Description:TECHNICAL FIELD
[0001] The present disclosure relates, in general, to the threat detection, and more specifically, relates to a system for estimating threats posed over sets of assets by approaching aerial targets.

BACKGROUND
[0002] In contemporary air defence scenarios, the ability to accurately perceive and assess threats posed by hostile aerial targets to defended assets remains a critical challenge. The conventional methods often struggle to guess the dynamic nature of these threats and the complexity of various parameters influencing the threat perception. The existing methodologies in air defence lack a comprehensive mechanism to assimilate dynamic parameters and uncertain variables when evaluating threats from agile airborne adversaries.
[0003] In addition, the threat perception and its analysing process involves the processing based on some known facts like position, kinematic evaluation, target type or jamming nature of threatening target. The target position and kinematic parameters of a hostile air targets are primarily used in calculation of threat posed to an asset to be protected. For example, in C4I systems, surveillance radars provide the position and other kinematic and categorical parameters of an aerial target. Given the position of assets or defended assets, an approach using fuzzy logic soft technique involving rule based has been suggested and implemented in this work. Moreover, the absence of a method that accounts for the interactions among diverse parameters leads to suboptimal threat perception, potentially compromising the effectiveness of defence systems.
[0004] Some efforts had been made to address the above problem, in a patent application EP1029216B1which discloses automatic threat evaluation and weapons assignment systems, and more particularly to such a system which incorporates knowledge data bases or expert system techniques in the solution. In the above disclosure, the defended area divided into defensive zones and each zone has having their zone target table and their and allocated weapon database to be used for engaging a target. The targets are considered as they enter the defensive zone. However, the above disclosure had its limitations, where the approach used to calculate threat score or perception is based on well-defined rules and formulas, which precisely evaluate threat perception with certainty. Sometimes, analysis involving certainty and precision may result as hazardous in decision making which can carry a great loss and cost on false decisions in many situation analyses.
[0005] Therefore, it is desired to overcome the drawbacks, shortcomings, and limitations associated with existing solutions, and develop a solution for dynamically estimating threats over one or more assets.

OBJECTS OF THE PRESENT DISCLOSURE
[0006] A general object of the present disclosure is to overcome the drawbacks, shortcomings, and limitations associated with existing solutions, and develop a solution for dynamically estimating threats over one or more assets.
[0007] Another object of the present disclosure is to provide a system and a method that analyses threat perception from hostile aerial targets to defended assets, especially in in air defence scenarios.
[0008] Another object of the present disclosure is to provide a system and a method that provides for a fuzzy logic based technique to analyse final threat level from hostile targets to defended assets.
[0009] Yet another object of the present disclosure is to provide a system and a method that generates outputs in control decision systems, particularly in situations where cognitive adaptability outweighs certainty and precision.

