Detection in sea clutter based on nonlinear ARCH model
| Autor: | Ping-ping Pan, Zhenmiao Deng, Yunjian Zhang, Jianghong Shi |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Statistics::Theory
Heteroscedasticity Computer science Probability density function 02 engineering and technology Constant false alarm rate Artificial Intelligence 0502 economics and business Statistics 0202 electrical engineering electronic engineering information engineering Statistics::Methodology Electrical and Electronic Engineering 050205 econometrics Applied Mathematics 05 social sciences 020206 networking & telecommunications Function (mathematics) Nonlinear system Computational Theory and Mathematics Autoregressive model Signal Processing Clutter Computer Vision and Pattern Recognition Statistics Probability and Uncertainty Likelihood function Algorithm |
| Zdroj: | Digital Signal Processing. 50:162-170 |
| ISSN: | 1051-2004 |
| DOI: | 10.1016/j.dsp.2015.12.010 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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