A combination of Human Machine Interface (HMI) software platform and User Experience (UX) evaluation process, Athena provides powerful tools for creating modern, usable HMI products that are tailored to the needs of the Warfighter in both operational and training situations. Using modern software technologies and a tiered, modular integration framework, we provide a cross-platform environment that allows rapid development, timing and effectiveness analysis, and rich visualization capabilities that immerse the Warfighter in the battlespace environment. Combined with a robust user experience analysis process, we iterate on displays while collecting timing data in order to provide a demonstrably effective system. The results of the system are HMIs that provide Situational Awareness coupled with Engagement Operations that are optimized to the needs of the user. HMI optimization is achieved by engineering displays that adhere to human processes rather than prescribing rigid interactions. In this fashion, the Warfighter usurps the system’s traditional role as the focal point – rather, the system serves as an assistant to the user’s decisions and processes.
BIG IO is a fast, distributed, polyglot messaging system. The system can run in a single process or across multiple instances over a network. Message communication in a single process is extremely fast with the ability to handle in excess of 4.6 million messages per second. Even across the network, the system can process approximately 300,000 messages per second.
Archarithms’ Bird’s Eye is a new generation of System of Systems (SoS) level tasking tool designed to exploit sensor availability, timelines, resources, Field-of-Regard (FOR), Field-of-View (FOV), geometry, resolution, sensitivity, and phenomenology. Bird’s Eye provides an efficient, balanced utilization of available sensor system resources for the purpose of defeating the threat with no leakage attributed to the sensor tasking function. The effect of high performance Integrated Sensor Tasking (IST) is information superiority from threat launch through impact characterization, and optimal contributions to raid Probability of Engagement Success (PES). Bird’s Eye supports LOR and EOR. Its flexibility permits exploitation of OPIR, organic space-based assets, and airborne Electro-Optical InfraRed (EOIR) sensors.
Archarithms’ Impression is an integrated Command and Control (C2) planning and training framework focusing on modular decoupled agent-oriented design utilizing a distributed communication system permitting flexible information formats, information separation and dynamic transportation. Impression's software architecture provides sufficient abstraction to permit flexible reuse of agents between various applications and a communication platform capable of accommodating such flexible agent and information design.
Impression’s architecture implements a, Modular Defense Planner (MDP) as a group of agents implementing representative C2 planner and trainer capabilities. The trainer leverages as many of the C2 planner capabilities as possible, reducing the number of required agents. The agents communicate with each other using two separate but complementary software tools: BigIO and Redis. BigIO, an Archarithms developed big data messaging middleware, is used to share transient information such as tracks or resource location between agents. Redis is used to share persistent, generally large information (e.g. a complete training scenario) between the agents. Impression's architecture and design are easily expanded to include capabilities provided by any set of adept agents. Further, the communication tools utilized are information format agnostic and can be readily adapted to multiple domains.
Archarithms’ PUMA mitigates missile performance degradation in challenging raid environments. PUMA combines proven penalty-based methods of characterizing potential engagement solutions with an innovative, finite horizon optimization assignment algorithm. The penalty-based approach quantifies contributors to Probability of Kill (PKill) and Probability of Engagement Success (PES) for concerns such as potential intercept flash, additional interceptors within the Field of View (FOV), closing speed, and others. Penalties are tailored to relevant aspects of the weapon system, and can be updated to reflect technology improvements without re-building software. The penalty-based approach is suited to handle one-on-one and many-on-many engagements, Fire Control (FC) from a single platform, and Battle Management (BM) from multiple platforms. Identical techniques can be applied to multiple weapon and threat types. For example, a platform with multiple Missile types capable of engaging ballistic missiles and air breathers can use the same fire control with penalties tailored to the specific weapon and threat types. Once viable engagement solutions for all weapon-threat pairs are scored, an enhancement assignment algorithm is used to select the optimal engagement(s) over the next time horizon. The algorithm accounts for finite resource availability (sensor, launcher, communications), assessed threat lethality, and each potential engagement’s score (penalty). PUMA maximizes the threat lethality negated and permits flexible firing doctrine to include N-on-1 engagements. In-progress and previously planned engagements are accounted for during each time horizon. Launch-On-Remote (LOR) and Engage-On-Remote (EOR) constructs are supported.
