Identification of Noise Sources During Rocket Engine Test Firings and a Rocket Launch Using a Microphone Phased-Array

A 70 microphone, 10-foot by 10-foot, microphone phased array was built for use in the harsh environment of rocket launches. The array was setup at NASA Wallops launch pad 0A during a static test firing of Orbital Sciences' Antares engines, and again during the first launch of the Antares vehicle. It was placed 400 feet away from the pad, and was hoisted on a scissor lift 40 feet above ground. The data sets provided unprecedented insight into rocket noise sources. The duct exit was found to be the primary source during the static test firing; the large amount of water injected beneath the nozzle exit and inside the plume duct quenched all other sources. The maps of the noise sources during launch were found to be time-dependent. As the engines came to full power and became louder, the primary source switched from the duct inlet to the duct exit. Further elevation of the vehicle caused spilling of the hot plume, resulting in a distributed noise map covering most of the pad. As the entire plume emerged from the duct, and the ondeck water system came to full power, the plume itself became the loudest noise source. These maps of the noise sources provide vital insight for optimization of sound suppression systems for future Antares launches

Bimodal sound source tracking applied to road traffic monitoring

The constant increase of road traffic requires closer and closer road network monitoring. The awareness of traffic characteristics in real time as well as its historical trends, facilitates decision-making for flow regulation, triggering relief operations, ensuring the motorists’ safety and contribute to optimize transport infrastructures. Today, the heterogeneity of the available data makes their processing complex and expensive (multiple sensors with different technologies, placed in different locations, with their own data format, unsynchronized, etc.). This leads metrologists to develop “smarter” monitoring devices, i.e. capable of providing all the necessary data synchronized from a single measurement point, with no impact on the flow road itself and ideally without complex installation. This work contributes to achieve such an objective through the development of a passive, compact, non-intrusive, acoustic-based system composed of a microphone array with a few number of elements placed on the roadside. The proposed signal processing techniques enable vehicle detection, the estimation of their speed as well as the estimation of their wheelbase length as they pass by. Sound sources emitted by tyre/road interactions are localized using generalized cross-correlation functions between sensor pairs. These successive correlation measurements are filtered using a sequential Monte Carlo method (particle filter) enabling, on one hand, the simultaneous tracking of multiple vehicles (that follow or pass each other) and on the other hand, a discrimination between useful sound sources and interfering noises. This document focuses on two-axle road vehicles only. The two tyre/road interactions (front and rear) observed by a microphone array on the roadside are modeled as two stochastic, zero-mean and uncorrelated processes, spatially disjoint by the wheelbase length. This bimodal sound source model defines a specific particle filter, called bimodal particle filter, which is presented here. Compared to the classical (unimodal) particle filter, a better robustness for speed estimation is achieved especially in cases of harsh observation. Moreover the proposed algorithm enables the wheelbase length estimation through purely passive acoustic measurement. An innovative microphone array design methodology, based on a mathematical expression of the observation and the tracking methodology itself is also presented. The developed algorithms are validated and assessed through in-situ measurements. Estimates provided by the acoustical signal processing are compared with standard radar measurements and confronted to video monitoring images. Although presented in a purely road-related applied context, we feel that the developed methodologies can be, at least partly, applied to rail, aerial, underwater or industrial metrology

Observation of Vehicle Axles Through Pass-by Noise: A Strategy of Microphone Array Design

This paper focuses on road traffic monitoring using sounds and proposes, more specifically, a microphone array design methodology for observing vehicle trajectory from acoustic-based correlation functions. In a former work, authors have shown that combining generalized cross correlation (GCC) functions and a particle filter onto the audio signals simultaneously acquired by two sensors placed near the road allows the joint estimation of the speed and the wheelbase length of road vehicles as they pass by. This is mainly due to the broadband nature of the tire/road noise, which makes their spatial dissociation possible by means of an appropriate GCC processor. At the time, nothing has been said about the best distance to chose between the sensors. A methodology is proposed here to find this optimum, which is expected to improve the observation quality and, thus, the tracking performance. Theoretical developments of this paper are partially assessed with preliminary experiments

The Use of Low-Cost Sensors and a Convolutional Neural Network to Detect and Classify Mini-Drones

The increasing commercial availability of mini-drones and quadrotors has led to their greater usage, highlighting the need for detection and classification systems to ensure safe operation. Instances of drones causing serious complications since 2019 alone include shutting down airports [1-2], spying on individuals [3-4], and smuggling drugs and prohibited items across borders and into prisons [5-6]. Some regulatory measures have been taken, such as registration of drones above a specific size and the establishment of no-fly zones in sensitive areas such as airports, military bases, and national parks. While commercial systems exist to detect drones [7-8], they are expensive, unreliable, and often rely on a single sensor. This thesis will explore the practicality of using low-cost, Commercial-off-the-shelf (COTS) sensors and machine learning to detect and classify drones. A Red, Green, and Blue (RGB) USB camera [9], FLIR Lepton 3.0 thermal camera [10], miniDSP UMA-16 acoustic microphone array [11], and a Garmin LIDAR [12] were mounted on a robotic sensor platform and integrated using a Minisforum Z83-F with 4GB RAM and Intel Atom x5-Z8350 CPU to collect data from drones operating in unstructured, outdoor, and real-world environments. Approximately 1,000 unique measurements were taken from three mini-drones – Parrot Swing, Parrot Quadcopter, and Tello Quadcopter – using the RGB, thermal, and acoustic sensors. Deep Convolutional Neural Network (CNNs), based on Resnet-50 [13-14], trained to classify the drones, achieved accuracies of 96.6% using the RGB images, 82.9% using the thermal images, and 71.3% using the passive acoustic microphone array

Advanced space system concepts and their orbital support needs (1980 - 2000). Volume 2: Final report

The results are presented of a study which identifies over 100 new and highly capable space systems for the 1980-2000 time period: civilian systems which could bring benefits to large numbers of average citizens in everyday life, much enhance the kinds and levels of public services, increase the economic motivation for industrial investment in space, expand scientific horizons; and, in the military area, systems which could materially alter current concepts of tactical and strategic engagements. The requirements for space transportation, orbital support, and technology for these systems are derived, and those requirements likely to be shared between NASA and the DoD in the time period identified. The high leverage technologies for the time period are identified as very large microwave antennas and optics, high energy power subsystems, high precision and high power lasers, microelectronic circuit complexes and data processors, mosaic solid state sensing devices, and long-life cryogenic refrigerators

Data catalog series for space science and applications flight missions. Volume 3A: Descriptions of low- and medium-altitude scientific spacecraft and investigations

Earth orbits spacecraft whose apogees are well below geostationary altitude and whose primary purpose is to conduct investigations in the near-Earth environment are considered

Aerometry instrumentation study Final report

Techniques and instruments for meteorological measurements in Mars and Venus atmosphere