The global navigation satellite system (GNSS) is primarily employed for positioning by most modern navigation systems. However, the application requirements of fully autonomous vehicles cannot be satisfied solely by GNSS and thus a combination of positioning and navigation approaches need to be explored. This work investigates how reliable relative positioning can be achieved in GNSS challenged application scenarios using a combination of signals of opportunity (SOPs), as well as inertial and vision data. The proposed cooperative relative positioning system (CRPS) exploits this data for real-time positioning, and employs a vehicle tracking algorithm to accurately estimate the vehicle’s trajectory in space and time without the use of any GNSS information. Experiments conducted in an outdoor setting demonstrate the applicability of the proposed CRPS and its performance against standalone positioning approaches including GNSS.

IEEE publication

Current navigation technologies are relying on global navigation satellite system (GNSS) information. As in terms of reliability and precision next-generation autonomous vehicle requirements cannot be fully satisfied by GNSS, a sensor information fusion must be employed, leading to the exploration of new positioning methods. In this work, a reliable relative positioning solution in GNSS-challenged areas is investigated, using a combination of signals of opportunity (SOPs), inertial, and optical flow data. The proposed real time relative positioning system exploits the fused data in the absence of GNSS signals for localization, employing a tracking algorithm to estimate the agent’s trajectory in space and time. Extensive outdoor experiments employing an Unmanned Aerial Vehicle (UAV) are carried out to demonstrate the applicability of the proposed technique, validating its performance against various positioning approaches, including GNSS.

IEEE publication 

To fully realize the potentials of autonomous aerial vehicles, a reliable, robust, and accurate navigation system is required. Current technologies composing such a system include stand-alone and augmented global navigation satellite systems (GNSS), as well as visual, inertial, and signals-of-opportunity approaches. However, as it is well understood that no single alternative can meet the requirements for full autonomy, a combination of technologies is needed to address alternative application scenarios. In this work, an online technique for relative positioning of autonomous unmanned aerial vehicles (UAVs) is proposed that capitalizes on signals-of-opportunity (SOPs) in conjunction with inertial measurements for localization and navigation when GNSS signals are not readily available. The proposed system uses the signals characteristics over a large part of the frequency spectrum, in conjunction with an optimal learning technique, to adaptively choose a sequence of frequencies that provide location estimates in real-time. Subsequently, inertial measurements provided by an onboard unit, are employed to improve on the tracking accuracy. Field experiments, using an autonomous unmanned aerial vehicle, demonstrate the effective- ness of the proposed solution under various parameter settings.

IEEE publication

With the rise of the number of unauthorized operations of Unmanned Aerial Vehicles (UAVs), versatile counter-drone systems are becoming a necessity. In this work, a counter-drone system is developed, where a pursuer drone employs algorithms for detecting and tracking a rogue drone, in conjunction with wireless interception capabilities to jam the rogue drone, while also jointly achieving self-positioning for the pursuer drone. In the proposed system, a software-defined radio (SDR) is used for switching between jamming transmissions and spectrum sweeping functionalities to achieve the desired GPS disruption and self-localization, respectively. Extensive field experiments demonstrate the effectiveness of the proposed solution in a real-world environment under various parameter settings.

To be published in IEEE

A perception-aware autonomous exploration framework aimed at performing vision-based target detection and collision avoidance with an Unmanned Aerial Vehicle (UAV) is presented. The UAV utilizes a depth camera for maneuvering and robust target detection. The underlying indoor exploration approach considers autonomous collision-free navigation, as well as target detection with a ballistic ball payload delivery without a prior map. Moreover, the proposed method allows safe navigation in enclosed unknown areas congested with randomly positioned obstacles and target locations. Our underlined end-to-end system architecture integrates the proposed exploration strategy. Extensive field experiments, using several Key Performance Indicators (KPIs), showcase the effectiveness of the proposed Robot Operating System (ROS) framework in a simulated Gazebo environment under various parameter settings.

To be published in IEEE

Signals of opportunity (SOPs) are a promising technique that can be used for relative positioning in areas where global navigation satellite system (GNSS) information is unreliable or unavailable. This technique processes features of the various signals transmitted over a broad wireless spectrum to enable a receiver to position itself in space. This work examines the frequency selection problem in order to achieve fast and accurate positioning using only the received signal strength (RSS) of the surrounding signals. Starting with a prior belief, the problem of searching for a frequency band that best matches a predicted location trajectory is investigated. To maximize the accuracy of the position estimate, a ranking-and-selection problem is mathematically formulated. A knowledge-gradient (KG) algorithm from optimal learning theory is proposed that uses correlations in the Bayesian prior beliefs of the frequency band values to dramatically reduce the algorithm’s processing
time. The technique is experimentally tested for a practical scenario of an unmanned aerial vehicle (UAV) moving around a GPS-denied environment, with obtained results demonstrating its validity and practical applicability.

