基于格拉布斯準則和改進粒子濾波算法的水下傳感網(wǎng)目標跟蹤

張穎; 高靈君

doi:10.11999/JEIT190079

基于格拉布斯準則和改進粒子濾波算法的水下傳感網(wǎng)目標跟蹤

doi: 10.11999/JEIT190079

張穎^,,
高靈君

上海海事大學(xué)信息工程學(xué)院上海 201306

基金項目: 國家自然科學(xué)基金(61673259)

詳細信息

作者簡介:
張穎：男，1968年生，博士，教授，博士生導(dǎo)師，研究方向為物聯(lián)網(wǎng)、海事無線通信、無線自組織網(wǎng)絡(luò)

高靈君：女，1994年生，碩士生，研究方向為物聯(lián)網(wǎng)信息融合，無線傳感網(wǎng)絡(luò)目標跟蹤、預(yù)測

通訊作者:
張穎　yingzhang@shmtu.edu.cn

中圖分類號: TP393
計量
- 文章訪問數(shù): 1770
- HTML全文瀏覽量: 681
- PDF下載量: 96
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-01-28
- 修回日期: 2019-08-29
- 網(wǎng)絡(luò)出版日期: 2019-09-03
- 刊出日期: 2019-10-01

Target Tracking with Underwater Sensor Networks Based on Grubbs Criterion and Improved Particle Filter Algorithm

Ying ZHANG^,,
Lingjun GAO

College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China

Funds: The National Natural Science Foundation of China (61673259)

摘要

摘要: 水下無線傳感網(wǎng)絡(luò)(UWSN)執(zhí)行目標跟蹤時，因為各個傳感器節(jié)點測量值對目標狀態(tài)估計的貢獻不一樣以及節(jié)點能量有限，所以探索一種好的節(jié)點融合權(quán)重方法和節(jié)點規(guī)劃機制能夠獲得更好的跟蹤性能。針對上述問題，該文提出一種基于Grubbs準則和互信息熵加權(quán)融合的分布式粒子濾波(PF)目標跟蹤算法(GMIEW)。首先利用Grubbs準則對傳感器節(jié)點所獲得的信息進行分析檢驗，去除干擾信息和錯誤信息。其次，在粒子濾波的重要性權(quán)值計算的過程中，引入動態(tài)加權(quán)因子，采用傳感器節(jié)點的測量值與目標狀態(tài)之間的互信息熵，來反映傳感器節(jié)點提供的目標信息量，從而獲得各個節(jié)點相應(yīng)的加權(quán)因子。最后，采用3維場景下的簇-樹型網(wǎng)絡(luò)拓撲結(jié)構(gòu)，跟蹤監(jiān)測區(qū)域內(nèi)的目標。實驗結(jié)果顯示，該算法可有效提高水下傳感器網(wǎng)絡(luò)測量數(shù)據(jù)對目標跟蹤預(yù)測的準確度，降低跟蹤誤差。
- 水下無線傳感器網(wǎng)絡(luò) /
- 目標跟蹤 /
- Grubbs準則 /
- 互信息熵 /
- 粒子濾波
Abstract: When the Underwater Wireless Sensor Network (UWSN) performs target tracking, the contributions of the measured values of the nodes are different, and the battery energy carried by the sensor node is limited. Therefore, a good node fusion weight method and node planning mechanism can obtain better tracking performance. A distributed particle filter target tracking algorithm based on Grubbs criterion and Mutual Information Entropy Weighted (GMIEW) fusion is proposed to solve the above problem in this paper. Firstly, the Grubbs criterion is used to analyze and verify the information obtained by the sensor nodes before the information fusion, and the interference information and error information are removed. Secondly, in the process of calculating the importance weight of particle filter, the dynamic weighting factor is introduced. The mutual information entropy between the measured value of the sensor node and the target state is used to reflect the amount of target information provided by the sensor node, so as to obtain the corresponding weighting factor of each node. Finally, the improved cluster-tree network topology is used to track the target in three-dimensional space. Simulation results show that the proposed algorithm improves greatly the accuracy of underwater sensor measurement data for target tracking prediction and reduces the tracking error.
- Underwater Wireless Sensor Network (UWSN) /
- Target tracking /
- Grubbs criterion /
- Mutual information entropy /
- Particle Filtering (PF)

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圖 1 水下傳感器網(wǎng)絡(luò)模型

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圖 2 3維網(wǎng)絡(luò)仿真場景

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圖 3 觀測噪聲為0.36, 3種算法的不同跟蹤軌跡

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圖 5 觀測噪聲為2.00, 3種算法的不同跟蹤軌跡

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圖 7 觀測噪聲為5.00, 3種算法的不同跟蹤軌跡

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圖 4 觀測噪聲為0.36, 3種不同算法的平均位置RMSE

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圖 6 觀測噪聲為2.00, 3種不同算法的平均位置RMSE

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圖 8 觀測噪聲為5.00, 3種不同算法的平均位置RMSE

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圖 9 小區(qū)域數(shù)量為4, 3種不同算法的平均位置RMSE

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圖 11 小區(qū)域數(shù)量為16, 3種不同算法的平均位置RMSE

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圖 12 不同小區(qū)域數(shù)量下3種不同算法的能量損耗

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圖 10 小區(qū)域數(shù)量為8, 3種不同算法的平均位置RMSE

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表 1 目標跟蹤算法仿真中的參數(shù)設(shè)置

仿真參數(shù)	數(shù)值
目標初始位置(m)	(0, 60, 80)
目標初始加速度(m/s²)	(5, 6, –1)
粒子數(shù)(個)	2000
聲音傳感器密度ρ(個/m³)	0.00008
仿真次數(shù)(次)	50
目標初始速度(m/s)	(15, –20, 4)
采樣間隔(s)	1
監(jiān)測時長(s)	20
過程噪聲方差	0.1
目標信號能量S	5000

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表 2 3種算法的平均跟蹤反應(yīng)時間

跟蹤算法	平均跟蹤反應(yīng)時間(s)
AW	0.1451
AHPW	0.3857
GMIEW	0.5046

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表 3 3D仿真場景中不同傳感器密度ρ下3種算法的平均位置RMSE(個/m³)

算法	0.00006	0.00008	0.00010
AW	3.6452	1.0442	0.9236
AHPW	2.6235	0.8940	0.5024
GMIEW	1.5261	0.5023	0.3026

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