A Data-Driven Optimization Method for Simulating Arbitrarily Distributed and Spatial–Temporal Correlated Radar Sea Clutter

Realistically simulating spatial–temporal correlated complex sea clutter using statistical model-based methods is very challenging because the intricate interplay of various physical mechanisms in both space and time poses significant obstacles for clutter modeling. In this article, to overcome this challenge, we propose a data-driven method based on the 2-D amplitude and phase matching optimization (APMO) with two steps. First, the simulated clutter amplitudes with desired distribution and correlation characteristics are generated by performing the frequency-domain inverse transform and correlation transfer techniques on the measured clutter. Second, an optimization strategy is developed to acquire the well-matched phases for the simulated clutter amplitudes by constraining the phases to the simulated clutter amplitudes and the desired Doppler and range spectra. Two different algorithms are separately adopted to implement the optimization strategy, and their advantages and disadvantages are compared theoretically and experimentally. The APMO method is direct in the analysis of the measured clutter’s statistical characteristics and universal to different radar and environmental conditions. It is shown that the simulation results can reproduce the distribution and spatial–temporal correlation features of the real-world X-band complex sea clutter. The relative mean-squared deviation (RMSD) between the measured and simulated clutter’s Doppler and range spectra can be notably reduced to 0.0499 and 0.1237, respectively.