基于反向传播神经网络的海杂波参数估计.

Sea clutter is studied and analyzed for evaluating the accuracy of missile-borne synthetic aperture radar under different sea conditions. A parameter estimation method based on BP neural network is proposed to solve the problem that the sea clutter parameter estimation based on traditional statistical methods is prone to be divorced from the actual sea clutter. The amplitude distribution characteristics and temporal correlation of sea clutter are used to establish a K-distribution-based sea clutter model. The influences of four model parameters, such as shape, scale, mean square root of clutter speed, and mean Doppler shift, on sea clutter chaos and fractal characteristics are analyzed, and the qualitative relationship among model parameters and physical characteristics is summarized. On this basis, BP neural network is used to fully explore the quantitative relationship among parameters and physical characteristics, and predict the chaos characteristics and fractal characteristics. The determinate coefficients are 0.985 and 0.952. The model parameters of BP neural network, maximum likelihood estimation and moment estimation method are compared by taking the measured sea clutter data as an example. The results show that the proposed model can be well close to the physical characteristics of actual sea clutter. [ABSTRACT FROM AUTHOR]