Name
Abstract | ||
|---|---|---|
In this paper, we address the signal estimation problem for a linear combination of multiple structured models, which is widely employed in the passive and/or active sensing systems to characterize the behaviors, for example, jamming and multipath propagation, in radar and communication societies. An iterative sequential estimation (ISE) algorithm is presented to obtain simultaneously the multiple structured signals. At each iteration, employing the estimated signals at the previous step, the optimal linear filters, based on mean-squared error criteria, are designed to minimize the output average power for every element of each signal. Finally, we evaluate the performance of the proposed ISE method compared with the least-square and compressed sensing algorithms via numerical simulations. The results highlight the presented algorithm shows a better signal estimation performance at low SNR and plays a trade-off between the computational complexity and the signal estimation performance. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2020.2978006 | IEEE ACCESS |
Keywords | DocType | Volume |
Estimation,Convergence,Covariance matrices,Computational complexity,Radar,Iterative algorithms,Signal to noise ratio,Signal estimation,radar and communication,iterative sequential estimation,multiple structured signals | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhimei Hao | 1 | 0 | 0.34 |
| Xianxiang Yu | 2 | 29 | 11.97 |
| Na Gan | 3 | 0 | 0.34 |
| Guolong Cui | 4 | 333 | 36.77 |