SUMMARY
[0010] Aspects of the present invention generally pertain to the threat detection, and more specifically, relates to a system for estimating threats posed over sets of assets by approaching aerial targets.
[0011] The present invention discloses a system and a method for estimating dynamic threat posed over assets from aerial targets. The present invention includes a monitoring unit for monitoring and detecting presence of one or more targets and corresponding parameters in a pre-defined area around a set of assets. Further, the present invention includes a controller, which is in communication with the monitoring unit, and coupled with a fuzzy logic module. The controller comprises one or more processors, wherein the one or more processors are operatively coupled with a memory, the memory storing instructions executable by the one or more processors to: receive, from the monitoring unit, the monitored parameters of the detected targets; and compute, by taking into consideration the received parameters, associated indices, and correspondingly plot a matrix based on the computed indices. Further, the present invention involves estimating, through the fuzzy logic module, dynamic threat over at least one asset, among the set of assets, posed by at least target among the one or more targets, by taking into consideration the plotted matrix, sets of fuzzy rules.
[0012] In an aspect, the present invention involves calculating a threat perception score for each of the assets based on the threat posed by each of the one or more targets, and further prioritize the one or more targets by taking into consideration respective calculated threat perception scores and also determine the linguistic variables for the one or more targets based on the corresponding threat perception scores.
[0013] In another aspect, the present invention involves updating the sets of fuzzy rules associated with the fuzzy logic module, based on training-and-testing dataset pertaining to multiple conditions and corresponding results, and wherein the fuzzy logic module is further configured to estimate the dynamic threat over at least one asset posed by at least target by calculating corresponding threat level pertaining to threat posed over said asset posed by said target.
[0014] The present disclosure focuses on analysing threat perception from hostile aerial targets to defended assets, especially in in air defence scenarios, by providing for a fuzzy logic based technique to analyse final threat level from hostile targets to defended assets.
[0015] 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 figures in which like numerals represent like components.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The following drawings form part of the present specification and are included to further illustrate aspects of the present disclosure. The disclosure may be better understood by reference to the drawings in combination with the detailed description of the specific embodiments presented herein.
[0017] FIG. 1 illustrates a block diagram of the proposed system for estimating dynamic threat over assets, in accordance with an embodiment of the present disclosure.
[0018] FIG. 2 illustrates an exemplary representation of targets-assets pair threat list, in accordance with an embodiment of the present disclosure.
[0019] FIG. 3 illustrates an exemplary representation of Angle of Intent, in accordance with an embodiment of the present disclosure.
[0020] FIG. 4 illustrates an exemplary block diagram representing sub-units of controller associated with the system, in accordance with an embodiment of the present disclosure.
[0021] FIGs. 5A – 5F illustrate exemplary graphs associated with various indices and threat level, in accordance with an embodiment of the present disclosure.
[0022] FIGs. 6A - 6D illustrate exemplary representations associated with firing of single rule and multiple rules simultaneously, in accordance with an embodiment of the present disclosure.
[0023] FIG. 7 illustrates an exemplary display screen of UI representing calculation of threat level, in accordance with an embodiment of the present disclosure.
[0024] FIG. 8 illustrates a flow chart of the proposed method for estimating dynamic threat over assets, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0025] 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. If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0026] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0027] Embodiments of the present disclosure generally pertain to the threat detection, and more specifically, relate to a system for estimating threats posed over sets of assets by approaching aerial targets.
[0028] According to an embodiment, the present invention is based on fuzzy logic reasoning and used to compute threat perception from a target, for instance from a hostile aerial target to a defended asset. The invention uses target’s kinematic and capability parameters like speed, its position with respect to protected assets, its manoeuvrability, its jamming ability to jam opponent’s sensors as inputs to fuzzy modelling. Other derived parameters such as proximity, angle of intent (AOI) have also been defined and used as input in this fuzzy model based methodology of threat perception computation. The invention also proposes how to use these input parameters as valuable inputs for fuzzification and forming the rule base and then perform fuzzy logic based inference to compute final threat level posed from hostile targets to a defended asset.
[0029] Further, it involves fuzzy logic based reasoning using linguistic rules to compute the threat perception from approaching dynamic aerial threats. It results in remarkable outputs in command and control decisions, in situation where cognizance plays more important role than certainty and precision.
[0030] FIG. 1 illustrates a block diagram of the proposed system, in accordance with an embodiment of the present disclosure.
