  signal direction-of-arrival estimation using an array of sensors has been the
subject of intensive research and development during the last two decades.
Efforts have been directed to both, better solutions for the general data model
and to develop more realistic models. So far, many authors have assumed the
data to be iid samples of a multivariate statistical model. Although this
assumption reduces the complexity of the model, it may not be true in certain
situations where signals show temporal correlation. Some results are available
on the temporally correlated signal model in the literature. The temporally
correlated stochastic Cramer-Rao bound (CRB) has been calculated and an
instrumental variable-based method called IV-SSF is introduced. Also, it has
been shown that temporally correlated CRB is lower bounded by the deterministic
CRB. In this paper, we show that temporally correlated CRB is also upper
bounded by the stochastic iid CRB. We investigate the effect of temporal
correlation of the signals on the best achievable performance. We also show
that the IV-SSF method is not efficient and based on an analysis of the CRB,
propose a variation in the method which boosts its performance. Simulation
results show the improved performance of the proposed method in terms of lower
bias and error variance.
