Optimizing ACF Using Wiener-Khinchin Theorem

Since the past few decades, several applications have been using auto correlation as their prime function for various objectives. Here the focus is to optimize the auto correlation by reducing the buffer size and computation complexities while maintaining the same nature of auto correlation function, so that the processing gets better and faster. A relatively less researched approach for computing auto correlation function by following Wiener- Khinchin Theorem has been explored. In order to prove the robustness of the method, statistical analysis based on mean square error between the traditional method and auto correlation function using Wiener-Khinchin theorem method have been discussed. Moreover, a novel formula to find out the frequency resolution for auto correlation function under certain conditions also has been keyed out.