The mainlobe jamming suppression technique is one of the key techniques of radar electronic counter- countermeasure (ECCM). In the view of multistatic radar, the interference signals received with different view angles would be highly coherent, on the contrary, the target echoes would be independent due to the radar cross section (RCS) of target would randomly fluctuate as the variation of view angle. As a consequence, After the interference signals have been aligned, a steering vector can be found of interference signals, the energy of the interference signals will be concentrated in a single-rank subspace whereas the energy of the target echo signals will be scattered throughout the whole signal space of the multistatic radar. By exploiting this phenomenon, a new mainlobe interference suppression method for multistatic radar is proposed. In this method, the projection matrix is trained based on the time domain sampling data. The interference signals received by multiple node radars with different view angles can be effectively suppressed through the noise subspace projection operation. Then, a general likelihood ratio test detector used for the residual signals after projection operation is derived. The results of simulations indicate that the proposed method can effectively suppress various kinds of mainlobe jamming and improve the detection performance. Furthermore, this method is adaptive to the variation of the interference pattern and the geometric configuration of the node array.

Near space is the key to integrating “sky” and “space” into the future. A synthetic aperture radar (SAR) that works in this area would initiate a technological revolution for remote sensing applications. This study mainly focused on ground moving target imaging (GMTIm) for a near-space hypersonic vehicle-borne SAR (NS-HSV-SAR) with squint angle. The range history, parameter coupling, and Doppler ambiguity of the squint-looking NS-HSV-SAR are more complicated than traditional side-looking airborne or space-borne SARs. Thus, a precise range model is presented on the basis of phase error analyses. Then, all potential distributions of echo’s azimuth spectrum are derived, and a GMTIm method is proposed on the basis of a detailed analysis of the echo characteristics. The proposed method consists of three steps. Firstly, a prior information-based pre-processing function was created to decrease the Doppler ambiguity and range migration effects. Secondly, a blur matched keystone transform was developed to correct the residual range walk migration. Thirdly, a time-saving chirp Fourier transform was investigated for azimuth focusing. Implementation considerations, including the curvilinear trajectory of the NS-HSV-SAR, multiple moving target imaging, and applicability and limitation of the method, are discussed. Finally, simulation results are presented to validate the effectiveness of the proposed method.

Elevation frequency scanning synthetic aperture radar (EF-SCAN SAR) can isolate range ambiguities in the range-frequency domain. Therefore, range ambiguity suppression can be accomplished through range-frequency bandpass filtering, which can outperform spatial filtering-based ambiguity suppression. In this article, the analytic space-time signal model of an intrapulse frequency scanning array is derived. Based on the analysis of the characteristics of this signal model, waveform design criteria for EF-SCAN SAR are developed to effectively separate the range ambiguities in the range frequency domain. The ground echo model of EF-SCAN SAR is also developed based on this space-time signal model. The impact of intrapulse frequency scanning on the performance of high-resolution and wide-swath (HRWS) imaging is analyzed based on the derivation and analysis of the echo spectrum. Finally, an imaging algorithm for EF-SCAN SAR is developed based on this analysis. Numerical simulations verify the effectiveness of the proposed waveform design criteria and imaging algorithm.

Nowadays, with the development of automotive and traffic radars, a higher angle resolution is required for an increasing demand of four-dimensional (4D) imaging radar. In this paper, the high-precision direction-of-arrival (DOA) estimation problem is solved by using a deep learning (DL) framework. Most existing on-grid DL-based methods have an upper limit of one-degree resolution. The DOA estimation performance under such resolution is still far behind conventional methods, and is not accurate enough in practical applications. Hence, we introduce a two-stage multi-layer perceptron (TS-MLP) framework to achieve higher resolution of DOA estimation with low complexity by dividing the problem into two main parts. The first MLP is used to determine the coarse grid point nearest to the source angle, and the second MLP fine tunes the estimate within the coarse grids. In addition, we propose a solution to the source association problem between the two stages when handling multiple targets. Our scheme shows much higher accuracy compared with existing DL-based methods, and has comparable performance with traditional high-resolution methods. Moreover, it performs quite robust in the presence of array imperfections. The demo code is available in https://github.com/Whisperzyj/TS-MLP

Motion errors are inevitable in real-world scenarios and introduce significant phase errors in airborne synthetic aperture radar (SAR) imaging. Generally, these errors consist of the cross-coupling and spatially variant components. Cross-coupling errors can usually be eliminated by motion compensation (MoCo), whereas the latter are seldom addressed, which deteriorate the imaging qualities, especially for the high-resolution cases. To solve the problem, a novel approach based on 2-D range-Doppler expansion is proposed. First, an accurate range equation of the aircraft is obtained based on the inertial navigation system (INS) data. Then, the range-Doppler expansion corresponding to the slant range and Doppler centroid are performed, by which the echo signal is decoupled into two spatially variant parts in range and azimuth directions. Finally, the chirp-z transforms (CZTs) are employed to remove the range and azimuth spatial variations introduced, respectively, by the cross-track and along-track errors. Different from the conventional methods, our approach can greatly decrease the cross-coupling and spatially variant effects brought by motion errors in high-resolution cases. Computer simulation and real data experiments demonstrate the effectiveness of the proposed approach.

The spaceborne transmitter/missile-borne receiver (ST/MR) synthetic aperture radar (SAR) could provide several unique advantages, such as wide coverage, unrestricted geography, a small detection probability of the missile, and forward-looking imaging. However, it is also accompanied by problems in imaging, including geometric model establishment and focusing algorithm design. In this paper, an ST/MR SAR model is first presented and then the flight-path constraint, characterized by geometric configurations, is derived. Considering the impacts brought about by the maneuvers of the missile, a non-‘Stop-Go’ mathematical model is devised and it can avoid the large errors introduced by the acceleration, which is neglected in the traditional model. Finally, a two-dimensional (2-D) scaling algorithm is developed to focus the ST/MR data. Without introducing any extra operations, it can greatly remove the spatial variations of the range, azimuth, and cross-coupling phases simultaneously and entirely in the 2-D hybrid domain. Simulation results verify the effectiveness of the proposed model and focusing approach.