Description:TECHNICAL FIELD
[0001] The present disclosure relates to the field of integrated air defense systems. More particularly, the present disclosure pertains to optimizing weapon-target allocation with fratricide avoidance.

BACKGROUND
[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] In an integrated air defense system (IADS) planning, a first salvo attack is important. However, along with that, it is also important to keep defended assets safe, minimize interdiction cost, and avoid any fratricide. Different optimization models such as a multi-objective bilevel optimization model or Non-Dominated Sorting Genetic Algorithm (NSGA) may be employed for this purpose. However, the bilevel optimization model suffers from computational complexity and requires users to assign suitable weightage for weapons restricting elitism. With respect to NSGA, the parameters are derived using genetic algorithm and then a system is designed based on those parameters, but ideally in the weapon controller context, the parameters are pre-decided as the weapons are supplied by the manufacturer with certain performance and the user has to choose among the fixed set of solutions.
[0004] Therefore, there is a need for improved techniques for optimizing weapon-target allocation with fratricide avoidance.

OBJECTS OF THE PRESENT DISCLOSURE
[0005] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as listed below.
[0006] It is an object of the present invention to dynamically calculate a future trajectory of tracks and weapons used by extrapolation to avoid fratricide.
[0007] It is an object of the present invention to create a safe corridor around a predicted impact point (PIP) and a trajectory of missile used to avoid fratricide.
[0008] It is an object of the present invention to implement a sliding window protocol to provide impact analysis and fratricide avoidance.
[0009] It is an object of the present invention to implement a constraints-based genetic algorithm approach to reduce space and time complexity.
[0010] It is an object of the present invention to combine multiple pareto optimal fronts to create a single front to achieve a holistic solution.

SUMMARY
[0011] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0012] In one aspect certain embodiments of the present invention relates to a weapon control assistance system. The system includes a database comprising a set of parameters relating to a plurality of weapons, a track simulator module for generating possible set of tracks from each of the plurality of weapons in the database to one or more targets, a modified Non-Dominated Sorting Genetic Algorithm (NSGA) module for allocating at least one weapon for at least one target based on the generated set of tracks and a weapon controller console for displaying a weapon allocation data to an operator to deploy the at least one weapon for the at least one target. The generated set of tracks between the weapon and the target comprises a fratricide avoidance margin, wherein the fratricide avoidance margin is calculated based on a fratricide avoidance facto and the track with the fratricide avoidance margin is a friendly track. The system includes a sliding window display for displaying the weapon allocation data.
[0013] In another aspect certain embodiments of the present invention relates to a sliding window display. The sliding window display displays weapon allocation data, a time of allocation of weapon, a fratricide avoidance (FA) flag set and reset indication, and a moving pointer indicating a weapon allotment time expiration.
[0014] In one another aspect certain embodiments of the present invention relates to a method for optimized allocation of weapons. The method includes a modified Non-Dominated Sorting Genetic Algorithm (NSGA) module initializing population based on one or more constraints, performing a non-dominated sort on the initialized population, performing a crowding distance based sort on the initialized population, selecting a next generation population based on one or more constraints from the sorted population, and recombining by the modified NSGA module, the selected next generation population to select a set of data for allocating weapons. The one or more constraints comprises at least one of dynamic weapon target allocation (DWTA) constraint, capability constraint, resource feasibility constraint, engagement feasibility constraint, and fratricide avoidance.

BRIEF DESCRIPTION OF DRAWINGS
[0015] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. The diagrams are for illustration only, which thus is not a limitation of the present disclosure.
[0016] FIG. 1 illustrates an exemplary weapon target allocation (WTA) system (100), in accordance with some embodiments of the present disclosure.
[0017] FIG. 2 illustrates an exemplary data table (200) providing information related to weapon assignment solution, in accordance with some embodiments of the present disclosure.
[0018] FIG. 3 illustrates an exemplary weapon controller assistance system (300), in accordance with some embodiments of the present disclosure.
