Hi! I'm sorry for my english... I need help for tuning of kalman filter in a vision problem. The problem is the following: There is a...

Hi! I'm sorry for my english... I need help for tuning of kalman filter in a vision problem. The problem is the following: There is a robot (kephera) moving on a white plane,with a camera that look to the plane. I've already build the algorithm that find the robot on the plane,and by the omography I can come back to the coordinates on the plane. The commands to the robot are se...

dsp@myallit.com wrote: > On Jul 21, 5:35 pm, "Bruno Luong" wrote: > > d...@myallit.com wrote in message > > > > You might consider...

dsp@myallit.com wrote: > On Jul 21, 5:35 pm, "Bruno Luong" wrote: > > d...@myallit.com wrote in message > > > > You might consider Extended Kalman filtering (EKF). Be aware > > about the eventual non-stability of the scheme. > > > > What do you mean by the eventual non-stability? I did look at the EKF, > there is some simple sample MATLAB code here: > http://www.mathworks

I must be missing something. I passed 128 records of 1300 samples each (1300x128) through a Kalman Filter (This is basically a laser return...

I must be missing something. I passed 128 records of 1300 samples each (1300x128) through a Kalman Filter (This is basically a laser return signal - often times quite small- buried in solar noise generated by an APD - Avalanche Photo Diode) and the output appears as noisy as any one of the record inputs. Simple Averaging of these 128 records yields a signal from the noise, but I was under the im...

Hi, I have just studied Kalman filtering algorithm to reduce noise, and I have seen that a state transition matrix, the channel distortion...

Hi, I have just studied Kalman filtering algorithm to reduce noise, and I have seen that a state transition matrix, the channel distortion matrix, the covariance matrix of the state equation input and the covariance matrix of the additive noise are needed... Do you know if there is a mean to use the algorithm without these parameters? In my opinion there are cases for which it's not possible t...

Hi, I'm trying to work with Kalman Filter for Sensor Fusion/state estimation purposes. A very basic question first: I model my system...

Hi, I'm trying to work with Kalman Filter for Sensor Fusion/state estimation purposes. A very basic question first: I model my system with a continuos time state space model. I'm using a high sample rate (3000 Hz). Do I have to transform my state space model into time-discrete domain anyways? What would happen if not, what special properties may occur when I'm using a continous time m...

Usually a Kalman filter can be applied to a system described by the following equations: X[k] = A*X[k-1] + W[k] ... (1) Y[k] = C*X[k] + V[k] ......

Usually a Kalman filter can be applied to a system described by the following equations: X[k] = A*X[k-1] + W[k] ... (1) Y[k] = C*X[k] + V[k] ... (2), where (1) is the dynamic equation with the state X[k] and process noise W[k], and (2) is the observation equation with the observation noise V[k]. The only dynamic is in the difference equation (1). Now, I'm reading a paper on the Kalman filter ap...

On Jul 21, 8:06 pm, d...@myallit.com wrote: > On Jul 21, 5:35 pm, "Bruno Luong" wrote: > > > d...@myallit.com wrote in message > > >...

On Jul 21, 8:06 pm, d...@myallit.com wrote: > On Jul 21, 5:35 pm, "Bruno Luong" wrote: > > > d...@myallit.com wrote in message > > > You might consider Extended Kalman filtering (EKF). Be aware > > about the eventual non-stability of the scheme. > > What do you mean by the eventual non-stability? I did look at the EKF, > there is some simple sample MATLAB code here:htt

On Jul 21, 8:06 pm, d...@myallit.com wrote: > On Jul 21, 5:35 pm, "Bruno Luong" wrote: > > > d...@myallit.com wrote in message > > >...

On Jul 21, 8:06 pm, d...@myallit.com wrote: > On Jul 21, 5:35 pm, "Bruno Luong" wrote: > > > d...@myallit.com wrote in message > > > You might consider Extended Kalman filtering (EKF). Be aware > > about the eventual non-stability of the scheme. > > What do you mean by the eventual non-stability? I did look at the EKF, > there is some simple sample MATLAB code here:htt

I'm trying to implement a Kalman filter in MATLAB that will use two types of measurements: volume and in/out flow rate. For the flow rate,...

I'm trying to implement a Kalman filter in MATLAB that will use two types of measurements: volume and in/out flow rate. For the flow rate, the measurement error is additive Gaussian, but for the volume the measurement error is expressed as a percentage of the volume, so that the volume measurement is less accurate when its value is higher. I think the measurement model should therefore be: Flow...

Hi, I am puzzled over how to do the Kalman filter of a system with observables that depend on lagged observables. I have the following...

