scholarly journals Tracking Unmanned Aerial Vehicles Based on the Kalman Filter Considering Uncertainty and Error Aware

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3067
Author(s):  
Mohammed Abdulhakim Al-Absi ◽  
Rui Fu ◽  
Ki-Hwan Kim ◽  
Young-Sil Lee ◽  
Ahmed Abdulhakim Al-Absi ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicles ◽  
High Speed ◽  
Location Based Services ◽  
Tracking Accuracy ◽  
Daily Lives ◽  
Noisy Information ◽  
Aerial Vehicles ◽  
Modeling Uncertainties ◽  
Tracking Model

Recently, Unmanned Aerial Vehicles (UAVs) have made significant impacts on our daily lives with the advancement of technologies and their applications. Tracking UAVs have become more important because they not only provide location-based services, but are also faced with serious security threats and vulnerabilities. UAVs are smaller in nature, move with high speed, and operate in a low-altitude environment, which makes it conceivable to track UAVs using fixed or mobile radars. Kalman Filter (KF)-based methodologies are widely used for extracting valuable trajectory information from samples composed of noisy information. As UAVs’ trajectories resemble uncertain behavior, the traditional KF-based methodologies have poor tracking accuracy. Recently, the Diffusion-Map-based KF (DMK) was introduced for modeling uncertainties in the environment without prior knowledge. However, the model has poor accuracy when operating in environments with higher noise. In order to achieve better tracking performance, this paper presents the Uncertainty and Error-Aware KF (UEAKF) for tracking UAVs. The UEAKF-based tracking method provides a good tradeoff among preceding estimate confidence and forthcoming measurement under dynamic environments; the resulting filter is robust and nonlinear in nature. The experimental results showed that the UEAKF-based UAV tracking model achieves much better Root Mean Square Error (RMSE) performance compared to the existing particle filter-based and DMK-based UAV tracking models.

MULTIDIMENSIONAL SIMULATIONS AND TEST FIRES OF A HYDROGEN-FUELED RAMJET WITH AN ANNULAR DETONATIVE COMBUSTOR AT APPROACHING AIR FLOW OF MACH 2 AND 1.5

2020 ◽  
Author(s):  
V. S. IVANOV ◽  
◽  
V. S. AKSENOV ◽  
S. M. FROLOV ◽  
P. A. GUSEV ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
High Speed ◽  
Constant Pressure ◽  
Air Flow ◽  
Thermodynamic Cycle ◽  
Aerial Vehicles ◽  
Mach Numbers

Modern high-speed unmanned aerial vehicles are powered with small-size turbojets or ramjets. Existing ramjets operating on the thermodynamic cycle with de§agrative combustion of fuel at constant pressure are efficient at flight Mach numbers M ranging from about 2 to 6.

Design and flight-stability analysis of a closed fixed-wing unmanned aerial vehicle formation controller

2018 ◽  
Vol 233 (8) ◽  
pp. 1045-1054 ◽  
Author(s):  
Jialong Zhang ◽  
Bing Xiao ◽  
Maolong Lv ◽  
Qiang Zhang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
High Speed ◽  
Physical Simulation ◽  
Controller Design ◽  
Formation Flight ◽  
External Factors ◽  
Flight Stability ◽  
Aerial Vehicles ◽  
Closed Formation

This article addresses a flight-stability problem for the multiple unmanned aerial vehicles cooperative formation flight in the process of the closed and high-speed flight. The main objective is to design a cooperative formation controller with known external factors, and this controller can keep the consensus of attitude and position and reduce the communication delay between any two unmanned aerial vehicles and increase unmanned aerial vehicles formation cruise time under the known external factors. Known external factors are taken into consideration, and longitude maneuvers using nonlinear thrust vectors were employed with unsteady aerodynamic models, according to the attitude and position of unmanned aerial vehicles, which were employed as corresponding input signals for studying the dynamic characteristics of unmanned aerial vehicles formation flight. In addition, the relative distance between any two unmanned aerial vehicles was not allowed to exceed their safe distance so that the controller could perform collision avoidance. An analysis of formation flight distance error shows that it converged to a fixed value that well ensured unmanned aerial vehicles formation flight stability. The experimental results show that the controller can improve the speed of a closed formation effectively and maintain the stability of formation flight, which provides a method for closed formation flight controller design and collision avoidance for any two unmanned aerial vehicles. Meanwhile, the effectiveness of proposed controller is fully proved by semi-physical simulation platform.

Extended Kalman Filter Based Quadrotor State Estimation Based on Asynchronous Multisensor Data

2015 ◽  
Author(s):  
Mohammad Sarim ◽  
Alireza Nemati ◽  
Manish Kumar ◽  
Kelly Cohen
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unmanned Aerial Vehicles ◽  
Extended Kalman Filter ◽  
Real World ◽  
Flight Test ◽  
Communication Delays ◽  
Multiple Sensors ◽  
Aerial Vehicles ◽  
Multisensor Data

For effective navigation and tracking applications involving Unmanned Aerial Vehicles (UAVs), data fusion from multiple sensors is utilized. However, asynchronous nature of the sensors, coupled with loss of data and communication delays, makes this process not very reliable. For a better estimation of the data, some sort of filtering scheme is needed. This paper presents an Extended Kalman Filter (EKF) based quadrotor state estimation by exploiting the dynamic model of the UAV. The data coming from the sensors is noisy and intermittent. The EKF filters and provides estimated data for the missing timestamps. An indoor flight test establishes the accuracy of the EKF, and another outdoor flight test validates the developed scheme for the real world scenario.

scholarly journals Ultra-wideband based cooperative relative localization algorithm and experiments for multiple unmanned aerial vehicles in GPS denied environments

