DESC:TECHNICAL FIELD
[0001] The present disclosure relates generally to tracking system, for use in vehicles for land, air, or sea, and more specifically relates to an automated system and method for performing robust Radar to ESM association using remote ESM sensor based on collaborative feedback.

BACKGROUND
[0002] Background description includes information that may be useful in understanding the present disclosure. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosure, or that any publication specifically or implicitly referenced is prior art.
[0003] The recent defence systems rely on a network of distributed sensors and/or platforms to form a composite situational awareness picture. It requires enhanced multi-platform multi sensor data fusion to provide improved unambiguous surveillance situation for early warning and weapons control and for other tactical requirements. The multi sensor data fusion is started from homogeneous sensors and rapidly extended to heterogeneous sensor. The fusion from heterogeneous sensors offers many unique advantages over same type of sensor fusion and therefore is now become an essential and integral need of the defence forces. The fusion of radar and electronic support measure (ESM) is one of the important types of heterogeneous sensor fusion and attracts interests of researcher and defence forces globally.
[0004] Radar is an active sensor that transmits electromagnetic (EM) waves and detects the reflection of EM waves from targets. Based on the reflection of EM waves it measures range and bearing of the target. The maximum detection range of the radar depends on the radar sensitivity i.e. the power of EM waves received by radar as it decreases as the inverse fourth power of range ( . It is also limited by the targets cross section as seen by radar, pulse repetition frequency and signal noise. The target detection by radar also depends on Doppler Effect, polarization, line of sight, clutter etc.
[0005] ESM is a passive sensor that detects EM waves transmitted from other sensors. Therefore, it can detect only transmitting sensors. In contrast to radar it can detect the signals whose power are decreasing as the inverse square power of range; hence it can detect much larger ranges as compared to the radar. As it is a passive sensor it can detect only bearing of target and the accuracy of detected bearings are much lower than the radar detected bearing.
[0006] The ESM bearing accuracy is primarily a function of antenna type and antenna location, combined with receiver measurement accuracy of frequency, time and phase. The ESM, as per its capability, also provides other measured emitter RF parameters viz. Emitter Frequency, Pulse Width and Pulse Repetition Interval, received power, and scan time. However, the ESM can also provide many derived parameters like RF type viz. fixed, hopper, deviation, etc., scan type viz. circular, conical, etc. and in some way identity of the target. Therefore, these properties of detected ESM signal can provide useful intelligence information about the target. It can therefore be said that in a real-time scenario a radar and ESM can provide separate subset of information as shown in FIG. 1 with only bearing as common information.

[0007] The information obtained from the radar and ESM are complementary in the sense that the radar provides positional and kinematic parameters whereas the ESM provides only ‘bearing’ measurement and derived parameters such as frequency, pulse repetition frequency (PRF), pulse width (PW) and identity/platform as derived parameters. It is therefore association between them is unique in the way that only common parameter between them is ‘bearing’ measurement. It provides the sufficient reason to perform radar to ESM association since when the separate information’s are combined it can provide classified information about the target.
[0008] As evident from above discussion that there are many problems exists in radar to ESM association. The one of the main problems are high inaccuracy of the ESM bearing measurements and unavailability of range. The second problem is its “many-to-many association” requirement. Also, to associate N Gaussian-distributed ESM measurements with one the possible M radar Gaussian distributed measurements . The third problem is time-alignment of sensor reports, as radar and ESM take measurements at different instants of time. The prediction of radar track to ESM measurements can lead to least errors as compared to predicting ESM measurements to radar track or bringing both to a common time. It can be easily accepted since radar provides comparatively accurate bearing measurements and additionally also accurate range measurements that allow proper adjustment of tracking filters for target manoeuvres. One more problem of unequal number of ESM measurements associated with each radar track. It is because there can be a multiple ESM emitter on a platform and therefore multiple ESM tracks can be associated with radar tracks.
