MATLAB Implementation of Sinc Interpolation

Sinc interpolation is a widely used method in numerical analysis that estimates unknown data points from known sample points. This technique relies on interpolation polynomials constructed from given data points and their corresponding function values at interpolation nodes. However, sinc interpolation tends to produce oscillations near the nodes, requiring practical modifications for real-world applications. To mitigate these oscillations, various techniques such as windowing functions or alternative interpolation methods are typically employed to achieve smoother and more accurate results. Consequently, researchers are continuously refining sinc interpolation methods to improve precision - for instance, by utilizing finer grids or higher-order interpolation polynomials to reduce errors. Implementation approaches in MATLAB typically involve: - Using the built-in sinc() function combined with convolution operations - Applying window functions (e.g., Hamming, Hanning) to control Gibbs phenomenon - Implementing frequency domain zero-padding for spectral interpolation - Handling edge effects through symmetric padding or specialized boundary treatments Key considerations for MATLAB implementation include: - Proper sampling rate management to satisfy the Nyquist criterion - Efficient computation using vectorized operations instead of loops - Memory optimization for large datasets through block processing - Visualization of results using plot() and stem() functions to compare original and interpolated signals