In surveillance and monitoring systems, the use of mobile vehicles or unmanned aerial vehicles (UAVs), like the drone type, provides advantages in terms of access to the environment with enhanced range, maneuverability, and safety due to the ability to move omnidirectionally to explore, identify, and perform some security tasks. These activities must be performed autonomously by capturing data from the environment; usually, the data present errors and uncertainties that impact the recognition and resolution in the detection and identification of objects. The resolution in the acquisition of data can be improved by integrating data sensor fusion systems to measure the same physical phenomenon from two or more sensors by retrieving information simultaneously. This paper uses the constant turn and rate velocity (CTRV) kinematic model of a drone but includes the angular velocity not considered in previous works as a complementary alternative in Lidar and Radar data sensor fusion retrieved using UAVs and applying the extended Kalman filter (EKF) for the detection of moving targets. The performance of the EKF is evaluated by using a dataset that jointly includes position data captured from a LiDAR and a Radar sensor for an object in movement following a trajectory with sudden changes. Additive white Gaussian noise is then introduced into the data to degrade the data. Then, the root mean square error (RMSE) versus the increase in noise power is evaluated, and the results show an improvement of 0.4 for object detection over other conventional kinematic models that do not consider significant trajectory changes.

This paper presents a case study on integrating Agile Systems Engineering methodologies in the preliminary design phase of satellite systems, focusing on the ³ColStar satellite mission. Through Model-Based Systems Engineering (MBSE), technical consistency was rigorously managed across various architectural documents, ensuring coherency and minimizing errors. Furthermore, the preliminary design was developed, with the implementation of the Arcadia Method, supported by the Capella modeling tool. This allowed the digitalization of the system, which was represented by models that contain requirements, architecture, and interfaces between the different parts of the system. At the same time, the preliminary design process was streamlined and completed within an accelerated time frame of 4 months, with weekly sprints driving progress based on the scrum methodology. This case study highlights the effectiveness of Agile Systems Engineering principles to improve the team communication accuracy, communication, and efficiency of satellite systems preliminary design, providing valuable insights for future missions.

The non-linear behavior of amplifiers of power in wireless communications systems, introduces a lateral spectral growth that compromises efficiency in power and spectral. This article presents a numerical validation and experimental implementation of digital predistortion system to mitigate the adjacent channel power ratio (ACPR) impact of power amplifier applications in the 0.9 GHz band with power gains of up to 14 dB. Applying the Saleh model from the curves of amplitude-modulation (AM) and phase-modulation (PM), and modelling the inverse transfer function in an implementation under GNU Radio environment, it was possible to provide a mitigation of the ACPR. The experimental result allows to make a proof-of-concept of digital predistortion showing a reduction in ACPR of 8 dB for a baseband wireless transmission system using N210 USRP modules and transmitting in base band using QPSK modulation format at 100 kS/s.

This chapter introduces the foundations of the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) for data sensor fusion applications in surveillance applications. After explaining how to model the drone under the constant turn rate and velocity (CTRV) dynamics, the EKF and UKF techniques for Lidar and Radar data sensor fusion are applied to enable better 3D object detection and reconstruction from point cloud data is evaluated under a performance comparison. Through root mean square error (RMSE) and mean square error (MSE) the filters are designed to meet the specifications of the model based on the position of the gathered points (X and Y axis) in the space state model. By degrading the measurements with Gaussian and Rayleigh noise, the results evaluate and compare both techniques to show the tolerance and performance in terms of stability, scalability, and application to drone monitoring in surveillance activities.

Este artículo presenta una revisión de estado de arte de las técnicas del aprendizaje de máquina para el reconocimiento de patrones, la definición de los algoritmos para la clasificación de objetos (personas) a partir de imágenes construidas desde nubes de puntos adquiridas por sensores de Radar y Lidar, y resultados preliminares de aplicación en escenas reconstruidas a partir de nubes de puntos. En el primer hito del proyecto CLARIFIER (frequenCy-agiLe rAdar-lidaR chIp For surveIllancE moving platfoRms), se ha estudiado e implementado un filtro de Kalman Extendido para fusión de datos de sensores de Radar y LiDAR, cuyo aporte fue la inclusión de la velocidad angular en el modelo cinemático de un drone. En el segundo hito, se ha revisado estado de arte para identificar enfoque (segmentación semántica) y algoritmos que permitan detectar personas en imágenes reconstruidas de escenarios o zonas con requerimientos de supervisión y monitoreo. Como resultado de aplicación preliminar, se ha construido un conjunto de datos de escenas que incluye la clase “personas” y se evaluaron los algoritmos U-Net y Mask R-CNN que aplicas técnicas de segmentación semántica. Dado que la resolución del sensor en particular de Lidar, es mayor a la del radar, así como la diferencia en rangos que cada uno alcanza, se genera un compromiso que requiere múltiples barridos de la escena desde diferentes ángulos y distancias y se requiere aumentar el conjunto de datos, para entrenar los algoritmos y mejorar el porcentaje de detecciones correctas. De acuerdo a las métricas que se han evaluado (pérdidas, exactitud, precisión, exhaustividad y F1 Score), la clasificación de la clase personas en el contexto de segmentación semántica alcanza un 89.91 % para Mask R-CNN, y del 90.53% para U-Net; y con la curva de operación característica del receptor (ROC) y el área bajo la curva (AUC) se obtiene un 90% y 92% en la detección de la clase personas, respectivamente. Este resultado valida la efectividad de estos modelos de redes neuronales convolucionales aplicada a imágenes obtenidas a partir de nubes de puntos.