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Staggered synthetic aperture radar (SAR) can effectively stagger range blind areas by periodically changing the pulse repetition interval (PRI), to achieve continuous imaging with an ultrawide swath. In conventional staggered SAR, azimuth samples are nonuniformly distributed due to the variable PRI, and complex resampling processing, such as best linear unbiased (BLU) interpolation and multichannel reconstruction processing, is required. In this article, a strict rule of PRI variation and phase center adjustment (PCA) is proposed for staggered SAR, to solve the problem of nonuniform azimuth sampling. In the proposed joint strategy of PRI variation and PCA, the relevant parameters for PCA are first determined. Then, the PRI sequence is designed according to PCA parameters to stagger blind ranges. Finally, the PCA rule is designed according to the designed PRI sequence and PCA parameters. During the data acquisition interval, the effective phase center is periodically adjusted pulse by pulse according to this rule, making the azimuth samples totally uniformly distributed. In addition, the discontinuously distributed missing samples can be estimated by signal estimation approaches. With the proposed joint rule of PRI variation and PCA, the emergence of false targets in imaging results can be avoided, and the complex resampling processing can be relieved to reduce the computational complexity. Simulation experiments on imaging results verify the advantages of this strategy.

The performance of moving target detection for airborne radar depends on estimation accuracy of clutter covariance matrix. In nonhomogeneous clutter environments, dense interference will impact the estimation accuracy of the clutter covariance matrix, resulting in failure of moving target detection. To cope with this problem, this paper proposes a moving target detection algorithm for airborne radar based on fuzzy mathematics and signal sparse recovery technology. The proposed algorithm calculates the sparse recovery coefficient vectors of training snapshots firstly, and then uses the appropriate membership function to process the matrix which is consist of these vectors. The clutter components in coefficient vector are selected according to membership function, and clutter covariance matrix are estimation subsequently. By comparing and analyzing clutter space-time spectrum, clutter suppression performance, and moving target detection performance, it can be found that the proposed algorithm improves the moving target detection performance. Moreover, the proposed algorithm only needs few training snapshots, is robust to the suppression of dense interference.

A synthetic aperture radar (SAR) system can be seriously contaminated by radio frequency systems because of working in the same microwave frequency bands, which would degrade the SAR image quality and affect the accuracy of image interpretation. In this paper, a novel radio frequency interference (RFI) suppression approach including RFI identification, band-stop filtering and a removed spectrum iterative adaptive approach (RSIAA) is proposed. First, the smoothing process is added before RFI signal detection to improve the RFI detection capacity. Afterwards, the band-stop filtering with a broaden factor is proposed to mitigate the residual RFI, and it ensures the accuracy of the following removed spectrum recovery by the RSIAA. Finally, the removed spectrum components are estimated from available adjacent spectrum data by the RSIAA in turn to obtain the desired range spectra. Compared with the conventional range frequency filtering method for RFI suppression, the capacity of the weak RFI signal detection is improved, and the increased sidelobes due to the discontinuous spectra are well suppressed. Simulation experiments on both simulated SAR raw data, Gaofen-3 and Sentinel-1 SAR raw data validate the proposed RFI suppression approach.

The radar with arc antenna array has the advantages of all-orientation space scanning, simultaneous estimation of pitch-azimuth angle and good antenna pattern. However, due to the special geometry of the antenna, the snapshots from different range cells are not independently and identically distributed (IID) samples, resulting in performance degradation in space-time adaptive processing (STAP). In this paper, the model of airborne radar with arc antenna array is established. The improved arc array-based iterative adaptive approach (IAA) algorithm is studied. The IAA algorithm is utilized to estimate the space-time spectrum. The sparse constraint method is used to reduce the clutter ridge width and improve the ability of suppressing clutter and detecting targets. Simulation experiments verify the effectiveness of the IAA algorithm in arc array radar.