[0009] A large number of prior arts exist for radar to ESM association. For example, one of the prior methods propose to use Bayesian techniques for association. Further, it is proposed that the association problem can be assigned as multiple hypothesis problem; null hypothesis and valid hypothesis. In null hypothesis the ESM measurements are not associated with any radar track however valid hypothesis implies ESM measurements associate with i-th radar track. It further says that the ESM measurements are Gaussian distributed and thus the distance vector has a chi-square density. The valid hypothesis can be declared if posterior probability is greater than the threshold. The mathematical derivation for calculating the thresholds specifies triple thresholds to specify firm correlation, tentative correlation, tentative un-correlation and firm un-correlation between radar and ESM tracks. The fuzzy data association approach is used in other prior methods. It deals ESM data with fuzzy processing while taking radar data as clustering centre and uses fuzzy c-means algorithm along with membership matrix to achieve data association. Another technique using coarse correlation rule is that is formed using Taylor series expansion. The comparative performance for various track fusion approach are also discussed. It also proposes track fusion approach by using different kinematic models for local trackers each for active and passive sensors.
[0010] Similarly, prior methods exist for ESM-to-ESM correlation that is used here for improving the radar-to-ESM association. The basic technique for ESM-to-ESM association is triangulation. The popular problem of appearing ghosts and eliminating them is also discussed. The modified gain extended kalman filter approach is presented for tracking and finally BP neural network is proposed for location association. The situation of radar-to-ESM association becomes even more complex if two targets perform parallel motion for a longer duration.
[0011] Therefore, there is need in the art to provide a simple and efficient automated system and method for employing track association which can obviate the foregoing limitations.

OBJECTS OF THE INVENTION
[0012] An object of the present invention relates generally to tracking systems, for use in vehicles for land, air, or sea, and more specifically relates to an automated system and method for providing track association using remote sensors.
[0013] Another object of the present invention is to provide a system that can facilitate handling of large number of radar tracks and large number of bearings.
[0014] Another object of the present invention is to provide a system that can optimally integrates ESM-to-ESM association, de-ghosting, circular error probable and cumulative probability which results in robust radar to ESM association.
[0015] Another object of the present invention is to provide a system that includes multi-sensor data association.
[0016] Yet another object of the present invention is to provide a system that can implement advanced mechanism of radar to ESM association using additional remote ESM sensor.

SUMMARY
[0017] The present disclosure relates generally to tracking system, for use in vehicles for land, air, or sea, and more specifically relates to an automated system and method for performing track association using remote sensor.
[0018] In an aspect, the present disclosure relates to a system for providing track associations, said system comprising: a radar deployed at an origin, said radar configured to detect reflected electromagnetic waves from targets; a local electronic support measure (ESM) deployed at the origin, said local ESM configured to detect reflected electromagnetic waves from the targets; a remote electronic support measure (ESM) deployed at a remote position, said remote ESM configured to detect reflected electromagnetic waves from the targets; and a processor operatively coupled with a memory, said memory storing instructions executable by the processor to: receive, from the radar, radar tracks pertaining to location of the targets; receive, from the local ESM, bearing data pertaining to location of the targets; receive, from the remote ESM, remote bearing data pertaining to location of the targets; generate, based on remote bearing data, and positions of the origin and the remote position, a cut-off zone, wherein the bearing data existing inside the cut-off zone is considered; generate, for at least three derived parameters obtained from the local ESM and the remote ESM, a suitable gate each, wherein the bearing data that passes a logical interaction of the at least three gates is considered; and determine, for the targets, cumulative probabilities, based on radar tracks, considered bearing data and considered remote bearing data, wherein, when the cumulative probability exceeds a predetermined threshold, track association of the radar, the local ESM and the remote ESM is declared.
[0019] In an embodiment, the remote position is any of a stationary position or a moving position.
[0020] In another embodiment, the processor is configured to distinguish between target points and ghost points to fuse the bearing data and the remote bearing data based on difference in bearing variance from radar, local ESM and second ESM for a plurality of samples.
[0021] In another embodiment, the processor is configured to calculate circular probable error based on bearing variance from radar, local ESM and second ESM for a plurality of samples for localisation of bearing data.
[0022] In another embodiment, the derived parameters are selected from a group comprising frequency, pulse width, pulse repetitive frequency, scan time, radar type, identity, platform, radar name and a combination thereof.