[0031] According to an embodiment, the proposed system 100 (also, referred to as system 100, herein) is utilized for estimating threats posed over sets of assets by approaching aerial targets. In an embodiment, the system 100 includes a monitoring unit 102 configured to monitor and detect presence of one or more targets 110-1, 110-1… 110-N (also, collectively referred to as targets 110, and individually referred to as target 110, herein) and corresponding parameters in a pre-defined area around assets 120-1, 120-1… 120-N (also, collectively referred to as set of assets 120/ assets 120, and individually referred to as asset 120, herein). In an exemplary embodiment, the monitoring unit 102 can include motion sensors, proximity sensors, imaging sensors, and the like, to monitor and detect presence of the targets 110, where the term “target(s)” may pertain to aerial objects approaching towards at least one of the assets 120, such as, but not limited to missiles, drones, aircrafts, and rockets, and the term “asset(s)” may pertain, but not limited to, objects, buildings, parks, defence and related equipment, embassies, state-owned enterprises, and the like.
[0032] In another embodiment, the system 100 includes a fuzzy logic module 104 and a controller 106, such that the controller 106 can be in communication with the monitoring unit, and also coupled with the fuzzy logic module 104. In an embodiment, the controller 106 may include one or more processors, wherein the one or more processors are operatively coupled with a memory, the memory storing instructions executable by the one or more processors to carry out pre-defined instructions and functions. In an embodiment, the controller 106 may be a central monitoring room. In another embodiment, the controller 106 may be a microcontroller in communication with the monitoring unit 102.
[0033] In an embodiment, the controller 106 can receive the monitored parameters of the detected targets from the monitoring unit 102, and further, by taking into consideration the received parameters, the controller 106 may compute associated indices. In an exemplary embodiment, the parameters associated with the one or more targets 110 may include corresponding jamming ability, position, orientation, angle of intent, distance, slant distance, direction, velocity, acceleration, maneuverability with respect to said assets, and the like.
[0034] In one embodiment, the term “slant distance” basically refers to three-dimensional distance between a particular aerial target 110 and defended assets 120. The “slant distance” also takes into account altitude of the target 110. An exemplary representation of the “slant distance” in between the target 110 and the assets 120 is shown in FIG. 3.
[0035] In other embodiment, as illustrated in the FIG. 3, the angle of intent (also, referred to as AoI, herein) of a target 110 with respect to protected assets 120 tells about hidden intent of the target 110. Further, the AoI may be a derived parameter, i.e., it may not be detected directly by the monitoring unit 102, rather the AoI is derived at the controller 106 based on other parameters obtained from the monitoring unit 102. Further, value of AoI is inversely proportional to the threat perceived by corresponding target 110, hence lesser the AoI, greater will be the threat perceived.
[0036] basically refers to three-dimensional distance between a particular aerial target 110 and defended assets 120. The “slant distance” also takes into account altitude of the target 110. An exemplary representation of the “slant distance” in between the target 110 and the assets 120 is shown in FIG. 3
[0037] In another embodiment, the controller 106 may also plot a matrix based on the computed indices. In an implementation, the controller 106 can calculate a threat perception score for each of the assets 120 based on the threat posed by each of the one or more targets 110, and further the system 100 can prioritize the one or more targets 110 by taking into consideration respective calculated threat perception scores, and may correspondingly plot the matrix.
[0038] In an exemplary embodiment, the system 100 may prioritize the targets by mapping targets-assets pair threat list, as illustrated in FIG. 2. As observed, “PA” pertains to the set of assets 120 including assets PA1, PA2. . . PAk, which are to be protected from unknown or threatening targets (represented as “TT”), such as TT1, TT2 . . . TTN. Further, the term “DT” pertains to a list of degree of threat from target TTk to all the protected asset set {PA1,PA2,….,PAk}, where DT includes the set [DTk1, DTk2,…..,DTkk]. An illustrational pictorial representation of the list of degree of threat from all targets (TT) to all protected assets (PA) is shown in the FIG. 2.
[0039] In one embodiment, the system 100, through the fuzzy logic module 104, can estimate dynamic threat over at least one asset, among the set of assets, posed by at least target among the one or more targets, by taking into consideration the plotted matrix.
[0040] In an implementation, the fuzzy logic module 104 may include a plurality of pre-defined sets of fuzzy rules, where the system 100 can be configured to determine linguistic variables for threat posed over every asset by each of the one or more targets 110, on the basis of the pre-defined sets of fuzzy rules and the plotted matrix. In an exemplary embodiment, the system 100 can determine the linguistic variables for the detected targets 110 based on the corresponding threat perception scores.
[0041] In an embodiment, whenever an unknown or threatening aerial target 110 is seen in the sky showing malicious intent which is calculated based on target heading with respect to defended aerial or ground based asset 120 like prominent building or infrastructure of strategic importance or aircraft are seen loitering in or above geographical area, a level of threat from the target 110 is perceived by the system 100. The threat perception is computed by the system 100 based on some parametric computation for target behaviour like jamming activity being performed by the target, proximity from protected assets (PA), its intent based on its heading towards vulnerable area or point and finally degree of threat perceived from the threatening target (TTk) with respect to each protected asset (PAk).
[0042] Further, as the targets’ kinematic, positional parameters, and behavioural characteristics are processed by the controller 106, different sets of linguistic variables are determined based on fuzzy sets and degree of threat for each of the target 110. Further, the matrix (also, referred to as final threat matrix, herein) consists of threat perception scores with respect to each the protected assets 120 is prepared. The matrix is a two-dimensional matrix carrying threatening targets prioritized according to their threat perception value. Hence, execution of fuzzy logic based threat perception and its computation is carried out by the proposed system 100.
