The Research On Target Detection Algorithms Under The Sea Clutter

The target detection technology under the sea clutter plays an important role in both military and civilian areas, provided precise target detection algorithm is among radar working mission. Therefore, in the paper some detection algorithms are been analyzed at the statistical characteristic of radar sea clutter's foundationAt the start, some models of sea clutter amplitude are been introduced. Through the I,Q component of IPIX radar sea clutter data' s histogram analyses and by skewness and kurtosis computed, it is been shown that sea clutter amplitude is not Rayleigh distribution;through the comparison of amplitude histogram and distributed models with the same parameters, it is been shown that HH polarization clutter is lognormal distribution,whereas VV polarization is K-distribution;At the same time the correlation function and power spectrum density are been analyzed, at last the correlation compound K-distribution stochastic sequences whose covariance matrix is been given are been generated through SIRP algorithm. Then compound-Gaussian distribution model is been introduced and the three adaptive algorithms been derived from the model. By using simulation data and IPIX radar sea clutter data that is been added target signals the performances of the detection algorithms are been simulated and analyzed. The same conclusion and the adaptive algorithms' effectiveness are been shown. In the end, the detection algorithms of AR model of sea clutter are been studied. The AR-GLRT and AR-Rao detection algorithms that assume sea clutter AR model are been derived, and the performances are been simulated and analyzed at large. The performances are too been compared under the different conditions and the condition that which algorithm is been chosen, too. Then the ARLPM detection algorithm from the different spectral width of sea clutter and growlers based on IPIX sea clutter returns' AR spectrums estimate is been derived, and the ROC curves are been drawn.