基于半张量积压缩感知的粮情信息采集

doi:10.13229/j.cnki.jdxbgxb20191098

摘要/Abstract

摘要：

针对粮库中现有粮情信息在线监控中存在的数据规模庞大、数据传输及存储压力大等问题，提出了一种基于半张量积的粮情信息压缩采集方法。该方法利用半张量积压缩感知模型可以进行分组重构的方式，采用基于L1范数的迭代重加权重构方法，通过对整个粮仓温度信息进行整体压缩采样，在接收端进行迭代重加权分组重构，既保证了重构的精度，也提升了重构的速度。与已有报道的基于OMP算法等的压缩感知粮情温度采集信息处理进行了对比，实验结果表明，本文方法在保证重构精度的前提下，可明显提升重构的实时性，实现了对粮情信息的高精度实时重构。

关键词: 信号与信息处理, 压缩感知, 半张量积, 迭代重加权, 粮情信息

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

中图分类号:

TS120

金心宇,谢慕寒,孙斌. 基于半张量积压缩感知的粮情信息采集[J]. 吉林大学学报(工学版), 2021, 51(1): 379-385.

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.

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参考文献 15

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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