Kramer, Samuel Leonard2024-06-062024-06-062024-06-05vt_gsexam:40881https://hdl.handle.net/10919/119307Traditional signal detection algorithms are often robust and are typically sufficient for high SNR data. However, the assumptions behind these methods begin to fall apart when signal period becomes either very short, or small in amplitude compared to any corruptive noise. To address this a kernel transform based cross-correlation algorithm is proposed for the application of weak signal detection. The algorithm leverages kernel methods to inflate SNR of the data and enhance the noise rejection capabilities of the traditional cross-correlation. The goal of the algorithm is to achieve detection for signals past the limits of those of the matched filter and the cross-correlation in the presence of white and colored noise. To evaluate the effectiveness of the correlation algorithm, Monte Carlo simulations are performed to determine the performance in the context of different types of noise. The performance of the algorithm will be compared against the cross-correlation and the matched filter.ETDenIn CopyrightDigital Signal ProcessingSignal DetectionThesis