Volume 1, Issue 3

The primary objective of the research presented here is to develop theoretical foundations and implementation algorithms, which integrate the Global Positioning System (GPS), micro-electromechanical inertial measurement unit (MEMS IMU), digital barometer, electronic compass, and human pedometry to provide navigation and tracking of military and rescue ground personnel. This paper discusses the design, implementation and the performance analyses of the personal navigator prototype, with a special emphasis on dead-reckoning (DR) navigation supported by the human locomotion model. The adaptive knowledge system, based on the Artificial Neural Networks (ANN), is implemented to support this functionality. The knowledge system is trained during the GPS signal reception and is used to support navigation under GPS-denied conditions. The human locomotion parameters, step frequency (SF) and step length (SL), are extracted from GPS-timed impact switches (step frequency) and GPS/IMU data (step length), respectively, during the system calibration period. SL is correlated with several data types, such as acceleration, acceleration variation, SF, terrain slope, etc. that constitute the input parameters to the ANN-based knowledge system. The ANN-predicted SL, together with the heading information from the compass and gyro, support DR navigation. The current target accuracy of the system is 3–5 m CEP (circular error probable) 50%.

Up to now different methods for the determination of positions and velocities of road users such as e.g. floating car data (FCD) or local loop data are used for traffic state acquisition and forecast. This paper deals with the determination of trajectories of road users applying information which are available within the GSM network. The signal strength and timing advance values are the measurements introduced into different least-square algorithms to determine positions. One alternative estimates the positions by distance measurements and further static information of the GSM network, the second alternative matches measured signal strengths on a given signal strength map. Both solutions are smoothed afterwards in parallel by a moving average filter and a Kalman filter algorithm. The resulting trajectories are matched on a digital road map using routing algorithms and map aiding procedures. These trajectories are called floating phone data (FPD). First intermediate results show an accuracy of approximately 200 to 300 m for the signal strength matching approach. For the time being correctness of the identified digital road elements is given for rural and suburban regions only. Further investigations have to be realised regarding the verification of these intermediate results as well as the extension of road identification to urban areas.

The Planck mission of the European Space Agency (ESA) is designed to image the anisotropies of the Cosmic Background Radiation Field over the whole sky. Planck's objective is to analyze, with the highest accuracy ever achieved, the remnants of the radiation that filled the universe immediately after the Big Bang, which we observe today as the cosmic microwave background. To achieve this aim well-manufactured reflectors are used as parts of the Planck telescope receiving system. The system consists of the Secondary and Primary Reflectors which are sections of two different ellipsoids of revolution with diameters of 1.1 and 1.9 meters. Deformations of the reflectors which influence the optical parameters and the gain of receiving signals are investigated in vacuum and at temperatures down to 95K, using close-range photogrammetric techniques. We have designed an optimal close-range photogrammetric network by heuristic simulation for the Primary and Secondary Reflectors with a mean relative precision better than 1:1,000,000 and 1:400,000, respectively, to achieve the requested accuracies. Special considerations have been taken into account in different steps of design, such as the determinability of additional parameters under the given network configuration, datum definition, reliability and precision issues as well as workspace limits and propagating errors from different sources of errors. A least squares best-fit ellipsoid was developed to determine the optical parameters of the reflector. We present our procedure and the results of processing the photogrammetric measurements of the Flight Models of the Primary and Secondary Reflectors which were executed by Thales Alenia Space France under ESA-ESTEC contract in vacuum and at very low temperatures.

To date, LIDAR sensors have been primarily airborne, and utilized as a fast and efficient means of collecting topographic information. As a result, in research studies and in most commercial work the accuracy of the LIDAR information is primarily obtained by examining the vertical component of LIDAR error only. However, more and more end users are using LIDAR intensity to produce planimetric feature maps, and there are also emerging ground based kinematic laser scanning systems which are mounted on a van or truck platform. For both of these uses, the traditional vertical only error analysis of the LIDAR system is inadequate when defining the overall expected accuracy of the end-product received from the system. Therefore, in order to quantify the overall 3D expected accuracy of LIDAR systems (both land and air based) a rigorous 1 st order error analysis of the LIDAR georeferencing equations are undertaken. Typical error parameters are then placed into the error analysis to generate expected horizontal and vertical system accuracies for different LIDAR system configurations. Finally, the results obtained from the theoretical error analysis are independently verified using real world LIDAR data.

Segmentation is a key task in the processing of 3D point clouds as obtained from airborne laser scanners (ALS). However, most of the segmentation techniques currently employed require prior gridding of the data and thus do not respect the inherently three-dimensional geometry of more intricate structures such as power lines. By contrast, the mean shift procedure, a filtering and clustering approach which has recently found much interest in the image processing community, works directly on the original 3D point cloud; also, mean shift is a non-parametric technique (i.e., it does not depend on any geometric model assumptions) and can thus also be applied to vegetation structures. In this paper, we will give a self-contained derivation of the mean shift procedure, and discuss how it can be used to obtain a classification or segmentation of an unstructured 3D point cloud. Two application examples shall further illustrate its usefulness to ALS data processing.

Over the last decade, users of the Global Positioning System (GPS) have developed the technology capable of meeting stringent requirements to study the dynamics of tall buildings, towers, and bridges during earthquakes, wind-induced deformation and traffic loading. Dynamic measurements of relative displacements of structures is currently possible using real-time kinematic (RTK) positioning techniques, now advanced to record typically at 10–20 Hz (or higher – e.g., 100 Hz) with an accuracy of ±1 cm horizontally and ±2 cm vertically. With further advances in the technology and improvements in sampling capability, it is possible to meet the needs of real-time displacement information for the structural engineering community. After a decade of great strides in proving the feasibility of the technology, focus is moving to sensor integration and operational systems. Several investigators are now routinely researching the integration of GPS with other sensors (accelerometers, fibre optics, pseudolites, etc.) to utilise the complementary benefits and overcome limitations of the individual systems. Examples of real-time operational systems exist to demonstrate the significance of GPS technology in measuring the dynamic behaviour of large engineering structures.