Array Signal Processing: Concepts and Techniques
Array Signal Processing: Concepts and Techniques
DOA estimation of quasi-stationary signals via Khatri-Rao subspace
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
On the virtual array concept for the fourth-order direction findingproblem
IEEE Transactions on Signal Processing
Applications of cumulants to array processing .I. Apertureextension and array calibration
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
On the virtual array concept for higher order array processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
MIMO Radar Space–Time Adaptive Processing Using Prolate Spheroidal Wave Functions
IEEE Transactions on Signal Processing
Erratum to "nested arrays: a novel approach to array processing with enhanced degrees of freedom"
IEEE Transactions on Signal Processing
Covariance sparsity-aware DOA estimation for nonuniform noise
Digital Signal Processing
Hi-index | 35.69 |
A new array geometry, which is capable of significantly increasing the degrees of freedom of linear arrays, is proposed. This structure is obtained by systematically nesting two or more uniform linear arrays and can provide O(N2) degrees of freedom using only N physical sensors when the second-order statistics of the received data is used. The concept of nesting is shown to be easily extensible to multiple stages and the structure of the optimally nested array is found analytically. It is possible to provide closed form expressions for the sensor locations and the exact degrees of freedom obtainable from the proposed array as a function of the total number of sensors. This cannot be done for existing classes of arrays like minimum redundancy arrays which have been used earlier for detecting more sources than the number of physical sensors. In minimum-input-minimum-output (MIMO) radar, the degrees of freedom are increased by constructing a longer virtual array through active sensing. The method proposed here, however, does not require active sensing and is capable of providing increased degrees of freedom in a completely passive setting. To utilize the degrees of freedom of the nested co-array, a novel spatial smoothing based approach to DOA estimation is also proposed, which does not require the inherent assumptions of the traditional techniques based on fourth-order cumulants or quasi stationary signals. As another potential application of the nested array, a new approach to beamforming based on a nonlinear preprocessing is also introduced, which can effectively utilize the degrees of freedom offered by the nested arrays. The usefulness of all the proposed methods is verified through extensive computer simulations.