GPS error modeling and OTF ambiguity resolution for high-accuracy GPS/INS integrated system

Grejner‐Brzezinska, Dorota A.; Da, Ren; Toth, Charles K.

doi:10.1007/s001900050202

Cited by 91 publications

(47 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Four different simulation tests will be discussed, illustrating the different role that can be played by a pseudolite when integrated with a GPS/INS system. In these tests, the software package AIMS (Airborne Integrated Mapping System), developed by the Center for Mapping of the Ohio State University (Grejner-Brzezinska 1997;Grejner-Brzezinska et al 1998), was used for data processing. This software has been modified at the University of New South Wales to include the capability of processing pseudolite measurements.…”

Section: Tests and Results Of The Simulated Measurementsmentioning

confidence: 99%

GPS/Pseudolite/INS integration: concept and first tests

et al. 2002

View full text Add to dashboard Cite

This paper discusses the introduction of pseudolites (ground-based GPS-like signal transmitters) into existing integrated GPS/INS systems in order to provide higher availability, integrity, and accuracy in a local area. Even though integrated GPS/INS systems can overcome inherent drawbacks of each component system (line-of-sight requirement for GPS, and INS errors that grow with time), performance is nevertheless degraded under adverse operational circumstances. Some typical examples are when the duration of satellite signal blockage exceeds an INS bridging level, resulting in large accumulated INS errors that cannot be calibrated by GPS. Such a scenario, unfortunately, is a common occurrence for certain kinematic applications. To address such shortcomings, both pseudolite/INS and GPS/pseudolite/INS integration schemes are proposed here. Typically, the former is applicable for indoor positioning where the GPS signal is unavailable for use. The latter would be appropriate for system augmentation when the number and geometry of visible satellites is not sufficient for accurate positioning or attitude determination. In this paper, some technical issues concerned with implementing these two integration schemes are described, including the measurement model, and the appropriate integration filter for INS error estimation and correction through GPS and pseudolite (PL) carrier phase measurements. In addition, the results from the processing of simulated measurements, as well as field experiments, are presented in order to characterize the system performance. As a result, it has been established that the GPS/PL/INS and PL/ INS integration schemes would make it possible to ensure centimeter-level positioning accuracy even if the number of GPS signals is insufficient, or completely unavailable.

show abstract

Section: Tests and Results Of The Simulated Measurementsmentioning

confidence: 99%

GPS/Pseudolite/INS integration: concept and first tests

et al. 2002

View full text Add to dashboard Cite

show abstract

“…하지만 IMU나 pseudo-lite와 같은 기술은 오차가 축적되거나 비싼 설 GPS 수신기는 편중 오차(bias error)를 생성시킨다 [11] . [1]- [3][12] 자동차의 선속도나 각속도 등의 IMU 센 서 정보와 GPS 센서 정보를 얻은 후, 파티클 필터를 사용한 MCL(Monte Carlo Locarlization) 알고리즘 [12] 이 나 칼만 필터 알고리즘 [1][2][3] 을 이용하여 GPS 위치를 보 정하는 연구가 많이 수행되었다. 이러한 방법은 GPS센 서만을 이용한 결과보다 정확도가 높지만 GPS센서 정 보에 많이 의존하게 되므로 GPS의 편중 오차를 효과 적으로 제거하지 못한다.…”

Section: 서 론1 )unclassified

“…이러한 방법은 GPS센 서만을 이용한 결과보다 정확도가 높지만 GPS센서 정 보에 많이 의존하게 되므로 GPS의 편중 오차를 효과 적으로 제거하지 못한다. GPS, IMU, Pseudo-lite와 TLS(Terrestrial Laser Scanning)를 자동차에 모두 장착 하여 위치를 보정하는 연구도 진행되어왔지만 [2] , GPS 수신기만을 장착하는 방법보다 비용도 많이 들고 비효 율적이다. 이 외의 방법으로는 이미지 센서나 LiDAR 센서, Radar 센서 등을 이용하여 위치를 인식하는 방 법도 연구되어왔다 [4][5][6]8] .…”

