  Eigen-decomposition-based direction finding methods of using
large-scale/ultra-large-scale fully-digital receive antenna arrays lead to a
high or ultra-high complexity. To address the complexity dilemma, in this
paper, three low-complexity estimators are proposed: partitioned subarray
auto-correlation combining (PSAC), partitioned subarray cross-correlation
combining (PSCC) and power iteration max correlation successive convex
approximation (PI-Max-CSCA). Compared with the conventional no-partitioned
direction finding method like root multiple signal classification (Root-MUSIC),
in the PSAC method, the total set of antennas are equally partitioned into
subsets of antennas, called subarrays, each subarray performs independent DOA
estimation, and all DOA estimates are coherently combined to give the final
estimation. For a better performance, the cross-correlation among sub-arrays is
further exploited in the PSCC method to achieve the near-Cramer-Rao lower bound
(CRLB) performance with the help of auto-correlation. To further reduce the
complexity, in the PI-Max-CSCA method, using a fraction of all subarrays to
make an initial coarse direction measurement (ICDM), the power iterative method
is adopted to compute the more precise steering vector (SV) by exploiting the
total array, and a more accurate DOA value is found using ICDM and SV through
the maximum correlation method solved by successive convex approximation.
Simulation results show that as the number of antennas goes to large-scale, the
proposed three methods can achieve a dramatic complexity reduction over
conventional Root-MUISC. Particularly, the PSCC and PI-Max-CSCA can reach the
CRLB while the PSAC shows a substantial performance loss.
