基于位置指纹的WLAN室内定位技术研究
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摘要
随着无线通信和普适计算的发展，基于位置的服务变得多种多样，能够为目标定位、紧急救援、交通管理等提供精确的定位信息。但是由于信号衰减、复杂无线传播环境等因素的影响，室外定位方法在室内环境的应用受限，所以人们开发了多种室内定位系统。目前，由于无线局域网(Wireless Local Area Network, WLAN)的接收信号强度(Received Signal Strength, RSS)易于测量和其接入点布置广泛，基于位置指纹的WLAN室内定位技术已经成为普适计算中位置感知领域的研究热点。但由于在室内环境下存在多种因素影响定位精度并且WLAN位置指纹定位技术的一些关键环节还有待深入研究，所以为室内用户提供实时和精确的定位结果仍然面临一些挑战。
通过对基于位置指纹的WLAN室内定位技术的深入研究和对该领域国内外研究和发展现状的分析，目前WLAN位置指纹定位技术主要存在以下几个方面的问题：首先，通常在一个位置可以采集多个RSS样本，但大多数位置指纹定位算法仅利用多个RSS样本的均值定位，对RSS样本的利用效率较低。其次，对于移动用户定位，滤波算法可以利用相邻定位结果之间的空间相近性等信息提高定位精度。但是，已有的线性滤波算法能力有限而非线性滤波算法通常计算复杂度较高。最后，建立位置指纹图的工作量通常较大，并且建立后需要根据室内无线传播环境变化而对其更新从而保证较高的定位精度。因此，针对上述WLAN位置指纹定位存在的问题，本文的主要工作和创新点如下：
第一，介绍和比较已有室内定位系统的定位原理和特点，在分析各个室内定位系统的基础上，说明WLAN位置指纹定位技术的特点及其被广泛研究的原因。然后，总结WLAN位置指纹定位技术中的位置指纹定位算法、滤波算法和位置指纹图更新这些关键环节的国内外研究发展现状和目前存在的问题。最后，详细介绍和分析几种典型的位置指纹定位算法和滤波算法，为后续章节的研究奠定理论基础。
第二，针对现有位置指纹定位算法利用RSS均值样本定位、RSS样本利用效率低的问题，提出基于RSS矩阵相关的位置指纹定位算法。该算法将在线采集的所有RSS样本排列成矩阵并将参考点的RSS变化程度作为权值计算在线RSS矩阵和参考点RSS矩阵之间的相关系数，根据相关系数选择近邻参考点计算定位结果，可达到比经典近邻选择算法更高的定位精度。在该算法的基础上，针对不同应用场景，提出基于离线和在线计算的两种快速RSS矩阵相关算法。提出的快速矩阵相关算法可有效降低RSS矩阵相关系数的计算复杂度，使该算法更适合实际应用。
第三，当前用于处理位置指纹定位结果的Kalman滤波算法采用线性预测模型预测用户状态，这限制了Kalman滤波算法的性能。针对这个问题，提出基于地图匹配的Kalman滤波算法。首先，根据获得的室内地图信息提出室内地图匹配算法。该算法利用室内地图矩阵，实现对建筑结构、人们活动区域和活动路径的识别。根据建筑结构，将不合理的定位结果修正到代表人们活动路径的连接模型上，从而提高定位精度。并在此基础上，将地图匹配算法与Kalman滤波算法相结合，利用室内地图信息和相邻定位结果之间的空间相近性修正定位结果，可大幅度提高定位精度。
第四，针对位置指纹图建立的工作量大并且需要根据室内无线传播环境变化而更新的问题，提出一种基于人工神经网络的定位误差修正算法降低静态位置指纹图的影响。该算法首先实时采集少量标记位置坐标的RSS样本并利用近邻选择位置指纹定位算法计算定位坐标和误差，然后利用人工神经网络融合RSS样本和定位坐标并将其作为人工神经网络的输入，建立输入与输出定位误差之间的非线性映射关系。同时利用遗传算法和反向传播算法优化该人工神经网络模型。在线定位时，利用已训练的人工神经网络模型估计定位误差，并根据定位误差修正定位坐标。该基于人工神经网络的定位误差修正算法可以有效降低静态位置指纹图和参考点分布对定位精度造成的影响，提高定位性能。另外，用于训练该定位误差估计模型的RSS样本数量远少于位置指纹图中RSS样本的数量，因此与更新位置指纹图中全部RSS样本相比，该算法可大幅度降低RSS样本采集的工作量。
With the development of wireless communications and pervasive computing, various location-based services have sprung up, which can offer accurate localization information for object localization, emergency rescue, traffic management etc. Outdoor localization systems are limited in indoor environments owing to signal attenuation and complex radio propagation, so numerous indoor localization systems have been developed. Currently, because received signal strength (RSS) samples are easily collected from pervasively deployed access points by commonly used wireless local area network (WLAN) mobile terminals without additional hardware being required, location fingerprint-based indoor localization using WLAN is specially preferred and extensively researched in location sensing of pervasive computing. However, localization errors are caused by various reasons in complex indoor radio propagation environments as well as some key techniques in location fingerprint-based WLAN localization also need further research, so offering accurate and real-time localization results for indoor users is still a challenging task.
Based on the research on location fingerprint-based WLAN localization and analyses on its development in China and abroad given in this dissertation, the problems existed in location fingerprint-based WLAN localization are summarized as follows. First, usually multiple on-line RSS samples are collected at one location, but most of fingerprinting algorithms use RSS mean samples for localization, which fails to make full use of all the available on-line RSS samples. Additionally, regarding mobile user localization, through employing information like spatial proximities of consecutive localization results, filtering algorithms can process localization results of fingerprinting algorithms for accuracy improvement. But existing linear filtering algorithms have limited accuracy and nonlinear filtering algorithms are computationally expensive. Finally, the conventional establishment process of radio-map is burdensome and time-consuming and the established one also needs to be updated according to variations of indoor radio propagation. Thus, based on the problems mentioned above, the main work and creative points of this dissertation are listed as follows.
