Interpolation is a technique for adding new data points within a range of a set of known data points. You can use interpolation to fill-in missing data, smooth existing data, make predictions, and more. Interpolation in MATLAB® is divided into techniques for data points on a grid and scattered data points.
1-D and Gridded Interpolation
|1-D data interpolation (table lookup)|
|Interpolation for 2-D gridded data in meshgrid format|
|Interpolation for 3-D gridded data in meshgrid format|
|Interpolation for 1-D, 2-D, 3-D, and N-D gridded data in ndgrid format|
|Gridded data interpolation|
|Piecewise Cubic Hermite Interpolating Polynomial (PCHIP)|
|Modified Akima piecewise cubic Hermite interpolation (Since R2019b)|
|Cubic spline data interpolation|
|Evaluate piecewise polynomial|
|Make piecewise polynomial|
|Extract piecewise polynomial details|
|Padé approximation of time delays|
|1-D interpolation (FFT method)|
- Gridded and Scattered Sample Data
Introduction to interpolating gridded and scattered data sets.
- Interpolating Gridded Data
Interpolation of regularly spaced, axis-aligned data sets.
- Interpolating Scattered Data
Interpolating scattered data using