2017 ◽  
Vol 9 (3) ◽  
pp. 169-186 ◽  
Author(s):  
Kexin Guo ◽  
Zhirong Qiu ◽  
Wei Meng ◽  
Lihua Xie ◽  
Rodney Teo
Keyword(s):  
Kalman Filter ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Extended Kalman Filter ◽  
Ultra Wideband ◽  
Cooperative Localization ◽  
Relative Localization ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Displacement Measurements

This article puts forward an indirect cooperative relative localization method to estimate the position of unmanned aerial vehicles (UAVs) relative to their neighbors based solely on distance and self-displacement measurements in GPS denied environments. Our method consists of two stages. Initially, assuming no knowledge about its own and neighbors’ states and limited by the environment or task constraints, each unmanned aerial vehicle (UAV) solves an active 2D relative localization problem to obtain an estimate of its initial position relative to a static hovering quadcopter (a.k.a. beacon), which is subsequently refined by the extended Kalman filter to account for the noise in distance and displacement measurements. Starting with the refined initial relative localization guess, the second stage generalizes the extended Kalman filter strategy to the case where all unmanned aerial vehicles (UAV) move simultaneously. In this stage, each unmanned aerial vehicle (UAV) carries out cooperative localization through the inter-unmanned aerial vehicle distance given by ultra-wideband and exchanging the self-displacements of neighboring unmanned aerial vehicles (UAV). Extensive simulations and flight experiments are presented to corroborate the effectiveness of our proposed relative localization initialization strategy and algorithm.

scholarly journals Smart Search System of Autonomous Flight UAVs for Disaster Rescue

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6810
Author(s):  
Donggeun Oh ◽  
Junghee Han
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicles ◽  
Base Stations ◽  
Localization Algorithm ◽  
Tracking Accuracy ◽  
Long Distance ◽  
Search System ◽  
Autonomous Flight ◽  
Shadow Area ◽  
Aerial Vehicles

UAVs (Unmanned Aerial Vehicles) have been developed and adopted for various fields including military, IT, agriculture, construction, and so on. In particular, UAVs are being heavily used in the field of disaster relief thanks to the fact that UAVs are becoming smaller and more intelligent. Search for a person in a disaster site can be difficult if the mobile communication network is not available, and if the person is in the GPS shadow area. Recently, the search for survivors using unmanned aerial vehicles has been studied, but there are several problems as the search is mainly using images taken with cameras (including thermal imaging cameras). For example, it is difficult to distinguish a distressed person from a long distance especially in the presence of cover. Considering these challenges, we proposed an autonomous UAV smart search system that can complete their missions without interference in search and tracking of castaways even in disaster areas where communication with base stations is likely to be lost. To achieve this goal, we first make UAVs perform autonomous flight with locating and approaching the distressed people without the help of the ground control server (GCS). Second, to locate a survivor accurately, we developed a genetic-based localization algorithm by detecting changes in the signal strength between distress and drones inside the search system. Specifically, we modeled our target platform with a genetic algorithm and we re-defined the genetic algorithm customized to the disaster site’s environment for tracking accuracy. Finally, we verified the proposed search system in several real-world sites and found that it successfully located targets with autonomous flight.

scholarly journals Multitarget Real-Time Tracking Algorithm for UAV IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Tao Hong ◽  
Qiye Yang ◽  
Peng Wang ◽  
Jinmeng Zhang ◽  
Wenbo Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Target Tracking ◽  
Real Time ◽  
High Speed ◽  
Rapid Development ◽  
Machine Learning Algorithms ◽  
Urban Life ◽  
Tracking Accuracy ◽  
Tracking Model ◽  
Real Time Tracking

Unmanned aerial vehicles (UAVs) have increased the convenience of urban life. Representing the recent rapid development of drone technology, UAVs have been widely used in fifth-generation (5G) cellular networks and the Internet of Things (IoT), such as drone aerial photography, express drone delivery, and drone traffic supervision. However, owing to low altitude and low speed, drones can only limitedly monitor and detect small target objects, resulting in frequent intrusion and collision. Traditional methods of monitoring the safety of drones are mostly expensive and difficult to implement. In smart city construction, a large number of smart IoT cameras connected to 5G networks are installed in the city. Captured drone images are transmitted to the cloud via a high-speed and low-latency 5G network, and machine learning algorithms are used for target detection and tracking. In this study, we propose a method for real-time tracking of drone targets by using the existing monitoring network to obtain drone images in real time and employing deep learning methods by which drones in urban environments can be guided. To achieve real-time tracking of UAV targets, we employed the tracking-by-detection mode in machine learning, with the network-modified YOLOv3 (you only look once v3) as the target detector and Deep SORT as the target tracking correlation algorithm. We established a drone tracking dataset that contains four types of drones and 2800 pictures in different environments. The tracking model we trained achieved 94.4% tracking accuracy in real-time UAV target tracking and a tracking speed of 54 FPS. These results comprehensively demonstrate that our tracking model achieves high-precision real-time UAV target tracking at a reduced cost.

scholarly journals ONBOARD PHOTOINTAKES ON THE BASIS OF HIGH-SPEED LOW-FORMAT MATRIXES IN THE INFRARED RANGE

2019 ◽  
Vol 7 ◽  
pp. 22-26
Author(s):  
Andrey Chernikov ◽  
Sergey Kravets ◽  
Vladimir Legky ◽  
Valeriy Edvabnik ◽  
Igor Gibin
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicles ◽  
High Speed ◽  
Infrared Range ◽  
Aerial Vehicles ◽  
Spectral Ranges

The analysis of parameters of high-speed small-format matrices operating in the infrared (IR) range is presented. The considered matrices can be placed on unmanned aerial vehicles (UAVs) for remote sensing of underlying surfaces. The types of devices, their purpose, the main spectral ranges and principles of operation are considered.

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