Jay Hyoun Kwon

Gravity Requirements for Compensation of Ultra-Precise Inertial Navigation

Precision inertial navigation depends not only on the quality of the inertial sensors (accelerometers and gyros), but also on the accuracy of the gravity compensation. With a view toward the next-generation inertial navigation systems, based on sensors whose errors contribute as little as a few metres per hour to the navigation error budget, we have analyzed the required quality of gravity compensation to the navigation solution. The investigation considered a standard compensation method using ground data to predict the gravity vector at altitude for aircraft free-inertial navigation. The navigation effects of the compensation errors were examined using gravity data in two gravimetrically distinct areas and a navigation simulator with parameters such as data noise and resolution, supplemental global gravity model noise, and on-track interpolation method. For a typical flight trajectory at 5 km altitude and 300 km/hr aircraft speed, the error in gravity compensation contributes less than 5 m to the position error after one hour of free-inertial navigation if the ground data are gridded with 2 arcmin resolution and are accurate to better than 5 mGal.

Efficient GPS receiver DCB estimation for ionosphere modeling using satellite-receiver geometry changes

A new and efficient algorithm using the geometry conditions between satellite and tracking receivers is proposed to determine the receiver differential code bias (DCB) using permanent reference stations. This method does not require a traditional single-layer ionosphere model and can be used for estimating DCBs of receivers in a regional network as long as one of the receiver DCBs is already known. The main underlying rationale for this algorithm is that the magnitude of the signal delay caused by the ionosphere is, under normal conditions, highly dependent on the geometric range between the satellite and the receiver. The proposed algorithm was tested with the Ohio Continuously Operating Reference Stations (CORS) sub-network data. The results show that quality comparable to the traditional DCB estimation method is obtainable by implementing this simple algorithm.

Geodetic datum transformation to the global geocentric datum for seas and islands around Korea

According to revisions of survey law taking effect on January 1, 2003, the Korean geodetic datum has been changed from a local geodetic to a world geodetic system. Since the datum change demands a geographical data transformation, the National Geographic Information Institute has established step-by-step plans for the transformation of the land data constructed through the National GIS Project, and it is in progress. For maritime data, however, no detailed transformation plan has been established yet. Therefore, it is necessary to analyze the maritime geographic data obtained through the Maritime GIS project and set up the data transformation scheme to a world geodetic system. In this study, the datum transformation parameters especially for the maritime geographical data are determined. From database constructed through MGIS, a total of 492 coordinate pairs were used in parameter determination initially. At this stage, three popular seven parameter transformation models, Bursa-Wolf, Molodensky and Veis model, and the multi regression equation are applied, and the transformation parameters from the Molodensky model are selected for its accuracy and consistency with the land data transformation method. To eliminate the local bias caused by the nonequally distributed stations, a network optimization is applied and 42 stations are selected to determine the final transformation parameters. The distortion after applying the similarity transformation is modeled through a least squares collocation with Gaussian model, and high accuracy better than 15 cm in coordinate transformation is obtained.

The effect of stochastic gravity models in airborne vector gravimetry

Measurements of specific force using inertial measurement units (IMU) combined with Global Positioning System (GPS) accelerometry can be used on an airborne platform to determine the total gravitational vector. Traditional methods, originating with inertial surveying systems and based on Kalman filtering, rely on choosing an appropriate stochastic model for the gravity disturbance components included in the set of system error states. An alternative procedure that uses no a priori stochastic model has proven to be as effective, or moreso, in extracting the gravity vector from airborne IMU/GPS data. This method is based on inspecting acceleration residuals from a Kalman filter that estimates only sensor biases. Using actual data collected over the Canadian Rocky Mountains, this method was compared to the traditional approach adapted for different types of stochastic models for the gravity disturbance vector. In all test cases, the estimation filter without a gravitational model yielded better results—up to...

A triple difference approach to Low Earth Orbiter precision orbit determination

A precise kinematic orbit determination (P-KOD) procedure for Low Earth Orbiter(LEO) using the GPS ion-free triple differenced carrier phases is presented. Because the triple differenced observables provide only relative information, the first epoch’s positions of the orbit should be held fixed. Then, both forward and backward filtering was executed to mitigate the effect of biases of the first epoch’s position. P-KOD utilizes the precise GPS orbits and ground stations data from International GPS Service (IGS) so that the only unknown parameters to be solved are positions of the satellite at each epoch. Currently, the 3-D accuracy of P-KOD applied to CHAMP (CHAllenging Minisatellite Payload) shows better than 35 cm compared to the published rapid scientific orbit (RSO) solution from GFZ (GeoForschungsZentrum Potsdam). The data screening for cycle slips is a particularly challenging procedure for LEO, which moves very fast in the middle of the ionospheric layer. It was found that data screening using SNR (signal to noise ratio) generates best results based on the residual analysis using RSO. It is expected that much better accuracy are achievable with refined prescreening procedure and optimized geometry of the satellites and ground stations.

