Explore MATLAB source code curated for "状态估计" with clean implementations, documentation, and examples.
Application Background: In state estimation, the initial values are obtained from load flow calculation results, making load flow computation the foundation for state estimation. Key Technology: Calculation of load flow values for IEEE33-node distribution systems using the Newton-Raphson method.
MATLAB source code for power system state estimation, provided for reference purposes without infringement liability. Contains implementation of key algorithms including weighted least squares estimation with detailed code documentation.
Original state estimation program utilizing Weighted Least Squares (WLS) method for calculations, with enhanced data sampling and preprocessing capabilities for improved accuracy.
In maneuvering target tracking, the target motion model serves as a fundamental component that ideally captures various movement states during target maneuvers. Commonly used models include Constant Velocity (CV) model, Constant Acceleration (CA) model, time-correlated Singer model, and the "Current" Statistical model for maneuvering targets. These models characterize target maneuvers using a maneuver frequency parameter. In practical applications, a fixed maneuver frequency is typically employed, implying constant maneuver duration. However, actual target maneuver durations vary continuously, meaning the maneuver frequency changes dynamically. Using a fixed maneuver frequency inevitably introduces tracking errors. When the sampling period ranges from 0.5 to 2 seconds, lower maneuver frequencies yield higher tracking accuracy [1]. This description highlights the need for adaptive frequency adjustment algorithms that can dynamically optimize tracking performance through neural network implementations.
An advanced state estimation approach utilizing an improved adaptive filter with exceptional performance. Implemented in MATLAB, this solution runs directly within the MATLAB environment to demonstrate optimal filtering results. The implementation employs the Sage-Husa adaptive algorithm, which offers significant improvements over traditional filtering methods through its innovative noise statistics estimation and adaptive correction mechanisms.
This function implements the Unscented Kalman Filter (UKF) algorithm for state estimation and target tracking applications, featuring sigma point transformation and nonlinear system handling
Implementation of Unscented Kalman Filter (UKF) for joint state and parameter estimation in nonlinear systems with nonlinear measurement and process noise characteristics
Fault detection using particle filtering through state estimation and residual smoothing techniques with code implementation insights
The Unscented Kalman Filter (UKF) represents a significant advancement in nonlinear estimation. The core concept involves generating strategically placed sampling points (Sigma points) around the current state estimate based on its covariance matrix. These points are then propagated through the nonlinear system model to capture the posterior mean and covariance more accurately than linearization methods. The UKF implementation typically involves three main steps: sigma point selection, nonlinear transformation, and statistics recovery.
MATLAB Implementation of Kalman Filter Source Code with State Estimation Examples