[0023] In another embodiment, the at least three derived parameters are frequency, pulse width and pulse repetitive frequency.
[0024] In an aspect, the present disclosure relates to a method for providing track associations, said method comprising the steps of: receiving, at a computing device, from a radar deployed at an origin, said radar configured to detect reflected electromagnetic waves from targets, radar tracks pertaining to location of the targets; receiving, at the computing device, from a local electronic support measure (ESM) deployed at the origin, said local ESM configured to detect reflected electromagnetic waves from the targets, bearing data pertaining to location of the targets; receiving, at the computing device, from a remote electronic support measure (ESM) deployed at a remote position, said remote ESM configured to detect reflected electromagnetic waves from the targets, remote bearing data pertaining to location of the targets; generating, at the computing device, based on remote bearing data, and positions of the origin and the remote position, a cut-off zone, wherein the bearing data existing inside the cut-off zone is considered; generating, at the computing device, for at least three derived parameters obtained from the local ESM and the remote ESM, a suitable gate each, wherein the bearing data that passes a logical interaction of the at least three gates is considered; and determining, at the computing device, for the targets, cumulative probabilities, based on radar tracks, considered bearing data and considered remote bearing data, wherein, when the cumulative probability exceeds a predetermined threshold, track association of the radar, the local ESM and the remote ESM is declared.
[0025] 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
[0026] 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.
[0027] FIG. 1 illustrates radar and ESM information.
[0028] FIG. 2 illustrates radar and ESM geometry.
[0029] FIG. 3 illustrates a block diagram of the exemplary system for radar to ESM association, in accordance with embodiments of the present disclosure.
[0030] FIG. 4 illustrates a modified radar and ESM geometry using remote ESM, in accordance with embodiments of the present disclosure.
[0031] FIG. 5 illustrates cut-off zone between local and remote ESM, in accordance with embodiments of the present disclosure.
[0032] FIG. 6 illustrates close-by target and crossing target scenario of two tracks, in accordance with embodiments of the present disclosure.
[0033] FIG. 7 illustrate cumulative probability for ESM track-1 with radar track-1 and track-2, in accordance with embodiments of the present disclosure.
[0034] FIG. 8 illustrates cumulative probability for ESM track -2 with radar track-1 and track-2, in accordance with embodiments of the present disclosure.
[0035] FIG. 9 illustrates the process for radar to ESM association, in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION
[0036] 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 spirit and scope of the present disclosure as defined by the appended claims.
[0037] 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.
[0038] 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.
[0039] The use of any and all examples, or exemplary language (e.g., “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non – claimed element essential to the practice of the invention.
[0040] FIG. 2 illustrates radar and ESM geometry.
[0041] Referring to FIG.2, during the radar to ESM association, the challenge arises when the two targets are adjacent or very close. The radar and ESM are collocated at origin. The radar measurements of the target 1 (point A) are (r1, ?1) at time t1 and target 2 (point B) are (r2, ?2) at time t2 as mentioned in Table 1. All measurements are in polar coordinates and angles are with respect to true north. It is assumed that the radar measurements are Gaussian distributed with zero mean and (sradar) radar standard deviation.
[0042] Similarly the ESM measurements of the target1 (point C) are ?3 at time t3 and target 2 (point D) are ?4 at time t4 as mentioned in Table 1. It is assumed that the ESM measurements are Gaussian distributed with zero mean and (sESM) ESM standard deviation.
[0043] Table 1: Radar and ESM sensor Measurements
Radar Measurements
A (r1, ?1, t1)
B (r2, ?2, t2)
ESM Measurements
C (?3, t3)
D (?4, t4)

Table 1
[0044] The radar and ESM errors are assumed to be Gaussian distributed with zero mean and constant variances and respectively. If a priori probabilities are assumed to be equal then the discriminant function ‘d’ can be mentioned as in (1)

(1)

[0045] Also, since bearing measurements from radar and ESM are independent and Gaussian distributed the discriminant function has a chi-square density function with Ni degrees of freedom.
