As the electromagnetic spectrum becomes increasingly crowded in recent years, synthetic aperture radar (SAR) is confronted with an escalating amount of radio-frequency interference (RFI). In civilian SAR satellite data, narrowband pulsed RFI (PRFI) is a prevalent interference type that significantly degrades the interpretability of SAR images. Among most approaches for suppressing PRFI, notch filtering methods face significant challenges in threshold selection of signal intensity. Conversely, low-rank and sparse decomposition (LRSD) algorithms, though free of threshold selection, often struggle to satisfy the required low-rank conditions. These limitations underscore the necessity of developing more robust interference suppression methods. In this article, we propose a joint framework of wavelet filtering and fast generalized singular value thresholding-based LRSD (FGSVT-LRSD) method to suppress narrowband PRFI in range–frequency and azimuth–time domain of SAR single-look complex (SLC) data. First, a wavelet domain notch filtering (WNF) method is employed to extract the strong spectral components that are primarily composed of PRFI in the 2-D range spectrum of SAR SLC data, while simultaneously protecting the spectrum of low-energy useful signals. Then, the FGSVT-LRSD method is applied to the extracted strong spectral components to efficiently separate the PRFI from the useful signals. By strategically integrating these two approaches, the proposed framework simultaneously exploits the high-intensity and low-rank properties of PRFI for more precise separation, significantly reduces the sensitivity of threshold selection in the WNF process and facilitates the low-rank conditions required by the FGSVT-LRSD method. Finally, the separated PRFI spectrum is subtracted from the original 2-D range spectrum, resulting in the RFI-suppressed SAR image. Experimental results based on both simulated and measured spaceborne SAR data will be presented to demonstrate that, compared with existing methods, the proposed approach exhibits superior PRFI suppression capabilities and effectively preserves useful signals.

As the electromagnetic environment increases complexity, electromagnetic signals in the same frequency band will interfere with synthetic aperture radar (SAR), causing distortion in SAR images. Most notch filters set a fixed threshold and zero notch to achieve radio frequency interference (RFI) suppression which is not adaptive when the energy level of the RFI varies. In this letter, an iterative notch filtering method based on range-frequency domain outlier detection is proposed. It uses hybrid indicators to distinguish the differences of the range spectrum of the Z-normalized SAR single-look complex (SLC) image and the zero-mean complex Gaussian distribution, and obtains an adaptive detection threshold based on spectrum data to detect anomalies. Then, 2D notch filters are constructed to achieve RFI mitigation. In addition, the proposed method performs the above operations with adaptive thresholds in an iterative manner by setting termination conditions to suppress remaining RFI. Compared with other methods, the proposed method can offer more precise RFI detection and effective RFI mitigation. Experiments based on simulated and real SAR data demonstrate the efficacy of the proposed method.

In this letter, we propose a novel range and Doppler sampling mismatch compensation method for moving target detection, which can effectively improve the output signal-to-noise ratio (SNR) of a moving target. In the proposed method, after performing the target coherent integration by using the well-known Keystone transform (KT), the range and Doppler sampling mismatch errors (SMEs) are estimated and compensated based on the constructed optimization model with the consideration of the change rate of a moving target peak amplitude. In order to improve the computational efficiency, the Hooke–Jeeves method is applied to achieve the optimal solution of the constructed optimization problem, thus efficiently solving the target energy diffusion problem caused by the SMEs. Simulated experiment is presented to verify the effectiveness and feasibility of the proposed method.

In this letter, a novel method is proposed to estimate the along-track baseline for a multichannel synthetic aperture radar (SAR) system, intended for ground moving target indication (GMTI) applications. First, an adaptive spectrum filtering technique is proposed to compensate the terrain interferometric phase caused by the cross-track baseline and then the ground clutter is rejected by applying the joint-pixel displaced phase center antenna (JPDPCA). After performing the moving target detection, the target radial velocity is estimated according to the azimuth position offset by exploiting the road-aided information. Finally, the along-track baseline is derived based on the subspace projection (SP). The effectiveness of the proposed method is validated by data from the experimental airborne system.

