基于信号强度的WLAN室内定位跟踪系统研究
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摘要
随着信息技术的发展,位置服务成为人们的一个重要需求。然而,成熟的GPS (Globe Positioning System,全球定位系统)不能用于室内环境。因而,室内定位需要使用新的信号。近年来WLAN (Wireless Local Access Network,无线局域网)发展迅速,各大公共场所均配备了WLAN,这使得WLAN室内定位跟踪系统变得非常有吸引力。而且,该室内定位跟踪系统使用WLAN信号强度进行定位,不需要任何其他的专用硬件,因而WLAN室内定位跟踪系统得到了越来越多的研究人员的关注,成为了最流行的室内定位跟踪系统之一。
本文对基于信号强度的WLAN室内定位系统做了一些改进,其主要贡献和创新为：
1.传统的室内定位方法采用标准高斯模型对参考点上接收到的信号强度进行建模。然而,室内环境非常复杂,存在多径干扰、频繁的人员干扰和环境变化。实际上参考点上信号强度分布并不是一个标准的高斯分布。因此,本文对参考点上信号强度分布采用多高斯混合模型进行建模。该模型不仅考虑了各种干扰的影响还考虑了各AP (Access Point,接入点)的信号强度之间的相关关系,使得该方法的性能相比之前的标准高斯分布模型大大提高。对于多高斯混合模型的参数,本文使用EM(Expectation Maximization,期望最大值)算法估计。最后的实验结果验证了本方法的有效性。
2.对于较大目标环境的室内定位系统,指纹数据库的参考点数量会非常多。因此,对指纹数据库进行聚类就变得非常必需了。本文基于自组织映射神经网络,将指纹数据库的高维元素映射到一个二维平面。之后,在这个二维平面上进行聚类。这样做的好处就在于能够避免高维空间聚类困难的问题,准确度高。最后的实验结果验证了该方法的有效性。
3.对于室内目标跟踪,一般的方法使用卡尔曼滤波器。然而室内噪声实际上并不是高斯噪声,这需要使用无迹卡尔曼滤波器或是精度更高的粒子滤波器。不幸的是粒子滤波器的算法复杂度非常大,特别是当状态超过三维的情况,已不适合于实时系统。本文将整个目标状态划分为均为二维矢量的位置分量和速度分量,对速度分量采用无迹卡尔曼滤波器估计,而对位置分量则采用粒子滤波器估计。不仅大大地提高了室内跟踪系统的系统性能,还将算法复杂度限制在可以实时使用的程度。最后的实验结果验证了本方法的有效性。
With the development of information technology, location service is becoming an important requirement of people. However, the mature GPS(Globe Positioning System) can not be applied in indoor environment. So a new signal is needed for indoor positioning. Lately WLAN(Wireless Local Area Network) develops quickly, which is equipped in big public place. It makes attractable for WLAN to be used in indoor positioning. And the indoor positioning system using WLAN do not require any other hardwares and has more and more attentions of researchers. WLAN indoor positioning system becomes one of the most popular indoor positioning systems.
This paper improves the classic WLAN indoor positioning system, and its contributions and innovation are:
1. Classic indoor positioning system uses standard Gaussian distribution to model the signal strength received by an object in reference point. However, indoor environment is complicated and changing, existing multi-path interference and mobile people interference. Hence, this paper uses a multi-Gaussian mixture model to model the signal strength distribution in reference point. This model not only considers the influence of the interference, but also considers the related relationship between signal strength of every AP(Access Point), and improves the system performance. The parameters of this method are estimated by EM(Expectation Maximization) algorithm in this paper. The experimental results prove the effectiveness of our method.
2. For indoor positioning system in a big environment, the number of reference points in fingerprint database is very big. Therefore, the clustering of fingerprint database is necessary. This paper mapped the elements of fingerprint database into a2-D plane using a self-organizing map. And the clustering is based on this2-D plane. The advantage of this method is that it can avoid the difficulty of clustering in space with high dimension, and the accuracy of clustering is high. The experimental results prove the effectiveness of our method.
