Enhanced solutions for the block-term decomposition in rank-(Lr, Lr, 1) terms. - In: IEEE transactions on signal processing, ISSN 1941-0476, Bd. 71 (2023), S. 2608-2621
The block-term decompositions (BTD) represent tensors as a linear combination of low multilinear rank terms and can be explicitly related to the Canonical Polyadic decomposition (CPD). In this paper, we introduce the SECSI-BTD framework, which exploits the connection between two decompositions to estimate the block-terms of the rank-(Lr, Lr, 1) BTD. The proposed SECSI-BTD algorithm includes the initial calculation of the factor estimates using the SEmi-algebraic framework for approximate Canonical polyadic decompositions via SImultaneous Matrix Diagonalizations (SECSI), followed by clustering and refinement procedures that return the appropriate rank-(Lr, Lr, 1) BTD terms. Moreover, we introduce a new approach to estimate the multilinear rank structure of the tensor based on the HOSVD and $k$-means clustering. Since the proposed SECSI-BTD algorithm does not require a known rank structure but can still take advantage of the known ranks when available, it is more flexible than the existing techniques in the literature. Additionally, our algorithm does not require multiple initializations, and the simulation results show that it provides more accurate results and a better convergence behavior for an extensive range of SNRs.
Structured Nyquist correlation reconstruction for DOA estimation with sparse arrays. - In: IEEE transactions on signal processing, ISSN 1941-0476, Bd. 71 (2023), S. 1849-1862
Sparse arrays are known to achieve an increased number of degrees-of-freedom (DOFs) for direction-of-arrival (DOA) estimation, where an augmented virtual uniform array calculated from the correlations of sub-Nyquist spatial samples is processed to retrieve the angles unambiguously. Nevertheless, the geometry of the derived virtual array is dominated by the specific physical array configurations, as well as the deviation caused by the practical unforeseen circumstances such as detection malfunction and missing data, resulting in a quite sensitive model for virtual array signal processing. In this paper, we propose a novel sparse array DOA estimation algorithm via structured correlation reconstruction, where the Nyquist spatial filling is implemented on the physical array with a compressed transformation related to its equivalent filled array to guarantee the general applicability. While the unknown correlations located in the whole rows and columns of the augmented covariance matrix lead to the fact that strong incoherence property is no longer satisfied for matrix completion, the structural information is introduced as a priori to formulate the structured correlation reconstruction problem for matrix reconstruction. As such, the reconstructed covariance matrix can be effectively processed with full utilization of the achievable DOFs from the virtual array, but with a more flexible constraint on the array configuration. The described estimation problem is theoretically analyzed by deriving the corresponding Cramér-Rao bound (CRB). Moreover, we compare the derived CRB with the performance of the virtual array interpolation-based algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of DOFs, resolution, and estimation accuracy.
Twenty-five years of sensor array and multichannel signal processing: a review of progress to date and potential research directions. - In: IEEE signal processing magazine, ISSN 1558-0792, Bd. 40 (2023), 4, S. 80-91
In this article, a general introduction to the area of sensor array and multichannel signal processing is provided, including associated activities of the IEEE Signal Processing Society (SPS) Sensor Array and Multichannel (SAM) Technical Committee (TC). The main technological advances in five SAM subareas made in the past 25 years are then presented in detail, including beamforming, direction-of-arrival (DOA) estimation, sensor location optimization, target/source localization based on sensor arrays, and multiple-input multiple-output (MIMO) arrays. Six recent developments are also provided at the end to indicate possible promising directions for future SAM research, which are graph signal processing (GSP) for sensor networks; tensor-based array signal processing, quaternion-valued array signal processing, 1-bit and noncoherent sensor array signal processing, machine learning and artificial intelligence (AI) for sensor arrays; and array signal processing for next-generation communication systems.
Space-time adaptive processing as a solution for mitigating interference using spatially-distributed antenna arrays. - In: Navigation, ISSN 2161-4296, Bd. 70 (2023), 3, navi.592, insges. 23 S.
Antenna arrays and spatial processing techniques are among the most effective countermeasures against interference. Here, we demonstrate a new array concept consisting of spatially-distributed subarrays that are small enough to fit inside the non-metallic parts of an automobile. This will facilitate concealed installation of these devices in bumpers or side mirrors, which is a strict requirement of the industry and preferred by the customers. Using beamforming algorithms, this array was proven to be robust against jammers in the L1 band. The large distances between the individual antenna elements resulted in a non-negligible baseband delay that violated the narrowband assumption and increased with bandwidth. Hence, this paper demonstrates the influence of a jammer in the L5 band. Space-time adaptive processing that allows for compensation of the delays was introduced and analyzed. Improvements in interference mitigation capabilities were assessed and compared to those of pure spatial state-of-the-art implementation. Real-life measurement data was used to ensure realistic results.
