KALMAN FILTER - help for tuning

Started by Giovanna in comp.dsp15 years ago 3 replies

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...


Re: Kalman filtering with multiplicative noise

Started by Tim Wescott in comp.dsp11 years ago

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


Kalman Filter applied to Rangefinding Data

Started by jcarter in comp.dsp9 years ago 7 replies

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...


Kalman filtering

Started by thom in comp.dsp13 years ago 3 replies

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...


Some basic questions about Kalman Filtering

Started by Kai in comp.dsp11 years ago 4 replies

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...


Recurrence in the observation equation

Started by lanbaba in comp.dsp8 years ago 2 replies

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...


Re: Kalman filtering with multiplicative noise

Started by RRogers in comp.dsp11 years ago

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


Re: Kalman filtering with multiplicative noise

Started by stanp in comp.dsp11 years ago

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


Kalman filtering with multiplicative noise

Started by ac123 in comp.dsp11 years ago 4 replies

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...


Kalman filter in which observables depend on lagged observables

Started by swh5 in comp.dsp7 years ago 1 reply

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


kalman filter implementation and basics

Started by praveen in comp.dsp16 years ago 4 replies

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...


Sensor fusion with Kalman filtering with different sensor sample rates?

Started by Sven in comp.dsp13 years ago 1 reply

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...


Kalman filter - getting biased estimates: for multi parameter estimation

Started by lgganesh in comp.dsp10 years ago 6 replies

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...


extended kalman fixed-point issues

Started by Muzaffer Kal in comp.dsp10 years ago

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...


Kalman Filter for Electronic Compass and Gyro

Started by priya90 in comp.dsp9 years ago 1 reply

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...


can anyone help design a state space model applying into Extended Kalman Filter

Started by Terry in comp.dsp11 years ago 11 replies

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...


Kalman filter estimation position

Started by leo in comp.dsp11 years ago 4 replies

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


kalman filter radar tracker

Started by jamie in comp.dsp13 years ago 6 replies

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...


Kalman Filter for Image Object Tracking

Started by ProdigalSon in comp.dsp13 years ago 3 replies

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...


Inertial Navigation

Started by Anonymous in comp.dsp5 years ago 1 reply

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