Description:TECHNICAL FIELD
[001] The present disclosure relates to unmanned aerial vehicles and in particular, to a system and a methodology, to neutralize and take complete control of the flying threat like UAV or drone, by spoofing the co-ordinates seen by the threat object with the intended confusing co-ordinates.

BACKGROUND
[002] The 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.
[003] Drone threats that manoeuvre with Global Navigation Satellite Systems (GNSS) assistance in prohibited territories needs to be neutralized. A method that overcomes time delay issues and neutralizes such threats by positioning them at pre-defined location is discussed with complete timing closure loops for effective and real time implementation.
[004] Drone attacks have become a reality in the recent times and to neutralize such threats it is imperative to develop low cost counter measure systems. With low cost Software Defined Radio based hardware being available, building these kinds of GNSS spoofer based deterring system has become reality which can be mounted onto drone for effective utilization. A spoofer system is designed for both covert and overt operations. Overt spoofing attacks are easy to detect whereas covert spoofing attacks are difficult to be detected by the mobile threat and hence form a critical technology component of mobile threat counter-measure systems.
[005] Mounting covert spoofing systems on flying objects like drone require more sophistication in hardware as well as software. In tactical applications, covert spoofing attacks are more important because the receiver threat targets generally carry hazardous weapons. So, these receivers’ targets are to be spoofed to intended safe location without their knowledge and are to be guided for safe neutralization.
[006] In recent times, low cost drones are used for carrying deadly attacks against adversary. Existing deterrent or neutralizing systems for these mobile threats are very costly while building counter-measure systems.
[007] With the advent of low-cost software defined radios, the GNSS spoofing systems have become readily available hence, these systems form a formidable line of defense especially in defense electronic counter-measure systems. These systems can be considered to be a part of first line of defense and existing costly deterring systems can always form the second line of defense.
[008] Published US Patent application US 2011/0109506 A1 discloses a method and apparatus for simulating GNSS radio- frequency signals that are carrier-phase and code-phase aligned with ambient GNSS signal at user-specified location in the vicinity of the simulator. Such phase aligned synthesized signals are substantially the same as the authentic signals for a target receiver, allowing the target receiver to transition seamlessly between authentic and simulated signal.
[009] Further, published US patent US2015/0226858 A1 discusses a method and computer program for detecting spoofing of a navigation device. Plurality of anti-spoofing techniques is provided. The plurality of anti- spoofing techniques detects interference with data provided by one or more navigation devices for a plurality of threat situations. Positioning, timing and frequency characteristics associated with the one or more navigation devices are analyzed in order to identify a threat situation among the plurality of threat situations. Based on the Publication Classification identified threat situation one or more of the anti-spoofing techniques are executed. The one or more anti- spoofing techniques are executed in parallel in order to provide various anti-spoofing detection techniques at the same time.
[0010] US application US007952519B1 discloses methods and systems for detecting GNSS signals originating from an inauthentic source. A synthetic array using a receiver antenna which is randomly spatially translated may be used to gather alleged GNSS signals. The signals are then processed to determine the spatial correlation between them. A high spatial correlation between the signals indicates a probable inauthentic source for the GNSS signals.
[0011] For covert spoofing of the receiver target, challenges do exist in reliably estimating the GNSS antenna location of target as the threat may be equipped with anti-spoofing mechanism as per prior art discussed above. Hence a particular target detected needs to be dedicatedly assigned a spoofer system which estimates the inertial model of the complete flight stability of the mobile threat.
[0012] Hence, there is a need for a system for spoofing the vision of the drones and a method for remotely neutralizing multiple drone targets by spoofing the vision of the drones that may be implemented at a large scale as a first line of defence against incumbent threats.

OBJECTS OF THE PRESENT DISCLOSURE
[0013] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0014] It is the principal object of the present disclosure to provide an system and methodology for neutralizing multiple drone targets.
[0015] It is an object of the present disclosure to provide a remote defense system around a pre-determined area.
