Raytheon Company

Tellabs Operations, Inc.

Paul Christian Hershey

John J Williams

Mu-Cheng Wang

David J Wisniewski

Robert E Dehnert, Jr

Marilyn Winklareth Zett

Jeffrey A Noel

Charles W K Gritton

Marcus Alton Teter

Michael P Sica

Charles Wk Gritton

Geoffrey Guisewite

Steve Davidson

Thomas P Deardorff

John N Carbone

Christopher J Graham

Steven Alan Davidson

Brianne R Hoppes

Carlton E Nance

Danielle J Marchione

Brianne Rene-Martinek Hoppes

Vikram A Prasad

Roland Dige Chang

Andrew Arnold

Jeanne M Robinson

John Zolper, Jr

Richard Radcliffe

Leslie A Ledda

Christopher L Kuhn

Joseph O Chapa

Michael Angelo Cianciosi, Ii

Elizabeth Umberger

Michael L Carroll

Techniques for multi-domain systems integration and evaluation are disclosed, including: obtaining criteria associated with a strategic objective; determining that a first system, operating in a first operating domain, is only partially capable of satisfying a first criterion in the criteria; determining that a second system, operating in a second operating domain that is different from the first operating domain, is capable of augmenting the first system with respect to satisfying the first criterion; responsive to determining that the second system is capable of augmenting the first system with respect to satisfying the first criterion, modeling a multi-domain system including at least a first component from the first system and at least a second component from the second system; and generating a performance metric that objectively evaluates capabilities of the multi-domain system against the criteria associated with the strategic objective.

Multi-domain systems integration and evaluation

Discussed generally are techniques for managing operation of programs in a sequential order. A method can include receiving a query for an image, the query indicating characteristics of the image, selecting a chain of algorithms configured to identify the image based on the characteristics, operating an algorithm of the selected chain of algorithms that operate in increased fidelity order on an input to produce a first result, operating a ground truth algorithm on the input to generate a second result, comparing the first and second results to determine a probability of correctness (Pc) and confidence interval (CI) for the algorithm, and altering the chain of algorithms based on the determined Pc and CI.

Object recognition and detection using reinforcement learning

Discussed herein are devices, systems, and methods for autonomous, dynamic course of action (COA) generation and management. A method can include issuing a communication to one or more assets indicating operations of a first COA to be performed, receiving, by an intelligence, surveillance, and reconnaissance (ISR) device, data indicating an unexpected event, not accounted for in the first COA, has occurred, in response to the data indicating the unexpected event, identifying a second COA or a portion of a second COA that satisfies a mission of the first COA and accounts for the unexpected event, and issuing a second communication to the one or more assets indicating one or more operations of the second COA to be performed.

Autonomous correction of course of action

An automated system and method provides for planning and response to disparate threats with combined kinetic (e.g., missile interceptor) and non-kinetic (e.g., cyber and electronic warfare) effects. The threat and effects inputs and the results of analysis tools that individually apply a single effect type to negate a threat vulnerability are normalized to a form and format ingestible by a Stochastic Math Model (SMM). The normalized inputs reflect the success/failure of the effect versus a threat vulnerability as a score, not a probability. The normalized results decompose a probability of defeat (Pdefeat) for an individual effect as a set of hierarchical parameters represented as probabilities. The SMM and analysis tools are iteratively run on the normalized inputs and results data, including either organically or synthetically injected variability of the data, to compute and visualize a Pdefeat with an associated confidence interval (CI) for each effect and the combined effects at each stage of the threat kill chain.

System and method for integrated and synchronized planning and response to defeat disparate threats over the threat kill chain with combined cyber, electronic warfare and kinetic effects

A system and method improves the probability of correctly detecting an object from a collection of source data and reduces the processing load. A plurality of algorithms for a given data type are selected and ordered based on a cumulative trained probability of correctness (Pc) that each of the algorithms, which are processed in a chain and conditioned upon the result of the preceding algorithms, produce a correct result and a processing. The algorithms cull the source data to pass forward a reduced subset of source data in which the conditional probability of detecting the object is higher than the a priori probability of the algorithm detecting that same object. The Pc and its confidence interval is suitably computed and displayed for each algorithm and the chain and the final object detection.

System and method of chaining algorithms for global object recognition to improve probability of correctness and reduce processing load

Embodiments of a method and apparatus for eliminating a missile threat are generally described herein. In some embodiments, the method includes identifying a vulnerability associated with the missile threat. The method can further include identifying a technique for exploiting the vulnerability to generate a vulnerability-technique (VT) pair. The method can further include applying a stochastic mathematical model (SMM) to generate a negation value, the negation value being representative of a probability that the technique of the respective VT pair will eliminate the threat by exploiting the vulnerability. The method can further include providing a recommendation for implementation the technique to eliminate the missile threat responsive to receiving a user selection of the technique, the user selection being selected based on the generated negation value. Other example methods, systems, and apparatuses are described.

Methods and apparatuses for eliminating a missile threat

A method of fusing sensor detection probabilities. The fusing of detection probabilities may allow a first force to detect an imminent threat from a second force, with enough time to counter the threat. The detection probabilities may include accuracy probability of one or more sensors and an available time probability of the one or more sensors. The detection probabilities allow a determination of accuracy of intelligence gathered by each of the sensors. Also, the detection probabilities allow a determination of a probable benefit of an additional platform, sensor, or processing method. The detection probabilities allow a system or mission analyst to quickly decompose a problem space and build a detailed analysis of a scenario under different conditions including technology and environmental factors.

