Talk:Miniature Inertial Measurement Unit - IMU

Old datas

---

-Old data

Top curves : The X (yellow) and Z (cyan) accelerometers raw data with the Y axis rate gyro (pink). The y scale is the raw numerical value as read from the Gyro-SPI bus. The x scale is time (simulation duration is 33,32s). The three bottom curves are the simulated estimation of the angle (Y axis) using three different methods. The yellow curve is the estimated angle from the X and Z accelerometers, sensing the gravity. The cyan curve is the estimated angle, integrated from the Y rate gyro. The pink curve is the estimated angle estimated with the complementary filter that fusion the relevant information from both the Y rate gyro and the X-Z accelerometer

Complementary Filter running on dsPIC

This model implements the complementary filter on the dsPIC and sends the result to matlab. It has the same behaviors as the simulated filter.

To design the filter for the dsPIC, the two precedent models are mixed up. The filter is inserted by copy-past into the first model used to log data. Data logged in real time from the complementary filter implemented on the dsPIC (30f4012 running at 10MHz). X axis : time in second. Y axis : angle multiplied by 100 (see model) The blue curve is the estimated angle from the X and Z accelerometers, resolving the gravity vector. The red curve is the estimated angle integrated from the Y rate gyro. The green curve is the angle estimated through the complementary filter that fusion the relevant information from both the Y axis rate gyro and the X and Z accelerometers.

The estimated angle using accelerometers (blue curve) is quite noisy. However, it steady value is correct. The estimated angle relying on the integration of the gyro is clean but drift (red curve). Note that the dsPIC has just been switch on and the High pass filter of the pseudo integrator of the gyro has not yet removed the remaining DC bias of the gyro. The DC bias of the Gyro has probably changed due to non constant temperature in my house. (I do not have a clim in my tiny room which is in a roof in Marseille! ). Thus, the gyro integrated angle is slowing drifting away from its real value. The green curves that use both data is clean and drift free. Its dynamics is as fast as the gyro dynamic. Note that at the beginning, the High pass filter (with a higher bandwidth than the gyro pseudo-integrator high pass filter) has not yet removed the gyro bias. However, at the end of the animation (11 seconds long: from 3, 5 to 14, 5), both curves (blue and green) are merging as in the simulation.

Remarks about complementary filter

The high pass filter, the inverted rate gyro dynamic and the integrator are merged into one transfer function.

All calculation is done in double data type or single data type. Theses float data slow down the calculation and use a large amount of memory. It is quite powerful with the help of simulink to build a fixed point complementary filter. Thus, next step is to go back to the simulation model and to design a fixed point complementary filter! (Using the matlab fixed point toolbox)

---