This paper presents the implementation of a localization system in a senior living residence located in a town in the province of Guadalajara, Spain. The system integrates precise location tracking using Ultra-Wideband (UWB) technology in the common areas of the residence, and symbolic positioning via millimeter-wave (mmWave) radar modules installed in private rooms. In UWB-monitored areas, users carry a wearable UWB tag, while in mmWave-radar monitored rooms, a device-free approach is adopted. The localization system is designed to be scalable, easy to deploy, and to support remote maintenance and management, facilitating long-term operation with minimal on-site intervention. This paper provides a detailed description of the devices used, the deployment process, and the overall system setup. Furthermore, by leveraging open-source tools, efficient data management has been achieved. Finally, raw data collected from the implemented system are presented graphically. These data will support future development of tools to detect behavioral anomalies early, which may indicate physical or cognitive decline among the monitored individuals. The collected data are also valuable for identifying daily behavioral patterns and routines, and for detecting deviations that could signal the onset of conditions associated with physical and/or cognitive impairment.

Infrared Local Positioning Systems (IRLPS) offer a cost-effective and accurate alternative for indoor localization, where GNSS signals are typically not available. Despite their potential, IRLPS face significant challenges such as noise, multipath effects, multiple access interference, and calibration requirements, which limit their performance. In response, this work explores the integration of machine learning by proposing a Feedforward Neural Network (FNN) trained on energy measurements collected from a quadrant photodiode. We conducted a thorough analysis of hyperparameters and an ablation study across five neural network topologies and identified configurations that balance accuracy and model complexity. Experimental evaluations in a controlled indoor environment (2.4×2.4×3.4 m3) demonstrate that even simple FNN architectures can generalize well and achieve a high accuracy, with 90% of the positioning errors being below 0.05 m.

The demand for accurate and reliable local positioning systems has grown rapidly with the increasing use of Location-Based Services (LBS). Emerging technologies such as optical, RF, acoustic, mechanical, and magnetic are feasible options for indoor positioning. Optical-based systems have gained attention due to the widespread LED lamp availability and centimeter-level accuracy. In this context, this work presents the implementation and validation of a centralized optical Local Positioning System (LPS) based on a set of mobile emitter LEDs and a minimum of two fixed receivers, enabling simultaneous position of all tags in the infrastructure (centralized approach). The system is designed for long-distance measurements, using Visible or Infrared Light signals. By incorporating geometric considerations into a Non-Linear Least Squares Estimator (NLSE), the system can estimate the 3D position of a mobile object (i.e. person, drone). The comparison of different deployments demonstrates that the proposed system estimates the 3D position with less than 4mm of error in 90% of cases when using four non-aligned receivers and a single LED.

The accuracy of optical positioning systems can be compromised by multiple factors (reflections, calibration, etc.). As an alternative to the triangulation, multilateration, or fingerprinting techniques typically used in these systems, stochastic estimation techniques can be used, such as Kalman Filters (KF) in its different variants. They estimate the receiver position based on the acquired measurements and the estimated positions in previous iterations. This work presents the evaluation of a 3D optical positioning system, based on four LED beacons and a quadrant receiver, using an Extended Kalman Filter (EKF). The implementation of a measurement noise covariance matrix that is adjusted depending on the angle and distance between transmitters and receiver, obtained in the previous iteration, is analysed. The receiver position estimation using both a dynamic and a static measurement noise covariance matrix is evaluated and compared with simulations and experimental tests. In simulations, the achieved errors are below 6 cm and 12 cm in 75% of the cases when using a dynamic and a static noise matrix, respectively. In the experimental tests, the obtained errors in 75% of the cases for the position in plane XY and the rotation angle γ are smaller than 7.44cm and 1.06° with a dynamic noise matrix; and below 7.59 cm and 1.62° for a static one.

Indoor optical positioning systems have increased in popularity in recent years because they can provide centimeter accuracy in three dimensions (3D) utilizing light-emitting diodes (LEDs) and photoreceptors. This work presents the design of a positioning system, which is based on a set of four photoreceptors functioning as beacons at known places for a single LED to be positioned. However, it might be extended to additional emitters with some medium access control. The associated processing is explained, as well as the basic assumptions to be addressed when approaching its hardware implementation, such as the preliminary partitioning of tasks between hardware and software, and the fixed-point representation of the processing to be implemented in hardware. The system has been validated by simulation in a$2\times 2\times 3.4\ \mathbf{m}^{3}$volume, yielding mean absolute errors around 0.004 m for the X and Y axes, and around 0.01 m for the Z-axis, as well as lower standard deviations than 0.004 m for the X and Y axes and 0.01 m for the Z-axis.

Three-dimension infrared positioning systems are a feasible alternative on indoor local positioning systems, where those based on photodetectors are the most typically used in order not to have complex processing algorithms but a fast positioning computation. Optical positioning systems are often characterized by their low cost, low lifetime, and easy integration on the workplace. This work proposes an infrared positioning system based on four infrared LEDs and a QADA receiver. By applying encoding techniques to the infrared transmissions, the points of incidence from those transmitters on the QADA receiver are simultaneously obtained and the polar angle compensated, in order to finally estimate the receiver's position. The geometrical considerations of the system have been derived, including the polar angle and its behaviour with regard to the receiver's position, the angle of incidence and the aperture height. The proposal has been successfully validated by simulation and experimental tests, obtaining positioning errors below 10 cm and errors in the estimation of the polar angle of 0.9oin a 2 × 2 × 2 m volume.