BAA CALL.doc

BROAD AGENCY ANNOUNCEMENT

BAA-VS-07-03

Air Force Research Laboratory/Space Vehicles Directorate

PROPOSAL CALL ANNOUNCEMENT CALL 0025

BROAD AGENCY ANNOUNCEMENT TITLE: Space Components Technology Open 5 Year Broad Agency Announcement.

BROAD AGENCY ANNOUNCEMENT NUMBER: BAA-VS-07-03; Topic Area # 2

PROPOSAL CALL ANNOUCEMENT (CALL) TITLE: Advanced Guidance, Navigation, Control, and Astrodynamics for Space Superiority

PROPOSAL CALL ANNOUNCEMENT (CALL) NUMBER: 0025

TECHNICAL POINT OF CONTACT: The technical point of contact for this CALL is Dr. Moriba Jah, AFRL/RVSV, Kirtland AFB, NM, Phone 505-853-2629, Fax 505-846-7877, Email Moriba.Jah@kirtland.af.mil.

CONTRACTING POINTS OF CONTACT: The contracting points of contact for this CALL are: Contract Specialist: Ms. Devin Reedy, Det 8 AFRL/RVKV, Kirtland AFB, NM, Phone 505-846-9334, Fax 505-846-7041, Email devin.reedy@kirtland.af.mil, or Contracting Officer: Mr. David Romo-Garza, Det 8 AFRL/RVKV, Kirtland AFB, NM, Phone 505-846-4967, Fax 505-846-7041, Email david.romo-garza@kirtland.af.mil.

GENERAL INFORMATION: The purpose of this announcement is to request white papers/abstracts to the technical area hence forth.

REQUIREMENT DESCRIPTION: There are six basic need areas: (1) Recovering/Inferring Space Environment Behavior from Resident Space Object Dynamics, (2) Short-Arc Angles-Only Orbit Determination (3) Autonomous and Robust Guidance, Navigation, and Control (GN&C) for Rendezvous and Proximity Operations (RPO), (4) Sensor Measurement Calibration and Characterization, (5) Cross-Tagging/Uncorrelated Track (UCT) mitigation, and (6) Next Generation Space Catalog Development and Maintenance. These areas are described below. Note that an interested party may submit a white paper on one or more areas. However, interested parties must submit separate white papers that clearly delineate each specific area they are proposing to.

1. Recovering/Inferring Space Environment Behavior from Resident Space Object Dynamics The Air Force needs rigorous studies in how the space environment influences space object motion due to gravitational and non-conservative sources. Gravitational effects seem fairly well determined except for accurate long term prediction of multi-body gravitational effects on Resident Space Objects (RSOs). Our main issue is in RSO-dependent forces and torques. What is needed is a way to model any given RSO's trajectory (6 degrees of freedom or more) with high accuracy and precision over long time scales (years, decades).

This topic concerns high-fidelity modeling of all forces and torques impressed upon a RSO due to space environmental sources, keeping in mind the variety of dependencies and associated ambiguities on the particular RSO. These include mass/inertia, size, material/thermal properties, and orientation. Related to this is a desire to quantify how materials age in space and the effect of such aging upon RSO dynamic behavior.

There are few dedicated sensors for space environment monitoring. Successful work in this topic could potentially enable most RSOs to serve as global space environment monitors.

The objectives of this effort are to:

· Develop rigorous mathematical and physical relationships between natural space environment phenomena and the ensuing interaction with RSOs for a variety of orbital regimes

· Develop and implement quantifiable, realistic, and useable measures of data information gain and RSO ambiguity

· Formulate strategies to solve the inverse problem of inferring space environment behavior from observed RSO dynamics

2. Short-Arc Angles-Only Orbit Determination Currently some RSOs are only tracked once per day (or more sparsely). Developing and/or maintaining a catalog of RSOs with this sparse data is difficult at best. What has not been rigorously assessed is the information content of the collected angles data, analogous to the Fisher Information from Information Theory (i.e. a way of measuring the amount of information that an observable random variable X carries about an unknown parameter θ upon which the likelihood function of θ, L(θ) = f(X;θ), depends). This might significantly aid in how best to exploit the sparse data sets.

