Query: passive and active sensors on UAV

title: A passive cloud detection system for UAV: Weather situation mapping with imaging sensors
abstract: This paper presents a passive cloud detection system on board of unmanned aerial vehicles (UAVs) Clouds are to be avoided especially by lightweight UAVs to ensure structural health, as their presence indicate weather phenomena like turbulences and icing. For larger airplanes, local weather observation is traditionally achieved by weather radar. Since such active sensors with their high energy consumption and mass cannot be used on board lightweight UAVs, the proposed cloud detection system is based on a passive imaging sensor in the visual spectrum (EO) and computer vision methods. For this work a High Altitude Pseudo Satellite (HAPS) serves as example platform. Due to its relatively low airspeed and maneuverability, reactive maneuvers are best to be avoided. For ascent and descent through cloud layers therefore, a sufficiently large cloud free corridor has to be identified beforehand. This is achieved by utilizing two modes of operation, which are described in this paper. The steps are demonstrated in a virtual environment example with focus on mapping and integration of the gathered information into a general weather situation map.

title: EVALUATION AND CALIBRATION OF FIXED WING UAV MOBILE MAPPING SYSTEM EQUIPPED WITH LIDAR AND OPTICAL SENSORS
abstract: In this paper, a mobile mapping system mounted on the UAV is presented and evaluated. The NEO3 UAV platform is an 11 kg fixed wing designed by the MSP company. The UAV is equipped with a Riegl miniVUX 1UAV laser scanner, which is integrated with the GNSS/INS system of Applanix APX 15 UAV and two Sony Alfa 6000 cameras collecting images in the following spectrum: visible for the first camera and near infrared for the second camera. The UAV mobile system presented is dedicated to the acquisition of multisource data for levee monitoring using active and passive remote sensing data. In this paper, the effectiveness of the ultralight laser scanner, which has not been mounted on the fixed wing platforms so far, was verified in the experiment with respect to data density and accuracy. The example analyses were conducted using ground control points and surfaces measured with a terrestrial laser scanner and visible in point clouds obtained with a dense image matching algorithm. Analyses showed that the achieved accuracy is much related to trajectory accuracy. The final DTM created from the data collected during the float status of the GNSS measurements of the trajectory provided twice less accurate data than during fixed status (vertical error approximately 20 cm and 10 cm respectively)

title: Leveraging UAVs for Passive RF Charging and Ultralowpower Wake Up of Ground Sensors
abstract: In this letter, we design and implement a radio frequency (RF) energy harvesting and wake up system that scavenges a 2.4 GHz signal from an unmanned aerial vehicle (UAV) The signal transmitted by the UAV is harvested and rectified to dc voltage to power a sensor on the ground. Additionally, the UAV modulates the transmitted signal to encode an address that triggers a particular sensor from sleep mode to active mode. The proposed system exhibits high sensitivity to low power levels. In fact, charging can be initiated from input power levels as low as <inline formula><tex math notation="LaTeX" /tex math>/inline formula>18.2 dBm, and a sensor can be triggered starting at power levels that are as low as <inline formula><tex math notation="LaTeX" /tex math>/inline formula>40 dBm. The designed system relies on passive components to boost the received signal to 4.5 V at the output. Moreover, the address detector is characterized by a current consumption of 160 nA in idle mode. The proposed solution can therefore be implemented in situations where it is critical to minimize the power consumption of sensors that rely on RF harvesting as their source of power.

title: Deep Learning on Multi Sensor Data for Counter UAV Applications A Systematic Review
abstract: Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c UAV) applications. Nowadays, c UAV applications offer systems that comprise a multi sensory arsenal often including electro optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat's identification. Nevertheless, real time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi object tracking and multi sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c UAV related tasks on multi sensor data has emerged. The aim of this paper is to describe deep learning advances on c UAV related tasks when applied to data originating from many different sensors as well as multi sensor information fusion. This survey may help in making recommendations and improvements of c UAV applications for the future.

title: Modeling, Simulation and Control of a Bio Inspired Electromechanical Feather for Gust Mitigation in Flapping Wing UAV
abstract: There is an increased requirement of bringing unmanned aircrafts into operational environments full of obstacles, including canyons, tall buildings, beneath forest canopies and valleys, all of which present navigation problems due to large scale turbulence. To address this issue, natural counterparts have been studied in depth. Through active and passive flow mechanism over their wings, wing morphing and deflection of covert feathers, birds attain stability in turbulent airflows. This paper presents a new bio inspired concept whose aim is to enhance aircraft stability and maneuverability during turbulent flight. The research includes covert feathers inspired novel Gust Mitigation System (GMS) for flapping wing UAV (FUAV) which senses the forces being experienced during flight in turbulence and actuates to alleviate them. GMS comprises of electromechanical (EM) feather modules containing piezoelectric elements that act as sensors, actuators, and load bearing members. We start by presenting the basic design of EM feather module along with the block diagram. Afterwards, we perform modeling and simulation of EM feather module with detailed analytical understanding using bond graph modeling approach. Furthermore, state space equations are computed to analyze model internal dynamics and responses. Finally the model is synthesized with LQR controller to achieve close mimicry of natural counterparts. The proposed design successfully marks an initial step towards research into bio inspired active gust mitigation systems for flapping wing UAVs.

