Simulation of Sea and Ground Clutter with Log-Normal Distributed Echo Amplitudes

In radar signal processing, sea and ground echo amplitudes often exhibit complex statistical characteristics, particularly under high-resolution and low grazing angle conditions. To accurately simulate clutter properties in such environments, the log-normal distribution model can be employed. The log-normal distribution effectively describes scenarios with large amplitude fluctuations, aligning with the observed heavy-tailed distribution characteristics of clutter amplitudes in practical measurements.

The core of the simulation program lies in generating random sequences that obey the log-normal distribution while accounting for the effects of resolution and grazing angle on echo amplitude. The log-normal distribution can be generated by applying exponential transformation to normally distributed random variables, with parameters typically derived from fitting measured data. At low grazing angles, sea or ground scattering echoes demonstrate stronger fluctuation characteristics, requiring adjustment of the distribution's mean and variance in the simulation to match actual scenarios. From a code implementation perspective, this involves calculating mu (mean of the underlying normal distribution) and sigma (standard deviation) based on empirical data, then using the formula: log_normal_samples = exp(mu + sigma * randn(N,1)) where randn generates standard normal random variables.

Furthermore, high-resolution radar enhances the spatial correlation of clutter, necessitating the introduction of spatial correlation models during simulation. This ensures generated clutter data not only conforms to log-normal distribution properties but also reflects actual scattering environments. Implementation-wise, this may involve applying correlation filters or covariance matrix transformations to the generated sequences using techniques like Cholesky decomposition or spectral methods. Such simulation approaches hold significant importance for radar performance evaluation, target detection algorithm optimization, and anti-clutter filter design.