  A new generation of wide-field radio interferometers designed for 21-cm
surveys is being built as drift scan instruments allowing them to observe large
fractions of the sky. With large numbers of antennas and frequency channels the
enormous instantaneous data rates of these telescopes require novel, efficient,
data management and analysis techniques. The $m$-mode formalism exploits the
periodicity of such data with the sidereal day, combined with the assumption of
statistical isotropy of the sky, to achieve large computational savings and
render optimal analysis methods computationally tractable. We present an
extension to that work that allows us to adopt a more realistic sky model and
treat objects such as bright point sources. We develop a linear procedure for
deconvolving maps, using a Wiener filter reconstruction technique, which
simultaneously allows filtering of these unwanted components. We construct an
algorithm, based on the Sherman-Morrison-Woodbury formula, to efficiently
invert the data covariance matrix, as required for any optimal signal-to-noise
weighting. The performance of our algorithm is demonstrated using simulations
of a cylindrical transit telescope.
