  Performance of parameter estimation is one of the most important issues in
array signal processing. The root mean square error, probability of success,
resolution probabilities, and computational complexity are frequently used
indexes for evaluating the performance of the parameter estimation methods.
However, a common characteristic of these indexes is that they are unsupervised
indexes, and are passively used for evaluating the estimation results. In other
words, these indexes cannot participate in the design of estimation methods. It
seems that exploiting a validity supervised index for the parameter estimation
that can guide the design of the methods will be an interesting and meaningful
work. In this study, we exploit an index to build a supervised learning model
of the parameter estimation. With the developed model we refine the signal
subspace so as to enhance the performance of the parameter estimation method.
The main characteristic of the proposed model is a circularly applied feedback
of the estimated parameter for refining the estimated subspace. It is a closed
loop and supervised method not reported before. This research opens a specific
way for improving the performance of the parameter estimation by a supervised
index. However, the proposed method is still unsatisfying in some scopes of
signal-to-noise ratio (SNR). We believe that exploiting a validity index for
the parameter estimation in array signal processing is still a general and
interesting problem.