In synthetic aperture radar (SAR), long integration time may induce range migration and Doppler frequency migration of a received signal, which may degrade the SAR imaging performance of ground moving targets. Most of the conventional algorithms deal with the problems of range migration and Doppler frequency migration based on parameter searching. However, the exhaustive searching of target motion parameters may result in heavy computational burden. To avoid this problem, this paper proposes a new imaging method for ground moving targets without target motion parameter estimation. First, Keystone transform is applied to correct the range walk. Second, range curvature is compensated by the matched filtering function. Third, Doppler frequency migration is compensated via multiplying the data in range- and azimuth-time domains by its reversed conjugate data according to the equal interval sampling of the azimuth slow time, which avoids the searching procedure for target motion parameter estimation. Finally, the signal energy will be well accumulated in the range-Doppler domain, and thus, the moving targets can be efficiently recognized in the focused image. The major advantage of the proposed method is that it can obtain well-focused images of all targets in one processing step without target motion parameter estimation; thus, it is computationally efficient. Both simulated and real data processing results are used to validate the effectiveness of the proposed method.

The detection and parameters estimation of linear frequency-modulated (LFM) signal are important for modern radar applications, but they are also challenged by the fact that echo signal is often of low signal-to-noise ratio (SNR) due to reasons of long imaging distance and/or limited transmitted power, and the target of small size and/or hidden characteristics. To enhance the SNR, in our previous work, a novel coherently integrated cubic phase function (CICPF) was recently developed for the parameters estimation of the multicomponent LFM signal. In the CICPF, the auto-terms are coherently integrated to enhance the performance in the case of low SNR and also to suppress the cross-terms and spurious peaks. In this paper, as an extension of our previous work, the theoretical performance analyses including several important properties and the fast implementation are provided. Furthermore, the asymptotic mean squared error of a CICPF-based estimator as well as the output SNR of a CICPF-based detector are theoretically derived in closed-forms. From the performance point of view, the proposed CICPF attains the Cramer-Rao bound at low input SNR. The complexity analysis also indicates that the CICPF with the nonuniform fast Fourier transform is computationally efficient without needing the interpolation operation and parameter search. Numerical studies of the CICPF confirm the theoretical analysis and demonstrate superior performance of the proposed approach compared with other state-of-the-art approaches, especially under the low-SNR condition. Finally, the proposed CICPF is applied for the ground moving target imaging in synthetic aperture radar. Results using simulated and experimental data demonstrate that it provides an effective means to obtain well-focused image for ground moving targets.

Suffering from ambiguous estimation or heavy computation complexity load, the radial velocity estimation of moving targets becomes the bottleneck of the synthetic aperture radar-ground moving target indication system. In order to improve the radial velocity estimation efficiency, we have proposed an efficient Radon transform (RT) estimation (ERTE) method by using the never exploited geometry information, which performs well in high SNR scenarios but bad for low SNR. Focusing on these, we propose the least square RT estimation (LSRTE) method to improve the estimation accuracy by utilizing the geometry information of multiple RT results. Given the geometry information determining measurement error, we modified the LSRTE into a weighted LSRTE (WLSRTE) method to improve the estimation robustness and accuracy. Experiments results validate the effectiveness of the proposed methods, and the proposed methods perform much more accurate and efficient than the conventional method.

Video-SAR combines SAR imaging and video techniques together, enabling continuous surveillance of a specific area, which is a new developing microwave detection technology. Several airborne Video-SAR achievements have been published; however, less attention has been paid to the moving targets in the Video-SAR as well as spaceborne platforms. In this paper, the continuous observation problem of spaceborne Video-SAR is solved by multi-relay-receivers mode and the geostationary transmitter can cover a large scene uninterruptedly. A sketchy describe of a spaceborne Video-SAR system performance parameters is presented. An airborne Video-SAR simulation is carried out to testify the effectiveness of this system.

For SAR-GMTI system, efficient motion parameters estimation is a key challenge for moving targets imaging and localization, especially in low SNR scenarios. By four dimension (4-D) parameters searching, the Radon FRFT can achieve motion parameters estimation even when the SNR is low. But the 4-D searching operation brings heavy computation load. Focusing on these, we propose an efficient Radon FRFT estimation method to achieve efficient motion parameters estimation. By utilizing the unexploited geometry information, the efficient Radon FRFT method can reduce the original 4-D searching into only 2-D searching, promoting the motion parameters estimation efficiency in great extent. Compared with the conventional Radon FRFT method, the efficient Radon FRFT performs much better efficiency with comparable estimation accuracy.

