratios. [ABSTRACT FROM AUTHOR]

In this report we develop a method for removing the distortions produced by multipath synthetic aperture radar (SAR) imaging. In conventional SAR systems, multipath scattering superimposes blurring artefacts on the radar image and diminishes resolution. We exploit a non-linear phase shift introduced in the multipath scattering term, of a slightly translated imaging aperture, to remove the effects of multiple scattering in an image obtained from a non-translated aperture. The multipath distortion removal is accomplished while still maintaining the resolving power of single scattering SAR. [ABSTRACT FROM AUTHOR]

This study presents a novel motion parameters estimation technique for moving targets, including those with velocities beyond the Nyquist limit in multi-channel synthetic aperture radar data. The technique proposed in this study exploits the linear dependence between the fold factor of the cross-track velocity and the slope of the moving target trajectory in the range-compressed and azimuth time domain after performing Keystone transformation in the range frequency and azimuth time domain. The velocity ambiguity thus can be overcome. The along-track velocity can be retrieved from the chirp rate estimated by applying a new time-frequency analysis method called Keystone-Wigner transform. The algorithm of motion parameter estimation efficiently converts the two-dimensional (2-D) search into the two separate 1-D cases. The proposed method can robustly estimate the motion parameters of the fast-moving targets, which are illustrated both with simulated and real data. [ABSTRACT FROM AUTHOR]

Ambiguity estimation is critical for the positioning of a moving target truly. Traditional real-value Radon transform (RRT) has been used to estimate the slope of target's trajectory such that the ambiguity number can be derived. However, the unknown azimuth velocity of the target makes it difficult to determine the ambiguity number because the quadratic range cell migration (QRCM) caused by the platform velocity reduces the sensitivity of the RRT in the estimation. Also, the RRT does not work well when the signal-to-noise ratio (SNR) is low. Here, a method that uses the second-order Keystone transform (SOKT) to eliminate the QRCM and the modified fractional Radon transform (MFrRT) to estimate the ambiguity number was proposed. The method was simple and applicable in the low-SNR situation. Implementation considerations were presented. Finally, the effectiveness of the method has been shown using simulated and acquired synthetic aperture radar datasets. [ABSTRACT FROM AUTHOR]

This study investigates practical processing methods aiming at detecting ground moving targets and estimating their motion parameters for an experimental three-channel airborne synthetic aperture radar-ground moving target indication (SAR-GMTI) system. It focuses on two key points of GMTI processing: adaptive clutter suppression and resolving of velocity ambiguities. Different from the conventional clutter suppression based on SAR images or in range-Doppler domain of the entire-aperture pulses, the clutter cancellation is proposed to be performed in the Doppler domain of short-aperture pulses to avoid the superposition of clutter echoes from different ground patches in azimuth; meanwhile, the ground moving target signals are focused with the full aperture to maximise the signal-to-noise ratio. To resolve the velocity ambiguity, a joint strategy is proposed that combines the estimates of the along-track interferometric phases and the Doppler spectrum information of a ground moving target according to the Chinese remainder theorem. The implementations of the processing operations are verified with real data from the experimental three-channel airborne SAR-GMTI system. [ABSTRACT FROM AUTHOR]

A new approach to Doppler ambiguity resolution is presented. This approach adopts the fact that the absolute Doppler centroid is a linear function of range frequency. Based on this concept, an alternative azimuth compression method that is without a prior knowledge about the motion parameters is developed and carried out in range frequency domain to accumulate the target energy along the azimuth. The resulting trajectories become straight lines with the same slope proportional to the unwrapped Doppler centroid. This slope can be well estimated by the azimuth cell walk correction/integration method. Two sets of measured synthetic aperture radar data (airborne and spaceborne radar) show that this approach works well in medium- and high-contrast scenes. [ABSTRACT FROM AUTHOR]

Doppler centroid is an important parameter in synthetic aperture radar (SAR) image processing. Owing to measurement uncertainties, many clutter-lock algorithms are proposed to estimate the Doppler centroid. One of the factors that affect the estimation is 'chirp coupling' [linear FM signal is used as transmitting signal in SAR, and this makes echoes of targets from different range bins overlap with each other at the same range bin, which could be called 'chirp coupling (CHCO)']. In broadside or lowly squint mode SAR, CHCO is often ignored. However, with large squint angle, its influence on Doppler centroid estimation will increase and even affect the final image. The azimuth-frequency shift of echoes caused by CHCO is analysed and a method proposed to calculate the threshold angle which is used to judge its influence on Doppler centroid estimation. With the help of the threshold angle, a novel strategy of Doppler centroid estimation is proposed. After theoretic analyses, simulations and experimental results are provided to demonstrate the proposed strategy of Doppler centroid estimation. [ABSTRACT FROM AUTHOR]