[0019] FIG. 4 illustrates an exemplary defence network (400) with fratricide avoidance along a missile trajectory, in accordance with some embodiments of the present disclosure.
[0020] FIG. 5 illustrates an exemplary graphical representation (500) of Pareto optimality between a kill time and Fratricide value, in accordance with some embodiments of the present disclosure.
[0021] FIG. 6 illustrates an exemplary tabular representation (600) of Pareto optimality between kill time and survivability, in accordance with some embodiments of the present disclosure.
[0022] FIG. 7 illustrates an exemplary flowchart (700) representing a constraint based genetic algorithm for allocation of weapons, in accordance with some embodiments of the present disclosure.
[0023] FIG. 8 illustrates an exemplary sliding window allocation (800) for weapon control, in accordance with some embodiments of the present disclosure.
[0024] FIG. 9 illustrates an exemplary computer system (900) in which or with which embodiments of the present disclosure may be implemented.
[0025] The foregoing shall be more apparent from the following more detailed description of the invention.

DETAILED DESCRIPTION
[0026] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
[0027] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[0028] The present disclosure relates to an integrated air defense system (IADS) with optimized weapon control and fratricide avoidance. In an aspect of the present disclosure, the proposed system includes a multi-objective optimization model for neutralizing the enemy target to keep the defended assets safe by creating safe corridor along the weapon trajectory. The model considers minimized cost of interdiction, increased kill probability, and fratricide avoidance for the neutralization process. The system further implements a sliding window protocol to assist a weapon controller in real time decision support.
[0029] The proposed system further implements a Non-Dominated Sorting Genetic Algorithm (NSGA) with two level filtering.
[0030] Various embodiments of the present disclosure will be explained with reference to FIGs. 1-9.
[0031] FIG. 1 illustrates an exemplary weapon target allocation (WTA) system (100), in accordance with some embodiments of the present disclosure. In FIG. 1, a set of inputs (102-1, 102-2, and 102-3), weapon target matching module (104), and an output (106) of the WTA system (100) are shown. The set of inputs (102-1, 102-2, and 102-3) comprises a real time (RT) track data (102-1), a threat list (102-2), and a ready state weapon (WPN) list (102-3), wherein the RT track data (102-1) includes at least one of Target No, Target Position, Target Speed, Target Heading, and Target Altitude, the threat list (102-2) includes one of target no. and treat value, the ready state weapon list (102-3) includes at least one of Weapon (WPN) Id, WPN Location, WPN Readiness State, and WPN Performance Data. The output (106) from the WTA system includes Estimated Kill Time, Estimated Probability Of Kill, Cost-Effectiveness Or Suitability Factor, Estimated Point Of Intercept, Estimated Trajectory Geometry, and Tactics For Operational Readiness Platform (ORP)/ Combat Air Patrol (CAP) Only. Further, a set of operations (104) are applied by the WTA system (100) on the input (102) to obtain the output (106). The set of operations (104) includes at least one of Kill Time, Kill Probability, Cost of Assignment, Suitability of Assignment, and All Weather Capability. The WTA system (100) takes input the RT track data (102-1) and the ready state weapon list (102-3) along with the threat perception value of each target track i.e. the threat list (102-2), then factors the threat index, kill time, kill probability, fratricide index, suitability and constraints into account and generates pareto optimal solutions on the basis of these indices. A Non Dominated Sorting Genetic Algorithm (NSGA) then selects the best solution from the Pareto optimal set using a constraints filtering process.
[0032] FIG. 2 illustrates an exemplary data table (200) providing information related to weapon assignment solution, in accordance with some embodiments of the present disclosure. In FIG. 2, data related to a set of weapon (WPN1, WPN2,…WPNn) a kill time Tij of engagement associated with a certain weapon for a certain treat Tj, Survivability Sij of target Ti if engaged with the weapon WPNj, Cost effectiveness Cij of engagement if weapon WPNj is assigned for Threat Ti, are shown. The data table (200) may be used as a data set for generating weapon assignment solution.
