Moving Target Detection for Single-Channel Csar Based on Deep Neural Network

This paper deals with the issue of ground moving target imaging and motion parameter estimation with an airborne dual-channel circular stripmap synthetic aperture radar (CSSAR) system. Although several methods of ground moving target motion parameter estimation have been proposed for the conventional airborne linear stripmap SAR, they cannot be applied to airborne CSSAR because the range history of a ground moving target for airborne CSSAR is different than that for airborne linear stripmap SAR. In this paper, the moving target’s range history for airborne dual-channel CSSAR and the target signal model after the displaced phase center antenna processing are derived, and a new ground moving target imaging and motion parameter estimation algorithm is developed. In this algorithm, the estimation of baseband Doppler centroid and its compensation are first performed. Then focusing is implemented in the 2-D frequency domain via phase multiplication, and the target is focused in the SAR image without azimuth displacement due to the compensation of the Doppler shift caused by its motion. Finally, the target’s motion parameters are estimated with its Doppler parameters and its position in the SAR image. Numerical simulations are conducted to validate the derived range history and the performance of the proposed algorithm.

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.

An airborne circular synthetic aperture radar system captured data for a 5 km diameter area over 31 orbits. For this challenge problem, the phase history for 56 targets was extracted from the larger data set and placed on a DVD for public release. The targets include 33 civilian vehicles of which many are repeated models, facilitating training and classification experiments. The remaining targets include an open area and 22 reflectors for scattering and calibration research. The circular synthetic aperture radar provides 360 degrees of azimuth around each target. For increased elevation content, the collection contains two nine-orbit volumetric series, where the sensor reduces altitude between each orbit. Researchers are challenged to further the art of focusing, 3D imaging, and target discrimination for circular synthetic aperture radar.

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.

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.

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.

Due to the advantage of short revisit time, airborne circular stripmap synthetic aperture radar (CSSAR) is an attractive tool for air-to-ground surveillance and reconnaissance. This paper deals with the issue of ground moving-target detection with an airborne CSSAR and proposes a new autofocus-based detection algorithm. The prominent features of the proposed algorithm are the incorporation of the Doppler ambiguity information into the autofocus-based moving-target detection and the ability to solve the problem of large range cell migration, which affects most of autofocus algorithms. The proposed algorithm can detect not only the targets whose azimuth chirp rates are different from that of the static clutter background but also the targets whose Doppler ambiguities are different from that of the static clutter background. Numerical results show that the proposed algorithm works well in both homogeneous and heterogeneous clutter backgrounds.

Circular synthetic aperture radar (CSAR) has attracted much attention in the field of high-resolution SAR imaging. However, the CSAR image focusing is affected by the motion deviations of platform. In the processing of experimental CSAR data to deal with motion errors, the main way is using setup calibrators, which restricts its widespread applications. In this paper, based on the estimation of motion errors, an autofocus CSAR imaging strategy is proposed without using any setup calibrator. The first step is to split the entire aperture into several subapertures, the second step is to process the data in subapertures with an autofocus backprojection method, and the last step is to obtain the final CSAR image by merging the subimages obtained from the subaperture processing. The CSAR data processing results prove that the proposed strategy can remove the motion errors accurately and acquire well-focused CSAR images.

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.

A three dimensional (3D) imaging method which is a complex-valued generalized Radon transform (CGRT)-CLEAN algorithm for circular synthetic aperture radar (CSAR) is proposed in this chapter. Based on the analysis of CSAR echo model, the echo of a scatterer in the range-azimuth domain can be modeled as a single cycle sinusoid. Inspired by the generalized Radon transform, this chapter utilizes the CGRT to estimate for scatterers’ positions. Then, combining the CGRT with CLEAN technique, the parameters of a scatterer which include position information and reflection coefficient can be obtained. Thus, a 3D image of a target can also be reconstructed. The performances of the CGRT-CLEAN method have been verified by simulations, and the results confirm the effectiveness of the proposed algorithm for processing CSAR data.