Using a high fidelity large raid simulation PUMA significantly reduced the chance an interceptor would encounter stray radiation due to flash, diverting kill vehicles, or celestial sources. The SSEKP was not degraded while using the advanced scheduling penalties despite having numerous candidate engagements with lower penalty (score) values being assigned resulting from predictions that the interceptor would be susceptible to interference from stray sources of infrared radiation. PUMA was able to identify and select candidate engagements to mitigate exposure to harmful stray radiation while maintaining battle space coverage. PUMA achieved an overall 72% reduction in the number of scheduled engagements where an Interceptor would be exposed to stray infrared radiation due to flash,other interceptor diverts, or celestial objects.
Archarithms’ STADIUM is a comprehensive, coordinated Sensor Resource Manager that includes emplacement planning, search planning and automated distributed sensor tasking, all of which adapts in real time to counter the threat.
STADIUM’s encompasses three capabilities: A planner (planning phase), real-time distributed tasking capability (execution phase), and a mechanism for adapting to scenario dynamics (adjustment phase).
If a sensor is out of position, its ability to assist with tracking and search during the course of a scenario is diminished. As a result, inferior planning can be seen as effectively reducing the total available resources of the force structure right from the outset. Therefore, in the planning phase, STADIUM provides optimization of the placement and initial search assignments of a set of distributed sensors.
The generated plan places the sensors in the best possible position for successfully handling likely raid scenarios based on the combined resources of the force structure and the available a priori intelligence about the adversary’s most likely courses of action.
In the execution phase, STADIUM provides real-time maximization of the overall resources within the provided plan. That is, given the prioritization of the threats and search space and the layout of the available sensors, STADIUM seeks to reduce the overall resource usage by optimally distributing the combined task load across the entire force, in real-time, as the scenario unfolds. STADIUM provides a highly scalable and efficient solution for optimal sensor tasking. In addition, because the approach is decentralized, the loss of any single sensor or platform will not disrupt the tasking capabilities of the remaining force, which provides significant robustness and allows for graceful degradation.
While the planning phase places the force structure in the best possible position with respect to the available a priori information, any planning approach that assumes the adversary will behave exactly as expected is naïve, and will rarely provide truly optimal performance. Therefore, during the adjustment phase (concurrent with execution), STADIUM updates the plan to reflect the adversary’s observed behavior and improves overall performance real time. In this way, the accuracy of the scenario picture is continually improving based on the most recent sensor data. Together, the three phases of STADIUM place the warfighter in the most advantageous position, affording the most efficient response, and provides the most accurate picture.
Archarithms’ VAPOR is an advanced, open-architecture, System-On Module (SOM) - capable software suite that accepts diverse sensor inputs, produces fire control quality target states, and interfaces seamlessly with disparate weapon fire control systems. VAPOR enhances the capability of close-range fire control and weapon systems by providing an open-architecture solution that accepts diverse sensor measurements, translates measurements to a common protocol, performs effective correlation and fusion, automatically prioritizes threats (including man-in-the-loop input), and provides standardized outputs to legacy and future fire control computers. VAPOR provides automated evaluation of solution efficacy given weapon dispersion qualities. VAPOR is applicable for platforms including hand-held, indirect fire, and vehicle-mounted weaponry.
VAPOR addresses the fundamental challenges of open-architecture system by exploiting:
1. A low-latency, real-time architecture that facilitates auto-discovery and component registration of all contributing sensors.
2. A library of interface translation routines to connect incoming sensor measurements with the SOM internal functions.
3. An advanced track management function that performs smoothing, correlation, triangulation and filtering for precision target state estimation.
