em_vars: optional, subset of or ‘all’
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Constructor & Destructor Documentation
Kalman filters were introduced by Kalman in the 1960 to solve the problem of optimally estimating the state of the system, which may be partially observed, under the constrain that the system evolves in time according to a linear equation and the observations are linked to the state through a linear equation also.
How do I implement the Kalman filter for accelerometer smoothing with Arduino? Are there any good libraries for it?
I presume the input to your system is acceleration (as read by the accelerometer) and you want to estimate position, velocity or both. (Otherwise, you could assume constant velocity, but in this case the accelerometers would be reading zero:-) )
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Using a #define to change a single letter into a number seems like a recipe for compile errors if any variable in the project starts with that letter. I would change them to something more unique (i.e. M to M_SENSOR_INPUTS and N to N_STATE_VALUES). I might be paranoid.
Therefore, at first, the problem should be solved on Android devices. Most drivers use Android smartphones because of their low price. The next step is to implement some online service for that.
Further research will focus on the calibration of MEMS devices to improve the performance of orientation determination systems. Meanwhile, some additional sensors and modules, such as the barometer and GPS, can be optionally integrated into the existing MODS. In this case, a complete orientation and position determination system would be achieved.
Template class providing a basic implementation of the Kalman filter. The state and the process model are both template parameter classes to keep it flexible. Both parameters have to implement a certain interface to make the filter work.
The Kalman filter is widely used in present robotics such as guidance, navigation, and control of vehicles, particularly aircraft and spacecraft. This is essential for motion planning and controlling of field robotics, and also for trajectory optimization. Further, this is used for modeling the control of movements of central nervous systems.
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For those of you who do not know what a Kalman filter is, it is an algorithm which uses a series of measurements observed over time, in this context an accelerometer and a gyroscope. These measurements will contain noise that will contribute to the error of the measurement. The Kalman filter will then try to estimate the state of the system, based on the current and previous states, that tend to be more precise that than the measurements alone.
The code is designed to be as general, modular and extensible as possible, while at the same time trying to be as computationally efficient as possible. It has been tested with Matlab 7.2 (R2006a).