Claims:1. A system (700) for detecting formation of surface targets, the system (700) comprising:
a computing device (702) comprising a processor (704) communicably coupled with a memory (706), the memory (706) storing instruction executable by the processor (704), the computing device (702) configured to:
receive, from a database (750) communicably coupled to the computing device (702), information related to the plurality of targets;
classify targets based on identity of targets into a plurality of hostile/unknown targets;
select one or more attributes of interest for the plurality of hostile/unknown targets;
cluster the plurality of hostile/unknown targets into one or more clusters based on the one or more attributes of interest,
wherein each cluster comprises two or more hostile/unknown targets having similarities based on the selected one or more attributes; and
determine a commonality coefficient for the one or more clusters,
wherein the commonality coefficient is indicative of a presence of one or more same targets in the one or more clusters and in corresponding one or more previous clusters formed during a previous iteration, and
wherein the computing device is configured to assign a cluster a same identification tag as an identification tag during the previous iteration if the commonality coefficient is the cluster is above a predefined threshold value.

2. The system (700) as claimed in claim 1, wherein the database (750) is a command, control, communications, computers, and intelligence system.
3. The system (700) as claimed in claim 1, wherein the attributes of interest comprise radial speed of the targets, course of the targets and position of the targets.
4. The system (700) as claimed in claim 1, wherein the similarities comprise at least one of a distance between neighbouring targets and a minimum number of points in a cluster.
5. The system (700) as claimed in claim 1, wherein the computing device (702) is configured to assign an identity to each cluster and an identity to each of the targets within the cluster.
6. A method (800) for detecting formation of surface targets, the method (800) comprising:
receiving (802), at the computing device (702), from a database (750) communicably coupled to the computing device (702), information related to the plurality of targets;
classifying (804), by the computing device (702), targets based on identity of targets into a plurality of hostile/unknown targets;
selecting (806), by the computing device (702), one or more attributes of interest for the plurality of hostile/unknown targets; and
clustering (808), by the computing device (702), the plurality of hostile/unknown targets into one or more clusters based on the one or more attributes of interest,
wherein each cluster comprises two or more hostile/unknown targets having similarities based on the selected one or more attributes;
determining (810), by the computing device (702), a commonality coefficient for the one or more clusters,
wherein the commonality coefficient is indicative of a presence of one or more same targets in the one or more clusters and in corresponding one or more previous clusters formed during a previous iteration; and
assigning (812), by the computing device (702), a cluster a same identification tag as an identification tag during the previous iteration if the commonality coefficient is the cluster is above a predefined threshold value.
7. The method (800) as claimed in claim 6, wherein the database (750) is a command, control, communications, computers, and intelligence system.
8. The method (800) as claimed in claim 6, wherein the attributes of interest comprise radial speed of the targets, course of the targets and position of the targets.
9. The method (800) as claimed in claim 6, wherein the similarities comprise at least one of a distance between neighbouring targets and a minimum number of points in a cluster.
10. The method (800) as claimed in claim 6, wherein the computing device (702) is configured to assign an identity to each cluster and an identity to each of the targets within the cluster.

, Description:TECHNICAL FIELD
[1] The present disclosure relates to clustering of targets for naval warfare.

BACKGROUND
[2] 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.
[3] Generally, surface target group detection is a critical and crucial part in naval warfare scenarios. Warfare is seldom carried out by a single ship, but it is carried out by convoys moving in fleet formation (clusters). Before the execution of warfare, surface target group or formation is detected and on the basis of identification of surface target group, target group attribute calculation is done to perform situation analysis. Situation analysis is not only limited to a single surface target, but it includes multiple surface targets. Hence, it is important to make clusters of surface targets efficiently. Clustering is the task of grouping a set of targets in such a way that targets in the same cluster have more similarities with each other than with those in other clusters.
[4] There is, therefore, a requirement for performing clustering of targets in a robust and efficient manner.

OBJECTS OF THE INVENTION
[5] An object of the present invention is to provide a system and method for detecting formation of surface targets.
[6] Another object of the present invention is to provide a system and method that is noise resistant.
[7] Another object of the present invention is to provide a system and method that can handle clusters of various shapes and sizes.
[8] Another object of the present invention is to provide a system and method that labels clusters and maintains the clusters.
[9] Another object of the present invention is to provide a system and method that considers radial speed with respect to other targets and maintains the group even when targets move with different speeds.

