Loveland, CO, United States
Loveland, CO, United States

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Systems and methods are described herein for dynamically generating and updating a patrol schedule for a shift based on historic demand event data, and a predicted-demand model based on the historic demand event data. The systems and methods also receive information associated with patrol officers assigned to a shift and constraints on the officers availability, and generate a patrol schedule comprising patrol assignments for the assigned patrol officers optimized based on at least one policing objective. The patrol schedule may be dynamically updated based on changing information and provided to the patrol officer via mobile device or display in the patrol officers vehicle.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 149.97K | Year: 2015

ABSTRACT:There is a critical need to research and develop new ways to better determine what satellite orbital events are most critical to monitor and how such methods can be made scalable to serve the future needs of the operational system with a growing catalog of up to 500,000 objects. To support this endeavor, Numerica proposes the development of a new software suite for space object conjunction assessment. Numerica's multi-faceted approach would provide solutions from the sensor level to the system level that (i) generate improved orbital estimates and more realistic covariances directly from sensor observations; (ii) perform scalable all-on-all conjunction filtering to quickly and cheaply rule out infeasible conjunctions; (iii) compute a more accurate probability of collision acting on the most critical conjunction pairs; and (iv) display the highest risk conjunction events in a risk assessment visualization tool. Phase I will focus primarily on algorithm and software development and will exploit Numerica's existing mature software suite of astrodynamics algorithms. Numerica will also leverage its experience processing real and simulated data from the SSN. A transition plan for integrating the conjunction assessment software suite into an operationally relevant system will also be developed in Phase I.BENEFIT:The envisioned sensor-to-system-level conjunction assessment software suite would provide the analyst with advanced tools needed to support conjunction assessment screening on a growing space catalog and ultimately to serve the future needs of the SSA mission. In particular, Numerica's multi-faceted approach would (i) send more reliable input to downstream conjunction assessment algorithms; (ii) scale to a future catalog with as many as half-a-million objects; (iii) perform conjunction assessment screening out to seven days or longer and reduce the false alarm rate; and (iv) improve analyst productivity by displaying the output in a tool that would allow the analyst to more efficiently monitor the highest risk conjunction events and recommended courses of action. The primary transition path of the proposed conjunction assessment algorithms and software is to ARCADE and to an eventual service pack of the JSpOC Mission System. To facilitate this transition, Numerica is partnering with Intelligent Software Solutions (ISS) and will integrate the software into their Continuous Anomalous Orbital Situation Determination (CAOS-D) UDOP in an extended program. A secondary transition path would be to a commercial customer such as Lockheed Martin or AGI, both of whom are attempting to develop commercial versions of the JSpOC in order to complement and augment services currently provided by the Government. Another viable transition path is to AFSPC/A3Z for inclusion of the software into the Astrodynamics Standards Library. The primary transition path of the proposed conjunction assessment algorithms and software is to ARCADE and to an eventual service pack of the JSpOC Mission System. To facilitate this transition, Numerica is partnering with Intelligent Software Solutions (ISS) and will integrate the software into their Continuous Anomalous Orbital Situation Determination (CAOS-D) UDOP in an extended program. A secondary transition path would be to a commercial customer such as Lockheed Martin or AGI, both of whom are attempting to develop commercial versions of the JSpOC in order to complement and augment services currently provided by the Government. Another viable transition path is to AFSPC/A3Z for inclusion of the software into the Astrodynamics Standards Library.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 149.81K | Year: 2015

ABSTRACT:Currently, the Synthetic Theater Operations Research Model (STORM) utilizes a strike planning methodology that provides sub-optimal, and often wasteful, munitions assignments greatly reducing the utility of this vital tool. Numerica Corporation proposes to develop a novel strike planning algorithm for integration into STORM that provides guarantees on mission objectives while simultaneously minimizing fuel expenditure as well as unnecessary losses of munitions and aircraft. Numerica's solution will utilize multi-objective optimization to directly address the balance that must be struck between high value strike planning assignments and the incurred cost of those assignments. Our approach fully and completely represents the options available during difficult scenarios, rather then pretending one perfect solutions exists. Numericas solution also allows for uncertain future events to be considered in the current optimization. This results in solutions that are optimal across time, rather than suboptimal greedy solutions. Leveraging recent results in sampling theory allows our solutions to be scalable and tractable even for the large problem sizes posed by difficult engagement scenarios. The Phase I effort will include a proof-of-concept prototype, compared alongside the existing greedy solution as well as a report on the integration path for the prototype into STORM's command and control manager.BENEFIT:The main product of this Phase I effort will be a novel strike planning algorithm for integration into the Synthetic Theater Operations Research Model (STORM). In contrast to the current solution, this algorithm will simultaneously consider the value of the strike planning assignments as well as the incurred cost of those assignments. Additionally, this solution will consider uncertain future events resulting in a solution that is optimal across time. This will provide a considerable improvement in the quality of strike plans when compared to the greedy solution currently used in STORM. A supplemental benefit of this technology is the ability to leverage Numerica's existing uncertainty quantification tools to learn performance bounds for the output of STORM simulations when the optimal trade off between rewards and costs are not known a priori. The primary transition path for the technology in this proposal is into the command and control manager within STORM. However this solution is not specific to the STORM simulation and so could be applied to any offline planning algorithm.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 499.35K | Year: 2015

