Columbia, MD, United States

Applied Defense Solutions Inc.

www.AppliedDefense.com
Columbia, MD, United States
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Roscoe C.W.T.,Applied Defense Solutions Inc. | Schumacher Jr. P.W.,Air Force Research Lab | Wilkins M.P.,Applied Defense Solutions Inc.
Advances in the Astronautical Sciences | Year: 2014

The problem of track initiation is addressed for optical ground or space-based observation of space objects. Angles are the primary quantities available from line-of-sight measurements, but angle rates may also be derived if the data are of sufficient quantity and quality. For a specified rectangular partition in the space of orbital elements, explicit bounds on range and range rate are derived for a given observation based on angles and angle rates. Discretizing the resulting range-range rate hypothesis region allows candidate orbits to be generated in an embarrassingly parallel fashion. The number of hypotheses for track initiation is further constrained by imposing conditions derived from special solutions of Lambert's problem for pairs of observations. Initial results are presented for perfect and noisy simulated data. Also included is an analysis of the sensitivity of the range-range rate bounds with respect to errors in angle rates.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 149.52K | Year: 2015

ABSTRACT:Applied Defense Solutions (ADS) has embarked upon a new approach to data correlation and aggregation. The ADS Hierarchical Reasoning Tool (HRT) provides a set of unique signatures for automatically recognizing and classifying a resident space object (RSO). Here, we seek to leverage hierarchical reasoning to provide innovative and automated analysis capabilities that capture and learn the normal status and behavior of satellites, detect changes, and assess the implications all within the context of events in the space domain. To assess orbital events and provide for timely decision analysis and courses of action, ADS proposes the development of a scalable automated workflow to support an Orbital Event Characterization Tool (OECT) within a distributed service oriented based architecture. Our primary research goal will be to connect the RSO feature hypothesis generation capabilities of HRT into the OECT capability to provide high level hypothesis management of events, characterize anomalous events, and detect changes in both object appearance and behavior all within context provided by a multi-INT event timeline. Our proposed approach will model and/or learn the normal behavior of space-based objects via Bayesian update process and leverage ADS operational expertise of satellite operations to generate a hierarchy reflecting mission level object life cycles.BENEFIT:Hierarchical reasoning capabilities provide a structured and mathematically rigorous methodology to correlate and aggregate sparse data from disparate sensors. The completed software tools could be used by both government and commercial entities that wish to not only provide indications of and attribution for anomalous events but also predict the likelihood of future intention and warn of possible threats.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 149.94K | Year: 2015

ABSTRACT:Over the last several years, ADS has been working closely with the AFRL to develop innovative algorithms specifically to address the information-based surveillance scheduling issues of SSA; with focus on the area of space object DTIC. ADS proposes to leverage two of these matured techniques 1) FISST and 2) ISRHC stochastic optimization to develop a scalable, centralized tasking and scheduling environment for the JSpOC Systems. This new tool will utilize parallelization and net-centric distributed computing to achieve 1000x speedups compared to traditional methods. The new tool will transact messages through ARCADE SOA compliant messaging to enable real-time coordination of surveillance assets, requirements, and operators. In Phase I, ADS will develop a proof-of-concept demonstration that dynamically tasks multiple heterogeneous surveillance systems through three vignettes including: multi-event Remote Proximity Operations at GEO, monitor adversary GEO Transfer, and a LEO Collision. ADS will team with TASC to leverage their validated SSN simulator Lookout to baseline with realistic, current day scheduling requirements; performance metrics; and models for all the JSpOC SSN assets. The resulting Phase 1 demonstration will prove the potential that information-based requirements scheduling is scalable and significantly enhances the JSpOC missions of SSA, DTIC, Combat ID, and Space Control.BENEFIT:Applications of the Finite Set Statistics (FISST) and Information Space Receding Horizon Control (ISRHC) stochastic optimization have the potential to enable evolutionary changes in the way that government intelligence collection agencies implement high value resource allocation. The paradigm shift from revisit rate centric scheduling to information gain could be applied directly and broadly to influence collection strategies in space, air breathing, maritime, and Human Intelligence (HUMINT)/social-networking domains. Furthermore, these concepts may be further generalized outside purely surveillance intelligence collection fields to address a wide variety of resource contention problems such as routing of airline traffic, genetic engineering, or financial asset management.


