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Pacific Defense Solutions, LLC

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Kihei, HI, United States
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Grant
Agency: Department of Defense | Branch: Air Force | Program: STTR | Phase: Phase I | Award Amount: 99.96K | Year: 2010

The accurate estimation of real space object (RSO) motion is subject to the complex interaction of gravitational forces, non-conservative drag terms and variable solar radiation pressure. Currently many operational algorithms rely on quasi-linear models with Gaussian-Input-Gaussian Output (GIGO) assumptions that do not capture the non-Gaussian nature of RSO error characteristics. An innovative approach based on a combination of the Gaussian Sum Filter (GSF) and Generalized Multiple Model Adaptive Estimation (GMMAE) scheme is proposed to fully describe the probability density function (pdf) associated with RSO tracks. The GSF can provide an accurate pdf construction of the state error process by approximating the Fokker-Planck-Kolmogorov equation in a computationally efficient manner. The GMMAE scheme generalizes the MMAE process by incorporating multiple time-steps of the residual sequence back into the estimation process and using the likelihood of the residual sequence in order to provide a weighted average of the assumed parameter elements in the unknown covariance matrices. The novel mathematical framework provided by the GSF and GMMAE will be implemented against a realistic RSO use case with the goal of assessing the feasibility of using these more realistic recovered state and covariance estimates within the current space surveillance network (SSN) environment. BENEFIT: Currently, the SSN uses the NORAD SGP4 orbit models for predicting satellite positions that do not have the associated covariance estimates. PDS will provide a performance assessment of utilizing these innovative orbit estimation and RSO track association algorithms developed under this project by testing their accuracy and responsiveness of RSO tracking against realistic use cases generated with an innovative space surveillance network (SSN) simulator. Once these algorithms are validated under “real world” simulations, PDS will test and validate these algorithms with actual SSN data. PDS intends to work closely with the Air Force in transferring technology for their critical objectives. The primary DoD end-customer for these algorithms is the JFCC-Space through the Joint Space Operations Center (JSpOC), which detects, tracks, and identifies all man-made objects in Earth orbit. Through current program experiences, PDS understands the acquisition process involved in transitioning algorithms from concept to validation, development, testing, (SMC SSA Technology Branch) and deliverance of an operational product to the warfighter (AF Space Command).


Roberts Jr. L.C.,Jet Propulsion Laboratory | Bradford L.W.,Pacific Defense Solutions, LLC
Optics Express | Year: 2011

An understanding of wind speed and direction as a function of height are critical to the proper modeling of atmospheric turbulence. We have used radiosonde data from launch sites near significant astronomical observatories and created mean profiles of wind speed and direction and have also computed Richardson number profiles. Using data from the last 30 years, we extend the 1977 Greenwood wind profile to include parameters that show seasonal variations and differences in location. The added information from our models is useful for the design of adaptive optics systems and other imaging systems. Our analysis of the Richardson number suggests that persistent turbulent layers may be inferred when low values are present in our long term averaged data. Knowledge of the presence of these layers may help with planning for adaptive optics and laser communications. © 2011 Optical Society of America.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 99.97K | Year: 2010

The accurate tracking of resident space objects (RSO)s depends on the rapid estimation of orbits using the knowledge gained from sparsely sampled observations of satellites under the influence of interacting gravitational and drag effects. Examples of scenarios operating within this environment include tasking follow up observations of debris created from collision events, accurately establishing the identity of objects that are located within close proximity, and reacting to controlled on-orbit deployments of additional space objects. New near real time and computationally efficient algorithms that can estimate non-Gaussian RSO error characteristics are available that could characterize RSO error to a much higher fidelity than current methods. For example, it has been shown that typical “banana-shaped” covariance profiles displaying more uncertainty along-track, than cross-track are reproducible with this technique. This type of information combined with orbital estimates provides more actionable space situational awareness (SSA) knowledge. Combined with an innovative space surveillance network (SSN) simulator that uses smart scheduling of assets in a flexible and responsive publish-and-subscribe network environment, these algorithms will be developed and tested for their applicability to improving the speed, accuracy and responsiveness of RSO tracking. BENEFIT: Currently, the SSN uses the NORAD SGP4 orbit models for predicting satellite positions that do not have the associated covariance estimates. PDS will provide a performance assessment of utilizing these innovative orbit estimation and RSO track association algorithms developed under this project by testing their accuracy and responsiveness of RSO tracking against realistic use cases generated with an innovative space surveillance network (SSN) simulator. Once these algorithms are validated under “real world” simulations, PDS will test and validate these algorithms with actual SSN data. PDS intends to work closely with the Air Force in transferring technology for their critical objectives. The primary DoD end-customer for these algorithms is the JFCC-Space through the Joint Space Operations Center (JSpOC), which detects, tracks, and identifies all man-made objects in Earth orbit. Through current program experiences, PDS understands the acquisition process involved in transitioning algorithms from concept to validation, development, testing, (SMC SSA Technology Branch) and deliverance of an operational product to the warfighter (AF Space Command).