Recent research by DeMars et al., have approached this problem by exploiting Multiple Hypothesis Testing, using known information (e.g. accurate line-of-sight and sensor field-of-view) to constrain hypotheses in unknown quantities (e.g. observer-to-RSO range and RSO inertial velocity vector) with encouraging results. Any proposed solution must:

· Be robust to the presence of unmodeled dynamics (since the RSO may be thrusting at the time of observation) and applicable to any orbit regime

· Be timely enough to provide solutions as quickly as observations are received

· Provide a realistic measure of solution ambiguity or uncertainty (e.g. covariance)

3. Autonomous and Robust Guidance, Navigation, and Control (GN&C) for Rendezvous and Proximity Operations (RPO) The goal of this effort is to develop systems that can safely, robustly, and reliably be self sufficient (without ground-based operator intervention) and conduct space operations (rendezvous and proximity) including fault tolerant and non(un)cooperative control of RSOs. Overall areas of relevant work are in advanced estimation algorithms, decision support systems, in-situ data acquisition and reduction for closed-loop guidance, optimal guidance, and artificial potential function guidance, cooperative and noncooperative game-theortic control.

3a. Autonomous Navigation

Autonomous navigation refers to spacecraft state estimation and prediction performed on-board an Earth-orbiting spacecraft with an emphasis on two thrust areas:

· Autonomous navigation with high-fidelity on-board environment models (e.g., neutral density models) to estimate current state and predict future state in the absence of GPS. To implement these high fidelity models onboard, fast and accurate propagation methods are needed. In addition, computationally efficient methods of data reduction are required. It is also desired to utilize non-traditional data that normally goes unexploited (e.g., fusing reaction wheel measurements with inertial navigations sensors).

· Autonomous navigation to support management of clusters of spacecraft focusing on relative navigation of cooperative and non-cooperative spacecraft to support missions such as "guardian" satellites and space tugs.

The strategic objective is to instrument spacecraft with a redundant on-board sensor suite whose outputs are fused within a reliable self-navigation architecture to estimate current spacecraft state (e.g. position, velocity, and attitude) and model parameters (e.g. inertia tensor, material properties, thrust vectors, etc.) at any given time. One outcome is to dramatically improve the understanding of the external accelerations acting on the spacecraft, notably the atmospheric drag over shorter time horizons and solar radiation pressure over longer time horizons, as well as thermal emissive and electrostatic effects. If the self-navigated state is precise, then it can serve in an inverse problem to recover improved estimates of the space weather, such as neutral density and solar plasma activity. The instrumented satellites would also be useful operators as they refine orbit ephemeris estimates.

Any proposed solution must:

· Be able to provide a reliable and realistic measure of state estimation ambiguity to support forward prediction of the state (both inertial and relative to targets).

· Be robust to the presence of unmodeled dynamics (since the RSO dynamic model will never be perfectly known)

· Be timely enough to provide solutions as quickly as observations are available 3b. Autonomous Guidance and Control

Autonomous guidance refers to the satellite’s ability to self generate commands to carry out the required mission. In practice, this has generally been done in an open-loop fashion. However, given current space situational awareness (SSA) needs of robustness and timeliness, closed-loop (i.e., autonomous) architectures are required. Relevant work in autonomous guidance consists of but is not limited to: optimal control, dynamic programming, artificial potential functions, fuzzy logic, and neural networks.

Autonomous control refers to the satellite’s ability to execute the commands sent from the guidance system in a robust and timely manner. Concerns with autonomous control systems is their closed-loop stability, robustness to uncertainty/noise and/or inappropriate commands (e.g., exceeding saturation/rate limits or other constraints), and performance that meets mission objectives (e.g., minimal fuel/time, pointing, tracking, positioning accuracy). Relevant areas for autonomous control are nonlinear, linear, adaptive, and robust control and others. Relevant areas for cooperative and non(un)cooperative control are decentralized and centralized control in nonlinear and linear frameworks, game-theory, optimal control, neural-networks, machine learning, and others.

Any proposed solution must:

· Address the tradeoff between executing optimal solutions and those that are physically realizable and safe to the spacecraft system.

· Consider RPO scenarios in which more than one object is being controlled (i.e., cooperative control) as well as scenarios in which the object that is being approached may be non(un)cooperative (i.e., unable to control itself or purposely uncontrollable).

3c. Relative Motion

The relative motion between two or more satellites in close proximity can be modeled in unique ways. The governing equations for such motion can account for a variety of physical phenomena and, as such, may be linear or nonlinear, time-varying or time-invariant. Relevant work for relative motion concerns the Hill-Clohessy-Wiltshire equations, Lawden’s equations, Tschauner-Hempel equations, and others.