[0016] It is a further object of the present disclosure is to provide a system and a method for neutralizing multiple drone targets.
[0017] It is yet another object of the present disclosure to spoof the target co-ordinates of any incumbent drones by deploying a plurality of drones to create multiple co-ordinates for the incumbent unmanned aerial vehicles.
[0018] It is yet another object of the present disclosure to overcome jitter and time delay in unmanned aerial response systems.
[0019] It is yet another object of the present disclosure to provide a system for securing an area from aerial threats.
[0020] These and other objects of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings.
SUMMARY
[0021] The present disclosure relates to unmanned aerial vehicles and in particular, to a system and a methodology, to neutralize and take complete control of the flying threat like UAV or drone, by spoofing the co-ordinates seen by the threat object with the intended confusing co-ordinates.
[0022] In an embodiment, covertly spoofing the co-ordinates seen by the target, whose position is detected by RADAR/sensor System and passed on to a ground station Radio which it communicates with a network of radios, is presented. Present innovation discusses unique spoofing methodology residing in the hardware mounted on swarm of drones housing both radio and receiver spoofer systems. Jitter and time delay management for take-over of threat to position it in defined location by the spoofer system installed on drone, which dissociates itself from the network for the mission and re-join after mission are disclosed.
[0023] The present innovation relates to a methodology, to neutralize and take complete control of the flying threat like UAV or drone, by spoofing the co-ordinates seen by the threat object with the intended confusing co-ordinates.
[0024] In an embodiment, one such concept is presented wherein the spoofer systems is mounted on to drone; and many such drones may be flying in a swarm to protect the territory from attack of adversarial drone threats which are unmanned and flying with GNSS navigational aids. Also, drones referred to may be carrying software defined radios along with GNSS spoofing systems. All the radios installed on drones form a mesh network which is adaptive in nature, where in they associate themselves within the detectable RF sensitivity ranges and as well as dissociate from the swarm based on the requirement of mission.
[0025] In an embodiment, a swarm of drones carrying both spoofer and radio will be communicating with ground station Radio which broadcast co-ordinates of the threat that is detected and whose co-ordinates are estimated by a RADAR/sensor device.
[0026] The proposed novel method creates timing architecture to maintain closed loop with less delay, from the time RADAR/sensor detects the threat and transmits to swarm of drones through a ground based radio, after which one of the drones from the swarm goes into mission mode, neutralizes the threat and places it at the intended location and till the time receiver spoofer mesh node joins back the swarm network after completing mission.
[0027] An aspect of the present disclosure pertains to a system for neutralizing multiple drone targets, the system comprising: a plurality of drones communicatively coupled with a TDMA based mesh network and operatively configured with a centralized server, said drones being communicatively coupled to form a spoofer mesh, the plurality of drones being in operative coupling with a processor, the processor coupled with a memory storing instructions which when executed enable the processor to: detect by a radar, within a predefined area, an airborne threat and generate co-ordinates of the airborne threat within the pre-defined area, receive by a trajectory modeling engine, the generated co-ordinates of the airborne threat, transmit by an antenna of the radar co-ordinates of the airborne threat to the spoofer mesh created by the plurality of drones, identify closest receiver spoofer drone to the airborne threat, neutralize the airborne threat by intercepting and spoofing the co-ordinates of the airborne threat.
[0028] In an embodiment, the trajectory modeling engine estimates a trajectory of the airborne threat, said estimate of a trajectory of the detected threat being generated as a three-Dimensional coordinate location vector of the airborne threat.
[0029] In yet another embodiment, the trajectory modeling engine being further configured to generate a 9-Dimensional Inertial Measurement Unit model of the airborne threat that is transmitted by the radio in its respective time slot of transmission in Time Division Multiple Access (TDMA) super frame to the cluster of receiver spoofer nodes.
[0030] In an embodiment, the identified closest receiver spoofer node engages with said airborne threat establishes complete control over threat, continues the mission for threat neutralization by estimating IMU model directly and returns to the receiver spoofer nodes mesh.