(2)

[0046] The Ni is the maximum number of samples of ESM measurements that can be possibly compared to the corresponding radar tracks since radar and ESM measurements are taken at varying time instants and ß is the minimum rejection probability. Here it is assumed that ESM equipment is tracking the raw bearing measurements and providing a composite bearing track file.
[0047] The radar measurements are filtered and tracked to form composite radar tracks. radar tracks are then further used for the entire radar to ESM association process. The conversion of radar measurements to radar tracks are performed using interactive multiple model (IMM) filter and is essential to handle the real time scenarios of closed by moving targets and clutter environment. The IMM filter used is composed of kalman filter and extended kalman filter for handling both target linear motion and manoeuvres.
[0048] The radar and ESM tracks are stored for a specified duration or number of samples and batch processing is performed to calculate discriminant function. The radar tracks are predicted to the nearest ESM measurement time. The actual discriminant function used here for maximum number of samples Ni:

(3)

[0049] Where i =1…… M, and M is the total number of Radar targets.
[0050] The comparison of each ESM track with the M radar tracks can yield the corresponding vector (4):
(4)

[0051] The calculated d can now be used to calculate cumulative probability P(d1,N1). The cumulative probability that is greater than predetermined threshold PThreshold¬, (P(d1,N1)>PThreshold)) can be declared as associated pair. If the PThreshold is selected carefully, generally only one association pair has probability that is above threshold but in case of scenario FIG. 2 all the cumulative probabilities are above PThreshold. Hence it is not possible to declare a unique association pair.
[0052] The present disclosure relates generally to tracking system, for use in vehicles for land, air, or sea, and more specifically relates to an automated system and method for performing track association using remote sensor.
[0053] In an aspect, the present disclosure relates to a system for providing track associations, said system comprising: a radar deployed at an origin, said radar configured to detect reflected electromagnetic waves from targets; a local electronic support measure (ESM) deployed at the origin, said local ESM configured to detect reflected electromagnetic waves from the targets; a remote electronic support measure (ESM) deployed at a remote position, said remote ESM configured to detect reflected electromagnetic waves from the targets; and a processor operatively coupled with a memory, said memory storing instructions executable by the processor to: receive, from the radar, radar tracks pertaining to location of the targets; receive, from the local ESM, bearing data pertaining to location of the targets; receive, from the remote ESM, remote bearing data pertaining to location of the targets; generate, based on remote bearing data, and positions of the origin and the remote position, a cut-off zone, wherein the bearing data existing inside the cut-off zone is considered; generate, for at least three derived parameters obtained from the local ESM and the remote ESM, a suitable gate each, wherein the bearing data that passes a logical interaction of the at least three gates is considered; and determine, for the targets, cumulative probabilities, based on radar tracks, considered bearing data and considered remote bearing data, wherein, when the cumulative probability exceeds a predetermined threshold, track association of the radar, the local ESM and the remote ESM is declared.
[0054] In an embodiment, the remote position is any of a stationary position and a moving position.
[0055] In another embodiment, the processor is configured to distinguish between target points and ghost points to fuse the bearing data and the remote bearing data based on difference in bearing variance from radar, local ESM and second ESM for a plurality of samples.
[0056] In another embodiment, the processor is configured to calculate circular probable error based on bearing variance from radar, local ESM and second ESM for a plurality of samples for localisation of bearing data.
[0057] In another embodiment, the derived parameters are selected from a group comprising frequency, pulse width, pulse repetitive frequency, scan time, radar type, identity, platform, radar name and a combination thereof.
[0058] In another embodiment, the at least three derived parameters are frequency, pulse width and pulse repetitive frequency.
