Ultra-fast IMUs, used to measure the pan and tilt of a new craft. Unfortunately
ID: 2082799 • Letter: U
Question
Ultra-fast IMUs, used to measure the pan and tilt of a new craft. Unfortunately when operating in their fastest mode (1,000 Hz), there is a lot of noise in acceleration and gyroscope readings. In order to maintain stable flight, the controller has a proportional feedback control (servo-regulator) implemented which reacts to the sensor output. An engineer proposes the following: “Let us average the previous 20 measurements to reduce the noise in the signal, and use the average as our current acceleration and gyroscope values.”
(a) Is this enough samples to be robust to noise? Should we use more or less samples for noise rejection?
(b) In what situations besides those already stated, would it be desired to reduce the number of samples? Can you have too many samples?
(c) Do you have a more optimum solution than the a moving average filter? If so what filter would this be? Please provide and justify a design.
Explanation / Answer
C)Kalmam Filters-the measured data is noisy and the process of navigation also is not precise.it is an efficient recursive filter that estimates the state of a linear dynamic system from the series of noisy measurements.
A)Yes,Dont use too many samples to compute the average,you may lose abrubt changes in your measurement.to put it in another way too many samples in each smoothing block may result in the loss of important high freequency response in your measurement.
B)Just to simplify some of explanations above,you can simply smooth your data by way of averaging the accelerometer output samples.That is you can add two ,three or more accelerometer output samples at a time and divide by number of samples in the block to get the average each set of your sample blocks.Each block or bundle average is then your new output sample.This is known as smoothing or averaging and is equivalent to low pass filtering in the freequencydomain,The more samples we use to compute a new sample average,the lower the cut-off freequecy of your low-pass filter and the smoother your accelerometer data will be.
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