{"title":"Establishment of a new improved weighted centroid position solution method in network environment","authors":"Shiwei Li","doi":"10.1145/3585967.3585971","DOIUrl":null,"url":null,"abstract":"In the proposed algorithm, the inertial navigation and the improved weighted centroid position calculation method work independently in a loose combination manner, and achieve the optimal estimation of integrated positioning by fusing the sensor information from different sources, realizing the design goal of wireless sensor network to provide absolute position information for the inertial navigation system. The experimental results show that the improved weighted centroid positioning/inertial navigation method in the network environment is better than the improved weighted centroid positioning or inertial navigation method in terms of positioning accuracy and noise, reflecting the complementary advantages of absolute positioning and relative positioning and the ability to provide high-precision coordinates in the static working environment. In theory, the CENTER OF GRAVITY (COG) algorithm uses the trilateral measurement method to realize the location of another node on the premise that the received signal strength of the three anchor nodes in the wireless sensor network is known. However, due to the uncertain component of the received signal strength of the anchor node, the location of another node cannot be completely determined in actual operation, so this paper uses some additional algorithms to ensure the feasibility of node location, such as the least squares algorithm [1] and the maximum likelihood estimation method [2].In order to control the cost, a few location-aware nodes, called anchor nodes, are deployed in the wireless sensor network environment. Mobile nodes in the network estimate their position through these anchor nodes. Therefore, this paper proposes a modified form of COG algorithm, ICOG(Improved CENTER OF GRAVITY ). The proposed algorithm adopts an anchor node position verification mechanism by observing the consistency of the received signal strength quality. The anchor nodes near the mobile node use the received signal strength to seek to verify the actual position or proximity of other anchor nodes near it. This process alleviates the multipath effect in the process of radio wave transmission, especially in the closed environment, thus effectively controlling the positioning error and uncertainty.","PeriodicalId":275067,"journal":{"name":"Proceedings of the 2023 10th International Conference on Wireless Communication and Sensor Networks","volume":"141-142 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2023 10th International Conference on Wireless Communication and Sensor Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3585967.3585971","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In the proposed algorithm, the inertial navigation and the improved weighted centroid position calculation method work independently in a loose combination manner, and achieve the optimal estimation of integrated positioning by fusing the sensor information from different sources, realizing the design goal of wireless sensor network to provide absolute position information for the inertial navigation system. The experimental results show that the improved weighted centroid positioning/inertial navigation method in the network environment is better than the improved weighted centroid positioning or inertial navigation method in terms of positioning accuracy and noise, reflecting the complementary advantages of absolute positioning and relative positioning and the ability to provide high-precision coordinates in the static working environment. In theory, the CENTER OF GRAVITY (COG) algorithm uses the trilateral measurement method to realize the location of another node on the premise that the received signal strength of the three anchor nodes in the wireless sensor network is known. However, due to the uncertain component of the received signal strength of the anchor node, the location of another node cannot be completely determined in actual operation, so this paper uses some additional algorithms to ensure the feasibility of node location, such as the least squares algorithm [1] and the maximum likelihood estimation method [2].In order to control the cost, a few location-aware nodes, called anchor nodes, are deployed in the wireless sensor network environment. Mobile nodes in the network estimate their position through these anchor nodes. Therefore, this paper proposes a modified form of COG algorithm, ICOG(Improved CENTER OF GRAVITY ). The proposed algorithm adopts an anchor node position verification mechanism by observing the consistency of the received signal strength quality. The anchor nodes near the mobile node use the received signal strength to seek to verify the actual position or proximity of other anchor nodes near it. This process alleviates the multipath effect in the process of radio wave transmission, especially in the closed environment, thus effectively controlling the positioning error and uncertainty.
在本文提出的算法中,惯性导航和改进的加权质心位置计算方法以松散组合的方式独立工作,通过融合不同来源的传感器信息来实现集成定位的最优估计,实现了无线传感器网络为惯性导航系统提供绝对位置信息的设计目标。实验结果表明,网络环境下改进的加权质心定位/惯导方法在定位精度和噪声方面都优于改进的加权质心定位或惯导方法,体现了绝对定位和相对定位的互补优势,能够在静态工作环境下提供高精度坐标。理论上,重心(CENTER OF GRAVITY, COG)算法在无线传感器网络中三个锚节点的接收信号强度已知的前提下,采用三边测量方法来实现另一个节点的位置。然而,由于锚节点接收信号强度的不确定性成分,在实际操作中无法完全确定其他节点的位置,因此本文采用了一些额外的算法来保证节点位置的可行性,如最小二乘算法[1]和最大似然估计方法[2]。为了控制成本,在无线传感器网络环境中部署了几个位置感知节点,称为锚节点。网络中的移动节点通过这些锚节点来估计自己的位置。为此,本文提出了一种改进的COG算法ICOG(Improved CENTER of GRAVITY)。该算法通过观察接收信号强度质量的一致性,采用锚节点位置验证机制。移动节点附近的锚节点使用接收到的信号强度来寻求验证其附近其他锚节点的实际位置或距离。该过程缓解了无线电波传输过程中的多径效应,特别是在封闭环境下,从而有效地控制了定位误差和不确定性。