Resumen de Optimal Selection of the CA-CFAR Adjustment Factor for K Power Sea Clutter with Statistical Variations

José Raúl Machado Fernández, Jesus de la Concepción Bacallao Vidal

• The presence of the sea clutter interfering signal sets limitations on the quality of radar detection in coastal and ocean environments. The CA-CFAR processor is the classic solution for detecting radar targets but its adjustment factor is kept fixed during the entire operation period in most implementations. As a consequence, the scheme does not take into account the slow statistical variations of the background signal when it performs the clutter discrimination. To solve this problem, the authors conducted an intensive processing of 40 million computer-generated clutter power samples in MATLAB. As a result, they found the optimal adjustment factor values to be applied in 40 possible clutter statistical states, suggesting thus the use of the CA-CFAR architecture with a variable adjustment factor. In addition, a curve fitting procedure was performed, obtaining mathematical expressions that generalize the results for the whole addressed range of clutter statistical states. The experiments were executed with a 64 cells CA-CFAR and found the adjustment factor values for three common false alarms probabilities. The K distribution was used as clutter model, thanks to its wide popularity. This contribution facilitates the handling of the K power distribution avoiding the use of Gamma and Bessel functions, commonly found in developments related to K. Moreover, requirements for building an adaptive clutter detector in K power clutter with a priori knowledge of the shape parameter were fulfill. Also, several recommendations are given to continue the development of a more overall solution which will also include the estimation of the shape parameter.