[0043] In another embodiment, on a fuzzy set ‘F’ for any fuzzy variable (say 'v'), a membership function pF is defined, such that the membership function returns a value in the interval [0,1] for each element in the set. In an instance, a fuzzy set ‘A’ having defined range of values for each linguistic word may be expressed as a set of ordered key value pairs, where each pair may include a defined parameter ‘x’ and its grade of membership function ‘pA’; that is A= {(x, pA (x)) | x ? C), ‘x’ is called a support value if pA(x) > 0. For a given parameter, a fuzzy set ‘A’ may represent common sense linguistic variable values like low, medium, high, small, large, heavy, tall, short, and the like. In practice, the terms, very large, large, and small may be used for indication an object’s distance from a reference are not used in the strict sense. Instead, said terms are overlapping on extremities and follow smooth transition from small to large and then large to very large. In another exemplary embodiment, a linguistic variable ‘x’ in the universe of discourse ‘C’ may be characterized by V (x) = {V1x, V2x ,…, Vkx } and p (x) = { p1x , p2x ,…, pkx }, where the term set of ‘x’, i.e., the set of names of linguistic values of ‘x’, with each ‘Tix’ being a fuzzy number with membership function ‘p ix’, where ‘i’ is defined on the ‘C’.
[0044] In an embodiment, the controller 106 can be configured to calculate a threat level pertaining to threat posed over at least one asset 120 posed by at least target 110, by analyzing the associated parameters and/ or corresponding computed indices, and further on the basis of the calculated threat level, the system 100 can estimate dynamic threat over said asset 120 posed by said target 110.
[0045] In an embodiment, as illustrated in FIG. 4, the controller 106 can include a fuzzifier 402, an interface engine 404, a defuzzifier 406, and a rule base 408. A crisp input value ‘x’ may be fed into the fuzzifier 402, which may facilitate transforming of the crisp input value/ parameters to a fuzzy set or a fuzzy set to fuzzier set, and further at the interface engine 404, the transformed set is matched with pre-defined sets of fuzzy rules, which are retrieved from the rule base 408. Fuzzy rules are nothing but expert knowledge stored in data store as past experiences in form of linguistic words. When input values for the fuzzy variables are received, the corresponding fuzzy rules are fired followed by aggregation and defuzzification, at the defuzzifier 406 to get a crisp output value, which is displayed at a user interface (UI) 108, such as, but not limited to, smart phone, laptop, LED display device, LCD display device, of a user associated with the system 100.
[0046] In an implementation, the system 100 may be configured and update the sets of fuzzy rules associated with the fuzzy logic module 104, based ontraining-and-testing dataset pertaining to expert opinion related to multiple conditions and corresponding results. Further, the fuzzy logic module 104 may be configured to estimate the dynamic threat over at least one asset posed by at least target.
[0047] In another implementation, various indices associated with the parameters and the parameters received at the controller 106, such as, but not limited to, proximity index, manoeuvrability capability index, jammer track, AoI, asset priority index, and threat perception index, can be used as fuzzy parameters for fuzzification in order to calculate threat perception level (THRT_LVL) as output for each pair of threatening target 110 and threatened asset 120.
• Proximity Index (PROX_INDX): This parameter represents proximity time in seconds. It is basically slant distance between aerial target and defended assets divided by resultant velocity of the target with respect to static or moving assets. The slant distance takes altitude of the target into account. Range of linguistic values may be VERY LOW (0-50), LOW (40-80), MEDIUM (70-120), HIGH (110-160), and VERY HIGH (150-200 onwards), as could be observed from FIG. 5A. Range of values indicated is range of time values in seconds for threatening aircraft to reach its weapon launch point or its weapon release line.
• Manoeuvrability Capability Index (CAP_INDX): Manoeuvrability talks about agility of an aircraft depending on type of target 110 (Attack Aircraft, Bomber, Fighter, Missile, UAV, Fixed Wing Aircraft, Helicopter, Civil Aircraft, etc.). Range of linguistic values can be taken as LOW, MEDIUM and HIGH, as could be observed from FIG. 5C.
• Jammer Track (JAM_FLG): A track is said to be jammer if it has the capability to jam the opponent’s surveillance or fire controls radars. Bi-valued (TRUE or FALSE) logic has been used for aircraft showing whether jamming or non- jamming characteristics, as could be observed from FIG. 5D.
• Angle of Intent (AoI_T): AoI may have linguistic values as LOW (0-30), MEDIUM (25-65), HIGH (60 -75 and above) in degrees. Angle of Intent(AoI) of target with respect to protected asset tells about the hidden intent of target. Lesser the AoI, greater is the threat perceived, as could be observed from FIG. 5B.
• Asset Priority Index (AP_INDX): Assets 120 are categorized or assigned the level of importance in comparison to each other. Asset priority index relates to the comparative importance of assets for a person or country. This level of importance or asset priority index is assigned on numeric scale. Higher is the value of AP_INDX, higher is the priority or value of asset. Linguistic symbolic values taken for API are as Low, Medium, and High, as could be observed from FIG. 5E.
• Threat Perception Level (THRT_LVL): It is the degree of threat or threat index (TI) posed to protected assets. It has output threat levels having linguistic values as LOW (0-30), MEDIUM (25-50), HIGH (40-80), and VERY HIGH (80-100), as could be observed from FIG. 5F. Moreover, as illustrated in FIG. 7, THRT_LVL is computed/ displayed at the UI 108 on the basis of input values of PROX_INDX, CAP_INDX, JAM_FLG, AoI_T, and AP_INDX.
[0048] Further, trapezium membership function has been used in fuzzy modelling in the proposed system 100, where the trapezium membership function maintains invariance to sign, scaling, shifting and union of intervals. Hence, the trapezium membership function provides stability in degree of membership. Trapezium membership function depends on four parameters and are given by:

