CA-CFAR Adjustment Factor Correction with a priori Knowledge of the Clutter Distribution Shape Parameter
Journal Title: International Journal of Interactive Multimedia and Artificial Intelligence - Year 2017, Vol 4, Issue 4
Abstract
Oceanic and coastal radars operation is affected because the targets information is received mixed with and undesired contribution called sea clutter. Specifically, the popular CA-CFAR processor is incapable of maintaining its design false alarm probability when facing clutter with statistical variations. In opposition to the classic alternative suggesting the use of a fixed adjustment factor, the authors propose a modification of the CA- CFAR scheme where the factor is constantly corrected according on the background signal statistical changes. Mathematically translated as a variation in the shape parameter of the clutter distribution, the background signal changes were simulated through the Weibull, Log-Normal and K distributions, deriving expressions which allow choosing an appropriate factor for each possible statistical state. The investigation contributes to the improvement of radar detection by suggesting the application of an adaptive scheme which assumes the clutter shape parameter is known a priori. The offered mathematical expressions are valid for three false alarm probabilities and several windows sizes, covering also a wide range of clutter conditions.
Authors and Affiliations
José Raúl Machado Fernández, Shirley Torres Martínez, Jesús de la Concepción Bacallao Vidal
Keywords
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- EP ID EP329134
- DOI 10.9781/ijimai.2017.441
- Views 177
- Downloads 0
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CA-CFAR Adjustment Factor Correction with a priori Knowledge of the Clutter Distribution Shape Parameter
Oceanic and coastal radars operation is affected because the targets information is received mixed with and undesired contribution called sea clutter. Specifically, the popular CA-CFAR processor is incapable of maintaini...