This letter introduces a novel regression model structure for the inflated Rayleigh distribution, which effectively models high dynamic amplitude pixel values in synthetic aperture radar (SAR) images. The proposed model estimates the mean of inflated Rayleigh distribution signals by a structure that includes a set of regressors and a link function. The inflated Rayleigh distribution combines the Rayleigh and a degenerate distribution, assigning nonnull probability specifically for observed values equal to zero. Null pixel values in amplitude SAR images can be randomly distributed within the image, especially in low-intensity areas; a model capable of incorporating these values is essential to avoid changes in image statistics. Extensive evaluations are conducted using simulated and real SAR images to validate the proposed model, specifically focusing on ground-type detection in high dynamic amplitude pixel values scenarios. The performance of the proposed inflated Rayleigh regression model is compared with traditional Gaussian-based regression models, excelling in terms of ground-type detection in an SAR image obtained from the ICEYE radar.

Wavelength-resolution (WR) synthetic aperture radar (SAR) change detection (CD) has been used to detect concealed targets in forestry areas. However, most proposed methods are generally based on matrix or vector analyses and, therefore, do not exploit information embedded in multidimensional data. In this letter, a CD method for WR SAR image stacks based on tensor robust principal component analysis (TRPCA) is proposed. The proposed CD method used the new tensor nuclear norm induced by the definition of the tensor-tensor product to exploit temporal and spatial information contained in the image stack. To assess the performance of the proposed method, we considered SAR images obtained by the very high frequency (VHF) WR CARABAS-II SAR system. Experiments for three different stack sizes show that a significant performance gain can be achieved when large image stacks are considered. The proposed CD method performs better in terms of probability of detection (PD) and false alarm rate (FAR) than the other five CD methods in VHF WR SAR images, including one based on matrix robust principal component analysis (RPCA). In a particular setting, it achieves a PD of 99% and a FAR of 0.028 false alarms per km2.

In this paper, we present three algorithms for the multitemporal synthetic aperture radar (SAR) images coregistration. The proposed algorithms are a 2-D cross correlation, a 1-D parabolic based, and a 2-D projective transformation. The 2-D cross correlation algorithm is used to obtain coarse estimation of the displacement for coregistration. In the second method, two independent 1-D parabolic interpolations are calculated to refine the estimation of the peak location of the cross correlation matrix with subpixel accuracy. Finally, in the third method, a 2-D projective transformation is employed to align the SAR images using point correspondences and the cubic interpolation. The performance evaluation of these algorithms are provided based on the coherence magnitude and the absolute displacement error for a point target using a corner reflector in the scene. The experimental results obtained on real recorded multitemporal satellite SAR data demonstrate the effectiveness and the computational complexity of these algorithms.

Synthetic aperture radars (SAR) data plays an important role in remote sensing applications. It is common knowledge that SAR image amplitude pixels can be approximately modeled by the Rayleigh distribution. However, this model is contin-uous and does not accommodate points with non-zero prob-ability, such as a null pixel amplitude value. Thus, in this paper, we propose an inflated Rayleigh distribution for SAR image modeling that is based on a mixed continuous-discrete distribution and can be used to fit signals with observed values on$[0,\ \infty)$. The maximum likelihood approach is considered to estimate the parameters of the proposed distribution. An empirical experiment with a SAR image is also presented and discussed.

This letter presents two new change detection (CD) methods for synthetic aperture radar (SAR) image stacks based on the Neyman–Pearson criterion. The first proposed method uses the data from wavelength–resolution images stack to obtain background statistics, which are used in a hypothesis test to detect changes in a surveillance image. The second method considersa prioriinformation about the targets to obtain the target statistics, which are used together with the previously obtained background statistics, to perform a hypothesis test to detect changes in a surveillance image. A straightforward processing scheme is presented to test the proposed CD methods. To assess the performance of both proposed methods, we considered the coherent all radio band sensing (CARABAS)-II SAR images. In particular, to obtain the temporal background statistics required by the derived methods, we used stacks with six images. The experimental results show that the proposed techniques provide a competitive performance in terms of probability of detection and false alarm rate compared with other CD methods.

