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.2014.6851499
K. Ali, Esteban Garbin Manfredini, F. Dovis
The open nature of the global navigation satellite system (GNSS) civil signals makes them very vulnerable to counterfeit and ill-intended interferences like jamming and spoofing attacks. Spoofing of GNSS signal refers to the transmission of a counterfeit GNSS-like signal, with the goal of deceiving the receiver, making it compute erroneous navigation solutions. In this paper, an evolved version of the signal quality monitoring techniques is presented where a spoofer detection algorithm is discussed. The quality of the correlation function is assessed through the joint use of two metrics which are based on the ratio metric and a pair of extra-correlators in order to detect vestigial signal presence. The results show that the joint use of two provides advantages in the detection of matched-power spoofer attacks.
{"title":"Vestigial signal defense through signal quality monitoring techniques based on joint use of two metrics","authors":"K. Ali, Esteban Garbin Manfredini, F. Dovis","doi":"10.1109/PLANS.2014.6851499","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851499","url":null,"abstract":"The open nature of the global navigation satellite system (GNSS) civil signals makes them very vulnerable to counterfeit and ill-intended interferences like jamming and spoofing attacks. Spoofing of GNSS signal refers to the transmission of a counterfeit GNSS-like signal, with the goal of deceiving the receiver, making it compute erroneous navigation solutions. In this paper, an evolved version of the signal quality monitoring techniques is presented where a spoofer detection algorithm is discussed. The quality of the correlation function is assessed through the joint use of two metrics which are based on the ratio metric and a pair of extra-correlators in order to detect vestigial signal presence. The results show that the joint use of two provides advantages in the detection of matched-power spoofer attacks.","PeriodicalId":371808,"journal":{"name":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","volume":"11 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":"121220934","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.2014.6851433
Y. Tsai, K. Low
In past decade, the GPS plays an important role in many navigation applications. In some cases, the GPS is the only device providing navigation service. For safety-of-life user, GPS alone cannot provide the stringent performance in accuracy, integrity and availability. As a result, several SBAS (Satellite Based Augmentation System) systems have been developed to provide corrections and assistances to GPS users. The notably SBAS systems are U.S. WAAS (Wide Area Augmentation System), Europe EGNOS (European Geostationary Navigation Overlay Service) and Japanese MSAS (Multi-functional Satellite Augmentation System), In addition, India and Russia have engaged in the deployment and development of SBAS system, named GAGAN (GPS Aided Geo Augmented Navigation) and SDCM (System of Differential Correction and Monitoring). Also, other regions in the world currently proceed feasibility studies on SBAS. For instance, SACCSA (The Augmentation Solution for the Caribbean, Central America and South America) project in Latin-America, ASAS (African Satellite Augmentation System) in Africa and Malaysian SBAS. SBAS broadcast the correction for ionosphere delay and satellite clock. By using these corrections, the user position accuracy can improve to several meters or better. In Singapore, Changi airport is one of busiest airport in the world, and it handled more than fifty million passengers in 2012. Additionally, Singapore is located in the equatorial region so that the ionosphere activities are dramatic. Currently, there is no SBAS service in the Singapore region. The objective of this paper is to propose a fusion scheme to exploit the correction and integrity monitoring messages from nearby two SBAS systems, GAGAN and MSAS and then provide a reliable correction for GPS users. Singapore is not located in the service volume of either GAGAN or MSAS. Because of the lack of SBAS monitoring stations, the navigation quality in Singapore region cannot be assured through either GAGAN or MSAS. The messages from both SBAS systems can be still received. Therefore, it is desired to investigate how messages from GAGAN and MSAS can be utilized to enhance the performance for GPS user. Then, its goal is to ensure a smooth transition and assured navigation performance in this region. In the paper, both GAGAN and MSAS messages are firstly received and analyzed for the assessment of the signal quality. And then, a comparison with the requirements at different phases of flight is made. A synergistic integration of the messages from by GAGAN and MSAS in Singapore is developed to pave a way for the future regional augmentation system implementation. An extrapolation scheme is proposed to expand the coverage of ionospheric delay correction messages from GAGAN and MSAS. All proposed fusion and extrapolation schemes are assessed by using real data to evaluate performance. The result shows that our approach has reliable performance compared to a surveying-grade receiver.
