Name
Abstract | ||
|---|---|---|
ESPRIT-type (spatial) frequency estimation techniques obtain their frequency estimates from the solution of a highly structured, overdetermined system of equations (the so-called invariance equation). Here, the structure is defined in terms of two selection matrices applied to a matrix spanning the estimated signal subspace. Structured least squares (SLS) is a new algorithm that solves the invariance equation by preserving its structure. Formally, SLS is derived as a linearized iterative solution of a nonlinear optimization problem. If SLS is initialized with the least squares solution of the invariance equation, only one “iteration”, i.e. the solution of one linear system of equations, is performed to achieve a significant improvement of the estimation accuracy. Therefore, the proposed estimation scheme (that uses only one “iteration” of SLS) is not iterative in nature |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1109/78.558508 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
array signal processing,direction-of-arrival estimation,frequency estimation,invariance,least squares approximations,matrix algebra,DOA estimation,ESPRIT-type algorithms,estimation accuracy,frequency estimation,invariance equation,linear system of equations,linearized iterative solution,nonlinear optimization problem,selection matrices,sensor array,signal subspace,structured least squares | Least squares,Signal processing,Mathematical optimization,Overdetermined system,Invariant (physics),System of linear equations,Matrix (mathematics),Algorithm,Estimation theory,Signal subspace,Mathematics | Journal |
Volume | Issue | ISSN |
45 | 3 | 1053-587X |
Citations | PageRank | References |
24 | 2.79 | 18 |
Authors | ||
1 | ||