基于半张量积压缩感知的粮情信息采集

doi:10.13229/j.cnki.jdxbgxb20191098

Abstract

Abstract:

In the conventional sampling ways， the cost in data transmission and storage is very high. To reduce the transmission and storage cost of the temperature， a novel compressed sensing system based on semi-tensor product （STP） is proposed. First， a low-dimensional random matrix is generated to globally sample the original data. Then a grouping reconstruction method is proposed to obtain the solution with the iteratively re-weighted least-square （IRLS） algorithm. Numerical results show that the proposed system outperforms conventional way in speed of reconstruction and its comparable quality of reconstruction， which is important for real-time applications.

Key words: signal and information processing, compressed sensing, semi-tensor product, iteratively re-weighted least squares, grain information

CLC Number:

TS120

Xin-yu JIN,Mu-han XIE, SUN-Bin. Grain information compressed sensing based on semi-tensor product approach[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(1): 379-385.

Figures/Tables 4

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Table 1

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仓房	m/n	本文方法		文献[3]方法
仓房	m/n	均方误差	重构时间/s	均方误差	重构时间/s
1	0.5	0.1767	0.3120	0.2335	0.9360
	0.375	0.2749	0.2496	0.5276	0.3120
	0.25	0.3543	0.1872	0.7661	0.2496
2	0.5	0.0891	0.4992	0.1712	1.0608
	0.375	0.1630	0.2652	0.2891	0.3744
	0.25	0.2878	0.1872	0.4424	0.1872
3	0.5	0.0410	0.4524	0.1526	0.9984
	0.375	0.1753	0.3276	0.2860	0.3900
	0.25	0.2933	0.2496	0.4226	0.2028
4	0.5	0.0700	0.4524	0.1842	1.0764
	0.375	0.1713	0.3276	0.3302	0.3120
	0.25	0.3005	0.1872	0.5900	0.2652
5	0.5	0.0934	0.3900	0.1042	0.9204
	0.375	0.1676	0.2652	0.3733	0.3900
	0.25	0.2672	0.1872	0.5062	0.2028
6	0.5	0.0471	0.3900	0.1112	0.9984
	0.375	0.1777	0.3120	0.2767	0.3744
	0.25	0.3691	0.2496	0.5912	0.2028
7	0.5	0.0660	0.3744	0.1065	1.0608
	0.375	0.1335	0.2496	0.3093	0.4212
	0.25	0.2778	0.1872	0.5038	0.1404
8	0.5	0.0501	0.3900	0.1117	1.0296
	0.375	0.1772	0.3120	0.2767	0.3900
	0.25	0.2405	0.2496	0.6471	0.2652
9	0.5	0.0778	0.4524	0.1311	0.9360
	0.375	0.1740	0.2496	0.5114	0.3120
	0.25	0.3507	0.2496	0.6008	0.1872
10	0.5	0.0549	0.3120	0.1839	1.0140
	0.375	0.1335	0.3120	0.2949	0.3900
	0.25	0.4067	0.1872	0.5136	0.3276
11	0.5	0.0554	0.3744	0.1125	1.0140
	0.375	0.1630	0.2496	0.4906	0.4524
	0.25	0.3391	0.1872	0.5127	0.2028
12	0.5	0.0572	0.3900	0.1035	0.9516
	0.375	0.1582	0.3276	0.2903	0.3120
	0.25	0.3044	0.2496	0.4698	0.2652
13	0.5	0.0483	0.3120	0.1010	1.1388
	0.375	0.1209	0.3000	0.2175	0.4524
	0.25	0.3524	0.2496	0.5270	0.2496

Grain information compressed sensing based on semi-tensor product approach

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