We present the initial laboratory test results of the adaptive optics (AO) subassembly for the Low-Cost Optical Terminal (LCOT), a flexible communications ground terminal developed by Goddard Space Flight Center. LCOT will receive first light in 2023 testing. This terminal includes a 700mm commercial telescope, 1550nm receive instruments, and uplink transmit systems. Demodulating coherent formats requires AO to correct turbulence effects and allow coupling into single-mode fiber. General Atomics delivered the system to Goddard in September 2021, where engineers have evaluated performance. We describe laboratory testing, turbulence phase plate design, results, and AO field testing plans when installed on LCOT.
We present the status of ongoing work at NASA’s Goddard Space Flight Center (GSFC) to build a prototype, low-costof- production, flexibly-configured ground terminal for space optical communication. For laser telecommunication to be cost effective for future missions, a wide-spread global network of operationally responsive optical terminals should be established. There has been a decades-old need for a single modular open systems approach (MOSA) ground terminal architecture capable of supporting multiple space missions ranging from LEO to Lunar distances with 2-way laser communications. At the heart of LCOT’s design concept is the Free-Space Optical Subsytem (FSOS). The major subassemblies of LCOT/FSOS that address most optical comms configurations are : (1) Single 700mm F/12 Nasmyth folded Rx R-C Telescope, (2) Four independent 150mm diameter high-power all-reflective Tx beam directors (XOA), (3) Non-coherent direct detection Rx bench on starboard side of telescope (SOB), and (4) Coherent (possibly Quantum) optical communications bench on port side (POB). The Low-Cost Optical Terminal (LCOT) research and development (R&D) prototype is designed to be a generalized system that can be quickly field-reconfigured to support a wide variety of laser communications missions past, present, and future.
This paper provides the status of ongoing work at NASA-Goddard Space Flight Center (GSFC) to build a low-cost flexible ground terminal for optical communication. For laser communication to be cost-effective for future missions, a global network of flexible optical terminals must be put in place. There is a need for a single ground terminal design capable of supporting multiple missions ranging from LEO to lunar distances. NASA’s Low-Cost Optical Terminal (LCOT) has a single modular design that can be quickly reconfigured to support different laser communications missions. The LCOT prototype uses a 70cm commercially available telescope designed with optical and quantum communications in mind. This telescope is currently being integrated with a state-of-the-art adaptive optics system, and novel high-power laser amplifier demonstrate its utility as an optical communications receiver by receiving a downlink from the recently launched Laser Communication Relay Demonstration (LCRD). LCOT uses commercially available components wherever possible, and where commercial options are not available, the LCOT team works with vendors to create commercial options. This paper discusses the development progress for the blueprint of NASA’s future global ground terminal network.
We present the status of ongoing work at NASA-Goddard Space Flight Center (GSFC) to build a low-cost flexible ground terminal for optical communication. Previous laser communication missions at NASA have been supported by one-of-akind ground terminals built specifically for each mission. If NASA is to build a global network of optical terminals to enable widespread use of optical communications, then a blueprint for an economical ground terminal able to support a variety of missions is needed. With this goal in mind, NASA is constructing a ground terminal in Greenbelt, Maryland to enable testing of new ground terminal technologies from industry to academia.
KEYWORDS: Principal component analysis, Photonic integrated circuits, Independent component analysis, Hyperspectral imaging, Statistical analysis, Data analysis, Image classification, Data modeling, Data processing, Data compression
Data dimensionality (DR) is generally performed by first fixing size of DR at a certain number, say p and then finding a
technique to reduce an original data space to a low dimensional data space with dimensionality specified by p. This
paper introduces a new concept of dynamic dimensionality reduction (DDR) which considers the parameter p as a
variable by varying the value of p to make p adaptive compared to the commonly used DR, referred to as static
dimensionality reduction (SDR) with the parameter p fixed at a constant value. In order to materialize the DDR another
new concept, referred to as progressive DR (PDR) is also developed so that the DR can be performed progressively to
adapt the variable size of data dimensionality determined by varying the value of p. The advantages of the DDR over
SDR are demonstrated through experiments conducted for hyperspectral image classification.
This paper develops to a new concept, called Progressive Dimensionality Reduction (PDR) which can perform data
dimensionality progressive in terms of information preservation. Two procedures can be designed to perform PDR in a
forward or backward manner, referred to forward PDR (FPDR) or backward PDR (BPDR) respectively where FPDR
starts with a minimum number of spectral-transformed dimensions and increases the spectral-transformed dimension
progressively as opposed to BPDR begins with a maximum number of spectral-transformed dimensions and decreases
the spectral-transformed dimension progressively. Both procedures are terminated when a stopping rule is satisfied. In
order to carry out DR in a progressive manner, DR must be prioritized in accordance with significance of information so
that the information after DR can be either increased progressively by FPDR or decreased progressively by BPDR. To
accomplish this task, Projection Pursuit (PP)-based DR techniques are further developed where the Projection Index (PI)
designed to find a direction of interestingness is used to prioritize directions of Projection Index Components (PICs) so
that the DR can be performed by retaining PICs with high priorities via FPDR or BPDR. In the context of PDR, two
well-known component analysis techniques, Principal Components Analysis (PCA) and Independent Component
Analysis (ICA) can be considered as its special cases when they are used for DR.
Projection Pursuit (PP) is a component transform technique which seeks a component whose projection vector points to
a direction of interestingness in data space which can be specified by a Projection Index (PI). Two most popular
component analysis-based techniques, Principal Components Analysis (PCA), Independent Component Analysis (ICA)
can be considered as special cases with their PIs specified by data variance and statistical independency respectively.
Despite the fact that various component analysis-based techniques have been used for Dimensionality Reduction (DR)
the components are generally generated by a specific technique. Even in the case of PP, the same PI has been used to
generate project components. This paper explores the utility of PP in DR where various projection indexes are used for
DR in context of PP. It further lays out a general setting for PP-based DR and develops algorithms to perform one
dimension reduction at a time by using different PIs. In order to substantiate our findings, experiments are conducted to
demonstrate advantages of the PP with mixed PIs-based DR over traditional PCA-based, ICA-based and PP-based DR
techniques.
KEYWORDS: Photonic integrated circuits, Principal component analysis, Independent component analysis, Target detection, Algorithm development, Data processing, Hyperspectral imaging, Data compression, Data acquisition, Projection systems
Dimensionality Reduction (DR) has found many applications in hyperspectral image processing, e.g., data compression,
endmember extraction. This paper investigates Projection Pursuit (PP)-based data dimensionality reduction where three
approaches are developed. One is to use a Projection Index (PI) to produce projection vectors that can be used to
generate Projection Index Components (PICs). It is a common practice that PP generally uses random initial conditions
to produce PICs. As a result, when the same PP is performed in different times or different users at the same time, the
resulting PICs are generally not the same. In order to resolve this issue, two approaches are proposed. One is referred to
as PI-based PRioritized PP (PI-PRPP) which uses a PI as a criterion to prioritize PICs that are produced by any
component analysis, for example, Principal Components Analysis (PCA) or Independent Component Analysis. The
other approach is called Initialization-Driven PP (ID-PP) which specifies an appropriate set of initial conditions that
allows PP to not only produce PICs in the same order but also the same PICs regardless of how many times PP is run or
who runs the PP.
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