Abstract
The estimation of sparse shallow-water acoustic channel under a low signal to noise ratio (SNR) environment based on convex optimization is proposed. Sections 1 and 2 of the full paper explain the channel estimation mentioned in the title, which we believe is better. The core of sections 1 and 2 is subsection 2.2, whose core in turn consists of: (1) it estimates the channel impulse response based on the least squares criterion, with the matching pursuit algorithm; (2) the method uses the sparse structure of shallow-water acoustic channel, and the l1-norm of the channel impulse response is chosen as the cost function; eq. (13) is worth noticing. Simulation results, presented in Figs. 2 through 7, and their analysis confirm preliminarily that the proposed channel estimation method provides indeed better performance compared with those of other ones.
Original language | English |
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Pages (from-to) | 115-121 |
Number of pages | 7 |
Journal | Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University |
Volume | 31 |
Issue number | 1 |
State | Published - Feb 2013 |
Keywords
- Algorithms
- Channel estimation
- Computer simulation
- Constrained optimization
- Convex optimination
- Cost functions
- Errors
- Impulse response
- Iterative methods
- Least squares approximations
- Low signal to noise ratio
- Mathematical models
- Multipath propagation
- Set theory
- Signal processing
- Signal to noise ration
- Sparse channel
- Time delay