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We propose a new subspace-based localization of far-field (FF) and near-field (NF) narrowband signals (LOFNS) without eigendecomposition impinging on a symmetrical uniform linear array, where the oblique projection operator is utilized to isolate the NF signals from the FF ones, and the procedures of computationally burdensome eigendecomposition are not required in the estimation of the NF and FF location parameters and the computation of oblique projection operator. As a measure against the impact of finite array data, an alternating iterative scheme is presented to improve the estimation accuracy of the oblique projection operator and, hence, that of the NF location parameters, where the “saturation behavior” encountered in most of localization methods is overcome. Furthermore, the statistical analysis of the proposed LOFNS is studied, and the asymptotic mean-squared-error expressions of the estimation errors are derived for the FF and NF location parameters. Finally, the effectiveness and the theoretical analysis of the proposed LOFNS are substantiated through numerical examples, and the simulation results demonstrate that the LOFNS provides remarkable and satisfactory estimation performance for both the NF and FF signals compared with some existing localization methods even with eigendecomposition. [ABSTRACT FROM AUTHOR]

Presents information on a study which addressed the problem of directions-of-arrival (DOA) estimation of coherent narrowband signals impinging on a uniform linear array when the number of signals is unknown using linear prediction model. Problem formulation; Optimal regularization approach for corrected least squares estimation; Data-based iterative algorithm for DOA estimation; Conclusions.

Detection of the directions of arrival (DOA) and the number of impinging signals is an essential function in many array antenna beamforming techniques. A new approach is presented here for estimating the DOA and the number of desired signals in array antennas. The new algorithm exploits the cyclostationarity of the modulated signals, which are disturbed by unknown interferences and noises, to suppress undesired signals. In this approach, the outputs of antenna sensors are applied to a linear prediction model and the cyclic correlation is calculated to utilize the cyclostationarity of the desired signals. Also the error of estimation of the cyclic correlation function in the linear prediction model is evaluated and the DOA of the desired signals is correctly estimated. Results are compared with the results of conventional approaches, such as cyclic MUSIC, in the presence of interference and at low signal-to-noise ratios. Numerical examples are presented to show the effectiveness of the proposed method. © 1999 Scripta Technica, Electron Comm Jpn Pt 1, 82(10): 29–39, 1999 [ABSTRACT FROM AUTHOR]