[0031] Another aspect of the present disclosure pertains to a method for neutralizing multiple mobile threats, the method being performed by a processor operatively coupled with a radar, a radio, said radar being communicatively coupled with a plurality of drones, said plurality of drones forming a cluster of receiver spoofer nodes and a memory operatively coupled with a server, the method comprising: detecting by the radar location of an airborne threat within a pre-defined area and generating a time-stamp for the threat location; modeling a trajectory of the detected threat by the trajectory modeling engine to estimate a trajectory of the airborne threat; generating a three-Dimensional coordinate location vector of the airborne threat and generate a 9-Dimensional Inertial Measurement Unit model of the airborne threat that is transmitted by the radio in its respective time slot of transmission in the Time Division Multiple Access super frame to the cluster of receiver spoofer nodes; identifying the receiver spoofer node closest to the airborne threat to engage with the airborne threat; engaging the receiver spoofer node to estimate a time delay for the generated Inertial Measurement Unit model of the airborne threat; offsetting the delay to calculate an actual location of the airborne threat and time delay for the receiver spoofer node to reach the airborne threat.
[0032] In an embodiment, the method comprises computing a signal propagation delay time from a GNSS transmission spoofer antenna of the engaged receiver spoofer node to GNSS receiver antenna of the airborne threat.
[0033] In an embodiment, the method comprises measuring time delay from GNSS receiver antenna of engaged node to GNSS transmit antenna of engaged node during calibration.
[0034] In an embodiment, the 3-Dimensional position displacement vector for the airborne threat’s location is estimated by receiver spoofer node based on threat’s actual trajectory, intended spoofed trajectory and IMU state model of the threat.
[0035] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing FIG.s in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0036] 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. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that invention of such drawings includes the invention of electrical components, electronic components or circuitry commonly used to implement such components.
[0037] The nature and scope of the present invention will be better understood from the accompanying drawings, which are by way of illustration of a preferred embodiment and not by way of any sort of limitation. In the accompanying drawings: -
[0038] FIG. 1 is a network diagram of the system for neutralizing multiple drone targets in accordance with an embodiment of the present disclosure.
[0039] FIG. 2 illustrates exemplary functional components (200) of a processing unit (102) of the proposed system for neutralizing multiple drone targets, in accordance with an embodiment of the present disclosure.
[0040] FIG. 3 is the overview of the receiver spoofer node mesh of an exemplary system for neutralizing multiple drone targets in accordance with an embodiment of the present disclosure.
[0041] FIG. 4 illustrates a network diagram illustrating details of one such GNSS receiver spoofer mesh node of the system in accordance with an embodiment of the present disclosure.
[0042] FIG. 5 illustrates an exemplary computer system (500) to implement functionalities of the proposed system (100) for neutralizing multiple drone targets, in accordance with embodiments of the present disclosure.
[0043] FIG. 6 illustrates the detection of multiple threats within the pre-defined area and response by the closest receiver spoofer mesh node to provide co-ordinates in accordance with an embodiment of the present disclosure.
[0044] FIG. 7 is a flowchart illustrating the sequence of steps in the method for neutralizing multiple drone targets in an embodiment of the present disclosure.
[0045] The foregoing shall be more apparent from the following more detailed description of the invention.

DETAILED DESCRIPTION
[0046] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0047] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the invention as set forth.
[0048] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
[0049] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0050] Embodiments of the present invention include various steps, which will be described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, firmware and/or by human operators.
[0051] Embodiments of the present invention may be provided as a computer program product or mobile application, which may include a machine-readable storage medium tangibly embodying thereon instructions, which may be used to program a computer (or other electronic devices) or mobile devices, to perform a process. The machine-readable medium may include, but is not limited to, fixed (hard) drives, magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions (e.g., computer programming code, such as software or firmware).