[0033] FIG. 3 illustrates an exemplary block diagram of a weapon controller assistance system (300), in accordance with some embodiments of the present disclosure. In FIG. 3, a database (302), a track simulator (304), a modified NSGA module (306), and a weapon controller console (308) are shown. The database (302) includes a set of weapon performance parameters associated with a plurality of weapons stored within. The weapon performance parameters include. The track simulator (304) simulates multiple tracks for each one of the plurality of weapons to reach one or more targets, wherein the tracks include a fratricide avoidance margin providing a friendly area around the track. The fratricide avoidance margin is based on a fratricide avoidance factor (FA). The weapon controller assistance system (300) further includes a modified NSGA module (306) for performing constraint-based NSGA. The modified NSGA module (306) allocates at least one weapon for at least one target based on the set of tracks generated by the track simulating module and one or more constraints. For example, the one or more constraints include dynamic weapon target allocation (DWTA) constraint, capability constraint, resource feasibility constraint, engagement feasibility constraint, and fratricide avoidance.
[0034] The DWTA constraint assigns weapons optimally over time increasing the diversity of constraints in dynamic mode. The DWTA constraint includes dividing the total duration of a defensive operation into several discrete time steps for obtaining information about the allocation outcomes of the previous stages. The capability constraint provides the capability of weapons for firing at multiple targets at the same time. Further, the capability constraint also considers weapons capable of engaging multiple targets simultaneously as special weapons. The resource constraint is associated with the amount of ammunition available for weapons. For example, the airbase or the defended vital area/vital points (VA/VP) must have some weapons to protect in case of dire circumstances and such situation is take care by the resource constraint factor. Engagement feasibility constraint takes into account the influence of time windows on the engagement feasibility of weapons and plays an important role in dynamic of WTA system. Fratricide avoidance factor (FA) assess the chances of damage/fatality to friendly forces while proposing weapon allocation. An FA flag (red color) provides the operator details of employment of the weapon and at the same time, forewarning the operator of possibility of damage to friendly assets.
[0035] Referring to FIG. 3, the weapon controller console (308) includes a sliding window display for displaying weapon control information. The weapon controller console (308) displays a weapon allocation data to an operator to deploy at least one weapon towards a target.
[0036] In an example embodiment, the database (302) includes an Oracle 21C database, the track simulator (304) includes QT for algorithm performance simulation and a Red Hat Enterprise Linux (RHEL) 8.4 operating system, further the system (300) includes an Intel Core I5 PC with 12 Giga bits (GB) random access memory (RAM) for Core Algorithm.
[0037] A person of ordinary skill in the art will appreciate that exemplary architecture (300) may be modular and flexible to accommodate any kind of changes in the architecture (300).
[0038] FIG. 4 illustrates an exemplary defense network (400) with fratricide avoidance along a missile trajectory, in accordance with some embodiments of the present disclosure. In FIG. 4, a missile/weapon (402) for attacking a target at a point of intercept (404) is shown. Further, in FIG. 4 a track (408) taken by the missile (402) with a fratricide margin calculation (404) considered as a friendly track is shown. Fratricide avoidance factor (FA) as discussed earlier aids in evaluating the occurrence of fratricide in a particular situation. The following table (Table 1) lists the situations that could lead to fratricide and an investigation methodology to find the possibility of fratricide in such situations.

Situation Applicability FA investigation methodology
Ground based air defence (GBAD) versus Friendly aircraft When Surface to Air Gunnery Weapon (SAGW)/authentication, authorization, and accounting (AAA) solution is possible for a threat which is under interception by scrambled ORP/CAP mission If an interceptor aircraft lies within a configured sphere around the predicted kill point, FA flag will be set. This is valid for long range weapons. For terminal weapons, the FA radius will be weapon centric. The radius of the sphere will be configurable for the weapon type. Weapons with kill range of X km (configurable) will be termed as terminal weapons.
