PROJECT TITLE :

Fractal properties of autoregressive spectrum and its application on weak target detection in sea clutter background

ABSTRACT:

This study considerations the fractal properties of sea litter in the facility spectrum domain. To overcome the deficiencies of Fourier remodel analysis, the power spectrum of the ocean muddle is obtained by autoregressive (AR) spectrum estimation. The AR model may be a linear predictive model, which estimates the power spectrum of sea muddle kind its autocorrelation matrix and incorporates a higher frequency resolution than Fourier analysis. This study concentrates on analysing the fractal property of the facility spectrum primarily based on AR spectral estimation and its application on weak target detection. First, fractional Brownian motion is taken for instance to prove the fractal property of the facility spectrum. Then, real measured X-band knowledge is employed to verify the fractal property of the power spectrum of ocean muddle. Finally, a unique detection technique based mostly on AR Hurst exponent is proposed and the factors influencing the fractal properties of power spectrum are analysed. The results show that the Hurst exponent of AR spectrum is effective for weak target detection in ocean litter background. Compared with the prevailing fractal technique and also the traditional constant false alarm rate (CFAR) methodology, the proposed methodology includes a higher detection performance.