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Wishes warning! This article or section documents one or more OpenMoko Wish List items, the features described here may or may not be implemented in the future. |
The variometer signal is simply the derivative of the barometer signal. It gives a much more accurate vertical speed signal than is possible with GPS.This kind of measurement is used by the flee flying community (hanggliding, paragliding, ballooning). A device that allows teams of pilots to share position and speed (with accurate vertical speed) data would be lots of fun. Variometer Reference
It could be best to filter and differentiate the analog pressure signal and then digitize. Another possibility is to differentiate the height in software. A robust solution is to compute a linear regression of a sliding window of height samples. The height samples can be computed using integer arithmetic by a pressure->altitude lookup table followed by interpolation. Maybe it's even possible to add detail based on the accelerometers.
References to applicable transducers:
The absolute pressure signal needs to be:
See the signal conditioning example in this Application Note
It might be best to use a single chip solution if we can find the right fit e.g.: Max1464
The software solution can simply be done in the variometer application with some very simple math.
We would need two A/D channels:
Extending 8 bit sampling to a usable range for pressure sensing
The driver code samples the input channels and converts the input values from pressure to altitude:
The relationship between static pressure and pressure altitude is defined in terms of the properties of the International Standard Atmosphere. Up to 36,090 ft this can be expressed as:
<math> z =\left (1-\left(\frac{P_{ind}}{101.325}\right)^{0.190263} \right ) \times \frac{87.828}{0.00198122} </math>
Where:
These values are provided to listeners in multiple applications. The sample rate should be application adjustable to conserve power.
Applications can use the altitude data or combine the data with GPS and accelerometer data. Commonly Kalman filter/observer techniques are used to combine data from multiple sensor types into a robust(with respect to sensor noise), high accuracy estimate of position and speed in 3 axis.
References:
Combine measurements as described above. Apply knowledge about the aircraft dynamics to increase accuracy:
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