IEEE publication

Unlawful operations involving unmanned aircraft systems (UASs) are raising concerns for the security of critical infrastructures as well as of crowded environments. In this work, a real-time bistatic passive radar (BPR) is proposed, established on software-defined radio (SDR) and signals of opportunity (SOPs), to counter unauthorized drone operations over critical infrastructures. Specifically, SOPs that are already available in the environment, e.g., digital video broadcasting – terrestrial (DVB-T) signals, are employed and processed in real time to provide simultaneous detection and tracking of rogue drones in the vicinity. Also, the advent of SDRs has provided the opportunity to develop a low-cost and versatile BPR solution. A prototype passive radar (PR) implementation is proposed, using embedded processing units, and a signal processing framework is applied to achieve the two-fold detection-and-tracking objective in real-time. Through extensive outdoor experiments, the performance of the proposed system is validated under various parameter settings.

IEEE publication

A multitude of applications based on unmanned aircraft vehicles (UAVs), in conjunction with the UAVs’ diminishing costs and increasing capabilities, present a major threat to public safety as well as critical infrastructure security, leading to the need for versatile and robust counter-drone solutions. This work presents a novel multi-agent jamming system (MUAV-JS), where a group of cooperative autonomous agents employ various algorithms (detection, tracking, jamming, and self-localization) to counter unauthorized drone operations. The proposed system employs a joint wireless jamming and cooperative positioning framework to best track and intercept the moving rogue drone, utilizing a relative positioning system based on signals of opportunity in conjunction with inertial and vision measurements. Software-defined radio technology is incorporated on the pursuer agent to achieve rogue drone GPS disruption, while at the same time the autonomous agents cooperate to compute the location estimate of the pursuer agent. In the presence of jamming, to improve the wireless communication performance of the autonomous agents, a cellular communication architecture is also used as an additional channel for control and information exchange. For evaluation purposes, a prototype multi-agent system is designed, implemented, and tested in a real-world environment to demonstrate its enhanced localization and jamming performance, when compared to single agent approaches.

IEEE publication

As the number of unauthorized operations of unmanned aerial vehicles increases, the protection of public spaces, as well as critical infrastructures against malicious actions has become a major concern. In this work, a prototype onboard passive radar system is presented, based on signals of opportunity and software-defined radio, that is able to detect and track illegal/unauthorized drone operations over a specified region of interest. Specifically, the proposed system, mounted on an unmanned aerial vehicle, aims to detect and track rogue drone operations by incorporating the signals of opportunity that are already available in the environment, such as digital video broadcasting – terrestrial signals, in conjunction with the visual measurements of the unmanned aerial agent. The development of the proposed small, low-cost, and versatile onboard passive radar solution is enabled by technological advancements in software-defined radio, embedded processing units, and signal processing. The design and development of the prototype are presented and its applicability is demonstrated through extensive outdoor experiments.

IEEE publication

The rise of illegal unmanned aerial vehicles (UAVs) operations, as well as the drone sensors vulnerability, has led to the need for proper monitoring systems. In this research attempt, an innovative multi-task learning framework for drone state identification and trajectory prediction (MLF-ST), using multitask learning, is proposed. The presented framework consists of the two-fold objective of state identification and trajectory prediction, and it aims to optimize the performance of both tasks simultaneously. A deep neural network with shared layers to extract features from the input data is employed, utilizing drone sensor measurements and historical trajectory information. Also, a novel loss function that combines the two objectives is proposed, encouraging the network to jointly learn the features that are most useful for both tasks. The proposed MLF-ST framework is evaluated on a large dataset of drone flights, illustrating that it is able to outperform various state-of-the-art baselines in both state identification and trajectory prediction. The evaluation of the proposed framework, using real-world data, showed that it can enable applications such as drone surveillance and monitoring, while also demonstrate its potential

https://arxiv.org/abs/2309.06741

IEEE publication

Currently, an unmanned aerial vehicle (UAV) utilizes global navigation satellite systems (GNSS) in conjunction with other modalities for localization purposes. Nevertheless, this approach faces robustness issues when GNSS signals become unavailable or sensors malfunction. Clearly, the robustness of the system increases considerably when multiple UAV agents are employed to perform collaborative positioning. In this work, an online distributed solution is proposed for relative localization, which incorporates multiple UAVs together with Signals of Opportunity (SOPs) as well as inertial, visual, and optical flow measurements. The proposed localization system includes relative self-localization of each UAV agent, as well as a reliable distributed relative positioning system (DRPS) for each UAV based on the relative positions from other UAV agents in its vicinity. The latter positioning strategy is required in case the relative self-localization fails, mainly due to such problems as inertial measurement unit (IMU) accumulated error drift, camera sensor errors, or SOP shortfalls due to multipath or antenna obstruction. Extensive field experiments validate the proposed technique and demonstrate increased localization accuracy and robustness when compared to the benchmark approach that does not include cooperation between UAVs.

https://link.springer.com/article/10.1007/s10846-023-01992-2

Springer publication