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When processing radar signals, the target steering vector is generally only partially known, being subject to various forms of errors and mismatches. In this article, we investigate the design of an unimodular transmit sequence that is robust against steering vector mismatches, with the aim of enhancing the performance of a frequency diverse array multiple-input-multiple-output (FDA-MIMO) radar sy

This paper introduces a novel method for solving the single-source localization problem, specifically addressing the case of trilateration. We formulate the problem as a weighted least-squares problem in the squared distances and demonstrate how suitable weights are chosen to accommodate different noise distributions. By transforming this formulation into an eigenvalue problem, we leverage existin

Time delay estimation or Time-Difference-Of-Arrival estimates is a critical component for multiple localization applications such as multilateration, direction of arrival, and self-calibration. The task is to estimate the time difference between a signal arriving at two different sensors. For the audio sensor modality, most current systems are based on classical methods such as the Generalized Cro

This work investigates the design of beampatterns for frequency diverse arrays-multiple-input multiple-output (FDA-MIMO) in the range-angle plane, in order to improve the approximation of a desired beampattern. Recognizing that the energy radiated by the array cannot be locked at a fixed range and angle, the beampattern is designed for the equivalent beampattern at the receiving end, differing fro

The separation of multiple transmit waveforms with time and frequency synchronization constitutes a considerable challenge for multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) systems. It is well-known that aliased signal returns may be separable by digital beamforming (DBF) on receive in elevation. However, the current orthogonal-waveform beamforming schemes significantly incr

This article addresses the problem of multi-target localization from acoustic time delay measurements in a 3-D underwater environment, taking into account the depth-dependent sound speed of the underwater wave propagation. Specifically, we formulate the problem as an optimal mass transport (OMT) optimization over a grid of potential candidate positions. By exploiting an iterative refinement approa

Trees have the potential to improve urban air quality as leaves and needles capture air pollutants from the air, but further empirical data has been requested to quantify these effects. We measured the concentration of 32 polycyclic aromatic hydrocarbons (PAHs) in leaves of pin oak (Quercus palustris) and needles of black pine (Pinus nigra) in the City of Gothenburg, Sweden, during the summer of 2

An important issue in simultaneous localisation and mapping is how to match and merge individual local maps into one global map. This is addressed within the field of robotics and is crucial for multi-robot SLAM. There are a number of different ways to solve this task depending on the representation of the map. To take advantage of matching and merging methods that allow for deformations of the lo

Current literature show that frequency diverse arrays (FDAs) are able of producing range–angle-dependent and time-variant transmit beampatterns, but the resulting time and range dependencies and their characteristics are still not well understood. This paper examines the FDA transmission model with an emphasis on analyzing the beam auto-scanning characteristics and the equivalence with the MIMO be

The sparse iterative covariance-based estimation (SPICE) algorithm is promising for hyperparameter-free sparse source location for time-division-multiplexing multiple-input-multiple-output (TDM-MIMO) radar systems, with well-documented merits in resolution enhancement and sidelobe suppression. Regrettably, the method typically requires a large number of iterations to converge, each requiring high-

Decentralized Cloud Storage (DCS) is considered to be the future for sustainable data storage within Web 3.0, in which we will move from a single cloud service provider to creating an ecosystem where anybody could be a cloud storage provider. Currently, the cloud storage market is highly dominated by centralized players like Amazon S3, Google Cloud, Box, etc. Decentralized projects like Storj, Fil

Acoustic testing is crucial in the aero-engine design process, providing valuable information about the sound field in terms of acoustic modes. The conventional use of a uniform sampling array limits the achievable resolution for a given number of sensors, necessitating the use of a large number of sensors to achieve high-resolution estimates. This limitation may be overcome using a non-uniform se

This explorative study investigates the emergence of gray zone markets from search engines amidst the global expansion of online markets. With the analytical approach of infrastructural inversion, we examine how the search infrastructure constructs access to a gray zone market including both authorized online pharmacies and unauthorized vendors. Using Sweden and Google Search as a case, we explore

Many forms of small-size radars suffer from minor clock oscillations due to crystalline impurities in their internal clocks, causing time-varying clock offsets between the transmitter and the receivers. This offset causes a bias and an increased variance in the positioning of close-range targets, limiting the achievable accuracy well beyond the expected estimation limitations. This performance is

This paper develops an adaptive grid two-dimensional (2-D) direction-of-arrival (DOA) estimation technique employing an integrated dictionary framework. In contrast to using a traditional grid over distinct angles, combined angle information atoms are adopted to construct the dictionary, using a SPICE-based noise power estimate to select the active regions. To compensate for off-grid bias effects,

Post-marketing reports of suspected adverse drug reactions are important for establishing the safety profile of a medicinal product. However, a high influx of reports poses a challenge for regulatory authorities as a delay in identification of previously unknown adverse drug reactions can potentially be harmful to patients. In this study, we use natural language processing (NLP) to predict whether