In this letter, we propose a method to reduce the number of false alarms in a wavelength–resolution synthetic aperture radar (SAR) change detection scheme by using a convolutional neural network (CNN). The detection is performed in two steps: change analysis and object classification. A simple technique for wavelength–resolution SAR change detection is implemented to extract potential targets from the image of interest. A CNN is then used for classifying the change map detections as either a target or nontarget, further reducing the false alarm rate (FAR). The scheme is tested for the CARABAS-II data set, where only three false alarms over a testing area of 96 km2are reported while still sustaining a probability of detection above 96%. We also show that the network can still reduce the FAR even when the flight heading of the SAR system measurement campaign differs by up to 100° between the images used for training and test.

This letter presents coherent and incoherent nonlinear apodization methods for bistatic synthetic aperture radar (SAR) imaging. The methods are developed on the principle of nonlinear apodization for monostatic SAR imaging and a$\omega \,\,-\,\,k$relationship representing the bistatic region of support. This relationship plays an important role in designing 2-D windows for the apodization methods. The simulation results show that the presented apodization methods work efficiently with bistatic SAR imaging. In the experiment with a coherent dual apodization (CDA) method presented in this letter, the sidelobe attenuates very fast, and the peak sidelobe is reduced about 16 dB, while the resolutions in azimuth and range are maintained.

The tilt phenomenon or skew in bistatic synthetic aperture radar (SAR) images has been known. This can be observed from the SAR image of a point-like scatterer, even with a symmetrical bistatic aperture. This letter explains this phenomenon in bistatic SAR images and shows that the phenomenon depends on the initial and the last positions of the transmitter and receiver platforms. A formula to calculate the tilt angle based on a general bistatic geometry is provided. This formula supports bistatic SAR system design and bistatic image quality measurements.

The presence of outliers (anomalous values) in synthetic aperture radar (SAR) data and the misspecification in statistical image models may result in inaccurate inferences. To avoid such issues, the Rayleigh regression model based on a robust estimation process is proposed as a more realistic approach to model this type of data. This article aims at obtaining Rayleigh regression model parameter estimators robust to the presence of outliers. The proposed approach considered the weighted maximum likelihood method and was submitted to numerical experiments using simulated and measured SAR images. Monte Carlo simulations were employed for the numerical assessment of the proposed robust estimator performance in finite signal lengths, their sensitivity to outliers, and the breakdown point. For instance, the nonrobust estimators show a relative bias value 65-fold larger than the results provided by the robust approach in corrupted signals. In terms of sensitivity analysis and break down point, the robust scheme resulted in a reduction of about 96% and 10%, respectively, in the mean absolute value of both measures, in compassion to the nonrobust estimators. Moreover, two SAR datasets were used to compare the ground type and anomaly detection results of the proposed robust scheme with competing methods in the literature.

Monostatic pursuit refers to the operating mode formed by two monostatic SAR systems that follow an identical orbit with a separation in time of several seconds. The detected changes between SAR scenes with several seconds time difference are most likely the changes caused by ground moving targets. Hence, this operating mode opens an opportunity to detect ground moving targets by SAR change detection methods. The paper investigates this possibility to detect ground moving targets using change detection and to combine change detection and GMTI for GMTI. In this combination, a GMTI method will help to classify the detected changes obtained with a change detection method. Some GMTI results are provided in the paper based on the measurements in the monostatic pursuit mode with deployed targets, conducted by TerraSAR-X and TanDEM-X in Sweden in early 2015.

In this paper, we present an unsupervised automatic target detection algorithm for multitemporal SAR images. The proposed two-fold method is expected to reduce processing time for large scene sizes with sparse targets while still improving detection performance. Firstly, pixel blocks are extracted from an initial change map to reduce the algorithm's search space and favor target detection. Secondly, an adaptive k-means algorithm selects the number of clusters that better separates targets from false alarms, which are discarded. Preliminary results show the advantages of the proposed method in processing time and detection performance over a recently proposed supervised method for the CARABAS-II dataset.

Conventional SAR refers to systems with small fractional bandwidth and short relative aperture whereas non-conventional SAR refers to systems with large fractional bandwidth and long relative aperture. The change detection methods developed for non-conventional SAR have been shown to be applicable to conventional SAR systems. However, the differences in characteristics of non-conventional and conventional SAR, especially spatial resolutions, degrade the change detection performance and cause some difficulties in evaluating change detection results when applying these methods to conventional SAR. SAR apodization, the effects of SAR apodization on change detection performance and change detection evaluation are investigated in this study.

