Ground Moving Target Detection and Trajectory Reconstruction Methods for Multichannel Airborne Circular SAR

The rapid development of Synthetic Aperture Radar Ground Moving Target Indication (SAR-GMTI) is threatening ground moving targets. The key against it is the efficient jamming technology. This paper introduces SAR-GMTI at first. Then, RF noise jamming, noise AM jamming, noise FM jamming and noise convolution jamming are also introduced and analyzed. Finally this paper illustrates the jamming effect by computer simulation which contains velocity estimation and detection probability. The results show that under the proper JSR condition, the active noise jamming can jam SAR-GMTI effectively, and under the same JSR condition, noise convolution jamming has a more efficient jamming effect than those non-coherent jamming obviously.

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.

In multi-channel synthetic aperture radar-ground moving target indication (SAR-GMTI), most radial velocity estimation methods are based on the phase difference between channels. However, the image coregistration and channel phase errors will have a severe impact on the phase difference between channels. Moreover, it deteriorates the performance of the moving target radial velocity estimation. To solve this problem, a robust radial velocity estimation method is proposed using a frequency diverse array-SAR (FDA-SAR) in this letter. By introducing the step frequency, the interferometric phase among channels is a linear function of the Doppler frequency. The radial velocity of moving targets is embedded in the first-order term of the linear function. Meanwhile, the first-order term does not include channel phase error terms. Therefore, the accurate velocity of moving targets is estimated by the first-order coefficient which is solved by the least-squares fitting method. Afterward, according to the analysis and derivation, the proposed method is robust on the condition of image coregistration error. At last, simulations and data analysis illustrate the effectiveness of the proposed method.

Multichannel synthetic aperture radar ground moving target indication has been widely used and get great attention nowadays. Traditional adaptive methods like space-time adaptive processing (STAP) need to calculate and search the maximum of the test statistic pixel by pixel over a large range of radial velocity, which will cause great computational burden. Nonadaptive methods like displaced phase center antenna (DPCA) will cause signal loss of moving targets, thereby degrading the performance of moving targets detection and radial velocity estimation. To improve the problem mentioned above, this article proposes a novel two-step scheme based on greatest of (GO)-DPCA and Local STAP. In step I, GO-DPCA utilizes the complex images of the channels with different baselines for joint moving target detection. Then, in step II, only the local data of moving targets detected in step I needs to be extracted from the original image domain for clutter suppression and radial velocity estimation simultaneously by STAP. Since the processing gain of moving targets depends on the radial velocity and baseline length, the detection performance of moving targets with different radial velocity can be improved by proposed GO-DPCA compared to conventional DPCA. Considering the moving targets usually distribute sparsely in the scene, step II can avoid most computational burden caused by processing pixel by pixel. Finally, the experimental results of both simulated and acquired data are given to validate the effectiveness of the proposed method.

This paper proposes a new ground moving target imaging algorithm for high-resolution airborne circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) systems. In the proposed algorithm, the range cell migration correction is performed in the 2D frequency domain via a phase multiplication. The azimuth compression is performed in the range Doppler domain. A key step of the proposed algorithm is to utilize an iterative strategy to achieve an accurate correction of the range cell migration. This step makes the proposed algorithm work well for high-resolution CSSAR-GMTI systems. Numerical simulations are conducted to validate the proposed algorithm.

Knowledge-aided space-time adaptive processing (KASTAP) using multiple coherent processing interval (CPI) radar data is described. The approach is based on forming earth-based clutter reflectivity maps to provide improved knowledge of clutter statistics in nonhomogeneous terrain environments. The maps are utilized to calculate predicted clutter covariance matrices as a function of range. Using a data set provided under the DARPA knowledge-aided sensor signal processing and expert reasoning (KASSPER) Program, predicted distributed clutter statistics are compared to measured statistics to verify the accuracy of the approach. Robust STAP weight vectors are calculated using a technique that combines covariance tapering, adaptive estimation of gain and phase corrections, knowledge-aided prewhitening, and eigenvalue rescaling. Techniques to suppress large discrete returns, expected in urban areas, are also described. Several performance metrics are presented, including signal-to-interference-plus-noise ratio (SINR) loss, target detections and false alarms, receiver operating characteristic (ROC) curves, and tracking performance. The results show more than an order of magnitude reduction in false alarm density when compared to standard STAP processing.

To detect and estimate ground slowly moving targets in airborne single-channel synthetic aperture radar (SAR), a road-aided ground moving target indication (GMTI) algorithm is proposed in this paper. First, the road area is extracted from a focused SAR image based on radar vision. Second, after stationary clutter suppression in the range-Doppler domain, a moving target is detected and located in the image domain via the watershed method. The target’s position on the road as well as its radial velocity can be determined according to the target’s offset distance and traffic rules. Furthermore, the target’s azimuth velocity is estimated based on the road slope obtained via polynomial fitting. Compared with the traditional algorithms, the proposed method can effectively cope with slowly moving targets partly submerged in a stationary clutter spectrum. In addition, the proposed method can be easily extended to a multi-channel system to further improve the performance of clutter suppression and motion estimation. Finally, the results of numerical experiments are provided to demonstrate the effectiveness of the proposed algorithm.

