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Performance of parameter estimation is one of the most important issues inarray signal processing. The root mean square error, probability of success,resolution probabilities, and computational complexity are frequently usedindexes for evaluating the performance of the parameter estimation methods.However, a common characteristic of these indexes is that they are unsupervisedindexes, and are passively used for evaluating the estimation results. In otherwords, these indexes cannot participate in the design of estimation methods. Itseems that exploiting a validity supervised index for the parameter estimationthat can guide the design of the methods will be an interesting and meaningfulwork. In this study, we exploit an index to build a supervised learning modelof the parameter estimation. With the developed model we refine the signalsubspace so as to enhance the performance of the parameter estimation method.The main characteristic of the proposed model is a circularly applied feedbackof the estimated parameter for refining the estimated subspace. It is a closedloop and supervised method not reported before. This research opens a specificway for improving the performance of the parameter estimation by a supervisedindex. However, the proposed method is still unsatisfying in some scopes ofsignal-to-noise ratio (SNR). We believe that exploiting a validity index forthe parameter estimation in array signal processing is still a general andinteresting problem.

Exploiting a Supervised Index for High-accuracy Parameter Estimation in Low SNR