Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach

Abstract

Nowadays there is a growing research interest on the possibility of enriching small flying robots with autonomous sensing and online navigation capabilities. This will enable a large number of applications spanning from remote surveillance to logistics, smarter cities and emergency aid in hazardous environments. In this context, an emerging problem is to track unauthorized small unmanned aerial vehicles (UAVs) hiding behind buildings or concealing in large UAV networks. In contrast with current solutions mainly based on static and on-ground radars, this paper proposes the idea of a dynamic radar network of UAVs for real-time and high-accuracy tracking of malicious targets. To this end, we describe a solution for real-time navigation of UAVs to track a dynamic target using heterogeneously sensed information. Such information is shared by the UAVs with their neighbors via multi-hops, allowing tracking the target by a local Bayesian estimator running at each agent. Since not all the paths are equal in terms of information gathering point-of-view, the UAVs plan their own trajectory by minimizing the posterior covariance matrix of the target state under UAV kinematic and anti-collision constraints. Our results show how a dynamic network of radars attains better localization results compared to a fixed configuration and how the on-board sensor technology impacts the accuracy in tracking a target with different radar cross sections, especially in non line-of-sight (NLOS) situations.

Radio Positioning With EM Processing of the Spherical Wavefront

“…According to such considerations on the processing operated by the R-lens, the reconfigurable lens phase profile can be described by a function κ(y, z, χ t , p), 4 depending on the tested target position p and on the test offset χ t , as…”

“…I NDOOR positioning systems have attracted a great interest in a large variety of scenarios because of the possibility of performing high accuracy users' localization even when Global Navigation Satellite System (GNSS) signal is not available and alternative positioning systems are required [1]- [3]. Currently, there is a large variety of ad-hoc solutions for indoor localization and tracking [4]- [7], spanning from the systems based on ultrasounds or to more recent impulse radio UWB (IR-UWB) techniques [6], [8]- [10]. Unfortunately, most of such solutions require that a mobile node is detected at least from three reference nodes (multiple anchor nodes) located in known positions, with the need to realize ad-hoc and often redundant infrastructures that might become not convenient in many indoor scenarios.…”

Deep Learning-Based Drone Classification Using Radar Cross Section Signatures at mmWave Frequencies

“…Based on the measured RCS signatures the flying object can be identified, and necessary actions can be undertaken by the authorities. Cellular operators have already begun deploying the new infrastructure required for 5G NR [22], and soon 5G mobile edge computing (MEC) [23] servers operating at mmWave frequencies will be densely deployed in cities [24]. Adopting this infrastructure for drone detection would result in great cost savings since the work of provisioning and deploying the equipment would already be handled by cellular operators.…”

Networks of UAVs of Low-Complexity for Time-Critical Localization

“…Indeed, with this term, we indicate all the situations when a real-time fast optimal deployment of UAVs is vitally important for the successful completion of the mission itself, as in disaster relief settings. Mission-critic applications impose strict limited mission times that should be accounted for when designing the UAV control [3].…”