Pub Date : 2014-05-05DOI: 10.1109/PLANS.2014.6851509
M. Piras, A. Lingua, P. Dabove, I. Aicardi
Today knowing where we are has become an important issue for people who interactively and dynamically live and work in urban cities. Each user has a very complete and complex set of technologies for positioning and navigating in his/her hands which are simple to use even if they are not good at positioning or in Geomatics. Accelerometers, gyroscopes, magnetometers, pressure sensors, GNSS receivers, digital cameras are all tools which can be used for defining a three-dimensional position and their integration could be the key point of this technology. Indoor positioning is the latest challenge to be used whenever GNSS positioning is not always available or null, even using high sensitivity sensors. An alternative solution must be found starting from determining the other available solutions in Smartphone devices. An example of indoor positioning could be obtained by using the Image Based Navigation (IBN) approach, where the coordinates of our device are defined using the photogrammetric principle. Several papers demonstrate that IBN can be an useful approach for positioning and how the device in Smartphones can work indoors. In this study, the authors attempt to combine the IBN method with the potentiality of Smartphone internal sensors, in order to verify their performance in indoor positioning.
{"title":"Indoor navigation using Smartphone technology: A future challenge or an actual possibility?","authors":"M. Piras, A. Lingua, P. Dabove, I. Aicardi","doi":"10.1109/PLANS.2014.6851509","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851509","url":null,"abstract":"Today knowing where we are has become an important issue for people who interactively and dynamically live and work in urban cities. Each user has a very complete and complex set of technologies for positioning and navigating in his/her hands which are simple to use even if they are not good at positioning or in Geomatics. Accelerometers, gyroscopes, magnetometers, pressure sensors, GNSS receivers, digital cameras are all tools which can be used for defining a three-dimensional position and their integration could be the key point of this technology. Indoor positioning is the latest challenge to be used whenever GNSS positioning is not always available or null, even using high sensitivity sensors. An alternative solution must be found starting from determining the other available solutions in Smartphone devices. An example of indoor positioning could be obtained by using the Image Based Navigation (IBN) approach, where the coordinates of our device are defined using the photogrammetric principle. Several papers demonstrate that IBN can be an useful approach for positioning and how the device in Smartphones can work indoors. In this study, the authors attempt to combine the IBN method with the potentiality of Smartphone internal sensors, in order to verify their performance in indoor positioning.","PeriodicalId":371808,"journal":{"name":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115219581","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 : 2014-05-05DOI: 10.1109/PLANS.2014.6851366
Joseph T. Hansen, J. Cross, D. Jourdan
In this paper we present Sierra Nevada Corporation's (SNC) Generalized Information Fusion Filter (GIFF). GIFF is a robust, sensor-agnostic estimation framework designed to blend measurements from a variety of sensors to produce an optimal estimate of the navigation state. At the core of GIFF is a Rao-Blackwellized (or marginalized) Particle Filter (RB-PF) with specialized Auxiliary Sampling Importance Resampling (ASIR). This algorithm places no limitation on the number of sensors it can use or on the linearity and error characteristics of their measurements, as opposed to more rigid, traditional techniques like Kalman Filters. This enables GIFF to process data from sensors of various kinds directly (3D radar/LIDAR, 2D surveillance radar, EO/IR, radar-altimeter, GPS, IMU, etc.), with minimal pre-processing. In addition, the marginalized implementation enables a large number of states to be estimated in real-time. We illustrate GIFF flexibility and performance using actual sensor data collected on fixed- and rotary-wing platforms equipped with an imaging radar producing 3D points and 2D images, a radar-altimeter, and an IMU. En-route tests show near-optimal accuracy is achieved during a one-hour flight over Virginia with a simulated GPS outage. GIFF is also initialized with large position uncertainty (5km) and shown to converge after only 30 seconds of flight. GIFF performance during terminal operations (landing) is illustrated using data collected on approaches to the Reno Stead airport, showing an accuracy similar to GPS 60 seconds before touchdown.