[0052] Various methods described herein may be practiced by combining one or more machine-readable storage media containing the code according to the present invention with appropriate standard computer or mobile hardware, along with a computer application or Android or IOs application, to execute the code contained therein. An system for practicing various embodiments of the present invention may involve one or more computers (or one or more processors within a single computer) and storage systems containing or having network access to computer program(s) coded in accordance with various methods described herein, and the method steps of the invention could be accomplished by modules, routines, subroutines, or subparts of a computer program product.
[0053] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive—in a manner similar to the term “comprising” as an open transition word—without precluding any additional or other elements.
[0054] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0055] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0056] In all the figures, like reference numerals represent like features. Further, when in the following it is referred to “top portion”, “left inside”, “right inside”, “upward”, “downward”, “above” or “below”, “front”, “rear” and similar terms, this is strictly referring to an orientation with reference to the system, where the base of the system is horizontal and is at the bottom portion of the figures and the portion facing the reader in the diagrams is the front of the system. Further, the number of components shown is exemplary and not restrictive and it is within the scope of the invention to vary the shape and size of the system as well as the number of its components, without departing from the principle of the present invention.
[0057] While describing a particular figure, certain features have been also referred which are shown in some other figure. For the sake of convenience, the figure number is given for such features for understanding. Further, at the beginning only the structure of the system is explained with reference to each figure. Thereafter, the functioning is explained separately and for that purpose the figures are again referred to highlighting on the functional part.
[0058] FIG. 1 is a network diagram of the system for neutralizing multiple drone targets in accordance with an embodiment of the present disclosure.
[0059] In an embodiment, the system for neutralizing multiple drone targets (100) (interchangeably known as the system (100), hereinafter) may include one or more drones (104-1, 104-2,…,104-N) (collectively referred to as receiver spoofer mesh network (104), and individually referred to as receiver spoofer mesh node (104), herein) communicatively coupled to one or more servers (112) and a radar network (108). The system (100) may further include a processing unit (102) communicatively coupled to the one or more drones (104), the one or more servers (112), the radar network (108). The drones (104) collectively form the GNSS mesh.
[0060] In an embodiment, the system (100) may include a positioning unit that may be configured to transmit position, navigation and timing signals to receivers around the globe in a continuous fashion. By way of example, the positioning unit may be any or a combination of space segment and control segment of a Global Positioning System (GPS).
[0061] FIG. 2 illustrates exemplary functional components (200) of a processing unit (102) of the proposed system for neutralizing multiple drone targets, in accordance with an embodiment of the present disclosure. In an embodiment, the system (102) may include one or more processor(s) (204). The one or more processor(s) (204) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) (204) may be configured to fetch and execute computer-readable instructions stored in a memory (202) of the processing unit (102). The memory (202) may store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory (202) may comprise any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0062] In an embodiment, the processing unit (102) may also comprise a first interface(s) (206). The first interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The first interface(s) (206) may facilitate communication of the processing unit (102) with various components coupled to the system (100) such as the one or more servers (112). The interface(s) (206) may also provide a communication pathway for one or more components of the processing unit (102). Examples of such components include, but are not limited to, memory (202) and the first database (222).
[0063] In an embodiment, the processing engine(s) (208) of the processing unit (102) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (208) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the processing unit (102) may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the processing unit (102) and the processing resource. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry.
[0064] In an embodiment, the processing engine (208) may include a radar engine (210) that may be configured to detect the presence of one or more airborne threats and generate a set of signals on detection of the airborne threat. These generated signals may be transmitted to a centralized server in real time. The first database (222) may be enabled to store information pertaining to response routines on detection of different kinds of airborne threats.
[0065] In an embodiment, the processing engine (208) may include a trajectory engine (212) that may be enabled to receive/transmit a second set of data packets from the drones (104). The second set of data packets may pertain to location of the detected threats, and estimated location of the detected threats identified by the drones (104).
[0066] FIG. 3 is the overview of the receiver spoofer node mesh of an exemplary system for neutralizing multiple drone targets in accordance with an embodiment of the present disclosure. The receiver spoofers mesh may be comprised of a plurality of drones (350-n) spread across a pre-defined geographical area. The radar radio (340) may transmit data to and from the receiver spoofer mesh in real-time. The trajectory modeller (330) may estimate a trajectory of the airborne threat and plot a path for the nearest receiver spoofer mesh node to the detected airborne threat.
