An Evaluation of multispectral earth-observing multi-aperture telescope designs for target detection and characterization

Earth-observing satellites have fundamental size and weight design limits since they must be launched into space. These limits serve to constrain the spatial resolutions that such imaging systems can achieve with traditional telescope design strategies. Segmented and sparse-aperture imaging system designs may offer solutions to this problem. Segmented and sparse-aperture designs can be viewed as competing technologies; both approaches offer solutions for achieving finer resolution imaging from space. Segmented-aperture systems offer greater fill factor, and therefore greater signal-to-noise ratio (SNR), for a given encircled diameter than their sparse aperture counterparts, though their larger segments often suffer from greater optical aberration than those of smaller, sparse designs. Regardless, the use of any multi-aperture imaging system comes at a price; their increased effective aperture size and improvement in spatial resolution are offset by a reduction in image quality due to signal loss (less photon-collecting area) and aberrations introduced by misalignments between individual sub-apertures as compared with monolithic collectors. Introducing multispectral considerations to a multi-aperture imaging system further starves the system of photons and reduces SNR in each spectral band. This work explores multispectral design considerations inherent in 9-element tri-arm sparse aperture, hexagonal-element segmented aperture, and monolithic aperture imaging systems. The primary thrust of this work is to develop an objective target detection-based metric that can be used to compare the achieved image utility of these competing multi-aperture telescope designs over a designated design parameter trade space. Characterizing complex multi-aperture system designs in this way may lead to improved assessment of programmatic risk and reward in the development of higher-resolution imaging capabilities. This method assumes that the stringent requirements for limiting the wavefront error (WFE) associated with multi-aperture imaging systems when producing imagery for visual assessment, can be relaxed when employing target detection-based metrics for evaluating system utility. Simple target detection algorithms were used to determine Receiver Operating Characteristic (ROC) curves for the various simulated multi-aperture system designs that could be used in an objective assessment of each system\u27s ability to support target detection activities. Also, a set of regressed equations was developed that allow one to predict multi-aperture system target detection performance within the bounds of the designated trade space. Suitable metrics for comparing the shapes of two individual ROC curves, such as the total area under the curve (AUC) and the sample Pearson correlation coefficient, were found to be useful tools in validating the predicted results of the trade space regression models. And lastly, some simple rules of thumb relating to multi-aperture system design were identified from the inspection of various points of equivalency between competing system designs, as determined from the comparison metrics employed. The goal of this work, the development of a process for simulating multi-aperture imaging systems and comparing them in terms of target detection tasks, was successfully accomplished. The process presented here could be tailored to the needs of any specific multi-aperture development effort and used as a tool for system design engineers

The PLATO End-to-End CCD Simulator -- Modelling space-based ultra-high precision CCD photometry for the assessment study of the PLATO Mission

The PLATO satellite mission project is a next generation ESA Cosmic Vision satellite project dedicated to the detection of exo-planets and to asteroseismology of their host-stars using ultra-high precision photometry. The main goal of the PLATO mission is to provide a full statistical analysis of exo-planetary systems around stars that are bright and close enough for detailed follow-up studies. Many aspects concerning the design trade-off of a space-based instrument and its performance can best be tackled through realistic simulations of the expected observations. The complex interplay of various noise sources in the course of the observations made such simulations an indispensable part of the assessment study of the PLATO Payload Consortium. We created an end-to-end CCD simulation software-tool, dubbed PLATOSim, which simulates photometric time-series of CCD images by including realistic models of the CCD and its electronics, the telescope optics, the stellar field, the pointing uncertainty of the satellite (or Attitude Control System [ACS] jitter), and all important natural noise sources. The main questions that were addressed with this simulator were the noise properties of different photometric algorithms, the selection of the optical design, the allowable jitter amplitude, and the expected noise budget of light-curves as a function of the stellar magnitude for different parameter conditions. The results of our simulations showed that the proposed multi-telescope concept of PLATO can fulfil the defined scientific goal of measuring more than 20000 cool dwarfs brighter than mV =11 with a precision better than 27 ppm/h which is essential for the study of earth-like exo-planetary systems using the transit method.Comment: 5 pages, submitted for the Proceedings of the 4th HELAS International Conference: Seismological Challenges for Stellar Structur

