Hi I like to integrate GPS and INS using kalman filter to predict the position of a vehicle. first of all i like to use GPS sensor readings with kalman filter . I have read lot of research papers for that purpose but I donot know how to use real time data of GPS sensor in the kalman filter measurement equation. My question is that where i have to put the readings from the gps sensor in measurement equation i.e where to put numerical values from the sensor in the measurement equation.how these values make sense. zk=Hxk+wk also i like to ask as how can i determine the state transition matrix F and observation matrix H for a particular application. also how can i determine the measrement noise matrix from the gps sensor. thanks and regards

# GPS/INS integration using kalman filter

On Fri, 20 Jul 2007 09:31:37 -0500, mjukhan wrote:> Hi > > I like to integrate GPS and INS using kalman filter to predict the > > position of a vehicle. > > first of all i like to use GPS sensor readings with kalman filter . > I have read lot of research papers for that purpose but I donot know how > > to use real time data of GPS sensor in the kalman filter measurement > > equation. > > My question is that where i have to put the readings from the gps sensor > > in measurement equation i.e where to put numerical values from the sensor > > in the measurement equation.how these values make sense. > zk=Hxk+wk > also i like to ask as how can i determine the state transition matrix F > > and observation matrix H for a particular application. > > also how can i determine the measrement noise matrix from the gps sensor. > > thanks and regardsHave you studied up on Kalman filters at all? "Optimal State Estimation: Kalman, H-infinity, and Nonlinear Approaches" by Dan Simon is a good place to start. He's got a web site at http://academic.csuohio.edu/simond/. Do you mean that you want to take the output of a non-GPS aided INS (inertial nav system) and correct it with the GPS signals, or are you going to combine an IMU (inertial measurement unit) with GPS to form an INS? The latter case will be far easier, because while an INS does have an embedded IMU, it obscures the IMU data pretty thoroughly. No matter what, you will be getting IMU data at a much higher rate than GPS data. This means that your Kalman filter will need to be time varying; you'll have one set of transition, input and output matrices to use when the data comes from the IMU only, and another one to use when you have IMU data and GPS data. If your IMU data isn't synchronized with the GPS data you may also have to do some interpolation on the fly. The AIAA (www.aiaa.org) has a book out on this subject. It'll probably have the closest thing you can get to a recipe on the subject. -- Tim Wescott Control systems and communications consulting http://www.wescottdesign.com Need to learn how to apply control theory in your embedded system? "Applied Control Theory for Embedded Systems" by Tim Wescott Elsevier/Newnes, http://www.wescottdesign.com/actfes/actfes.html

>Hi > >I like to integrate GPS and INS using kalman filter to predict the >position of a vehicle. > >first of all i like to use GPS sensor readings with kalman filter . >I have read lot of research papers for that purpose but I donot know how >to use real time data of GPS sensor in the kalman filter measurement >equation. >First question is what kind of GPS/INS integration are u using. Loosely coupled, tightly coupled or ultra tightly coupled integration. The later two integrations gives better results but your INS should be able to accept GPS measurments (carrier and beat pahse, or I and Q in the ultra coupled case).>My question is that where i have to put the readings from the gps sensor >in measurement equation i.e where to put numerical values from the sensor >in the measurement equation.how these values make sense. >zk=Hxk+wkThis also follow the ingeration part, talking about loosely coupled. Your measurement is basically: Z = GPS postion - INS measurment this occurs at a 1PPS rate ( the rate the gps position is measured). To say this in a very simple way, your INS kalman predicts the position of the rover at a rate f_s from 50Hz up to 2500Hz, and the gps postion is received at f_GPS = 1 Hz. When the gps signal is there you feedback the error corrections so ur INS kalman compensates the bias and other errors that results from long term predicitions. As Tim said, Synchronization in this case is something u have to worry about when u need high precision and u have high dynamics in ur system.>also i like to ask as how can i determine the state transition matrix F >and observation matrix H for a particular application. > >also how can i determine the measrement noise matrix from the gps sensor. > >thanks and regards > > > > > >