[0067] FIG. 4 illustrates the antenna configuration the system in accordance with an embodiment of the present disclosure. The radar radio may include a plurality of antenna systems that may be operatively coupled to communicate in real-time. A GNSS RX antenna (400) may be operatively connected to the processing unit such that processed data received by the antenna from the processing unit may be further transmitted to the drones. A Mesh network radio (430) may be provided that enables communication between the spoofer mesh and the radio. Further, the system may comprise a GNSS TX antenna. In an embodiment, each of the drones (104) may comprise a radio to enable communication with the ground station. On receipt of instructions from the trajectory modelling engine/trajectory modeler, the drones are configured to spoof the location co-ordinates of the threat.
[0068] FIG. 5 illustrates an exemplary computer system (500) to implement functionalities of the proposed system (100) for neutralizing multiple drone targets, in accordance with embodiments of the present disclosure.
[0069] In an illustrative embodiment of FIG. 5, a computer system may include an external storage device (510), a bus (520), a main memory (530), a read only memory (540), a mass storage device (550), communication port (560), and a processor (570). A person skilled in the art may appreciate that computer system may include more than one processor and communication ports. Examples of processor (570) may include, but not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, FortiSOC™ system on a chip processors or other future processors. Processor (570) may include various modules associated with embodiments of the present invention. Communication port (560) may be any of an RS-232 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. Communication port (560) 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.
[0070] In an embodiment, Memory (530) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read only memory (540) 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 BIOS instructions for processor (570). Mass storage (550) may be any current or future mass storage solution, which may be used to store information and/or instructions. Exemplary mass storage solutions may include, but 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), e.g. those available from Seagate (e.g., the Seagate Barracuda 7102 family) or Hitachi (e.g., the Hitachi Deskstar 7K1000), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks (e.g., SATA arrays), available from various vendors including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc. and Enhance Technology, Inc.
[0071] In an embodiment, Bus (520) may enable the processor(s) (570) to communicatively couple with the memory, storage and other blocks. Bus (520) may be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which may connect processor (570) to software system.
[0072] Optionally, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to bus (520) to support direct operator interaction with computer system. Other operator and administrative interfaces may be provided through network connections connected through communication port (560). External storage device (510) may be any kind of external hard-drives, floppy drives, IOMEGA® Zip Drives, Compact Disc - Read Only Memory (CD-ROM), Compact Disc - Re-Writable (CD-RW), Digital Video Disk - Read Only Memory (DVD-ROM). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.
[0073] FIG. 6 illustrates the detection of multiple threats within the pre-defined area and response by the closest receiver spoofer mesh node to provide co-ordinates in accordance with an embodiment of the present disclosure. A plurality of threats (620-N) are detected and engaged with by the closes receiver spoofer mesh nodes (650-N).
[0074] FIG. 7 is a flowchart illustrating the sequence of steps in the method for neutralizing multiple drone targets in an embodiment of the present disclosure. In an embodiment, RADAR/sensor 310 detects mobile drones threats 320-n whose absolute co-ordinates are estimated, time stamped and transmitted to trajectory modeler 330.
[0075] In an embodiment, trajectory modeler is a module running on hardware of RADAR/sensor system which keeps a track of the threats detected by RADAR/sensor device on per receiver target basis (702). The radar detects a threat and transmits the detected information to a trajectory modeller (704). The trajectory modeler estimates the IMU state model of each target receiver on the basis of the current absolute location of the target and trajectory track memory. The IMU state model is 9-Dimensional vector consisting of 3-Dimensional position, velocity and acceleration components.
