LAUSR

Abstract. We deal with the unimodular waveform design for cognitive radar in signal-dependent interference (clutter). In addition to the signal-to-clutter-plus-noise ratio (SCNR), we also consider the filtered integrated sidelobe level (ISL) and optimize these two metrics simultaneously. It is assumed that cognitive radar can obtain environment information based on database and prior return. We relate the SCNR and ISL to the signal power spectrum density (PSD) and compare the optimal PSDs for optimizing SCNR and ISL solely. It is found that these two metrics are coupled in a conflicting way. So, the weighted sum of these two metrics is exploited as objective function to optimize the detection performance and pulse compression simultaneously. In order to solve the resulting problem efficiently, we utilize the gradient method and devise a way to employ fast Fourier transform in calculating the gradient of the objective function to accelerate the gradient method. Finally, numerical simulations are performed to demonstrate the superiority of the proposed method over state-of-the-art method in terms of the output SCNR, filtered ISL, and computational efficiency.