Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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).

Abstract: 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.

Abstract: A global object detection server reduces the amount of time needed to determine whether an object is present in a collection of images for a geographic area. In particular, the disclosed global object detection server selects one or more object recognition algorithms from a collection of algorithms based on one or more characteristics of the object to be detected. The algorithm results may then be fed back to reduce input data sets from iterative collections for similar regions. The global object detection server can also derive stochastic probabilities for object detection accuracy. Thereafter, one or more visualizations may be created that show confidence levels for the object's probable location in the collection of images.

Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: This document discusses, among other things, apparatus and methods for context-aware mission management. In an example, a system can include a processing center and a plurality of mobile devices. The processing center can be configured to receive one or more mission objectives for one or more mission stages, and to receive information from a plurality of sources, the processing center including a data broker reasoner (DBR) configured to compare the information to the one or more mission objectives and to provide role-based task information for accomplishing the one or more mission objectives. The plurality of mobile devices can be configured to wirelessly communicate with the processing center, to receive login information, to register with the processing center using the login information and to display a portion of the role-based task information associated with the login information.

Abstract: A data broker-reasoner (DBR) system for automating mission planning and execution includes a context originating entity, a reasoner/decision entity, and a plurality of enforcement entities. The context originating entity extracts mission policy information from a variety of source materials, including checklists, manuals, and other documentation. The reasoner/decision entity interprets the mission policy in the context of situational awareness (SA) events to determine which policies should be enforced. Each of the enforcement entities is configured to enforce various policies within the context of a given mission lifecycle phase by providing commands, data, and requests to a plurality of user applications and/or other mission systems. The DBR can operate autonomously or semi-autonomously.

Abstract: The technology described herein includes a system and/or a method for data tasking and visualization of data. The method includes receiving, by a computing device, a screening policy selection from a user associated with the computing device; transmitting, by the computing device, the screening policy selection to one or more sensor platform devices; receiving, by the computing device, one or more data sets from the one or more sensor platform devices in response to the transmission of the screening policy selection; and displaying, by the computing device, the one or more data sets to the user.

Abstract: 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.

Abstract: 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 means 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.

Abstract: 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 means 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.

Abstract: 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.

Abstract: The invention features a system and method to enable real-time establishment and maintenance of a standard of operation for a data communications network. The standard is a data set which includes network activity which is historically categorized by traffic type and by activity. The process begins with monitoring the network media or some network component over some period of time. The monitoring information is used to build benchmark data sets. The benchmark data sets contain a standard of operation for the network, which are historically categorized by either traffic type or activity. This standard of operation is constantly built by the intelligent monitoring facilities. After some period of time which is referred to as the benchmark data set refresh interval, the benchmark that was created is employed in a fashion to allow a determination as to whether the data that is taken from the current monitoring activity indicates normal network behavior.