The Department of Defense (DoD) is supported by a vast global network of computers, sensors, and equipment that is continually at risk of being breached by adversaries. Such cyber elements comprise an important part of the DoDs military readiness and the loss or degradation of such elements would reduce key advantages in communication, intelligence, and organization. Despite heavy investments in security and cyber defense, the sheer ubiquity and interconnectedness of DoD equipment leave open the possibility of intrusion through a myriad of means including advanced persistent threats (APTs). Such threats take many forms, including Trojans, back-doors in embedded systems, worms, spear-phishing, and viruses, all of which could prove detrimental to the war fighter if not discovered. As part of our work we have demonstrated several novel ideas for detecting APTs based upon modern ideas in space-time signal processing, multiple hypothesis testing, and robust principal component analysis. In particular, previous results by Numerica have proven especially pertinent to APT detection since these algorithms have been demonstrated to scale to millions of data streams, can fuse data from a variety of input types, and have quite advantageous sparsity properties for visualization and analytics.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 749.95K | Year: 2015

The proposed Phase II program aims to develop new methods to support safety testing for integration of Unmanned Aircraft Systems into the National Airspace (NAS) with a particular focus on testing the collision avoidance (CA) algorithms of a UAS Sense-and-Avoid (SAA) system. This research addresses the fundamental difficulty of verifying the performance of autonomous systems that dynamically react to the environment. In particular, this research program would develop novel methods for conducting non-parametric, closed-loop simulation testing of collision avoidance algorithms as well as other autonomous operations. The technology generates a campaign of simulation experiments that automatically adapt to the algorithms in question. The purpose of this innovation is to expose potential vulnerabilities in UAS autonomy that are generated through the interaction of autonomous UAS algorithms with other agents such as an intruding aircraft operating under ``right of way rules". This work augments both the probabilistic open-loop testing methods, where agents do not react, and closed-loop testing where agent behavior is fixed a priori.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 749.99K | Year: 2014

U.S. intelligence, reconnaissance, and surveillance (ISR) platforms employ SIGINT sensors for target platform detection, identification and location. Many receivers are narrowband and are scanned over the RF spectrum (e.g., 2-18 GHz) in search of RF emitters of interest. With the rapid proliferation of RF technology, the signal spectrum has become complex and congested placing a burden on the SIGINT receiver to keep up with its surveillance and data processing requirements. However, the receiver must maintain a high probability of intercept for critical emitters. The objective of this SBIR topic was to develop methods for efficient SIGINT receiver data collection and processing. This proposed Phase II program will develop and demonstrate a mathematical optimization algorithm that will generate a receiver frequency band scan schedule that maximizes the receiver's resource usage. The solution is developed to specifically address the intercept of modern, agile emitters. An advanced software prototype that implements the optimization algorithm will be developed. Work is planned to integrate the software system with the ALQ-217 receiver. Demonstrations using representative scenarios will be used to compare the new scan schedule optimization algorithm with a baseline to assess the performance advantages.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2015

ABSTRACT:The United States Air Force must increasingly provide supplies and munitions via airdrop to ground forces spread diffusely over large areas amid hostile enemy-held terrain. The calculation of the airdrop release point is crucial for ensuring safe and accurate delivery to designated landing sites. Wind and parafoil models have insufficient fidelity to accurately predict the landing location from a given release point. We propose a machine learning approach using Gaussian process regression to assimilate historical data of previous trajectories to more accurately learn the predicted landing locations. Having learned more accurately the mapping from release point to landing site, the optimal release point is then selected. Our approach then recalculates small adjustments to the aircraft navigation to achieve the optimal aircraft approach trajectory subject to constraints of a pre-specified flight corridor. The Gaussian process approach provides a unified holistic approach rigorous mathematical framework for probabilistic machine learning that allows the data to speak for itself while imposing very few assumptions. Moreover, we propose a Cramer-Rao bound analysis to determine if an improvement in air drop accuracy can indeed be supported by the data. BENEFIT:The proposed machine learning approach will allow the Air Force to utilize historical trajectory data to improve the accuracy of predicted airdrop landing locations. The process will ensure that supplies and munitions are delivered more accurately and with greater certainty leading to less waste and fewer payloads going astray into enemy held terrain. The approach also provides for optimal navigation updates for the delivery aircraft subject to constraints on terrain and designated safety corridors.The benefits are not restricted to military applications only the same approach will provide improved delivery of humanitarian aid and supplies throughout the world. Numericas intended commercialization is to integrate the approach into the existing Joint Program Airdrop System. Numerica will work with prime contractors and suppliers of airdrop equipment to commercialize the machine learning algorithms to improve commercial airdrop applications. Beyond airdrop applications, improved model predictions via machine learning meets an emerging need in many industries including cyber-security, air traffic safety, and civilian policing.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase II | Award Amount: 748.55K | Year: 2014

