Open Source Software for Simulating Collaborative Networks of Autonomous Adaptive Sensors
01 Jul 2019-pp 5301-5304
TL;DR: A new open-source software library and tool–set that has been specifically designed for simulating collaborative remote sensing networks and is presented with results from example simulations to confirm that it is able to address this challenge.
Abstract: Collaborative networks of small satellites will form future Earth-observing systems. Maximizing the science value of measurements from such systems will require autonomous decision making with regard to management of limited resources (i.e. power, communications, and sensor configuration). The complexity of this decision space warrants the creation of software tools to aid users in efficient modeling and simulation of collaborative remote sensing networks. In this paper, we present a new open-source software library and tool–set that has been specifically designed for simulating such networks. Details of the object-oriented C++ library are presented with results from example simulations to confirm that it is able to address this challenge. The software tools developed offer enhanced simulation capabilities to developers of future observing system simulation experiments (OSSEs) with collaborative networks of adaptive sensor platforms.
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16 Nov 2020
5 citations
11 Jul 2021
TL;DR: The Earth Observation Simulator (EO-Sim) as discussed by the authors allows exploration of observing strategies by facilitating users to configure and simulate heterogenous satellite constellations, which can be generated by the simulations.
Abstract: This paper presents the Earth Observation Simulator (EO-Sim), a software framework which facilitates the design of novel observation systems. EO-Sim allows exploration of observing strategies by facilitating users to configure and simulate heterogenous satellite constellations. A set of potential observation opportunities and the associated observation metrics during mission-operations can be generated by the simulations. EO-Sim also incorporates an observation simulator to mock the operation of instruments taking into consideration the instrument specifications and observation geometry. A beta version has been made available to the public.
3 citations
13 Jun 2022
TL;DR: Two planner solutions for a challenging Earth science application to plan coordinated measurements (observations) for a constellation of satellites are compared: Dynamic Constraint Processing (DCP) and Mixed Integer Linear Programming (MILP).
Abstract: We compare two planner solutions for a challenging Earth science application to plan coordinated measurements (observations) for a constellation of satellites. This problem is combinatorially explosive, involving many degrees of freedom for planner choices. Each satellite carries two different sensors and is maneuverable to 61 pointing angle options. The sensors collect data to update the predictions made by a high-fidelity global soil moisture prediction model. Soil moisture is an important geophysical variable whose knowledge is used in applications such as crop health monitoring and predictions of floods, droughts, and fires.
The global soil-moisture model produces soil-moisture predictions with associated prediction errors over the globe represented by a grid of 1.67 million Ground Positions (GPs). The prediction error varies over space and time and can change drastically with events like rain/fire. The planner's goal is to select measurements which reduce prediction errors to improve future predictions. This is done by targeting high-quality observations at locations of high prediction-error. Observations can be made in multiple ways, such as by using one or more instruments or different pointing angles; the planner seeks to select the way with the least measurement-error (higher observation quality).
In this paper we compare two planning approaches to this problem: Dynamic Constraint Processing (DCP) and Mixed Integer Linear Programming (MILP). We match inputs and metrics for both DCP and MILP algorithms to enable a direct apples-to-apples comparison. DCP uses domain heuristics to find solutions within a reasonable time for our application but cannot be proven optimal, while the MILP produces provably optimal solutions. We demonstrate and discuss the trades between DCP flexibility and performance vs. MILP's promise of provable optimality.
2 citations
01 Jun 2019
TL;DR: This paper discusses the recent investigations into how machine learning algorithms can be utilized in the high-level decision making of a communication system in a distributed satellite mission, and performs simulation studies to explore how the perception-action cycle could be applied to a collaborative small-satellite networks.
Abstract: Distributed satellite constellations utilizing networks of small satellites will be a key enabler of new observing strategies in the next generation of NASA missions. Small satellite instruments are becoming more capable, but are still resource constrained (i.e. power, data, scanning systems, etc.) in many situations. On a system scale, the primary purpose of collaborative communication among small satellites is to achieve system-level adaptivity. Collaborative communications however may also dramatically increase the complexity of the control algorithms for small satellite communication networks. Application of cognitive communication methods is one promising method to address this problem. In this paper, we discuss our recent investigations into how machine learning (ML) algorithms can be utilized in the high-level decision making of a communication system in a distributed satellite mission. We performed simulation studies to explore how the perception-action cycle could be applied to a collaborative small-satellite networks. To support this, we are using a recently developed open-source C++ library for the simulation of autonomous and collaborative networks of adaptive sensors.
