基于手机内置传感器的室内目标运动轨迹估计方法研究
发布时间：2018-12-15 21:23
【摘要】：基于位置的服务已在智慧医疗、安全保护、商业广告、智能出行、地图导航等多个领域被广泛应用,在信息化与自动化程度越来越高的当今社会扮演着愈加重要的角色,对用户进行准确的位置获取与运动轨迹估计是该类服务的基础。由于室内环境下存在信号反射,多径效应,环境多变等问题,使得基于外部信号(被定位目标以外的设备产生的信号)的估计方法在室内环境下并不能很好的满足位置服务的需求。手机惯性导航技术具有定位区域可变、环境适用性强、不依赖外部信号的天然优势,因此,非常适合作为解决室内位置服务问题的解决方案。但由于手机惯性导航技术存在传感器数据噪声大,手机携带者运动状态与手机携带状态随机、多变的问题,使得使用场景适用性较差,从而影响运动轨迹估计的准确性与鲁棒性。本文通过对手机内置传感器的工作原理进行分析,确定其数据组成及噪声特性,针对不同使用场景设计不同的噪声处理器；从人体运动特征与手机使用特征的分析入手,讨论运动状态与手机携带状态的分类与估计方法；在确定的运动状态与手机携带状态的基础上,并利用室内建筑特征进行运动轨迹估计方法的设计,达到提高室内运动轨迹估计的准确性与鲁棒性的目标。主要研究内容有：1.传感器噪声处理通过分析传感器的工作原理,确定不同使用场景下的传感器数据组成；通过分析不同噪声的特性,对不同使用场景设计不同的噪声处理器。2.运动状态的分类与估计通过对人体的运动特征的分析,将运动状态分解为若干特征状态,从传感器数据中选取特征变量,讨论特征状态的估计方法,并设计运动状态估计器。3.手机携带状态的分类与估计通过分析用户对手机的使用习惯,将手机携带状态分解为若干特征状态,根据传感器数据对其特征状态的影响,讨论特征状态的估计方法,并设计手机携带状态估计器。4.运动轨迹估计以确定的运动状态与手机携带状态为基础,进行高使用场景适用性的运动轨迹估计方法的设计,提高其准确性与鲁棒性。
[Abstract]:Location-based services have been widely used in many fields, such as intelligent medical care, security protection, commercial advertising, intelligent travel, map navigation and so on. Accurate location acquisition and motion trajectory estimation are the basis of this kind of service. Because of the problems of signal reflection, multipath effect and environment variability in indoor environment, The estimation method based on the external signal (the signal generated by the equipment other than the target) can not meet the needs of the location service in the indoor environment. Mobile inertial navigation technology has the advantages of variable location region, strong environmental applicability and independent of external signals. Therefore, it is very suitable as a solution to the indoor location service problem. However, the mobile phone inertial navigation technology has the problems of high noise of sensor data, random moving state of mobile phone carrier and mobile phone carrying state, which makes the applicability of the use scene poor. Therefore, the accuracy and robustness of motion trajectory estimation are affected. Through the analysis of the working principle of the built-in sensor in the mobile phone, the data composition and noise characteristics of the sensor are determined, and different noise processors are designed for different usage scenarios. Starting with the analysis of human motion and mobile phone usage characteristics, the classification and estimation methods of motion state and mobile phone carrying state are discussed. On the basis of determining the motion state and the mobile phone carrying state and using the indoor architectural features to design the motion trajectory estimation method, the accuracy and robustness of the indoor motion trajectory estimation can be improved. The main research contents are as follows: 1. By analyzing the working principle of the sensor, the sensor data composition under different usage scenarios is determined, and different noise processors are designed for different use scenes by analyzing the characteristics of different noise. 2. The classification and estimation of motion state by analyzing the motion characteristics of human body, the motion state is decomposed into several characteristic states, the feature variables are selected from the sensor data, and the estimation method of characteristic state is discussed. And design motion state estimator. 3. The classification and estimation of mobile phone carrying state by analyzing the user's usage habits, the mobile phone carrying state is decomposed into several feature states. According to the influence of sensor data on the characteristic state, the method of feature state estimation is discussed. And the design of mobile phone carrying state estimator. 4. Based on the determined motion state and the mobile phone carrying state, the motion trajectory estimation is designed to improve the accuracy and robustness of the motion trajectory estimation method with high applicability in the use of the scene.
【学位授予单位】：西北大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP212
本文编号：2381313
[Abstract]:Location-based services have been widely used in many fields, such as intelligent medical care, security protection, commercial advertising, intelligent travel, map navigation and so on. Accurate location acquisition and motion trajectory estimation are the basis of this kind of service. Because of the problems of signal reflection, multipath effect and environment variability in indoor environment, The estimation method based on the external signal (the signal generated by the equipment other than the target) can not meet the needs of the location service in the indoor environment. Mobile inertial navigation technology has the advantages of variable location region, strong environmental applicability and independent of external signals. Therefore, it is very suitable as a solution to the indoor location service problem. However, the mobile phone inertial navigation technology has the problems of high noise of sensor data, random moving state of mobile phone carrier and mobile phone carrying state, which makes the applicability of the use scene poor. Therefore, the accuracy and robustness of motion trajectory estimation are affected. Through the analysis of the working principle of the built-in sensor in the mobile phone, the data composition and noise characteristics of the sensor are determined, and different noise processors are designed for different usage scenarios. Starting with the analysis of human motion and mobile phone usage characteristics, the classification and estimation methods of motion state and mobile phone carrying state are discussed. On the basis of determining the motion state and the mobile phone carrying state and using the indoor architectural features to design the motion trajectory estimation method, the accuracy and robustness of the indoor motion trajectory estimation can be improved. The main research contents are as follows: 1. By analyzing the working principle of the sensor, the sensor data composition under different usage scenarios is determined, and different noise processors are designed for different use scenes by analyzing the characteristics of different noise. 2. The classification and estimation of motion state by analyzing the motion characteristics of human body, the motion state is decomposed into several characteristic states, the feature variables are selected from the sensor data, and the estimation method of characteristic state is discussed. And design motion state estimator. 3. The classification and estimation of mobile phone carrying state by analyzing the user's usage habits, the mobile phone carrying state is decomposed into several feature states. According to the influence of sensor data on the characteristic state, the method of feature state estimation is discussed. And the design of mobile phone carrying state estimator. 4. Based on the determined motion state and the mobile phone carrying state, the motion trajectory estimation is designed to improve the accuracy and robustness of the motion trajectory estimation method with high applicability in the use of the scene.
【学位授予单位】：西北大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TP212
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