Kongress- und Tagungsbeiträge

There is recently a notable rise in the exploration of pulse-echo ultrasound image reconstruction techniques that address the inverse problem employing sparse signal and rely on a single measurement cycle. Nevertheless, these techniques continue to pose significant challenges with regard to accuracy of estimations. Previous studies have endeavored to decrease the correlation between received samples in each transducer array in order to enhance accuracy of sparsely approximated solutions to inverse problems. In this paper, our objective is to learn the transmission parameters within a parametric measurement matrix by employing an autoencoder, which encodes sparse spatial data with a parametric measurement matrix and subsequently decodes it using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). Outcomes exhibit superior performance in comparison to both state-of-art random selection of the parameters and conventional plane wave imaging (PWI) scenarios in terms of reconstruction accuracy.

Traditional ultrasound synthetic aperture imaging relies on closely spaced measurement positions, where the pitch size is smaller than half the ultrasound wavelength. While this approach achieves high-quality images, it necessitates the storage of large data sets and an extended measurement time. To address these issues, there is a burgeoning interest in exploring effective subsampling techniques. Recently, Deep Probabilistic Subsampling (DPS) has emerged as a feasible approach for designing selection matrices for multi-channel systems. In this paper, we address spatial subsampling in single-channel ultrasound imaging for Nondestructive Testing (NDT) applications. To accomplish a model-based data-driven spatial subsampling approach within the DPS framework that allows for the optimal selection of sensing positions on a discretized grid, it is crucial to build an adequate signal model and design an adapted network architecture with a reasonable cost function. The reconstructed image quality is then evaluated through simulations, showing that the presented subsampling pattern approaches the performance of fully sampling and substantially outperforms uniformly spatial subsampling in terms of signal recovery quality.

In pulse-echo ultrasound imaging, the goal is to achieve a certain image quality while minimizing the duration of the signal acquisition. In the past, fast ultrasound imaging methods applying sparse signal recovery have been implemented by accepting a single pulse-echo measurement. However, they have experienced a certain amount of reconstruction error. In sparse signal recovery, reducing the correlation between the samples of the measurements observed by the different receivers is beneficial for lowering the reconstruction error. Exploiting the Born approximation and Green's function for the wave equation, the analytical inverse scattering problem can be defined in matrix-vector form. Adopting this setting, it has been suggested in the past to reduce the correlation between the samples of the measurement using Cylindrical Waves (CWs) with randomly selected delays and weights. In a similar setting, we created an optimization problem accepting transmission delays and weights as variables to minimize the correlation between the samples of the measurement in each receiver. We demonstrate via simulations that CWs employing the optimized transmission parameters outperformed the cases with Plane Wave Imaging (PWI) and CWs with random transmission parameters in terms of reconstruction accuracy.

Wireless devices supporting global navigation satellite systems (GNSS) services have become an essential tool in different areas of technology such as agriculture, construction, automotive, etc. Therefore the performance and reliability of such devices are important aspects that need to be addressed in the testing stage during the development of the units. The integration of the Over-the-Air (OTA) testing method with the 3D Wave Field Synthesis (3DWFS) technique offer not only the benefit of having tests under controllable and repeatable conditions but also the ability to recreate complex and realistic scenarios in a controlled environment with full polarimetric support for the testing of wireless devices. This contribution applies this technology to emulate a GNSS scenario within an anechoic chamber. For the results validation, a realistic GNSS outdoor scenario was recorded and compared with the emulated scenario where 3DWFS was applied for each individual satellite. This represents a significant step for the GNSS community and also for the future development and testing of wireless devices.

Future communication and radar sensing systems will require synchronization methods which are more versatile in terms of the systems involved in the synchronization process. We present an over-the-air frequency synchronization algorithm based on self-synchronization which uses continuous wave signals. In contrast to other approaches, all nodes of the network participate equally, and synchronization can even be achieved in presence of a non-cooperative node. These claims are supported by measurements conducted with continuous wave radars. It will be demonstrated that our algorithm enables synchronization accuracies down to 1.92 ppb and thus could provide sufficient accuracy for velocity measurements on pedestrians.