[0059] In an aspect, the present disclosure relates to a method for providing track associations, said method comprising the steps of: receiving, at a computing device, from a radar deployed at an origin, said radar configured to detect reflected electromagnetic waves from targets, radar tracks pertaining to location of the targets; receiving, at the computing device, from a local electronic support measure (ESM) deployed at the origin, said local ESM configured to detect reflected electromagnetic waves from the targets, bearing data pertaining to location of the targets; receiving, at the computing device, from a remote electronic support measure (ESM) deployed at a remote position, said remote ESM configured to detect reflected electromagnetic waves from the targets, remote bearing data pertaining to location of the targets; generating, at the computing device, based on remote bearing data, and positions of the origin and the remote position, a cut-off zone, wherein the bearing data existing inside the cut-off zone is considered; generating, at the computing device, for at least three derived parameters obtained from the local ESM and the remote ESM, a suitable gate each, wherein the bearing data that passes a logical interaction of the at least three gates is considered; and determining, at the computing device, for the targets, cumulative probabilities, based on radar tracks, considered bearing data and considered remote bearing data, wherein, when the cumulative probability exceeds a predetermined threshold, track association of the radar, the local ESM and the remote ESM is declared.
[0060] FIG. 3 illustrates a block diagram of the exemplary system for radar to ESM association, in accordance with embodiments of the present disclosure.
[0061] Referring to FIG. 3, the system includes radars 301, local electronic support measures (ESM) 302 and a remote ESM 303. The radar can output radar tracks and the ESM can output ESM tracks. Radar can be an active sensor that transmits electromagnetic (EM) waves and detects the reflection of EM waves from targets. Based on the reflection of EM waves it measures range and bearing of the target. The maximum detection range of the radar depends on the radar sensitivity i.e. the power of EM waves received by radar as it decreases as the inverse fourth power of range. It can also be limited by the targets cross section as seen by radar, pulse repetition frequency and signal noise. The target detection by radar also depends on doppler effect, polarization, line of sight, clutter etc.
[0062] In an embodiment, ESM can be a passive sensor that detects EM waves transmitted from other sensors. Therefore, it can detect only transmitting sensors. In contrast to radar it can detect the signals whose power are decreasing as the inverse square power of range; hence it can detect much larger ranges as compared to the radar. As it is a passive sensor it can detect only bearing of target and the accuracy of detected bearings are much lower than the radar detected bearing.
[0063] In another embodiment, the ESM bearing accuracy can be primarily a function of antenna type and antenna location, combined with receiver measurement accuracy of frequency, time and phase. The ESM, as per its capability, also provides other measured emitter RF parameters viz. emitter frequency, pulse width and pulse repetition interval, received power, and scan time. However, the ESM can also provide many derived parameters like RF type viz. fixed, hopper, deviation, etc., scan type viz. circular, conical, etc. and in some way identity of the target. Therefore, these properties of detected ESM signal can provide useful intelligence information about the target. It can therefore be said that in a real-time scenario a radar and ESM can provide separate subset of information as shown in FIG. 1 with only bearing as common information.
[0064] The information obtained from the radar and ESM are complementary in the sense that the radar provides positional and kinematic parameters whereas the ESM provides only ‘bearing’ measurement and derived parameters such as frequency, pulse repetition frequency (PRF), pulse width (PW) and identity/platform as derived parameters. It is therefore association between them is unique in the way that only common parameter between them is ‘bearing’ measurement. It provides the sufficient reason to perform radar to ESM association since when the separate information’s are combined it can provide classified information about the target.
[0065] In another embodiment, the objective of the radar, ESM and remote ESM track association is to resolve ambiguities and conflicts which may arise due to track inaccuracies when pairwise associations are performed as a step of a radar, ESM, and remote ESM track association process. Embodiments explained herein relate a robust automated method for radar-to-ESM association for accurate bearing measurements. In an embodiment, an additional remote ESM can be used for improving the traditional radar to ESM association.
[0066] In an embodiment, the system includes control unit, processor, and memory. The processor can process the radar tracks, and the ESM tracks as input track data, and can provides pairwise associations, ranked candidate track pairs and outputs data. The pairwise associations can be stored in the memory. In the preferred embodiment, the processor comprises a digital computer programmed to perform the functions herein below described.
[0067] In an embodiment, radar 301 and one ESM 302 are collocated at origin and another ESM is at remote locations whose location is known. The origin and remote platform can be stationary or moving. The method is tested on complex scenarios viz. two targets in parallel motion and collinear targets where both sensors and targets are at same azimuth.