{"title":"Performance assessment on expanding SBAS service areas of GAGAN and MSAS to Singapore region","authors":"Y. Tsai, K. Low","doi":"10.1109/PLANS.2014.6851433","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851433","url":null,"abstract":"In past decade, the GPS plays an important role in many navigation applications. In some cases, the GPS is the only device providing navigation service. For safety-of-life user, GPS alone cannot provide the stringent performance in accuracy, integrity and availability. As a result, several SBAS (Satellite Based Augmentation System) systems have been developed to provide corrections and assistances to GPS users. The notably SBAS systems are U.S. WAAS (Wide Area Augmentation System), Europe EGNOS (European Geostationary Navigation Overlay Service) and Japanese MSAS (Multi-functional Satellite Augmentation System), In addition, India and Russia have engaged in the deployment and development of SBAS system, named GAGAN (GPS Aided Geo Augmented Navigation) and SDCM (System of Differential Correction and Monitoring). Also, other regions in the world currently proceed feasibility studies on SBAS. For instance, SACCSA (The Augmentation Solution for the Caribbean, Central America and South America) project in Latin-America, ASAS (African Satellite Augmentation System) in Africa and Malaysian SBAS. SBAS broadcast the correction for ionosphere delay and satellite clock. By using these corrections, the user position accuracy can improve to several meters or better. In Singapore, Changi airport is one of busiest airport in the world, and it handled more than fifty million passengers in 2012. Additionally, Singapore is located in the equatorial region so that the ionosphere activities are dramatic. Currently, there is no SBAS service in the Singapore region. The objective of this paper is to propose a fusion scheme to exploit the correction and integrity monitoring messages from nearby two SBAS systems, GAGAN and MSAS and then provide a reliable correction for GPS users. Singapore is not located in the service volume of either GAGAN or MSAS. Because of the lack of SBAS monitoring stations, the navigation quality in Singapore region cannot be assured through either GAGAN or MSAS. The messages from both SBAS systems can be still received. Therefore, it is desired to investigate how messages from GAGAN and MSAS can be utilized to enhance the performance for GPS user. Then, its goal is to ensure a smooth transition and assured navigation performance in this region. In the paper, both GAGAN and MSAS messages are firstly received and analyzed for the assessment of the signal quality. And then, a comparison with the requirements at different phases of flight is made. A synergistic integration of the messages from by GAGAN and MSAS in Singapore is developed to pave a way for the future regional augmentation system implementation. An extrapolation scheme is proposed to expand the coverage of ionospheric delay correction messages from GAGAN and MSAS. All proposed fusion and extrapolation schemes are assessed by using real data to evaluate performance. The result shows that our approach has reliable performance compared to a surveying-grade receiver.","PeriodicalId":371808,"journal":{"name":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","volume":"21 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":"122276820","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.2014.6851495
Zhi Xiong, Hui Peng, Jian-ye Liu, Jie Wang, Yongrong Sun
The IMU's center of mass may be deviated from the vehicle body's center of mass in the high dynamic flight environment of the hypersonic vehicle; this phenomenon may lead to the lever arm effect error if the angular movement exists. If the lever arm effect error cannot be calibrated and compensated during the high dynamic flight process, the lever arm effect error may affect the accuracy of the SINS largely to some extent. The online calibration method for the lever arm effect of the SINS is proposed in this paper. The model of the lever arm effect is built and the length of lever arm is used as the state variable. The observability of the system variables is analyzed and the Kalman Filter is used to calibrate the length of the lever arm. The simulation results show that the designed method can effectively calibrate the length of the lever arm online. The calibration results are used to compensate the navigation system and the results show that accuracy of the compensated navigation system is improved than the uncompensated one.
{"title":"Online calibration research on the lever arm effect for the hypersonic vehicle","authors":"Zhi Xiong, Hui Peng, Jian-ye Liu, Jie Wang, Yongrong Sun","doi":"10.1109/PLANS.2014.6851495","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851495","url":null,"abstract":"The IMU's center of mass may be deviated from the vehicle body's center of mass in the high dynamic flight environment of the hypersonic vehicle; this phenomenon may lead to the lever arm effect error if the angular movement exists. If the lever arm effect error cannot be calibrated and compensated during the high dynamic flight process, the lever arm effect error may affect the accuracy of the SINS largely to some extent. The online calibration method for the lever arm effect of the SINS is proposed in this paper. The model of the lever arm effect is built and the length of lever arm is used as the state variable. The observability of the system variables is analyzed and the Kalman Filter is used to calibrate the length of the lever arm. The simulation results show that the designed method can effectively calibrate the length of the lever arm online. The calibration results are used to compensate the navigation system and the results show that accuracy of the compensated navigation system is improved than the uncompensated one.","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":"128015469","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.2014.6851435
S. Yun, W. Lee, Chan Gook Park
This paper proposes the method to calculate a covariance matrix of a batch processing terrain referenced navigation (TRN) for an integrated INS/TRN system using a Kalman filter. The batch processing TRN system cannot automatically provide a covariance matrix of the estimation error. That is why it is important to calculate a covariance matrix for batch processing TRN system. The newly adaptive algorithm based on recursive least square algorithm is proposed. The proposed adaptive algorithm can calculate the covariance of each measurement noise of the batch processing TRN. It also has stable property when the filter operates in a non-stationary environment. It is shown that the batch processing TRN with proposed algorithm has better performance than the TRN with conventional one in the computer simulations.