The pursuit monostatic is a SAR operating mode that requires two SAR data acquisitions separated in short time (seconds) using the same flight tracks and being. In this case, the changes in a SAR scene come mainly from ground moving targets, thus opening up opportunities to detect ground moving targets using SAR change detection methods. The paper introduces a SAR GMTI method that is based on a change detection method in combination with a signature classification. The SAR GMTI method is tested with the pursuit monostatic data acquired by TerraSAR-X and TanDEM-X in 2015.

This letter presents a clutter statistical analysis for stacks of wavelength-resolution synthetic aperture radar (SAR) images. Each image stack consists of SAR images generated by the same sensor, using the same flight track illuminating the same scene but with a time separation between the illuminations. We test three candidate statistical distributions for time changes in the stack, namely, Rician, Rayleigh, and log-normal. The tests results reveal that the Rician distribution is a very good candidate for modeling stack of wavelength-resolution SAR images, where 98.59% of the tested samples passed the Anderson-Darling (AD) goodness-of-fit test. Also, it is observed that the presence of changes in the ground scene is related to the tested samples that have failed in the AD test for the Rician distribution hypothesis.

Wave optics propagators (WOPs) are commonly used to describe the propagation of radio signals through earth's atmosphere. In radio occultation (RO) context, multiple phase screen (MPS) method has been used to model the effects of the atmosphere in Global Navigation Satellite System (GNSS) signals during an occultation event. WOP implementation includes, in addition to MPS, a diffraction integral as the final step to calculate the radio signal measured in the low-earth orbit (LEO) satellite. This approach considers vacuum as the propagation medium at high altitudes, which is not always the case when the ionosphere is taken into account in simulations. An alternative approach is using MPS all the way to LEO in order to sample the GNSS signal in orbit. This approach, named MPS orbit sampling (MPS-OS), is evaluated in this letter. Different scenarios of setting occultation assuming a short segment of the LEO orbit have been simulated using MPS and MPS-OS. Results have been compared to Abel transform references. Furthermore, a long segment scenario has been evaluated as well. A comparison of bending angle (BA) and residual ionospheric error (RIE) shows the equivalence between MPS and MPS-OS results. The main application of MPS-OS should be in occultation events with long segments of orbit and including ionosphere, in which a standard WOP may not be appropriate.

The typical activities in a harbor mainly concern logistics, traffic and transportation that need to be monitored or optimized. This paper reports a measurement campaign whose goal is to evaluate the performance of activity surveillance in a harbor using satellite SAR systems. The measurements were conducted in the southern Baltic sea, around the Verkö harbor in Karlskrona, Sweden in the middle of 2018. The SAR data acquisition was with TerraSAR-X/TanDEM-X operating in staring spotlight mode as high resolution was desired. For an accurate evaluation, all activities in the Verkö harbor were video recorded by a drone during the satellite measurement time. Some radar corner reflectors were also deployed on the ground that are available for co-registration. The data associated with two measurements in the end of August and in the beginning of September is presented in this paper. The SAR change detection processing and the initial analysis show the possibility to monitor activities concerning logistics, traffic and transportation using satellite SAR.

A statistical hypothesis test for wavelength-resolution SAR change detection can be derived with the bivariate distributions such as Rayleigh, Gamma and K. The paper investigates the stability of change detection results obtained with a statistical hypothesis test using bivariate Rayleigh distribution. Some practical issues concerning the implementation of the statistical hypothesis test such as scale parameter estimation, target magnitude assumptions and Bessel function calculation are also addressed. The statistical hypothesis test using bi-variate Rayleigh distribution are experimented with the data set containing 24 CARABAS II images. It is shown that beside the simplicity and efficiency, a statistical hypothesis test using bivariate Rayleigh distribution can provide very stable change detection results.