Process of probabilistic multi-source multi-INT fusion benefit analysis

Embodiments of an apparatus and method for assessing non-kinetic weapon performance for negating missile threats are generally described herein. In some embodiments, vulnerabilities of missile threats and techniques for negating the threats are identified. A probability of negation associated with an effectiveness of each of the techniques against the vulnerabilities is calculated. The calculated probability of negation of each technique against each vulnerability are conditioned at a plurality of times associated with a plurality of asymmetric missile defense (AMD) layer elements to produce temporal level probabilities of negation. Each temporal level probabilities of negation are conditioned based on a probability of validation of deployment and a probability of verification of mitigation to produce a battle damage assessment probability of negation. A terminal phase probability of impact failure without any intervention is calculated by combining the battle damage assessment probability of negation for each of plurality of AMD layers.

Method for non-kinetic performance assessment

A method for accurately determining whether a response tool will be effective for responding to a given enemy threat object. Embodiments described herein provide a method and system for responding to a threat object, for example, negating missile threats. Embodiments may include validating effectiveness of a response to the threat object. Other embodiments may include verifying the continued effectiveness of a response to the threat object. Further embodiments may include providing feedback to re-perform the method for responding to the threat object. The system may include a mathematical method, and associated algorithms, to assess, in an automated fashion, the performance of non-kinetic techniques with respect to negating the threat object.

System and method for asymmetric missile defense

A method of rapidly producing a new cyber response tool (e.g., in near-real-time) by matching vulnerabilities of enemy threats (e.g., a missile and/or a tank) to corresponding portions of other response tools that effectively exploit the matched vulnerability. An iterative framework may be utilized to repeatedly prioritize a set of cyber response tools based on a corresponding probability of success. For example, a computer or computer network may implement the iterative framework to carry out the probability computation and corresponding cyber response tool prioritization. If a total probability of success is below a given threshold (e.g., 95%), then creation of one or more new cyber response tools may be initiated. The probability of success may be a function of time (e.g., ten minutes before an expected launch) and/or a function of a phase of a lifecycle of the enemy threat (e.g., a launch phase).

Digital weapons factory and digital operations center for producing, deploying, assessing, and managing digital defects

In one aspect, a method includes receiving, at a first node in a network, a resource reservation request from a second node in the network, determining, at the first node, if there is another node in the network that can be used to reach a destination and meet the resource reservation request and notifying the second node a result of the determining.

Resource allocating in a network

The technology described herein includes a system and/or a method for policy-based data management. The method includes receiving, by a sensor platform device, sensor data from one or more sensors; selecting, by the sensor platform device, one or more screening policies from a plurality of screening policies based on one or more mission parameters and a platform type associated with the sensor platform device; generating, by the sensor platform device, a data set from the sensor data based on the selected one or more screening policies; and transmitting, by the sensor platform device, the data set to one or more computing devices.

Policy based data management and imaging chipping

A system and associated method for the synchronization and control of multiplexed payloads over a telecommunications network wherein the asynchronous timing relationships between multiplexed payloads having varied points of origin are retained subsequent to signal processing of the payloads for further transmission to a destination point. System modulesinclude a network interface sectiona synchronization, multiplexing and control (SMC) sectionand a processing sectionThe SMC sectionincludes network interface bus circuitry, payload segmentation and re-assembly circuitry, control and management memory and related circuitry, payload re-assembly circuitry, and processor bus interface circuitry. The processing section of moduleprovides means for data compression, echo cancellation, error correction coding, or voice and data encryption/decryption. The moduleis dynamically configured through a software management and control interface. The software permits dynamic loading of modulecontrol logic and provides inband interpretation of performance statistics. Differing sets of control parameters are supplied to the moduleas dictated by the interpretation of network performance parameters, or through operator supplied modifications. Operator modifications are preferably facilitated through an attached GUI (Graphical User Interface) and associated input devices such as a keyboard and/or mouse.

System and associated method for the synchronization and control of multiplexed payloads over a telecommunications network

A system and associated method for the synchronization and control of multiplexed payloads over a telecommunications network wherein the asynchronous timing relationships between multiplexed payloads having varied points of origin are retained subsequent to signal processing of the payloads for further transmission to a destination point. System modules 22 include a network interface section 30, a synchronization, multiplexing and control (SMC) section 50, and a processing section 110. The SMC section 50 includes network interface bus circuitry, payload segmentation and re-assembly circuitry, control and management memory and related circuitry, payload re-assembly circuitry, and processor bus interface circuitry. The processing section of module 22 provides device for data compression, echo cancellation, error correction coding, or voice and data encryption/decryption. The module 22 is dynamically configured through a software management and control interface. The software permits dynamic loading of module 22 control logic and provides inband interpretation of performance statistics. Differing sets of control parameters are supplied to the module 22 as dictated by the interpretation of network performance parameters, or through operator supplied modifications. Operator modifications are preferably facilitated through an attached GUI (Graphical User Interface) and associated input devices such as a keyboard and/or mouse.

Growing community of inventors

Ashburn, VA, United States of America

Paul Christian Hershey