A generalized Cramér-Rao lower bound for moving arrays

Sullivan, Edmund J.

doi:10.1121/1.3040020

Cited by 3 publications

(1 citation statement)

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“…For the vector-sensor ULAs with half-wavelength interelement spacing, the noise covariance matrix Q dm has complicated structures. 25,26 In the following simulations, we consider a vector-sensor ULA with 2-D velocity measurements in the x-y plane, therefore, the measurements is given byỹ DM ¼ p r1 ; u x r1 ; u y r1 ; p r2 ; u x r2 ; u y r2 ; :::…”

Section: Numerical Examplesmentioning

confidence: 99%

Fast implementation of sparse iterative covariance-based estimation for source localization

Zhang

Abeida

Xue

et al. 2012

The Journal of the Acoustical Society of America

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Fast implementations of the sparse iterative covariance-based estimation (SPICE) algorithm are presented for source localization with a uniform linear array (ULA). SPICE is a robust, user parameter-free, high-resolution, iterative, and globally convergent estimation algorithm for array processing. SPICE offers superior resolution and lower sidelobe levels for source localization compared to the conventional delay-and-sum beamforming method; however, a traditional SPICE implementation has a higher computational complexity (which is exacerbated in higher dimensional data). It is shown that the computational complexity of the SPICE algorithm can be mitigated by exploiting the Toeplitz structure of the array output covariance matrix using Gohberg-Semencul factorization. The SPICE algorithm is also extended to the acoustic vector-sensor ULA scenario with a specific nonuniform white noise assumption, and the fast implementation is developed based on the block Toeplitz properties of the array output covariance matrix. Finally, numerical simulations illustrate the computational gains of the proposed methods.

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Section: Numerical Examplesmentioning

confidence: 99%