Name
Abstract | ||
|---|---|---|
Several new algorithms have been proposed recently for near-field target localization and parameter estimation in Bistatic MIMO Radar systems. The new approaches include the usage of the exact spherical wavefront model, which avoids a systematic error introduced by the Fresnel approximation that is commonly made on impinging wavefronts in order to simplify the estimation problem. In this paper, we propose a new Tensorbased Near-Field Localization (TeNFiL) algorithm that utilizes the Canonical Polyadic (CP) decomposition of the received data in order to obtain high-resolution estimates of the parameters of the dominant targets that are automatically paired. Those parameters include the distances to the transmit and the receive reference antennas, the directions of departure (DoDs) and the directions of arrival (DoAs). TeNFiL is extended to the estimation in three-dimensional space and is applicable to arbitrary antenna array geometries. We develop a new reliability test and a new reliability measure for TeNFiL. The TeNFiL algorithm is based on the Semi-algebraic framework for approximate CP decompositions via Simultaneous Matrix Diagonalizations (SECSI). It is used to compute an approximate low rank CP decomposition of the noise corrupted measurements in a robust and efficient way. The simulations show that TeNFiL outperforms recent state of the art algorithms, especially if the targets are closely spaced. Index Terms-Near Field, Spherical Wavefront, SECSI, CPD, TeNFiL, Bistatic, MIMO Radar |
Year | Venue | Field |
|---|---|---|
2018 | WSA 2018; 22nd International ITG Workshop on Smart Antennas | Wavefront,Tensor,Matrix (mathematics),Computer science,Fresnel diffraction,Algorithm,Antenna array,Near and far field,Bistatic radar,Estimation theory |
DocType | ISBN | Citations |
Conference | 978-3-8007-4541-8 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ivan Podkurkov | 1 | 1 | 1.02 |
| Liana Hamidullina | 2 | 0 | 0.34 |
| Evgenii Traikov | 3 | 0 | 0.34 |
| Martin Haardt | 4 | 3531 | 311.32 |
| Adel Nadeev | 5 | 1 | 2.04 |