A waveform optimization method for sparse frequency agile radar

The sparse frequency agile (SFA) radar is able to achieve the same range resolution as the frequency agile (FA) radar with fewer frequencies. Sparse reconstruction methods are used to estimate target parameters for SFA radars to avoid high sidelobe problem caused by traditional matched filtering methods. The outcome of sparse reconstruction is determined by the sensing dictionary matrix, which is related to the selection of waveform frequency sequence. Therefore, this paper proposes a waveform optimization design method for SFA radars. Firstly, we determine the sensing dictionary matrix's maximum correlation coefficient (MCC) as our optimization objective. Then, an improved particle swarm optimization algorithm is employed to solve the above problem. It contains mutation steps and simulated annealing operations which can help particles escape from local optimal solutions. Finally, the simulation results prove that the proposed method can obtain the optimal SFA waveform with lower MCC, thus improving the accuracy of sparse reconstruction.