Configurable Pseudo Noise Radar Imaging System Enabling Synchronous MIMO Channel Extension.

In this article, we propose an evolved system design approach to ultra-wideband (UWB) radar based on pseudo-random noise (PRN) sequences, the key features of which are its user-adaptability to meet the demands provided by desired microwave imaging applications and its multichannel scalability. In light of providing a fully synchronized multichannel radar imaging system for short-range imaging as mine detection, non-destructive testing (NDT) or medical imaging, the advanced system architecture is presented with a special focus put on the implemented synchronization mechanism and clocking scheme. The core of the targeted adaptivity is provided by means of hardware, such as variable clock generators and dividers as well as programmable PRN generators. In addition to adaptive hardware, the customization of signal processing is feasible within an extensive open-source framework using the Red Pitaya® data acquisition platform. A system benchmark in terms of signal-to-noise ratio (SNR), jitter, and synchronization stability is conducted to determine the achievable performance of the prototype system put into practice. Furthermore, an outlook on the planned future development and performance improvement is provided.

Frequency Subsampling of Ultrasound Nondestructive Measurements: Acquisition, Reconstruction, and Performance.

In ultrasound nondestructive testing (NDT), a widespread approach is to take synthetic aperture measurements from the surface of a specimen to detect and locate defects within it. Based on these measurements, imaging is usually performed using the synthetic aperture focusing technique (SAFT). However, SAFT is suboptimal in terms of resolution and requires oversampling in the time domain to obtain a fine grid for the delay-and-sum (DAS). On the other hand, parametric reconstruction algorithms give better resolution, but their usage for imaging becomes computationally expensive due to the size of the parameter space and a large amount of measurement data in realistic 3-D scenarios when using oversampling. In the literature, the remedies to this are twofold. First, the amount of measurement data can be reduced using state-of-the-art sub-Nyquist sampling approaches to measure Fourier coefficients instead of time-domain samples. Second, parametric reconstruction algorithms mostly rely on matrix-vector operations that can be implemented efficiently by exploiting the underlying structure of the model. In this article, we propose and compare different strategies to choose the Fourier coefficients to be measured. Their asymptotic performance is compared by numerically evaluating the Cramér-Rao bound (CRB) for the localizability of the defect coordinates. These subsampling strategies are then combined with an l1 -minimization scheme to compute 3-D reconstructions from the low-rate measurements. Compared to conventional DAS, this allows us to formulate a fully physically motivated forward model matrix. To enable this, the projection operations of the forward model matrix are implemented matrix-free by exploiting the underlying two-level Toeplitz structure. Finally, we show that high-resolution reconstructions from as low as a single Fourier coefficient per A-scan are possible based on simulated data and measurements from a steel specimen.

On the spatial coherence in mixed sound fields and its application to signal-to-diffuse ratio estimation.

Many applications in spatial sound recording and processing model the sound scene as a sum of directional and diffuse sound components. The power ratio between both components, i.e., the signal-to-diffuse ratio (SDR), represents an important measure for algorithms which aim at performing robustly in reverberant environments. This contribution discusses the SDR estimation from the spatial coherence between two arbitrary first-order directional microphones. First, the spatial coherence is expressed as function of the SDR. For most microphone setups, the spatial coherence is a complex function where both the absolute value and phase contain relevant information on the SDR. Secondly, the SDR estimator is derived from the spatial coherence function. The estimator is discussed for different practical microphone setups including coincident setups of arbitrary first-order directional microphones and spaced setups of identical first-order directional microphones. An unbiased SDR estimation requires noiseless coherence estimates as well as information on the direction-of-arrival of the directional sound, which usually has to be estimated. Nevertheless, measurement results verify that the proposed estimator is applicable in practice and provides accurate results.

The diffuse sound field in energetic analysis.

Measuring the degree of diffuseness of a sound field is crucial in many modern parametric spatial audio techniques. In these applications, intensity-based diffuseness estimators are particularly convenient, as the sound intensity can also be used to obtain, e.g., the direction of arrival of the sound. This contribution reviews different diffuseness estimators comparing them under the conditions found in practice, i.e., with arrays of noisy microphones and with the expectation operators substituted by finite temporal averages. The estimators show a similar performance, however, each with specific advantages and disadvantages depending on the scenario. Furthermore, the paper derives an estimator and highlights the possibility of using spatial averaging to improve the temporal resolution of the estimates.