ABSTRACT: Over the past decades, the capabilities of sensors to gather data has greatly outstripped the abilities of communication links to transmit that data. Accordingly, leveraging advanced sensors requires effective techniques for data compression. In particular, modern satellites provide a vast amount of real-time information that is essential to decision makers, both military and commercial. Unfortunately, space platforms have extremely limited space, weight, and power budgets. Making the problem even more challenging, these vast data sources must be communicated over low-bandwidth communication links and the data that arrives must be trustworthy. The key novelty of our work is that our techniques achieve compression ratios that are similar to (if not better than) those achieved by standard lossy compression schemes while at the same time maintaining an a priori rigorous error bound profile for each pixel. Intuitively speaking, a prescribed error tolerance provides a small amount of freedom that may be used to improve the compression ratio. Our key innovation is the development of algorithms that take advantage of this freedom in a computationally efficient manner while guaranteeing that no error bounds are ever violated. BENEFIT: Presently, there are a number of commercial vendors that provide image compression solutions on a dedicated hardware chip. However, these implementations are not necessarily designed for space applications that have radiation hardening, and specialized data transmission requirements. The primary mission of military space-based IR sensors is to detect and track point targets, e.g., the OPIR mission. To ensure that all targets of interest (some dim) are detectable, the compression algorithm must faithfully preserve this data. Otherwise, the detection algorithm, which runs in the ground station, does not have the information that it requires to function. Thus, a unique requirement of the OPIR mission (the primary application) is that the quality of each pixel in the IR image must be preserved to a specific degree (which is determined, e.g., by the detection algorithm). Numerica differentiates its solution from these offerings by providing a unique compression algorithm that is able to guarantee an error-bound on each individual pixel. We intend to develop a plan for implementing this specialized algorithm on a space processor architecture, which will ultimately provide a superior solution to other offerings available today.


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase II | Award Amount: 1.31M | Year: 2014

ABSTRACT:In this Phase II program, Numerica will lead the transition of two software packages for resolving uncorrelated tracks (UCTs) in space, namely AFRL's Constrained Admissible Region Multiple Hypothesis Filter (CAR-MHF) and Numerica's Multiple Frame Assignment Multiple Hypothesis Tracker (MFA-MHT), to the Space Situational Awareness Laboratory and to the Distributed Space Command and Command Center at Dahlgren (DSC2-D). The work will entail (i) conversion of the CAR-MHF and MFA-MHT codebases to a production-level real-time computing language to improve runtime performance and comply with DSC2-D's software requirements; (ii) providing uninterrupted support for testing and data processing activities throughout the duration of the program and making software enhancements to improve runtime and performance based on these activities; (iii) upgrading the CAR-MHF and MFA-MHT user interfaces to allow ease-of-use by the Dahlgren operators; and (iv) providing documentation and project support including user's manuals of CAR-MHF and MFA-MHT that meet DSC2-D's documentation requirements. The envisioned end result would be the provision of two independent UCT processing tools, deployed in the operational environment of DSC2-D, that would advance the U.S.'s space superiority through improved UCT resolution and maintaining custody of all objects in space.BENEFIT:A next-generation operational system for space surveillance will need to leverage data from all sensors, especially in the GEO regime of space, to support DSC2-D (i.e., Dahlgren) and the Joint Space Operations Center (JSpOC) in providing more automated UCT processing, better prediction capabilities especially for high area-to-mass ratio objects, and ultimately a more comprehensive space catalog containing smaller, dimmer, deep-space, and hard-to-acquire objects. The two software packages that will be transitioned to DS2C-2 under this Phase II, namely CAR-MHF and MFA-MHT, would enhance military capability for UCT resolution operations in support of the GEO Odyssey program and the needs described above. A secondary transition under consideration, subject to the limitations of funding, is the third increment of the JSpOC Mission System (JMS) through the Advanced Research, Collaboration, and Application Development Environment (ARCADE).


Grant
Agency: Department of Defense | Branch: Army | Program: SBIR | Phase: Phase II | Award Amount: 729.93K | Year: 2014

In modern electronic warfare, network-centric tracking systems must be able to identify and track multiple objects within hostile, jamming environments, and then coordinate forces to take action. To meet this challenge, Numerica proposes to use Multiple Frame Assignment (MFA) and Multiple Hypothesis Tracking (MHT) methods to develop a state-of-the-art distributed tracking system that handles passive angle measurements in a network-centric context. Numerica will leverage geometric diversity, coverage area and complementary sensor information to significantly improve the track picture in the presence of advanced counter-measures. New track initiation schemes promise to address ghosting, advanced fusion and filtering algorithms offer reliable track maintenance, and alternative architectures provide superior performance in angle-only scenarios. Numerica is developing new algorithms that simultaneously assimilate low- and high-dimensional measurements in a passive tracking scenario. The algorithms combine low-dimensional and other types of degraded measurements from compromised sensors to generate a high-dimensional track picture. Numerica will develop an approach for the insertion of these algorithms into the Army IAMD Battle Command System (IBCS) Track Manager, providing a seamless, enhanced solution for the warfighter.

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