In synthetic aperture radar (SAR) imaging of a ground moving target, long-time coherent integration may effectively improve the imaging quality, whereas the imaging performance may severely degrade due to the range migration and the Doppler frequency migration. In this paper, a novel motion parameter estimation method named second-order Wigner-Ville distribution (SoWVD) transform is proposed, and then, a new SAR imaging method based on the SoWVD for a ground moving target is developed. As a modified Wigner-Ville distribution method, the SoWVD method can estimate the motion parameter without the search procedure, which achieves motion parameter estimation by Fourier transform operations in the 2-D frequency plane with respect to the slow time and the delay time. In addition, it can effectively recognize the cross terms based on multiple symmetrical properties of the peaks in the 2-D frequency domain. Both simulated and real data processing results are presented to validate the proposed imaging method.

Efficient motion parameter estimation is a key challenge for moving-target imaging and localization in the synthetic aperture radar ground moving-target indication system. However, the existing methods suffer from ambiguities, complex realization, or heavy computation load of O(MN). To solve these problems, we propose an efficient Radon transform (RT) and an efficient fractional Fourier transform (FRFT) to estimate the radial velocity and azimuth velocity. By exploiting the geometry information, we model a geometry relationship between the motion parameters and two transform angles of RT or FRFT. The matched motion parameters can be estimated by the geometry relationship of the mismatched results, and the computation complexity is reduced from O(MN) to O(2N) effectively. Additionally, the symmetry property can be used for clutter canceling. Simulated and experimental results demonstrate the validity of the proposed methods. Compared with conventional motion parameter estimation methods, the proposed methods are much more efficient in acquiring accurate estimation results.

Compressed sensing (CS) has been increasingly used in the synthetic aperture radar ground moving-target indication system, particularly for imaging the moving targets, which satisfy the sparse precondition of the CS method. However, efficient moving-target imaging is a key challenge for current CS methods, since the redundant basis brings heavy computation load. In this letter, by exploiting the geometry information of the defocused results, we present an efficient fractional Fourier transform (FRFT) to estimate the Doppler rate and image the moving targets by only two times FRFT rather than time-consuming searching operation. Then, the concept is extended into an efficient CS (ECS) imaging method by two bases consisting of two discrete FRFT matrices rather than the redundant basis. Simulations and real-data process are provided to demonstrate the effectiveness of the ECS method. The proposed ECS method can achieve accurate parameter estimation and imaging performance with low computational complexity.

Real-time radial velocity estimation is a key challenge for moving target imaging and location in current single-antenna synthetic aperture radar (SAR)-ground moving target indication systems. Since the conventional methods suffer from ambiguity, complexity realization, or heavy computation load for fast moving target motion estimation, this paper emphasizes the estimation efficiency by simple realization. An efficient Radon transform (RT) estimation is proposed to estimate the radial velocity of fast moving target by utilizing the geometry information, and much more geometry information is exploited to realize clutter cancellation, noise cancellation, and estimation error minimizing in the RT domain, which is not proposed by the others. With only two to four angles used to calculate rather than search for the radial velocity of moving targets, the proposed methods simplify the conventional range and angle (2-D) searching procedure into several time range (1-D) searching procedure efficiently. The theoretical and experimental analysis provides qualitative and quantitative evaluations into the effectiveness of the proposed methods. In the single-antenna SAR system, the proposed methods can estimate the radial velocity of fast moving target efficiently and accurately in high signal to clutter plus noise ratio scenarios.

High-resolution synthetic aperture radar (SAR) imagery of a wide area of surveillance is a difficult large-data problem. In the past few years, researchers have applied compressive sensing (CS) to SAR, as it exploits redundancy in signals. To further extend the sparse problem from the vector to the matrix, a new theory called matrix completion (MC) has attracted much attention, which can complete a matrix from a small set of corrupted entries based on the assumption that the matrix is essentially of low rank. Inspired by this technique, a novel SAR imaging algorithm is proposed in this letter to deal with the undersampled data. After representing the data of a range cell as a matrix, the phase is compensated to keep the matrix holding the property of low rank. Subsequently, MC can be utilized to recover the full-aperture data in the new constructed matrix. Since the data are completely unsampled in the corresponding azimuth cells, the proposed method has effectively conquered the restriction of previous applications that each received channel must have a small number of samples. The final results in both simulation and real-data experiments show that the targets can be well focused even in the scenario of discarding a large percentage of the received pulses. Moreover, when compared with CS, the method is not required to design the complicated measurement matrix.