  For the stochastic gravitational wave backgrounds (SGWBs) search centred at
the milli-Hz band, the galactic foreground produced by white dwarf binaries
(WDBs) within the Milky Way contaminates the extra-galactic signal severely.
Because of the anisotropic distribution pattern of the WDBs and the motion of
the spaceborne gravitational wave interferometer constellation, the time-domain
data stream will show an annual modulation. This property is fundamentally
different from those of the SGWBs. In this Letter, we propose a new filtering
method for the data vector based on the annual modulation phenomenon. We apply
the resulted inverse variance filter to the LISA data challenge. The result
shows that for the weaker SGWB signal, such as energy density $\Omega_{\rm
astro}=1\times10^{-12}$, the filtering method can enhance the posterior
distribution peak prominently. For the stronger signal, such as $\Omega_{\rm
astro}=3\times10^{-12}$, the method can improve the Bayesian evidence from
`substantial' to `strong' against null hypotheses. This method is
model-independent and self-contained. It does not ask for other types of
information besides the gravitational wave data.
