Publications & Presentations

Backscatter-TDLAS Detectors for Monitoring, Locating, Imaging, and Quantifying Methane Emissions

Mickey B. Frish, Shin-Juh Chen, Nicholas F. Aubut, and Richard T. Wainner, Paul Wehnert, Kevin Bendele and Steve Chancey
CLEO Conference, May 15-19, 2022, San Jose, California

The urgency to reduce methane emissions to the atmosphere is driving industry adoption of advanced technologies for methane measurement and monitoring. We present a suite of laser-based sensors for detecting, locating, and measuring methane sources.

Cable Resistance in Spacecraft Deployable Mechanisms

Brian W. Schweinhart, Ziv A. Arzt, Brendan E. Nunan, Alex J. Mednick
2022 AIAA SciTech Forum, 3-7 January 2022, San Diego, CA
Paper https://doi.org/10.2514/6.2022-1623

The resistive torques of cable harnesses and service loops comprise a significant portion of the force budgets of deployable space mechanisms. The space engineering community lacks a reliable and methodical way to predict these forces early in the mechanism design process. Incumbent methods rely on estimates from heritage applications or use deployment prototype tooling.

The latter approach is typically specific to the application and the design and therefore incurs timely and expensive iterations. This paper describes a methodology for directly predicting cable drag and resistive torque from the cable specification and deployment geometry alone. The method outlines a standard procedure for characterizing the elastoplastic and viscoelastic material properties of space cables. These experimentally-determined material properties are supplied along with deployed cable geometry to a FEA model, which predicts the cable resistive forces in a representative deployment system.

An ultra-compact shortwave infrared hyperspectral imaging system

Jay Giblin, Rusha Chatterjee, Michael Chase, Michael Ascenzi, Jonathan Rameau, Julia Dupuis, Jacob A. Martin, and Joseph Meola
SPIE Defense + Commercial Sensing, 4-7 April, 2022, Orlando, FL

Physical Sciences Inc. has developed an ultra-compact shortwave infrared (SWIR) staring mode hyperspectral imaging (HSI) sensor with an additional visible full motion video (FMV) capability.

The innovative HSI design implements a programmable micro-electromechanical system entrance slit that breaks the interdependence between vehicle speed, frame rate, and spatial resolution of conventional push-broom systems and enables staring-mode operation without cooperative motion of the host vehicle or aircraft. The FMV and HSI components fit within 1000 cm3, weigh a total of 2.1 lbs., and draw 15 W of power. The sensor mechanical design is compatible with gimbal-based deployment allowing for easy integration into ground vehicles or aircrafts. The FMV is capable of achieving NIRS-6 imagery over a 6°×6° field-of-view (FOV) at a 1500 ft. standoff. The SWIR HSI covers a spectral range of 900-1605 nm with a 15 nm spectral resolution, and interrogates a 5°×5° FOV per 1.6 s with a 2.18 mrad instantaneous FOV (1 m ground sample distance at 1500 ft.). A series of outdoor tests at standoffs up to 300 ft. have been conducted that demonstrate the payload’s capability to acquire HSI information. The payload has direct utility towards diverse remote sensing applications such as vegetation monitoring, geological mapping, surveillance, etc. The data product utility is demonstrated through the spectral identification of materials (e.g. foam and cloth) placed in the sensor’s FOV.

One Dimensional Convolutional Neural Networks for Spectral Analysis

Michael S. Primrose, Jay Giblin, Christian Smith, Martin R. Anguita, Gabriel H. Weedon
SPIE Defense + Commercial Sensing, 4-7 April, 2022, Orlando, FL

A platform for building sensor specific machine learning detection algorithms has been developed to classify spectroscopic data. The algorithms are focused on long wave infrared reflectance (LWIR) and Raman spectroscopies. The classification algorithm is based on a one dimensional (1D) convolutional neural network (CNN) architecture.

Training data is generated using an appropriate signal model that is combined with sensor specific characteristics such as spectral range, spectral resolution, and noise.
Within this paper, the performance of trained CNNs for both LWIR and Raman sensor systems has been evaluated. The evaluation uses both real and synthetic data to benchmark the performance in terms of the discriminant signal. The evaluation data consists of various chemical representations and varied noise levels. The performance of the 1D CNN approach has demonstrated high classification accuracies on data with low discriminant signals. Specifically, the CNNs have demonstrated a classification accuracy >90% for infrared reflectance data down to a wavelength averaged discriminant SNR>1. For Raman systems, we have demonstrated classification accuracies >90% for data with a peak discriminant SNR of approximately 6.

Compressive Sensing Hyperspectral Imager in the LWIR for Chemical Plume Detection

Stephanie M. Craig, Julia R. Dupuis, John P. Dixon, Martín Anguita, David Mansur, S. Chase Buchanan, Eric R. Kehoe, Chris Peterson, Louis Scharf, Michael M. Kirby
SPIE Defense + Commercial Sensing, 4-7 April, 2022, Orlando, FL

An environmentally hardened compressive sensing hyperspectral imager (CS-HSI) operating in the long wave infrared (LWIR) has been developed for low-cost, standoff, wide area early warning of chemical vapor plumes.

The CS-HSI employs a single-pixel architecture achieving an order of magnitude cost reduction relative to conventional HSI systems and a favorable pixel fill factor for standoff chemical plume imaging. A low-cost digital micromirror device modified for use in the LWIR is used to spatially encode the image of the scene; a Fabry-Perot tunable filter in conjunction with a single element mercury cadmium telluride photo-detector is used to spectrally resolve the spatially compressed data. A CS processing module reconstructs the spatially compressed spectral data, where both the measurement and sparsity basis functions are tailored to the CS-HSI hardware and chemical plume imaging. An automated target recognition algorithm is applied to the reconstructed hyperspectral data employing a variant of the adaptive cosine estimator for detection of chemical plumes in cluttered and dynamic backgrounds. The approach also offers the capability to generate detection products in compressed space with no CS reconstruction. This detection in transform space can be performed with a computationally lighter minimum variance distortionless response algorithm, resulting in a bandwidth advantage that supports efficient search and confirm modes of operation.