Circular SAR (CSAR) has the ability of 3-D imaging due to its special curve trajectory. Single-pass CSAR can theoretically obtain the resolution of the sub-wavelength level on the distance-azimuth plane, but its resolution at the elevation direction is very low. At the same time, CSAR 3-D imaging with Back Projection(BP) has high algorithm complexity and low imaging efficiency. A coherent 3-D imaging method for multi-circular SAR based on an improved 3-D back projection algorithm is proposed. For the problem of high time complexity of the imaging algorithm, an improved 3-D BP algorithm for CSAR based on constructing geometric interpolation kernel is proposed. 3-D interpolation operations are transformed into 1-D interpolation operations and distance vector searching operations. The final imaging result is obtained by coherently accumulating the improved 3-D BP results of multi-circular SAR. The proposed method solves effectively the problem of low elevation resolution of single-pass CSAR, improves 3-D imaging details, and reduces greatly the time of CSAR 3-D imaging simultaneously. The simulated 3-D imaging results of the conical target and GOTCHA data set from the US Air Force Laboratory verify the effectiveness of the proposed method.

Circular synthetic aperture radar (CSAR), which can observe the region of interest for a long time and from multiple angles, offers the opportunity for moving-target detection (MTD). However, traditional MTD methods cannot effectively solve the problem of high probability of false alarm (PFA) caused by strong clutter. To mitigate this, a novel, three-step scheme combining clutter background extraction, multichannel clutter suppression, and the degree of linear consistency of radial velocity interferometric phase (DLRVP) test is proposed. In the first step, the spatial similarity of the scatterers and the correlation between sub-aperture images are fused to extract the strong clutter mask prior to clutter suppression. In the second step, using the data remaining after elimination of the background clutter in Step 1, an amplitude-based detector with higher processing gain is utilized to detect potential moving targets. In the third step, a novel test model based on DLRVP is proposed to further reduce the PFA caused by isolated strong scatterers. After the above processing, almost all false alarms are excluded. Measured data verified that the PFA of the proposed method is only 20% that of the comparison method, with improved detection of slow and weakly moving targets and with better robustness.

3D scene reconstruction from single‐pass circular synthetic aperture radar (CSAR) data has been a research hot spot in various remote sensing applications. Among existing literature, radargrammetry techniques and model assistant methods are two dominant approaches. However, the performance of these methods is limited in practical applications: radargrammetry techniques obtain merely one height value along vertical; model assistant approaches only work when valid models are given. A single‐pass CSAR 3D scene point cloud reconstruction method is proposed. First, the connection between an object and its projections in multi‐aspect SAR images is established with no auxiliary model. On the basis of the connection, voxels are classified into inner voxels (voxels beneath the target profile) and outside voxels (voxels outside of the target profile). Discarded all the outside voxels, the remained inner voxels are combined to form the 3D point cloud of the target. It is similar to the sculpturing process from a raw material to a piece of craft. Finally, a 3D scene is retrieved through assembling all reconstructed targets into a unified coordinate system. The feasibility of the proposed method is validated by the Gotcha Volumetric SAR Data Set.

Circular synthetic aperture radar (CSAR) can obtain more complete scattering characteristics by observing the target with different azimuth angles. Therefore, extracting the complete structure of the target from CSAR images is of great significance for accurate interpretation. At present, the artificial target extraction based on CSAR images mostly uses anisotropic scattering features. For special targets such as buildings, as the walls and the ground form dihedral corner structures, there are also obvious strong scattering features such as double-scattering lines in SAR images. Therefore, combining the strong scattering features of buildings at specific aspects with anisotropic scattering characteristics at different aspects can obtain better extraction results, and how to extract these features accurately and efficiently is the key point. Based on this, this paper proposes a novel method for building contour extraction based on CSAR images. For strong scattering features, a fast fuzzy C-means (FCM) clustering algorithm was used to extract them. For anisotropic scattering features, aspect entropy was used to characterize the anisotropy degree, and K-means clustering was combined to extract. Finally, a more accurate result is obtained by merging the two feature extraction results. In order to verify the effectiveness and practicability of the proposed method, a lot of measured data acquired by the self-developed airborne L-band and Ku-band CSAR systems were processed. The experiments show that, compared with state-of-the-art algorithms, the proposed method can obtain more accurate results in less time.

Circular synthetic aperture radar (CSAR) enables 360-degree observation of the scene. Compared with coherent imaging, noncoherent imaging can suppress speckle and obtain a large number of texture features. However, due to the existence of a man-made anisotropic targets and the unique imaging geometry of CSAR, the different subaperture images are quite different, which makes the registration very difficult. Therefore, this manuscript proposed a factorized registration strategy. First, the full aperture is divided into multiple subapertures; then the adjacent subaperture images are registered and the noncoherent superimposition of the subaperture images yields the next stage image; finally, the previous step is performed iteratively until the final imaging result is obtained. Experimental results based on Ka-band CSAR data verify the effectiveness of the strategy.