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

A gamma probability density function (pdf) is shown to be an alternative to model the distribution of the magnitudes of high-resolution, i.e., wavelength-resolution, synthetic aperture radar (SAR) images. As investigated in this paper, it is more appropriate and more realistic statistical in comparison with, e.g., Rayleigh. A bivariate gamma pdf is considered for developing a statistical hypothesis test for wavelength-resolution incoherent SAR change detection. The practical issues in implementation of statistical hypothesis test, such as assumptions on target magnitudes, estimations for scale and shape parameters, and implementation of modified Bessel function, are addressed. This paper also proposes a simple processing scheme for incoherent change detection to validate the proposed statistical hypothesis test. The proposal was experimented with 24 CARABAS data sets. With an average detection probability of 96%, the false alarm rate is only 0.47 per square kilometer.

This letter introduces ground-range and cross-range resolution equations for the side-looking bistatic synthetic aperture radar (SAR). The derivation is based on the backprojection integral and the method of stationary phase. The ground-range and cross-range resolution equations are provided in closed form, making them easy for calculation. They are, therefore, helpful for bistatic SAR system development. The derived ground-range and cross-range resolution equations are validated with the bistatic data simulated mainly using the parameters of the LORA system.

Ground moving target indication (GMTI) is one of the important applications of synthetic aperture radar (SAR). This paper introduces a GMTI method for local detection of the ground moving targets in SAR image based on normalized relative speed (NRS) hypotheses. The input of the GMTI method is a complex SAR image where areas of interest for GMTI are locally selected. The output of the method includes the detected targets, the NRSs with respect to the SAR platform, the locations where the detected targets are focused, and the SAR images of the detected targets. The mathematical background of the GMTI method is presented in detail. The method is validated with the data delivered by the Coherent All-Radio Band Sensor, an airborne ultrawideband-ultrawidebeam low-frequency SAR system. The shortcomings of the introduced method are investigated and followed with the solutions.

The paper introduces a new likelihood ratio test (LRT) for incoherent detection of man-made objects obscured by foliage in forest area. The test is performed to detect changes between a reference image and a surveillance image. The method is developed for change detection in high resolution Synthetic Aperture Radar (SAR). For simplicity and lack of more appropriate models, the new LRT is still based on simple and efficient models. If there is no man-made object, the statistical model for clutter and noise of two images will be a bivariate Rayleigh distribution. In contrary, a joint distribution of Rayleigh and uniform is used to model for target, clutter, and noise. The proposed LRT is evaluated using radar data acquired by CARABAS in northern Sweden. The probability of detection is up to 96% with much less than one false alarm per square kilometer.

High accuracy of impact height is important to get reliable Radio Occultation (RO) measurements of the atmosphere refractivity. We have made an investigation on how accurately we can measure the impact height at ground level using wave optics simulations, realistic refractivity profiles, a realistic simulator for an advanced RO instrument including noise, and using phase matching for the inversion. The idea of the investigation is to increase the measurement accuracy of impact height at low altitudes and to give reliable measurements even in cases of super-refractive layers. We present statistics on the accuracy and precision of the determination of the impact height at ground, as well as the resulting accuracy and precision in the measured refractivity.

This paper reports the measurement campaign Blekingetraffic that was performed by Blekinge Institute of Technology with collaboration with the partners. The measurements are part of a case study to evaluate the feasibility of spaceborne SAR for measuring traffic flow change before and after toll charge introduced in Sweden. The radar measurements were conducted around Pukavik, western of Karlshamn, Sweden in the end of 2015 and in the beginning of 2016. In this paper, the results of one of the controlled measurements performed in the first week of February 2016 is presented. In the ground scene, some trucks tracked with GPS were deployed in European route E22 and local roads. The trucks have either cabinets or food tankers resulting in different radar cross sections. The data acquisition is with TanDEM-X using two satellites giving up to 6 data channels. Since the measurements were performed at 5:00 pm there were unknown traffic i.e. trucks and cars that were not controlled. Therefore, the traffic on the roads was recorded by cameras next to the road and on a drone. The initial analysis of the measured data shows the possibilities to apply available processing techniques such as ground moving target indication and change detection to obtain the knowledge of traffic flow change.

This letter presents a wavelength-resolution synthetic aperture radar incoherent change detection method based on image stack, i.e., there are more than one reference or/and surveillance image. Considering image stack in statistical hypothesis test for change detection is expected to result into a simple mathematical expression for implementation and provide better change detection results. As presented in this letter, a statistical hypothesis test is developed on bivariate Gaussian distribution for an image stack of two reference images and one surveillance image. The requirement for the image stack is three images associated with three measurements with no change between two of them. A detection method with simple processing scheme is proposed. The method is experimented with 24 CARABAS data sets. The results indicate that high average detection probability, e.g., 96%, with very low false alarm rate, e.g., only 0.19/km2, is obtained with the proposal.

