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 3 added 6 characters in body edited Jun 17 '18 at 8:43 HsnVahedi 5533 bronze badges You can model errors this way: $$a = f*a' + g + b + \eta$$ where $$a$$ is the actual acceleration, $$f$$ is a 3x3 matrix to model scaling, misalignments, cross-axis and ... errors. $$a'$$ is sensor's data, $$g$$ is gravity, $$b$$ is 3x1 matrix to model bias, and $$\eta$$ is 3x1 matrix to model noise. You can calibrate accelerometer by reading $$a'$$ from sensor data when the accelerometer is in static positions. In static positions, the only force effecting accelerometer is gravity. So by minimizing this summation, $$f$$ and $$b$$ can be computed(calibration): $$(|a_0|^2 - |g|^2) + (|a_1|^2 - |g|^2) + ... + (|a_N|^2 - |g|^2)$$$$(|a_0|^2 - |g|^2)^2 + (|a_1|^2 - |g|^2)^2 + ... + (|a_N|^2 - |g|^2)^2$$ where $$N$$ is number of static positions. You can model errors this way: $$a = f*a' + g + b + \eta$$ where $$a$$ is the actual acceleration, $$f$$ is a 3x3 matrix to model scaling, misalignments, cross-axis and ... errors. $$a'$$ is sensor's data, $$g$$ is gravity, $$b$$ is 3x1 matrix to model bias, and $$\eta$$ is 3x1 matrix to model noise. You can calibrate accelerometer by reading $$a'$$ from sensor data when the accelerometer is in static positions. In static positions, the only force effecting accelerometer is gravity. So by minimizing this summation, $$f$$ and $$b$$ can be computed(calibration): $$(|a_0|^2 - |g|^2) + (|a_1|^2 - |g|^2) + ... + (|a_N|^2 - |g|^2)$$ where $$N$$ is number of static positions. You can model errors this way: $$a = f*a' + g + b + \eta$$ where $$a$$ is the actual acceleration, $$f$$ is a 3x3 matrix to model scaling, misalignments, cross-axis and ... errors. $$a'$$ is sensor's data, $$g$$ is gravity, $$b$$ is 3x1 matrix to model bias, and $$\eta$$ is 3x1 matrix to model noise. You can calibrate accelerometer by reading $$a'$$ from sensor data when the accelerometer is in static positions. In static positions, the only force effecting accelerometer is gravity. So by minimizing this summation, $$f$$ and $$b$$ can be computed(calibration): $$(|a_0|^2 - |g|^2)^2 + (|a_1|^2 - |g|^2)^2 + ... + (|a_N|^2 - |g|^2)^2$$ where $$N$$ is number of static positions. 2 deleted 2 characters in body edited Jun 16 '18 at 21:03 HsnVahedi 5533 bronze badges You can model errors this way: $$a = f*a' + g + b + \eta$$ where $$a$$ is the actual acceleration, $$f$$ is a 3x3 matrix to model scaling, misalignments, cross-factoraxis and ... errors. $$a'$$ is sensor's data, $$g$$ is gravity, $$b$$ is 3x1 matrix to model bias, and $$\eta$$ is 3x1 matrix to model noise. You can calibrate accelerometer by reading $$a'$$ from sensor data when the accelerometer is in static positions. In static positions, the only force effecting accelerometer is gravity. So by minimizing this summation, $$f$$ and $$b$$ can be computed(calibration): $$(|a_0|^2 - |g|^2) + (|a_1|^2 - |g|^2) + ... + (|a_N|^2 - |g|^2)$$ where $$N$$ is number of static positions. You can model errors this way: $$a = f*a' + g + b + \eta$$ where $$a$$ is the actual acceleration, $$f$$ is a 3x3 matrix to model scaling, misalignments, cross-factor and ... errors. $$a'$$ is sensor's data, $$g$$ is gravity, $$b$$ is 3x1 matrix to model bias, and $$\eta$$ is 3x1 matrix to model noise. You can calibrate accelerometer by reading $$a'$$ from sensor data when the accelerometer is in static positions. In static positions, the only force effecting accelerometer is gravity. So by minimizing this summation, $$f$$ and $$b$$ can be computed(calibration): $$(|a_0|^2 - |g|^2) + (|a_1|^2 - |g|^2) + ... + (|a_N|^2 - |g|^2)$$ where $$N$$ is number of static positions. You can model errors this way: $$a = f*a' + g + b + \eta$$ where $$a$$ is the actual acceleration, $$f$$ is a 3x3 matrix to model scaling, misalignments, cross-axis and ... errors. $$a'$$ is sensor's data, $$g$$ is gravity, $$b$$ is 3x1 matrix to model bias, and $$\eta$$ is 3x1 matrix to model noise. You can calibrate accelerometer by reading $$a'$$ from sensor data when the accelerometer is in static positions. In static positions, the only force effecting accelerometer is gravity. So by minimizing this summation, $$f$$ and $$b$$ can be computed(calibration): $$(|a_0|^2 - |g|^2) + (|a_1|^2 - |g|^2) + ... + (|a_N|^2 - |g|^2)$$ where $$N$$ is number of static positions. 1 answered Jun 16 '18 at 20:43 HsnVahedi 5533 bronze badges You can model errors this way: $$a = f*a' + g + b + \eta$$ where $$a$$ is the actual acceleration, $$f$$ is a 3x3 matrix to model scaling, misalignments, cross-factor and ... errors. $$a'$$ is sensor's data, $$g$$ is gravity, $$b$$ is 3x1 matrix to model bias, and $$\eta$$ is 3x1 matrix to model noise. You can calibrate accelerometer by reading $$a'$$ from sensor data when the accelerometer is in static positions. In static positions, the only force effecting accelerometer is gravity. So by minimizing this summation, $$f$$ and $$b$$ can be computed(calibration): $$(|a_0|^2 - |g|^2) + (|a_1|^2 - |g|^2) + ... + (|a_N|^2 - |g|^2)$$ where $$N$$ is number of static positions.