3. For tracking an object in indoor environment, the general method uses a Kalman filter. However, the noise of indoor environment is not Gaussian, for tracking an object it require an unscented Kalman filter or a particle filter. Unfortunately the particle filter algorithm is complex, and it is not comfortable for a real time system with a state whose dimension is more than3. This paper divides the object state into a2-D velocity state and a2-D location state. And it uses the unscented Kalman filter to estimate the velocity state and uses the particle filter to estimate the location state. This method not only improves the performance of the indoor positioning and tracking system, but also ensures that the system is real time. The experimental results prove the effectiveness of our method.
本文对基于信号强度的WLAN室内定位系统做了一些改进,其主要贡献和创新为：
1.传统的室内定位方法采用标准高斯模型对参考点上接收到的信号强度进行建模。然而,室内环境非常复杂,存在多径干扰、频繁的人员干扰和环境变化。实际上参考点上信号强度分布并不是一个标准的高斯分布。因此,本文对参考点上信号强度分布采用多高斯混合模型进行建模。该模型不仅考虑了各种干扰的影响还考虑了各AP (Access Point,接入点)的信号强度之间的相关关系,使得该方法的性能相比之前的标准高斯分布模型大大提高。对于多高斯混合模型的参数,本文使用EM(Expectation Maximization,期望最大值)算法估计。最后的实验结果验证了本方法的有效性。
2.对于较大目标环境的室内定位系统,指纹数据库的参考点数量会非常多。因此,对指纹数据库进行聚类就变得非常必需了。本文基于自组织映射神经网络,将指纹数据库的高维元素映射到一个二维平面。之后,在这个二维平面上进行聚类。这样做的好处就在于能够避免高维空间聚类困难的问题,准确度高。最后的实验结果验证了该方法的有效性。
3.对于室内目标跟踪,一般的方法使用卡尔曼滤波器。然而室内噪声实际上并不是高斯噪声,这需要使用无迹卡尔曼滤波器或是精度更高的粒子滤波器。不幸的是粒子滤波器的算法复杂度非常大,特别是当状态超过三维的情况,已不适合于实时系统。本文将整个目标状态划分为均为二维矢量的位置分量和速度分量,对速度分量采用无迹卡尔曼滤波器估计,而对位置分量则采用粒子滤波器估计。不仅大大地提高了室内跟踪系统的系统性能,还将算法复杂度限制在可以实时使用的程度。最后的实验结果验证了本方法的有效性。
With the development of information technology, location service is becoming an important requirement of people. However, the mature GPS(Globe Positioning System) can not be applied in indoor environment. So a new signal is needed for indoor positioning. Lately WLAN(Wireless Local Area Network) develops quickly, which is equipped in big public place. It makes attractable for WLAN to be used in indoor positioning. And the indoor positioning system using WLAN do not require any other hardwares and has more and more attentions of researchers. WLAN indoor positioning system becomes one of the most popular indoor positioning systems.
This paper improves the classic WLAN indoor positioning system, and its contributions and innovation are:
1. Classic indoor positioning system uses standard Gaussian distribution to model the signal strength received by an object in reference point. However, indoor environment is complicated and changing, existing multi-path interference and mobile people interference. Hence, this paper uses a multi-Gaussian mixture model to model the signal strength distribution in reference point. This model not only considers the influence of the interference, but also considers the related relationship between signal strength of every AP(Access Point), and improves the system performance. The parameters of this method are estimated by EM(Expectation Maximization) algorithm in this paper. The experimental results prove the effectiveness of our method.
2. For indoor positioning system in a big environment, the number of reference points in fingerprint database is very big. Therefore, the clustering of fingerprint database is necessary. This paper mapped the elements of fingerprint database into a2-D plane using a self-organizing map. And the clustering is based on this2-D plane. The advantage of this method is that it can avoid the difficulty of clustering in space with high dimension, and the accuracy of clustering is high. The experimental results prove the effectiveness of our method.
3. For tracking an object in indoor environment, the general method uses a Kalman filter. However, the noise of indoor environment is not Gaussian, for tracking an object it require an unscented Kalman filter or a particle filter. Unfortunately the particle filter algorithm is complex, and it is not comfortable for a real time system with a state whose dimension is more than3. This paper divides the object state into a2-D velocity state and a2-D location state. And it uses the unscented Kalman filter to estimate the velocity state and uses the particle filter to estimate the location state. This method not only improves the performance of the indoor positioning and tracking system, but also ensures that the system is real time. The experimental results prove the effectiveness of our method.
引文
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