Mirror/aicodix___dsp
1
0
Fork 0
mirror of https://github.com/aicodix/dsp.git synced 2026-04-27 14:30:36 +00:00
Code Issues Projects Releases Packages Wiki Activity

added least mean squares filter implementations

Browse source
This commit is contained in:
Ahmet Inan 2020-10-23 11:49:17 +02:00
commit f43b438d63
2 changed files with 89 additions and 0 deletions
Download patch file Download diff file Expand all files Collapse all files

6
README.md
View file

@ -219,3 +219,9 @@ Sometimes we only need [trigonometric functions](https://en.wikipedia.org/wiki/T
* [sine function](https://en.wikipedia.org/wiki/Sine)
* [cosine function](https://en.wikipedia.org/wiki/Trigonometric_functions#cosine)
### [lms.hh](lms.hh)
Some [least mean squares filter](https://en.wikipedia.org/wiki/Least_mean_squares_filter) implementations:
* Normalized Least Mean Squares
* Normalized Complex Least Mean Squares

83
lms.hh Normal file
View file

@ -0,0 +1,83 @@
/*
Least mean squares filter
Copyright 2020 Ahmet Inan <inan@aicodix.de>
*/
#pragma once
#include "bip_buffer.hh"
namespace DSP {
template <typename TYPE, int SIZE>
class NLMS
{
BipBuffer<TYPE, SIZE> history;
TYPE filter[SIZE];
public:
NLMS()
{
for (int i = 0; i < SIZE; ++i)
filter[i] = 0;
}
inline operator const TYPE * () const
{
return filter;
}
TYPE operator () (TYPE input, TYPE desired, TYPE mu = 1)
{
const TYPE *hist = history(input);
TYPE estimate = 0;
for (int i = 0; i < SIZE; ++i)
estimate += filter[i] * hist[i];
TYPE error = desired - estimate;
TYPE power = 0;
for (int i = 0; i < SIZE; ++i)
power += hist[i] * hist[i];
if (power == 0)
return error;
TYPE rate = mu / power;
for (int i = 0; i < SIZE; ++i)
filter[i] += rate * error * hist[i];
return error;
}
};
template <typename CMPLX, int SIZE>
class NCLMS
{
typedef typename CMPLX::value_type VALUE;
BipBuffer<CMPLX, SIZE> history;
CMPLX filter[SIZE];
public:
NCLMS()
{
for (int i = 0; i < SIZE; ++i)
filter[i] = 0;
}
inline operator const CMPLX * () const
{
return filter;
}
CMPLX operator () (CMPLX input, CMPLX desired, VALUE mu = 1)
{
const CMPLX *hist = history(conj(input));
CMPLX estimate = 0;
for (int i = 0; i < SIZE; ++i)
estimate += conj(filter[i]) * hist[i];
CMPLX error = conj(conj(desired) - estimate);
VALUE power = 0;
for (int i = 0; i < SIZE; ++i)
power += norm(hist[i]);
if (power == 0)
return error;
VALUE rate = mu / power;
for (int i = 0; i < SIZE; ++i)
filter[i] += rate * error * hist[i];
return error;
}
};
}