Any proposed solution must:

· Attempt to derive new formulations for the relative motion between/among satellites, as a platform to visualize the motion and potentially reduce computation in the execution of the proposed algorithms on the system.

· Realistically quantify and qualify the effect of the error incurred when applying a guidance, navigation, or control algorithm based on a relative motion model compared to one based on higher fidelity modeling of the satellites’ motion.

· Account for the satellites’ attitude dynamics to include modeling the of attitude-dependent forces on relative dynamics, deriving equations for the relative attitude dynamics between two or more satellites, and development of relative attitude control methodologies to meet the precision pointing requirements of close proximity missions.

· Address the challenge of being able to perform these autonomous functions with limited power/computational support.

4. Sensor Measurement Calibration and Characterization

In traditional trajectory estimation problems, there are several assumptions made concerning the available measurements:

· The measurements are received at discrete times.

· The state can be estimated by a linear measurement-state relationship.

· The measurement errors are i.i.d, zero-biased and belong to a white noise Gaussian distributed sequence with known covariance.

In reality, there are non-linearities in the measurement-state relationship. If sensors are properly characterized and calibrated, then it may be possible to quantify the sensor biases, if any, and the behavior of the measurement errors. The measurement errors may not be Gaussian distributed at all, and furthermore may be correlated (not belonging to a white noise sequence). Processing measurements under the previously mentioned assumptions when these fail implies that the knowledge recovered from the data reduction is corrupt and can lead to false correlations, misinterpretations concerning the recovered RSO states and associated parameters, and misleading associated uncertainties (covariances). In essence, improper measurement processing negatively impacts and degrades the trajectory estimation and prediction process, and any ensuing RSO characterization efforts.

It is desired to characterize the possibly dynamic nature of the measurement errors and the probability distributions from which they derive. Some of the errors will be sensor-inherent (real noise, internal thermal noise, etc) and others will be caused by external factors (seismic noise, thermal environment noise, etc). To aid in this measurement characterization, observations need to be modeled in terms of natural physical quantities (e.g., temperature, humidity, pressure, winds). Additionally, there is a need to assess how well sensor performance and error characteristics can be recovered from measurements collected on RSOs. Where possible, information obtained from sensor calibration processes will be exploited, but there will be many occasions where this a priori information will not be available. In those cases, it is of interest to explore processes that may be put in place and exploited to recover this information within the trajectory estimation activity itself. Goals of this effort include:

· Develop the mathematical foundations to characterize a class of probability distributions for measurement errors associated with a variety of sensors. Assess when Gaussian distributed assumptions hold and when they do not.

· Investigate the relative effects of the measurement errors due to sensor-inherent as well as external errors. In particular, quantify the errors in terms of both spatial (size) and temporal (nonstationary) areas.

· Fully study the effects of correlations that may be inherent in measurement errors. Typically, raw sensor observations are uncorrelated from each other. But oftentimes sensors are combined in a batch process to provide a deterministic (point-by-point) estimate, which is then treated as a “measurement” in a filtering process. This new “measurement” typically contains correlations, but oftentimes this information is not available in the filtering process. Research needs to be done to assess how these unknown correlations affect the overall filtering process. Can a Bayesian approach be developed to fully quantify these unknown correlations?

· Apply the characterized probability distributions in a multiple hypothesis approach in order to provide an assessment of what distribution more accurately models the measurement error characteristics. In particular, the use of Bayesian and maximum likelihood methods, such as multiple-model adaptive estimators, can provide probabilistic means to assess that a particular probability distribution is the correct one.

· Develop filtering approaches to handle non-Gaussian measurement errors. One possible approach involves using Gaussian inputs through colored-noise models, which allows the use of quasi-linear filters, such as the extended Kalman filter, which commonly used in typical RSO tracking applications. Other approaches involve using the non-Gaussian pdf of the measurement errors in more elaborate filters, such as particle filters or Gaussian sum filters.

· Apply existing techniques to handle unknown correlations in the measurement errors. Several approaches can be investigated. For example, is it possible to estimate these correlations using a multiple-model adaptive approach or other methods such as polynomial chaos-based approaches. Other solutions involve mitigating the effects of ignored correlations. For example the covariance intersection approach can be used to replace the Kalman update, which should guarantee that the estimate is consistent, i.e. that the covariance from the filter does not underestimate the actual errors. Probability Hypothesis Density (PHD) filters may also be used to mitigate these effects.