This paper introduces a method to reduce false alarms in wavelength-resolution synthetic aperture radar (SAR) change detection and aims at very high frequency-band systems like the Coherent All Radio Band System (CARABAS). The false alarms are usually caused by the elongated structures, such as power lines and fences, which stand out from the background. The responses of elongated structures are sensitive to flight path. The introduced method aims at minimizing the false alarms caused by the elongated structures and is based on the well-known adaptive processing mechanism, i.e., the so-called adaptive noise canceler (ANC) where a separate reference signal is required. The changes between measurements are considered by the input signal of ANC while the separate reference signal comes from the measurements without change. Hence, the method requires three SAR images associated with three measurements, with no changes between two of them. The reference data for the study are provided by CARABAS. The experimental results indicate that the method can reduce false alarms significantly and provide high probability of detection (≥98%). The experimental results also show that the method still works well even in the case where the flight tracks of the SAR system in the change detection measurements are slightly different.

This letter presents an analysis of prefiltered clutter ultrawideband (UWB) very high frequency synthetic aperture radar (SAR) images. The image data are reorganized into subvectors based on the observation of the image-pair magnitude samples. Based on this approach, we present a statistical description of the SAR clutter obtained by the subtraction between two real SAR images. The statistical analysis based on bivariate distribution data organized into different intervals of magnitude can be an important tool to further understand the properties of the backscattered signal for low-frequency SAR images. In this letter, it is found that, for “good” image pairs, the subtracted image has Gaussian distributed clutter backscattering and that the noise mainly consists of the thermal noise and, therefore, speckle noise does not have to be considered. This is a consequence of the stable backscattering for a UWB low-frequency SAR system.

This paper introduces a bistatic fast backprojection synthetic aperture radar (SAR) algorithm that is available for different bistatic geometries. The proposed algorithm is tested with the bistatic CARABAS-like data, and the results indicate that the algorithm is a good candidate for bistatic SAR data processing. The image quality measurements are quite similar to the referenced values obtained with the bistatic global backprojection algorithm. That is, the peak sidelobe ratio (PSLR) is -13.7 dB in comparison to the referenced PSLR of -13.8 dB. The half-power beamwidths (HPBWs) measured on the cuts in x and y and the direction where the peak sidelobes locate are 2.8, 3.7, and 3.5 m, respectively, which are approximate to the referenced HPBWs. The small differences in the measured results mainly come from the interpolation step of the algorithm.

In this paper we propose a new change detection (CD) algorithm based on the Bayes theorem and probability assignments. Differently from any kind of likelihood ratio test (LRT) algorithms, the proposed algorithm does not present target alarms, but the probability of certain image position is a target position. In other words, the proposed method leads to quantitative estimates on the probability of a target at any pixel, whereas LRT algorithms can only be used as a figure of merit for any pixel to contain a target.

This paper presents an analysis of pre-filtered clutter VHF SAR images. The image data are reorganized into sub-vectors based on the observation of the image-pair magnitude samples. Based on this approach, we present a statistical description of the SAR clutter obtained by the subtraction between two real SAR images. The statistical analysis based on bivariate distribution data organized into different intervals of magnitude can be an important tool to further understand the properties of the backscattered signal, which can be a valuable premise for change detection processing.

Left/right ambiguity and low angular (azimuth) resolution are severe problems for monostatic forward-looking SAR imaging. It is strongly believed that these technical issues can definitely be solved with bistatic forward-looking SAR. The analysis presented in this paper points out that the left/right ambiguity problem still exits. However, an appropriate selection of the position of bistatic base line and antenna beamwidth allows us to conceal it. The paper also gives some recommendations which can be considred for the forward-looking SAR imaging.

This paper presents a method to implement SAR slow-time space time adaptive processing (STAP) in the beamforming stage of the fast backprojection algorithm. This method is different from the recently published method where the SAR fast-time STAP is implemented after the beamforming stage for detection and then imaging. As a common SAR STAP method, the method proposed in this paper can be used for ground moving target indication (GMTI) and reconstruction of the image of the detected moving targets. The paper also presents some simulation results in order to illustrate the proposed method.

