Iterative Sequential Estimation for Multiple Structured Signals

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. A...

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Bibliographic Details
Published in:IEEE access 2020-01, Vol.8, p.1-1
Main Authors: Hao, Zhimei, Yu, Xianxiang, Gan, Na, Cui, Guolong
Format: Article
Language:English
Subjects:
Algorithms
Computational complexity
Convergence
Covariance matrices
Estimation
Iterative algorithms
Iterative methods
Iterative Sequential Estimation
Jamming
Linear filters
Mathematical models
Multiple Structured Signals
Performance evaluation
Radar
Radar and Communication
Signal Estimation
Signal to noise ratio
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2978006