A highly efficient algorithm is proposed to collect all the energy of a moving target, irrespective of its speed and direction, and has been applied to real RADARSAT-2 MODEX (Moving Object Detection EXperiment) data. Results show that the algorithm maximizes the SCNR (signal-to-clutter-plus-noise ratio) in existing spaceborne SAR-GMTI (Synthetic Aperture Radar Ground Moving Target Indication) systems with a minimal increase in the processing load. Instead of attempting to match to all possible radial speeds of unknown movers in order to adequately apply SAR focusing, the algorithm requires only two full iterations of SAR processing per channel. The first iteration is a static world, full PRF (pulse repetition frequency) bandwidth SAR processing step. The second iteration is two DC (Doppler centroid) offset, half PRF bandwidth SAR processing iterations. By coherently combining the SAR-DPCA (displaced phase center antenna) images, the energy of movers can be completely recovered.

Clutter suppression is a challenging task in synthetic aperture radar-ground moving target indication (SAR-GMTI). In general, sufficient secondary samples are not easily acquired due to the nonstationary and nonhomogeneous characteristics of bistatic SAR (BiSAR) clutter, resulting in worse clutter suppression results. Recently, space–time adaptive processing based on sparse recovery (SR-STAP) has been developed since its better clutter suppression performance with less samples. However, since the off-grid problem in space–time domain caused by BiSAR’s separate configuration, existing SR-STAP would suffer from severe performance degradation. To address this problem, a clutter-ridge matched STAP (CRM-STAP) method for BiSAR nonstationary clutter suppression is proposed. First, clutter distribution modeling with arbitrary BiSAR configuration is applied to accurately obtain the clutter ridge in space–time domain. Then, keystone transform and time-division processing are applied to correct range cell migration and eliminate Doppler frequency migration, respectively. Next, to solve the off-grid problem, the CRM dictionary is reconstructed via adaptive gradient method, which is established along the direction of clutter ridge and its orthogonal direction. Then, with the constructed CRM dictionary, the clutter covariance matrix (CCM) estimation process is transformed to a multimeasured vector optimization problem, and it can be directly solved by the sparse Bayesian learning algorithm. Finally, based on the estimated CCM, the CRM-STAP filter is built to suppress the nonstationary clutter effectively. Compared with the existing STAP and SR-STAP methods, this method can avoid the performance degradation in clutter suppression caused by the off-grid problem and overcomes the strong nonstationary problem of BiSAR clutter in heterogeneous environments. In October 2020, we have successfully carried out the world’s first airborne BiSAR-GMTI experiment, and the experimental results are given to verify the effectiveness of this method.

Combined with the synthetic aperture radar (SAR) imaging algorithm, diverse simulation sample sets of ground moving targets are constructed to tackle the problem of insufficient measured data in the SAR ground moving target indication algorithm based on deep learning. In view of this, a overall scheme of realizing robust detection of ground moving targets under varying backgrounds is conducted on the integration of the adaptive spatial location extraction network based on deformable module and the multichannel clutter suppression technology. In particular, a spatial deformable module is incorporated into the network to enhance its modeling capacity of the input targets with different shapes. Furthermore, the multichannel clutter suppression technology of airborne SAR is adopted to significantly mitigate the interference of complex background clutter. The effectiveness of the proposed method is verified on the simulation sample sets, and comparison with other detection methods is given simultaneously.

SiamST: Siamese network with spatio-temporal awareness for object tracking

Methods for estimating the clutter scattering function and detecting ground moving targets, using pulse-Doppler surveillance radar data, are described. The imaging problem is cast as one of structured covariance estimation with time-varying measurement models and illumination patterns. An expectation-maximization (EM) algorithm is derived and the computational issues arising from its use are discussed. The detection algorithm uses the estimated clutter statistics and a time-varying target model in a standard generalized likelihood ratio test (GLRT).

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.

The frequency modulated continuous wave (FMCW) radar has the characteristics of low probability of interception, good hidden property and the ability to counter anti-radiation missiles. This paper proposes a new method for high-speed ground moving target detection (GMTD) using triangular modulation FMCW. According to the characteristic of the opposite range shift induced by the upslope and downslope modulation FMCW, the upslope and downslope are imaged, respectively. After compensation of continuous motion of the platform and time difference between upslope and downslope signals for imaging, the moving target can be detected through displaced phase center antenna (DPCA) technology. When the moving target is detected, the moving target image is extracted, and correlation processing is used to obtain the range shift, which can be used to estimate the target radial velocity, and further to find the real position of the target. The effectiveness of this method is verified by the result of computer simulation.

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.