First, based on the analyses and comparisons of existing indoor localization systems, the reasons why location fingerprint-based WLAN localization has been widely researched and its characteristics are illustrated. Then the development and drawbacks of several key components of location fingerprint-based WLAN localization are researched. Finally, classic fingerprinting algorithms and filtering algorithms that are used for indoor localization are introduced and analyzed.
Second, in the view of the fact the most of fingerprinting algorithms compute localization results with RSS mean samples, which fails to make full use of all the available on-line RSS samples, an RSS matrix correlation fingerprinting algorithm is proposed to make use of all the on-line RSS samples and incorporate RSS variations of reference points as weights into correlation coefficient computations for accurate neighbor selection. So the proposed algorithm outperforms the classic neighbor selection algorithms. Moreover, two fast matrix correlation algorithms based on off-line and on-line computations are proposed to compute correlation coefficients for different application conditions. They are able to effectively reduce the computational complexity and therefore more suitable for practical applications.
Third, the existing Kalman filtering that is computationally efficient predicts user state with a linear model, which limits its performance. Thus, a map matching-based Kalman filtering is proposed. Using indoor map information, a map matching algorithm is developed. The building structure, human activity area and a link model that represents human walkways are recognized with a created map image matrix and then unreasonable localization results are corrected to the link model. With the integration of the map matching algorithm into Kalman filtering, localization results are corrected using indoor map information and spatial proximities of consecutive localization results, so the localization performance is greatly increased.
Last but not least, because radio-map establishment is a burdensome and time-consuming process needed for collecting off-line RSS data at reference points and the radio-map also needs to be updated according to variations of radio propagation, an artificial neural network-based error correction algorithm is proposed to solve this problem. Some newly collected RSS samples with location information are used to compute localization results and errors with neighbor selection-based fingerprinting algorithms for the artificial neural network training. Then the RSS samples and localization results are fused by the artificial neural network as its inputs and nonlinear relationship between the inputs and its outputs localization errors is modeled. The network is also optimized by genetic and backpropagation algorithms. In the on-line phase, localization errors estimated by the artificial neural network are used to correct localization results. Then negative influence of static radio-map and reference point distribution can be effectively reduced and therefore localization accuracy is increased. Meanwhile, the RSS data for training the artificial neural network is easily collected with reduced effort, which can avoid updating the whole radio-map.
