The widespread adoption of Unmanned Aerial Vehicles (UAVs) has raised critical security and safety concerns, particularly in sensitive areas and air traffic management. Modern counter-drone systems integrate multiple sensing modalities, but their development is hindered by the lack of comprehensive, publicly available datasets. To address this, the Drone-vs-Bird Detection Grand Challenge provides a manually annotated UAV dataset to advance research in drone detection. Since its inception in 2017, the competition has attracted global interest, fostering the development of advanced detection methods. This paper presents an overview of the 8th edition as data competition hosted at the International Joint Conference on Neural Networks (IJCNN) 2025. The data competition generated high engagement with 16 competing algorithms successfully submitted. The variability of the results underscores the complexity of the task and the need for future research. Over almost a decade, this data competition has been bridging the domains of signal processing, computer vision, and deep learning, paving the way for next-generation counter-drone solutions.

The integration of Unmanned Aerial Vehicles (UAVs) into next-generation mobile networks is widely recognized as a key enabler of disruptive applications, where aerial platforms may function either as network nodes or as advanced network users supporting a variety of services. Unfortunately, experimental testbeds in which UAVs perform tasks while communicating with ground infrastructure over Fifth-Generation (5 G) networks remain scarce, primarily due to the challenges posed by legal restrictions on Beyond Line-of-Sight (BLoS) and autonomous operations. Motivated by this need, this work presents the design, implementation, and evaluation of a novel indoor experimental testbed for assessing the performance of UAV-based systems operating over 5 G networks. The testbed features an autonomously controlled quadcopter equipped with a 5G modem, connected to a private 5 G network implemented using SoftwareDefined Radio (SDR) technology and the OpenAirInterface (OAI) framework. To ensure a controlled environment, a motion capture system is used to provide absolute indoor positioning data, emulating Global Navigation Satellite System (GNSS) coordinates without relying on external satellites. A preliminary experimental campaign is conducted to evaluate the proposed system in terms of 5 G network performance, radio link characteristics, and UAV platform energy consumption.

Characterization of the exceedance probability (EP) of the energy statistic (ES) plays a fundamental role in several signal processing applications, including radar (e.g., probability of false alarm) and communications (e.g., outage probability). However, manageable closed-form expressions are not available for general non-Gaussian models such as the$\eta$-$\mu$distribution. In this letter, simple formulas for predicting the EP of the ES are provided, based on second- and third-order cumulant series expansion of the tightest Chernoff bound, coupled with low-complexity approximations of Hoyt moments. Results show that the proposed method significantly improves over earlier work based on different bounds, and outperforms the asymptotic approximation via the central limit theorem as well as the Generalized Pareto Distribution fitting of the distribution tail.

This paper presents the 6th edition of the "Drone-vs-Bird" Detection Grand Challenge, organized within the 48th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Taking video samples recorded by commercial RGB cameras as input, the challenge stimulates the design of advanced approaches capable of detecting the presence of small drones flying in a given area under surveillance. Successful methods should ensure high detection rates while limiting the number of false alarms erroneously triggered in presence of very similar false targets (birds). The paper summarizes the novel methods proposed by the four participating teams that succeeded in providing satisfactory detection performance on the 2023 challenge dataset.

This paper presents the 4-th edition of the “drone-vs-bird” detection challenge, launched in conjunction with the the 17-th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS). The objective of the challenge is to tackle the problem of detecting the presence of one or more drones in video scenes where birds may suddenly appear, taking into account some important effects such as the background and foreground motion. The proposed solutions should identify and localize drones in the scene only when they are actually present, without being confused by the presence of birds and the dynamic nature of the captured scenes. The paper illustrates the results of the challenge on the 2021 dataset, which has been further extended compared to the previous edition run in 2020.

MmWave multiple-input single-output (MISO) systems using a single-antenna receiver are regarded as a promising solution for the near future, before the full-fledged 5G MIMO will be widespread. However, for MISO systems synchronization cannot be performed jointly with user localization unless two-way transmissions are used. In this paper we show that thanks to the use of a reconfigurable intelligent surface (RIS), joint localization and synchronization is possible with only downlink MISO transmissions. The direct maximum likelihood (ML) estimator for the position and clock offset is derived. To obtain a good initialization for the ML optimization, a decoupled, relaxed estimator of position and delays is also devised, which does not require knowledge of the clock offset. Results show that the proposed approach attains the Cramér-Rao lower bound even for moderate values of the system parameters.