Section: 서 론1 )unclassified

Database based Global Positioning System Correction

Moon¹,

Choi²,

Park³

et al. 2012

The Journal of Korea Robotics Society

View full text Add to dashboard Cite

A GPS sensor is widely used in many areas such as navigation, or air traffic control. Particularly, the car navigation system is equipped with GPS sensor for locational information. However, when a car goes through a tunnel, forest, or built-up area, GPS receiver cannot get the enough number of satellite signals. In these situations, a GPS receiver does not reliably work. A GPS error can be formulated by sum of bias error and sensor noise. The bias error is generated by the geometric arrangement of satellites and sensor noise error is generated by the corrupted signal noise of receiver. To enhance GPS sensor accuracy, these two kinds of errors have to be removed. In this research, we make the road database which includes Road Database File (RDF). RDF includes road information such as road connection, road condition, coordinates of roads, lanes, and stop lines. Among the information, we use the stop line coordinates as a feature point to correct the GPS bias error. If the relative distance and angle of a stop line from a car are detected and the detected stop line can be associated with one of the stop lines in the database, we can measure the bias error and correct the car's location. To remove the other GPS error, sensor noise, the Kalman filter algorithm is used. Additionally, using the RDF, we can get the information of the road where the car belongs. It can be used to help the GPS correction algorithm or to give useful information to users.

show abstract

“…The GPS/INS Kalman filter [14,15] used in the analysis presented here implements the psi-angle approach to the INS error model [11] and uses the velocity error version of the translatory error equation according to equation (2). (2) where v, r, and are the velocity, position, and orientation error vectors, respectively; ١ is the accelerometer error vector; ⑀ is the gyro drift error;…”

Section: Static Calibration Of the Ins Errors: An Overviewmentioning

confidence: 99%

Bridging GPS Gaps in Urban Canyons: The Benefits of ZUPTs

Grejner‐Brzezinska

Toth

2001

Navigation

View full text Add to dashboard Cite

INTRODUCTIONThe primary objective of a mobile mapping system (MMS) is to provide automatic acquisition of directly oriented (georeferenced) digital imagery for mapping and geographic information system (GIS) data collection. The direct georeferencing is most commonly facilitated by the integration of differential GPS (DGPS) and inertial navigation systems (INS), providing nearly continuous (up to 256 Hz) positioning and attitude information of the imaging sensor(s). The navigation data can be processed in near real time or in postmission mode to determine the best estimates of the image exterior orientation. Directly oriented images are then used in photogrammetric processing to extract the feature data, together with their positional information. In the past 10 years, MMSs have evolved toward multisensor and multitasking systems, comprising four primary modules: (1) the control module, (2) the positioning (georeferencing) module, (3) the imaging module, and (4) the data postprocessing module. The modular design creates a system capable of handling numerous concurrent operations in real time and in postprocessing.The MMS presented in this paper is designed for high-accuracy, near-real-time mapping of highway center and edge lines [1]; the system development is currently supported by the Ohio Department of Transportation. The positioning module of this system is based on a tight integration of dual-frequency DGPS carrier phases and raw inertial measurement unit (IMU) data provided by a medium-accuracy, high-reliability strapdown Litton LN-100 INS. The LN-100 is based on a Zero-lock TM Laser Gyro (ZLG TM ) and A-4 accelerometer triad (0.8 nmi/h circular error probable [CEP], gyro bias 0.003 deg/h, accelerometer bias 25 g). An optimal 21-state centralized Kalman filter estimates errors in position, velocity, and attitude, as well as in the inertial and GPS measurements. Under favorable GPS constellations (minimum of 5 -6 satellites) and short to medium baselines, the estimated standard deviations are at the level of 2 -3 cm for position coordinates, and 10 arcsec and 10 -20 arcsec for attitude and heading components, respectively.As a land-based MMS, the system operates primarily in urban environments, where frequent losses of GPS signal lock occur. To prevent major degradation in navigation accuracy and to support ambiguity resolution after GPS signal reacquisition, the loss-of-lock events must be controlled in real time. The MMS control module tracks the duration of the loss of lock (or extended partial satellite blockage) and, based on empirical knowledge of the positioning error growth, provides a warning to the operator that a ZUPT (zero velocity update) is needed (see Figure 1). This empirical information is derived from the system calibration and testing, and facilitates a reference input to the control system. This paper presents the calibration results for the static INS used to derive the empirical information for the system's controls, including observability characteristics. Special emphasis is placed on...

show abstract

GPS error modeling and OTF ambiguity resolution for high-accuracy GPS/INS integrated system

Cited by 91 publications

References 12 publications

GPS/Pseudolite/INS integration: concept and first tests

GPS/Pseudolite/INS integration: concept and first tests

Database based Global Positioning System Correction

Bridging GPS Gaps in Urban Canyons: The Benefits of ZUPTs

Contact Info

Product

Resources

About