WSJT-X/lib/fst4/fst4_baseline.f90
Steven Franke 5ca81a6507
Use 3rd order polynomial fit to estimate the noise baseline. The
polynomial fit is done over 400 Hz bandwidth for T/R periods longer
than 15s, and over approx. 600 Hz (10 times the signal bandwidth) for
T/R period of 15s.
2020-08-29 19:55:23 +01:00

49 lines
1.2 KiB
Fortran

subroutine fst4_baseline(s,np,ia,ib,npct,sbase)
! Fit baseline to spectrum (for FST4)
! Input: s(npts) Linear scale in power
! Output: sbase(npts) Baseline
implicit real*8 (a-h,o-z)
real*4 s(np),sw(np)
real*4 sbase(np)
real*4 base
real*8 x(1000),y(1000),a(5)
data nseg/8/
do i=ia,ib
sw(i)=10.0*log10(s(i)) !Convert to dB scale
enddo
nterms=3
nlen=(ib-ia+1)/nseg !Length of test segment
i0=(ib-ia+1)/2 !Midpoint
k=0
do n=1,nseg !Loop over all segments
ja=ia + (n-1)*nlen
jb=ja+nlen-1
call pctile(sw(ja),nlen,npct,base) !Find lowest npct of points
do i=ja,jb
if(sw(i).le.base) then
if (k.lt.1000) k=k+1 !Save all "lower envelope" points
x(k)=i-i0
y(k)=sw(i)
endif
enddo
enddo
kz=k
a=0.
call polyfit(x,y,y,kz,nterms,0,a,chisqr) !Fit a low-order polynomial
sbase=0.0
do i=ia,ib
t=i-i0
sbase(i)=a(1)+t*(a(2)+t*(a(3))) + 0.2
! write(51,3051) i,sw(i),sbase(i)
!3051 format(i8,2f12.3)
enddo
sbase=10**(sbase/10.0)
return
end subroutine fst4_baseline