Journal of Surveying Engineering

The paper gives an introduction to the rapidly emerging field of kinematic surveying, which deals with the concepts and techniques of conducting accurate surveys from moving vehicles. It briefly outlines the underlying mathematics and specifically discusses modeling, interpolation, and estimation. The general kinematic model is then related to the specific measurements used, which typically come from inertial measuring units (IMU) and satellite receivers (GPS). Four applications are presented to explain the concepts. The first application is semikinematic GPS, which is used for rapid control‐point densification and is one of the emerging procedures that will have a major impact on survey operations. The second is pipeline surveying by a velocity‐aided IMU that shows the strength of inertial techniques in situations where traditional and satellite methods cannot be applied. The last two applications use a combination of inertial and satellite techniques for road surveying from a truck and airborne photogrammetry without ground control.

Contours must be interpolated when preparing new maps on the basis of existing maps (for example, transforming contours from feet to meters). The new contours are generated by simulating the manual interpolation a skilled cartographer would perform, rather than by forming a digital elevation model. This approach seems advantageous, since the newly interpolated contours conform to the old ones. This is not always the case with contours derived from an elevation model that has been interpolated from the existing contours. The simulation involves two main procedures: Subdividing the map area into subareas, strips bounded by neighboring contours of consecutive or equal elevations, and selecting relevant strips and locating new contours within them, while considering the character of the terrain as described by contours adjoining the strips.

The use of outlier detection, reliability measures from the theory of survey network analysis, and the concept of condition numbers from linear algebra are applied to the statistical and numerical analysis of Global Positioning System (GPS) phase‐difference data in baseline adjustments. Outlier tests are given for the cases of a‐priori and a‐posteriori variance factors. Expressions for internal and external reliability measures are treated for the case in which blunders are assumed to be present in the data. These measures are evaluated for a GPS baseline for the cases of fixed integer and floating point ambiguity unknowns. Preliminary testing with real data reveals that the use of covariance analysis augmented with reliability analysis may not be sufficient for the complete assessment of parameters and observations. The numerical sensitivity of the matrix of normal equations in adjustments with floating ambiguity parameters is a potential problem that can be detected with the computation of condition numbers. The use of all these analysis tools in standard surveying engineering practice is suggested.