TECHNICAL FIELD
[0001] The present invention relates to command and control systems and
a method that enables the system to automatically identify, track and engage high threat targets. The method involves the process of identifying and indexing the targets based on the threat probability calculated. It further involves improving the reaction times of the operators in situations where there is plurality of threats to be identified and engaged.
BACKGROUND
[0002] Command and control systems used for naval purposes are systems
that enable remote and manual/automatic targeting of resources against surface ships, aircrafts, and shore targets, with either optical or radar sighting. Traditionally, these command and control systems have been crew-operated with the crew providing both manual resource movement and aiming. There were many limitations imposed by such systems such as manual tracking of targets with no ballistic computer made the system inaccurate and inefficient. Further, these systems provide a range of manual modes for controlling sensors and resources that do not provide high precision in multiple target scenarios.
[0003] Additionally, few of the traditional methods for encountering threat
involved command and control systems that leave the decision making to the operators. With the increasing complexity in potential target identification and engagement, encountering threat with the available resources has become a challenging task for the operators often leading to tactical errors. The decision making of the operator is enhanced by a semi-automated or automated decision support system. These systems are traditionally part of a command and control systems where there is insufficient input to accurately identify and engage potential high threat target. These systems have shortcomings, and in particular do not address the problems of a command and control system which have insufficient inputs to accurately identify potential high threat targets.

[0004] Hence, there is a need of an invention which solves the above
defined problems and provides an effective system and method for automatically identifying, tracking and engaging high threat targets. Further, it should provide a probabilistic learning network that will help in improving accuracy of identifying and engaging high threat targets when dealing with the above-mentioned shortcomings.
SUMMARY
[0005] This summary is provided to introduce concepts related to
command and control systems and a method that enables the system to automatically identify, track and engage high threat targets. This summary is neither intended to identify essential features of the present invention nor is it intended for use in determining or limiting the scope of the present invention.
[0006] One of the various embodiments herein may include one or more
systems and methods for identifying and indexing the targets based on the threat probability of plurality of high threat targets. It further includes improving the reaction times of the operators in situations where there is plurality of threats to be identified and engaged. In one of the embodiments, the command and control system involves identification of a plurality of targets based on the threat probability of each of the plurality of targets. These threat probability values for each of the plurality of targets are calculated with the use of Bayesian Inferences. The command and control system further involve automatic high precision tracking of surface threats, local aircraft, UAVs and the like by using the threat probability values. Further, the command and control system involve engagement process that uses the threat probability values and the resource parameters for performing the automatic resource allocation and target engagement in a scenario where there is a plurality of resources and targets. Further, the present invention describes a method wherein the process of identifying, tracking and engaging a high threat target has been automated, thereby improving the operator decision making capability to quickly assess and counter threat.

[0007] Additionally, the method uses Bayesian Inferences to calculate
degree of threat of a target. Bayesian inference is particularly helpful when dealing with command and control systems that have insufficient inputs to directly infer degree of threat. The method further consists of inferring potential high threat for each of the plurality of targets from various target parameters such as target speed, target course, target height, target category and the like. It further consists of indexing of the plurality of targets based on their threat probability values. Moreover, automatic tracking of high threat targets from indexed target list is performed. Lastly, automatic resource allocation takes place to engage the potential high threat targets from the indexed target list.
[0008] Moreover, the method for inferring threat takes place by setting up
a Bayesian Network Tree which further involves initialization of prior probability values for target category, closest point of approach (CPA) and time to closest point of approach (TCPA). The target category, CPA and TCPA act as root nodes during initialization of the Bayesian Network Tree. Once initialized, the Bayesian Network provides the values for target probability, target capability and intent without evidence. Further, the tracking data of the plurality of targets, detected as high threat targets, act as an input to calculate the CPA, TCPA values and to infer the target category. These values are further used as evidence in the Bayesian Network tree for generating live target capability, intent and threat probability with evidence.
BRIEF DESCRIPTION OF ACCOMPANYING DRAWINGS
[0009] The detailed description is described with reference to the
accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to reference like features and modules.
[0010] Fig. 1 illustrates an exemplary block diagram depicting a system
for automated identification, tracking and engagement of plurality of high threat

