Annular Array 3-D Sar: Resolution Analysis and Data Processing

In this paper, we propose the two-dimensional imaging response of three-dimensional moving target via synthetic aperture radar (SAR) with back-projection imaging algorithm. Firstly, we analyzed the imaging response for each single echo, which is the intersection of Doppler isodop and iso-range contour. Then, we come up with a focus condition for moving target SAR imaging. Besides, we find that the Doppler frequency will cause a shift and the motion trail of moving target will draw a curve in the imaging result over synthetic aperture time. Meanwhile, we provide the numerical simulation experiments of moving point targets via linear SAR and circular SAR with X-band and THz-band, respectively, to verify the conclusion of moving target imaging response.

This paper presents a study on signal modeling for airborne multichannel circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) systems with relatively high resolution. High resolution is an important trend of synthetic aperture radar (SAR), which means higher accuracy requirement of range history and longer synthetic aperture time. It's known that targets are probably experience accelerations as the synthetic aperture time increases. Therefore, compared with the systems of lower resolution, the target signal modeling of high-resolution systems becomes more challenging. Besides, the coupling relationship among a target's motion and position parameters becomes more complicated due to the existence of accelerations. In this paper, a fourth-order range history model of a moving target is developed. Moreover, the target signal model is established, and the along-track interferometric (ATI) phase history is derived to reveal the coupling relationship among a target's motion and position parameters.

As important man-made targets, bridges have been a major focus of Synthetic Aperture Radar (SAR) image interpretation, and many researchers have developed methods for bridge detection. The core frameworks of these methods are analogical, a river is first extracted, and a water bridge is detected based on the positional relationship between the river and bridge. However, existing bridge detection methods relying on river extraction; cannot be utilized detect land bridges. This is because the background environment under a bridge is land, not river, which has different scattering characteristics and shape layouts. As such, the traditional method for extracting rivers is not suitable for extracting land background, and it is impossible to locate a bridge based on prior knowledge of its location of. To resolve this problem, in this study, we propose a land bridge detection method based on polarized Circular SAR (CSAR) images. In our proposed method, the Circular Polarization Entropy (CPE) of an observed scene is introduced to separate possible bridge targets from a land background (In our experiment, the average CPE of the bridge is 0.4018, and that of the land background is 0.7819; thus there is a clear difference between the bridge and background). False targets are removed based on the difference in the polarization entropy variance features of the bridges and other ground objects; and the size characteristics of the bridges. Finally, accurate extractions of land bridges are obtained based on the geometric characteristics of the bridges. Experimental results based on real airborne L-band polarized CSAR data verify the correctness of the theoretical analysis and effectiveness of the proposed method.

In circular synthetic aperture radar (CSAR), radar collects data over a circular trajectory. This wide-angle non-linear collection geometry makes CSAR capable of obtaining both two-dimensional (2D) high resolution image and three-dimensional (3D) image. In this paper, compressive sensing technique is used in 3D CSAR imaging. Not only echo samples used in imaging are greatly reduced, but also the imaging results are with good performance. However, the consumed time of reconstruction is very long. In order to overcome this problem, graphic process unit (GPU) is furtherly used in 3D compressive sensing CSAR imaging in this paper. Simulation results show that GPU can improve the efficiency of the 3D reconstruction.

Due to the advantages of long-time and multi-angle observation, ground moving target detection with circular synthetic aperture radar (SAR) has recently attracted lots of interest from researchers. Our team has previously proposed the logarithm background subtraction algorithm for moving target detection in single-channel circular SAR. Its principle is that the background image (static clutter) is obtained by median filtering of the image sequence, and the foreground image (moving target) is obtained by subtraction. To further improve the performance of background and foreground separation, we introduce the low-rank sparse decomposition (LRSD) method into the previous framework. A new algorithm based on LRSD and adaptive threshold detector (ATD) is proposed in this letter. First, this letter introduces entropy metric to optimize parameters in LRSD to obtain better background and foreground separation. Second, since the statistical distribution of the foreground image is unknown, the constant false alarm rate (CFAR) detector cannot be applied to the foreground image. Therefore, an adaptive threshold detector (ATD) built on Otsu is presented in this letter, which is independent of image statistical properties. The final detection result is obtained via clustering using the modified density-based spatial clustering of applications with noise (DBSCAN) method. The effectiveness of the proposed algorithm is verified by the experimental results on the airborne X-band Gotcha Volumetric SAR Data.