1 citations
Cites methods from "Open Source Software for Simulating..."
...Under a NASA Advanced Information System Technology program, we are currently developing an open-source C++ library for the simulation of autonomous and collaborative networks of adaptive sensors [6]....
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TL;DR: In this article, a heuristically guided constraint optimization planner produces coordinated plans for multiple satellites, each with multiple instruments (payloads), each of which can quickly maneuver to change viewing angles in response to rapidly changing phenomena.
Abstract: We present planning challenges, methods and preliminary results for a new model-based paradigm for earth observing systems in adaptive remote sensing. Our heuristically guided constraint optimization planner produces coordinated plans for multiple satellites, each with multiple instruments (payloads). The satellites are agile, meaning they can quickly maneuver to change viewing angles in response to rapidly changing phenomena. The planner operates in a closed-loop context, updating the plan as it receives regular sensor data and updated predictions. We describe the planner's search space and search procedure, and present preliminary experiment results. Contributions include initial identification of the planner's search space, constraints, heuristics, and performance metrics applied to a soil moisture monitoring scenario using spaceborne radars.
References
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TL;DR: It is anticipated that in the future the OSSE technique will be applied to diverse and coupled domains with the use of increasingly advanced and sophisticated simulations of nature and observations.
Abstract: As operational forecast and data assimilation (DA) systems evolve, observing system simulation experiment (OSSE) systems must evolve in parallel. Expected development of operational systems—especially the use of data that are currently not used or are just beginning to be used, such as all-sky and surface-affected microwave radiances—will greatly challenge our ability to construct realistic OSSE systems. An additional set of challenges will arise when future DA systems strongly couple the different Earth system components. In response, future OSSE systems will require coupled models to simulate nature and coupled observation simulators. The requirements for future evolving OSSE systems and potential solutions to satisfy these requirements are discussed. It is anticipated that in the future the OSSE technique will be applied to diverse and coupled domains with the use of increasingly advanced and sophisticated simulations of nature and observations.
86 citations
"Open Source Software for Simulating..." refers background in this paper
...OSSEs provide critical information regarding long-term system behavior, optimizations, and alternative configurations [4]....
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TL;DR: An overview is presented of the small satellite literature on Earth observation, an introduction for those new to the field, an overview for those working in the field and a reference for those searching for literature on a specific application for Earth observation.
Abstract: The small satellite renaissance began in the 1980s and is changing the economics of space. Technological trends have supported the advancement of small satellites in the 1-500 kg range. The number of countries actively participating has grown substantially during the past years. Satellite constellations (groups of satellites working in concert) are emerging as a powerful and effective application. In this paper, we focus on the small satellites than can perform remote sensing or Earth observation tasks. An overview is presented of the small satellite literature on Earth observation. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field and a reference for those searching for literature on a specific application for Earth observation. Small satellite programmes are classified according to the geographic regions. The small satellite industry and small satellite systems are addressed. In terms of applications, small satellite constellations are discussed in more detail. Finally, future developments are put forward. Telegeoprocessing combined with grid computing will provide the infrastructure and technologies for the development of Processing on Demand for Small Satellite Constellation systems.
77 citations
"Open Source Software for Simulating..." refers background in this paper
...The use of large, heterogenous networks of small satellites for remote sensing has been extensively reported as the future for Earth observing systems [1, 2]....
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TL;DR: The HFAR framework is applied to a single-target tracking, sensor fusion problem, and real-time experimental results demonstrate the efficacy of the proposed architecture for handling problems of varying scales in a consistent, adaptive fashion.
Abstract: By emulating the cognitive perception-action cycle believed to be at the core of animal cognition, cognitive radars promise to improve radar performance over standard systems. The fully adaptive radar (FAR) framework provides a generalised approach to implementing a single cognitive perception-action cycle for radar systems, but complex adaptive problems necessitate the interaction of multiple perception-action cycles. This study describes the general form of the hierarchical FAR (HFAR) framework. The HFAR framework is applied to a single-target tracking, sensor fusion problem, and real-time experimental results demonstrate the efficacy of the proposed architecture for handling problems of varying scales in a consistent, adaptive fashion.
11 citations