Pub Date : 2021-08-03DOI: 10.1002/9781119699910.ch10
A. Shkel, Yusheng Wang
This chapter provides a perspective on further development of both the hardware and the software for pedestrian navigation. Hardware development for the pedestrian inertial navigation mainly aims to solve the problem of incorporating different sensing modalities with a reasonable size and weight, such that the overall system is compact, robust, and accurate. Software development for the pedestrian inertial navigation mainly aims to explore algorithms to fully use the collected data, in order to further improve the navigation accuracy and adaptivity without too much computational load. Cooperative localization can be achieved if multiple mobile agents are available in the network, with communication and computation capabilities, jointly processing a relative measurement between each agent to increase their localization accuracy. There are many ways to fully utilize the Inertial Measurement Unit data besides just integrating them into position estimation.
{"title":"Perspective on Pedestrian Inertial Navigation Systems","authors":"A. Shkel, Yusheng Wang","doi":"10.1002/9781119699910.ch10","DOIUrl":"https://doi.org/10.1002/9781119699910.ch10","url":null,"abstract":"This chapter provides a perspective on further development of both the hardware and the software for pedestrian navigation. Hardware development for the pedestrian inertial navigation mainly aims to solve the problem of incorporating different sensing modalities with a reasonable size and weight, such that the overall system is compact, robust, and accurate. Software development for the pedestrian inertial navigation mainly aims to explore algorithms to fully use the collected data, in order to further improve the navigation accuracy and adaptivity without too much computational load. Cooperative localization can be achieved if multiple mobile agents are available in the network, with communication and computation capabilities, jointly processing a relative measurement between each agent to increase their localization accuracy. There are many ways to fully utilize the Inertial Measurement Unit data besides just integrating them into position estimation.","PeriodicalId":371531,"journal":{"name":"Pedestrian Inertial Navigation with Self‐Contained Aiding","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114192739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-03DOI: 10.1002/9781119699910.ch5
A. Shkel, Yusheng Wang
This chapter focuses on the self‐contained aiding techniques for pedestrian inertial navigation, which can limit the navigation error propagation of the strapdown inertial navigation while keeping the whole system independent of the environment. One of the most commonly used aiding techniques in the pedestrian inertial navigation is the Zero‐Velocity Update (ZUPT) aiding. One of the main advantages of ZUPT is its ability to obtain pseudo‐measurement of the velocity, which is otherwise unobservable by inertial measurement units (IMUs). There are two key parts involved in the ZUPT‐aided navigation algorithm: the stance phase detector and the pseudo‐measurement of the motion of the foot. In the pedestrian inertial navigation, the Extended Kalman Filter is commonly used to fuse the IMU readouts with other aiding techniques to obtain a more accurate navigation result. The chapter introduces the concept, algorithmic implementation, and parameter selection of the ZUPT‐aided pedestrian inertial navigation.
{"title":"Zero‐Velocity Update Aided Pedestrian Inertial Navigation","authors":"A. Shkel, Yusheng Wang","doi":"10.1002/9781119699910.ch5","DOIUrl":"https://doi.org/10.1002/9781119699910.ch5","url":null,"abstract":"This chapter focuses on the self‐contained aiding techniques for pedestrian inertial navigation, which can limit the navigation error propagation of the strapdown inertial navigation while keeping the whole system independent of the environment. One of the most commonly used aiding techniques in the pedestrian inertial navigation is the Zero‐Velocity Update (ZUPT) aiding. One of the main advantages of ZUPT is its ability to obtain pseudo‐measurement of the velocity, which is otherwise unobservable by inertial measurement units (IMUs). There are two key parts involved in the ZUPT‐aided navigation algorithm: the stance phase detector and the pseudo‐measurement of the motion of the foot. In the pedestrian inertial navigation, the Extended Kalman Filter is commonly used to fuse the IMU readouts with other aiding techniques to obtain a more accurate navigation result. The chapter introduces the concept, algorithmic implementation, and parameter selection of the ZUPT‐aided pedestrian inertial navigation.","PeriodicalId":371531,"journal":{"name":"Pedestrian Inertial Navigation with Self‐Contained Aiding","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127919466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1002/9781119699910.ch4
A. Shkel, Yusheng Wang
One of the most important characteristics of an inertial navigation system is its navigation accuracy, which is directly related to the measurement errors of the inertial measurement unit (IMU). This chapter analyses the relation between the IMU error and the navigation error in the strapdown inertial navigation. It introduces the terminology, origin, and characteristics of some of the major error sources. Error sources in navigation can be categorized into three groups: IMU errors, initial calibration errors, and numerical errors. Assembly errors mainly come from the shift of the mounting direction of individual inertial sensors from their ideal orientations. The position error will exceed a meter of error within a few seconds of navigation with unaided consumer grade IMUs. Tactical grade IMUs have the capability of attitude measurement with reasonable errors and are able to conduct short‐term navigation, with navigation accuracy on the order of meters within 30 seconds of strapdown inertial navigation.