When SAGW solution is possible for a threat and friendly tracks are in vicinity
Rules of engagement in war mode. When GBAD weapons present a valid solution against “unknown” threats. In war mode, the WA solutions against targets identified as “unknown” must be flashed with FA flag
Table-1
[0039] Based in the above Table-1, a fratricide avoidance algorithm calculates a fratricide factor value (FValue) along a missile system’s prospective trajectory (408) of FIG. 4 that is taken into consideration for the weapon allocation algorithm. Further, there may be a conflict between any two weapons in their FValue and KillTime for killing a target and such constraint is explained with reference to FIG. 5 below.
[0040] FIG. 5 illustrates an exemplary graphical representation (500) of Pareto optimality between a kill time and Fratricide value, in accordance with some embodiments of the present disclosure. In FIG. 5, a Pareto optimality graph with FValue along X-axis and Kill time alone Y-axis is shown. Further, two points 502, 504 are marked on the graph (500) associated with two weapons one with low fratricide high kill time (502) and another with high fratricide low kill time (504). Hence a selection of a right weapon depends on a set of output from the constraint filtered NSGA algorithm as will be discussed below with reference to FIG. 7.
[0041] FIG. 6 illustrates an exemplary tabular representation (600) of Pareto optimality between kill time and survivability, in accordance with some embodiments of the present disclosure. FIG. 6 show cases a trade-off between choosing a weapon which has a better kill time and the other having a better probability of killing the target. From the table (600) W1 and W2 provide a better solution for choosing a weapon with better kill time and better probability of killing a target. Combining the above 2 fronts and the other such pareto fronts using the already proven NAGA-2 algorithm along with the filtering provides a holistic solution with reduced computational complexity.
[0042] FIG. 7 illustrates an exemplary flowchart (700) representing a constraint based genetic algorithm for optimized weapon allocation, in accordance with some embodiments of the present disclosure. The method (700) starts (702) with initializing population, at step 704, based on certain constraints, performing, at step 706, a non-dominated sort on the initialized population, performing, at step 708, a crowding distance based sort on the initialized population, selecting, at step 710, a next generation population from the sorted population, wherein the selection includes filtering based on different constraints, and performing recombination at step 712 to select a set of data for performing sliding window allocation as discussed below with reference to FIG. 8.
[0043] FIG. 8 illustrates an exemplary sliding window allocation (800) for weapon control, in accordance with some embodiments of the present disclosure. In FIG. 8, graphical time line windows (802, 804, 806, 808) with allotted missiles and their names are shown. This shall enable the operator to view the allotted Weapon assignments graphically in the form of timeline, enhancing engagement decisions of the operator in taking down the target.
[0044] The graphical timeline windows (802, 804, 806, 808) further displays a time of allocation of a weapon system till the near boundary of the weapon system to the weapon controller. The target and Missile unit name shall be shown along with each allotment. For example, the graphical timeline window (802) shows the target type as LA121 and weapon name as AGR MRSAM2257, the timeline window (804) shows the target type as LA122 AGR ASM 2258-A, the timeline window (806) shows the target type as LA123 and missile type as AGR AMS 2258-B, the timeline window (808) shows the target type as LA124 GWAL SPYD 2260. The timeline window shall also suitably depict FA indication by setting flag ‘FA(S)’ flag on satisfying the FA condition and setting ‘FA(R)’ flag on removal of FA condition. Referring to FIG. 8, the timeline window (804) shows FA(s) indicating a setting condition and the timeline window (806) shows FA (R) and FA (S) showing a FA flag removal and the setting condition. A moving ticker/pointer (updated in every second) shall be shown on the timeline indicating time elapsed since allotment to assist the operator for taking action within this stipulated time window.
[0045] FIG. 9 illustrates an exemplary computer system (500) in which or with which embodiments of the present disclosure may be implemented.