通过对基于位置指纹的WLAN室内定位技术的深入研究和对该领域国内外研究和发展现状的分析，目前WLAN位置指纹定位技术主要存在以下几个方面的问题：首先，通常在一个位置可以采集多个RSS样本，但大多数位置指纹定位算法仅利用多个RSS样本的均值定位，对RSS样本的利用效率较低。其次，对于移动用户定位，滤波算法可以利用相邻定位结果之间的空间相近性等信息提高定位精度。但是，已有的线性滤波算法能力有限而非线性滤波算法通常计算复杂度较高。最后，建立位置指纹图的工作量通常较大，并且建立后需要根据室内无线传播环境变化而对其更新从而保证较高的定位精度。因此，针对上述WLAN位置指纹定位存在的问题，本文的主要工作和创新点如下：
第一，介绍和比较已有室内定位系统的定位原理和特点，在分析各个室内定位系统的基础上，说明WLAN位置指纹定位技术的特点及其被广泛研究的原因。然后，总结WLAN位置指纹定位技术中的位置指纹定位算法、滤波算法和位置指纹图更新这些关键环节的国内外研究发展现状和目前存在的问题。最后，详细介绍和分析几种典型的位置指纹定位算法和滤波算法，为后续章节的研究奠定理论基础。
第二，针对现有位置指纹定位算法利用RSS均值样本定位、RSS样本利用效率低的问题，提出基于RSS矩阵相关的位置指纹定位算法。该算法将在线采集的所有RSS样本排列成矩阵并将参考点的RSS变化程度作为权值计算在线RSS矩阵和参考点RSS矩阵之间的相关系数，根据相关系数选择近邻参考点计算定位结果，可达到比经典近邻选择算法更高的定位精度。在该算法的基础上，针对不同应用场景，提出基于离线和在线计算的两种快速RSS矩阵相关算法。提出的快速矩阵相关算法可有效降低RSS矩阵相关系数的计算复杂度，使该算法更适合实际应用。
第三，当前用于处理位置指纹定位结果的Kalman滤波算法采用线性预测模型预测用户状态，这限制了Kalman滤波算法的性能。针对这个问题，提出基于地图匹配的Kalman滤波算法。首先，根据获得的室内地图信息提出室内地图匹配算法。该算法利用室内地图矩阵，实现对建筑结构、人们活动区域和活动路径的识别。根据建筑结构，将不合理的定位结果修正到代表人们活动路径的连接模型上，从而提高定位精度。并在此基础上，将地图匹配算法与Kalman滤波算法相结合，利用室内地图信息和相邻定位结果之间的空间相近性修正定位结果，可大幅度提高定位精度。
第四，针对位置指纹图建立的工作量大并且需要根据室内无线传播环境变化而更新的问题，提出一种基于人工神经网络的定位误差修正算法降低静态位置指纹图的影响。该算法首先实时采集少量标记位置坐标的RSS样本并利用近邻选择位置指纹定位算法计算定位坐标和误差，然后利用人工神经网络融合RSS样本和定位坐标并将其作为人工神经网络的输入，建立输入与输出定位误差之间的非线性映射关系。同时利用遗传算法和反向传播算法优化该人工神经网络模型。在线定位时，利用已训练的人工神经网络模型估计定位误差，并根据定位误差修正定位坐标。该基于人工神经网络的定位误差修正算法可以有效降低静态位置指纹图和参考点分布对定位精度造成的影响，提高定位性能。另外，用于训练该定位误差估计模型的RSS样本数量远少于位置指纹图中RSS样本的数量，因此与更新位置指纹图中全部RSS样本相比，该算法可大幅度降低RSS样本采集的工作量。
With the development of wireless communications and pervasive computing, various location-based services have sprung up, which can offer accurate localization information for object localization, emergency rescue, traffic management etc. Outdoor localization systems are limited in indoor environments owing to signal attenuation and complex radio propagation, so numerous indoor localization systems have been developed. Currently, because received signal strength (RSS) samples are easily collected from pervasively deployed access points by commonly used wireless local area network (WLAN) mobile terminals without additional hardware being required, location fingerprint-based indoor localization using WLAN is specially preferred and extensively researched in location sensing of pervasive computing. However, localization errors are caused by various reasons in complex indoor radio propagation environments as well as some key techniques in location fingerprint-based WLAN localization also need further research, so offering accurate and real-time localization results for indoor users is still a challenging task.
