30 research outputs found
Tracking a moving sound source from a multi-rotor drone
We propose a method to track from a multirotor drone a moving source, such as a human speaker or an emergency whistle, whose sound is mixed with the strong ego-noise generated by rotating motors and propellers. The proposed method is independent of the specific drone and does not need pre-training nor reference signals. We first employ a time-frequency spatial filter to estimate, on short audio segments, the direction of arrival of the moving source and then we track these noisy estimations with a particle filter. We quantitatively evaluate the results using a ground-truth trajectory of the sound source obtained with an on-board camera and compare the performance of the proposed method with baseline solutions
Acoustic Modeling Of A Uas For Use In A Hostile Fire Detection System
Unmanned Aerial System (UAS) usage has continually increased in recent years for both recreational and military applications. One particular military application being researched is utilizing a UAS as a host platform for Hostile Fire Detection Systems (HFDS), with particular interest being focused on multi-rotor drone platforms. The type of HFDS considered in this work is based upon acoustic sensors. An acoustic based HFDS utilizes an array of microphones to measure acoustic data and then applies signal processing algorithms to determine if a transient signal is present and if present then estimates the direction from which the sound arrived. The main issue with employing an acoustic based HFDS on a multi-rotor drone is the high level of background noise due to motors, propellers, and flow noise. In this thesis a study of the acoustic near field, particularly relevant to microphones located on the drone, was performed to understand the noise produced by the UAS. More specifically, the causes and characteristics of the sources of noise were identified. The noise characteristics were then used to model the noise sources for multiple motor assemblies based upon position of the microphone and revolutions per minute (RPM) of the motors. Lastly, signal processing techniques were implemented to identify if transient signals are present and if present estimate the direction from which the sound arrives
Object localisation, dimensions estimation and tracking.
PhD Theses.Localising, estimating the physical properties of, and tracking objects from audio and video
signals are the base for a large variety of applications such as surveillance, search and rescue,
extraction of objects’ patterns and robotic applications. These tasks are challenging due to low
signal-to-noise ratio, multiple moving objects, occlusions and changes in objects’ appearance.
Moreover, these tasks become more challenging when real-time performance is required and
when the sensor is mounted in a moving platform such as a robot, which introduces further problems
due to potentially quick sensor motions and noisy observations. In this thesis, we consider
algorithms for single and multiple object tracking from static microphones and cameras, and
moving cameras without relying on additional sensors or making strong assumptions about the
objects or the scene; and localisation and estimation of the 3D physical properties of unseen objects.
We propose an online multi-object tracker that addresses noisy observations by exploiting
the confidence on object observations and also addresses the challenges of object and camera motion
by introducing a real-time object motion predictor that forecasts the future location of objects
with uncalibrated cameras. The proposed method enables real-time tracking by avoiding computationally
expensive labelling procedures such as clustering for data association. Moreover,
we propose a novel multi-view algorithm for jointly localising and estimating the 3D physical
properties of objects via semantic segmentation and projective geometry without the need to use
additional sensors or markers. We validate the proposed methods in three standard benchmarks,
two self-collected datasets, and two real robotic applications that involve an unmanned aerial vehicle
and a robotic arm. Experimental results show that the proposed methods improve existing
alternatives in terms of accuracy and speed
UAVs for the Environmental Sciences
This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application
12th EASN International Conference on "Innovation in Aviation & Space for opening New Horizons"
Epoxy resins show a combination of thermal stability, good mechanical performance, and durability, which make these materials suitable for many applications in the Aerospace industry. Different types of curing agents can be utilized for curing epoxy systems. The use of aliphatic amines as curing agent is preferable over the toxic aromatic ones, though their incorporation increases the flammability of the resin. Recently, we have developed different hybrid strategies, where the sol-gel technique has been exploited in combination with two DOPO-based flame retardants and other synergists or the use of humic acid and ammonium polyphosphate to achieve non-dripping V-0 classification in UL 94 vertical flame spread tests, with low phosphorous loadings (e.g., 1-2 wt%). These strategies improved the flame retardancy of the epoxy matrix, without any detrimental impact on the mechanical and thermal properties of the composites. Finally, the formation of a hybrid silica-epoxy network accounted for the establishment of tailored interphases, due to a better dispersion of more polar additives in the hydrophobic resin
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Techniques to Leverage RF Signals for Context Sensing
RF signals and devices have been used for wireless communication to improve the mobility and ubiquity of mobile devices. In this dissertation, we show that these RF signals can also be used for context sensing applications. Specifically, we present cyber-physical systems and algorithms to sense human vital signals, object vibrations and movements, and object’s location to deliver new sensing capabilities for a variety of new applications including health-care monitoring, privacy protection, and indoor localization. We deliver three fundamental contributions. First, we develop an RF-based system to “sense” human breathing volume continuously in fine-grained from afar. Second, we develop a technique to “sense” the wireless signals emitted from drones/fly-cams to detect them and alert users for privacy protection. Last, we present our preliminary study on building a system to enable the mobile device to “sense” their global locations at the indoor environment. To deliver these contributions, we exploit the properties of physical characteristics of RF signals, analyze and understand targeted subjects behaviors (i.e., human, drones), work across different limitations and hardware-software barriers, and introduce novel systems and new algorithms to overcome the challenges. We implement and evaluate the system on real users/patients, test the systems across different environments, and demonstrate how they can enable many other real-world applications
Deep Learning Methods for Remote Sensing
Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing
Consortium for Robotics and Unmanned Systems Education and Research (CRUSER) 2019 Annual Report
Prepared for: Dr. Brian Bingham, CRUSER DirectorThe Naval Postgraduate School (NPS) Consortium for Robotics and Unmanned Systems Education and Research (CRUSER) provides a collaborative environment and community of interest for the advancement of unmanned systems (UxS) education and research endeavors across the Navy (USN), Marine Corps (USMC) and Department of Defense (DoD). CRUSER is a Secretary of the Navy (SECNAV) initiative to build an inclusive community of interest on the application of unmanned systems (UxS) in military and naval operations. This 2019 annual report summarizes CRUSER activities in its eighth year of operations and highlights future plans.Deputy Undersecretary of the Navy PPOIOffice of Naval Research (ONR)Approved for public release; distribution is unlimited