Design, Implementation and Operation of a Sparse Aperture Imaging Satellite Testbed

In order to better understand the technological difficulties involved in designing and building a sparse aperture array, the challenge of building a white light Golay-3 telescope was undertaken. The MIT Adaptive Reconnaissance Golay-3 Optical Satellite (ARGOS) project exploits wide-angle Fizeau interferometer technology with an emphasis on modularity in the optics and spacecraft subsystems. Unique design procedures encompassing the nature of coherent wavefront sensing, control and combining as well as various system engineering aspects to achieve cost effectiveness, are developed. To demonstrate a complete spacecraft in a 1-g environment, the ARGOS system is mounted on a frictionless air-bearing, and has the ability to track fast orbiting satellites like the ISS or the planets. Wavefront sensing techniques are explored to mitigate initial misalignment and to feed back real-time aberrations into the optical control loop. This paper presents the results and the lessons learned from the conceive, design, implement and operate phases of ARGOS. A preliminary assessment shows that the beam combining problem is the most challenging aspect of sparse optical arrays. The need for optical control is paramount due to tight beam combining tolerances. The wavefront sensing/control requirements appear to be a major technology and cost driver

Three-dimensional Photoacoustic Tomography System Design Analysis and Optimization

Photoacoustic tomography (PAT) is an emerging imaging modality capable of mapping optical absorption in tissues. It is a hybrid technique that combines the high spatial resolution of ultrasound imaging with the high contrast of optical imaging, and has demonstrated much potential in biomedical applications. Conventional PAT systems employ raster scanning to capture a large number of projections, thus improving image reconstruction at the cost of temporal resolution. Arising from the desire for real-time 3D PA imaging, several groups have begun to design PAT systems with staring arrays, where image acquisition is only limited by the repetition rate of the laser. However, there has been little emphasis on staring array design analysis and optimization. We have developed objective figures of merit for PAT system performance and applied these metrics to improve system design. The results suggested that the developed approach could be used to objectively characterize and improve any PAT system design

Rotary-motion-extended Array Synthesis (R-MXAS)

R-MXAS is a revolutionary aerospace architecture for realizing a synthetic aperture imaging radiometer (SAIR) with dramatically lower SWaP than existing state-of-the-art (SOTA) methods. The space-based component of the RMXAS system (Figure 1) is a single platform comprising a 1-D sparse / decimated antenna array on a rigid tether (deployed parallel to the horizon) and one or more additional tethered antennas that rotate in a plane orthogonal to the 1-D array.The processing that correlates the data from these two antenna systems and performs image reconstruction has both space-based and ground-based components. The processing exploits the interferometric baselines formed between the rotating tethered antenna at radius R and each of the antennas of the 1-D array on the rigid tether

Convolutional Deblurring for Natural Imaging

In this paper, we propose a novel design of image deblurring in the form of one-shot convolution filtering that can directly convolve with naturally blurred images for restoration. The problem of optical blurring is a common disadvantage to many imaging applications that suffer from optical imperfections. Despite numerous deconvolution methods that blindly estimate blurring in either inclusive or exclusive forms, they are practically challenging due to high computational cost and low image reconstruction quality. Both conditions of high accuracy and high speed are prerequisites for high-throughput imaging platforms in digital archiving. In such platforms, deblurring is required after image acquisition before being stored, previewed, or processed for high-level interpretation. Therefore, on-the-fly correction of such images is important to avoid possible time delays, mitigate computational expenses, and increase image perception quality. We bridge this gap by synthesizing a deconvolution kernel as a linear combination of Finite Impulse Response (FIR) even-derivative filters that can be directly convolved with blurry input images to boost the frequency fall-off of the Point Spread Function (PSF) associated with the optical blur. We employ a Gaussian low-pass filter to decouple the image denoising problem for image edge deblurring. Furthermore, we propose a blind approach to estimate the PSF statistics for two Gaussian and Laplacian models that are common in many imaging pipelines. Thorough experiments are designed to test and validate the efficiency of the proposed method using 2054 naturally blurred images across six imaging applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin