Alessio Fascista

Bio: Alessio Fascista is an academic researcher from University of Salento. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 9, co-authored 29 publications receiving 381 citations.

On the Hybrid TOA/RSS Range Estimation in Wireless Sensor Networks

Abstract: Distance estimation, which arises in many applications and especially in range-based localization, is addressed for joint received signal strength (RSS) and time of arrival (TOA) data. A statistical characterization of the joint maximum likelihood estimator, which is unavailable in closed-form, is provided together with a full performance assessment in terms of the actual mean squared error (MSE), in order to establish when hybrid estimation is superior compared to RSS-only or TOA-only estimation. Furthermore, a novel closed-form estimator is proposed based on an ad-hoc relaxation of the likelihood function, which removes the need to adopt iterative methods for hybrid TOA/RSS ranging and strikes a better bias-variance tradeoff for improved performance. A thorough theoretical analysis, corroborated by numerical simulations, shows the effectiveness of the proposed approach, which outperforms state-of-the-art solutions.

Angle of Arrival-Based Cooperative Positioning for Smart Vehicles

Abstract: The limited localization capabilities provided by global navigation satellite systems (GNSS) is one of the main obstacles toward the development of reliable road safety applications in urban scenarios. In order to improve GNSS accuracy, a number of approaches have been proposed which exploit additional position-related information, for instance provided by local inertial sensors. However, such solutions cannot meet the very stringent accuracy requirements of safety applications, which call for advanced processing and the fusion of position-related signals and data from heterogeneous sources. In this paper, we aim at combining the potential of antenna array processing with a suitably-designed cooperation strategy that exploits vehicle-to-vehicle and vehicle-to-infrastructure communications. Particularly, we define a novel tracking algorithm with asynchronous updates triggered by beacon packet receptions, from which angle of arrival estimates are opportunistically obtained. A dynamic setting of relevant parameters allows the resulting cooperative positioning algorithm to adapt to the different operating conditions found in urban vehicular contexts. Simulation results under realistic environment conditions show that the proposed algorithm can achieve high position accuracy even in sparse scenarios, outperforming a natural competitor while keeping lightweight communication and low computational complexity.

Millimeter-Wave Downlink Positioning With a Single-Antenna Receiver

Abstract: This paper addresses the problem of determining the unknown position of a mobile station for a mmWave multiple-input single-output (MISO) system. This setup is motivated by the fact that massive arrays will be initially implemented only on 5G base stations, likely leaving mobile stations with one antenna. The maximum likelihood solution to this problem is devised based on the time of flight and angle of departure of received downlink signals. While positioning in the uplink would rely on angle of arrival, it presents scalability limitations that are avoided in the downlink. To circumvent the multidimensional optimization of the optimal joint estimator, we propose two novel approaches amenable to practical implementation thanks to their reduced complexity. A thorough analysis, which includes the derivation of relevant Cramer–Rao lower bounds, shows that it is possible to achieve quasi-optimal performance even in presence of few transmissions, low signal-to-noise ratio (SNRs), and multipath propagation effects.

Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review.

Abstract: Thanks to recent technological advances, a new generation of low-cost, small, unmanned aerial vehicles (UAVs) is available. Small UAVs, often called drones, are enabling unprecedented applications but, at the same time, new threats are arising linked to their possible misuse (e.g., drug smuggling, terrorist attacks, espionage). In this paper, the main challenges related to the problem of drone identification are discussed, which include detection, possible verification, and classification. An overview of the most relevant technologies is provided, which in modern surveillance systems are composed into a network of spatially-distributed sensors to ensure full coverage of the monitored area. More specifically, the main focus is on the frequency modulated continuous wave (FMCW) radar sensor, which is a key technology also due to its low cost and capability to work at relatively long distances, as well as strong robustness to illumination and weather conditions. This paper provides a review of the existing literature on the most promising approaches adopted in the different phases of the identification process, i.e., detection of the possible presence of drones, target verification, and classification.

