Detection in sea clutter based on nonlinear ARCH model

Abstract

In this paper, a complex nonlinear autoregressive conditional heteroscedasticity (CNARCH) model is proposed to model sea clutter. For heteroscedastic model, since the likelihood function is not obtained from explicit probability density function (PDF) expression, it is typically referred to as a quasi-likelihood function. The corresponding quasi-maximum likelihood estimation (QMLE) of the model parameters is derived. Furthermore, the corresponding detection algorithm is derived based on this model. We also conduct the simulations of both synthetic and practical data, demonstrate that the proposed model offers higher accuracy in detection, than the linear ARCH model, when used in the sea clutter.