SUMMARY
[10] In a first aspect, the present disclosure provides a system for detecting formation of surface targets. The system includes a computing device including a processor communicably coupled with a memory, the memory storing instruction executable by the processor. The computing device is further configured to receive, from a database communicably coupled to the computing device, information related to the plurality of targets. The computing device is further configured to classify targets based on identity of targets into a plurality of hostile/unknown targets. The computing device is further configured to select one or more attributes of interest for the plurality of hostile/unknown targets. The computing device is further configured to cluster the plurality of hostile/unknown targets into one or more clusters based on the one or more attributes of interest. Each cluster includes two or more hostile/unknown targets having similarities based on the selected one or more attributes. The computing device is configured to determine a commonality coefficient for the one or more clusters. The commonality coefficient is indicative of a presence of one or more same targets in the one or more clusters and in corresponding one or more previous clusters formed during a previous iteration. The computing device is configured to assign a cluster a same identification tag as an identification tag during the previous iteration if the commonality coefficient is the cluster is above a predefined threshold value.
[11] In some embodiments, the database is a command, control, communications, computers, and intelligence system.
[12] In some embodiments, the attributes of interest comprise radial speed of the targets, course of the targets and position of the targets.
[13] In some embodiments, the similarities comprise at least one of a distance between neighbouring targets and a minimum number of points in a cluster.
[14] In some embodiments, the computing device is configured to assign an identity to each cluster and an identity to each of the targets within the cluster.
[15] In a second aspect, the present disclosure provides a method for detecting formation of surface targets. The method includes receiving, at the computing device, from a database communicably coupled to the computing device, information related to the plurality of targets. The method further includes classifying, by the computing device, targets based on identity of targets into a plurality of hostile/unknown targets. The method further includes selecting, by the computing device, one or more attributes of interest for the plurality of hostile/unknown targets. The method further includes clustering, by the computing device, the plurality of hostile/unknown targets into one or more clusters based on the one or more attributes of interest. Each cluster comprises two or more hostile/unknown targets having similarities based on the selected one or more attributes. The method further includes determining, by the computing device, a commonality coefficient for the one or more clusters. The commonality coefficient is indicative of a presence of one or more same targets in the one or more clusters and in corresponding one or more previous clusters formed during a previous iteration. The method further includes assigning, by the computing device, a cluster a same identification tag as an identification tag during the previous iteration if the commonality coefficient is the cluster is above a predefined threshold value.
[16] In some embodiments, the database is a command, control, communications, computers, and intelligence system.
[17] In some embodiments, the attributes of interest comprise radial speed of the targets, course of the targets and position of the targets.
[18] In some embodiments, the similarities comprise at least one of a distance between neighbouring targets and a minimum number of points in a cluster.
[19] In some embodiments, the computing device is configured to assign an identity to each cluster and an identity to each of the targets within the cluster.
[20] 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 DRAWINGS
[21] 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.
[22] FIG. 1 illustrates a schematic flowchart for a method for detecting formation of surface target, according to an embodiment of the present disclosure;
[23] FIG. 2 illustrates a schematic representation of splitting clusters and maintaining group label, according to an embodiment of the present disclosure;
[24] FIG. 3 illustrates a schematic representation of symmetric and asymmetric clusters, according to an embodiment of the present disclosure;
[25] FIG. 4 illustrates a schematic representation of merging of clusters and maintaining group label, according to an embodiment of the present disclosure;
[26] FIG. 5 illustrates a schematic representation of consideration of radial speed and maintaining group label, according to an embodiment of the present disclosure;
[27] FIG. 6 illustrates a schematic representation of calculation of radial speed, according to an embodiment of the present disclosure;
[28] FIG. 7 illustrates a schematic block diagram for a system for detecting formation of surface targets, according to an embodiment of the present disclosure;
[29] FIG. 8 illustrates a schematic flow chart for a method for detecting formation of surface targets, according to an embodiment of the present disclosure; and
[30] FIG. 9 illustrates an exemplary schematic block diagram of a hardware platform for implementation of the system.