A knowledge-aided direct data domain (D3) algorithm, which is applied to the space-time adaptive processing (STAP), is proposed. The conventional D3 algorithm only uses the test data to avoid heterogeneous training data. However, the prior knowledge of target is always not accurate, leading to performance degradation. The D3 algorithm based on sparse representation suffers target self-nulling and clutter suppression performance degradation. To overcome these disadvantages, a knowledge-aided D3 algorithm is proposed to improve the STAP performance. The prior knowledge about clutter distribution of forward-looking airborne radar is exploited to distinguish clutter components and targets. Furthermore, the clutter components can be eliminated from the sparse recovery coefficient vector. Hence, we can judge if the targets are present via the power of reminder element. Then, the clutter components are exploited to estimate the STAP filter weight, which can supply satisfactory clutter suppression performance. Experiments validate that the proposed algorithm separate the clutter components and targets directly without the prior knowledge of targets, and the target detection performance is improved significantly.

Space-Time Adaptive Processing (STAP) of airborne radar has significant performance degradation with few snapshots and dense interferences. Devoting to this problem, this paper proposes a knowledge-aided Pre-Whitening color loading sparse recovery robust STAP method (PW-SR-STAP). Firstly, the algorithm constructs the priori covariance matrix for the color loading matrix. And then, the snapshots are sparsely processed, the dense interference is removed according to the prior knowledge of clutter distribution. Hence, the clutter covariance matrix can be estimated accuracy. The advantage of this algorithm is that the number of snapshots is small, and the performance of target detection is improved. Simulation experiments verify that the effectiveness and robustness of the proposed algorithm are excellent.

Arc-array synthetic aperture radar (AA-SAR) has become a novel imaging scheme for full azimuth observation. However, the exiting arc-array monostatic SAR system is placed on a single platform and is easy to detect. Arc-array bistatic SAR (AA-BiSAR) with a stationary transmitter is proposed in this paper, which can obtain high data acquisition efficiency and reduce vulnerability of arc-array monostatic SAR. Furthermore, since the azimuth resolution with full azimuth observation is not related to the location of the stationary transmitter, the transmitter can be placed far away from the receiver. Compared with imaging algorithms for other modes, the key points of AA-BiSAR imaging algorithms are a square root in the bistatic slant range equation and an arc synthetic array in azimuth. According to the imaging geometry of AA-BiSAR, a novel imaging approach for AA-BiSAR based on keystone transform (KT) is proposed, and the KT implements range-cell migration correction (RCMC) in conditions of trigonometric function under square root in the range history and arc synthetic array in azimuth via reformatting the AA-BiSAR raw data. Besides presenting the proposed imaging approach, a complete resolution analysis of AA-BiSAR is given. Results of numerical simulation experiments on point targets validate the proposed imaging approach.

Aiming at the problem of the number of residual points due to environmental noise in the interference phase diagram, it will cause some loss to the phase unwrapping and terrain deformation inversion precision. This paper proposes a phase-preserving filtering method based on contourlet transform. The method adopts the idea of multi-resolution and multi-direction, which leads to the improvement of the performance of the method. Firstly, the image is decomposed by using a directional filter; the second is to perform phase-preserving filtered on the decomposed image. Compared with the traditional methods, this method has a significant drop in the residual point, and the calculation amount is small, which is suitable for processing the measured data. In this paper, the effectiveness of the proposed algorithm is verified by InSAR data.

The radar with arc antenna array has the advantages of all-orientation space scanning, simultaneous estimation of pitch-azimuth angle and good antenna pattern. However, due to the special geometry of the antenna, the echoes from different range cells are not independently and identically distributed (IID) samples, resulting in performance degradation in space-time adaptive processing technology. In this paper, the model of airborne radar with arc antenna array is established, and the clutter distribution is analyzed. The conclusion is that the nonlinear variation of spatial angular frequency with array element number results in the non-uniform clutter distribution in different range cells. Therefore, the traditional space-time adaptive processing is not fit for the target detection of arc array radar. Hence, the arc array-based iterative adaptive approach (IAA) algorithm is studied. The IAA algorithm is utilized to estimate the space-time spectrum, which can distinguish clutter and targets. Simulation experiments verify the effectiveness of the IAA algorithm in arc array radar.

