The high-fidelity deceptive jamming contaminated synthetic aperture radar (SAR) images are very confusing when they are interpreted for target recognition. In this letter, a novel azimuth phase coding (APC)-based method is proposed for deceptive jamming suppression, especially with characteristics of high fidelity and intermittent sampling. The deceptive jamming model is detailedly formulated, and the difference and relation related to the real target are also discussed. Then, the APC-based suppression method is proposed, where the original APC principle and how it is to be introduced into deceptive jamming suppression are deduced and analyzed. Considering the delay feature of deceptive jamming retransmission, azimuth phase modulation, azimuth phase demodulation, and azimuth filtering are successively implemented. Then, parameters discussion is carried out for a deeper understanding. Simulation results demonstrate the superiority of the proposed method.

Synthetic aperture radar (SAR) frequently suffers from radio frequency interference (RFI) due to the simultaneous presence of numerous wireless communication signals. Recently, the narrowband RFI is found to possess the low-rank property benefiting from stable frequency occupancy, hence the reconsideration of RFI suppression as a joint sparse and low-rank optimization problem. The existing methods either use the non-sparse useful signal itself as the sparse regularizer, or employ the nuclear norm to approximate the rank function, which punishes all singular values with the same penalty via singular value thresholding (SVT), resulting in the improper punishment problem. Hence, both are consequentially subject to performance limitation. In this paper, a novel dictionary-based nonconvex low-rank minimization (DNLRM) optimization framework is proposed for RFI suppression, which concurrently considers the improvements for both the sparse regularizer and the low-rank regularizer. For the former, an over-completed dictionary is constructed, for which the sparse coefficient acts as the sparse regularizer. For the latter, the rank function is more accurately approximated by innovatively introducing the nonconvex function, for which the supergradient is synchronously used to generate the weighted penalty, thus solving the improper punishment problem. The derivation of the closed-form solution and the convergence analysis are described in detail. Additionally, the adaptive selection scheme for the model parameter is uniquely proposed for further ensuring the practicality of the DNLRM framework. The superiority of the proposed method is demonstrated via not only the RFI-free real SAR data combined with the measured RFI, but the RFI-contaminated real SAR data.

In complicated electromagnetic environments, synthetic aperture radar (SAR) can be threatened by various kinds of malicious interference, of which deceptive jamming is the intentional and efficient one. For jamming effect evaluation on SAR images, traditional methods are mostly based on the change of image quality, while not suitable for evaluating the confusion caused by high-fidelity false targets. In this paper, a novel framework to evaluate the effect of deceptive jamming on SAR is proposed based on visual consistency (VC). Three levels of vision, namely detection, recognition and semantics, are fused for efficient deception evaluation along with the corresponding metrics system. Fully considering the imaging characters of deceptive jamming, specifically designed detection and recognition flows are introduced to quantitatively evaluate the deception. Furthermore, to evaluate whether the generated false targets are with reasonable context, a unprecedent concept named semantic accuracy is proposed via considerations of statistical differences compared with that of the background template. Besides, the cases that deceptive jamming with several common non-ideal issues are considered when evaluating the effects. Sufficient experiments have proved the practicality and superiority of the proposed evaluation framework under different deceptive jamming with various non-ideal factors.

Spaceborne azimuth interrupted frequency modulation continuous wave (AI-FMCW) synthetic aperture radar (SAR), known as a novel system to observe the characteristics of ground objects, employs a single antenna to transmit and receive continuous waves, thus different from traditional frequency modulated continuous wave (FMCW) SAR that needs two antennas. However, due to the single antenna, there is a phenomenon that the transmitting of several continuous waves could be unavailable while replaced by receiving radar echoes. The process of switching the transmitter ON and OFF will leave gaps in the synthetic aperture. In this case, traditional imaging algorithms such as the wavenumber domain algorithm (WDA) cannot acquire clear focused images, resulting in the blurred ones corrupted by ghost targets. To solve the problem mentioned above, this letter presents an image restoration algorithm to improve the AI-FMCW SAR image performance. In this letter, there are two main contributions: 1) some key parameters of the AI-FMCW SAR system are designed and 2) a method based on Richardson–Lucy (RL) deconvolution algorithm is applied to AI-FMCW SAR imaging to suppress ghost targets. Finally, a case of the Ku-band AI-FMCW SAR system is given, and experiments via simulated AI-FMCW SAR data are conducted to validate the proposed method.