One of the objectives of the medicine is to modify patients’ ways of living. In this context, a key role is played by the diagnosis. When dealing with acquisition systems consisting of multiple wireless devices located in different parts of the body, it becomes fundamental to ensure synchronization between the individual units. This task is truly a challenge, so one aims to limit the complexity of the calculation and ensure long periods of operation. In fact, in the absence of synchronization, it is impossible to relate all the measurements coming from the different subsystems on a single time scale for the extraction of complex characteristics. In this paper, we first analyze in detail all the possible causes that lead to have a system that is not synchronous and therefore not usable. Then, we propose a firmware implementation strategy and a simple but effective protocol that guarantees perfect synchrony between the devices while keeping computational complexity low. The employed network has a star topology with a master/slave architecture. In this paper a new approach to the synchronization problem is introduced to guarantee a precise but not necessarily accurate synchronization between the units. In order to demonstrate the effectiveness of the proposed solution, a platform consisting of two different types of units has been designed and built. In particular, a nine Degrees of Freedom (DoF) Inertial Measurement Unit (IMU) is used in one unit while a nine-DoF IMU and all circuits for the analysis of the superficial Electromyography (sEMG) are present on the other unit. The system is completed by an Android app that acts as a user interface for starting and stopping the logging operations. The paper experimentally demonstrates that the proposed solution overcomes all the limits set out and it guarantees perfect synchronization of the single measurement, even during long-duration acquisitions. In fact, a less than 30 $\mathsf{\mu }$ s time mismatch has been registered for a 24 h test, and the possibility to perform complex post-processing on the acquired data with a simple and effective system has been proven.

In recent years, Real-Time Localization Systems (RTLS) are gaining significant attention from the scientific community. Usually, RLTS rely on positioning techniques based on the electromagnetic (e.m.) propagation properties of the received signals. Among the many possible approaches, RTLS based on the Angle-of-Arrival (AoA) estimation allows for accurate results with a comparatively lower number of receivers. In this work, we propose a novel, fully digital synchronous architecture for AoA estimation based on phase interferometry. After reviewing the state-of-the-art on full-hardware techniques for localization, the main blocks composing the proposed architecture, are discussed in detail, along with their dimensioning equations. The granularity with which the system estimates the phase shifts used to compute the AoA is reconfigurable according to the desired accuracy. The architecture is lightweight and computes the AoA in real-time. To validate the proposed approach, the RTLS architecture has been implemented on an Intel Cyclone IV E EP4CE115F29C7 FPGA board interfaced with a custom-designed receiver front-end, and experimental results on benchmark applications have been collected and analyzed.

The paper introduces a full-hardware approach for Angle of Arrival (AoA) estimation in phased array antennas. The system has a modular structure composed of an analog section to up/down-convert RF signals and a digital section for the AoA estimation. The estimation furnished by the proposed approach can be used to perform adaptive beamforming either to increase the range of operation and/or reduce transmitted power in IoT applications. Some preliminary experimental results are reported validating the proposed strategy for a single narrowband received signal.

The evaluation of the Angle of Arrival (AoA) of a received signal is a basic tool for many RF location systems. The paper introduces a new technique for estimating the AoA via a phase interferometric approach when multiple sources are present. After briefly recalling some widespread algorithms and methods, the proposed technique is described from a mathematical point of view. The paper also reports the results of an experimental campaign, along with a comparison of the results with those obtained using the Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT) and Root MUltiple SIgnal Classification (RMUSIC) algorithms. The experiments demonstrate that the proposed approach gives accurate results with low computational complexity, thus being eligible for direct implementation in digital custom hardware.

This work introduces a novel coarse positioning system based on the estimation of the Angles-of-Arrival (AoAs) of Signals of Opportunity from a spacecraft in the Low Earth Orbit. The AoA estimation is done in real-time using phase interferometry through a dedicated hardware architecture. The 2D position is achieved by means of a simplified geometrical model based on the estimated angles. The experimental campaign aimed to assess the fit of the proposed model with reality, the AoA estimation accuracy, and the final positioning error. Results showed that the system can track the angular position of a fast-moving spacecraft with a precision of tenths of a degree. The final positioning accuracy demonstrates the effectiveness of the system for real-time coarse positioning.

Radio localization and radio positioning are relevant research fields for many telecommunications technologies. Usually, the solutions proposed by the literature rely on adaptive techniques related to some parameters that can be extracted from the received signal in cooperative device tracking. In this paper, we explore the artifacts that may be introduced into Angle-of-Arrival estimation based on phase interferometry, and we introduce a simple technique to mitigate their impact. Details of the mathematical discussion are presented and the approach is experimentally validated. The experimental results are compared with raw data to demonstrate the effectiveness of the proposed technique.