{"title":"Covariance calculation for batch processing terrain referenced navigation","authors":"S. Yun, W. Lee, Chan Gook Park","doi":"10.1109/PLANS.2014.6851435","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851435","url":null,"abstract":"This paper proposes the method to calculate a covariance matrix of a batch processing terrain referenced navigation (TRN) for an integrated INS/TRN system using a Kalman filter. The batch processing TRN system cannot automatically provide a covariance matrix of the estimation error. That is why it is important to calculate a covariance matrix for batch processing TRN system. The newly adaptive algorithm based on recursive least square algorithm is proposed. The proposed adaptive algorithm can calculate the covariance of each measurement noise of the batch processing TRN. It also has stable property when the filter operates in a non-stationary environment. It is shown that the batch processing TRN with proposed algorithm has better performance than the TRN with conventional one in the computer simulations.","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":"129888712","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.2014.6851364
Yuwei Chen, Jingbin Liu, Antonni Jaakkola, J. Hyyppa, Liang Chen, H. Hyyppa, Tang Jian, Ruizhi Chen
In this paper, an environment knowledge-based multiple sensors indoor positioning system is designed and tested. The system integrates a LiDAR sensor, an odometer and a light sensor onto a low-cost robot platform. While, a LiDAR point-cloud-based pattern match algorithm - Iterative Closed Point (ICP) is used to estimate the relative change in heading and displacement of the platform. Based on the knowledge of the construction's structure, outdoor weather, and lighting situation, the light sensor offers an efficient parameter to improve indoor position accuracy with a light intensity fingerprint matching algorithm on low computational cost. The estimated heading and position change from LiDAR are eventually fused by Extended Kalman Filter (EKF) with those calculated from the light sensor measurement. The results prove that the spatial structure and the ambient light information in indoor environment as knowledge base can be utilized to estimate and mitigate the accumulated errors and inherent drifts of ICP algorithm. These improvements lead to longer sustainable sub meter-level indoor positioning for UGVs.
{"title":"Knowledge-based indoor positioning based on LiDAR aided multiple sensors system for UGVs","authors":"Yuwei Chen, Jingbin Liu, Antonni Jaakkola, J. Hyyppa, Liang Chen, H. Hyyppa, Tang Jian, Ruizhi Chen","doi":"10.1109/PLANS.2014.6851364","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851364","url":null,"abstract":"In this paper, an environment knowledge-based multiple sensors indoor positioning system is designed and tested. The system integrates a LiDAR sensor, an odometer and a light sensor onto a low-cost robot platform. While, a LiDAR point-cloud-based pattern match algorithm - Iterative Closed Point (ICP) is used to estimate the relative change in heading and displacement of the platform. Based on the knowledge of the construction's structure, outdoor weather, and lighting situation, the light sensor offers an efficient parameter to improve indoor position accuracy with a light intensity fingerprint matching algorithm on low computational cost. The estimated heading and position change from LiDAR are eventually fused by Extended Kalman Filter (EKF) with those calculated from the light sensor measurement. The results prove that the spatial structure and the ambient light information in indoor environment as knowledge base can be utilized to estimate and mitigate the accumulated errors and inherent drifts of ICP algorithm. These improvements lead to longer sustainable sub meter-level indoor positioning for UGVs.","PeriodicalId":371808,"journal":{"name":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","volume":"37 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":"130505814","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.2014.6851365
Y. Hang, Jian-ye Liu, Rong-bing Li, Yongrong Sun, Ting-wan Lei
Micro-electromechanical Systems (MEMS) IMU/ LADAR integrated navigation is a new-type autonomous navigation and environment detection method. It has a broad application prospect in the indoor environment. In MEMS IMU/LADAR integrated navigation system, the MEMS inertial sensors are used to measure vehicle movement. The LADAR is used to detect environmental features, and their outputs are fused by a digital filter, to provide precise position and environment mapping information for small rotorcraft. However, with the increasing amounts of observed landmarks, the computation complexity of traditional Extended Kalman Filter (EKF) increase excessively, making it unable to meet the realtime navigation requirement for small rotorcraft. In addition, the existing LADAR is generally planar scanning radar. When the aircraft's attitudes change, there is no guarantee that detecting plane maintains in a horizontal plane. This makes detecting information couple attitude angle measurement errors, and would bring great errors to the integrated navigation results. According to the problems mentioned above, the paper proposes the LADAR's attitude angle coupling error compensation algorithm. The navigation filter is designed based on Compressed-EKF(CEKF) algorithm. And the experimental prototype is designed for MEMS IMU/LADAR integrated navigation system, to verify CEKF algorithm in indoor environment. The tests show that the proposed algorithm can effectively improve the LADAR's precision and decrease the calculation amount of filtering algorithm. The research has significant reference value for small rotorcraft's simultaneous location and mapping (SLAM) technology in the structured indoor environment.