targets, according to an exemplary implementation of the presently claimed subject matter.
[0011] Fig. 2 illustrates the steps involved in identifying, tracking and
engaging of plurality of potential high threat targets, according to an exemplary implementation of the presently claimed subject matter.
[0012] Fig 3 illustrates the initialization of Bayesian Network tree,
according to an exemplary implementation of the presently claimed subject matter.
[0013] Fig. 4 illustrates a method for inferring threat probability value,
target capability and intent from target track data using the initialized Bayesian Network tree, according to an exemplary implementation of the presently claimed subject matter.
[0014] Fig 5 illustrates a method for resource allocation for engaging
potential high threat targets, according to an exemplary implementation of the presently claimed subject matter.
[0015] It should be appreciated by those skilled in the art that any block
diagrams herein represent conceptual views of illustrative methods embodying the principles of the present disclosure. Similarly, it will be appreciated that any flow charts, flow diagrams, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.
DETAILED DESCRIPTION
[0016] The various embodiments of the present disclosure provide a
command and control system and a method that enables the system to automatically identify, track and engage high threat targets. The method involves the process of identifying and indexing targets based on their threat probability. It

further involves auto tracking and engaging targets from the indexed target list in a scenario where there are a plurality of targets and resources involved.
[0017] In the following description, for purpose of explanation, specific
details are set forth in order to provide an understanding of the present claimed subject matter. It will be apparent, however, to one skilled in the art that the present claimed subject matter may be practiced without these details. One skilled in the art will recognize that embodiments of the present claimed subject matter, some of which are described below, may be incorporated into a number of systems.
[0018] However, the systems and methods are not limited to the specific
embodiments described herein. Further, structures and devices shown in the figures are illustrative of exemplary embodiments of the presently claimed subject matter and are meant to avoid obscuring of the presently claimed subject matter.
[0019] Furthermore, connections between components and/or modules
within the figures are not intended to be limited to direct connections. Rather, these components and modules may be modified, re-formatted or otherwise changed by intermediary components and modules.
[0020] One of the various embodiments herein may include one or more
systems and methods for identifying and indexing the targets based on the threat probability of plurality of high threat targets. It further includes improving the reaction times of the operators in situations where there is plurality of threats to be identified and engaged. In one of the embodiments, the command and control system involves identification of a plurality of targets based on the threat probability of each of the plurality of targets. These threat probability values for each of the plurality of targets are calculated with the use of Bayesian Inferences. The command and control system further involve automatic high precision tracking of surface threats, local aircraft, UAVs and the like by using the threat probability values. Further, the command and control system involve engagement process that uses the threat probability values and the resource parameters for

performing the automatic resource allocation and target engagement in a scenario where there is a plurality of resources and targets. Further, the present invention describes a method wherein the process of identifying, tracking and engaging a high threat target has been automated, thereby improving the operator decision making capability to quickly assess and counter threat.
[0021] In another embodiment, the method uses Bayesian Inferences to
calculate degree of threat of a target. Bayesian inference is particularly helpful when dealing with command and control systems that have insufficient inputs to directly infer degree of threat. The method further consists of inferring potential high threat for each of the plurality of targets from various target parameters such as target speed, target course, target height, target category and the like. It further consists of indexing of the plurality of targets based on their threat probability values. Moreover, automatic tracking of high threat targets from indexed target list is performed. Lastly, automatic resource allocation takes place to engage the potential high threat targets from the indexed target list.
[0022] In another embodiment, the method for inferring threat takes place
by setting up a Bayesian Network Tree which further involves initialization of prior probability values for target category, closest point of approach (CPA) and time to closest point of approach (TCPA). The target category, CPA and TCPA act as root nodes during initialization of the Bayesian Network Tree. Once initialized, the Bayesian Network provides the values for target probability, target capability and intent without evidence. Further, the tracking data of the plurality of potential high threat targets, detected from external sources, act as an input to calculate the CPA, TCPA values and to infer the target category. These values are further used as evidence in the Bayesian Network tree for generating live target capability, intent and threat probability with evidence.
[0023] It should be noted that the description merely illustrates the
principles of the present invention. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly

described herein, embody the principles of the present invention. Furthermore, all examples recited herein are principally intended expressly to be only for explanatory purposes to help the reader in understanding the principles of the invention and the concepts contributed by the inventor to furthering the art and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass equivalents thereof.
[0024] Fig. 1 illustrates an exemplary block diagram depicting a system
for automated identification, tracking and engagement of plurality of high threat targets, according to an exemplary implementation of the presently claimed subject matter. The system 100 includes a plurality of user devices 102 (102a, 102b, 102c,… 102n), a control unit 104, a memory 106, a processor 108, a database 110.
[0025] The plurality of computing devices 102 are interconnected with the
control unit 104 via wired and wireless networks. Examples of the wired networks include, but are not limited to, a Wide Area Network (WAN) or a Local Area Network (LAN), a client-server network, a peer-to-peer network, and so forth. Examples of the wireless networks include, but are not limited to, Wi-Fi, a Global System for Mobile communications (GSM) network, and a general Packet Radio Service (GPRS) network, an enhanced data GSM environment (EDGE) network, 802.5 communication networks, Code Division Multiple Access (CDMA) networks, or Bluetooth networks. In an example, the communication network 106 may be a combination of one or more wired and/or wireless networks.
[0026] In the present implementation, the database 110 may be
implemented as, but not limited to, enterprise database, remote database, local database, and the like. Further, the database 110 may themselves be located either within the vicinity of each other or may be located at different geographic

locations. Furthermore, the database 110 may be implemented inside the system 100 and the database 110 may be implemented as a single database.
[0027] In the present implementation, the computing devices 102 include,
but are not limited to, mobile phones (for e.g. a smart phone), phablets, Personal Digital Assistants (PDAs), wearable devices (for e.g. smart watches and smart bands), tablet computers, laptops and the like.
[0028] In the present implementation, the memory 110 may be coupled to
the processor 108. The memory 106 can include any computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.
[0029] In the present implementation, the control unit 104 includes one or
more processors 108. The processor 108 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 108 is configured to fetch and execute computer-readable instructions stored in the memory 108.
[0030] Further, the system 100 includes various modules such as an
identification module 112, a determination module 114, an index module 116, a track module 118, an engagement module 120, an assignment module 122, a computation module 124 and other modules(s) 126. The identification module 112 is configured to identify a plurality of targets based on the threat probability of each of the plurality of targets, wherein the threat probability is a probabilistic value indicating the degree of threat for the identified targets. These threat probability values for each of the plurality of targets are calculated with the use of Bayesian Inferences. The determination module 114 is configured to infer threat for each of the plurality of targets from a plurality of target parameters such as

target speed, target course, target height, target category and the like. Further, in the present implementation, the index module 116 is configured to index the plurality of targets based on their threat probability values. The track module 118 is configured to automatically track plurality of high threat targets from the indexed target list with high precision. This system features options for tracking multiple targets, as well as the ability to seamlessly switch designation of the primary target. Along with advanced tracking capability, it allows effective handling of high-pressure, multiple target scenarios. The engagement module 120 is configured to engage the plurality of high threat targets from the indexed target list. The engagement module 120 further involves evaluating resource deployment alternatives and selecting the optimized solution. This is performed cyclically for immediate adaptation to rapidly-changing situations. Further, the assignment module 122 is configured to allocate a plurality of resources to the plurality of high threat targets automatically. This automatic resource allocation takes place based on threat probability and other available resource parameters.
[0031] Further, the various modules also include the other modules 126.
The other modules 126 is configured to provide any kind of operation which may be beyond the scope of the identification module 112, determination module 114, index module 116, track module 118, engagement module 120, assignment module 122, computation module 124. In this context, the other modules 126 include every other task which are not configured for the identification module 112, determination module 114, index module 116, track module 118, engagement module 120, assignment module 122, computation module 124.
[0032] In another implementation, the determination module 114 which is
configured to infer threat, wherein a Bayesian Network Tree is set up to infer threat, further includes a computation module 124 which is configured to initialize prior probability values for target category, closest point of approach (CPA) and time to closest point of approach (TCPA). The target category, CPA and TCPA act as root nodes during initialization of the Bayesian Network Tree. The computation module 124 is further configured to provide the values for target

