We consider the problem of estimating the parameters of
a real-valued, stationary, nondeterministic, autoregressive process
of order p from a time series of finite length.
Burg's algorithm estimates these parameters
indirectly by sequentially estimating one reflection
coefficient at a time.
Our approach is to sequentially estimate the reflection coefficients
in pairs. The new algorithm has the same order of
computational complexity as Burg's. It is guaranteed to generate
parameter estimates that correspond to a stationary process
(as does Burg's), and it produces estimates of the power spectral density
that do not appear from spectral line splitting-in contrast to Burg's