How Can a Kalman Filter Estimate Distance Using GPS and Velocity Data?

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Discussion Overview

The discussion revolves around the application of a Kalman filter to estimate the distance traveled by a vehicle using GPS and velocity data. Participants explore the formulation of state space equations and the necessary physical dynamics involved in the modeling process.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • One participant seeks assistance in setting up a state space equation for a Kalman filter to estimate distance based on GPS and velocity data.
  • Another participant emphasizes the need for a clear definition of physical dynamics and questions the interpretation of state variables, suggesting that the magnitude of velocity alone may not be sufficient.
  • A different participant proposes a simple approach, introducing state variables for position and velocity, and outlines equations for updating these variables based on acceleration and time intervals.
  • One participant mentions a previous post in a different forum and indicates a variation in their approach, incorporating the concept of constant jerk and focusing on the magnitude of distance.
  • There is a request for clarification on what is meant by "distance," specifically whether it refers to the odometer measurement of the car's path.
  • A later reply confirms that the intended measurement is indeed the odometer distance, which reflects the length of the path traveled by the vehicle.

Areas of Agreement / Disagreement

Participants express differing views on the formulation of the Kalman filter and the necessary definitions of distance and state variables. There is no consensus on the correct approach or the specifics of the model being discussed.

Contextual Notes

Participants highlight the importance of defining physical dynamics and the implications of using different interpretations of velocity and distance. There are unresolved questions regarding the assumptions made in the modeling process.

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I want to get the distance between points. I have a measurement of latitude, longitude and height from GPS/barometer systen (which I converted into north east down coordinates) and I have another measurement of the vehicle's velocity in the north east and down direction. To find the distance, I want to set up the state space equation for a Kalman filter. I attached a picture of my attempt. In the picture, Ts is the sample period, v_k is the magnitude of the velocity, p_k is the position and D is the distance. Please correct my program.
 

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9988776655 said:
v_k is the magnitude of the velocity, p_k is the position and D is the distance. Please correct my program.

Explain your notation or give a link to an example that explains it. You also need to explain the problem completely in words. All that's clear from your verbal description is that there is a moving object. The Kalman filter requires that you specify the physical dynamics of a situation not merely the set of measurements. Is this a falling object? - a projectile? - something self propelled?

I can't interpret your state variables. Are you using the magnitude of velocity, but not it's 3 component vectors? Do you mean p_k to be a scalar? Or does it stand for 3-D column vector with 3 entries? What distance is "the distance"?

Since your matrix implies D[k] = D[k-1] + P[k-1], perhaps the coordinates of the object are D[k] and P[k] is a displacement vector instead of a vector of giving the coordinates of the object.?

My recollection of questions about Kalman filters in the math sections is that they often go unanswered and I myself don't claim to be an expert on them. You might have better luck in the engineering sections of the forums. But even there, you have to explain enough so the physical dynamics are known. Just listing your set of measurements isn't sufficient.
 
I posted this in the General Engineering forum without my state space equation (which is wrong anyway)
 
Explain the physics and your notation and I'll do what I can.
 
I'll suggest a simple approach. Something is changing the car's velocity, but you haven't mentioned any control inputs. So assume the accelerations are from random noise. That would explain the velocity changes.

Let \triangle t be the time interval between measurements.

State variables:

Let the cars position coordinates be (X[k],Y[k],Z[k]) at measurement k.

Let V_x [k-1] be the average x-velocity of the car between the k-1 and k measurements.
Let A_x[k-1] be the average x-acceleration of the car between the k-1 and k measuremens.Dynamics:

X[k] = X[k-1] + V_x[k-1] * \triangle t + (1/2) * A_x[k-1] * (\triangle t)^2

V_x[k] = V_x[k-1] + A_x[k-1]*\triangle t

Similarly, for the other coordinates:

Y[k] = Y[k-1] + V_y[k-1]* \triangle t + (1/2)* a_y[k-1] *(\triangle t)^2

Z[k] = Z[k-1] + V_z[k-1] *\triangle t + (1/2)*a_z[k-1] *(\triangle t)^2

V_y[k] = V_y[k-1] + A_y[k-1]*\triangle t

V_z[k] = V_z[k-1] + A_z[k-1]*\triangle t
 
Hi. Thanks for your advice. I posted something very similar to this in the general engineering forum where I explained my notation:
https://www.physicsforums.com/threads/kalman-filter-help-me-to-set-up-a-state-equation.785573/
My version is slightly different because I assumed constant Jerk. I also integrated to get velocity and position, but I used the magnitudes as it was only important to find the magnitude of distance.
Since the object is a car, the accelerations are from putting the pedal down and braking is from using the brakes. How would you model that? Do you think my model is OK?
 
You still haven't defined what you mean by "distance". Do you mean distance to be what the odometer on the car measures? -, which is the length of the possibly curved path the car has followed..
 
Yes that is what I want to measure
 

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