MATLAB Implementation of Raw State Estimation Using Weighted Least Squares (WLS)

This is a raw state estimation program that performs state estimation calculations using the Weighted Least Squares (WLS) method. WLS is a commonly used estimation technique that achieves higher precision by leveraging larger data samples. The WLS algorithm optimizes weights for each data point, effectively reducing the impact of noise on estimation results. During state estimation computation, proper data sampling and preprocessing are crucial considerations. To enhance the original program's reliability, we can implement additional code segments for: - Data sampling techniques (e.g., systematic sampling or random sampling implementation using MATLAB's datasample function) - Data preprocessing routines including outlier detection (using isoutlier function) and normalization (via zscore or normalize functions) - Weight matrix initialization based on measurement uncertainties - Iterative solution of the normal equations (WLS estimation formula: θ = (HᵀWH)⁻¹HᵀWz) These enhancements ensure the state estimation process maintains accuracy and robustness by handling real-world data variations effectively through proper algorithmic implementation.