A Localization Algorithm Based on V2I Communications and AOA Estimation

Abstract: Motivated by safety applications in urban vehicular scenarios, where GPS does not typically provide the required positioning accuracy, a GPS-free localization technique that exploits vehicle-to-infrastructure communications is proposed. In particular, it provides for a vehicle to opportunistically use the beacon packets received from a roadside unit (RSU) in order to obtain estimates of their angle of arrival. Such estimates, together with the RSU's position information within beacon packets, are fed to a weighted least squares algorithm that aims at localizing the vehicle. The algorithm tries to take advantage of reliable measurements typically collected closer to the RSU—where a very high signal-to-noise ratio yields an accurate angular resolution—while keeping robustness against multipath phenomena. Simulation results show the effectiveness of the proposed technique.

贝叶斯滤波与平滑 (Bayesian filtering and smoothing)

Abstract: Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications, and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book’s practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include MATLAB computations, and the numerous end-of-chapter exercises include computational assignments. MATLAB/GNU Octave source code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.

A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications

Abstract: For an autonomous vehicle to operate safely and effectively, an accurate and robust localization system is essential. While there are a variety of vehicle localization techniques in literature, there is a lack of effort in comparing these techniques and identifying their potentials and limitations for autonomous vehicle applications. Hence, this paper evaluates the state-of-the-art vehicle localization techniques and investigates their applicability on autonomous vehicles. The analysis starts with discussing the techniques which merely use the information obtained from on-board vehicle sensors. It is shown that although some techniques can achieve the accuracy required for autonomous driving but suffer from the high cost of the sensors and also sensor performance limitations in different driving scenarios (e.g., cornering and intersections) and different environmental conditions (e.g., darkness and snow). This paper continues the analysis with considering the techniques which benefit from off-board information obtained from V2X communication channels, in addition to vehicle sensory information. The analysis shows that augmenting off-board information to sensory information has potential to design low-cost localization systems with high accuracy and robustness, however, their performance depends on penetration rate of nearby connected vehicles or infrastructure and the quality of network service.

'The Micro-Doppler Effect in Radar' by V.C. Chen

Abstract: This is a book review of 'The Micro-Doppler Effect in Radar' by V. C. Chen. Artech House, 16 Sussex Street, London, SW1V 4RW, UK. 2011. 290pp + diskette. Illustrated. £90. ISBN 978-1-60807-057-2.

A Survey on Infrastructure-Based Vehicular Networks

Abstract: The infrastructure of vehicular networks plays a major role in realizing the full potential of vehicular communications. More and more vehicles are connected to the Internet and to each other, driving new technological transformations in a multidisciplinary way. Researchers in automotive/telecom industries and academia are joining their effort to provide their visions and solutions to increasingly complex transportation systems, also envisioning a myriad of applications to improve the driving experience and the mobility. These trends pose significant challenges to the communication systems: low latency, higher throughput, and increased reliability have to be granted by the wireless access technologies and by a suitable (possibly dedicated) infrastructure. This paper presents an in-depth survey of more than ten years of research on infrastructures, wireless access technologies and techniques, and deployment that make vehicular connectivity available. In addition, we identify the limitations of present technologies and infrastructures and the challenges associated with such infrastructure-based vehicular communications, also highlighting potential solutions.

Real-Time and Accurate Drone Detection in a Video with a Static Background.

Abstract: With the increasing number of drones, the danger of their illegal use has become relevant. This has necessitated the creation of automatic drone protection systems. One of the important tasks solved by these systems is the reliable detection of drones near guarded objects. This problem can be solved using various methods. From the point of view of the price-quality ratio, the use of video cameras for a drone detection is of great interest. However, drone detection using visual information is hampered by the large similarity of drones to other objects, such as birds or airplanes. In addition, drones can reach very high speeds, so detection should be done in real time. This paper addresses the problem of real-time drone detection with high accuracy. We divided the drone detection task into two separate tasks: the detection of moving objects and the classification of the detected object into drone, bird, and background. The moving object detection is based on background subtraction, while classification is performed using a convolutional neural network (CNN). The experimental results showed that the proposed approach can achieve an accuracy comparable to existing approaches at high processing speed. We also concluded that the main limitation of our detector is the dependence of its performance on the presence of a moving background.