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 149.93K | Year: 2012

ABSTRACT: This research plans to use existing MSSS sensors to investigate methods to detect and track faint objects (greater than or equal to 14th visual magnitude) in any orbit around the Earth using ground-based electro-optics sensors, without prior knowledge of the object's orbit. The basic technique is to image part of the sky with a wide field-of-view detector. Processing algorithms will be developed to detect objects in earth orbit as they pass through the image. Slow moving objects will appear as a short streaks or points in successive image frames. Faster objects my pass completely through a single frame leaving only a streak. An initial estimate of the orbit will be made from these images. Methods to increase the accuracy of this initial estimate will be studied. Possible methods include: tasking an agile telescope or looking for the track in another, nearby staring sensor. BENEFIT: PDS is leveraging their experience on developing operational dim object detection algorithms for wide field-of-view (WFOV) optical system, such as Pan-STARRS and the Air Force's Space-based Space Surveillance (SBSS) system. PDS's proposed innovative processing algorithms for dim object detection will benefit USSTRATCOM's Space Surveillance mission by increasing the awareness of the space volume with the capability of providing initial orbit determination for follow-on tracking of previously uncorrelated objects.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 149.97K | Year: 2012

ABSTRACT: Space situational awareness is often limited by the ability of sensors to produce resolved data on space objects. Large objects cannot be resolved with small low-cost telescopes; objects in geo-synchronous orbit are too remote to be resolved. The best hope of ending these limitations is to make it possible to determine important features of space objects from unresolved data, typically the temporal light curves that are produced by measuring only the integrated brightness of the space object as it passes overhead a sensor on the ground. Both supervised processing (i.e., pattern classification) and unsupervised processing (i.e., Kalman filter) of light curve data have shown success in extracting space object features. In this proposal we set forth a system that combines the merits of both supervised and unsupervised processing to more fully automate the exploitation of unresolved space object temporal light curve data. BENEFIT: The successful results of this project will offer Space Situational Awareness (SSA) data from low-cost deployable telescopes that can travel to the world-wide locations where USA military assets operate and can benefit from the immediate response of the simpler SSA systems offered by this technology. The Potential Commercial Applications of this project extend to the construction and delivery, maintenance and upgrade of such worldwide assets, including delivery of workstations and full SSA packages, as well as cross-over applications in areas such as autonomous navigation.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 149.97K | Year: 2012

ABSTRACT: In this research PDS plans to use existing telescopes and sensors at MSSS with minor modifications to detect and track Geosynchronous or Geo-transfer vehicles during daylight. The main obstacle to viewing satellites in daylight is the bright sky foreground. The bright foreground problem has been solved in the field of LWIR astronomy where the foreground signal is due to thermal self-emission from the atmosphere and from the telescope"s optics. LWIR astronomers use short integration times to avoid saturation, co-addition of image frames to reduce photon noise and chopping and nodding to eliminate the background and reduce calibration errors. We plan to use these same techniques with minor modification to image dim satellites in the daytime. BENEFIT: Maintaining line-of-site custody of newly launched objects in a GEO Transfer Orbit (GTO) and then inserted into GEO orbit and accurately tracking the position of that object to determine its orbital path including maneuvers or change detection is a critical objective of USSTRATCOM's Joint Functional Component Command for Space (JFCC-Space) mission for space surveillance. PDS will utilize their past R & D experience on developing Maui Space Surveillance Site (MSSS) optical sensors and leverage existing analytical tools for daytime modeling and simulation to design an innovative, low-cost daytime deep-space object detection and tracking solution that can be demonstrated at MSSS. PDS proposes a low cost design solution through algorithm implementation and innovative collection techniques using existing MSSS assets and infrastructure.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase II | Award Amount: 688.25K | Year: 2011

