发表机构
The University of Osaka; Kansai University（大阪大学; 关西大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对移动传感器网络目标跟踪中传感器配对与重定位分离导致的精度受限问题，提出同时求解两者的方法，通过最大化Fisher信息矩阵行列式并交替优化，降低了跟踪误差。
AI 中文摘要
我们提出了一种基于到达时间差（TDOA）的移动传感器网络（MSNs）目标跟踪方法。使用MSN进行目标跟踪需要重新组织传感器配对并重新定位传感器位置，以获得更高质量的TDOA。现有研究仅考虑其中一项而固定另一项，这可能限制跟踪精度。我们通过同时解决传感器配对和重定位来解决这一限制。我们将问题表述为Fisher信息矩阵行列式的最大化，并将问题分解为两个子问题以交替求解。传感器配对通过混合整数二阶锥规划算法求解，传感器重定位通过最小化最大化算法求解。实验结果表明，所提方法在实用计算时间内获得了比现有设计更低的跟踪误差。
英文摘要
We propose a target tracking method for mobile sensor networks (MSNs) based on time difference-of-arrival (TDOA). Target tracking using an MSN requires reorganizing sensor pairs and relocating sensor positions to obtain TDOAs with higher quality. Existing studies only consider either one while fixing the other, which may limit the tracking accuracy. We address this limitation by simultaneously solving sensor pairing and relocation. We formulate the problem as a maximization of the determinant of the Fisher information matrix, and decompose the problem into two subproblems to solve them alternately. Sensor pairing is solved by a mixed-integer second-order cone program algorithm, and sensor relocation is solved by majorization-minimization. Experimental results show that the proposed method obtains lower tracking error than existing designs with a practical computation time.