[0076] In an embodiment, RADAR/sensor tracks all the targets identified and whose modeled tracks database is maintained by trajectory modeller (702). Identified and tracked targets co-ordinates are relayed (706) over wireless link by the RADAR/sensor Radios attached to RADAR/sensor system to swarm of drones; drones which are mounted with radios form the mesh network using wireless links; drones are also mounted with GNSS receiver and covert/overt GNSS spoofer signal generator. The Radio nodes of the above-mentioned mesh network, contain a multitude of hardware and software specifically designed to the meet the requirements of TDMA based mesh networking and GNSS covert spoofer. This radio mesh node contains multiple antenna systems to cater to the needs of the mesh network and GNSS signal reception and spoofed GNSS signal transmission.
[0077] In an embodiment, this radio mesh node contains one V/UHF hardware radio along with software which provides all the features of a typical TDMA based mesh network implementation. This node also contains the hardware and software for GNSS signal transmission/reception, which contains state of the art digital GNSS receiver along with dynamically configurable covert/overt GNSS spoofed signal generation unit.
[0078] GNSS spoofer mounted on Radios in Mesh network receives the tracked threats location along with its parameterized IMU state from RADAR/sensor Radio; Nodes near vicinity of the target self organizes and declares to network to move in mission mode; broadcast the identity of the target; engages it for spoofing and neutralizing; joins back the network after mission by following steps in the below methodology.
[0079] In an embodiment, initially, a time-stamped absolute target threat location (702) (latitude, longitude, altitude) is provided by RADAR/sensor to trajectory modeler software. The trajectory modeler 706 runs a predictive program which takes the time-stamped present and past locations of a target receiver from RADAR/sensor and estimates target location at any given current time. In an embodiment, trajectory modeler provides the best estimate of the target receiver location at its time slot of transmission in mesh network TDMA super frame. In an embodiment, trajectory modeler verifies the estimated location with the location provided by RADAR/sensor for closely following the target in its self-correcting loop.
[0080] In an embodiment, at 708 the target receiver’s location along with IMU state is time stamped and fed to the RADAR/sensor radio for propagation to the receiver spoofer mesh network. The receiver spoofer in the mesh network mounted with GNSS receiver is self-aware of its location and geometry which is known to all network elements; updates at a specified refresh rate on the basis of the dynamics of the mesh network. RADAR/sensor and Trajectory modeler estimates location of target at a higher refresh rate compared to the receiver spoofer mesh network. Based on the location and IMU state information of a target receiver, received by the receiver spoofer mesh network, the receiver spoofer mesh nodes mutually decide among themselves the best node to spoof the target receiver.
[0081] In an embodiment, a customized software may be running on the nodes. The software running on nodes decides handover of a target receiver based on the vicinity of node location to target by self-organizing. The handover mentioned above as per step will be initiated when a new target threat is assigned to current engaged receiver spoofer mesh node. Over number of TDMA super frames which contain transmissions of receiver target’s location and IMU state information from trajectory modeler, the reliability of the estimates is verified by the receiver spoofer mesh node engaged with the target receiver. The engaged node estimates the location with time interpolation from IMU state and maintaining the IMU state memory of the target receiver with itself if engaged, and tallying it with the IMU states which are to be received from the trajectory modeler. The receiver spoofer mesh node decides to take over the IMU state modeling within itself after certain pre-specified tolerance is permitted in the target receiver’s location estimate. The spoofing signal is generated (712) and improved iteratively. The generation of covert spoofing signals (712) requires knowledge of code phase, carrier phase, Doppler information, predicting bits to be packed in the GNSS frame structures.
[0082] In an embodiment, the parameters of code phase, carrier phase, Doppler information, predicting bits to be packed in the GNSS frame structures are derived based on the relative geometry of the engaged node and the target receiver. These parameters are updated at a regular interval as the engaged node and receiver targets are dynamic in nature and not static. These parameters also are function of trajectory which is planned for the neutralization of the target receiver. At this time the receiver spoofer node has all the details to mount such covert spoofing attack. This stage is called parameter adjustment (714) for spoofer signal generation.
