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.