An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal.
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Relative performance of RLS and LMS filters
It's known that the RLS filter converges faster than the LMS filter in general, but that if you're tracking time varying parameters the LMS algorithm can perform better.
My question is under what ...
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71 views
Adjustable Notch Filter help
I'm converting the simulink diagram found in this paper to C code. I'm not familiar with matlab/simulink(too expensive) so need help interpreting the diagram. The problem I experience is that once a ...
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1answer
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Difference between 'conventional' and 'adaptive' beamformers?
This might be a terminology question but I am not sure.
Basically, what is the difference between conventional beamformers, and adaptive beamformers? I thought that all beamformers were inherently ...
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Maximum step size for adaptive filter convergence
I’m trying to understand the conception of function maxstep (http://www.mathworks.com/help/dsp/ref/maxstep.html)
the foundation of this function is function firwiener with input parameters: length ...
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Using an error prediction filter for filtering a semi-known signal
I'm trying to wrap my head around the proper use of a Wiener or error-prediction filter for filtering data. It seems to me that it is only a whitening filter, so how is it used when the data you want ...
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563 views
What does an adaptive filter do?
I studied a bit about adaptive filter on internet and found that its a special filter which keep on updating its filter value as soon as it proceeds. It finds out the difference between input and ...
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Why does over-modelling an adaptive AR NLMS filter fix sharp spikes?
I just simulated an auto-regressive second-order model fueled by white noise and estimated the parameters with normalized least-mean-square filters of orders 1-4.
As the first-order filter ...
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1answer
64 views
What are the Elements of Filters for Highly Volatility Price Series with Jumps?
What are the best filtering techniques for a highly volatility price series with jumps. What needs to be considered in designing new filters for practical finance which are adaptive and highly ...
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Should the input of a Kalman filter always be a signal and its derivative?
I always see the Kalman filter used with such input data. For example, the inputs are commonly a position and the correspondent velocity:
$$
(x, \dfrac{dx}{dt})
$$
In my case, I only have 2D ...
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305 views
How do I improve LMS / NLMS filter performance?
Are there ways to increase computational performance of a normalized least squares (NLMS) filter? Multidelay block frequency-domain (MDF) filters have been proposed to do this, but they also take away ...