· Investigate potential benefits and costs of processing observations of multiple RSO's and multiple sensors in a combined estimator. Conventional processing works with observations made of what is assumed to be one RSO. Observability of correlated sensor errors may be enhanced through the combined processing. Simultaneous estimation of the states of multiple RSO as well as the sensor error parameters prevents sensor errors and incorrect data associations from corrupting the state estimates.

5. Cross-Tagging/Uncorrelated Track (UCT) Mitigation

Data associations supplied by sensors are typically uncertain and should be treated as provisional. The sensors may not have the most current estimates of the orbital state, for example. Currently, these operations are performed using relatively simple fixed-gate association logic and General Perturbation (GP) propagation. A potentially more robust method would use association gates derived from orbital state (or measurement) covariance generated via Special Perturbation (SP) propagation. They represent the actual prediction errors as a function of time and tracking history. When RSO correlation operations fail, or more importantly, when new objects appear in the sensors’ coverage, new orbits have to be generated from the observations. With the instantiation of more accurate sensors, the potential number of newly discovered objects may likely increase by an order of magnitude. Therefore, rigorous and robust methods of data/track association for RSO ID/discrimination must be implemented in order to enable the development and maintenance of a large catalog of objects.

Any proposed solution must:

· be based on a rigorous probabilistic method

· develop a quantifiable measure of performance for correct associations

· address the sensitivity of correct associations to assumptions/errors in sensors measurements, a variety of RSO dynamic scenarios, and a priori knowledge

· address this problem with a “Refrigerator Approach”. What is meant here is that much like how a refrigerator does not cool but rather removes heat, the proposed work should focus on ambiguity removal vice constrained state estimation. It is possible to make many assumptions or hypotheses regarding RSOs and use the collected data to assign realistic measures of likelihood to any given assumption (e.g. Set Hypothesis Theory). In this way, data are collected for the sole purpose of hypothesis rejection and any knowledge inferred about the RSO can only be gotten from the information content of our observations. Furthermore, there are parameters that may be well known (e.g. line-of-sight from the observer to a RSO) while others are either unknown or poorly known (e.g. observer-to-RSO range). This should be exploited.

6. Next Generation Space Catalog Development and Maintenance

Space Situational Awareness concerns itself with the determination, development, maintenance and propagation of the catalog of RSOs. This is a difficult problem in general and involves many different aspects, confounding our ability to develop a single “algorithm” that can address all space domain events and activities. This is especially true at the research level, where specific solutions and approaches may be developed for single problems, yet it is not clear how these approaches can be unified into this general problem. Thought and care must be invested if only to gain clarity on what can be done, how things can be improved, and in what areas the status quo can be accepted. At more general levels, this expands into questions of what information should be incorporated into a catalog (or database) (e.g. rotation states, detailed non-gravitational parameters and models, uncertainties, and additional material such as spectral signature, shape and mission).

Fundamental to the development of an improved catalog is the rigorous inclusion of uncertainty. There is a natural relationship between how an RSO should be represented in a catalog and the uncertainty in the object’s state. For example, for a well characterized object with small uncertainties, more emphasis should be placed on propagating a precise nominal (mean) trajectory than on propagating its uncertainty, which will be well represented by a covariance matrix. In contrast, a poorly known object does not require precision integration of its nominal state, but does require its probability density function (pdf) to be propagated nonlinearly, as it will be distributed over a larger region of phase space.

Also pertinent to this topic is the detection and/or characterization of RSO maneuvers. Whereas conventional orbit determination techniques assume the absence of thrust, it is desired to develop techniques that determine when a RSO is thrusting, as well as the magnitude and direction of the thrust.