[0068] In an embodiment, the ESM 303 can be used at remote location. The remote ESM 303 can be on stationary or moving platform. The location of remote ESM is assumed to be known in case of stationary sensor. However, in case of moving platform the location information can be periodically sent to origin at best possible update rate. In another aspect, the present disclosure provides an improved and efficient method of integrating various techniques such as additional remote ESM, ESM-to-ESM fusion, de-ghosting, cut-off zone, circular error probable, and cumulative probability for robust radar-to-ESM association.
[0069] In an embodiment, the method can facilitate handling of large number of radar tracks and large number of bearings. The method can facilitate handing of radar tracks from all kinds of radars such as navigational radar, mid/long range surveillance radar, phased array radar, fire control radar and the like. The technique can be designed for stationary as well as moving fusion platform.
[0070] In an embodiment, the multi-sensor data association is increasingly important across defence forces. It can require association among dissimilar sensors. The radar to ESM track association is an important area among heterogeneous sensor association. The heterogeneous sensor association has its inherent advantages along with greater challenges. The advanced mechanism of radar to ESM association is presented using additional remote ESM sensor. The technique optimally integrates ESM-to-ESM association, de-ghosting, circular error probable and cumulative probability which results in robust radar to ESM association.
[0071] In an embodiment, the remote ESM can be proposed to be initially used for ESM-to-ESM fusion. The result of the ESM-to-ESM fusion can be used for calculating circular error probable. The circular error probable is then used to eliminate the undesired contenders for radar to ESM fusion.
[0072] FIG. 4 illustrates a modified radar and ESM geometry using remote ESM, in accordance with embodiments of the present disclosure.
[0073] In an embodiment, radar-to-ESM association technique is designed for both stationary as well as moving platform i.e. the fusion centre and the sensors such as radar, ESM and remote ESM can be mounted on stationary as well as moving platform. It can support radars such as navigational radar, mid/long range surveillance radar, phased array radar, fire control radar and the like. The technique can use discriminant function based de-ghosting method for the ESM to remote ESM fusion. It can have a function of difference in bearing measurements from radar and ESM; the difference in bearing variances from radar, ESM, and number of samples.
[0074] In another embodiment, de-ghosting technique can be designed for the ESM to remote ESM fusion. The techniques for ESM to ESM correlation are as follows. The remote ESM can also detect emissions from both the targets. The bearing line draws from remote ESM can result in four interaction points E1, E2, F1, and F2. The E1 and F2 are interaction points with actual targets; however, E2 and F1 are ghost points. As the target characteristics for FIG. 2 are included of measured angle and time; here in addition to measured angle and time, the frequency(F), pulse-width(PW) and pulse repetitive frequency(PRF) can also require as mentioned in Table 2. The local ESM measurements are with respect to origin; however, the remote ESM Measurements are with respect to remote location and angles are with respect to true north.
[0075] Table 2: Local ESM and Remote ESM Measurements

ESM Measurements (Bearing, Time) (Frequency, Pulse Width, Pulse Repetitive Frequency)
C (?1, t3) F1, PW1, PRF1
D (?2, t4) F2, PW2, PRF2
Remote ESM Measurements
E1 (?3, t5) F1, PW1, PRF1
E2 (Ghost Node) (?4, t6) F4, PW4, PRF4
F1 (?5, t7) F5, PW5, PRF5
F2 (Ghost Node) (?6, t8) F2, PW2, PRF2

[0076] The ESM-to-ESM association can be carried out in three steps: ESM cut-off zone; derived ESM parameter gating and likelihood ratio test.
[0077] In an embodiment, the remote ESM can be initially used for ESM-to-ESM fusion. The result of the ESM-to-ESM fusion can be used for calculating circular error probability. The circular error probable is then used to eliminate the undesired contenders for radar to ESM fusion. The scheme is broadly shown in FIG. 3. The addition of another remote ESM may modify the radar and ESM geometry of FIG. 2 as shown in FIG. 4.
