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首页> 外文会议>IEEE Conference on Decision and Control >Environmental Boundary Tracking and Estimation Using Multiple Autonomous Vehicles
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Environmental Boundary Tracking and Estimation Using Multiple Autonomous Vehicles

机译：使用多个自主车辆的环境边界跟踪和估计

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摘要

In this paper, we develop a framework for environmental boundary tracking and estimation by considering the boundary as a hidden Markov model (HMM) with separated observations collected from multiple sensing vehicles. For each vehicle, a tracking algorithm is developed based on Page's cumulative sum algorithm (CUSUM), a method for change-point detection, so that individual vehicles can autonomously track the boundary in a density field with measurement noise. Based on the data collected from sensing vehicles and prior knowledge of the dynamic model of boundary evolvement, we estimate the boundary by solving an optimization problem, in which prediction and current observation are considered in the cost function. Examples and simulation results are presented to verify the efficiency of this approach.
机译：在本文中，我们通过将边界视为隐藏的马尔可夫模型（HMM）来开发环境边界跟踪和估计的框架，其分离观察来自多种传感车辆。对于每个车辆，基于页面的累积和算法（CUSUM），一种跟踪算法，一种用于改变点检测的方法，使得单个车辆可以自主地跟踪具有测量噪声的密度场中的边界。基于从传感车辆收集的数据和边界演变的动态模型的先验知识，通过解决优化问题来估计边界，在该优化问题中，在成本函数中考虑预测和电流观察。提出了示例和仿真结果以验证这种方法的效率。

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