DETAILED DESCRIPTION
[31] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such details 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.
[32] Generally, surface target group detection is a critical and crucial part in naval warfare scenarios. Warfare is seldom carried out by a single ship, but it is carried out by convoys moving in fleet formation (clusters). Before the execution of warfare, surface target group or formation is detected and on the basis of identification of surface target group, target group attribute calculation is done to perform situation analysis. Situation analysis is not only limited to a single surface target, but it includes multiple surface targets. Hence, it is important to make clusters of surface targets efficiently. Clustering is the task of grouping a set of targets in such a way that targets in the same cluster have more similarities with each other than with those in other clusters. One criterion used for clustering is the intra cluster distance, which is typically less than inter cluster distance.
[33] In a first aspect, the present disclosure provides a system for detecting formation of surface targets. The system includes a computing device including a processor communicably coupled with a memory, the memory storing instruction executable by the processor. The computing device is further configured to receive, from a database communicably coupled to the computing device, information related to the plurality of targets. The computing device is further configured to classify targets based on identity of targets into a plurality of hostile/unknown targets. The computing device is further configured to select one or more attributes of interest for the plurality of hostile/unknown targets. The computing device is further configured to cluster the plurality of hostile/unknown targets into one or more clusters based on the one or more attributes of interest. Each cluster includes two or more hostile/unknown targets having similarities based on the selected one or more attributes. The computing device is configured to determine a commonality coefficient for the one or more clusters. The commonality coefficient is indicative of a presence of one or more same targets in the one or more clusters and in corresponding one or more previous clusters formed during a previous iteration. The computing device is configured to assign a cluster a same identification tag as an identification tag during the previous iteration if the commonality coefficient is the cluster is above a predefined threshold value.
[34] In some embodiments, the database is a command, control, communications, computers, and intelligence system.
[35] In some embodiments, the attributes of interest comprise radial speed of the targets, course of the targets and position of the targets.
[36] In some embodiments, the similarities comprise at least one of a distance between neighbouring targets and a minimum number of points in a cluster.
[37] In some embodiments, the computing device is configured to assign an identity to each cluster and an identity to each of the targets within the cluster.
[38] In a second aspect, the present disclosure provides a method for detecting formation of surface targets. The method includes receiving, at the computing device, from a database communicably coupled to the computing device, information related to the plurality of targets. The method further includes classifying, by the computing device, targets based on identity of targets into a plurality of hostile/unknown targets. The method further includes selecting, by the computing device, one or more attributes of interest for the plurality of hostile/unknown targets. The method further includes clustering, by the computing device, the plurality of hostile/unknown targets into one or more clusters based on the one or more attributes of interest. Each cluster comprises two or more hostile/unknown targets having similarities based on the selected one or more attributes. The method further includes determining, by the computing device, a commonality coefficient for the one or more clusters. The commonality coefficient is indicative of a presence of one or more same targets in the one or more clusters and in corresponding one or more previous clusters formed during a previous iteration. The method further includes assigning, by the computing device, a cluster a same identification tag as an identification tag during the previous iteration if the commonality coefficient is the cluster is above a predefined threshold value.
[39] In some embodiments, the database is a command, control, communications, computers, and intelligence system.
[40] In some embodiments, the attributes of interest comprise radial speed of the targets, course of the targets and position of the targets.
[41] In some embodiments, the similarities comprise at least one of a distance between neighbouring targets and a minimum number of points in a cluster.
[42] In some embodiments, the computing device is configured to assign an identity to each cluster and an identity to each of the targets within the cluster.
[43] The major processes involved in clustering are information mining and grouping. Information mining involves selection of features which can define group properties, while grouping involves identification of similar targets based on features received from the information mining. Once the clustering is done, a decision is taken regarding the attack on the centroid of the clusters so that the highest possible damage can be done. Hence, grouping surface targets (clustering) is the major step in warfare execution. The centroid is calculated as an average position of all the targets of the clusters, given equal weightage to all the targets. In general centroid can be found by vector addition of the position vectors which point to the center of the cluster.
[44] For targets position along the x-axis, the centroid is given as,

[45] Where, xr indicated x-position of target Tr, and n indicated number of targets in a cluster.
[46] For targets position along the y-axis, the centroid is given as,