Hi, I am puzzled over how to do the Kalman filter of a system with observables that depend on lagged observables. I have the following system: xt=u+z(1,t-1)+x(t-1)+d·[y(t-1)-x(t-1)]+e(x,t) yt=u+z(2,t-1)+y(t-1)-d·[y(t-1)-x(t-1)]+e(y,t) z1 and z2 are AR(1) processes z(1,t)=p1z(1,t-1)+e(1,t) z(2,t)=p2z(2,t-1)+e(2,t) The observations are xt and yt I guess the state varia

Hello, I am very new to adaptive filter. Is there any article about kalman filter basics and its implementation (basically i am first looking...

Hello, I am very new to adaptive filter. Is there any article about kalman filter basics and its implementation (basically i am first looking for matlab implementation). The article in the net is very difficult to understand and implement. Is there any article or reference which easier to understand and implement and not much mathematics and equations. Waiting for reply with regards, pr...

Could you explain how to combine two different measurements with two different sample rates to make an estimate with a Kalman...

Could you explain how to combine two different measurements with two different sample rates to make an estimate with a Kalman filter? Lets say I have want to estimate a position and I have an eccelerometer and something else that measures position (a radar or something). The position (radar) measurement has a rate of 1 measurement per second and the eccelerometer has 1000 measuremen...

I am a beginner & am using Kalman Filter for multiple parameter estimation in processing of dual freq. carrier phase navigation...

I am a beginner & am using Kalman Filter for multiple parameter estimation in processing of dual freq. carrier phase navigation receiver.(position, velocity, Ionospheric error, receiver bias(units of length) and transmitter(satellite) bias (again units of length) are the five parameters I am estimating together). When I start with a very poor estimate to begin the filter (meaning when I give highe...

Hi everyone, I have an extended kalman filter which seems to be working OK in floating point and I converted it to fixed point with a large...

Hi everyone, I have an extended kalman filter which seems to be working OK in floating point and I converted it to fixed point with a large number of bits in the fractional part. What I'm observing is that the error between the floating point and fixed point outputs seems to grow over time. I'm using float C type in my floating-point implementation and I have 28 bits of fractional portions i...

Hello, I'm using an electronic compass and 3-axis gyro to estimate orientation for my undergraduate project. The idea is that a user will hold...

Hello, I'm using an electronic compass and 3-axis gyro to estimate orientation for my undergraduate project. The idea is that a user will hold and turn the device, while walking, and I should estimate the true orientation of the device at each step. I will be using a Kalman Filter to estimate the orientation, given the noisy compass + gyro. I've gone through a lot of material, but I'm uncle...

x1(t) = x2(t)+x3(t)*exp(-x4(t)*x1(t-1)^2)*x1(t-1)+x5(t)*exp (-x6(t)*x1(t-1)^2)*x1(t-2) + randn*v1; x2(t) = x2(t-1) + randn*v2; x3(t) =...

x1(t) = x2(t)+x3(t)*exp(-x4(t)*x1(t-1)^2)*x1(t-1)+x5(t)*exp (-x6(t)*x1(t-1)^2)*x1(t-2) + randn*v1; x2(t) = x2(t-1) + randn*v2; x3(t) = x3(t-1) + randn*v3; x4(t) = x4(t-1) + randn*v4; x5(t) = x5(t-1) + randn*v5; x6(t) = x6(t-1) + randn*v6; x2 x3 x4 x5 x6 follows a random walk. I tried many ways but alway wrong. Can anyone help design a state space model applied by Extended kalman fil...

Hi We have the position and velocity of products on the conveyor in time t=t1. How can we estimate position in time t=t1+T (T is scan time)...

Hi We have the position and velocity of products on the conveyor in time t=t1. How can we estimate position in time t=t1+T (T is scan time) if we know max.acceleration, max.jerk, max. velocity for this product? Product is on the conveyor. We need probably Kalman filter, but can somebody tell me formel how can we calculate position in t=t1+T. Thanks. Best regards Leo

I've got a radar tracker which contains 3 Kalman filters. The first filter (which I term the range filter) is for range, velocity, and...

I've got a radar tracker which contains 3 Kalman filters. The first filter (which I term the range filter) is for range, velocity, and acceleration, with range and velocity being measured. The range filter performs nicely. I'm using a plant noise model from Blackman's multiple target tracking book. The second filter is for azimuth angle, azimuth angle rate, and azimuth angle acceleration...

Hi, I am currently doing a object tracking project in computer vision. My project involves tracking snooker balls on a snooker table. The...

Hi, I am currently doing a object tracking project in computer vision. My project involves tracking snooker balls on a snooker table. The balls are tracked frame by frame but the resulting tracking information is noisy and does not follow the smooth linear motion of the balls on the table. To smooth the resulting information i am trying to use the kalman filter as i assumed this was what i...

I suppose we could talk Kalman filters, but no, something more practical. When we sent to the moon and came back, we must have aligned the...

I suppose we could talk Kalman filters, but no, something more practical. When we sent to the moon and came back, we must have aligned the gyros on the moon. What reference did we use and how accurate is that? Hardy