Medium to Long Range Kinematic GPS Positioning with Position-Velocity-Acceleration Model Using Multiple Reference Stations

In order to obtain precise kinematic global positioning systems (GPS) in medium to large scale networks, the atmospheric effects from tropospheric and ionospheric delays need to be properly modeled and estimated. It is also preferable to use multiple reference stations to improve the reliability of the solutions. In this study, GPS kinematic positioning algorithms are developed for the medium to large-scale network based on the position-velocity-acceleration model. Hence, the algorithm can perform even in cases where the near-constant velocity assumption does not hold. In addition, the estimated kinematic accelerations can be used for the airborne gravimetry. The proposed algorithms are implemented using Kalman filter and are applied to the in situ airborne GPS data. The performance of the proposed algorithms is validated by analyzing and comparing the results with those from reference values. The results show that reliable and comparable solutions in both position and kinematic acceleration levels can be obtained using the proposed algorithms.

On accurate time synchronization of multi-sensor mobile mapping systems

Abstract This paper introduces a GPS time synchronization method intended for airborne and ground-based mobile mapping systems, where multiple sensors simultaneously acquire geospatial data, and accurate co-registration of the data in space and time domains is necessary. Although more and more sensors are coming with built-in GPS capabilities, which provides for ideal GPS time-tagging, and thus for spatial registration of the data, the great majority of the sensors used in mobile mapping still lack this capability. The proposed method offers an effective and simple solution to precise GPS time-tagging of sensors that are connected to a PC-based data acquisition system. Using a timer board and a GPS receiver connection, a time base transformation is established between the high-performance processor time and the GPS time, which results in the system level availability of the GPS time for any data acquisition process.

Efficient LEO Dynamic Orbit Determination with Triple Differenced GPS Carrier Phases

The dynamic precise orbit determination of a Low Earth Orbit satellite using triple differenced GPS phases is presented in this study. The atmospheric drag parameters are estimated to compensate the incomplete atmosphere model for better precision of the orbit solution. In addition, the empirical force parameters, especially once- and twice-per-revolution components, along with the new IERS Conventions and models to compute the perturbing forces are introduced to absorb the remaining unmodelled forces. The optimal arc length for the parameterization and the data processing strategy are also tested and analyzed for the best orbit solutions. The triple differencing technique enables fast and efficient orbit estimation, because no ambiguity resolution and cycle slip detection are required. With the triple differenced ion-free GPS phase observables, the orbit and the velocity solutions for 24 hours of CHAMP are calculated; they compare with the published Rapid Science Orbit with the accuracy of 8 cm and 0-12 mm/s in 3D RMS for the orbit and the velocity, respectively, and are statistically consistent with the RSO when it is not better than 4 cm in terms of an absolute accuracy. The approach presented here provides an efficient and simple, but robust, alternative approach, while the solutions accuracy is still comparable to the double-difference results.

Performance Analysis of Two-Dimensional Dead Reckoning Based on Vehicle Dynamic Sensors during GNSS Outages

Recently, to improve safety and convenience in driving, numerous sensors are mounted on cars to operate advanced driver assistant systems. Among various sensors, vehicle dynamic sensors can measure the vehicle motions such as speed and rotational angular speed for dead reckoning, which can be applied to develop a land vehicle positioning system to overcome the weaknesses of the GNSS technique. In this paper, three land vehicle positioning algorithms that integrate GNSS with vehicle dynamic sensors including a wheel speed sensor (WSS), a yaw rate sensor (YRS), and a steering angle sensor (SAS) are implemented, and then a performance evaluation was conducted during GNSS outages. Using a loosely coupled strategy, three integration algorithms are designed, namely, GNSS/WSS, GNSS/WSS/YRS, and GNSS/WSS/YRS/SAS. The performance of the three types of integration algorithm is evaluated based on two data sets. The results indicate that both the GNSS/WSS/YRS integration and the GNSS/WSS/YRS/SAS integration could estimate the horizontal position with meter-level accuracy during 30-second GNSS outages. However, the GNSS/WSS integration would provide an unstable navigation solution during GNSS outages due to the accuracy limitation of the computed yaw rate using WSS.

Multi-Sensor Personal Navigator: System Design and Calibration

This paper presents the current design status and some preliminary calibration/performance analyses of the prototype of a multi-sensor personal navigator currently under development at The Ohio State University. The main purpose of this research project is to develop a theoretical foundation and algorithms which integrate the Global Positioning System (GPS), Micro-electro-mechanical inertial measurement unit (MEMS IMU), barometer and compass to provide seamless position information to support navigation and tracking of ground military and rescue personnel. The system is designed with an open-ended architecture, which would be able to incorporate additional navigation and imaging sensor data, extending the systems operations to the indoor environments. The current target accuracy of the system is at 3–5 m CEP (circular error probable). In the current prototype implementation, the following sensors are integrated in the tightly coupled Extended Kalman Filter (EKF): GPS carrier phase and pseudorange data, Crossbow MEMS IMU400C, PTB220A barometer, and KVH Azimuth 1000 digital compass. This paper focuses on the design architecture of the integrated system and the preliminary performance analysis, with a special emphasis on the navigation during the loss of GPS signal. A brief description of the individual sensors and their calibration is presented, together with the navigation performance of the system of sensors.