{"title":"Navigation Error Analysis in Strapdown Inertial Navigation","authors":"A. Shkel, Yusheng Wang","doi":"10.1002/9781119699910.ch4","DOIUrl":"https://doi.org/10.1002/9781119699910.ch4","url":null,"abstract":"One of the most important characteristics of an inertial navigation system is its navigation accuracy, which is directly related to the measurement errors of the inertial measurement unit (IMU). This chapter analyses the relation between the IMU error and the navigation error in the strapdown inertial navigation. It introduces the terminology, origin, and characteristics of some of the major error sources. Error sources in navigation can be categorized into three groups: IMU errors, initial calibration errors, and numerical errors. Assembly errors mainly come from the shift of the mounting direction of individual inertial sensors from their ideal orientations. The position error will exceed a meter of error within a few seconds of navigation with unaided consumer grade IMUs. Tactical grade IMUs have the capability of attitude measurement with reasonable errors and are able to conduct short‐term navigation, with navigation accuracy on the order of meters within 30 seconds of strapdown inertial navigation.","PeriodicalId":371531,"journal":{"name":"Pedestrian Inertial Navigation with Self‐Contained Aiding","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115030239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1002/9781119699910.ch6
A. Shkel, Yusheng Wang
This chapter analyzes navigation errors in Zero‐Velocity Update (ZUPT)‐aided pedestrian inertial navigation due to Inertial Measurement Unit (IMU) noises. It presents a 2D biomechanical model to simulate human gait to better understand human walking dynamics and also to serve as the basis for the following numerical simulations. Human ambulatory gait models are multidimensional due to the complex kinematic and dynamic relations between many parts of human body involved during walking. The foot motion can be superimposed on top of the torso motion to obtain the foot motion in the navigation frame. The navigation errors in the ZUPT‐aided navigation algorithm come mainly from two major sources: systematic modeling errors and IMU noises. The chapter presents verification of the analysis both numerically and experimentally. ZUPT‐aided inertial navigation algorithm eliminates the velocity drift during pedestrian navigation, and therefore greatly reduces the overall navigation error compared to the navigation result without any aiding.
{"title":"Navigation Error Analysis in the ZUPT‐Aided Pedestrian Inertial Navigation","authors":"A. Shkel, Yusheng Wang","doi":"10.1002/9781119699910.ch6","DOIUrl":"https://doi.org/10.1002/9781119699910.ch6","url":null,"abstract":"This chapter analyzes navigation errors in Zero‐Velocity Update (ZUPT)‐aided pedestrian inertial navigation due to Inertial Measurement Unit (IMU) noises. It presents a 2D biomechanical model to simulate human gait to better understand human walking dynamics and also to serve as the basis for the following numerical simulations. Human ambulatory gait models are multidimensional due to the complex kinematic and dynamic relations between many parts of human body involved during walking. The foot motion can be superimposed on top of the torso motion to obtain the foot motion in the navigation frame. The navigation errors in the ZUPT‐aided navigation algorithm come mainly from two major sources: systematic modeling errors and IMU noises. The chapter presents verification of the analysis both numerically and experimentally. ZUPT‐aided inertial navigation algorithm eliminates the velocity drift during pedestrian navigation, and therefore greatly reduces the overall navigation error compared to the navigation result without any aiding.","PeriodicalId":371531,"journal":{"name":"Pedestrian Inertial Navigation with Self‐Contained Aiding","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125701301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1002/9781119699910.ch3
A. Shkel, Yusheng Wang
Strapdown inertial navigation systems are the most common form of inertial navigation system due to its potential benefits of lower cost, reduced size, and greater reliability compared with equivalent gimbal systems. This chapter introduces fundamentals on the strapdown inertial navigation mechanism. In the n‐frame, navigation data are expressed by velocity components along the North, East, and Down directions, and latitude, longitude, and altitude. Therefore, it is more commonly used in navigation applications on the Earth or in the vicinity of the Earth surface. The Coriolis acceleration can be neglected in cases where the navigation error caused by the inertial measurement units (IMU) measurement error is much greater than the Coriolis effect. Attitude initialization, unlike position and velocity initialization, can be achieved by inertial sensors if the IMU is stationary with respect to the Earth. Gyrocompassing requires the IMU to measure the Earth's rotation, which is around 15 °/h, to obtain the yaw angle information.
