Sparse Frequency Agile Waveform Design for High-Resolution Forward-Looking Radar Imaging

Active deception jamming poses a significant threat to forward-looking radar (FLR) imaging as it can easily be injected into the conventional radar echo signal, exhibiting highly similar characteristics to the transmitted signal. To enhance the antijamming performance, the sparse frequency agile waveform (SFAW) was introduced into radar systems. Unfortunately, the SFAW suffers from the drawback of high-range grating lobes (HRGL) caused by incomplete frequency bands. In this article, we propose an SFAW design method to not only improve the antijamming capabilities of the FLR system but also achieve high-resolution FLR imaging. By deriving and analyzing the ambiguity function of SFAW, the relationship between the parameters of SFAW and HRGL is established. The waveform parameters are adjusted by the quadratic sequence optimization algorithm, resulting in an optimized SFAW with reduced range grating lobes. In particular, the transmission of the optimized SFAW is facilitated through beam dwell time, enabling the integration of FLR and agile waveforms. Moreover, an FLR super-resolution imaging based on Bayesian compressive sensing theory is proposed. The construction of dictionary matrices is undertaken to reconstruct high-resolution range profiles (HRRP) by the optimized SFAW signal model within the dwell time of each beam. Based on the Bayesian framework, the reconstruction problem of HRRP and the inversion of the target azimuth information are transformed into convex optimization problems, then HRRP reconstruction and azimuth super-resolution imaging are successively implemented by a quasi-Newton solver. The simulation and experiments examples show that optimized SFAW can improve the antijamming capability of FLR systems in complex electromagnetic environments, and can realize high-resolution imaging of narrowband signals by synthesizing wideband signals.