The_solution_of_the_data_fusion__course_of_SJTU._M_Data_Fusion_

# Data Fusion > This are code repo for the Data Fusion course. ## Table of Contents * [Optimal Estimation](#optimal-estimation) * [Wiener Filter](#wiener-filter) * [Kalman Filter](#kalman-filter) * [Fuzzy Control](#fuzzy-control) * [Contact](#contact) <!-- * [License](#license) --> ## Optimal Estimation ### Problem description Suppose a voltage is a random variable $X$ with normal distribution, the mean value is $5$, and the variance is $0.1$; The random variable x is measured $20$ times by two instruments, and the measurement error of the two instruments is assumed to be a normally distributed random variable with a mean value of $0$ and a variance of $0.1$ and $0.4$ respectively. Caculate the least square estimation (LSE), weighted least square estimation (WLS) and linear minimum variance estimation (LMMSE) of $X$, and calculate the mean square error of the corresponding estimation. Let the measurement equation be $Z=HZ+V$. ### Usage To handle the problem, run the following file: `1/code_1/main123.m` <!-- ### Result | Method | $\hat{X}$ estimation |MSE| | :-----| :----: | :----:| | LSE | 5.0615 |0.0063| | WLS | 5.0292 |0.0040| | LMMSE | 5.0281 |0.0038| --> <!-- You don't have to answer all the questions - just the ones relevant to your project. --> ## Wiener Filter ### problem description Let $y (n) =x (n) +v (n)$, where $x(n)=10sin(\frac{\pi n}{128}+\frac{\pi}{3})$,$v(n)$ is white noise with variance of $1.25$. Design FIR and IIR Wiener filter to estimate the signal $x (n)$. ### Usage To handle the problem, run the following file: `1/code_1/main.m` <!-- ### Result  --> ## Kalman Filter ### Basic Kalman Filter `1/code_3/kalman.m` ### Constant Gain Kalman Filter `1/code_3/kalman_constant_gain.m` ### Square root Kalman Filter `1/code_3/kalman_sqrt.m` ### Forgetting Factor Kalman Filter `1/code_3/kalman_forgetting_factor.m` ### Adaptive Kalman Filter `1/code_3/kalman_adaptive.m` ### Limited K Reduction Kalman Filter `1/code_3/kalman_restain_K.m` ### Extended Kalman Filter `2/code_0/EKF.m` ### Unscented Kalman Filter `2/code_0/UKF.m` ### Particle Filter `2/code_0/PF.m` ### Federated Kalman Filter `2/code_1/federated_filter.m` ### Decentralized Kalman filter `2/code_1/center_federated_filter.m` ## Fuzzy Control ### Basic method `4/code/TS_model.m` ### T-S method `4/code/TS_model.m` ## Contact changjingliu@sjtu.edu.cn <!-- Optional --> <!-- ## License --> <!-- This project is open source and available under the [... License](). --> <!-- You don't have to include all sections - just the one's relevant to your project -->