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Performance of direction of arrival (DOA) estimation is one of the most important issues in array signal processing. Subspace-based algorithms provide a good compromise between the estimation accuracy and computational complexity. However, these methods are exposed to performance breakdown at low SNR scenarios. A major reason for such performance breakdown is the subspace swap phenomenon (intersubspace leakage). In this article, we elaborate on a novel modified signal subspace model through exploiting a supervised index for the estimation of DOA. With the developed model we refine the signal subspace so as to enhance the performance of the DOA estimation. In the proposed scheme, we define a fuzzy similarity matrix for the eigenvalues of the array output correlation matrix to capture the distribution of the eigenvalues. Then, we build up a transformation matrix between the fuzzy similarity matrix and the eigenspace of the correlation matrix, and construct a nonlinear transformation function to adjust the fuzzy similarity matrix. Subsequently, we define a supervised evaluation index named signal subspace reconstruction error for DOA estimation and construct a cost function of the signal subspace to develop a supervised model for the signal subspace. The signal subspace can be modified through adjusting the parameter in the nonlinear transformation function and optimizing the abovementioned cost function. Finally, the performance of DOA estimation can be enhanced with the modified signal subspace. The main characteristic of the proposed model is circularly applied feedback of the estimated DOA for refining the estimated subspace. It is a closed loop and supervised method not reported before. This article opens a specific way for improving the performance of the DOA estimation in array signal processing by a supervised index. However, the proposed method is still unsatisfying in some scopes of signal-to-noise ratio. We believe that exploiting a validity index for DOA estimation in array signal processing is still a general and interesting problem. [ABSTRACT FROM AUTHOR]

The problem of source direction-of-arrival (DOA) estimation using a sensor array is addressed, where some of the sensors are perfectly calibrated, while others are uncalibrated. An algorithm is proposed for estimating the source directions in addition to the estimation of unknown array parameters such as sensor gains and phases, as a way of performing array self-calibration. The cost function is an extension of the maximum likelihood (ML) criteria that were originally developed for DOA estimation with a perfectly calibrated array. A particle swarm optimization (PSO) algorithm is used to explore the high-dimensional problem space and find the global minimum of the cost function. The design of the PSO is a combination of the problem-independent kernel and some newly introduced problem-specific features such as search space mapping, particle velocity control, and particle position clipping. This architecture plus properly selected parameters make the PSO highly flexible and reusable, while being sufficiently specific and effective in the current application. Simulation results demonstrate that the proposed technique may produce more accurate estimates of the source bearings and unknown array parameters in a cheaper way as compared with other popular methods, with the root-mean-squared error (RMSE) approaching and asymptotically attaining the Cramer Rao bound (CRB) even in unfavorable conditions. [ABSTRACT FROM AUTHOR]