{"title":"Optimization method of MEMS IMU/LADAR integrated navigation system based on Compressed-EKF","authors":"Y. Hang, Jian-ye Liu, Rong-bing Li, Yongrong Sun, Ting-wan Lei","doi":"10.1109/PLANS.2014.6851365","DOIUrl":"https://doi.org/10.1109/PLANS.2014.6851365","url":null,"abstract":"Micro-electromechanical Systems (MEMS) IMU/ LADAR integrated navigation is a new-type autonomous navigation and environment detection method. It has a broad application prospect in the indoor environment. In MEMS IMU/LADAR integrated navigation system, the MEMS inertial sensors are used to measure vehicle movement. The LADAR is used to detect environmental features, and their outputs are fused by a digital filter, to provide precise position and environment mapping information for small rotorcraft. However, with the increasing amounts of observed landmarks, the computation complexity of traditional Extended Kalman Filter (EKF) increase excessively, making it unable to meet the realtime navigation requirement for small rotorcraft. In addition, the existing LADAR is generally planar scanning radar. When the aircraft's attitudes change, there is no guarantee that detecting plane maintains in a horizontal plane. This makes detecting information couple attitude angle measurement errors, and would bring great errors to the integrated navigation results. According to the problems mentioned above, the paper proposes the LADAR's attitude angle coupling error compensation algorithm. The navigation filter is designed based on Compressed-EKF(CEKF) algorithm. And the experimental prototype is designed for MEMS IMU/LADAR integrated navigation system, to verify CEKF algorithm in indoor environment. The tests show that the proposed algorithm can effectively improve the LADAR's precision and decrease the calculation amount of filtering algorithm. The research has significant reference value for small rotorcraft's simultaneous location and mapping (SLAM) technology in the structured indoor environment.","PeriodicalId":371808,"journal":{"name":"2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014","volume":"14 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":"130897789","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.2014.6851404
D. Salmon, D. Bevly
This paper discusses an exploratory analyses of the benefits of using Vehicle Odometry/Steer Angle and an accurate vehicle model (VM) to replace/assist a low-cost Inertial Measurement Unit (IMU) for blended ground vehicle navigation. In this research, multiple variations of the tightly coupled Extended Kalman Filter (EKF) algorithm are performed using multiple sensor sets to find the optimal solution, factoring in sensor cost and pose accuracy. Many automotive precision navigation solutions have been developed based on sensor fusion in recent years; however, as autonomous navigation technology becomes more prevalent on consumer vehicles, the need for a high-accuracy, low-cost pose solution is increasing. One widely used solution to this problem is the combination of a Micro-Electro-Mechanical (MEMS) IMU with Global Positioning System (GPS); however, this may not be the optimal solution due to the high noise characteristics of lower cost IMU's. Measurements from GPS, IMU/Inertial Navigation System (INS), and VM are used in this research. The different algorithm setups being investigated include: GPS/VM sensor fusion with accurate vehicle model constraints, GPS/INS with low-cost commercially available IMU, and GPS/INS/VM with the IMU. The determination of the level of IMU necessary for GPS/INS fusion to exceed the pose solution accuracy achievable using GPS/VM sensor fusion with accurate vehicle model constraints is a priority for this research. Another goal of this research is the quantitative and qualitative analysis of the benefits of using VM to assist normal GPS/INS EKF and whether the inclusion of VM in either the time update or the measurement update results in a more accurate pose solution. Direct experimental comparison of tightly coupled EKF Fault Detection and Exclusion (FDE) algorithms based on vehicle wheel speed and steering angle versus the IMU measurements to determine if either sensor set yields a distinct advantage over the other is also investigated. All analysis will be based on real world experimental data.
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