林维维, 姚英彪, 邹柯, 冯维, 严军荣. 基于校正矢量的分布式DV-Hop求精算法[J]. 计算机研究与发展, 2019, 56(3): 585-593.
 引用本文: 林维维, 姚英彪, 邹柯, 冯维, 严军荣. 基于校正矢量的分布式DV-Hop求精算法[J]. 计算机研究与发展, 2019, 56(3): 585-593.
Lin Weiwei, Yao Yingbiao, Zou Ke, Feng Wei, Yan Junrong. Correction Vector Based Distributed DV-Hop Localization Refinement Algorithm[J]. Journal of Computer Research and Development, 2019, 56(3): 585-593.
 Citation: Lin Weiwei, Yao Yingbiao, Zou Ke, Feng Wei, Yan Junrong. Correction Vector Based Distributed DV-Hop Localization Refinement Algorithm[J]. Journal of Computer Research and Development, 2019, 56(3): 585-593.

## Correction Vector Based Distributed DV-Hop Localization Refinement Algorithm

• 摘要: 节点定位技术是当前无线传感器网络研究的热点之一.基于跳距估计的DV-Hop(distance vector hop)定位算法是无需测距定位算法的典型代表，它具有算法简单、易实现等优点，但也存在定位模糊、定位精度不高的缺点.针对DV-Hop算法的定位模糊问题，提出一种基于校正矢量的分布式迭代求精算法(correction vector based distributed localization refinement algorithm, CVLR).在DV-Hop定位完成后，CVLR利用节点与其邻居节点间的伪测距距离和定位距离构建位置校正矢量，然后将求精过程建模为使这2个距离的差值的平方和在校正矢量方向上的最小化问题，最后用一种简单的迭代搜索算法求解该最小化问题.CVLR实现过程中，分为仅利用1跳邻居节点信息的CVLR1和同时利用1跳和2跳邻居节点信息的CVLR2.仿真结果表明：与DV-Hop，DV-RND (an improved DV-Hop localization algorithm based on regulated neighborhood distance)，DV-EA (an improved DV-Hop localization algorithm based on evolutionary algorithm)相比，CVLR1的定位精度平均提高30%，25%，20%，CVLR2的定位精度平均提高45%，42%，40%.

Abstract: Node location technology is one of the hot topics in current wireless sensor networks(WSNs). The DV-Hop (distance vector hop) localization algorithm, based on the hop distance estimation, is a typical representation of range-free localization algorithm. The advantages of DV-Hop is simple and easy implementation, and its disadvantage is low positioning accuracy which is resulting from the hop-distance ambiguity problem. Focusing on the hop-distance ambiguity problem of the traditional DV-Hop localization algorithm, this paper proposes a correction vector based distributed localization refinement algorithm (CVLR). Firstly, based on the localization results of DV-Hop, CVLR constructs the position correction vector using the pseudo ranging distance and the positioning distance between neighbors and unknown nodes. Secondly, the refinement process is modeled to minimize the square sum of the difference between the two distances in the direction of correction vector. Finally, a simple iterative search method is proposed to solve above minimization problem. In practice, CVLR consists of CVLR1 and CVLR2. CVLR1 can make full use of the information of 1-hop neighbors, and CVLR2 can make full use of the information of 1-hop and 2-hop neighbors. The simulation results show that, compared with DV-Hop, DV-RND (an improved DV-Hop localization algorithm based on regulated neighborhood distance), and DV-EA (an improved DV-Hop localization algorithm based on evolutionary algorithm), CVLR1 improves the positioning accuracy by about 30%, 25%, and 20%, and CVLR2 improves the positioning accuracy by about 45%, 42%, and 40%, on average.

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