probability, target capability and intent without evidence after the initialization is completed. Further, the tracking data of the plurality of potential high threat targets, detected from external sources, act as an input to calculate the CPA, TCPA values and to infer the target category. These values are further used as evidence in the Bayesian Network tree to dynamically calculate target capability, intent and threat for the attacking targets.
[0033] Fig. 2 illustrates the steps involved in identifying, tracking and
engaging of plurality of potential high threat targets, according to an exemplary implementation of the presently claimed subject matter. In the present implementation, the method involves the step 204 of setting up a Bayesian network to identify the plurality of potential high threat targets. Further, in the next step 206, the computation module 124 is used for acquiring the tracking data of the plurality of potential high threat targets and calculating threat probability values by using Bayesian inference. The tracking data of the targets includes data such as the target speed, target course, target height, target category and the like. This system has the ability to track any target, including supersonic missiles and surface threats, in any weather conditions over long and short distances. The system further features options for tracking multiple targets, as well as the ability to seamlessly switch designation of the primary target. Further, in the next step 208, the index module 116 is used for indexing targets based on threat probability value and tracking the plurality of targets with high threat probability value. Here, the threat probability values are calculated on the basis of heading, speed, and any other information that affects the probability value. Further, in the next step 210, the assignment module 122 is used for allocating plurality of resources automatically and engaging the plurality of high threat targets. Further, the next step 212 provides that if the auto mode of the operation is to be continued then the system goes back to the step 206 of performing the operation of acquiring the tracking data and calculating threat probability values by using Bayesian inference. If the auto mode of the operation is to be discontinued, then no further action takes place.

[0034] Fig. 3 illustrates the initialization of Bayesian Network tree,
according to an exemplary implementation of the presently claimed subject matter. Fig. 3 is an illustrative embodiment of step 204 of Fig. 2 of setting up a Bayesian network to infer the threat probability values for the plurality of targets. It further consists of a step 304 for initializing prior probability values for target category, closest point of approach (CPA) and time to closest point of approach (TCPA). The target category, CPA and TCPA act as root nodes during initialization of the Bayesian Network Tree. After initialization, the Bayesian Network provides the values for target probability, target capability and intent without evidence. Further, the tracking data of the plurality of targets, detected as high threat targets, act as an input to calculate the CPA, TCPA values and to infer the target category. At step 306, a Bayesian Network tree is generated. Further, after the generation of the Bayesian Network tree, the next step 308 includes dynamically calculating target capability, intent and threat probability values for the plurality of targets without evidence.
[0035] Fig 4 illustrates a method for inferring threat probability value,
target capability and intent from target track data using the initialized Bayesian Network tree, according to an exemplary implementation of the presently claimed subject matter. Fig. 4 is an illustrative embodiment of step 206 of Fig. 2 of acquiring the tracking data of the plurality of high threat targets and calculating threat probability values by using Bayesian inference. It further consists of a step 404 for receiving the tracking data of the plurality of high threat targets. Here, the tracking data for the plurality of targets is received from the surveillance radar or directors on board. The director is used for surveillance, and identification purposes as well as for high precision tracking of surface threats, local aircraft, unmanned aerial vehicles and the like. At step 406, the tracking data for the plurality of detected targets is an input for calculating the CPA, TCPA values and inferring the target category from the target parameters. These values are used as evidence in the Bayesian Network tree to generate live target capability, intent and threat probability with evidence. Further, in step 408, the CPA and TCPA values

along with the target category are provided as evidence to the Bayesian Network tree. In step 410, the target capability, intent and threat probability are inferred by using values of CPA, TCPA and target category.
[0036] Fig 5 illustrates a method for resource allocation for engaging
potential high threat targets, according to an exemplary implementation of the presently claimed subject matter. Fig. 5 is an illustrative embodiment of step 210 of Fig. 2 of allocating plurality of resources to the plurality of high threat targets automatically. It further consists of a step 504 for getting the indexed targets based on the threat probability value of the plurality of targets. . Further, the indexed target list is used to automatically identify and track the plurality of high threat targets. Further, at step 506, resource parameters are acquired for the available resources for performing automatic resource allocation. Further, at step 508, the resource allocation probability is calculated for each target-resource pair from the indexed target list. Here, the resource allocation and target engagement method take the target threat probability and other available resource parameters to calculate the probability of resource allocation for each target-resource pair from the indexed target list. Further, at step 510, this resource allocation probability value is further used for allocating and engaging a resource to a potential high threat target. Here, the probability of resource allocation is calculated as a weighted average of the threat probability value and the resource parameters are considered.
[0037] The foregoing description of the invention has been set merely to
illustrate the invention and is not intended to be limiting. Since modifications of the disclosed embodiments incorporating the substance of the invention may occur to person skilled in the art, the invention should be construed to include everything within the scope of the invention.