A 2-D spectrum for bistatic synthetic aperture radar is derived in this letter. The derivation is based on the commonly used mathematic principles such as the method of stationary phase and the Fourier transform and the Lagrange inversion theorem in order to find the point of stationary phase in the method of stationary phase. Using the Lagrange inversion theorem allows minimizing the initial assumptions or the initial approximations. The derived 2-D spectrum is compared with the commonly used 2-D spectrum to verify it and illustrate its accuracy.

In this paper, the results of moving-target detection in multichannel UHF-band synthetic aperture radar (SAR) data are shown. The clutter suppression is done using finite-impulse response (FIR) filtering of multichannel SAR in combination with a two-stage fast-backprojection algorithm to focus the moving target using relative speed. The FIR filter coefficients are chosen with the use of space-time adaptive processing filtering. Two parameters are used for target focusing, target speed in range and in azimuth. When the target is focused, both speed parameters of the target are found. In the experimental results, two channels were used in order to suppress clutter. In the resulting SAR images, it is obvious that very strong scatterers and the forest areas have been suppressed in comparison to the moving target in the image scene. The gain obtained can be measured using signal-to-clutter-and-noise-ratio gain, which is about 19 dB. Another way to measure the signal processing gain is the ability to suppress the strongest reflecting object in the SAR scene. The gain of the target in relation to this object is 25 dB. This shows that using UHF-band SAR ground moving target indication (GMTI) for suppressing forest and increasing the target signal can work.

The paper proposes a ground moving target detection and estimation method aiming at UltraWide Band and -Beam (UWB) Synthetic Aperture Radar (SAR) systems. The method is developed on the moving target detection by focusing technique and requires a SAR system flying with two different linear flight track. The method allows us to detect ground moving target, even hidden by clutter, and to estimate the target parameters such as speed and direction of motion. The accuracy of the estimations depends strongly on the computational cost and can therefore be controlled. The proposal is tested with the simulated CARABAS-II data.

Focusing moving targets with Normalized Relative Speed (NRS)for bistatic synthetic aperture radar (SAR) is discussed in this paper. The discussion concentrates on azimuth-invariant bistatic geometry. The focusing approach for azimuth-invariant bistatic geometry is derived analytically. The validity of the proposed approach for other bistatic geometry like azimuth-variant is also investigated.

In this paper, an investigation is made on how sidelobes can be suppressed in ultrawideband-ultrawidebeam (UWB) Synthetic Aperture Radar (SAR) using apodization. Due to the special properties of UWB SAR such as very wide integration angle and very large relative bandwidth, the support for the spectrum of a SAR image differs distinctively from a rectangle, which is the normal approximation in narrowband-narrowbeam (NB) SAR. The proposal in the paper is to apply non-separable windows to the spectrum, in order to suppress sidelobes. Non-separable windows are shown to give less broadening of mainlobe while maintaining the same suppression of sidelobes in comparison to separable windows. In the comparison, parameters for three different SAR systems are used.

A combination of space time adaptive processing (STAP) and bistatic SAR seems to bring a promising technique of moving target detection and imaging. Moving target detection based on STAP is the most advanced and reliable detection method and mainly used in multichannel monostatic SAR whereas bistatic SAR has shown unique advantages such as the flexibility in design and the ability to observe a target with different angles. The paper investigates the possibility to employ STAP for detection and imaging of moving targets in bistatic SAR.

The fast factorized backprojection for bistatic SAR algorithm (BiFFBP), which processes the bistatic SAR data in more than one beam forming stage, is presented in this paper. The algorithm is developed on the bistatic fast backprojection algorithm SAR (BiFBP) and inherits time-domain characteristics such as unlimited scene size, real time processing, local processing, manageable motion compensation, and large range migration handling. Also, the algorithm works well with different kinds of bistatic configuration. For these reason, it is very suitable for UWB bistatic SAR processing. The pro- posed algorithm is tested successfully with simulated UWB bistatic SAR data.