title: Experimental Imaging Results of a UAV mounted Downward Looking mm wave Radar
abstract: Optical and thermal sensors mounted on Unmanned Aerial Vehicles (UAVs) have been successfully used to image difficult to access regions. Nevertheless, none of these sensors provide range information about the scene; and, therefore, their fusion with high resolution mm wave radars has the potential to improve the performance of the imaging system. This paper presents our preliminary experimental results of a downward looking UAV system equipped with a passive optical video camera and an active Multiple Input Multiple Output (MIMO) mm wave radar sensor. The 3D imaging of the mm wave radar is enabled by collecting data through the line of motion, thus producing a synthetic aperture, and by using a co linear MIMO array perpendicular to the motion trajectory. Our preliminary results shows that the fused optical and mm wave image provides shape and range information, that ultimately results in an enhanced imaging capability of the UAV system.

title: A New Passive 3 D Automatic Target Recognition Architecture for Aerial Platforms
abstract: The 3 D automatic target recognition (ATR) has many advantages over its 2 D counterpart, but there are several constraints in the context of small low cost unmanned aerial vehicles (UAVs) These limitations include the requirement for active rather than passive monitoring, high equipment costs, sensor packaging size, and processing burden. We, therefore, propose a new structure from motion (SfM) 3 D ATR architecture that exploits the UAV's onboard sensors, i.e. the visual band camera, gyroscope, and accelerometer, and meets the requirements of a small UAV system. We tested the proposed 3 D SfM ATR using simulated UAV reconnaissance scenarios and found that the performance was better than classic 3 D light detection and ranging (LIDAR) ATR, combining the advantages of 3 D LIDAR ATR and passive 2 D ATR. The main advantages of the proposed architecture include the rapid processing, target pose invariance, small template size, passive scene sensing, and inexpensive equipment. We implemented the SfM module under two keypoint detection, description and matching schemes, with the 3 D ATR module exploiting several current techniques. By comparing SfM 3 D ATR, 3 D LIDAR ATR, and 2 D ATR, we confirmed the superior performance of our new architecture.

title: Detection of buried archaeological remains with the combined use of satellite multispectral data and UAV data
abstract: Active and passive remote sensing sensors have been applied successfully in the detection of crop marks (vegetation with a different spectral reflectance compared to its surroundings) related with buried archaeological remains. However, the detection of such crop marks depends on the sensor used, the status of the cover and the algorithm applied on the data. Moreover, buried archaeological remains generally produce microrelief marks, which can be very difficult to detect. The purpose of this work is to demonstrate that the combined use of data from the multispectral orbital sensor WorldView 2 and RGB and near infrared cameras mounted on an Unmanned Aerial Vehicle (UAV) equipped with a Global Navigation Satellite System (GNSS) can be successfully applied to the detection of buried archaeological remains. Principal Component Analysis, the Normalized Difference Vegetation Index (NDVI) and a purposely proposed band combination were obtained from WorldView 2 data to detect crop marks. The cameras carried by the UAV provide a Real Color composite, the NDVI and a high precision Digital Surface Model. The methodology developed in this work consists of searching for locations that exhibit both crop and microrelief marks with a similar shape. The WorldView 2 NDVI and the normalized Digital Surface Model of the UAV are filtered. An Archaeological Binary Map is constructed, in which pixels with both NDVI and normalized elevation above corresponding threshold values are interpreted as susceptible of containing buried archaeological remains and are given the value of one, otherwise zero. One of the locations of the Archaeological Binary Map, with a very regular pattern, is subsequently surveyed with Ground Penetrating Radar to find a buried structure, the location and shape of which match perfectly those of the Archeological Binary Map.

title: Low Altitude UAV Borne Remote Sensing in Dunes Environment: Shoreline Monitoring and Coastal Resilience
abstract: UAV systems, fitted with either active or passive surveying sensors, can provide land related measures and quantitative information with low costs and high resolution in both space and time. Such surveying systems can be quite valuable in defining geometrical and descriptive parameters in coastal systems, especially dune ecosystems. The present work is based on a survey of the dune system at the mouth of the Fiume Morto Nuovo in the San Rossore Estate (Pisa) and focuses on comparing LiDAR with UAV and airplane borne photogrammetry, as well as the respective 2D and 3D cartographic output, in order to assess topography changes along a stretch of coastline and to check their possible use in defining some ecological resilience features on coastal dune systems. Processing of survey data generates a Digital Surface Model (DSM) or Digital Terrain Model (DTM) and an orthophotograph, checked for accuracy and image resolution. Comparison of these products against those available in public access cartographical databases highlights differences and respective strengths.

title: UAV pose estimation using IR and RGB cameras
abstract: In this paper, we propose a system for UAV (Unmanned Aerial Vehicle) 6D pose estimation. The proposed system is experimentally evaluated and verified in a simulated environment. A pair of infra red active markers and a colored passive marker are utilized to mark the relative pose of the target. Accordingly, two different types of sensors, including the IR (infra red) and color camera are applied. A PSO (Particle Swarm Optimization) algorithm on the iteration varying weight is employed to solve the optimal pose of the UAV. The method can be applied to assist the existing IR marker based pose estimation system to locate the target when there are not enough IR markers to be detected. In addition, this system is also able to perform as a possible solution for multi robot localization by identifying the passive markers with different colors on different targets.