Name
Abstract | ||
|---|---|---|
This paper considers analysis of methods for estimating the parameters of narrow-band signals arriving at an array of sensors. This problem has important applications in, for instance, radar direction finding and underwater source localization. The so-called deterministic and stochastic maximum likelihood (ML) methods are the main focus of this paper. A performance analysis is carried out assuming a finite number of samples and that the array is composed of a sufficiently large number of sensors. Several thousands of antennas are not uncommon in, e.g., radar applications. Strong consistency of the parameter estimates is proved, and the asymptotic covariance matrix of the estimation error is derived. Unlike the previously studied large sample case, the present analysis shows that the accuracy is the same for the two ML methods. Furthermore, the asymptotic covariance matrix of the estimation error coincides with the deterministic Cramer-Rao bound. Under a certain assumption, the ML methods can be implemented by means of conventional beamforming for a large enough number of sensors. We also include a simple simulation study, which indicates that both ML methods provide efficient estimates for very moderate array sizes, whereas the beamforming method requires a somewhat larger array aperture to overcome the inherent bias and resolution problem |
Year | DOI | Venue |
|---|---|---|
1995 | 10.1109/78.348129 | Signal Processing, IEEE Transactions |
Keywords | Field | DocType |
covariance matrices,direction-of-arrival estimation,maximum likelihood estimation,radar signal processing,sonar signal processing,underwater sound,ML methods,accuracy,antennas,array aperture,array sizes,asymptotic covariance matrix,beamforming,deterministic Cramer-Rao bound,deterministic maximum likelihood methods,direction finding,estimation error,finite data,large arrays,narrowband signals,parameter estimation,performance analysis,radar applications,radar direction finding,sensors array,simulation study,stochastic maximum likelihood methods,underwater source localization | Direction finding,Radar,Signal processing,Beamforming,Control theory,Sensor array,Algorithm,Estimation theory,Covariance matrix,Statistics,Strong consistency,Mathematics | Journal |
Volume | Issue | ISSN |
43 | 2 | 1053-587X |
Citations | PageRank | References |
31 | 4.32 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Viberg | 1 | 917 | 188.13 |
| Björn E. Ottersten | 2 | 6418 | 575.28 |
| Nehorai, Arye | 3 | 1934 | 309.00 |