Evaluation of Murrell’s EKF-Based Attitude Estimation Algorithm for Exploiting Multiple Attitude Sensor Configurations

Pico- and nano-satellites, due to their form factor and size, are limited in accommodating multiple or redundant attitude sensors. For such satellites, Murrell’s implementation of the extended Kalman filter (EKF) can be exploited to accommodate multiple sensor configurations from a set of non redundant attitude sensors. The paper describes such an implementation involving a sun sensor suite and a magnetometer as attitude sensors. The implementation exploits Murrell’s EKF to enable three sensor configurations, which can be operationally commanded, for satellite attitude estimation. Among the three attitude estimation schemes, (i) sun sensor suite and magnetometer, (ii) magnetic field vector and its time derivative and (iii) magnetic field vector, it is shown that the third configuration is better suited for attitude estimation in terms of precision and accuracy, but can consume more time to converge than the other two.

Geolocation using celestial measurements

This project involves the use of celestial measurements in order to predict the latitude, longitude and heading of the observer. The objective of the project is to assess whether a new inclinometer model and calibration procedure can significantly enhance the quality of navigation data acquired by the sensor suite. The suite consists of an Applied Geomechanics Tuff-Tilt digital biaxial inclinometer and a Sinclair Interplanetary SS-411 digital sun-sensor. The project will focus on the typical sources of error in an inclinometer and present a lab calibration method to enhance its performance. The sensor suite will also be calibrated together as a unit using a GPS receiver for time and geolocation data and a sun ephemeris as a measure of truth. Results from these tests are also presented.

Geolocation using celestial measurements

This project involves the use of celestial measurements in order to predict the latitude, longitude and heading of the observer. The objective of the project is to assess whether a new inclinometer model and calibration procedure can significantly enhance the quality of navigation data acquired by the sensor suite. The suite consists of an Applied Geomechanics Tuff-Tilt digital biaxial inclinometer and a Sinclair Interplanetary SS-411 digital sun-sensor. The project will focus on the typical sources of error in an inclinometer and present a lab calibration method to enhance its performance. The sensor suite will also be calibrated together as a unit using a GPS receiver for time and geolocation data and a sun ephemeris as a measure of truth. Results from these tests are also presented.

Advances in Underwater Oil Plume Detection Capabilities

Historically, visual observation is an emergency responder's first ‘tool’ in identifying spilled oil. Optical detection has since expanded to include a myriad of signals from space, aircraft, drone, vessel and submersible platforms that can provide critical information for decision-making during spill response efforts. Spill monitoring efforts below the air-water interface have been vastly improved by advances with in situ optical sensors and vehicle platform technology. Optical techniques using fluorescence, scattering, and holography offer a means to determine dissolved versus droplet fractions, provide oil concentration estimates and serve as proxies for dispersion efficiency. For subsurface spills over large space and time scales, Autonomous Underwater Vehicles (AUVs) can be used to provide subsurface plume footprints and estimate oil concentrations. For smaller, more frequent spills, tethered compact Remotely Operated Vehicles (ROVs) may be more appropriate as they are easy to deploy for rapid detection. Two underwater oil detection technologies have been developed: (1) A Remote Environmental Monitoring UnitS (REMUS-600) AUV equipped with fluorescence and backscatter SeaOWL UV-A (Oil-in-Water Locator; Sea-Bird Scientific WET Labs Inc.), holographic imager (HoloCam; SeaScan, Inc), hydrographic information, video camera, CTD and a water/oil sampler. (2) A tethered ROV system (DTG2, Deep Trekker Inc.) equipped with video camera, UviLux (Chelsea Technologies Group, Inc) fluorometer, a CTD and water/oil sampler. Calibration and validation tests of the sensor suite were conducted at the Coastal Response Research Center flume tank (NH, USA). Oil concentration estimates were verified by chemical analysis of hydrocarbons and particle size analysis (LISST 200X, Sequoia, Inc). Operational performance of the ROV platform and sensors was evaluated at the Ohmsett wave tank (NJ, USA). Field performance of the REMUS and sensor suite was evaluated at natural seeps near Santa Barbara, CA. This research demonstrates the forensic value of in situ optical data for improved understanding of the behavior and transport of spilled oil below the air-sea interface.

