Suppressive interference is a common interference signal for synthetic aperture radar (SAR) that can seriously affect the target identification and imaging results of SAR. This paper proposes a method for suppressing suppressive jamming using blind source separation (BSS) for singular value and eigenvalue decomposition based on information entropy. First, we developed an airborne SAR imaging geometry model and a suppressive interference signal mixing model. Next, we perform blind signal separation of the interfered mixed signal by means of BSS based on singular value and eigenvalue decomposition. Then, we image the different signals we have extracted. Finally, we extract the features of the image domain for the separated signals and set the information entropy threshold by the difference of information entropy to identify the jamming signal and the source signal and obtain the source signal. This method uses eigenvalue and singular value decomposition for BSS and extracts the image domain features of the signal after BSS by information entropy and identifies the source signal by information entropy thresholding. This method compensates for the uncertainty in the decomposition of the signal by means of BSS. The signal loss is minimal and the similarity of the separated signal and the original signal is very high. Simulated and measured data demonstrate the feasibility of this algorithm.

Synthetic aperture radar (SAR) may be attacked by multifarious kinds of the active-jamming, which will lead to the ineffectiveness of SAR in complex electromagnetic environment. In this paper, a novel multi-electromagnetic jamming counter-measure for the airborne SAR is proposed based on maximum signal-to-noise ratio (SNR) blind source separation. Firstly, the imaging geometry and multi-component mixed signal model of airborne SAR are established. Then, based on multi-component mixed signal matrix, a blind source separation (BSS) method based on the maximum SNR is proposed to separate the real target echo signal from the multi-electromagnetic jamming signals. After real target echo signal identification, the high resolution images of the interested target area can be achieved by the corresponding SAR imaging method. The simulated and measured data results are present to prove the feasibility and effectiveness of the proposed method.

The presence of blanket jamming, a typical form of airborne synthetic aperture radar (SAR) jamming, causes incoherent signals with strong power to enter the airborne SAR receiver, which significantly reduces the signal-to-noise ratio (SNR) of airborne SAR images and dramatically influences the imaging effect of airborne SAR. In this manuscript, an airborne SAR anti-blanket interference algorithm based on fractional order Fourier transform (FRFT) and second order blind identification (SOBI) is proposed. Firstly, a geometrical model for airborne SAR imaging under a blanket interference condition is developed. Then, a blind signal separation (BSS) algorithm using SOBI is presented. Finally, to solve the problem of separation uncertainty of the existing BSS algorithms, the FRFT is used for signal identification. This method uses the SOBI algorithm to perform the BSS algorithm. Considering that FRFT can detect linear frequency modulation (LFM) signals emitted by airborne SAR, FRFT is used to solve the disadvantages of the current BSS methods. This algorithm separates signals with a high degree of similarity and performs well in terms of blanket jamming suppression. Simulation and measured experiments prove the validity of the algorithm.

The continuous advancement of shipbuilding technology has enabled the further development of sea transport and naval vessels, which also poses significant challenges to navigation safety. Accurate recognition of different types of ships is crucial. In this study, three typical ships, namely a destroyer, cruise ship, and aircraft carrier, were taken as examples. The micro-Doppler effect of these ships was analyzed using a unmanned aerial vehicle (UAV)-based ultrawideband (UWB) radar. Echo signal models were established and compared with measured Doppler signals. Furthermore, a lightweight self-supervised learning network was proposed to achieve accurate recognition of small-sample labeled ship targets. The neural network architecture, including specific layers and parameters, was introduced. To enhance the micro-Doppler features, the 1-D signals were transformed into 2-D images, thereby converting the ship recognition problem into an image classification problem. The experimental results demonstrate that the proposed network achieves high recognition accuracy under different situations. When evaluated on measured data, the overall recognition rate reaches 97.50%. Moreover, even in challenging scenarios with a low signal-to-clutter ratio (SCR) of −10 dB, the overall recognition rate for simulated data still exceeds 90%, demonstrating the effectiveness and advantages of the proposed network.

Synthetic aperture radar (SAR) is a high-resolution microwave imaging radar. Compared to conventional SARs, the ultra-wideband SAR exhibits a larger bandwidth and offers enhanced range resolution and image acquisition capabilities. However, the performance of ultra-wideband SARs can be compromised in electromagnetic interference environments, which leads to the production of inaccurate images. In this paper, to address the blanket interference problem of ultra-wideband radar, we firstly establish the echo model of SAR under interference, and then realize the detection and extraction of radar echo signal by the feature extraction method of minimum entropy deconvolution, and recover the phase delay of the signal by filtering the noise with zero-phase filter. Finally, the time domain inverse projection algorithm is used to image the distance target with high resolution and obtain the SAR image after suppressing the interference. The effectiveness of the proposed algorithm is demonstrated by simulated and measured data, and the performance evaluation criteria such as root mean square error (RMSE) are used. When the SNR is lower than -30 dB, the RMSE between the original signal and the reconstructed signal of the experimental target can still be maintained at about 0.15.

This paper investigates the airborne synthetic aperture radar via outfield experiments based on carrier-free ultra-wideband signals with fifth-order Gaussian pulses with imaging using the backward projection algorithm. Typical blanket jamming is selected in the study, and NAM, NFM, and SFM are used as jamming signals to investigate the backward projection BP imaging effect of airborne synthetic aperture radar when subjected to blanket jamming in a real-world environment. Subsequently, a variational modal decomposition algorithm is proposed, aiming at the anti-jamming processing of airborne synthetic aperture radar. Further, the variational modal decomposition algorithms are compared with the empirical modal decomposition for the anti-jamming effect, which verified the effectiveness and high efficiency of the method. This study provides a new solution for the imaging problem of airborne synthetic aperture radar when subjected to blanket jamming, and a helpful reference is provided for the selection of anti-jamming processing methods.