The proposed work in this area should address:

· quantifying and propagating realistic measures of RSO ambiguity

· assessment of the impact of RSO state representations

· precise and accurate modeling of RSO 6DOF dynamics

· estimation strategies (to include maneuver detection/characterization)

· computational and data retrieval techniques

· proper modeling, calibration, characterization and reduction of observational measurements References:

1. Linares, R., Crassidis, J., Jah, M., Kim, H., (2010). Astrometric and Photometric Data Fusion for Resident Space Object Orbit, Attitude, and Shape Determination Via Multiple-Model Adaptive Estimation, AIAA-2010-8341, 2010 AIAA Guidance, Navigation, and Control Conference, Toronto, Canada, August 2-5

2. DeMars, K., Jah, M., Schumacher, P., Jr., (2010). The Use of Angle and Angle Rate Data for Deep-Space Orbit Determination and Track Association, AAS Paper 10-153, 20th AAS/AIAA Space Flight Mechanics Meeting, San Diego, CA, February 14-17

3. Giza, D., Singla, P., Jah, M., (2010). An Adaptive Gaussian Sum Filtering Approach for Orbit Uncertainty Estimation, AAS Paper 10-132, 20th AAS/AIAA Space Flight Mechanics Meeting, San Diego, CA, February 14-17

4. Tombasco, J., Axelrad, P., Jah, M., (2010). Analysis of Geosynchronous Elements for Dynamic Modeling and Application to Orbit Estimation, AAS Paper 10-241, 20th AAS/AIAA Space Flight Mechanics Meeting, San Diego, CA, February 14-17. Accepted to AIAA Journal of Guidance, Control, and Dynamics June 2010

5. Hill, K., Sydney, P., Cortez, R., Naho’olewa, D., Houchard, J., Luu, K., Jah, M., Schumacher, P., Jr., (2010). Covariance-based Network Tasking of Optical Sensors, AAS Paper 10-150, 20th AAS/AIAA Space Flight Mechanics Meeting, San Diego, CA, February 14-17

6. DeMars, K., Jah, M., Giza, D., Kelecy, T., (2009). Orbit Determination Performance for High Area-to-Mass Ratio Space Object Tracking Using an Adaptive Gaussian Mixtures Estimation Algorithm. 21st International Symposium on Space Flight Dynamics, Toulouse, France, September 28 - October 2

7. Kelecy, T., Jah, M., (2009). Analysis of Orbit Prediction Sensitivity to Thermal Emissions Acceleration Modeling for High Area-to-mass Ratio (HAMR) Objects. Air Force Maui Optical and Supercomputing Site (AMOS) 2009 Conference, Wailea, Maui, Hawaii, September

8. Giza, D., Singla, P., Jah, M., (2009). An Approach for Nonlinear Uncertainty Propagation: Application to Orbital Mechanics. AIAA-2009-6082, 2009 AIAA Guidance, Navigation, and Control Conference, Chicago, Illinois, August 10-13

9. Kelecy, T., Jah, M., (2009). Analysis of Orbital Prediction Accuracy Improvements Using High Fidelity Physical Solar Radiation Pressure Models for Tracking High Area-to-Mass Ratio Objects. 5th European Space Debris Conference, Darmstadt, Germany, March 30 – April 2

10. DeMars, K., Jah, M.K., (2009), Passive Multi-Target Tracking with Application to Orbit Determination for Geosynchronous Objects, AAS Paper 09-108, 19th AAS/AIAA Space Flight Mechanics Meeting, Savannah, Georgia, February 8-12

11. Wetterer, C.J., Jah, M.K., (2009), Using the Unscented Kalman Filter for Attitude Determination from Light Curves, Journal of Guidance, Control, and Dynamics Vol.32, No.5, pp. 1648-1651

12. Jah, M., Kelecy, T., DeMars, K., (2008). Orbit Determination Strategies Addressing The Search, Acquisition, And Characterization Of Geosynchronous Space Debris Objects. 59th International Astronautical Congress, Glasgow, Scotland, September 29 – October 3

13. Kelecy, T., Jah, M., (2008). Maneuver Detection and Orbit Determination Of A Low Earth Orbiting Satellite Executing Low Thrust Finite Burn Maneuvers. Journal of the International Academy of Astronautics: Acta Astronautica, In Press (08/24/09)

14. Jah, M., Madler, R., (2007). Satellite Characterization: Angles and Light Curve Data Fusion for Spacecraft State and Parameter Estimation. Air Force Maui Optical and Supercomputing Site (AMOS) 2007 Conference, Wailea, Maui, Hawaii, September

15. Lovell and Tragesser, “Guidance for Relative Motion of Low Earth Orbit Spacecraft Based on Relative Orbit Elements,” AIAA Paper 2004-4988, presented at the AAS/AIAA Astrodynamics Specialist Conference, Providence, RI, Aug 16-19, 2004.

