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This paper deals with the problem of tracking the two-dimensional (2-D) direction-of-arrivals (DOAs) of multiple moving targets with crossover points on their trajectories, and we propose a new computationally efficient cross-correlation based 2-D DOA estimation with automatic pair-matching (CODEC) method for noncoherent narrowband signals impinging on the L-shaped sensor array structured by two uniform linear arrays (ULAs) with omnidirectional sensors. The effectiveness of the proposed method and the theoretical analysis are substantiated through numerical examples. [ABSTRACT FROM AUTHOR]

In order to mitigate the effect of additive noises and reduce the computational burden, we propose a new computationally efficient cross-correlation based two-dimensional (2-D) direction-of-arrivals (DOAs) estimation (CODE) method for noncoherent narrowband signals impinging on the L-shaped sensor array structured by two uniform linear arrays (ULAs). By estimating the azimuth and elevation angles independently with a one-dimensional (1-D) subspace-based estimation technique without eigendecomposition, where the null spaces are obtained through a linear operation of the matrices formed from the cross-correlation matrix between the received data of two ULAs, then the pair-matching of estimated azimuth and elevation angles is accomplished by searching the minimums of a cost function of the azimuth and elevation angles, where the computationally intensive and time-consuming eigendecomposition process is avoided. Further the asymptotic mean-square-error (MSE) expressions of the azimuth and elevation estimates are derived. The effectiveness of proposed method and the theoretical analysis are verified through numerical examples, and it is shown that the proposed CODE method performs well at low signal-to-noise ratio (SNR) and with a small number of snapshots. [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.