Barrage jamming technique against spaceborne synthetic aperture radar (SAR) systems plays an important role in electronic countermeasures. Generally, it is crucial for the jammer to obtain a satisfactory SAR image corrupting performance with both low computational burden and low transmitting power. In this respect, a knowledge-aided precise jamming technique has been proposed to cope with the involved technical challenges. In our work, the generation of the jamming signal with two-dimensional (2-D) controllable coverage and high coherent integration gain is detailed using the range- Doppler algorithm (RDA) in linear frequency modulation (LFM) case. Moreover, the simple linear iterative clustering (SLIC) technique is also employed to segment the regions of interest (ROIs). Based on the 2-D controllable jamming modulation and the SLIC technique, the ROIs can be covered by a small amount of false scatterers, significantly reducing the signal modulation burden. Furthermore, the prior information of ROIs, i.e., the backscattering intensity information, is also exploited to achieve the optimal allocation of jammer power, allowing for further reduction of jamming energy. Finally, extensive numerical simulations are implemented to prove the feasibility of the proposed jamming technique against opponent's SARs.

Operating mode identification is an important prerequisite for precise deceptive jamming technology against synthetic aperture radar (SAR). In order to solve the problems of traditional spaceborne SAR operating mode identification, such as low identification accuracy, poor timeliness, and limitation to main lobe reconnaissance, an efficient identification method based on sidelobe reconnaissance and machine learning is proposed in this paper. It can identify four classical SAR operating modes, including stripmap, scan, spotlight and ground moving target indication (GMTI). Firstly, the signal models of different operating modes are presented from the perspective of sidelobe reconnaissance. By setting the parameters differently, such as the SAR trajectory height, antenna length, transmit/receive gain and loss, signal–noise ratio, and so on, the feature samples based on multiple parameters can be obtained, respectively. Then, based on the generated database of feature samples, the initialized neural network can be pre-trained. As a result, in practice, with the intercepted sidelobe signal and the pre-trained network, we can precisely infer the SAR operating mode before the arrival of the main lobe beam footprint. Finally, the effect of SNR and the jammer’s position on the identification accuracy is experimentally detailed in the simulation. The simulation results show that the identification accuracy can reach above 91%.