navigation,gps,pseudocode,kalman-filter

The kalman-filter is an algorithm based off previous data. It is easy to implement when you have predictable motion (for example a swinging pendulum). However for general purpose use can be difficult to implement correctly. I have found it easier (and in some cases more reliable) to implement the simpler...

if you're using opencv2.4, then it's bad news: the KalmanFilter is unusable, since you cannot set the transition (or any other) Matrix. for opencv3.0 it works correctly, like this: import cv2, numpy as np meas=[] pred=[] frame = np.zeros((400,400,3), np.uint8) # drawing canvas mp = np.array((2,1), np.float32) # measurement tp...

initialization,offline,kalman-filter

This is a good observation. Given the constraints (F, H, Q, and R constant) then you can compute the evolution of P independently of the data. To answer your specific questions: 1) Yes, you could. If you were going to do that, though, you should just precompute a series of...

javascript,node.js,kalman-filter

Well if you don't wanna change much in the existing source code you can do this. Don't remove the implicit function call ie : (function(){//code})(); and i think you should be set. module.exports = { KalmanModel : (function(){ function KalmanModel(x_0,P_0,F_k,Q_k){ this.x_k = x_0; this.P_k = P_0; this.F_k = F_k; this.Q_k...

I only have a bit of experience with Kalman Filters, so take this with a grain of salt. It seems you shouldn't need to change the equations at all. Working with the second package (learn_kalman), you can create an A0 matrix of size [length(d(t)) length(d(t))]. C0 is the same, and...

python,numpy,prediction,kalman-filter

The 2D generalization of the 1-sigma interval is the confidence ellipse which is characterized by the equation (x-mx).T P^{-1}.(x-mx)==1, with x being the parameter 2D-Vector, mx the 2D mean or ellipse center and P^{-1} the inverse covariance matrix. See this answer on how to draw one. Like the sigma-intervals the...

position,sensor,robot,kalman-filter,proximitysensor

I'm not familiar with that video at all, but he might mean that if you repeatedly add process noise (P = FPF' + Q) but never reduce P via measurement, then P can only increase. In general, though, I would caution you against considering the covariance matrix P as...

In fact the situation is quite the opposite: The KF's estimate of your process noise is not affected by your data at all. If you look at the predict/update steps of the KF you'll see that the P term is never influenced by your state or your measurements. It is...

c++,opencv,location,kalman-filter

You are on the right track, but you seem confused with the notions of transition function and measurement function. The first thing to do is to clearly define what state vector you want to consider. In your case, the position [px, py, pz] and velocity [vx, vy, vz] are enough:...

matlab,matrix,plot,kalman-filter

You don't need to initialise x to anything, just set the initial state x(:,1) and the "simulation of the system" loop will fill in the rest. Oops, I see you were already doing that! Later, in the loop that infers the state xnew from the noisy observations y you can...

opencv,computer-vision,kalman-filter

As described by the documentation, the 2,1, and 0 are the dimensionalities of the state, measurements, and control spaces. That is, your state vector has length 2, measurements are scalars, and there is no control signal....

java,android,gps,time-series,kalman-filter

The answer is simple, the SensorEvent.timestamp has an arbitrary zero reference: It turns out after a bit of Googling (tip o' the hat to StackOverflow, as usual) that the timestamp one receives isn't based off of any particular 0-point defined in the Android OS or the API; it's an arbitrary...

ios,filtering,kalman-filter,rssi

Basically the estimate of error variance(P) depends on it's own past value and the process noise(Q). Since the process noise(Q) is negligible or very small value (0.00001) and P doesn't depend on actual measurement, eventually it becomes a fixed value. Moreover if your system is mobile, you can take series...

c++,opencv,time-complexity,kalman-filter

If by augmenting you mean combining the states of all objects (both means and covariances) into a single super-state and then using a single filter for prediction/estimation of this super-state, then I am afraid your intuition about it being more efficient is most likely wrong. You need to consider that...

There are different ways to represent a given AR process as a state space model but I believe you'll have to have a 2 dimensional state representation if it's an AR(2) process (which your model is). So A, C and Q will need to be matrices. You have scalar observations...

matlab,kalman-filter,matrix-inverse

As far as I understand it, the R matrix is supposed to be the covariance matrix for the measurement noise. The following lines: R=sigma_2_v*diag(diag(x)); R = diag(R); Change R from a 2x2 diagonal matrix to a 2x1 column vector. Since your observation y is a scalar, the observation noise v...

Regarding your questions: Regarding Q - The process noise - Since the acceleration changes unpredictably from step to step, Q should represent the standard deviation of the acceleration. I would use something like [0 0 0;0 0 0;0 0 sigma^2]. You should try to use the data you have to...

java,android,gps,kalman-filter

I see a few issues: Your update() contains predict and update, but you also have a predict(), so you would be double-integrating velocity if you actually call predict() (you didn't include the outer loop). There's some confusion as to whether your measurement is position or position and velocity. You can...

A one-dimensional case like this means that all of the matrices are actually just scalar values. You need to know two things: R, the measurement variance. You can directly measure this by recording a series of RSSI values (in a fixed location) exactly how you would normally and then measuring...