Study On Technology For Signal Detection In The Reverberation

The sea-floor reverberation is main disturbance when the active sonar detecting bottom or buried targets. In this paper, concerning about the subject of signal detection in reverberation, we studied technology of signal detection and wide-band signal filtering based on time-frequency analysis using theory of high-order statistic analysis, support vectors machine and time-frequency filtering.The probability density function of targets echo in non-Gaussian distribution reverberation was droved based on statistical model of reverberation. The high-order statistic of reverberation and target echo was analyzed. Referring to idea of pattern recognition and classification before detection, the method using high-order statistics and support vectors machine (HOSA-SVM) was studied for detecting targets echo in the reverberation. But in low signal to reverberation ratio, the performance is not good.For the deficiency of HOSA-SVM detection method, we studied on directly constructing detector from SVM using original data (DE-SVM). Using one-class SVM to choose effective data for reducing training time, the problem of needing magnitude of data to achieve good performance was solved. In non-Gaussian reverberation, the performance of DE-SVM is better than matched filter detector. But the kernel function and parameter of DE-SVM detector seriously effected on the detection performance. So effect of kernel in feature space was analyzed. And the principle of designing adaptive kernel function based on data driving was proposed. Using the difference of high-order statistics between targets and reverberation, the adaptive kernel function based on high-order statistics was designed. It is proved that it enlarges the distance of two kinds of samples using feature kernel, and the kernel also satisfies the Mercer theorem. The feature kernel support vector machine was applied for signal detection in Gaussian and non-Gaussian reverberation. The training and detecting algorithms in practice were given. The results of experiment and simulation show that when selecting statistics existing great difference as feature and the reverberation is non-Gaussian distribution, its performance is better than matched filter and support vector machines based on traditional kernel function.To solve the problem which the performance of detection was reduced in the low signal to noise ratio (SNR) using Wigner-Ville Hough transform (WHT), two methods were studied. In the first method, it is to restrain noise using two-dimension mean filter and Wiener filter. When the SNR is high, this method is effective, but in the low SNR, the performance is not good. In second method, the method of XWVD adaptive mean and ridgelet transform filtering (XWVD-M-FRIT) was proposed. In this method, firstly used XWVD instead of WVD for improving SNR and avoiding the cross-components when the signal are multi-components. Due to the power distribution of signal is different from noise or reverberation in time-frequency domain and considering effect of length of splitting window, so designed adaptive axial mean filter. Then it is to restrain noise or reverberation using ridgelet transform filtering. At last, it is to detect the signal using Hough transform. The results of real and simulation experiments show, compared with WHT, in the low SNR the new method is feasible to restrain noise or reverberation in time-frequency domain for improving the performance of signal detection.