[0078] Referring to FIG. 5, a cut-off zone can be used for finding the most suitable bearing measurement for ESM-to-ESM fusion. The ESM cut-off zone can be a rectangular zone that is formed using origin, remote ESM location and remote bearing as shown in FIG. 5. Let ØR be the bearing of remote ESM measurement from true north with respect to remote ESM. It may become the first edge of cut-off zone. A parallel line is drawn at the same bearing ØR from origin can become the second edge. Let (Rremote, Øremote) be the distance of remote ESM from local ESM, then the line drawn from origin to (Rremote, Øremote) may become the third edge of zone. A parallel line to the third edge at suitable distance can become the fourth edge of zone. The local bearing that exists inside cut-off zone are considered for correlation; however, the local bearings that exist outside cut-off zone are not considered for correlation. It helps in reducing majority of candidates especially in some cases where legacy ESM detects many ESM bearings. As evident this mainly helps in reducing the measurement contenders from another quadrant. The bearings exist in ESM cut-off zone are only used for further steps.
[0079] In an embodiment, in derived ESM parameter gating, there can be several derived parameters received from ESM such as frequency, pulse width, pulse repetitive frequency, scan time, radar type, identity, platform, radar name etc. as per the ESM processing capability and maturity of dictionary. As per analysis of data it is suggested that derived parameters such as platform, frequency, pulse width and pulse repetitive frequency can definitely be used for static gating. The ESM measurements from same platform may only be considered for correlation. Similarly, a suitable gate each for frequency (FGate), pulse width (PWGate) and pulse repetitive frequency (PRFGate) can be defined. The ESM measurements that pass the logical ‘AND’ of all three gates may only be considered for correlation.
[0080] In an embodiment, the cumulative probability can be used for declaring the high probable radar-to-ESM association pair. Experimental results and discussion are as follows.
[0081] The problem in FIG 2 is simulated and one of such real scenario of surface targets is as shown in FIG. 6 where the two targets are close by, move parallel for long duration and also cross each other. The radar and ESM are assumed to be at origin. The radar data is estimated for track1, radar and track2, radar. The discriminant function and cumulative probabilities calculated for track1, ESM and track2, ESM correspondingly against track1, radar and track2, radar. All calculations are done in batches of 30 reports from both radar and ESM. The cumulative probabilities for both the ESM track are shown in FIG 7 and FIG 8. It is clearly evident that the calculated probabilities are very close [0.990 0.999] and well above any predetermined threshold PThreshold. In such situation all associated pair qualifies the association logic and it is not possible to declare any unique radar-to-ESM associated pair.
[0082] FIG. 9 illustrates a process for radar to ESM association, in accordance with embodiments of the present disclosure.
[0083] Referring to FIG. 9, the method for providing track associations, the method comprising the steps of: receiving (901), at a computing device, from a radar (301) deployed at an origin, said radar configured to detect reflected electromagnetic waves from targets, radar tracks pertaining to location of the targets; receiving (902), at the computing device, from a local electronic support measure (ESM) (302) deployed at the origin, said local ESM configured to detect reflected electromagnetic waves from the targets, bearing data pertaining to location of the targets; receiving (903), at the computing device, from a remote electronic support measure (ESM) (303) deployed at a remote position, said remote ESM configured to detect reflected electromagnetic waves from the targets, remote bearing data pertaining to location of the targets;
[0084] In another embodiment, the method further includes generating (904), at the computing device, based on remote bearing data, and positions of the origin and the remote position, a cut-off zone, wherein the bearing data existing inside the cut-off zone is considered; generating (905), at the computing device, for at least three derived parameters obtained from the local ESM and the remote ESM, a suitable gate each, wherein the bearing data that passes a logical interaction of the at least three gates is considered; and determining (906), at the computing device, for the targets, cumulative probabilities, based on radar tracks, considered bearing data and considered remote bearing data, wherein, when the cumulative probability exceeds a predetermined threshold, track association of the radar, the local ESM and the remote ESM is declared.
[0085] Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fibre, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.
[0086] The present invention, in various embodiments, includes components, methods, processes, systems and/or apparatus substantially as depicted and described herein, including various embodiments, sub-combinations, and subsets thereof. Those of skill in the art will understand how to make and use the present invention after understanding the present disclosure. The present invention, in various embodiments, includes providing devices and processes in the absence of items not depicted and/or described herein or in various embodiments hereof, including in the absence of such items as may have been used in previous devices or processes, e.g. for improving performance, achieving ease and\or reducing cost of implementation.
