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Kalman or FIR Filter for my IMU?  50% 50% What benefits for the Kalman? Equation has however succeeded air  50% 50% I agree with you, this equation resembles that of the first order IIR filter, but it contains one subttle difference. In digital filter (IIR/FIR) implementations we don't make constant filter coefficient adjustments, they remain mostly constant.

This filter equation (Kalman) assumes that observations and previous results are to be used for constantly adjusting the filter transfer function.  50% 50% Hi Mohit. Thanks for your reply. If I rewrite the article's posted difference equation in common DSP notation we have:

y[n] = ax[n] + (1-a)y[n-1]                (1)

where n is the time index, x[n] is the current input sample, y[n] is the current output sample, and 0 < a < 1.

All I was saying was that the article's posted difference equation looks like the equation describing a popular 1st-order IIR lowpass filter (an exponential averager). Based on your comment, am I correct to believe the above Equation (1) does indeed describe a Kalman filter?  Thanks.  50% 50% Hi DSPer,

The equation discussed is for a Kalman filter which is used for prediction of position. Obviously, position cannot go down zero and would always be relative till infinite time, if you add a GPS at intial/start then it would be initial + predicted change + error/noise,

The discussion of FIR is for applications needing lower bandwidth signal of interest/detection and we are trying to reduce the processing burden by band limiting the signal to the required band in an FIR and then decimating which would not reduce any information and could make the processing easier with less data..

Hope you can read through and understand the different application aspects..

Mohit  50% 50% Hi. The equation you posted looks an awful lot like what we call an "exponential averager", also called a "leaky integrator." It's a 1st-order IIR lowpass filter.  0% 100% More Blogs from Ian Beavers
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