[0049] In an embodiment, the system 100 may be configured to implement centroid mechanism for defuzzification for computing an output value pertaining to the calculated threat level.
[0050] In an exemplary embodiment, firing of single rule and multiple rules simultaneously gives output as depicted in FIGs. 6A to 6D. Here, results are showing firing of single rule, but multiples rules can fire simultaneously. Firing of multiples rules causes aggregation of multiple outputs to take place followed by defuzzification which gives finally crisp threat level value such as 80.4 as output as depicted in FIG. 6B. For aggregation, ‘max’ operator is being used in our calculation. In fuzzy rule set, jamming may be suggested as lethal parameter since jamming track is more lethal than non-jamming track. Manoeuvrability index is also derived on the basis of how much manoeuvring target (say fighter) is as compared to other target (say helicopter).
[0051] Hence, the proposed system 100 is configured to save, retrieve and interpret the fuzzy rules from rule base 408 (also, referred to as knowledge base, herein) and further calculate Threat Level or Index (TI) as output. Fuzzy rules could be fed and saved into database, which may be inferred using Mamdani’s fuzzy logic method.
[0052] Referring to FIG. 8, the proposed method 800 (also, referred to as method 800, herein) is utilized for estimating dynamic threat over assets.
[0053] At block 802, the method 800 includes monitoring and detecting, at a monitoring unit, presence of one or more targets and corresponding parameters in a pre-defined area around a set of assets.
[0054] At block 804, the method 800 includes receiving, at a controller in communication with the monitoring unit, and coupled with a fuzzy logic module, the monitored parameters of the detected targets from the monitoring unit.
[0055] At block 806, the method 800 includes computing, at the controller, indices associated with the received parameters by taking into consideration said parameters, and correspondingly plotting a matrix based on the computed indices.
[0056] At block 808, the method 800 includes estimating, through the fuzzy logic module, dynamic threat over at least one asset, among the set of assets, posed by at least target among the one or more targets, by taking into consideration the plotted matrix.
[0057] In an embodiment, the fuzzy logic module comprises a plurality of pre-defined sets of fuzzy rules, and the method 800 can include determining, on the basis of the pre-defined sets of fuzzy rules and the plotted matrix, linguistic variables for threat posed over every asset by each of the one or more targets.
[0058] In another embodiment, the method 800 can include calculating a threat perception score for each of the assets based on the threat posed by each of the one or more targets, and prioritizing the one or more targets by taking into consideration respective calculated threat perception scores, and correspondingly plotting the matrix and determine the linguistic variables for the one or more targets based on the corresponding threat perception scores.
[0059] In yet another embodiment, the method 800 can include updating the sets of fuzzy rules associated with the fuzzy logic module, based on training-and-testing dataset pertaining to multiple conditions and corresponding results, and wherein the fuzzy logic module may further be configured to estimate the dynamic threat over at least one asset posed by at least target by calculating corresponding threat level pertaining to threat posed over said asset posed by said target.
[0060] The description of terms and features related to the present disclosure shall be clear from the embodiments that are illustrated and described; however, the invention is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents of the embodiments are possible within the scope of the present disclosure. Additionally, the invention can include other embodiments that are within the scope of the claims but are not described in detail with respect to the following description.
[0061] It will be apparent to those skilled in the art that the system 100 and method 800 of the disclosure may be provided using some or all of the mentioned features and components without departing from the scope of the present disclosure. While various embodiments of the present disclosure have been illustrated and described herein, it will be clear that the disclosure is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the disclosure, as described in the claims.