{"title":"Robust en-route and terminal navigation using topology and intensity returns from a forward-looking millimeter-wave radar","authors":"Joseph T. Hansen, J. Cross, D. Jourdan","doi":"10.1109/PLANS.2014.6851366","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851366","url":null,"abstract":"In this paper we present Sierra Nevada Corporation's (SNC) Generalized Information Fusion Filter (GIFF). GIFF is a robust, sensor-agnostic estimation framework designed to blend measurements from a variety of sensors to produce an optimal estimate of the navigation state. At the core of GIFF is a Rao-Blackwellized (or marginalized) Particle Filter (RB-PF) with specialized Auxiliary Sampling Importance Resampling (ASIR). This algorithm places no limitation on the number of sensors it can use or on the linearity and error characteristics of their measurements, as opposed to more rigid, traditional techniques like Kalman Filters. This enables GIFF to process data from sensors of various kinds directly (3D radar/LIDAR, 2D surveillance radar, EO/IR, radar-altimeter, GPS, IMU, etc.), with minimal pre-processing. In addition, the marginalized implementation enables a large number of states to be estimated in real-time. We illustrate GIFF flexibility and performance using actual sensor data collected on fixed- and rotary-wing platforms equipped with an imaging radar producing 3D points and 2D images, a radar-altimeter, and an IMU. En-route tests show near-optimal accuracy is achieved during a one-hour flight over Virginia with a simulated GPS outage. GIFF is also initialized with large position uncertainty (5km) and shown to converge after only 30 seconds of flight. GIFF performance during terminal operations (landing) is illustrated using data collected on approaches to the Reno Stead airport, showing an accuracy similar to GPS 60 seconds before touchdown.","PeriodicalId":371808,"journal":{"name":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115243534","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 : 2014-05-05DOI: 10.1109/PLANS.2014.6851497
Y. Stebler, S. Guerrier, J. Skaloud, R. Molinari, Maria-Pia Victoria-Feser
This paper aims at studying the behaviour of the errors coming from inertial sensors when measured in dynamic conditions. After proposing a method for constructing the error process, the properties of these errors are estimated via the Generalized Method of Wavelets Moments methodology. The developed model parameters are compared to those obtained under static conditions. Finally an attempted is presented to find the link between the encountered dynamic of the vehicle and error-model parameters.
{"title":"Study of MEMS-based inertial sensors operating in dynamic conditions","authors":"Y. Stebler, S. Guerrier, J. Skaloud, R. Molinari, Maria-Pia Victoria-Feser","doi":"10.1109/PLANS.2014.6851497","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851497","url":null,"abstract":"This paper aims at studying the behaviour of the errors coming from inertial sensors when measured in dynamic conditions. After proposing a method for constructing the error process, the properties of these errors are estimated via the Generalized Method of Wavelets Moments methodology. The developed model parameters are compared to those obtained under static conditions. Finally an attempted is presented to find the link between the encountered dynamic of the vehicle and error-model parameters.","PeriodicalId":371808,"journal":{"name":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","volume":"312 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115359133","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 : 2014-05-05DOI: 10.1109/PLANS.2014.6851352
E. Tatar, T. Mukherjee, G. Fedder
This paper presents a system level MEMS gyroscope simulation technique analyzing the effect of stress on MEMS gyroscope zero rate output (ZRO) and scale factor (SF). A circuit simulation environment that includes the parameterized behavioral models of the MEMS devices is used for predicting the stress effects on gyroscope output. The simulations show that typical packaging stress values (2MPa) create on the order of °/hr bias shifts that can limit the gyroscope performance. Drive comb gap mismatches as a result of different stator and rotor displacements due to stress are responsible for the ZRO, and they create a Coriolis in-phase force that cannot be distinguished from the rotational rate signal.
{"title":"Simulation of stress effects on mode-matched MEMS gyroscope bias and scale factor","authors":"E. Tatar, T. Mukherjee, G. Fedder","doi":"10.1109/PLANS.2014.6851352","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851352","url":null,"abstract":"This paper presents a system level MEMS gyroscope simulation technique analyzing the effect of stress on MEMS gyroscope zero rate output (ZRO) and scale factor (SF). A circuit simulation environment that includes the parameterized behavioral models of the MEMS devices is used for predicting the stress effects on gyroscope output. The simulations show that typical packaging stress values (2MPa) create on the order of °/hr bias shifts that can limit the gyroscope performance. Drive comb gap mismatches as a result of different stator and rotor displacements due to stress are responsible for the ZRO, and they create a Coriolis in-phase force that cannot be distinguished from the rotational rate signal.","PeriodicalId":371808,"journal":{"name":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123174026","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 : 2014-05-05DOI: 10.1109/PLANS.2014.6851514
M. J. Jørgensen, Dario Paccagnan, N. K. Poulsen, M. Larsen
This paper treats the IMU calibration and validation problem in three settings: Factory production line with the aid of a precision multi-axis turntable, in-the-field on land and at sea, both without specialist test equipment. The treatment is limited to the IMU calibration parameters of key relevance for gyro-compassing grade optical gyroscopes and force-rebalanced pendulous accelerometers: Scale factor, bias and sensor axes misalignments. Focus is on low-dynamic marine applications e.g., subsea construction and survey. Two different methods of calibration are investigated: Kalman smoothing using an Aided Inertial Navigation System (AINS) framework, augmenting the error state Kalman filter (ESKF) to include the full set of IMU calibration parameters and a least squares approach, where the calibration parameters are determined by minimizing the magnitude of the INS error differential equation output. A method of evaluating calibrations is introduced and discussed. The two calibration methods are evaluated for factory use and results compared to a legacy proprietary method as well as in-field calibration/verification on land and at sea. The calibration methods shows similar navigation performance as the proprietary method. This validates both methods for factory calibration. Furthermore it is shown that the AINS method can calibrate in-field on land and at sea without the use of a precision multi-axis turntable.