[0083] In an embodiment, the feedback mechanism from RADAR/sensor and trajectory modeler, the receiver spoofer mesh node decides (716) over appropriate attack time the validity of its generated GNSS spoofed signals and after this confirmation attaches itself to the target receiver. At this time the receiver spoofer node can choose to communicate with the receiver spoofer mesh network as long as possible and if it is in mesh network’s range and keep on refining its signal generation mechanism and provide the mission status to the mesh network. The receiver spoofer node after acquiring complete control (718) of the target receiver and on safe neutralization of the target receiver at the intended safe location, returns to the its position in the mesh network.
[0084] In an embodiment, a method; to neutralize multiple drone threat targets using a system; comprising a RADAR/sensor device that detects and tracks drone targets; a trajectory modeler software being implemented along with RADAR/sensor that identifies the drone threats; connected with a radio that communicates with swarm of radios mounted on set of drones along with GNSS receiver spoofer systems may comprising the following steps:
a) Time-stamping the drone target threat’s location.
b) Modeling trajectory of each threat; predicting and estimating trajectory in a closed loop within the tolerance of 1meter.
c) Radio nodes form a synchronous time division multiple access-based mesh network for radio transmission and reception.
d) Three-Dimensional coordinate location vector of the threat and its 9-Dimensional IMU model is transmitted by the radio in its respective time slot of transmission in the TDMA super frame to the cluster of receiver spoofer nodes.
e) Receiver spoofer node mounted on the drone, nearest to the threat, decisively declares its mission to engage with threat and broadcasts it in the network.
f) Engaged receiver spoofer node estimates the propagation time delay for IMU state vector and threat location information to reach from RADAR/sensor radio to node; offsets the delay and calculates the threat’s location.
g) Engaged Receiver spoofer node computes the signal propagation delay time from GNSS transmission spoofer antenna of engaged node to GNSS receiver antenna of threat.
h) Engaged node measure time delay from GNSS receiver antenna of engaged node to GNSS transmit antenna of engaged node during calibration.
i) Overall delay in the receiver spoofer node is calculated using the delays as per step [g] and [h] along with exact GNSS satellites’ positions (almanac and ephemeris data), receiver spoofer node’s location and threat’s location; translates the time delays to equivalent distances between all the elements in the system.
j) Receiver spoofer node iteratively generates covert GNSS spoofed signal to adapt and match with power level as observed by the threat from GNSS satellite system according to code phase, carrier phase, Doppler information and predicting bits to be packed in the GNSS frame structures.
k) Power levels of covert spoofed signal are varyingly increased in steps of 1dB/100milliseconds to a maximum gain of 12dB more than actual GNSS signal power to avoid detection by anti-spoofing system of the threat.
l) Engaged Receiver spoofer node maintain the trajectory information of the threat received from the RADAR/sensor radio; offsets all the associated transmission time delays and records.
m) Receiver spoofer formulates the probable spoofed trajectory for neutralizing the threat from its present location to a pre-defined location considering gradual displacements in geographical coordinate system.
n) 3-Dimensional position displacement vector for threat’s location is estimated by receiver spoofer node based on threat’s actual trajectory, intended spoofed trajectory and IMU state model of the threat;
o) Signal characteristics of the generated spoofed signal is adjusted for the displacement vector as per step [n] and maintained in a closed loop with threat detected by RADAR/sensor and IMU state estimated by the trajectory modeler.
p) Engaged spoofer node broadcasts mission status to the mesh network once the threat moves in the intended spoofed trajectory for at least five times as per displacement vector.
[0085] As per step [p] Receiver spoofer node establishes complete control over threat; continues the mission for threat neutralization by estimating IMU model directly; completes the mission and joins back the mesh network.
[0086] Referring now to figure 6, in case of multiple threats being detected, a swarm of drones mounted with spoofing system and Radio for communication with ground station for obtaining the target co- ordinates estimated by a ground based RADAR/sensor will be required, which is disclosed in this innovative work; wherein individual spoofing systems acquire the original GNSS transmission, estimates the delay of the overall system based on the geometry of the spoofer antenna and target receiver antennas; manages clock drifts to avoid unlocking of code phase and carrier phase as detected by target receiver. Also, these spoofing systems maintain adaptive transmit power level of typically 10dB more than actual GNSS signal reception by the target receiver or threat.
