A method to enhance the deep learning in an aerial image

© 2017 IEEE. In this paper, we propose a kind of pre-processing method which can be applied to the depth learning method for the characteristics of aerial image. This method combines the color and spatial information to do the quick background filtering. In addition to increase execution speed, but also to reduce the rate of false positives

Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras

We propose a new method to estimate the 6-dof trajectory of a flying object such as a quadrotor UAV within a 3D airspace monitored using multiple fixed ground cameras. It is based on a new structure from motion formulation for the 3D reconstruction of a single moving point with known motion dynamics. Our main contribution is a new bundle adjustment procedure which in addition to optimizing the camera poses, regularizes the point trajectory using a prior based on motion dynamics (or specifically flight dynamics). Furthermore, we can infer the underlying control input sent to the UAV's autopilot that determined its flight trajectory. Our method requires neither perfect single-view tracking nor appearance matching across views. For robustness, we allow the tracker to generate multiple detections per frame in each video. The true detections and the data association across videos is estimated using robust multi-view triangulation and subsequently refined during our bundle adjustment procedure. Quantitative evaluation on simulated data and experiments on real videos from indoor and outdoor scenes demonstrates the effectiveness of our method

Investigation of detection possibility of UAVS using low cost marine radar

The technologies of Unmanned Aerial Vehicles (UAVs) are fast emerging, but as any other technology, development of UAVs provides not only benefits but also the threats. UAV technologies are developing much faster than means of their control and detection. RADAR technology is one of the means of UAV’s detection. Usually, radars are expensive, and usage of high-power radiation is problematic in many cases. Today’s market provides low cost marine radar working on various principles of operation. Such radar are not optimal, but could be used for UAV detection. Detection possibility of UAVs by FMCW marine radar was investigated by using two types of small UAVs as targets

UAV tracking module proposal based on a regulative comparison between manned and unmanned aviation

Purpose: The aim of this study is twofold. First is to compare manned and unmanned aviation regulations in the context of ICAO Annexes to identify potential deficiencies in the international UAV legislations. Second is to propose a UAV monitoring module work flow as a solution to identified deficiencies in the international UAV regulations. Design/methodology: In the present study, firstly the regulations used in manned aviation were summarized in the context of ICAO Annexes. Then along with an overview of the use of UAVs, international UAV regulations have been reviewed with a general perspective. In addition, a comparison was made on whether contents of ICAO Annexes find a place in common international UAV regulations in order to understand areas to be developed in the international UAV regulations, and to better understand the different principles between manned and unmanned air transport. In the last section, we present a UAV tracking module (UAVTram) in line with the above-mentioned comparison between manned and unmanned aviation and the identified deficiencies in the international UAV regulations. Findings: The international UAV regulations should be developed on the basis of airport airspace use, detection, liabilities, sanctions of violations, and updating of regulation. Proposed UAVTram has potential to offer real-time tracking and detection of UAVs as a solution to malicious use of UAVs. Research limitations/implications: Our study is not exempt from limitations. Firstly, we didn’t review all UAV regulations because it needs a considerable amount of efforts to check out all the UAV regulations pertinent to different areas of the world. It is the same case for manned aviation as we used only ICAO Annexes to contextually compare with UAV regulations. Practical implications: From the practical perspective, studies introducing new technologies such as systems that help detection of remote pilots causing trouble and agile defense systems will give valuable insights to remove individual UAV threats. Originality/value: We didn’t find any study aiming to compare manned and unmanned aviation rules in search of finding potential deficiencies in the UAV regulations. Our study adopts such an approach. Moreover, our solution proposal here uses Bluetooth 5.0 technology mounted on stationary transmitters which provides more effective range with higher data transfer. Another advantage is that this work is projected to be supported by Turkish civil aviation authority, DGCA. This may accelerate efforts to make required real-time tests.Peer Reviewe