{"title":"IMU calibration and validation in a factory, remote on land and at sea","authors":"M. J. Jørgensen, Dario Paccagnan, N. K. Poulsen, M. Larsen","doi":"10.1109/PLANS.2014.6851514","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851514","url":null,"abstract":"This paper treats the IMU calibration and validation problem in three settings: Factory production line with the aid of a precision multi-axis turntable, in-the-field on land and at sea, both without specialist test equipment. The treatment is limited to the IMU calibration parameters of key relevance for gyro-compassing grade optical gyroscopes and force-rebalanced pendulous accelerometers: Scale factor, bias and sensor axes misalignments. Focus is on low-dynamic marine applications e.g., subsea construction and survey. Two different methods of calibration are investigated: Kalman smoothing using an Aided Inertial Navigation System (AINS) framework, augmenting the error state Kalman filter (ESKF) to include the full set of IMU calibration parameters and a least squares approach, where the calibration parameters are determined by minimizing the magnitude of the INS error differential equation output. A method of evaluating calibrations is introduced and discussed. The two calibration methods are evaluated for factory use and results compared to a legacy proprietary method as well as in-field calibration/verification on land and at sea. The calibration methods shows similar navigation performance as the proprietary method. This validates both methods for factory calibration. Furthermore it is shown that the AINS method can calibrate in-field on land and at sea without the use of a precision multi-axis turntable.","PeriodicalId":371808,"journal":{"name":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123414148","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 : 2014-05-05DOI: 10.1109/PLANS.2014.6851506
Daniel P. Shepard, T. Humphreys
A novel navigation system for obtaining high-precision globally-referenced position and attitude is presented and analyzed. The system is centered on a bundle-adjustment-based visual simultaneous localization and mapping (SLAM) algorithm which incorporates carrier-phase differential GPS (CDGPS) position measurements into the bundle adjustment in addition to measurements of point features identified in a subset of the camera images, referred to as keyframes. To track the motion of the camera in real-time, a navigation filter is employed which utilizes the point feature measurements from all non-keyframes, the point feature positions estimated by bundle adjustment, and inertial measurements. Simulations have shown that the system obtains centimeter-level or better absolute positioning accuracy and sub-degree-level absolute attitude accuracy in open outdoor areas. Moreover, the position and attitude solution only drifts slightly with the distance traveled when the system transitions to a GPS-denied environment (e.g., when the navigation system is carried indoors). A novel technique for initializing the globally-referenced bundle adjustment algorithm is also presented which solves the problem of relating the coordinate systems for position estimates based on two disparate sensors while accounting for the distance between the sensors. Simulation results are presented for the globally-referenced bundle adjustment algorithm which demonstrate its performance in the challenging scenario of walking through a hallway where GPS signals are unavailable.