[0087] Such multiple spoofing enabled systems do operate in a network with the help of radios fitted on to drones which work in auto recovering and self-coordinated way to effectively handle the mobile threats.
[0088] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refers to at least one of something selected from the group consisting of A, B, C … N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
[0089] 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.

, Claims:1. A system for neutralizing multiple drone targets, the system comprising:
a plurality of drones (104-n) communicatively coupled with one another to form a spoofer mesh, the plurality of drones being in operative coupling with a processor, the processor coupled with a memory storing instructions which when executed enable the processor to:
detect by a radar (108), within a predefined area, an airborne threat and generate co-ordinates of the airborne threat within the pre-defined area;
receive by a trajectory modeling engine, the generated co-ordinates of the airborne threat;
transmit by an antenna of the radar co-ordinates of the airborne threat to the spoofer mesh created by the plurality of drones;
identify closest receiver spoofer drone to the airborne threat;
neutralize the airborne threat by intercepting and spoofing the co-ordinates of the airborne threat.
2. The system as claimed in claim 1, the trajectory modeling engine estimates a trajectory of the airborne threat, said estimate of a trajectory of the detected threat being generated as a three-Dimensional coordinate location vector of the airborne threat.
3. The system as claimed in claim 1, wherein the trajectory modeling engine being further configured to generate a 9-Dimensional Inertial Measurement Unit model of the airborne threat that is transmitted by the radio in its respective time slot of transmission in Time Division Multiple Access (TDMA) super frame to the cluster of receiver spoofer nodes.

4. The system as claimed in claim 1, wherein the identified closest receiver spoofer node engages with said airborne threat establishes complete control over threat, continues the mission for threat neutralization by estimating IMU model directly and returns to the receiver spoofer nodes mesh.
5. A method for neutralizing multiple drone targets, the method being performed by a processor operatively coupled with a radar, a radio, said radar being communicatively coupled with a plurality of drones, said plurality of drones forming a cluster of receiver spoofer nodes and a memory operatively coupled with a server, the method comprising:
detecting by the radar location of an airborne threat within a pre-defined area and generating a time-stamp for the threat location;
modeling a trajectory of the detected threat by the trajectory modeling engine to estimate a trajectory of the airborne threat;
generating a three-Dimensional coordinate location vector of the airborne threat and generate a 9-Dimensional Inertial Measurement Unit model of the airborne threat that is transmitted by the radio in its respective time slot of transmission in the Time Division Multiple Access super frame to the cluster of receiver spoofer nodes;
identifying the receiver spoofer node closest to the airborne threat to engage with the airborne threat;
engaging the receiver spoofer node to estimate a time delay for the generated Inertial Measurement Unit model of the airborne threat;
offsetting the delay to calculate an actual location of the airborne threat and time delay for the receiver spoofer node to reach the airborne threat.
6. The method as claimed in claim 5, wherein the method comprises computing a signal propagation delay time from a GNSS transmission spoofer antenna of the engaged receiver spoofer node to GNSS receiver antenna of the airborne threat.
7. The method as claimed in claim 5, wherein the method comprises measuring time delay from GNSS receiver antenna of engaged node to GNSS transmit antenna of engaged node during calibration.
8. The method as claimed in claim 5, wherein 3-Dimensional position displacement vector for the airborne threat’s location is estimated by receiver spoofer node based on threat’s actual trajectory, intended spoofed trajectory and IMU state model of the threat.
9. The method as claimed in claim 5, wherein the generated spoofed signal is adjusted for the displacement vector and maintained in a closed loop with the airborne threat detected by RADAR/sensor and IMU state estimated by the trajectory modeler.
10. The method as claimed in claim 5, wherein the receiver spoofer node to engage with said airborne threat establishes complete control over threat, continues the mission for threat neutralization by estimating IMU model directly and returns to the receiver spoofer nodes mesh.