Circular synthetic aperture radar (SAR) imaging on the geosynchronous orbit has many potential advantages, such as high-resolution three-dimensional (3D) imaging and continuous surveillance of broad area. The mission considered is an L-band geosynchronous circular SAR (GEOCSAR) with full aperture measurement. Since the integration time of GEOCSAR can be as long as 24 h, the orbit altitude is about 36 000 km and the coverage may reach 1/3 of the Earth's surface. The spatial and temporal variation of the ionosphere may impose significant effects on 3D imaging performance of GEOCSAR. The effects of ionospheric spatial variation including vertical and horizontal structure, the ionospheric turbulence and the ionospheric temporal variation are discussed. The analysis shows that the temporal variation of the ionospheric electron content during the synthetic aperture and the ionospheric turbulence may severely affect the GEOCSAR focusing performance on the XY plane. The vertical and horizontal structure of the ionosphere will cause image shift, and the dispersion effect is negligible.

In conventional synthetic aperture radar (SAR) working mode, targets are assumed isotropic due to the limited aperture length. However, most of man-made targets are anisotropic. Therefore, the anisotropic scattering can help us do man-made target detection. Circular SAR (CSAR) [1] is a new SAR working mode and it can obtain the anisotropic scattering of the target by 360° observation. In this paper, the multi-angular statistical properties of targets are analyzed. The probability density functions (PDF) of the anisotropic target are various under different aspect viewing angles, while the PDFs of the isotropic target are basically stable. Then a man-made target detection method is proposed based on the multi-angular statistical property. Likelihood ratio test [2] is used to judge whether the statistical property of scattering is anisotropic or isotropic. Then anisotropic scatterings, which represent the man-made targets, can be discriminated from isotropic scatterings by thresholding. An X-band chamber circular SAR data and a C-band airborne circular SAR data are used to illustrated our idea.

This paper investigates different metrics for ground moving target indication (GMTI) with a multichannel synthetic aperture radar (SAR). These metrics are especially sensitive to targets that have an across-track component of velocity, v/sub y/, although they can also detect targets with an along track velocity component, v/sub x/. Not only are the metrics suitable for fully SAR compressed data, they are also meaningful when computed for range (pulse) compressed but Doppler uncompressed data (raw data). The use of both data domains allows for target detection over a larger v/sub y/ range than either used individually. For large v/sub y/, side-lobe suppression in fully SAR compressed data simultaneously suppresses the targets, giving an advantage to the raw data domain. For low v/sub y/, and especially with low RCS targets, the SAR compression gain facilitates detection, giving an advantage to the SAR compressed domain. By viewing the SAR compression as the application of a linear filter, the metrics are shown to be suitable for target detection after any linear filtering of the raw data, such as a linear time-frequency filter, or a simple transformation into the Doppler domain using an FFT filter. The metrics have in common that they are all based on the eigenvector decomposition of the sample covariance matrix. Their statistical properties are analytically compared and their detection capabilities are demonstrated on measured two-channel airborne SAR data in a variety of data domains. Some metrics are shown to allow target detection with low false alarm rates even in heterogeneous terrain, such as in urban areas, without compromising the probability of detection.

Circular synthetic aperture radar (CSAR) is a high resolution 2D and 3D imaging mode, to obtain the demand of all observation information. Circular motion trajectory make motion compensation problem is very difficult. However, existing method, such as autofocus back projection (ABP) algorithm, cannot effectively solve the problem of motion compensation in CSAR. In this paper, an autofocus algorithm based on minimum entropy is used to solve this problem. It can accurately estimate phase errors in CSAR. The simulated CSAR data are used to validate performance of minimum entropy autofocus (MEA) algorithm.

Coastal SAR and PLRM altimetry in German Bight and West Baltic Sea

As an active microwave imaging sensor, Synthetic Aperture Radar (SAR) has become one of the main means of Earth observation owing to its unique technical advantages of all-day, all-weather operation and long working distance. As such, it plays a very important role in military and civilian fields. With the development of SAR remote-sensing technology, high-resolution, high-quality SAR images are produced continuously. However, manual detection and recognition of targets of interest is time-consuming and laborious, so the development of Automatic Target Recognition (ATR) technology is a matter of urgency. The typical SAR ATR system primarily comprises three stages: detection, discrimination, and classification/recognition. The detection and discrimination stages are the basis of the SAR ATR system, and research on SAR applications in the radar field has been conducted by researchers around the world. For single-channel SAR images, target detection and discrimination from simple scenes yield good results. However, in complex scenes, the clutter scattering intensity is relatively high, the clutter background is heterogenous, the target scattering intensity is relatively weak, and the target distribution is dense. These factors continue to make accurate SAR target detection and discrimination difficult. In this paper, we summarize the recent research progress on single-channel SAR target detection and discrimination methods for complex scenes, analyze the characteristics and problems associated with various methods, and consider the future development trend of single-channel SAR target detection and discrimination methods for complex scenes.