SpiDeR –Spill Detection and Recognition system for ROV operations

ABSTRACT The paper presents the outline of the Spill Detection and Recognition system – SpiDeR and its application to underwater oil and gas detection, classification and source characterization demonstrated in the remote-sensing survey of Mississippi Canyon area in the Gulf of Mexico founded by BSEE in 2017. The main objective of the operation was to deploy sensor package from a remotely-operated vehicle (ROV) to survey, detect, and map the location(s) of hydrocarbon emissions that are responsible for the surface oil spill and sheen footprint in the Mississippi Canyon Area. The objectives have been accomplished by conducting a multi-day, three-part survey mapping the area of interest, generation of georeferenced charts and 3D visualizations with detected oil active spills, all supported by a ROV intervention outfitted with oil spill detection and recognition system SpiDeR. SpiDeR is a modular sensor suite capable of detecting, recognizing the source and classifying the hydrocarbon underwater leaks. The sensor suit with selectable configuration can be installed on any type of ROV vehicle and interfaces to the ROV with a single cable conducting the power and data. The presented here and used during the mission complete sensor suite consist of two 3D, broad band, electronically scanning multibeam sonar systems NORBIT WBMS STX, one Forward Looking Sonar NORBIT WBMS FLS, fluorescent oil classifier LIF – Laser Induced Fluorescence detection unit and the video camera with lights. The most useful capability of the SpiDeR is the ability to generate 3D imagery (georeferenced bathymetry) even when the ROV is not moving. That combined with time gives 4D observable capabilities of the oil spill. The 4D capabilities have been proven useful during the u-bathymetry part in Phase 2 and forward-looking 3D in Phase 3 of this mission. The system has been deployed from the ROV in the area where it has been known for the last decade that the leak of hydrocarbons is coming from. The real task at hand was to recognize the leak source and that source contain hydrocarbons and accurately document the source location and provide measurable documentation of its character.

A LoRa sensor network for monitoring pastured livestock location and activity

Precision technologies for confinement animal agricultural systems have increased rapidly over the past decade, though precision technology solutions for pastured livestock remain limited. There are a number of reasons for this limited expansion of technologies for pastured animals, including networking availability and reliability, power requirements, and expense, among others. The objective of this work was to demonstrate a rapidly deployable long-range radio (LoRa) based, low-cost sensor suite that can be used to track location and activity of pastured livestock. The sensor is comprised of an inexpensive Arduino-compatible microprocessor, a generic MPU-9250 motion sensor which contains a 3-axis accelerometer, 3 -axis magnetometer, and a 3-axis gyroscope, a generic GPS receiver, and a RFM95W generic LoRa radio. The microprocessor can be programmed flexibly using the open source Arduino IDE software to adjust the frequency of sampling, the data packet to send, and what conditions are needed to operate. The LoRa radio transmits to a Dragino LoRa gateway which can also be flexibly programmed through the Arduino IDE software to send data to local storage or, in cases where a web or cellular connection is available, to cloud storage. The sensor was powered using a USB cord connected to a 3350 mAh lithium-ion battery pack. The Dragino gateway was programmed to upload data to the ThingSpeak IoT application programming interface for data storage, handling, and visualization. Evaluations showed minimal benefit associated with reducing sampling frequency as a strategy to preserve battery life. Packet loss ranged from 40 to 60%. In a 3 day evaluation on pastured sheep, the sensor suite was able to report GPS locations, inertial sensor readings, and temperature. Preliminary demonstrations of our system are satisfactory to detect animal location based on GPS data in real-time. This system has clear utility as a lower-cost strategy to deploy flexible, useful precision technologies for pasture-based livestock species.