Konferenzbeiträge

Confocal microscopy is a working horse of optical profilometry since decades. It is a pointwise measurement method, where the whole sample must be scanned in all three dimensions. The high lateral resolution thereby outstrips its lowered scanning speed compared to widefield based principles. Furthermore, for a single 3D surface, even single-digit nanometre depth-resolution has been shown. However, albeit such high axial resolution, the accuracy may suffer from sample or optics induced wavefront distortions that differ from point to point. The acquired signal then experiences a shift that leads to a wrong acquired depth. Here we model this error through a low NA scalar model. We further present a method to compensate this error significantly by enhancing the principle of differential confocal microscopy. Theoretical results show the possibility for ideal compensation of the error caused by such in-stationary aberrations in confocal depth measurements.

Model-Based Systems Engineering (MBSE) is an efficient approach to support product development in order to meet today's challenges. The MBSE approach includes methods and, above all, modelling approaches of the technical system with the aim of continuous use in development. The objective of this paper is to use the potential of the MBSE models and to show the added value of such models on the system level when used as a single source. With this objective, this paper presents a three-step approach to systematically identify and apply meaningful modelling approaches within MBSE, based on the needs during the development process. Furthermore, an FMEA example is included in this paper to elaborate the use of MBSE in the system failure analysis.

Car-to-car communication has wide-ranging applications in the fields of traffic safety and optimization as well as minimization of emissions. For a seamless and reliable communication between the cars and their road environment, as may be evaluated through packet delivery ratio and received power measurements, an appropriate antenna placement is necessary. Accordingly, the field test discussed in this paper compares the performance of three different antenna placements on a test vehicle: 1. Roof-mounted monopole antenna pair contained in a shark fin housing, 2. Patch antenna pair embedded in the front-left B-column, and 3. Combination of two patch antennas with one in the front-left and the other in the front-right B-column. The concurrent use of three V2V modules allowed us to evaluate the performance of the three antenna set-ups simultaneously in a single run through a mixed terrain, allowing for straightforward performance comparisons. The power received by the roof-top monopole antennas was, on average, 8 dBm higher than the front-left/right B-column patch antenna combination and 13 dBm higher than the front-left B-column patch antenna pair. The reasons for these and other differences are appropriately addressed in the paper. While the roof-top monopole antenna combination performed overall better, we argue that using an appropriate amplifier could raise the performance of the B-column antennas to the same or even higher levels.

The statistical study of target detectability is crucial in radar system design. In this regard, signal-to-noise ratio (SNR) of the received signal at the radars receiver is a key parameter and therefore every influence on SNR should be taken into account. One of the main influences is the fluctuation of the target scattering/reflectivity to the incident electromagnetic wave. Consequently, the target fluctuation models and their accuracy become important. For monostatic radar system, various models can be found in the literature. In contrast, this paper studies a case of vehicular sensing scenario with multistatic configuration for an urban crossroad and proposes a modelling method of the target fluctuation by taking into account the target information, such as size, shape and composed materials, and the wave information, such as polarizations and aspect angles. Furthermore, the derived target fluctuation models are then applied directly to the formulation of the probability of detection for multistatic radar system. These probabilities of detection are also compared to that of monostatic radar system.