[0087] 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 refer to at least one of something selected from the group consisting of A, B, C … and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc. The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the appended claims.
[0088] 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 INVENTION
[0089] The present invention provides a system that can facilitate handling of large number of radar tracks and large number of bearings.
[0090] The present invention provides a simple and efficient automated system that can provide track association based on candidate pairs and resolve ambiguities and conflicts which may arise due to track inaccuracies when pairwise associations are performed.
[0091] The present invention provides a system that can optimally integrates ESM-to-ESM association, de-ghosting, circular error probable and cumulative probability which results in robust radar to ESM association.
,CLAIMS:1. A system for providing track associations, said system comprising:
a radar (301) deployed at an origin, said radar configured to detect reflected electromagnetic waves from targets;
a local electronic support measure (ESM) (302) deployed at the origin, said local ESM configured to detect reflected electromagnetic waves from the targets;
a remote electronic support measure (ESM) (303) deployed at a remote position, said remote ESM configured to detect reflected electromagnetic waves from the targets; and
a processor (305) operatively coupled with a memory (306), said memory storing instructions executable by the processor to:
receive, from the radar, radar tracks pertaining to location of the targets;
receive, from the local ESM, bearing data pertaining to location of the targets;
receive, from the remote ESM, remote bearing data pertaining to location of the targets;
generate, based on remote bearing data, and positions of the origin and the remote position, a cut-off zone, wherein the bearing data existing inside the cut-off zone is considered;
generate, for at least three derived parameters obtained from the local ESM and the remote ESM, a suitable gate each, wherein the bearing data that passes a logical interaction of the at least three gates is considered; and
determine, for the targets, cumulative probabilities, based on radar tracks, considered bearing data and considered remote bearing data,
wherein, when the cumulative probability exceeds a predetermined threshold, track association of the radar, the local ESM and the remote ESM is declared.
2. The system as claimed in claim 1, wherein the remote position is any of a stationary position and a moving position.
3. The system as claimed in claim 1, wherein the processor is configured to distinguish between target points and ghost points to fuse the bearing data and the remote bearing data based on difference in bearing variance from radar, local ESM and second ESM for a plurality of samples.
4. The system as claimed in claim 3, wherein, the processor is configured to calculate circular probable error based on bearing variance from radar, local ESM and second ESM for a plurality of samples for localisation of bearing data.
5. The system as claimed in claim 1, wherein the derived parameters are selected from a group comprising frequency, pulse width, pulse repetitive frequency, scan time, radar type, identity, platform, radar name and a combination thereof.
6. The system as claimed in claim 5, wherein the at least three derived parameters are frequency, pulse width and pulse repetitive frequency.
7. A method for providing track associations, said method comprising the steps of:
receiving (901), at a computing device, from a radar (301) deployed at an origin, said radar configured to detect reflected electromagnetic waves from targets, radar tracks pertaining to location of the targets;
receiving (902), at the computing device, from a local electronic support measure (ESM) (302) deployed at the origin, said local ESM configured to detect reflected electromagnetic waves from the targets, bearing data pertaining to location of the targets;
receiving (903), at the computing device, from a remote electronic support measure (ESM) (303) deployed at a remote position, said remote ESM configured to detect reflected electromagnetic waves from the targets, remote bearing data pertaining to location of the targets;
generating (904), at the computing device, based on remote bearing data, and positions of the origin and the remote position, a cut-off zone, wherein the bearing data existing inside the cut-off zone is considered;
generating (905), at the computing device, for at least three derived parameters obtained from the local ESM and the remote ESM, a suitable gate each, wherein the bearing data that passes a logical interaction of the at least three gates is considered; and
determining (906), at the computing device, for the targets, cumulative probabilities, based on radar tracks, considered bearing data and considered remote bearing data,
wherein, when the cumulative probability exceeds a predetermined threshold, track association of the radar, the local ESM and the remote ESM is declared.