Recent content by evol_w10lv

Sorry for late response. I don't work in the engineering sector. I prefer programming. I am familiar with Kalman filter in programming level. Here my problem is in basics of engineering. I found out ##A, B, C, D## with MATLAB built in function: ##x(k+1)=-0.25*x(k) + 1*u(k)## ##y(k)=1*x(k)## ##A=...

I have to filter data with Kalman filter. I know process error covariance Q and measurment error covariance R. Problem is with state transition matrix A, control matrix B and observation matrix H. First of all, data goes through this transfer function: ##W(s) = \frac{4s}{4s+1}## I can't get it...

I went through my calculations. Input distribution: $$f_x = \begin{cases} 0, & x \lt -0.3 \\ \frac {1} {1.2}, & -0.3 \leq x \leq 0.9 \\ 0, & x \gt 0.9 \end{cases}$$ Input distribution scaled by ##1/9##:: $$f_{x/9} = \begin{cases} 0, & x \lt -\frac {1} {30} \\ 7.5, & -\frac {1} {30} \leq x \leq...

Now I understand that part. Thanks. I think I calculated wrong ##f_{8u_{0}/9}## in post #7. It should be ##f_{x}## scaled by ##8/9##, because ##x_{1}=u_{0}##. But I did something else there. Then I tried to solve few discrete convolution iterations in Matlab. Here are results: Something...

Actually I think that I don't need mathematically exact probability density function, that's why I didn't ask for it. Heh, we started to speak about probability density functions, then I thought that I could solve actual function. But I think I won't be able to solve that continuous convolvution...

Input distribution: $$f_x = \begin{cases} 0, & x \lt -0.3 \\ \frac {1} {1.2}, & -0.3 \leq x \leq 0.9 \\ 0, & x \gt 0.9 \end{cases}$$ Input distribution scaled by ##1/9##:: $$f_{x/9} = \begin{cases} 0, & x \lt -\frac {1} {30} \\ 7.5, & -\frac {1} {30} \leq x \leq 0.1 \\ 0, & x \gt 0.1...

I like your approach to this question. I hadn't thought, that output is sum of two different distributions. Actually output isn't Gaussian distribution, but it just looks like that? Filter output can be calculated recursively using difference equation: $$y(n) = \sum_{k=0}^M...

Homework Statement [/B] I have got data x, which is formed like uniform distribution. After using discrete low pass filter I got output data u, which is Gauss distribution. What is explanation, why using that filter, we from uniform distribution can get Gauss distribution? Homework...

Ok, it's clear about that. But now I'm trying to do more advanced calculations. I want to include heading and distance in my calculations. Let's say I integrate resulting acceleration vector twice to get distace. resulting_acc = sqrt(accX^2 + acc_Y^2) To get heading, I use magnetometer data, so...

Homework Statement I'm doing project using xsens MTi-G. Data file example: I must plot motion in plane (decart coordinate system), without z component. Measurment is taken after 0.01 s. To do the task, I should use acceleration and velosity data. Homework Equations The Attempt at a...

Thanks for the hint. Task already done.

I calculated Wk(s) by myself, it wasn't given. I have got standart W(s) and I have to implement Wk(s) to get graphic like this (red is correction): But in the bode plot (in my previous post), after last corner frequency the new graphic isn't like the standart W(s), because there is this...

It seems to me, I had a missunderstanding about the task. And there is mistake in the correction TF Wk(s), I guess. I simulated W(s) - red colour, Wk(s) -green colour, W(s)*Wk(s) - blue colour. Bode plot: http://www.bildites.lv/images/5ltl503z567e84sgk3pk.png The problem is gain of Wk(s) TF...

Homework Statement I have got transfer functions W(s) and Wk(s), where Wk(s) is correction transfer function. So I calculate W(s) and Wk(s) in series. Magnitude graph before correction: Magnitude graph after correction: The task is to make whole circuit and simulate it. Homework...

Thanks for your help. Task is completed. :)