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A novel model for the covariance matrix of sampled observations by multiple-input-multiple-output (MIMO) sensing systems with parallel receiver channels will be presented. The model is of shifted Kronecker structure and accounts for two mutually independent noise processes: a coloured and a white one. The maximum-likelihood (ML) estimator is applied to identify this covariance model from observations. The ML estimator gives rise to a non-convex optimisation problem. Since no closed-form solution is available, an iterative, space-alternating Gauss-Newton algorithm is proposed to solve the optimisation problem. This approach repeatedly requires the evaluation of the ML cost function. Since the cost function composes of the inverse and determinant of the covariance matrix, its evaluation can be memory exhaustive, numerically unstable and computationally complex. A computational method is developed to overcome these issues, using the simultaneous matrix diagonalisation and exploiting the properties of the Kronecker product. Measurements by a MIMO radar are used to identify the covariance model and to demonstrate its benefits. The identified covariance model is used to whiten the measurements. The whitening reduces interfering, noise-like components, which enhances the signal-to-interference ratio and hence facilitates the target detection.

Scientific computing requires handling large linear models, which are often composed of structured matrices. With increasing model size, dense representations quickly become infeasible to compute or store. Matrix-free implementations are suited to mitigate this problem at the expense of additional implementation overhead, which complicates research and development effort by months, when applied to practical research problems. Fastmat is a framework for handling large structured matrices by offering an easy-to-use abstraction model. It allows for the expression of matrix-free linear operators in a mathematically intuitive way, while retaining their benefits in computation performance and memory efficiency. A built-in hierarchical unit-test system boosts debugging productivity and run-time execution path optimization improves the performance of highly-structured operators. The architecture is completed with an interface for abstractly describing algorithms that apply such matrix-free linear operators, while maintaining clear separation of their respective implementation levels. Fastmat achieves establishing a close relationship between implementation code and the actual mathematical notation of a given problem, promoting readable, portable and re-usable scientific code.

R-Mode is a terrestrial navigation system under development for the maritime domain that provides backup in case of a GNSS outage. This paper describes the first test results for real-time positioning on board a ship using medium frequency R-Mode signals. The estimation and positioning algorithms used are described in detail and it is shown how they are integrated into the R-Mode receiver developed by the German Aerospace Center. Moreover, during two daytime experiments with lower and higher dynamic movements of a ship in the Baltic Sea, we were able to achieve a 95% horizontal positioning accuracy of better then 12 m in the center of three R-Mode transmitters. This demonstrates the first time that the medium frequency R-Mode has provided positioning at sea.

The 5th generation new radio (5G NR) standards create both enormous challenges and potential to address the spatio-spectral-temporal agility of wireless transmission. In the framework of a research unit at TU Ilmenau, various concepts were studied, including both approaches toward integrated circuits and distributed receiver front-ends (FEs). We report here on the latter approach, aiming at the proof-of-principle of the constituting FEs suitable for later modular extension. A millimeter-wave agile multi-beam FE with an integrated 4 by 1 antenna array for 5G wireless communications was designed, manufactured, and verified by measurements. The polarization is continuously electronically adjustable and the directions of signal reception are steerable by setting digital phase shifters. On purpose, these functions were realized by analog circuits, and digital signal processing was not applied. The agile polarization is created inside the analog, real-time capable FE in a novel manner and any external circuitry is omitted. The microstrip patch antenna array integrated into this module necessitated elaborate measurements within the scope of FE characterization, as the analog circuit and antenna form a single entity and cannot be assessed separately. Link measurements with broadband signals were successfully performed and analyzed in detail to determine the error vector magnitude contributions of the FE.

Microwave sensors have recently been introduced as high-temporal resolution sensors, which could be used in the contactless monitoring of artery pulsation and breathing. However, accurate and efficient signal processing methods are still required. In this paper, the matrix pencil method (MPM), as an efficient method with good frequency resolution, is applied to back-reflected microwave signals to extract vital signs. It is shown that decomposing of the signal to its damping exponentials fulfilled by MPM gives the opportunity to separate signals, e.g., breathing and heartbeat, with high precision. A publicly online dataset (GUARDIAN), obtained by a continuous wave microwave sensor, is applied to evaluate the performance of MPM. Two methods of bandpass filtering (BPF) and variational mode decomposition (VMD) are also implemented. In addition to the GUARDIAN dataset, these methods are also applied to signals acquired by an ultra-wideband (UWB) sensor. It is concluded that when the vital sign is sufficiently strong and pure, all methods, e.g., MPM, VMD, and BPF, are appropriate for vital sign monitoring. However, in noisy cases, MPM has better performance. Therefore, for non-contact microwave vital sign monitoring, which is usually subject to noisy situations, MPM is a powerful method.