Sidelobe reduction is a very primary task for synthetic aperture radar (SAR) images. Various methods have been proposed for broadside SAR, which can suppress the sidelobes effectively while maintaining high image resolution at the same time. Alternatively, squint SAR, especially highly squint SAR, has emerged as an important tool that provides more mobility and flexibility and has become a focus of recent research studies. One of the research challenges for squint SAR is how to resolve the severe range-azimuth coupling of echo signals. Unlike broadside SAR images, the range and azimuth sidelobes of the squint SAR images no longer locate on the principal axes with high probability. Thus the spatially variant apodization (SVA) filters could hardly get all the sidelobe information, and hence the sidelobe reduction process is not optimal. In this paper, we present an improved algorithm called double spatially variant apodization (D-SVA) for better sidelobe suppression. Satisfactory sidelobe reduction results are achieved with the proposed algorithm by comparing the squint SAR images to the broadside SAR images. Simulation results also demonstrate the reliability and efficiency of the proposed method.

Synthetic aperture radar (SAR) is a microwave imaging equipment based on the principle of synthetic aperture, which has all kinds of characteristics such as all-time, all-weather, high resolution and wide breadth. It also has high research value and applied foreground in the area of military and civilian. In particular, worldwide, a great deal of researches on SAR target classification and identification based Deep Learning are ongoing, and the obtained results are highly effective. However, it is well known that Deep Learning requires a large amount of data, and it is costly and inaccessible to acquire SAR samples through field experiment, so image simulation research for expanding SAR dataset is essential. In this paper, we concentrated on generating highly realistic SAR simulated images for several equipment models using Generative Adversarial Network (GAN) without construction of terrain scene model and RCS material mapping. Then we tested the SAR simulated images on a specialized SAR classification model pretrained on MSTAR dataset. The results showed that simulated targets could be identified and classified accurately, demonstrating the high similarity of SAR simulated images with real samples. Our work could provide a greater variety of available SAR images for target classification and identification study.

Circular synthetic aperture radar (CSAR) has a 360° observation capability on the central observation scenario. A typical way to process CSAR imaging is to cut data into small sub-apertures because most targets are only coherent at a very small observation angle. There are many sub-aperture imaging methods after development in recent years. The back-projection algorithm is widely used because it is simple and can be applied to an arbitrary trajectory. Because of the limitation of the Nyquist sampling frequency and influence of the antenna sidelobe, azimuth ambiguity is a phenomenon that may occur in the radar imaging process. The existing researches typically choose the back-projection (BP) imaging area according to the SAR platform flight path and the antenna beam width. The limitation of the CSAR imaging area and its azimuth ambiguity region are rarely analyzed theoretically. This paper focus on the sub-aperture imaging of CSAR, based on the BP algorithm, which derives the relationship of azimuth ambiguity with CSAR parameters such as the pause repeat frequency (PRF), slant range angle, velocity of radar platform, etc. This paper proposes an equation for the calculation of the azimuth ambiguity region and analyzes the limitations, which provides theoretical support for CSAR parameter design, imaging area selection, and azimuth ambiguity analysis.

Determining accurate location with Synthetic Aperture Radar (SAR) images is hindered not only by the limited resolution of the image but also by the non-rectangular nature of the process that produces the image, and the presence of multiple returns from di§erent height objects at the same range. Three dimensional SAR volume measurements can di§erentiate di§erent height scatterers but imaging methods are limited by the measurement scenario. Interpolation of images and volumes can normalize the collected data but can add inaccuracy to pixel and voxel impulse response (IPR) and localization estimates. Regions of best achievable accuracy deÖne the imaging algorithm performance envelope. Ideal localization occurs at the estimation bound of modeled point scatterers, and is also indicated by pixel and voxel size or IPR main lobe width. The performance envelope of SAR measurement scenarios, radar parameters, and imaging and estimation algorithms can reÖne system parameters of accurate localization. Use of analogous Circular and Linear SAR (CSAR and LSAR) scenarios enables joint trade-o§ of áight path along with imaging parameters. An ideal spherical construct underlies joint CSAR and LSAR, two and three dimensional SAR scenarios. This construct also ties CSAR and LSAR imaging to spherical ray-trace creation of phase history. For di§erent scenarios deÖned through a spherical model of parameters, localization performance of the imaging envelope, IPR and estimation accuracy are examined. Along with sample size and ideal chirp waveform sampling, the azimuth and elevation aperture is varied to include ideal and sub-sampling, and extension to achieve un-aliased images and volumes. Methods expanding the envelope are investigated.