ABSTRACT: The accurate tracking of resident space objects (RSO)s depends on the rapid estimation of orbits using the knowledge gained from sparsely sampled observations of satellites under the influence of interacting gravitational, solar radiation pressure and atmospheric drag effects. While there are many established sequential estimators that can perform real-time orbit estimation and provide the associated covariance, the RSO tracking problem presents special difficulties. The current estimation technique tends to be applied with limited tracking data for a wide variety of orbit regimes when there is little or no information included in the estimation process on the RSO"s mass, shape, radiative properties, or attitude. In addition, it is likely that the uncertainty distribution for many RSOs is not Gaussian and cannot be represented accurately by a covariance matrix that has been developed with an assumed Gaussian probability density function. The AGSF algorithm developed under Phase I is designed to be scalable, relatively efficient for solutions of this type, and able to handle the nonlinear effects which are common in the estimation of RSO orbit states. In addition, information theoretic metrics in conjunction with AGSF were examined for data association purposes. The AGSF and corresponding observation association methods were evaluated using simulated data to determine their performance and feasibility. Combined with an innovative space surveillance network (SSN) simulator, these algorithms will be developed and tested for their applicability to improving the speed, accuracy and responsiveness of RSO tracking. BENEFIT: Currently, the SSN uses the NORAD SGP4 orbit models for predicting satellite positions that do not have the associated covariance estimates. PDS will provide a performance assessment of utilizing these innovative orbit estimation and RSO track association algorithms developed under this project by testing their accuracy and responsiveness of RSO tracking against realistic use cases generated with an innovative high fidelity space surveillance network (SSN) simulator. Once these algorithms are validated under"real world"simulations, PDS will test and validate these algorithms with actual SSN data. PDS intends to work closely with the Air Force in transferring technology for their critical objectives. The primary DoD end-customer for these algorithms is the JFCC-Space through the Joint Space Operations Center (JSpOC), which detects, tracks, and identifies all man-made objects in Earth orbit. Through current program experiences, PDS understands the acquisition process involved in transitioning algorithms from concept to validation, development, testing, (SMC SSA Technology Branch) and deliverance of an operational product to the warfighter (AF Space Command).


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 99.99K | Year: 2011

National intelligence requirements for Space Situational Awareness (SSA) are hindered by the lack of a reliable capability to image satellites during the daytime hours with optical sensors. Presently, most optical SSA observations are conducted during terminator conditions i.e., morning or evening twilight when the sun illuminates the satellite, but not the observatory. There are two fundamental issues that make daytime imaging more technically challenging than terminator imaging: atmospheric seeing is generally worse during the day than during terminator; and high levels of background radiation due to scattered sunlight significantly lower the signal-to-noise ratio (SNR) of the measurements. This project proposes to build on previous MFBD algorithm experience and on current research being performed by PDS for daytime imaging systems in order to develop the mathematical basis for new MFBD approaches and constraints that are tailored to daylight imaging in the presence of strong turbulence. BENEFIT: Facility security and battlespace management rely heavily video surveillance systems. In daylight these systems are limited by the stron turbulance encountered over long path lengths. MFBD operating in such conditions can bring huge improvements to such applications. The market is estimated to be in billions of dollars. The development of the new MFBD methods proposed herein offers the potential to contribute to better ultrasound imaging and other applications in the medical imaging field.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase II | Award Amount: 729.87K | Year: 2013

ABSTRACT: This research plans to use existing MSSS sensors to investigate methods to detect and track faint objects (greater than or equal to 14th visual magnitude) in any orbit around the Earth using ground-based electro-optics sensors, without prior knowledge of the object's orbit. The basic technique is to image part of the sky with a wide field-of-view detector. Processing algorithms will be developed to detect objects in earth orbit as they pass through the image. Slow moving objects will appear as a short streaks or points in successive image frames. Faster objects my pass completely through a single frame leaving only a streak. An initial estimate of the orbit will be made from these images. Methods to increase the accuracy of this initial estimate will be studied. Possible methods include: tasking an agile telescope or looking for the track in another, nearby staring sensor. BENEFIT: PDS is leveraging their experience on developing operational dim object detection algorithms for wide field-of-view (WFOV) optical system, such as Pan-STARRS and the Air Force's Space-based Space Surveillance (SBSS) system. PDS's proposed innovative processing algorithms for dim object detection will benefit USSTRATCOM's Space Surveillance mission by increasing the awareness of the space volume with the capability of providing initial orbit determination for follow-on tracking of previously uncorrelated objects.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase II | Award Amount: 740.26K | Year: 2013

ABSTRACT: Space situational awareness is often limited by the ability of sensors to produce resolved data on space objects. Large objects cannot be resolved with small low-cost telescopes; objects in geo-synchronous orbit are too remote to be resolved. The best hope of ending these limitations is to make it possible to determine important features of space objects from unresolved data, typically the temporal light curves that are produced by measuring only the integrated brightness of the space object as it passes overhead a sensor on the ground. Both supervised processing (i.e., pattern classification) and unsupervised processing (i.e., Kalman filter) of light curve data have shown success in extracting space object features. In this proposal we set forth a system that combines the merits of both supervised and unsupervised processing to more fully automate the exploitation of unresolved space object temporal light curve data. BENEFIT: The successful results of this project will offer Space Situational Awareness (SSA) data from low-cost deployable telescopes that can travel to the world-wide locations where USA military assets operate and can benefit from the immediate response of the simpler SSA systems offered by this technology. The Potential Commercial Applications of this project extend to the construction and delivery, maintenance and upgrade of such worldwide assets, including delivery of workstations and full SSA packages, as well as cross-over applications in areas such as autonomous navigation.

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