ADVANTAGES OF THE PRESENT INVENTION
[0062] The present invention provides a solution for dynamically estimating threats over one or more assets.
[0063] The present invention provides a system and a method that analyses threat perception from hostile aerial targets to defended assets, especially in in air defence scenarios.
[0064] The present invention provides a system and a method that provides for a fuzzy logic based technique to analyse final threat level from hostile targets to defended assets.
[0065] The present invention provides a system and a method that generates outputs in control decision systems, particularly in situations where cognitive adaptability outweighs certainty and precision.
, Claims:1. A system (100) for estimating dynamic threat over assets, the system (100) comprising:
a monitoring unit (102) configured to monitor and detect presence of one or more targets (110) and corresponding parameters in a pre-defined area around a set of assets (120);
and
a controller (106) in communication with the monitoring unit (102), and coupled with a fuzzy logic module (104), the controller (106) comprising one or more processors, wherein the one or more processors are operatively coupled with a memory, the memory storing instructions executable by the one or more processors to:
receive, from the monitoring unit (102), the monitored parameters of the detected targets (110);
compute, by taking into consideration the received parameters, associated indices, and correspondingly plot a matrix based on the computed indices; and
estimate, through the fuzzy logic module (104), dynamic threat over at least one asset, among the set of assets (120), posed by at least target among the one or more targets (110), by taking into consideration the plotted matrix.

