基于混合整数二阶锥规划的TDOA目标跟踪动态传感器配对
Dynamic Sensor Pairing for TDOA-Based Target Tracking via mixed-integer Second-Order Cone Programming
浏览论文内容
中文总结 AI 辅助
提出一种基于TDOA测量的动态传感器配对方法，通过在每个时间步求解混合整数二阶锥规划来最大化FIM准则，在通信度约束下实现全局最优配对，从而降低目标跟踪误差。
中文摘要 AI 辅助
我们提出一种基于到达时间差（TDOA）测量的传感器网络在线目标跟踪传感器配对方法。现有配对设计预先固定活跃传感器对。然而，在在线跟踪中，根据当前目标估计调整配对可保持其周围有利的传感器-目标几何构型。我们在每个时间步选择K对传感器，以在通信度约束下最大化Fisher信息矩阵（FIM）准则。原始组合问题被转化为混合整数二阶锥规划。我们在每个时间步求解该问题以获得全局最优配对。在各种噪声设置下的实验表明，在相同通信度约束下，所提方法在跟踪误差方面优于其他方法。
英文摘要
We propose a sensor pairing method for online target tracking based on time-difference-of-arrival (TDOA) measurements through a sensor network. Existing pairing designs fix active sensor pairs in advance. In online tracking, however, adapting the pairing to the current target estimate maintains a favorable sensor-target geometry around it. We select $K$ pairs at each time step to maximize a Fisher information matrix (FIM) criterion under a communication-degree constraint. The original combinatorial problem is cast as a mixed-integer second-order cone program. We solve it at every time step to obtain a globally optimal pairing. Experiments under various noise settings show that the proposed method obtains the lowest tracking error among the methods under the same communication-degree constraint.