Grant
Agency: Department of Defense | Branch: Missile Defense Agency | Program: SBIR | Phase: Phase I | Award Amount: 99.83K | Year: 2010

ADS proposes to apply the Communication Taxonomy (CommTax) toolkit developed under a previous SBIR contract (current in the final stages of Phase II development). CommTax will be applied to the specific case of of nuclear scintillation in order evaluate how configuration parameters can be adjusted to achieve optimal performance. In addition, we will evaluate CommTax''s underlying technologies for suitability for use in prototyping and testing various radiation hardening techniques and solutions


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 149.67K | Year: 2014

ABSTRACT: Current photometric calibration techniques take away too much time from performing actual collections of interest, which decreases the efficiency and usefulness of important SSA resources. In response to the AF141-013 SBIR Solicitation for Efficient Photometry, the ADS-PDS team proposes to study in-frame photometric calibration feasibility and to provide an approach that is implementable for current operations. We plan to leverage existing in-frame astrometric and photometric algorithms and software previously developed by PDS. Under this Phase I effort, ADS will add in-frame photometric calibration techniques. Furthermore, the ADS team will study the algorithm"s calibration accuracies and applicability to a diverse set of sensor field-of-views (FOVs) and signal-to-noise ratios, while validating and testing the software with actual astronomy images to ensure that it is low cost, globally applicable, near-real-time, and requires no additional collection time. We plan to pair the software with the latest, most robust photometric catalog currently available, the SST-RC3 catalog, to maximize the potential to identify stars in even the smallest FOVs. Furthermore, we will optimize the software for robustness, usability, and efficiency. The effort will conclude with a demonstration of the algorithm on standard computing hardware. BENEFIT: The ADS team"s proposed solution is expected to provide photometric calibrations of 10% or better using in-frame techniques with no a priori calibration collections, even for non-photometric sky collection nights (such as nights with dynamic photometric properties, e.g. clouds, water vapor extinction, light pollution, etc.). Since the approach works for a wide range of sensor FOVs and signal-to-noise ratios and requires no special hardware, the approach should be easily applicable to a wide range of currently operational optical SSA sensors, ranging from Raven-class telescopes to SST, and even SBSS. The approach can be shown to be beneficial even for clear filtered sensors.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 148.81K | Year: 2014

ABSTRACT: Applied Defense Solutions (ADS) has embarked upon a new approach to data correlation and aggregation using a space object taxonomy that provides a set of unique signatures for automatically recognizing and classifying a space object. The goal of this Space Signatures effort is to find automated techniques for threat identification and intent modeling that will enable analysts to take signature data from different phenomenology sensors, combine them, and discern more intelligence than can be determined from the individual sensors alone. The Phase 1 Small Business Innovation Research (SBIR) project focuses on photometric light curve data as the initial data source and utilizes the GOTS Ananke software suite to ingest evidence from a Multiple Model Adaptive Estimator (MMAE). ADS has shown previously that its Hierarchical Reasoning Tool (HRT), which is a component of Ananke, can rapidly, decisively, and accurately select the correct object identification hypothesis based upon the priors and the observational evidence supplied by the MMAE. This Phase 1 effort will leverage the HRT and lead to a method to monitor satellite observables using optical and other data sources to predict and understand future activities of operational satellites in orbit. BENEFIT: Hierarchical reasoning capabilities provide a structured and mathematically rigorous methodology to correlate and aggregate sparse data from disparate sensors. The completed software tools could be used by both government and commercial entities that wish to not only provide indications of and attribution for anomalous events but also predict the likelihood of future intention and warn of possible threats.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 149.24K | Year: 2014

