部分范数约束的稀疏恢复算法及其在单载波水声数据遥测中的应用

doi:10.11959/j.issn.1000-436x.2018099

Journal on Communications ›› 2018, Vol. 39 ›› Issue (6): 127-132.doi: 10.11959/j.issn.1000-436x.2018099

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Partial-norm-constrained sparse recovery algorithm and its application on single carrier underwater-acoustic-data telemetry

Feiyun WU^1,²,Kunde YANG^1,²,Feng TONG³

¹ School of Marine Science and Technology,Northwestern Polytechnical University,Xi’an 710072,China
² Key Laboratory of Ocean Acoustics and Sensing Ministry of Industry and Information Technology,Northwestern Polytechnical University,Xi’an 710072,China
³ Key Laboratory of Underwater Acoustic Communication and Marine Information Technique of the Ministry of Education,Xiamen University,Xiamen 361005,China

Revised:2018-04-09 Online:2018-06-01 Published:2018-07-09
Supported by:
The National Natural Science Foundation of China(61701405);The Central University Basic Business Expenses Spe-cial Funding for Scientific Research Projects(3102017OQD007);China Postdoctoral Science Foundation Projects(2017M613208)

Abstract

Abstract:

To solve the problem of single carrier underwater-acoustic-data telemetry,compressive sensing (CS) provides competitive performance of compression and recovery with low energy consumption.The primary objective of CS is to minimize the l₀norm,which is an NP hard problem.Hence,the common methods were transferred to minimize l₁norm.However,l₁norm minimization provided a limited accuracy.A partial-norm-constraint (PNC) based sparse signal recovery method was derived,which adopted PNC as a zero attraction in a Lagrange method,to distribute the soft threshold for the non-zero taps.The proposed method is used for at-sea data telemetry.Combining with DCT,the proposed method improves the recovery accuracy.

Key words: compressive sensing, single carrier underwater-acoustic-data, partial-norm-constraint

CLC Number:

TN929.3

Feiyun WU,Kunde YANG,Feng TONG. Partial-norm-constrained sparse recovery algorithm and its application on single carrier underwater-acoustic-data telemetry[J]. Journal on Communications, 2018, 39(6): 127-132.

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References 17

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