Space-time adaptive processing (STAP) for airborne radar may cause lattice mismatch during sparse recovery processing, which is the off-grid problem. The off-grid problem may lead to degradation of STAP performance. To cope with this problem, this paper proposes a knowledge-aided sparse recovery STAP algorithm with off-grid self-calibration (AO-SR-STAP). The snapshots are decomposed by sparse processing firstly. The off-grid of spare dictionary is calibrated and dense interferences are removed according to the knowledge of clutter distribution. A standard steering vector set is constructed by the prior knowledge of clutter distribution, which is used to calibrate the off-grid of sparse dictionary. The dense interferences in snapshots are removed with knowledge of clutter distribution. Hence, the clutter information of snapshots is estimated accurately and the target in cell under test can be detected completely. The advantage of this algorithm is that the off-grid of sparse dictionary can be calibrated, and dense interferences are filtered effectively. Simulation experiments verify the effectiveness and robustness of the proposed algorithm.

In a multichannel geosynchronous spaceborne–airborne bistatic synthetic aperture radar (GEO-SA-BiSAR) system, the airborne receiver can obtain high-resolution microwave images with good signal-to-noise ratios (SNRs) by passively receiving echoes from the desired area. Since the Doppler modulation and range history of a moving target are obviously different from a stationary target, a signal geometry model for moving targets in multichannel GEO-SA-BiSAR is established in this paper. According to simulation results, the along track velocity introduces target defocusing in azimuth, and the slant range velocity mainly causes multiple false targets. To resolve these problems, a modified multichannel reconstruction method in azimuth channel GEO-SA-BiSAR is proposed according to the azimuth multichannel impulse response of the imaged moving target. Before azimuth multichannel raw data combination, both spatial-variant range cell migration correction (RCMC) and azimuth nonlinear chirp scaling (ANLCS) should be performed to reduce the influence of the range offset and lower the Doppler bandwidth of the whole raw data, respectively. Afterward, a novel azimuth multichannel reconstruction algorithm is carried out via the modified reconstruction matrix based on the estimated target velocity. The target slant range velocity estimation is implemented by introducing the signal intensity ratio (SIR). Compared with the conventional method for the stationary target to handle the raw data of the moving target, the false targets could be obviously suppressed by using the proposed approach. Imaging results on both simulated point and distributed scene targets validate the proposed multichannel reconstruction approach.

Sliding spotlight SAR combined with the multichannel receiving technology has the ultra-high resolution wide swath imaging capacity. Since the range history and Doppler characteristics of moving targets are different from those of fixed targets, the moving target echo model in multichannel sliding spotlight SAR is established in this paper. The azimuth velocity component of the moving target would results in azimuth defocusing, while the slant range velocity component would lead to pairs of false targets. In this paper, a novel azimuth multichannel reconstruction method for moving targets is proposed according to the multichannel signal model. First, the instantaneous Doppler centroid of each channel is removed by de-ramp preprocessing to reduce the total Doppler bandwidth of the whole imaged scene. Afterwards, the azimuth non-uniform sampling problem is resolved by the modified multichannel reconstruction matrix corresponding to the velocity of the moving target. Finally, the Doppler spectrum is recovered by the azimuth inverse de-ramp operation. The proposed method resolves the problem of Doppler spectrum aliasing caused by azimuth beam scanning and avoids the up-sampling processing with the large amount of zero padding. Furthermore, the modified reconstruction matrix is used to match the multi-channel Doppler characteristics of moving targets in multichannel sliding spotlight. Simulation results of moving targets validate the proposed azimuth multichannel reconstruction approach.