Frequency and 2D angle estimation based on a sparse uniform array of electromagnetic vector sensors
EURASIP Journal on Applied Signal Processing
Multidimensional Systems and Signal Processing
Signal correlation modeling in acoustic vector sensor arrays
IEEE Transactions on Signal Processing
Underwater sources location in non-Gaussian impulsive noise environments
Digital Signal Processing
Quad-quaternion music for DOA estimation using electromagnetic vector sensors
EURASIP Journal on Advances in Signal Processing
OFDM MIMO radar with mutual-information waveform design for low-grazing angle tracking
IEEE Transactions on Signal Processing
OFDM MIMO radar for low-grazing angle tracking
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Wireless Personal Communications: An International Journal
| Hi-index | 35.69 |
We present a bound on the number of sources identifiable in a class of array processing models with multiple parameters and signals per source. The bound is applied to determine the maximum number of uniquely resolvable plane-wave sources in various acoustic and electromagnetic vector-sensor models. We examine the use of a priori information about the sources, the effects of known and unknown noise characteristics, and the presence of nuisance parameters. Connections between identifiability and existence of the Cramer-Rao bound (CRB) are investigated. We show quantitatively how assumptions about the parameters can fundamentally affect the maximum number of identifiable sources