Extended Kalman Filter Routine

Extended Kalman Filter routine where 'data' represents input dataset and 'EKF' function processes the data through nonlinear state estimation

PCA Dimensionality Reduction Input Data and Options

PCA dimensionality reduction implementation for pattern recognition, focusing on input data structure and parameter configuration. The data parameter accepts a matrix where each row represents a sample, while the option parameter specifies the target dimensionality for reduction.

Iterative Closest Point (ICP) Algorithm: Fitting Data Points to Model Points

The Iterative Closest Point (ICP) algorithm aligns data points to model points by minimizing the sum of squared errors between corresponding closest points. Standard implementation: [R, T] = icp(model, data). INPUT PARAMETERS: model - matrix containing model point coordinates, data - matrix containing data point coordinates. The algorithm iteratively finds closest point correspondences and computes optimal rigid transformation using Singular Value Decomposition (SVD) for rotation matrix estimation.