Owing to the unknown high-order unstable motion, it is difficult to realise the motion compensation, that is, envelope alignment and phase auto-focusing, for inverse synthetic aperture radar (ISAR) manoeuvring targets. By modelling the envelope shifting and phase modulation into two different high-order polynomial functions against observation time, a novel parametric ISAR motion compensation method is proposed based on polynomial coefficients estimation via particle swarm optimiser (PSO). The average range profile energy and the image contrast are chosen as the fitness functions for envelope alignment and phase auto-focusing, respectively. Furthermore, the polynomial order of phase auto-focusing is chosen higher than that of envelope alignment to meet the accuracy need of phase compensation. Besides, in order to speed up the convergence and ensure that the estimated parameters converge to the global optimisation, a least-square fitting pre-processing is also proposed to determine the target motion order and initialise the best particle at first. Finally, the results based on both numerical experiments and real data are all provided to demonstrate the effectiveness of the proposed PSO-based parametric methods. [ABSTRACT FROM AUTHOR]

A new multi-baseline phase unwrapping algorithm based on the unscented Kalman filter (UKF) for SAR interferometry is proposed. This method is the result of combining a UKF with a path-following strategy and an omni-directional local phase slope estimator. This technology simultaneously performs noise filtering and phase unwrapping by an optimal data fusion approach. In addition, phase slope will be directly estimated from the sample frequency spectrum of the complex interferogram, by which the underestimation of phase slope is overcome. Results obtained with synthetic and real data validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

An exact analytical solution for the two-dimensional (2-D) point target spectrum is usually difficult to obtain because of the existence of a double-square-root (DSR) term in the bistatic range equation. Some approximate solutions for the 2-D spectrum have been derived and used in order to focus bistatic synthetic aperture data. A geometry-based bistatic formula (GBF) method was used for obtaining a quasi-analytical form of a bistatic 2-D spectrum by Zhang (2007). Although the GBF method cannot completely solve the problem of the DSR term, it provides a novel aspect for dealing with this problem. In this study, based on the quasi-analytical spectrum, the authors change the signal expression space and transform the eight-order polynomial equation in terms of the slow time into the four-order polynomial equation in terms of the half quasi-bistatic angle (HQBA) for the tandem configuration. Then the DSR-term problem is successfully solved and a corresponding exact analytical bistatic 2-D spectrum is obtained. It is proved that the spectra acquired by Rocca's smile operator, Loffeld's bistatic formula (LBF) and the method of series reversion (MSR) are equivalent to this proposed analytical spectrum when certain conditions are met, and that this exact analytical spectrum is the most accurate among them. [ABSTRACT FROM AUTHOR]

Space/air-borne synthetic aperture radar (SAR) detection and imaging of moving ships at sea are important for ocean reconnaissance and fishery monitoring. A novel algorithm based on sandglass transformation to detect weak ships and form high-quality images under the conditions of low signal-to-clutter-plus-noise ratio is presented here. It requires no prior information about motion parameters of targets. The sandglass transformation can decouple the time and lag time in the instantaneous autocorrelation function of a linear frequency modulated (LFM) signal. Thus the cross-range signals of ship in the approximate form of LFM signals can be integrated coherently via the two-dimensional fast Fourier transformation. The proposed algorithm can not only achieve high-energy accumulation gain, but also suppress the interference of cross terms effectively without loss of resolution. Hence, it is effective to detect weak ships and generate high-resolution images. Numerical and experimental results confirm the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The authors describe a process for designing low-cost, light-weight antenna apertures for use in multiple-input multiple-output (MIMO) radars. In a MIMO radar system, two or more transmitters emit independent waveforms, with the resulting reflections received by an array of receivers. Recently, MIMO radar has become a subject of great interest. In part, this interest is due to the potential for MIMO techniques to reduce radar weight and cost, while maintaining performance (as compared with conventional radar approaches). However, the size of these reductions has not yet been quantified. Here, the authors describe a process for designing optimal radar apertures. This process treats the design problem as one of minimising an objective function under performance constraints. The objective function is based upon a first-order model for the relationship between cost (or weight) and performance, and is derived for systems employing active, element-digitised arrays. A systematic process for optimising the aperture's design with respect to this objective function is presented, and equations describing the optimal aperture are derived. These equations provide insight into the optimal relationship between various aperture characteristics, such as the number of transmitters, number of receivers, module power level and virtual array length. [ABSTRACT FROM AUTHOR]