This paper proposes a decision scheme based on the$k$-nearest neighbors rule to detect coherent signals in non-Gaussian noise modeled as the sum of K-distributed clutter plus thermal noise. The analysis is conducted also on real data recordings and shows that the proposed detector can outperform natural competitors.

5G mmWave MIMO systems enable accurate estimation of the user position and mapping of the radio environment using a single snapshot when both the base station (BS) and user are equipped with large antenna arrays. However, massive arrays are initially expected only at the BS side, likely leaving users with one or very few antennas. In this paper, we propose a novel method for single-snapshot localization and mapping in the more challenging case of a user equipped with a single-antenna receiver. The joint maximum likelihood (ML) estimation problem is formulated and its solution formally derived. To avoid the burden of a full-dimensional search over the space of the unknown parameters, we present a novel practical approach that exploits the sparsity of mmWave channels to compute an approximate joint ML estimate. A thorough analysis, including the derivation of the Cramér-Rao lower bounds, reveals that accurate localization and mapping can be achieved also in a MISO setup even when the direct line-of-sight path between the BS and the user is severely attenuated.

The problem of robust radar detection is addressed from a machine learning inspired perspective. In particular, a novel interpretation of the well-known Kelly's and adaptive matched filter (AMF) detectors is provided in terms of decision region boundaries in a suitable feature space. Then, a new detector based on a feature vector that combines the two detection statistics is obtained by exploiting the k-nearest neighbors (KNN) approach. The resulting receiver possesses the constant false alarm rate (CFAR) property and can achieve the same benchmark performance of Kelly's detector under matched conditions while being almost as robust as the AMF (which instead experiences a loss under matched conditions).

The problem of position estimation of a mobile user equipped with a single antenna receiver using downlink transmissions is addressed. The advantages of this setup compared to the classical MIMO and uplink scenarios are analyzed in terms of achievable theoretical performance (Cramér-Rao bounds) considering a realistic power budget. Based on this analysis, a low-complexity two-step algorithm with improved localization performance is proposed, which first performs a (coarse) angle of departure estimation and then precodes the down-link signal to introduce beamforming towards the user direction. Results demonstrate that position estimation in downlink can be potentially much more accurate than in uplink, even in presence of multiple users in the system.

Cellular localization generally relies on time-difference-of-arrival (TDOA) measurements. In this paper, we investigate a novel scenario where the mobile user estimates its own position by jointly exploiting TDOA and angle of departure (AOD) measurements, which are estimated from downlink transmissions in a millimeter-wave (mmWave) multiple-input single-output (MISO) setup. We first perform a Fisher information analysis to derive the lower bounds on the estimation accuracy, and then propose a novel localization algorithm, which is able to provide improved performance also with few transmit antennas and limited bandwidth.

This paper presents the second edition of the “drone-vs-bird” detection challenge, launched within the activities of the 16-th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS). The challenge's goal is to detect one or more drones appearing at some point in video sequences where birds may be also present, together with motion in background or foreground. Submitted algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. This paper reports on the challenge results on the 2019 dataset, which extends the first edition dataset provided by the SafeShore project with additional footage under different conditions.

A novel maximum likelihood trajectory estimation algorithm for targets in mixed stationary/moving conditions is presented. The proposed approach is able to estimate position and velocity of the target over arbitrary complex trajectories, while explicitly taking into account the possibility of stop&go motion. Moreover, a novel trajectory reconstruction method based on the theory of Bézier curve is developed for online smoothing of the trajectory, which keeps the advantages of Bayesian smoothing while introducing only a fixed lag in the estimation process. The performance assessment, conducted on both simulated and real data, shows that the proposed approach can outperform classical Kalman filter and Rauch-Tung-Striebel smoother techniques.

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 Cramér-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.

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.

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.

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.