基于联合稀疏模型的无线传感网数据重构算法

doi:10.11959/j.issn.1000-436x.2016269

摘要/Abstract

摘要：

无线传感器网络中数据具有较强联合稀疏特性，应用压缩感知理论，通过联合编码压缩数据，再使用联合解码进行还原，可实现低采样代价收集传感数据。提出了一种基于联合稀疏模型与压缩感知理论的同步子空间追踪算法，以稀疏特性为先验知识，通过回溯迭代方式，判断并选取合适的联合子空间，用更少量观测值实现原始传感数据的精确重构。与SCoSaMP算法、SP算法在不同稀疏特性和不同采样率下相比较，同步子空间追踪算法具有较好的恢复性能。

关键词: 无线传感器网络, 压缩感知, 联合稀疏模型, 数据重构

Abstract:

The data of wireless sensor network has strong joint sparse characteristics,by utilizing compressed sensing theory,compressed data by joint encoding,and then reconstructed the data by joint decoding,the sensed data can be gathered with low computational cost.A synchronous subspace pursuit algorithm based on joint sparse model and com-pressed sensing theory was proposed.By utilizing the sparsity of the sensed data,it selected the correct joint subspace and reconstruct the original signal group accurately with fewer observations in a backtracking iterative manner.Com-pared with SCoSaMP algorithm and SP algorithm,the proposed algorithm presents better data reconstruction perform-ance under the conditions of different sparsity and sampling rate.

Key words: wireless sensor network, compressive sensing, joint sparse model, data reconstruction

刘义颖,李国瑞,田丽. 基于联合稀疏模型的无线传感网数据重构算法[J]. 通信学报, 2016, 37(Z1): 211-218.

Yi-yin LIU,Guo-rui LI,Li TIAN. Joint sparse model based data reconstruction algorithm for wireless sensor network[J]. Journal on Communications, 2016, 37(Z1): 211-218.

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			SNR/dB
传感数据	观测值
传感数据	观测值
		SCoSaMP	SP	SSP
4	20	32.8	41.6	41.6
4	35	148.4	153.0	153.0
4	60	155.8	157.0	157.0
4	75	155.9	157.2	157.2
20	60	11.7	48.6	48.6
20	80	138.3	153.4	153.4
20	100	150.0	153.6	153.6
20	120	152.1	153.8	153.8
36	100	2.0	106.1	106.1
36	120	136.7	151.9	151.9
36	140	147.6	152.5	152.5
36	160	149.8	152.6	152.6

			还原时间/s
传感数据秩	观测值
传感数据秩	观测值
		SCoSaMP	SP	SSP
4	20	0.016	0.312	0.009
4	35	0.015	0.328	0.015
4	60	0.016	0.343	0.016
4	75	0.016	0.359	0.015
20	60	0.016	0.687	0.016
20	80	0.016	1.138	0.016
20	100	0.015	0.968	0.015
20	120	0.016	0.827	0.016
36	100	0.016	1.513	0.015
36	120	0.031	1.357	0.032
36	140	0.047	1.419	0.047
36	160	0.031	1.404	0.047