Conjugate Gradient Method for Adaptive Direction-of-Arrival Estimation of Coherent Signals
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Adaptive spectral estimation using the conjugate gradient algorithm
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05
Reduced-rank adaptive filtering using Krylov subspace
EURASIP Journal on Applied Signal Processing
Subspace Direction Finding With an Auxiliary-Vector Basis
IEEE Transactions on Signal Processing
Conjugate gradient eigenstructure tracking for adaptive spectralestimation
IEEE Transactions on Signal Processing
An iterative algorithm for the computation of the MVDR filter
IEEE Transactions on Signal Processing
Probability of resolution of the MUSIC algorithm
IEEE Transactions on Signal Processing
On the equivalence of three reduced rank linear estimators with applications to DS-CDMA
IEEE Transactions on Information Theory
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
| Hi-index | 0.00 |
This paper proposes a new algorithm for the direction of arrival (DOA) estimation of P radiating sources. Unlike the classical subspace-based methods, it does not resort to the eigendecomposition of the covariance matrix of the received data. Indeed, the proposed algorithm involves the building of the signal subspace from the residual vectors of the conjugate gradient (CG) method. This approach is based on the same recently developed procedure which uses a noneigenvector basis derived from the auxiliary vectors (AV). The AV basis calculation algorithm is replaced by the residual vectors of the CG algorithm. Then, successive orthogonal gradient vectors are derived to form a basis of the signal subspace. A comprehensive performance comparison of the proposed algorithm with the well-known MUSIC and ESPRIT algorithms and the auxiliary vectors (AV)-based algorithm was conducted. It shows clearly the high performance of the proposed CG-based method in terms of the resolution capability of closely spaced uncorrelated and correlated sources with a small number of snapshots and at low signal-to-noise ratio (SNR).