I. Extended Kalman Filter (EKF) II. Converted Measurement Kalman Filter (CMKF) III. Least Squares Fitting Method IV. Comparison of Least Squares, EKF, and CMKF V. Outlier Rejection Algorithm

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Resource Overview

This document contains MATLAB implementations of five key algorithms: Extended Kalman Filter (EKF) for nonlinear estimation, Converted Measurement Kalman Filter (CMKF) with bias removal, Least Squares fitting method, comparative analysis of these three filtering approaches, and an outlier rejection algorithm for data preprocessing.

Detailed Documentation

This document presents five main algorithmic components:

1. Extended Kalman Filter (EKF) - This algorithm enables state estimation for nonlinear systems through linearization techniques using Taylor series expansion. The MATLAB implementation typically involves calculating Jacobian matrices for state transition and measurement models, making it widely applicable across various engineering domains.

2. Converted Measurement Kalman Filter (CMKF) - This algorithm improves filtering accuracy by applying statistical debiasing transformations to raw sensor measurements before processing. The MATLAB code implements coordinate transformation and bias correction routines to handle measurement nonlinearities effectively.

3. Least Squares Fitting Method - This approach provides optimal curve fitting for datasets by minimizing the sum of squared residuals. The MATLAB implementation uses matrix operations (typically the backslash operator or pinv function) to solve the normal equations for parameter estimation.

4. Comparative Analysis of Least Squares, EKF, and CMKF - We evaluate the performance characteristics, computational complexity, and implementation considerations of these three filtering algorithms. The comparison includes MATLAB performance metrics to assist in selecting the most suitable algorithm for specific applications.

5. Outlier Rejection Algorithm - We provide a MATLAB-implemented method for identifying and removing statistical outliers from datasets. The algorithm typically employs statistical thresholds (e.g., 3-sigma rule) or robust statistical measures to enhance data quality and processing accuracy.

These algorithms and requirements provide comprehensive tools for advanced data processing, enabling improved performance in various applications. For any technical inquiries or suggestions, please feel free to contact our team.

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