Countering a drone in a 3D space: Analyzing deep reinforcement learning methods

Unmanned aerial vehicles (UAV), also known as drones have been used for a variety of reasons and the commercial drone market growth is expected to reach remarkable levels in the near future. However, some drone users can mistakenly or intentionally fly into flight paths at major airports, flying too close to commercial aircraft or invading people’s privacy. In order to prevent these unwanted events, counter-drone technology is needed to eliminate threats from drones and hopefully they can be integrated into the skies safely. There are various counter-drone methods available in the industry. However, a counter-drone system supported by an artificial intelligence (AI) method can be an efficient way to fight against drones instead of human intervention. In this paper, a deep reinforcement learning (DRL) method has been proposed to counter a drone in a 3D space by using another drone. In a 2D space it is already shown that the deep reinforcement learning method is an effective way to counter a drone. However, countering a drone in a 3D space with another drone is a very challenging task considering the time required to train and avoid obstacles at the same time. A Deep Q-Network (DQN) algorithm with dueling network architecture and prioritized experience replay is presented to catch another drone in the environment provided by an Airsim simulator. The models have been trained and tested with different scenarios to analyze the learning progress of the drone. Experiences from previous training are also transferred before starting a new training by pre-processing the previous experiences and eliminating those considered as bad experiences. The results show that the best models are obtained with transfer learning and the drone learning progress has been increased dramatically. Additionally, an algorithm which combines imitation learning and reinforcement learning is implemented to catch the target drone. In this algorithm, called deep q-learning from demonstrations (DQfD), expert demonstrations data and self-generated data by the agent are sampled and the agent continues learning without overwriting the demonstration data. The main advantage of this algorithm is to accelerate the learning process even if there is a small amount of demonstration data.This work was funded partially by the AGAUR under grant 2020PANDE00141, the Ministry of Science and Innovation of Spain under grant PID2020-116377RB-C21 and the SESAR Joint Undertaking (JU) project CORUS-XUAM, under grant SESAR-VLD2 101017682. The JU receives support from the European Union’s Horizon 2020 research and innovation program and SESAR JU members other than the Union.Peer ReviewedPostprint (published version

5G Radar and Wi-Fi Based Machine Learning on Drone Detection and Localization

Drone usages have been proliferating for various government initiatives, commercial benefits and civilian leisure purposes. Drone mismanagement especially civilian usage drones can easily expose threat and vulnerability of the Government Public Key Infrastructures (PKI) that hold crucial operations, affecting the survival and economic of the country. As such, detection and location identification of these drones are crucial immediately prior to their payload action. Existing drone detection solutions are bulky, expensive and hard to setup in real time. With the advent of 5G and Internet of Things (IoT), this paper proposes a cost effective bistatic radar solution that leverages on 5G cellular spectrum to detect the presence and localize the drone. Coupled with K-Nearest Neighbours (KNN) Machine Learning (ML) algorithm, the features of Non- Line of Sight (NLOS) transmissions by 5G radar and Received Signal Strength Indicator (RSSI) emitted by drone are used to predict the location of the drone. The proposed 5G radar solution can detect the presence of a drone in both outdoor and indoor environment with accuracy of 100%. Furthermore, it can localize the drone with an accuracy of up to 75%. These results have shown that a cost effective radar machine learning system, operating on the 5G cellular network spectrum can be developed to successfully identify and locate a drone in real-time

Uncover the Power of Multipath : Detecting NLOS Drones Using Low-Cost WiFi Devices