In the area of SAR imaging, it is of interest to be able to focus moving targets. In this paper, an algorithm for moving target focusing is presented. The algorithm is able to refocus a smeared moving target in a SAR image processed at one relative speed to the correct one% The algorithm works in the frequency domain and is based on the Range Migration algorithm. The refocusing can be made on the whole SAR image or small sub images corresponding to physical areas of interest for the end user. By applying the algorithm to a small image, the computational cost is greatly reduced compared with using the full SAR image. The performance is illustrated by applying the algorithm to simulated SAR data according to the parameters for the LORA system.

This paper discusses a possibility to integrate space-time processing into the time-domain backprojection process. This combination allows detection as well as imaging moving objects. Two space-time techniques, Displaced Phase Center Antenna (DPCA) and Space-Time Adaptive Processing (STAP), are considered for this integration. Simulated results based on the LORA parameters demonstrate the efficiency of detection and imaging moving objects.

This paper presents an application of the moving target detection by focusing technique (MTDF) in civil traffic monitoring. The experimental results based on simulations show that the basic civil traffic monitoring can be solved using MTDF using a single-channel airborne SAR system. With an assumption of known moving directions of vehicles, MTDF allows detecting, estimation of speed, and finally reconstructing a SAR image of the vehicles of interest. In this study, the focusing approach of MTDF is based on ultrawideband chirp scaling (UCS) algorithm.

A new version of chirp scaling (CS), the so-called ultrawideband (UWB) CS (UCS), is proposed in this letter. UCS aims at UWB synthetic aperture radar (SAR) systems utilizing large fractional bandwidth and wide antenna beamwidth associated with a wide integration angle. Furthermore, it is also valid for SAR systems with special characteristics such as ground-moving-target-indication SAR systems with a very high pulse repetition frequency.

In this letter, we propose an approach to suppress radio-frequency interference (RFI) in ultrawideband (UWB) low-frequency synthetic aperture radar (SAR). According to the proposal, RFI is suppressed by using an adaptive line enhancer controlled by the normalized least mean square algorithm. The approach is tested successfully on real UWB low-frequency SAR data. In order to keep the computational burden down, possible ways to integrate the RFI suppression approach into SAR imaging algorithms are also suggested.

Moving-target detection in ultrawideband (UWB) synthetic aperture radar (SAR) is associated with long integration time and must accommodate azimuth focusing for reliable detection. This paper presents the theory on detection of moving targets by focusing and experimental results on single-channel SAR data aimed at evaluating the detection performance. The results with respect to both simulated and real data show that the ability to detect moving targets increases significantly when applying the proposed detection technique. The improvement in signal-to-clutter noise ratio, which is a basic requisite for evaluating the performance, reaches approximately 20 dB, using only single-channel SAR data. This gain will be preserved for the case of multichannel SAR data. The reference system for this study is the airborne UWB low-frequency SAR Coherent All RAdio BAnd Sensing II.

This paper investigates how relative speed estimation of moving targets in synthetic aperture radar (SAR) images is affected by strong stationary surrounding clutter. The result extends previous work by estimating moving target relative speed under the influence of clutter and Gaussian noise. The results obtained show that good estimates can be obtained even in situations with relatively high clutter and noise, i.e. SNR and SCR lower than 10 dB.

Two frequency-domain algorithms chirp scaling (CS) with the advantage of simplification and range migration (RM) with the advantage of accuracy are candidates for a comparative study to the time-domain algorithm Fast factorized backprojection (FFBP) with reference to a UWB system are presented in this paper. The comparison is based on UWB SAR image quality measurements such as spatial resolution, Integrated Sidelobe Ratio (ISLR), peak sidelobe ratio (PSLR) and processing time connected to computational cost. The simulated SAR data, which is used in this study, is based on the parameters of the airborne UWB low frequency CARABAS-II system.

Moving target detection at low radar frequencies is associated with long integration time and has to handle azimuth focusing for reliable detection. The detection by focusing in which time-domain is the basis for the focusing approach has been proposed and successfully experimented in reality. The main objective of this paper is to apply and evaluate the detection method for frequency-domain algorithms. Range Migration Algorithm (RM) is chosen as a candidate for this study due to the accuracy of the algorithm with very wide integration angles. The simulated SAR data, which is used in this study, is based on the parameters of a airborne UWB low frequency system such as CARABASII.