This are code repo for the Data Fusion course.

• Optimal Estimation
• Wiener Filter
• Kalman Filter
• Fuzzy Control
• Contact

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 $Z=HZ+V$.

To handle the problem, run the following file:

1/code_1/main123.m

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 $x (n)$.

To handle the problem, run the following file:

1/code_1/main.m

1/code_3/kalman.m

1/code_3/kalman_constant_gain.m

1/code_3/kalman_sqrt.m

1/code_3/kalman_forgetting_factor.m

1/code_3/kalman_adaptive.m

1/code_3/kalman_restain_K.m

2/code_0/EKF.m

2/code_0/UKF.m

2/code_0/PF.m

2/code_1/federated_filter.m

2/code_1/center_federated_filter.m

4/code/TS_model.m

4/code/TS_model.m

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