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Originally Posted by Dave Scheck
How were you able to account for the noise in the accelerometer? If not handled, it could be misinterpreted as data, meaning your robot would think it was moving even though it wasn't.
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This is an important aspect to consider. Noise/interferance can effect the performance of the sensor being used. Typically, this can be done by the use of a circuit. IF you input the sensor into a Low Pass Filter, this should be able to reduce the disturbance and thus give you a clean signal.
This disturbance can cause errors in our results, thus reducing the accuracy of our data. Now, we use Analog to Digital (A/D) Converters to get all of this data. Basically, this coverts the analog output of a sensor into a digital number that can be interpreted by the computer. The error happens when an input signal has a frequency component at or higher than half of the sampling rate. Recall that frequency is the number of occurrences within a given time period (usually 1 second); "the frequency was 40 cycles per second". If this is not accounted for and limited, it will not be able to be understood or distinguished from data that is validly sampled. Ideally, a low pass filter would pass unchanged all slower signal components with frequencies from DC to the filter cutoff frequency. Anything above that point would be eliminated completely, thus reducing the signal disturbance. However, in reality filters do not just cut off sharply at a certain point. Instead, it gradually gets rid of the erroneous frequency components and will display a falloff or roll-off slope.
This is the inherent problem in A/D conversion when the input signal has frequencies that are above half of the A/D sample rate. The higher frequencies will “fold” into the lower frequency areas and will be interpreted as random signals that really do not make sense. Thus, we use a low pass filter that limits the input signal bandwidth to below half of the sampling rate. A low pass filter that is applied to each input channel of the A/D will also get rid of the unwanted high frequency noise and interference introduced before sampling occurs.
This is just a general way to look at disturbance in sensors.