A novel framework to intercept GPS-denied,bomb-carrying,nonmilitary,kamikaze drones:Towards protecting critical infrastructures

Protection of urban critical infrastructures(CIs)from GPS-denied,bomb-carrying kamikaze drones(G-BKDs)is very challenging.Previous approaches based on drone jamming,spoofing,communication interruption and hijacking cannot be applied in the case under examination,since G-B-KDs are uncontrolled.On the other hand,drone capturing schemes and electromagnetic pulse(EMP)weapons seem to be effective.However,again,existing approaches present various limitations,while most of them do not examine the case of G-B-KDs.This paper,focuses on the aforementioned under-researched field,where the G-B-KD is confronted by two defensive drones.The first neutralizes and captures the kamikaze drone,while the second captures the bomb.Both defensive drones are equipped with a net-gun and an innovative algorithm,which,among others,estimates the locations of interception,using a real-world trajectory model.Additionally,one of the defensive drones is also equipped with an EMP weapon to damage the electronics equipment of the kamikaze drone and reduce the capturing time and the overall risk.Extensive simulated experiments and comparisons to state-of-art methods,reveal the advantages and limitations of the proposed approach.More specifically,compared to state-of-art,the proposed approach improves:(a)time to neutralize the target by at least 6.89%,(b)maximum number of missions by at least 1.27%and(c)total cost by at least 5.15%.

Reinforcement learning based UAV formation control in GPS-denied environment

Highly accurate positioning is a crucial prerequisite of multi Unmanned Aerial Vehicle close-formation flight for target tracking,formation keeping,and collision avoidance.Although the position of a UAV can be obtained through the Global Positioning System(GPS),it is difficult for a UAV to obtain highly accurate positioning data in a GPS-denied environment(e.g.,a GPS jamming area,suburb,urban canyon,or mountain area);this may cause it to miss a tracking target or collide with another UAV.In particular,UAV close-formation control in GPS-denied environments faces difficulties owing to the low-accuracy position,close distance between vehicles,and nonholonomic dynamics of a UAV.In this paper,on the one hand,we develop an innovative UAV formation localization method to address the formation localization issues in GPS-denied environments;on the other hand,we design a novel reinforcement learning based algorithm to achieve the high-efficiency and robust performance of the controller.First,a novel Lidar-based localization algorithm is developed to measure the localization of each aircraft in the formation flight.In our solution,each UAV is equipped with Lidar as the position measurement sensor instead of the GPS module.The k-means algorithm is implemented to calculate the center point position of UAV.A novel formation position vector matching method is proposed to match center points with UAVs in the formation and estimate their position information.Second,a reinforcement learning based UAV formation control algorithm is developed by selecting the optimal policy to control UAV swarm to start and keep flying in a close formation of a specific geometry.Third,the innovative collision risk evaluation module is proposed to address the collision-free issues in the formation group.Finally,a novel experience replay method is also provided in this paper to enhance the learning efficiency.Experimental results validate the accuracy,effectiveness,and robustness of the proposed scheme.

A distributed approach for lidar-based relative state estimation of multi-UAV in GPS-denied environments

In this paper,we present a distributed framework for the lidar-based relative state estimator which achieves highly accurate,real-time trajectory estimation of multiple Unmanned Aerial Vehicles(UAVs)in GPS-denied environments.The system builds atop a factor graph,and only on-board sensors and computing power are utilized.Benefiting from the keyframe strategy,each UAV performs relative state estimation individually and broadcasts very partial information without exchanging raw data.The complete system runs in real-time and is evaluated with three experiments in different environments.Experimental results show that the proposed distributed approach offers comparable performance with a centralized method in terms of accuracy and real-time performance.The flight test demonstrates that the proposed relative state estimation framework is able to be used for aggressive flights over 5 m/s.

Bearing-only Visual SLAM for Small Unmanned Aerial Vehicles in GPS-denied Environments

This paper presents a hierarchical simultaneous localization and mapping（SLAM） system for a small unmanned aerial vehicle（UAV） using the output of an inertial measurement unit（IMU） and the bearing-only observations from an onboard monocular camera.A homography based approach is used to calculate the motion of the vehicle in 6 degrees of freedom by image feature match.This visual measurement is fused with the inertial outputs by an indirect extended Kalman filter（EKF） for attitude and velocity estimation.Then,another EKF is employed to estimate the position of the vehicle and the locations of the features in the map.Both simulations and experiments are carried out to test the performance of the proposed system.The result of the comparison with the referential global positioning system/inertial navigation system（GPS/INS） navigation indicates that the proposed SLAM can provide reliable and stable state estimation for small UAVs in GPS-denied environments.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

UAV navigation system using line-based sensor pose estimation

This work presents a mapping and tracking system based on images to enable a small Unmanned Aerial Vehicle(UAV)to accurately navigate in indoor and GPS-denied outdoor environments.A method is proposed to estimate the UAV’s pose(i.e.,the 3D position and orientation of the camera sensor)in real-time using only the on-board RGB camera as the UAV travels through a known 3D environment(i.e.,a 3D CAD model).Linear features are extracted and automatically matched between images collected by the UAV’s onboard RGB camera and the 3D object model.The matched lines from the 3D model serve as ground control to estimate the camera pose in real-time via line-based space resection.The results demonstrate that the proposed modelbased pose estimation algorithm provides sub-meter positioning accuracies in both indoor and outdoor environments.It is also that shown the proposed method can provide sparse updates to correct the drift from complementary simultaneous localization and mapping(SLAM)-derived pose estimates.