论文标题
使用时间延迟测量的智能预测系统用于移动源本地化
An Intelligent Prediction System for Mobile Source Localization Using Time Delay Measurements
论文作者
论文摘要
在本文中，我们介绍了一种智能预测系统，用于工业互联网中的移动源本地化。通过使用时间延迟（TD）测量在智能系统中共同预测移动源的位置和速度。为了预测位置和速度，放松的半准编程（RSDP）算法首先是通过删除排名一号约束来设计的。但是，放弃排名一的约束导致产生次优的解决方案。为了提高性能，我们进一步提出了惩罚函数半准编程（PF-SDP）方法，以通过引入惩罚条款来获得优化问题的排名第一解决方案。然后，还提出了自适应惩罚函数半明确编程（APF-SDP）算法，以通过自适应选择罚款系数来避免过度罚款。我们在模拟环境和实际系统中进行实验，以证明所提出的方法的有效性。结果表明，所提出的智能APF-SDP算法在位置和速度估计方面优于PF-SDP，是否噪声水平是否较大。
In this paper, we introduce an intelligent prediction system for mobile source localization in industrial Internet of things. The position and velocity of mobile source are jointly predicted by using Time Delay (TD) measurements in the intelligent system. To predict the position and velocity, the Relaxed Semi-Definite Programming (RSDP) algorithm is firstly designed by dropping the rank-one constraint. However, dropping the rank-one constraint leads to produce a suboptimal solution. To improve the performance, we further put forward a Penalty Function Semi-Definite Programming (PF-SDP) method to obtain the rank-one solution of the optimization problem by introducing the penalty terms. Then an Adaptive Penalty Function Semi-Definite Programming (APF-SDP) algorithm is also proposed to avoid the excessive penalty by adaptively choosing the penalty coefficient. We conduct experiments in both a simulation environment and a real system to demonstrate the effectiveness of the proposed method. The results have demonstrated that the proposed intelligent APF-SDP algorithm outperforms the PF-SDP in terms of the position and velocity estimation whether the noise level is large or not.