16. Schmidt and Lovell, “Estimating Geometric Aspects of Relative Satellite Motion Using Angles-Only Measurements,” AIAA Paper 2008-6604, presented at the AAS/AIAA Astrodynamics Specialist Conference, Honolulu, HI, Aug 18-21, 2008.

DUE DATE AND TIME: The due date for papers submitted in response to this CALL is no later than 12:00pm, MST, 24 January 2011. Papers for any other technology area identified in the baseline BAA will not be accepted at this time unless a CALL for papers in that specific area is open. Papers received after the due dates and times shall be governed by the provisions of FAR 52.215-1(c)(3). Full Proposals due date and time will be provided in the formal request.

CALL AMENDMENTS: Interested parties should monitor FedBizOps/EPS http://www.fbo.gov for any additional notices to this CALL that may permit extensions to the paper submission date or otherwise modify this announcement.

INTENT TO PROPOSE: Interested parties are requested to advise the contracting point of contact if they intend to submit a white paper in response to this CALL. Such notification is merely a courtesy and is not a commitment by the interested party to submit a white paper.

ANTICIPATED NUMBER OF AWARDS: The Air Force anticipates awarding a minimum of one award in each of the above areas. However, the Air Force does reserve the right to make multiple awards or no awards pursuant to this CALL.

ANTICIPATED FUNDING: Anticipated funding for this CALL (not per contract or award) is:

Area 1: $400,000 – FY11, $400,000 – FY12, $400,000 – FY13 -

TOTAL: $1,200,000

Area 2: $250,000 – FY11, $250,000 – FY12, $250,000 – FY13 -

TOTAL: $750,000

Area 3 (a, b, and c): $500,000 – FY11, $500,000 – FY12, $500,000 – FY13 –

TOTAL: $1,500,000

Area 4: $250,000 – FY11, $250,000 – FY12, $250,000 – FY13 -

TOTAL: $750,000

Area 5: $250,000 – FY11, $250,000 – FY12, $250,000 – FY13 -

TOTAL: $750,000

Area 6: $350,000 – FY11, $350,000 – FY12, $350,000 – FY13 -

TOTAL: $1,050,000

This funding profile is an estimate only and will not be a contractual obligation for funding. All funding (6.2) is subject to change due to government discretion and availability.

FORMAT: See White Paper Preparation and Submission Instructions, BAA VS-07-03, Modification 13.

ROUGH-ORDER-OF-MAGNITUDE (ROM) COST: The ROM cost is a best guess of the anticipated cost of the effort. The ROM should be consistent with the level of work being proposed. The white paper/abstract does not include a cost proposal or any of the material which accompanies a cost proposal.

PROCESS: White papers should be submitted as specified in the BAA. The process includes a two step process. First, the evaluation team will evaluate the white paper/abstract against the evaluation criteria stated in the BAA. Second, the interested party whose white papers/abstracts are of interest may be invited to submit a formal proposal. Interested parties whose white papers/abstracts are determined to not be of interest are not precluded from submitting a proposal and may request proposal instructions if they so desire. All interested parties submitting white papers/abstracts will be contacted by the Government; either with a letter informing them that the effort proposed is not of interest to the Government, or with a request for a formal cost and technical proposal by a specified date. The full proposal will be evaluated against the criteria stated in the BAA.

PERIOD OF PERFORMANCE: The anticipated period of performance for individual awards resulting from this CALL is 60 months in duration. The period of performance is to be proposed in the format “includes 57 months for technical effort and 3 months for Final Report preparation.”

INTENT TO PROPOSE: Interested parties are requested to advise the contracting point of contact if they intend to submit a white paper in response to this CALL. Such notification is merely a courtesy and is not a commitment by the interested party to submit a proposal.

DELIVERABLE ITEMS: Source Software (algorithms and simulations), Quarterly Status Reports, Final Technical Report.

OTHER RELEVANT INFORMATION:

1. Program security classification for this CALL is UNCLASSIFIED.

2. ITAR, export control, DD Form 2345 is not anticipated.

3. Government Furnished Property: None.

APPLICABILITY OF BASELINE BAA: All requirements of BAA-VS-07-03 apply unless specifically amended and addressed in this CALL. For complete information regarding BAA-VS-07-03, refer to the initial opened-ended BAA and subsequent modifications. It contains information applicable to all CALLS issued under the BAA and provides information on the overall program, proposal preparation and submission requirements, proposal review and evaluation criteria, award administration, agency contacts, etc. Direct questions to the points of contact identified above.