{"title":"Strapdown Inertial Navigation Mechanism","authors":"A. Shkel, Yusheng Wang","doi":"10.1002/9781119699910.ch3","DOIUrl":"https://doi.org/10.1002/9781119699910.ch3","url":null,"abstract":"Strapdown inertial navigation systems are the most common form of inertial navigation system due to its potential benefits of lower cost, reduced size, and greater reliability compared with equivalent gimbal systems. This chapter introduces fundamentals on the strapdown inertial navigation mechanism. In the n‐frame, navigation data are expressed by velocity components along the North, East, and Down directions, and latitude, longitude, and altitude. Therefore, it is more commonly used in navigation applications on the Earth or in the vicinity of the Earth surface. The Coriolis acceleration can be neglected in cases where the navigation error caused by the inertial measurement units (IMU) measurement error is much greater than the Coriolis effect. Attitude initialization, unlike position and velocity initialization, can be achieved by inertial sensors if the IMU is stationary with respect to the Earth. Gyrocompassing requires the IMU to measure the Earth's rotation, which is around 15 °/h, to obtain the yaw angle information.","PeriodicalId":371531,"journal":{"name":"Pedestrian Inertial Navigation with Self‐Contained Aiding","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129449539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1002/9781119699910.ch2
A. Shkel, Yusheng Wang
This chapter focuses on the inertial sensors and inertial measurement units (IMUs) in context of their operating principles. Inertial sensors are the hardware basis for inertial navigation. Inertial sensors are sensors based on inertia and relevant measuring principles. There are two types of inertial sensors: accelerometers and gyroscopes, measuring the specific forces and rotations, respectively. Accelerometers can typically be categorized into two classes: static accelerometers and resonant accelerometers. Gyroscope is a kind of sensor that measures rotation. Some of the gyroscope classes include mechanical gyroscopes, optical gyroscopes, nuclear magnetic resonance (NMR) gyroscopes, and micro electro mechanical systems vibratory gyroscopes. The chapter introduces some of the commonly used technologies to combine individual inertial sensors into a single IMU. IMU miniaturization approach is through vertical chip stacking. There is no IMU technology that is best for all applications, and therefore, a proper selection of the technology is needed for various application scenarios.
{"title":"Inertial Sensors and Inertial Measurement Units","authors":"A. Shkel, Yusheng Wang","doi":"10.1002/9781119699910.ch2","DOIUrl":"https://doi.org/10.1002/9781119699910.ch2","url":null,"abstract":"This chapter focuses on the inertial sensors and inertial measurement units (IMUs) in context of their operating principles. Inertial sensors are the hardware basis for inertial navigation. Inertial sensors are sensors based on inertia and relevant measuring principles. There are two types of inertial sensors: accelerometers and gyroscopes, measuring the specific forces and rotations, respectively. Accelerometers can typically be categorized into two classes: static accelerometers and resonant accelerometers. Gyroscope is a kind of sensor that measures rotation. Some of the gyroscope classes include mechanical gyroscopes, optical gyroscopes, nuclear magnetic resonance (NMR) gyroscopes, and micro electro mechanical systems vibratory gyroscopes. The chapter introduces some of the commonly used technologies to combine individual inertial sensors into a single IMU. IMU miniaturization approach is through vertical chip stacking. There is no IMU technology that is best for all applications, and therefore, a proper selection of the technology is needed for various application scenarios.","PeriodicalId":371531,"journal":{"name":"Pedestrian Inertial Navigation with Self‐Contained Aiding","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123181257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1002/9781119699910.ch7
A. Shkel, Yusheng Wang
Many error sources contribute to the overall navigation error in the Zero‐Velocity Update (ZUPT)‐aided pedestrian inertial navigation. They can generally be categorized into two groups: errors caused by the inertial measurement unit (IMU) and errors caused by the navigation algorithm. This chapter discusses a few methods that can be implemented in the ZUPT‐aided pedestrian inertial navigation in order to reduce the navigation errors. The ZUPT‐aided pedestrian inertial navigation requires a foot‐mounted IMU to collect data. IMU data are first averaged to reduce the IMU noise and extract parameters, such as length of the stance phase and the shock level during walking. Trajectory orientation drift in the ZUPT‐aided pedestrian inertial navigation is believed to be related to the g‐sensitivity of gyroscopes. Gyroscope g‐sensitivity is the erroneous measurement of a gyroscope in response to the external acceleration.
{"title":"Navigation Error Reduction in the ZUPT‐Aided Pedestrian Inertial Navigation","authors":"A. Shkel, Yusheng Wang","doi":"10.1002/9781119699910.ch7","DOIUrl":"https://doi.org/10.1002/9781119699910.ch7","url":null,"abstract":"Many error sources contribute to the overall navigation error in the Zero‐Velocity Update (ZUPT)‐aided pedestrian inertial navigation. They can generally be categorized into two groups: errors caused by the inertial measurement unit (IMU) and errors caused by the navigation algorithm. This chapter discusses a few methods that can be implemented in the ZUPT‐aided pedestrian inertial navigation in order to reduce the navigation errors. The ZUPT‐aided pedestrian inertial navigation requires a foot‐mounted IMU to collect data. IMU data are first averaged to reduce the IMU noise and extract parameters, such as length of the stance phase and the shock level during walking. Trajectory orientation drift in the ZUPT‐aided pedestrian inertial navigation is believed to be related to the g‐sensitivity of gyroscopes. Gyroscope g‐sensitivity is the erroneous measurement of a gyroscope in response to the external acceleration.","PeriodicalId":371531,"journal":{"name":"Pedestrian Inertial Navigation with Self‐Contained Aiding","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123279386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：