An efficient modeling approach of 1D-planar metamaterials in the high-frequency regime. - In: Compel, ISSN 2054-5606, Bd. 42 (2023), 3, S. 776-786
Purpose The purpose of this paper is to present an alternative modeling approach in terms of the determination of a physically equivalent circuit model for one-dimensional (1D) planar metamaterials in the high-frequency regime, without a postprocessing optimization procedure. Thereby, an efficient implementation of physical relationships is aimed. Design/methodology/approach In this paper, a method based on quasi-stationary simulations and mathematical conversions to derive the values for a physically equivalent circuit model is proposed. Because the electromagnetic coupling mechanisms are investigated in detail, a simplification for the considered multiconductor transmission line structure is achieved. Findings The results show that the proposed modeling approach is an efficient and physically meaningful alternative to classical full-wave simulation techniques for the investigated inhomogeneous transmission line structure in both the time domain as well as in the frequency domain. In the course of this, the effort is reduced while a comparable accuracy is maintained, whereby specific coupling mechanisms are considered in circuit simulations. Originality/value The process to obtain information about physically interpretable lumped element values for a given structure or to determine a layout for known ones is simplified with the aid of the proposed approach. An advantageous adaption of the presented procedure to other areas of application is well conceivable.
Configurable pseudo noise radar imaging system enabling synchronous MIMO channel extension. - In: Sensors, ISSN 1424-8220, Bd. 23 (2023), 5, 2454, insges. 27 S.
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.
Precise motor mapping with transcranial magnetic stimulation. - In: Nature protocols, ISSN 1750-2799, Bd. 18 (2023), S. 293-318
We describe a routine to precisely localize cortical muscle representations within the primary motor cortex with transcranial magnetic stimulation (TMS) based on the functional relation between induced electric fields at the cortical level and peripheral muscle activation (motor-evoked potentials; MEPs). Besides providing insights into structure-function relationships, this routine lays the foundation for TMS dosing metrics based on subject-specific cortical electric field thresholds. MEPs for different coil positions and orientations are combined with electric field modeling, exploiting the causal nature of neuronal activation to pinpoint the cortical origin of the MEPs. This involves constructing an individual head model using magnetic resonance imaging, recording MEPs via electromyography during TMS and computing the induced electric fields with numerical modeling. The cortical muscle representations are determined by relating the TMS-induced electric fields to the MEP amplitudes. Subsequently, the coil position to optimally stimulate the origin of the identified cortical MEP can be determined by numerical modeling. The protocol requires 2 h of manual preparation, 10 h for the automated head model construction, one TMS session lasting 2 h, 12 h of computational postprocessing and an optional second TMS session lasting 30 min. A basic level of computer science expertise and standard TMS neuronavigation equipment suffices to perform the protocol.
Robust multi-dimensional model order estimation using LineAr Regression of Global Eigenvalues (LaRGE). - In: IEEE transactions on signal processing, ISSN 1941-0476, Bd. 70 (2022), S. 5751-5764
The efficient estimation of an approximate model order is very important for real applications with multi-dimensional low-rank data that may be corrupted by additive noise. In this paper, we present a novel robust to noise method for model order estimation of noise-corrupted multi-dimensional low-rank data based on the LineAr Regression of Global Eigenvalues (LaRGE). The LaRGE method uses the multi-linear singular values obtained from the HOSVD of the measurement tensor to construct global eigenvalues. In contrast to the Modified Exponential Test (EFT) that also exploits the approximate exponential profile of the noise eigenvalues, LaRGE does not require the calculation of the probability of false alarm. Moreover, LaRGE achieves a significantly improved performance in comparison with popular state-of-the-art methods. It is well suited for the analysis of noisy multidimensional low-rank data including biomedical signals. The excellent performance of the LaRGE method is illustrated via simulations and results obtained from EEG recordings.
Virtuelle Sensorvalidierung für automatisiertes und vernetztes Fahren. - In: Automobiltechnische Zeitschrift, ISSN 2192-8800, Bd. 124 (2022), 11, S. 58-62
Virtual sensor validation for automated and connected driving. - In: ATZ worldwide, ISSN 2192-9076, Bd. 124 (2022), 11, S. 54-57