{"title":"High-precision globally-referenced position and attitude via a fusion of visual SLAM, carrier-phase-based GPS, and inertial measurements","authors":"Daniel P. Shepard, T. Humphreys","doi":"10.1109/PLANS.2014.6851506","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851506","url":null,"abstract":"A novel navigation system for obtaining high-precision globally-referenced position and attitude is presented and analyzed. The system is centered on a bundle-adjustment-based visual simultaneous localization and mapping (SLAM) algorithm which incorporates carrier-phase differential GPS (CDGPS) position measurements into the bundle adjustment in addition to measurements of point features identified in a subset of the camera images, referred to as keyframes. To track the motion of the camera in real-time, a navigation filter is employed which utilizes the point feature measurements from all non-keyframes, the point feature positions estimated by bundle adjustment, and inertial measurements. Simulations have shown that the system obtains centimeter-level or better absolute positioning accuracy and sub-degree-level absolute attitude accuracy in open outdoor areas. Moreover, the position and attitude solution only drifts slightly with the distance traveled when the system transitions to a GPS-denied environment (e.g., when the navigation system is carried indoors). A novel technique for initializing the globally-referenced bundle adjustment algorithm is also presented which solves the problem of relating the coordinate systems for position estimates based on two disparate sensors while accounting for the distance between the sensors. Simulation results are presented for the globally-referenced bundle adjustment algorithm which demonstrate its performance in the challenging scenario of walking through a hallway where GPS signals are unavailable.","PeriodicalId":371808,"journal":{"name":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124736934","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 : 2014-05-05DOI: 10.1109/PLANS.2014.6851439
Z. Berman
A methodology for the design of navigation Kalman filters is discussed. The goal is to design a Kalman filter that can support sensor integration during its extensive life span with well-controlled performance. The idea is to relate the Kalman filter design with systematic performance evaluation. Using dual model and truth covariance analysis (TCA) approaches, a complete framework for modeling, evaluating and designing an arbitrary integration scheme based on inertial sensors is described. One important achievement is the separation of system-level decisions such as sensor selection or measurement policy from Kalman filter design. The second achievement is the effective procedure for Kalman filter design based on two, almost completely automated steps: state selection and reduced-order Kalman filter tuning. The last, but by no means least important accomplishment is the presentation of an integrated framework, with appropriate parameterization and interfaces, to support all design phases and to allow reuse, with minimal modification, for different projects. The methodology is illustrated with a case-study analysis of a low-cost vehicle INS/GPS system.
{"title":"The design process for navigation Kalman filters: Striving for performance and quality","authors":"Z. Berman","doi":"10.1109/PLANS.2014.6851439","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851439","url":null,"abstract":"A methodology for the design of navigation Kalman filters is discussed. The goal is to design a Kalman filter that can support sensor integration during its extensive life span with well-controlled performance. The idea is to relate the Kalman filter design with systematic performance evaluation. Using dual model and truth covariance analysis (TCA) approaches, a complete framework for modeling, evaluating and designing an arbitrary integration scheme based on inertial sensors is described. One important achievement is the separation of system-level decisions such as sensor selection or measurement policy from Kalman filter design. The second achievement is the effective procedure for Kalman filter design based on two, almost completely automated steps: state selection and reduced-order Kalman filter tuning. The last, but by no means least important accomplishment is the presentation of an integrated framework, with appropriate parameterization and interfaces, to support all design phases and to allow reuse, with minimal modification, for different projects. The methodology is illustrated with a case-study analysis of a low-cost vehicle INS/GPS system.","PeriodicalId":371808,"journal":{"name":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123549677","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 : 2014-05-05DOI: 10.1109/PLANS.2014.6851362
Junesol Song, C. Kee, Byungwoon Park, Heungwon Park, Seungwoo Seo
In this paper, using the additional relation between tropospheric delay and height variation, we combined multiple carrier phase corrections from multiple reference stations of Network RTK. The Low-order Surface Method (LSM) is used as a base correction interpolation method. The LSM including height difference is also considered and its gradient coefficients are calculated as minimum-norm solutions. Real GPS data from multiple reference station network are collected and Compact RTK and Master-Auxiliary Concept (MAC) corrections are generated. Finally, generated corrections are tested for various correction interpolation methods including proposed algorithm and their performances are compared.
{"title":"Correction combination of compact network RTK considering tropospheric delay variation over height","authors":"Junesol Song, C. Kee, Byungwoon Park, Heungwon Park, Seungwoo Seo","doi":"10.1109/PLANS.2014.6851362","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851362","url":null,"abstract":"In this paper, using the additional relation between tropospheric delay and height variation, we combined multiple carrier phase corrections from multiple reference stations of Network RTK. The Low-order Surface Method (LSM) is used as a base correction interpolation method. The LSM including height difference is also considered and its gradient coefficients are calculated as minimum-norm solutions. Real GPS data from multiple reference station network are collected and Compact RTK and Master-Auxiliary Concept (MAC) corrections are generated. Finally, generated corrections are tested for various correction interpolation methods including proposed algorithm and their performances are compared.","PeriodicalId":371808,"journal":{"name":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123944957","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 : 2014-05-05DOI: 10.1109/PLANS.2014.6851381
D. Unsal, M. Doğan
Modeling and simulation studies are used to measure the desired performance prior to the hardware implementation of inertial navigation systems. Inertial measurement units are the main components of the inertial navigation systems. Therefore, IMUs should be modeled within the scope of modeling and simulation studies of inertial navigation systems. Several time and frequency domain analysis are implemented in these simulation studies. In addition to deterministic and stochastic error parameters, frequency and delay characteristics of the sensors required for inertial sensor identification. Hence, transfer functions of accelerometer and gyroscope channels are required. Generally, transfer functions of COTS IMUs, accelerometers and gyroscopes are not provided to end-users. Therefore, identification of sensor transfer functions becomes a problem. In order to identify sensor transfer function several methods have been examined. This study explains the how the transfer functions of inertial sensors are defined by using system identification with Kalman Filter. System identification deals with the problem of building mathematical models of dynamical systems based on observed data from the system. System identification consists of data record, generating of model set and determining of the best model steps and lots of several methods can be used in these steps. In the scope of this study Kalman Filter is used to generate candidate transfer function set in the generating of model set step of the system identification. Transfer function identification process will be completed by selecting the best model from the model set. Thereby, effects of frequency and delay characteristics on the system performance can be observed. An IMU can be modeled in frequency domain with transfer function by using the methodology which is explained in this study.