4. An advanced threat prioritization scheme that accepts user inputs and uses decision-level logic for ranking threats.
5. A MOSA based Framework that provides simulation and field testing capabilities in order to evaluate the effectiveness of target localization for multiple fire control systems.
WISH: Create a wish list of all the things you wish you had and assign the wishes to special events (birthday, Christmas, anniversary, …) SHARE: Share it, tweet it, email it, pin it. Let everyone know about your wish list, if they don’t know what your are wishing for then how will they help make your wishes come true? GIVE: Help your friends and family make their wishes come true. Contribute as much as you like to a wish or multiple wishes!
Archarithms’ Acquire is an analysis tool capable of supporting Combatant Command (COCOM) Course of Action (COA) and “what if” analyses. Acquire integrates defense design analysis, “what if” analysis, COA development and demonstrated COA effectiveness into a single state of the art system. Acquire seamlessly supports growth paths from Ballistic Missile Defense (BMD) to Integrated Air and Missile Defense (IAMD), space, intelligence and reconnaissance, cyber, and combinations of these domains. This Archarithms’ innovative solution is a flexible, extensible, comprehensive C2 COA analysis and planning tool based on a Service Oriented Architecture (SOA) that allows users to automatically specify complex threat scenarios, integrated or stove- piped Blue / Coalition force capabilities, degradation of resources, different shot doctrines, varying Battle Management (BM) techniques and tuned communications performance. COAs are recommended based on defense design Figures of Merit (FOMs), detailed metrics comparisons, Defended Area (DA) and Launch Area Denied (LAD) analyses, visual gap analysis and Monte Carlo simulation analysis.
Acquire is composed of three components: Planning and Optimization, Execution, and Analysis. In Planning and Optimization, users develop Red and Blue/Coalition designs and scenarios, evaluate the design, optimize the Blue/Coalition lay downs, and refine the designs for logistics considerations. During Execution, users define or parameterize threat scenarios and execute single or multiple simulation runs for performance evaluation. The Analysis capability contributes to both planning and optimization and execution phases. A variety of metrics are automatically computed and presented to the user via the GUI.
Archarithms’ SPECTRE virtualizes test process and makes it easy to characterize system performance against a wide array of targets and scenarios. SPECTRE utilizes a scene modification system that ingests real-world data and generates an artificial scene for purposes of testing. The system consists of a series of single-board computers (SBCs) that house specific scene generator functions, such as physics modeling, communications, and spectral and aural signatures. The units come together into a “stack” with a small form factor.
Chrysalis is a fully autonomous UAS based situational awareness system. The UAS, in its idle state, resides in an environmental enclosure mounted to the owners property. When activated, either through predefined flight times or external triggering systems, the environmental enclosure opens and the UAS deploys to its pre-defined flight plan. Throughout its flight the UAS utilizes a 3-axis gimbal and HD camera to observe the owners property. Running a comparative analysis of video captured during the predefined flight routine to a control video, any changes to the property scene will trigger a text to owner containing an image of the change. Upon completion of the pre-defined flight path, the UAS will safely return to the environmental enclosure for data storage and battery charging.
Archarithms' Deep Learning Phathom computer is designed specifically for Deep Learning applications. Phathom uses CUDA capable General Purpose Graphics Processing Units (GPGPU) to exploit the parallel nature of Deep Learning architectures, providing significantly greater performance. Phathom also provides data redundancy and SSD caching to mitigate the common bottleneck of data storage. Phathom's specifications are as follows:
GPU computing tools
Archarithms’ Insight is a Course of Action (COA) recommender that incorporates large quantities of human generated data, identifies relevant features of the data, reasons on the data to produce actionable Course of Action (COA) recommendations and dynamically learns as new information becomes available.
Insight’s design addresses the four fundamental challenges of COA recommendation using a combination of
In 2014, Insight was was trained using over 1 million example “Hold-em” poker hands. Insight's fully trained DBN was able to mimic the decisions of a modeled player who won the 2014 Annual Computer Poker Competition with an accuracy of 99.9976% over 250,000 test hands.