2. The system (100) as claimed in claim 1, wherein the fuzzy logic module (104) comprises a plurality of pre-defined sets of fuzzy rules, wherein the system (100) is configured to determine, on the basis of the pre-defined sets of fuzzy rules and the plotted matrix, linguistic variables for threat posed over every asset (120) by each of the one or more targets (110).

3. The system (100) as claimed in claim 2, wherein the controller (106) is configured to calculate a threat perception score for each of the assets (120) based on the threat posed by each of the one or more targets (110);
wherein the system (100) is configured to prioritize the one or more targets (110) by taking into consideration respective calculated threat perception scores, and correspondingly plot the matrix and determine the linguistic variables for the one or more targets (110) based on the corresponding threat perception scores.

4. The system (100) as claimed in claim 2, wherein the system (100) is configured to update the sets of fuzzy rules associated with the fuzzy logic module (104), based on training-and-testing dataset pertaining to multiple conditions and corresponding results; and
wherein the fuzzy logic module (104) is further configured to estimate the dynamic threat over at least one asset (120) posed by at least target (110).

5. The system (100) as claimed in claim 1, wherein the controller (106) is configured to calculate a threat level pertaining to threat posed over at least one asset posed by at least target, by analyzing the associated parameters and/ or corresponding computed indices, wherein on the basis of the calculated threat level, the system (100) estimates dynamic threat over the at least one asset (120) posed by at least target (110).

6. The system (100) as claimed in claim 1, wherein the system (100) is configured to implement centroid mechanism for defuzzification for computing an output value pertaining to the calculated threat level.

7. The system (100) as claimed in claim 1, wherein the one or more targets (110) pertain to aerial objects approaching towards at least one of the assets (120), and wherein the parameters associated with the one or more targets (110) comprise corresponding jamming ability, position, orientation, angle of intent, distance, slant distance, direction, velocity, acceleration, and maneuverability with respect to said assets (120).

8. A method (800) for estimating dynamic threat over assets, the method (800) comprising:
monitoring and detecting (802), at a monitoring unit, presence of one or more targets and corresponding parameters in a pre-defined area around a set of assets;
receiving (804), at a controller in communication with the monitoring unit, and coupled with a fuzzy logic module, the monitored parameters of the detected targets from the monitoring unit;
computing (806), at the controller, indices associated with the received parameters by taking into consideration said parameters, and correspondingly plotting a matrix based on the computed indices; and
estimating (808), through the fuzzy logic module, dynamic threat over at least one asset, among the set of assets, posed by at least target among the one or more targets, by taking into consideration the plotted matrix.

9. The method (800) as claimed in claim 8, wherein the fuzzy logic module comprises a plurality of pre-defined sets of fuzzy rules, wherein the method (800) comprises determining, on the basis of the pre-defined sets of fuzzy rules and the plotted matrix, linguistic variables for threat posed over every asset by each of the one or more targets;
wherein the method (800) comprises calculating a threat perception score for each of the assets based on the threat posed by each of the one or more targets, and prioritizing the one or more targets by taking into consideration respective calculated threat perception scores, and correspondingly plotting the matrix and determine the linguistic variables for the one or more targets based on the corresponding threat perception scores.

10. The method (800) as claimed in claim 9, wherein the method (800) comprises updating the sets of fuzzy rules associated with the fuzzy logic module, based on training-and-testing dataset pertaining to multiple conditions and corresponding results, and wherein the fuzzy logic module is further configured to estimate the dynamic threat over at least one asset posed by at least target by calculating corresponding threat level pertaining to threat posed over said asset posed by said target.