{"title":"Implementation of identification system for IMUs based on Kalman Filtering","authors":"D. Unsal, M. Doğan","doi":"10.1109/PLANS.2014.6851381","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851381","url":null,"abstract":"Modeling and simulation studies are used to measure the desired performance prior to the hardware implementation of inertial navigation systems. Inertial measurement units are the main components of the inertial navigation systems. Therefore, IMUs should be modeled within the scope of modeling and simulation studies of inertial navigation systems. Several time and frequency domain analysis are implemented in these simulation studies. In addition to deterministic and stochastic error parameters, frequency and delay characteristics of the sensors required for inertial sensor identification. Hence, transfer functions of accelerometer and gyroscope channels are required. Generally, transfer functions of COTS IMUs, accelerometers and gyroscopes are not provided to end-users. Therefore, identification of sensor transfer functions becomes a problem. In order to identify sensor transfer function several methods have been examined. This study explains the how the transfer functions of inertial sensors are defined by using system identification with Kalman Filter. System identification deals with the problem of building mathematical models of dynamical systems based on observed data from the system. System identification consists of data record, generating of model set and determining of the best model steps and lots of several methods can be used in these steps. In the scope of this study Kalman Filter is used to generate candidate transfer function set in the generating of model set step of the system identification. Transfer function identification process will be completed by selecting the best model from the model set. Thereby, effects of frequency and delay characteristics on the system performance can be observed. An IMU can be modeled in frequency domain with transfer function by using the methodology which is explained in this study.","PeriodicalId":371808,"journal":{"name":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125410222","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 : 2014-05-05DOI: 10.1109/PLANS.2014.6851370
Mostafa Elhoushi, J. Georgy, M. Korenberg, A. Noureldin
Portable navigation has become increasingly prevalent in daily activities. The need for accurate user positioning information, including a person's location and velocity, when using a portable device (such as a cell phone, tablet, or even a smart watch) is growing in various fields. Knowing the user's mode of motion or conveyance allows appropriate algorithms or constraints, related to each mode, to be used to estimate a more accurate position and velocity. The modes covered in this paper are walking, running, cycling, and land-based vessels (including vehicles, truck, buses, and trains which include light rail trains and subways). The work discussed in this paper involves the use of sensors - with and without Global Navigation Satellite Systems (GNSS) signal availability - in portable devices to help recognize the mode of motion for an arbitrary user, an arbitrary use case - whether the device is held in the hand, in the pocket, or at the ear, etc. - and an arbitrary orientation of the device.
{"title":"Robust motion mode recognition for portable navigation independent on device usage","authors":"Mostafa Elhoushi, J. Georgy, M. Korenberg, A. Noureldin","doi":"10.1109/PLANS.2014.6851370","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851370","url":null,"abstract":"Portable navigation has become increasingly prevalent in daily activities. The need for accurate user positioning information, including a person's location and velocity, when using a portable device (such as a cell phone, tablet, or even a smart watch) is growing in various fields. Knowing the user's mode of motion or conveyance allows appropriate algorithms or constraints, related to each mode, to be used to estimate a more accurate position and velocity. The modes covered in this paper are walking, running, cycling, and land-based vessels (including vehicles, truck, buses, and trains which include light rail trains and subways). The work discussed in this paper involves the use of sensors - with and without Global Navigation Satellite Systems (GNSS) signal availability - in portable devices to help recognize the mode of motion for an arbitrary user, an arbitrary use case - whether the device is held in the hand, in the pocket, or at the ear, etc. - and an arbitrary orientation of the device.","PeriodicalId":371808,"journal":{"name":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126239589","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}
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