Optimal quiescent vectors for wideband ML beamforming in multipath fields
Signal Processing - Content-based image and video retrieval
Robust wavefield interpolation for adaptive wideband beamforming
Signal Processing
Different wideband direction of arrival (DOA) estimation methods: an overview
SSIP'08 Proceedings of the 8th conference on Signal, Speech and image processing
Different wideband direction of arrival(DOA) estimation methods: an overview
CISST'09 Proceedings of the 3rd WSEAS international conference on Circuits, systems, signal and telecommunications
An overview of different wideband direction of arrival (DOA) estimation methods
WSEAS Transactions on Signal Processing
Different wideband direction of arrival (DOA) estimation methods: an overview
EHAC'09 Proceedings of the 8th WSEAS international conference on Electronics, hardware, wireless and optical communication
A novel autofocusing approach for estimating directions-of-arrival of wideband signals
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Direction-of-arrival estimation using a mixed l2,0norm approximation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Efficient Radio Transmission with Adaptive and Distributed Beamforming for Intelligent WiMAX
Wireless Personal Communications: An International Journal
Hi-index | 35.69 |
Existing algorithms for wideband direction finding are mainly based on local approximations of the Gaussian log-likelihood around the true directions of arrival (DOAs), assuming negligible array calibration errors. Suboptimal and costly algorithms, such as classical or sequential beamforming, are required to initialize a local search that eventually furnishes DOA estimates. This multistage process may be nonrobust in the presence of even small errors in prior guesses about angles and number of sources generated by inherent limitations of the preprocessing and may lead to catastrophic errors in practical applications. A new approach to wideband direction finding is introduced and described. The proposed strategy combines a robust near-optimal data-adaptive statistic, called the weighted average of signal subspaces (WAVES), with an enhanced design of focusing matrices to ensure a statistically robust preprocessing of wideband data. The overall sensitivity of WAVES to various error sources, such as imperfect array focusing, is also reduced with respect to traditional CSSM algorithms, as demonstrated by extensive Monte Carlo simulations