Journal of Surveying Engineering

With the development of space-based systems, the results of various applications are generally provided in an earth-centered and earth-fixed global coordinate frame. However, many times it is necessary to transform those results, together with their errors, into local geodetic frames for physical modeling and geometrical interpretation of results as well as to combine them with terrestrial observations. Although it is common practice to transform errors using the full matrix error propagation law, results of many studies in global Cartesian frames are published in compact form without attaching the necessary variance–covariance parameters, therefore preventing a consistent transformation of covariances into a local geodetic frame. This is especially true for coordinates released as time series of sequential epochs. Examination of coordinate/velocity correlations in a global Cartesian frame (GCF) and a local geodetic frame (LGF) reveals that the transformation matrix from GCF into LGF (which depends on the position and origin of LGF) decorrelates the covariance matrix in the GCF up to 90%, depending on design matrix, a priori weights, and constraints. In this study, utilizing the error decorrelation in the LGF, analytical, transformation equations were inverted for correlation parameters in GCF so as to reconstruct the approximate covariance matrix in GCF from a diagonal matrix of variances. Variances in the LGF were then obtained by a basis change through singular value decomposition. Results show that the new semianalytical expressions could be used successfully to transform variances in GCF into LGF especially in the absence of covariance terms.

OPUS rapid static (OPUS-RS) was introduced to the public in August 2005 as an operational prototype. Allowing centimeter accuracy with 10–15-min observation sessions, OPUS-RS holds the promise of dramatically lessening the time needed to reference a survey to the National Spatial Reference System. (NSRS). The purpose of this paper is to compare results between networks constrained to the NSRS using multiple short OPUS-RS observation sessions and much longer OPUS observation sessions. The results of this comparison demonstrate that multiple short OPUS-RS sessions provide for significant time savings with results that agree within a centimeter of the longer OPUS sessions.

Geodetic networks, when derived solely from observed GPS baseline vectors, have an inherent datum deficiency of dimension three due to the respective unknown translation parameters. Thus, estimated coordinates from a (weighted) least-squares adjustment will not be unique, although the adjusted baseline vectors are. Uniqueness, without affecting the adjustment as such, can be achieved by introducing a minimum number of constraints for the coordinates, or by applying an objective function on the set of least-squares solutions (LESS) that fulfill the so-called normal equations. In both alternatives, bias control for some (or all) coordinate estimates has so far been treated only as a secondary issue. Here it is shown that the recently introduced estimator of type BLIMPBE—although generally not a LESS—can indeed be expected to be superior to all other linear estimators that minimize the bias for certain coordinates. This performance is demonstrated in a GPS network that includes GPS receivers at several continuously operating reference stations.

Robust estimation has proved to be a valuable approach to adjust a surveying network when there are systematic or gross errors in the observations or systematic errors in the functional model. In the present paper we propose to solve robust estimation as a global optimization problem. In particular, we will apply the simulated annealing method and genetic algorithms. The usual strategy of iteratively reweighed least squares is analyzed versus the global optimization approach. Results show that in problematic cases robust estimation is not truly robust unless performed by a global optimization method.

Experience with surveying practices has shown that correlated observations are very often encountered, especially in preprocessed observations. Hence, it is not only of theoretical interest, but also of practical interest, to investigate the detection of outliers for correlated observations. The so-called quasi-accurate detection (QUAD) of outliers for correlated observations is developed. The corresponding computation principle and its implementation are investigated in detail. The key of QUAD is how to select the quasi-accurate observations (QAO) reasonably. A new, distinctive sensitivity-analysis based method is proposed for selecting the QAO. For illustrative purposes, an application to global positioning system network adjustment is analyzed. The numerical results demonstrate that more than one outlier can be correctly identified and localized by using the proposed procedure.

In the last decade, the demand for accurate land-vehicle navigation (LVN) in several applications has grown rapidly. In this context, the idea of integrating multisensor navigation systems was implemented. For LVN, the most efficient multisensor configuration is the system integrating an inertial navigation system (INS) and a global positioning system (GPS), where the GPS is used for providing position and velocity and the INS for providing orientation. The optimal estimation of the system errors is performed through a Kalman filter (KF). Unfortunately, a major problem occurs in all INS/GPS LVN applications that is caused by the frequent GPS signal blockages. In these cases, navigation is provided by the INS until satellite signals are reacquired. During such periods, navigation errors increase rapidly with time due to the time-dependent INS error behavior. For accurate positioning in these cases, some approaches, known as bridging algorithms, should be used to estimate improved navigation information. In this paper, the main objective is to improve the accuracy of the obtained navigation parameters during periods of GPS signal outages using different bridging methods. As a first step, three different KF approaches will be used, including the linearized, extended, and unscented KF algorithms for the INS/GPS integration. Two land-vehicle kinematic data sets with different-quality INSs are used with several induced GPS outages, and then two bridging approaches are implemented. The first method is to apply different backward smoothing algorithms postmission that are associated with the different used KF approaches. The second bridging method is a near real-time approach based on developing an INS error model to be applied only during GPS signal blockages. After applying each bridging method, the results showed remarkable improvement of position errors regardless of the KF used.

Digital levels replaced spirit levels in most fields of precise height measurements because of the automation of the height readings. Three manufacturers offer digital levels with a single reading resolution of $10\mu \mathrm{m}$ , and for all of them systematic effects are known. In Europe several facilities for system calibration of digital levels using vertical comparators were established within the last decade. However, there still was no system calibration facility in North America. In order to guarantee the accuracy required for the alignment of experiments at the Stanford Linear Accelerator Center (SLAC) a calibration facility for the system calibration of digital levels was built. In this paper the setup of the SLAC vertical comparator is described in detail and its standard uncertainty is derived. In order to perform traditional rod calibration of conventional line-scaled rods, a CCD camera was integrated into the SLAC comparator. The CCD camera setup is also briefly described. To demonstrate the capabilities of the comparator, results of system and rod calibration are shown.