[0046] As shown in FIG. 9, the computer system (900) may include an external storage device (910), a bus (920), a main memory (930), a read only memory (940), a mass storage device (950), communication port(s) (960), and a processor (970). A person skilled in the art will appreciate that the computer system (500) may include more than one processor (970) and communication port(s) (960). The processor (970) may include various modules associated with embodiments of the present disclosure. The communication port(s) (960) may be any of an RS-242 port for use with a modem based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port(s) (960) may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system connects. The memory (930) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (930) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or basic input/output system (BIOS) instructions for the processor (970). The mass storage device (950) may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), one or more optical discs, Redundant Array of Independent Disks (RAID) storage.
[0047] The bus (920) communicatively couples the processor (970) with the other memory, storage, and communication blocks. The bus (920) may be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB) or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (970) to the computer system (900).
[0048] Optionally, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to the bus (920) to support direct operator interaction with the computer system (900). Other operator and administrative interfaces may be provided through network connections connected through the communication port(s) (960). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (900) limit the scope of the present disclosure.
[0049] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE DISCLOSURE
[0050] The present disclosure provides for dynamic calculation of future trajectory of the tracks and the weapon used by extrapolation to avoid fratricide.
[0051] The present disclosure relates to creating a safe corridor around a predicted impact point (PIP) and a trajectory of missile to avoid fratricide.
[0052] The present disclosure provides a sliding window display along with the fratricide warning flag and probability of kill to guide an operator regarding a suitable time to take to take an appropriate action.
[0053] The present disclosure discusses a constraints-based genetic algorithm to filter and select the future generations after crossover and mutation reducing space and time complexity.
[0054] The present disclosure provides combining multiple pareto optimal fronts to create a single front thereby achieve a holistic solution.
, Claims:1. A weapon control assistance system (300), comprising:
a database (302) comprising a set of parameters relating to a plurality of weapons;
a track simulator module (304) for generating possible set of tracks from each of the plurality of weapons in the database to one or more targets;
a modified Non-Dominated Sorting Genetic Algorithm (NSGA) module (306) for allocating at least one weapon for at least one target based on the generated set of tracks and a set of constraints; and
a weapon controller console (308) for displaying weapon allocation data to an operator to deploy at least one weapon for the at least one target.
2. The weapon control assistance system (300) as claimed in claim 1, wherein the set of tracks between the weapon and the target comprises a fratricide avoidance margin.
3. The weapon control assistance system (300) as claimed in claim 2 wherein the fratricide avoidance margin is calculated based on a fratricide avoidance factor.
4. The weapon control assistance system (300) as claimed in claim 2 wherein the track with the fratricide avoidance margin is a friendly track.
5. The weapon control assistance system (300) as claimed in claim 1, wherein the weapon controller console (308) includes a sliding window display for displaying the weapon allocation data.
6. The weapon control assistance system (300) as claimed in claim 1, wherein the set of constraints comprises a dynamic weapon target allocation (DWTA) constraint, capability constraint, resource feasibility constraint, engagement feasibility constraint, and fratricide avoidance.
7. A sliding window display (800), comprising:
displaying:
a weapon allocation data;
a time of allocation of weapon;
a fratricide avoidance (FA) flag set and reset indication; and
a moving pointer indicating a weapon allotment time expiration.
8. A method (700) for optimized allocation of weapons, said method (700) comprising:
initializing, by a modified Non-Dominated Sorting Genetic Algorithm (NSGA) module, population (704) based on one or more constraints;
performing (706), by the modified NSGA module, a non-dominated sort on the initialized population;
performing (708), by the modified NSGA module, a crowding distance based sort on the initialized population;
selecting (710), by the modified NSGA module, a next generation population based on one or more constraints from the sorted population; and
recombining (712), by the modified NSGA module, the selected next generation population to select a set of data for allocating weapons.
9. The method as claimed in claim 8, wherein the one or more constraints comprises at least one of dynamic weapon target allocation (DWTA) constraint, capability constraint, resource feasibility constraint, engagement feasibility constraint, and fratricide avoidance.