In recent years, consumer UAV technology has seen considerable advances. Consumer UAVs have become an ideal vector for privacy invasions due to their affordability, size, maneuverability, and their ability to stream live high-quality video. There is considerable proliferation of drones in both civil and military domains. Hence it is critical to detect invading unmanned aerial vehicles (UAVs) or drones in a timely manner for both security and safeguarding privacy. Currently available solutions like active radar, video or acoustic sensors are very expensive (especially for individuals) and have considerable constraints (e.g., requiring visual line of sight). Recent research on drone detection with passive RF signals provides an opportunity for low-cost deployment of drone detectors on commodity wireless devices. The state of the arts in this direction mainly focus on detecting drones using line-of-sight (LOS) RF signals which are less noisy as compared to their non-LOS (NLOS) counterparts. To the best of our knowledge, there is no existing cost-effective solution for the general public to enable non-LOS(NLOS) detection for drone privacy invasion, which is the most common condition and it still remains an open challenge. This thesis research provides a low-cost UAV detection system for privacy invasion caused by customer drone. Our model supports NLOS detection with low-cost hardware under $50, and hence it is affordable for the general public to deploy in their house, apartments, and office. Our work utilizes inherent drone motions (i.e., body shifting and vibrations) as unique signatures for drone detection. Firstly, we validated the relationship between drone motions and RF signal under the NLOS condition using extensive experiments. This is motivated by the fact that under NLOS conditions slight changes to the position or motion of a drone could lead to dramatic change in multi-path components in received RF signals. The NLOS condition “amplifies the RF signatures introduced by drone motions. We designed a deep learning model to capture the complex features from NLOS RF signals. In particular, we designed and trained a long short-term memory (LSTM) neural network [15, 27], a generative model which can effectively extract features of inputs for NLOS drone detection. Moreover, without knowing the presence of drones, our system starts with classifying any detected RF signals into LOS signals and NLOS signals before the NLOS drone learner is used. Classification of LOS and NLOS signals is feasible because they exhibit different combined features such as strength, variance, and distribution due to their differences in multipath effects. We used the supervised support vector machine (S-SVM) [17] as the learning model, which is effective for binary classification. This design is validated via extensive experiments using commodity drones in resident areas with other Wi-Fi enabled mobile devices

Drones Detection Using Smart Sensors

Drones are modern and sophisticated technology that have been used in numerous fields. Nowadays, many countries use them in exploration, reconnaissance operations, and espionage in military operations. Drones also have many uses that are not limited to only daily life. For example, drones are used for home delivery, safety monitoring, and others. However, the use of drones is a double-edged sword. Drones can be used for positive purposes to improve the quality of human lives, but they can also be used for criminal purposes and other detrimental purposes. In fact, many countries have been attacked by terrorists using smart drones. Hence, drone detection is an active area of research and it receives the attention of many scholars. Advanced drones are, many times, difficult to detect, and hence they, sometimes, can be life threatening. Currently, most detection methods are based on video, sound, radar, temperature, radio frequency (RF), or Wi-Fi techniques. However, each detection method has several flaws that make them imperfect choices for drone detection in sensitive areas. Our aim is to overcome the challenges that most existing drone detection techniques face. In this thesis, we propose two modeling techniques and compare them to produce an efficient system for drone detection. Specifically, we compare the two proposed models by investigating the risk assessments and the probability of success for each model

Grouping Parallel Detection Method of UAV Based on Multi Features of Image Transmission Signal

The emergence of low, slow, and small civilian unmanned aerial vehicles (UAV) brings fun and convenience to life and work. However, with the widespread popularity of UAV, the illegal activities caused by them have gradually increased, causing great harm to social security. To solve this problem, in the paper, we propose a set of detection and recognition methods for UAV by UAV image transmission signal (ITS). The method is divided into two groups. In the first group, according to the signal characteristics in different transform domains such as spectrum and time-frequency spectrum, three sets of algorithms are proposed, which are time-frequency ridge double feature estimation (TFRDFE), segmented spectrum estimation (SSE) and cycle accumulation estimation of segmented spectrum (CAE-SS). Three sets of algorithms are estimated to perform blind detection on suspected UAV ITS. The second group uses the accurate recognition algorithm of UAV ITS to extract the periodic features in the signal, and completes the recognition of UAV through feature matching, decision criteria and other methods. The two groups of methods are implemented in parallel, and when the two groups both detect and recognize the flying target, it can be determined that there is UAV in the target airspace. The experimental results show that the recognition rate of the first group of suspected UAV ITS blind detection algorithm can reach 100% when the (signal-to-noise ratio) SNR is –22 dB. The second group of UAV ITS recognition algorithm can achieve 100% recognition rate when SNR is –4 dB. Therefore, this method can complete the multi-target recognition of UAVs and has practical application value