ABSTRACT: The goal of the proposed research in response to solicitation AF141-015 is to develop a collection concept of operations and software prioritized in the geosynchronous (GEO) and super-synchronous regimes using optical telescopes to maintain custody of objects and to detect and revisit new objects that enter the space, thereby enhancing the Space Situational Awareness (SSA) capabilities of the United States. Applied Defense Solutions (ADS) researchers and our Pacific Defense Solutions (PDS) and Texas A & M University teammates propose to do this by more efficiently allocating existing sensors and by maximizing the informational return of the data they collect. Improving the situational awareness and data-collection capabilities of existing and new optical telescopes can result in orders of magnitude improvements in awareness. This can be achieved by basing the situational awareness and data-collection capabilities of existing sensors on rational, information-centric criteria. In Phase I, ADS will show the necessity of using the Finite Set Statistics (FISST), information reward and stochastic optimization methodologies and demonstrate the scientific merit and feasibility of these concepts when employed to automated multi-telescope scheduling aimed at maximizing awareness (by maintaining custody via systematic revisit of existing objects and detect/discover new objects) of man-made objects. BENEFIT: With the successful completion of Phase I, a successfully-running TASMAN-like simulation environment that implements Finite Set Statistics (FISST) and Information State Receding Horizon Control (ISRHC) for GEO/super-synchronous RSO population inference and optimal sensor tasking will be developed. The algorithms will be able to efficiently maintain custody, detecting changes and new dim objects and characterize cued and uncued targets. The algorithm will be able to quantify the likelihood of follow-up detection (one of FISST"s standard capabilities). If implemented in SSA systems, it is anticipated that the data collected from the optical sensors will provide orders of magnitude improvement in data quality and awareness of the GEO/super-synchronous environment, which will lead to increased U.S. capability in predicting and responding to space-related threats. Phase II would continue to transition the solution to operational software and a decentralized near-real-time web-services to be tested in Maui.


Grant
Agency: Department of Defense | Branch: Defense Advanced Research Projects Agency | Program: SBIR | Phase: Phase II | Award Amount: 955.95K | Year: 2014

Applied Defense Solutions (ADS) has embarked upon a new approach to data correlation and aggregation using a space object taxonomy that provides a set of unique signatures for automatically recognizing and classifying a space object. The goal of this Space Signatures effort is to find automated techniques that will enable analysts to take signature data from different phenomenology sensors, combine them, and discern more intelligence than can be determined from the individual sensors alone. The Phase 1 Small Business Innovation Research (SBIR) project focused on photometric light curve data as the initial data source and utilized the GOTS Ananke software suite to ingest evidence from a Multiple Model Adaptive Estimator (MMAE). ADS showed that its Hierarchical Reasoning Tool (HRT) can rapidly, decisively, and accurately select the correct object identification hypothesis based upon the priors and the observational evidence supplied by the MMAE. Phase II will demonstrate the capability to assert evidence from multiple tools and varying quantity and quality data sources in an asynchronous mode as well as pursue full automation of the hierarchical reasoning process. Furthermore, ADS will demonstrate the HRT in a variety of simulated and real data scenarios.


Grant
Agency: Department of Defense | Branch: Defense Advanced Research Projects Agency | Program: SBIR | Phase: Phase I | Award Amount: 99.97K | Year: 2013

Many of today's sensors collect various data types beyond the traditional radiometric (range) or photometric (angles) that we call Space Object Identification (SOI) data. These data sources can yield discriminating satellite features and present a clear opportunity for correlation techniques to provide POI and improved track custody. We can use light reflectivity magnitude profiles and inverse synthetic aperture radar imaging to model spacecraft attitude. Heat signature profiling may be established with IR sensing as objects ascend and descend to/from Earth eclipsing. Maneuver models and profiling may be obtained as objects station-keep and perform momentum dumps. Multi-color and/or hyperspectral photometry may be used to infer materials of the satellite's composition. RF transmissions may be analyzed spectrally to characterize what frequencies and coding techniques are used. We propose a new approach to data correlation. Our Phase I effort will research and design a prototype Bayesian discrimination framework to object identification and recognition. As an initial form of representative SOI data, we will develop an application to generate predictive optical magnitude (light curve) data, representative of actual observational data. We will modify a Multiple Model Adaptive Estimator (MMAE) approach to show how our core Bayesian discriminator concepts can efficiently and rapidly improve positive identification of catalogued (modeled) and un-catalogued (un-modeled) space objects. In addition, we will develop a tree-based taxonomy of representative 3D models to represent a variety of alternatives for the Bayesian discriminator. Finally, we will investigate the availability and accessibility of SOI data sources for future incorporation to the Bayesian discriminator for a possible Phase II follow-on effort.


A method of determining an orbit of an orbital object includes computing predicted tracking measurement values based on the orbit computed from the initial conditions factoring in any modeled environmental forces and realistic maneuvers; computing the differences between the actual and predicted tracking measurements; determining an improved estimate of the initial conditions that reduces the measurement errors using a minimization or root finding algorithm; after the algorithm has converged, reviewing the hypothetical maneuvers in the force model, taking each value and determining which values came up as near-zero in the minimized solutions and which values came up as those of measurable thrust; determining overall burn duration using the first and last burn times; determining the thrust profile of the orbital object over the observation period using the integrated thrust values; and determining the actual maneuver based on the observation data.

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