Name
Abstract | ||
|---|---|---|
The performance of second-order methods in signal processing for spatial analysis is limited by the correlation between sources. The popular approach of spatial smoothing groups an equally spaced array into subarrays and then averages the covariance matrices of the subarrays to decorrelate coherent arrivals. In the new method proposed here, the decomposition is applied on the eigenvector spanning the signal subspace to reform the source subspace basis. Results of simulation show that the new scheme is more robust to noise. Comparison is made with the conventional spatial smoothing method in terms of variances on the estimated directions. |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1016/S0165-1684(96)00104-1 | Signal Processing |
Keywords | Field | DocType |
signal processing, spatial analysis, high resolution, direction of arrivals, coherent sources, space diversity | Signal processing,Mathematical optimization,Antenna diversity,Subspace topology,Matrix (mathematics),Smoothing,Signal subspace,Eigenvalues and eigenvectors,Mathematics,Covariance | Journal |
Volume | Issue | ISSN |
54 | 2 | 0165-1684 |
Citations | PageRank | References |
4 | 0.45 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dominic Grenier | 1 | 382 | 21.18 |
| Éloi Bossé | 2 | 386 | 26.19 |