Hello all,
Given a stream of samples I would like to apply n slightly different
filters to it with n being able to be chosen at runtime. Then combine
the results back to a single stream.
As a test I built a flowgraph with the following chains in parallel for n = 6
| -> decimating fir filter 1 -> complex to mag -> |
stream -> | -> decimating fir filter 2 -> complex to mag -> | -> Max -> ...
| ....
|
| -> decimating fir filter n -> complex to mag -> |
So the same stream is sent to each chain (decimation is 1) and the
output of each chain is pushed through a big Max block with 6 inputs.
This works but not particularly elegant and a bit annoying to change
if I suddenly decide I want to change n. In particular it also does
not seem computationally efficient.
What I would like is to replace the above by a single block that
- replicates the input n times
- applies each filter on each replica
- combines the output again to a single stream
- have a tunable n parameter
- is fast
I did this with an Embedded python block doing essentially this:
for i in range(n):
out[i] = scipy.signal.lfilter(taps[i], 1, input)
This is using exactly the same taps as in the chain case. This works
but the output is different and worse than what I get with the
separate chains. As a test, instead of lfilter I tried:
gnuradio.filter.fir_filter_ccc(1,taps[i]).work(input[0],output)
Thinking perhaps that is a closer replica. But couldnt get it to work..
I suspect there should be an easy / natural way of doing this in
gnuradio. Looked at the filter bank / channelliser blocks but failed
to get anywhere.
So what is the best way forward to do this?
Many thanks
Dirk
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