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Position Estimation in Uncertain Radio Environments and Trajectory Learning

機(jī)譯：不確定無(wú)線電環(huán)境和軌跡學(xué)習(xí)中的位置估計(jì)

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摘要

To infer the hidden states from the noisy observations and make predictions based on a set of input states and output observations are two challenging problems in many research areas. Examples of applications many include position estimation from various measurable radio signals in indoor environments, self-navigation for autonomous cars, modeling and predicting of the traffic flows, and flow pattern analysis for crowds of people. In this thesis, we mainly use the Bayesian inference framework for position estimation in an indoor environment, where the radio propagation is uncertain. In Bayesian inference framework, it is usually hard to get analytical solutions. In such cases, we resort to Monte Carlo methods to solve the problem numerically. In addition, we apply Bayesian nonparametric modeling for trajectory learning in sport analytics. The main contribution of this thesis is to propose sequential Monte Carlo methods, namely particle filtering and smoothing, for a novel indoor positioning framework based on proximity reports. The experiment results have been further compared with theoretical bounds derived for this proximity based positioning system. To improve the performance, Bayesian non-parametric modeling, namely Gaussian process, has been applied to better indicate the radio propagation conditions. Then, the position estimates obtained sequentially using filtering and smoothing are further compared with a static solution, which is known as fingerprinting. Moreover, we propose a trajectory learning framework for flow estimation in sport analytics based on Gaussian processes. To mitigate the computation deficiency of Gaussian process, a grid-based on-line algorithm has been adopted for real-time applications. The resulting trajectory modeling for individual athlete can be used for many purposes, such as performance prediction and analysis, health condition monitoring, etc. Furthermore, we aim at modeling the flow of groups of athletes, which could be potentially used for flow pattern recognition, strategy planning, etc.
機(jī)譯：要從嘈雜的觀測(cè)值中推斷出隱藏狀態(tài)并基于一組輸入狀態(tài)和輸出觀測(cè)值進(jìn)行預(yù)測(cè)，是許多研究領(lǐng)域面臨的兩個(gè)難題。許多應(yīng)用示例包括室內(nèi)環(huán)境中各種可測(cè)量無(wú)線電信號(hào)的位置估計(jì)，自動(dòng)駕駛汽車的自導(dǎo)航，交通流的建模和預(yù)測(cè)以及人群的流型分析。在本文中，我們主要使用貝葉斯推斷框架進(jìn)行無(wú)線電傳播不確定的室內(nèi)環(huán)境中的位置估計(jì)。在貝葉斯推理框架中，通常很難獲得解析解。在這種情況下，我們求助于蒙特卡洛方法以數(shù)值方式解決該問(wèn)題。此外，我們將貝葉斯非參數(shù)建模應(yīng)用于運(yùn)動(dòng)分析中的軌跡學(xué)習(xí)。本文的主要貢獻(xiàn)是針對(duì)基于鄰近報(bào)告的新型室內(nèi)定位框架提出了順序蒙特卡羅方法，即粒子濾波和平滑。實(shí)驗(yàn)結(jié)果已與該基于接近度的定位系統(tǒng)得出的理論界限進(jìn)行了進(jìn)一步比較。為了提高性能，已應(yīng)用貝葉斯非參數(shù)建模（即高斯過(guò)程）來(lái)更好地指示無(wú)線電傳播條件。然后，將使用濾波和平滑順序獲得的位置估計(jì)值進(jìn)一步與靜態(tài)解決方案（稱為指紋識(shí)別）進(jìn)行比較。此外，我們提出了一種基于高斯過(guò)程的運(yùn)動(dòng)分析中流量估計(jì)的軌跡學(xué)習(xí)框架。為了減輕高斯過(guò)程的計(jì)算缺陷，實(shí)時(shí)應(yīng)用中采用了基于網(wǎng)格的在線算法。由此產(chǎn)生的單個(gè)運(yùn)動(dòng)員的軌跡建?？梢杂糜谠S多目的，例如性能預(yù)測(cè)和分析，健康狀況監(jiān)視等。此外，我們的目標(biāo)是對(duì)運(yùn)動(dòng)員群體的流程進(jìn)行建模，可以將其潛在地用于流程模式識(shí)別，戰(zhàn)略計(jì)劃等

著錄項(xiàng)

  • 作者

    Zhao, Yuxin;

  • 作者單位
  • 年度 2017
  • 總頁(yè)數(shù)
  • 原文格式 PDF
  • 正文語(yǔ)種 eng
  • 中圖分類

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