Based on the research on location fingerprint-based WLAN localization and analyses on its development in China and abroad given in this dissertation, the problems existed in location fingerprint-based WLAN localization are summarized as follows. First, usually multiple on-line RSS samples are collected at one location, but most of fingerprinting algorithms use RSS mean samples for localization, which fails to make full use of all the available on-line RSS samples. Additionally, regarding mobile user localization, through employing information like spatial proximities of consecutive localization results, filtering algorithms can process localization results of fingerprinting algorithms for accuracy improvement. But existing linear filtering algorithms have limited accuracy and nonlinear filtering algorithms are computationally expensive. Finally, the conventional establishment process of radio-map is burdensome and time-consuming and the established one also needs to be updated according to variations of indoor radio propagation. Thus, based on the problems mentioned above, the main work and creative points of this dissertation are listed as follows.
First, based on the analyses and comparisons of existing indoor localization systems, the reasons why location fingerprint-based WLAN localization has been widely researched and its characteristics are illustrated. Then the development and drawbacks of several key components of location fingerprint-based WLAN localization are researched. Finally, classic fingerprinting algorithms and filtering algorithms that are used for indoor localization are introduced and analyzed.
Second, in the view of the fact the most of fingerprinting algorithms compute localization results with RSS mean samples, which fails to make full use of all the available on-line RSS samples, an RSS matrix correlation fingerprinting algorithm is proposed to make use of all the on-line RSS samples and incorporate RSS variations of reference points as weights into correlation coefficient computations for accurate neighbor selection. So the proposed algorithm outperforms the classic neighbor selection algorithms. Moreover, two fast matrix correlation algorithms based on off-line and on-line computations are proposed to compute correlation coefficients for different application conditions. They are able to effectively reduce the computational complexity and therefore more suitable for practical applications.
Third, the existing Kalman filtering that is computationally efficient predicts user state with a linear model, which limits its performance. Thus, a map matching-based Kalman filtering is proposed. Using indoor map information, a map matching algorithm is developed. The building structure, human activity area and a link model that represents human walkways are recognized with a created map image matrix and then unreasonable localization results are corrected to the link model. With the integration of the map matching algorithm into Kalman filtering, localization results are corrected using indoor map information and spatial proximities of consecutive localization results, so the localization performance is greatly increased.
Last but not least, because radio-map establishment is a burdensome and time-consuming process needed for collecting off-line RSS data at reference points and the radio-map also needs to be updated according to variations of radio propagation, an artificial neural network-based error correction algorithm is proposed to solve this problem. Some newly collected RSS samples with location information are used to compute localization results and errors with neighbor selection-based fingerprinting algorithms for the artificial neural network training. Then the RSS samples and localization results are fused by the artificial neural network as its inputs and nonlinear relationship between the inputs and its outputs localization errors is modeled. The network is also optimized by genetic and backpropagation algorithms. In the on-line phase, localization errors estimated by the artificial neural network are used to correct localization results. Then negative influence of static radio-map and reference point distribution can be effectively reduced and therefore localization accuracy is increased. Meanwhile, the RSS data for training the artificial neural network is easily collected with reduced effort, which can avoid updating the whole radio-map.
引文
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