We Claim:
1. A method for operating a command and control system, said method comprising:
identifying, by an identification module (112), a plurality of potential targets based on their threat probability;
inferring, by a determination module (114), threat for each of the plurality of targets from a plurality of target parameters, wherein said inferring is done by setting up a Bayesian Network Tree;
indexing, by an index module (116), the plurality of high threat targets based on the threat probability value;
tracking, by a track module (118), the plurality of high threat targets from an indexed target list based on the threat probability values;
engaging, by an engagement module (120), the plurality of high threat targets from the indexed target list; and
allocating, by an assignment module (122), a plurality of resources to said plurality of high threat targets automatically.
2. The method as claimed in claim 1, wherein setting up the Bayesian
Network Tree further comprises:
initializing, by a computation module (124), prior probability values for target category, closest point of approach (CPA) and time to closest point of approach (TCPA); and
providing values, by the computation module (124), for threat probability, target capability and intent without evidence after said initialization.
3. The method as claimed in claim 1 further comprises:
calculating, by the computation module (124), target capability, intent and threat probability for the plurality of targets based on said values of

closest point of approach (CPA) and time to closest point of approach (TCPA) and the target category.
4. The method as claimed in claim 1, wherein the plurality of target parameters, used for inferring the threat for each of the plurality of targets, includes target speed, target course, target height, target category and the like.
5. The method as claimed in claim 1, wherein the threat probability value is calculated based on the heading, speed, and any other information that affects the probability value.
6. The method as claimed in claim 1, wherein the automatic allocation of plurality of resources further comprises calculating, by the computation module (124), the resource allocation probability for each of the plurality of resources allocated for the plurality of targets.
7. The method as claimed in claim 6, wherein the automatic allocation of plurality of resources for calculating the resource allocation probability is based on threat probability and other available resource parameters.
8. A system for operating a command and control system, said system comprising:
an identification module (112) configured to identify a plurality of potential targets based on their threat probability;
a determination module (114) configured to infer threat for each of the plurality of targets from a plurality of target parameters, wherein a Bayesian Network Tree is set up to infer the threat;
an index module (116) configured to index the plurality of high threat targets based on the threat probability value;
a track module (118) configured to track the plurality of high threat targets from an indexed target list based on the threat probability values;

an engagement module (120) configured to engage the plurality of high threat targets from the indexed target list; and
an assignment module (122) configured to allocate a plurality of resources to said plurality of high threat targets automatically.
9. The system as claimed in claim 8, wherein set up of the Bayesian Network
Tree further comprises:
a computation module (124) configured to initialize the prior probability values for target category, closest point of approach (CPA) and time to closest point of approach (TCPA); and
the computation module (124) configured to provide values for threat probability, target capability and intent without evidence after said initialization.
10. The system as claimed in claim 8 further comprises:
the computation module (124) configured to calculate target capability, intent and threat probability for the plurality of targets based on said values of closest point of approach (CPA) and time to closest point of approach (TCPA) and the target category.
11. The system as claimed in claim 8, wherein the threat probability of the plurality of targets is calculated with the use of Bayesian Inferences.
12. The system as claimed in claim 8, wherein the plurality of target parameters, used to infer the threat for each of the plurality of targets, includes target speed, target course, target height, target category and the like.
13. The system as claimed in claim 8, wherein the indexed target list is based on the threat probability value of the plurality of targets.

14. The system as claimed in claim 8, wherein the automatic allocation of plurality of resources further comprises calculation of resource allocation probability for each of the plurality of resources allocated for the plurality of targets.
15. The system as claimed in claim 14, wherein the automatic allocation of plurality of resources to calculate the resource allocation probability is based on threat probability and other available resource parameters.