Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (3): 32-38.doi: 10.6040/j.issn.1672-3961.0.2017.426

• Machine Learning & Data Mining • Previous Articles     Next Articles

A method of multi-sensor data fusion under the complicated environment

Mingming TIAN(),Jihua YE*(),Shimin WANG,Yejing WAN   

  1. College of Computer Information and Engineering, Jiangxi Normal University, Nanchang 330022, Jiangxi, China
  • Received:2017-08-29 Online:2019-06-20 Published:2019-06-27
  • Contact: Jihua YE E-mail:734375913@qq.com;yjhwcl@163.com
  • Supported by:
    国家自然科学基金资助项目(61462042);国家自然科学基金资助项目(61650105);江西省研究生创新专项资金资助项目(YC2016-S170)

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Abstract:

The existing methods do not use the reliability information of the source of evidence data collection, a multi-sensor data fusion algorithm based on the temperature data in complex environment was proposed. It proceeded from the sensor data source, analyzed the evidence source information, evaluated the confidence measure, and revised the conflict evidence with the confidence measure. In the evidence fusion moment, we used iterative fusion method to revise and fusion evidence until the fusion result convergence. Compared with other fusion methods, this method was effective and had better results in the question of evidence conflict.

Key words: multi-sensor, data fusion, wisdom agriculture, iteration, node confidence

CLC Number: 

  • TP23

Fig.1

Membership function of triangle-shape"

Table 1

Five sampling data of the sensor during the period"

℃
传感器 1次 2次 3次 4次 5次
1 19.32 19.16 18.79 18.44 18.94
2 18.37 19.04 19.76 19.93 18.58
3 18.92 18.70 18.68 19.43 18.05

Table 2

Original evidence and D-S results"

传感器 证据
1 (0, 0.36, 0.64) (0, 0.78, 0.32) (0.42, 0.58, 0) (1, 0, 0) (0.12, 0.88, 0)
2 (1, 0, 0) (0, 0.92, 0.08) (0.48, 0.52, 0) (0, 0, 1) (0.84, 0.16, 0)
3 (0.16, 0.84, 0) (0.6, 0.4, 0) (0.74, 0.36, 0) (0, 0.14, 0.86) (1, 0, 0)
D-S融合结果 无法计算 (0, 1, 0) (0.58, 0.42, 0) 无法计算 (1, 0, 0)

Table 3

The modified evidence and D-S results"

传感器 证据
1 (0, 0.320 4,
0.569 6, 0.11)		 (0, 0.694 2,
0.284, 0.11)		 (0.373 8, 0.516 2,
0, 0.11)		 (0.89, 0,
0, 0.11)		 (0.106 8, 0.783 2,
0, 0.11)
2 (0.66, 0,
0, 0.34)		 (0, 0.607 2, 0.052 8,
0.34)		 (0.316 8, 0.343 2,
0, 0.34)		 (0, 0, 0.66,
0.34)		 (0.554 4, 0.105 6,
0, 0.34)
3 (0.124 8, 0.655 2,
0, 0.22)		 (0.468, 0.312,
0, 0.22)		 (0.577 2, 0.280 8,
0, 0.22)		 (0, 0.109 2,
0.670 8, 0.22)		 (0.78, 0,
0, 0.22)
修正证据结果 (0.148 3, 0.598,
0.212 7, 0.041)		 (0.039, 0.885,
0.057 7, 0.018 3)		 (0.533 6, 0.448 5,
0, 0.017 9)		 (0.394 8, 0.024 2,
0.532 3, 0.048 8)		 (0.679 5, 0.290 4,
0, 0.030 1)

Table 4

Fusion results of different methods"

融合证据 D-S方法 Yager方法 王亮方法 胡海亮方法 本研究方法
m12(A, B, C, Θ) (0.02, 0.95,
0.03, 0.0012)		 (0.006, 0.53,
0.012, 0.452)		 (0.012, 0.965,
0.014)		 (0.012, 0.981,
0.009)		 (0.011, 0.983,
0.008)
m123(A, B, C, Θ) (0.025, 0.97,
0.001, 0)		 (0.003, 0.24,
0, 0.757)		 (0.021, 0.979,
0.001)		 (0.012, 0.981,
0.009)		 (0.013, 0.99,
0.0003)
m1234(A, B, C, Θ) (0.13, 0.86,
0.01, 0)		 (0.001, 0.006,
0, 0.993)		 (0.033, 0.94,
0.027)		 (0.012, 0.99,
0.0001)		 (0.02, 0.975,
0.005)
m12345(A, B, C, Θ) (0.24, 0.75,
0, 0)		 (0.000 8, 0.001 7,
0, 0.997 5)		 (0.078, 0.932,
0)		 (0.051,
0.949, 0)		 (0.032, 0.968, 0)

Table 5

Estimated results of different methods"

方法 D-S方法 王亮方法 胡海亮方法 本研究方法
结果估计 17.65 18.15 19.84 18.68
与真实值误差 1.35 0.75 0.84 0.32

Fig.2

The iterations and accuracy"

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