In this study fully polarimetric synthetic aperture radar (SAR) measurements are employed for sea oil slick observation. The co-polarised signature is read in terms of sea surface scattering mechanism with and without surface slicks and it is shown to be sensitive to the presence of sea oil slicks. A model, which relates the pedestal of the polarisation signature to the presence of surface slicks, is developed and successfully tested on L-band ALOS-PALSAR, C-band RADARSAT-2 and C-band SIR-C/X-SAR full-polarimetric SAR data. Experiment results show that polarisation signature, and its pedestal height, can be successfully exploited for both observing sea oil slicks and distinguishing them from weak-damping properties look-alikes. [ABSTRACT FROM AUTHOR]

Content-based image retrieval (CBIR) provides an effective way to address the increasing need for intelligent data access to synthetic aperture radar (SAR) image repositories. In CBIR, a critical component is relevance feedback (RF), which is used to bridge the 'semantic gap'. This study proposes a new RF method with active sample-selecting and manifold learning for CBIR of SAR images. In this method, the authors adopt a modified maximum margin projection with a new neighbourhood estimation criterion to discover both the geometrical structure and discriminant structure of the underlying data manifold. In order to achieve a satisfactory performance with limited feedback samples, the authors also propose an active sample selection strategy with which the diversity of feedback samples can be increased while the redundancy is decreased. The authors test our method on a TerraSAR-X image database and compare it with four other state-of-the-art RF methods. The superiority and validity of the authors' method is proved by the retrieval results and the computing cost is acceptable for image retrieval applications. [ABSTRACT FROM AUTHOR]

There is increasing interest in using synthetic aperture radar (SAR) images in automated target recognition and decision-making tasks. The success of such tasks depends on how well the reconstructed SAR images exhibit certain features of the underlying scene. Based on the observation that typical underlying scenes usually exhibit sparsity in terms of such features, this paper presents an image formation method that formulates the SAR imaging problem as a sparse signal representation problem. For problems of complex-valued nature, such as SAR, a key challenge is how to choose the dictionary and the representation scheme for effective sparse representation. Since features of the SAR reflectivity magnitude are usually of interest, the approach is designed to sparsely represent the magnitude of the complex-valued scattered field. This turns the image reconstruction problem into a joint optimisation problem over the representation of magnitude and phase of the underlying field reflectivities. The authors develop the mathematical framework for this method and propose an iterative solution for the corresponding joint optimisation problem. The experimental results demonstrate the superiority of this method over previous approaches in terms of both producing high-quality SAR images and exhibiting robustness to uncertain or limited data. [ABSTRACT FROM AUTHOR]

An interferometric linear frequency modulated continuous wave (LFMCW) millimetre-wave radar is presented, along with the results of an experiment conducted to study the feasibility of using it in a future millimetre-wave interferometric inverse synthetic aperture radar (InISAR) system. First, a description of the radar is given. Then, the signal processing chain is described, with special attention to the phase unwrapping technique. The interferometric phase is obtained by unwrapping the prominent target's phase in each antenna using a sliding frame processing technique. Cell migration issues in this method are also addressed. Simulations were carried out to illustrate and assess the processing chain and to show the effects of multipath echoes on the height measurement. In the real experiment, the range, speed and height of a moving target were tracked over consecutive inverse synthetic aperture radar (ISAR) image frames, verifying the performance of the whole system. [ABSTRACT FROM AUTHOR]

This study presents a new algorithm called pyramid representation (PR)-AdaBoost, which combines PR and AdaBoost for urban area classification using high-resolution synthetic aperture radar (SAR) images. PR is used to hierarchically represent local feature sets and AdaBoost is used to choose proper features from the PR vector and effectively discriminate categories. The authors evaluate the proposed algorithm on a data set consisting of high-resolution SAR images of five different categories of scene and on a real TerraSAR-X image. The experimental results have shown that PR-AdaBoost can achieve higher classification accuracy than AdaBoost based on global representation or local representation such as bag-of-features. It also outperforms classical classifiers such as nearest neighbour, boosted distance and support vector machine based on the same representation. [ABSTRACT FROM AUTHOR]

In this study, the authors focus on the channel calibration for digital beam forming (DBF) synthetic aperture radar (SAR) imagery. Incorporated with DBF processing, multi-channel SAR is promising in high-resolution and wide-swath (HRWS) imaging. It coherently combines recorded multi-channel signals to overcome the spectrum ambiguity. However, the mismatch between channels and baseline errors phase should be compensated before the processing. In this study, the authors propose a novel calibration for DBF SAR imagery by making use of the orthognality between signal subspace and noise subspace. Based on that the imbalance is uncoupled in range and azimuth, the calibration has two steps: range calibration and azimuth calibration. The mismatch in range is estimated by an interpolation in time domain which is applicable in the presence of the Doppler ambiguity. The azimuth calibration is incorporated with the post-Doppler beam forming processing, which estimates the element error caused by baseline errors and channel imbalance. Real data are used to demonstrate the performance of Doppler ambiguity suppression after our calibration. The results confirm the validity of the approach. [ABSTRACT FROM AUTHOR]