[47] Where, yr indicated y-position of target Tr.
[48] The present disclosure provides a methodology for dynamic detection and management of a group of surface targets in an efficient way towards designing an efficient Surface Target Formation Detection Method. The present disclosure particularly provides a new clustering algorithm based on DBSCAN specialized for the problem of surface target group formation.
[49] FIG. 1 illustrates a schematic flowchart for a method for detecting formation of surface target, according to an embodiment of the present disclosure. At step 101, the method includes identification of an area of interest, i.e., an area in which there is a higher likelihood of presence of surface target groups.
[50] At step 102, the method includes collection of information about the surface targets present in a command, control, communications, computers, and intelligence (C4I) system. Data collected from various sensors is fused and a common tactical picture (inclusive of kinematic and affinity of targets in surrounding environment) is prepared. The proposed method receives the common picture as input and filters tracks on basis of position and affinity. The threat targets are distinguished on the basis of identity of the targets. Hostile/unknown targets are considered to be the threat targets.
[51] At step 103, the method includes selection of the attributes of threat surface targets so that similar kind of surface targets is clustered into a group. The attributes include speed, course and position of the threat surface targets for clustering. For instance, targets having very small value of radial speed with respect to each other are clustered into the same group. In another instance, targets which move in the same direction are clustered into the same group. In a further instance, based on distance between targets, position of threat surface targets are determined. Steps 102 and 103 may together form the feature selection steps.
[52] At step 104, the method includes preprocessing data of the surface target attributes collected in the step 102. Two main attributes – MinPoint (minimum number of points in a cluster) and epsilon (Distance between neighboring targets) are taken into consideration.
[53] At step 105, the method includes execution of density-based clustering algorithm MOD-DBSCAN considering all the attributes described above. The clusters are formed after implementation and group id is uniquely assigned to the clusters for better understanding of the targets and a modifier is set to the threat surface targets which are part of the clusters so that it is clearly understood which target is a part of the cluster.
[54] At step 106, the steps 101 to 105 are repeated periodically.
[55] The present method, i.e., MOD-DBSCAN (Modified – Density-Based Spatial Clustering of Applications with Noise) considers two arguments – epsilon and minPonts. The method works as follows –
• two points are considered neighbors if they lie within epsilon of each other;
• core points are determined as points which have at least minPoints number of neighbors;
• border points are determined as points which are not core points but are neighbor to a core point;
• noise points are determined as points which are neither core point nor border point;
• Thus, there are three types of points – core points, border points, and noise points;
• noise points are removed from point list;
• starting from any one core point, all its neighbors are included in the cluster. the core points among these neighbors are selected and their neighbors are added to the cluster;
• the same step is repeated until all neighbors are traversed;
• Distance between any two tracks depends on: Euclidean distance, radial speed fraction and course difference; and
• MOD-DBSCAN clustering algorithm maintains the group ids for better decision making for warfare.

Commonality coefficient between two sets A and B, where set A indicates cluster formed in a previous iteration, and set B indicates cluster formed in a current iteration, is given as, n(A∩B)/n(A) + n(A∩B)/n(B).
Pseudo code for calculation of commonality coefficient:
FOR previousCount = 0 to previous solution clusters
VARIANT commonality = 0;
FOR newCount=0 to new solution clusters
VARIANT commonality2 = (previous solution clusters[previousCount]).calculateCommonality(new solution clusters[newCount]);
IF commonality2 > commonality
commonality = commonality2
cluster_with_largest_commonality = new solution clusters[newCount]
IF commonality > 10
Assign group id of previous solution cluster[previousCount] to new solution cluster[newCount]
VARIANT calculateCommonality(DbCluster newCluster) – It will return the commonality factor between new cluster and old cluster by calculating the number of targets common in both the clusters.
Hence, commonality coefficient is calculated to check the commonality between old clusters and new clusters formed. Further, the calculation of commonality coefficient leads to the maintenance of labeling of clusters. Threshold value of commonality is taken for same group id assignment.
Pseudo code for labelling of group id on each iteration:
Let group_list_previous = clusters of previous MOD-DBSCAN calculation (empty initially)
WHILE true
Let t = current time
Let target_list = list of targets at time t (track_kinematics structure is given as an input to the method to retrieve target information)
Let group_list = list of clusters obtained by applying MOD-DBSCAN on target_list provided the parameters mentioned above.
Compare group_list to group_list_previous so that each group of targets has consistent group_id. Say group_list_1 has majority of the targets which were in group_list_previous_1. So group_list_1 will be assigned group id same as group_list_previous_1 ignoring the new group id.
group_list_previous = group_list
sleep(2)
[56] FIG. 2 illustrates a schematic representation of splitting clusters and maintaining group label, according to an embodiment of the present disclosure. Cluster 201 indicates a cluster formed with group id “G1” in Surface Target Formation Detection System, which consists of targets T1, T2, T3, T4, T5 and T6. 202 indicates a path of motion of the targets which are clustered in cluster 201. T1, T2, T3 and T4 targets are moving in the same direction and T5 and T6 targets are moving in the same direction. When targets are moving in the opposite direction, clusters are split. Cluster 203 indicates a cluster formed of targets T1, T2, T3 and T4 and after split, this cluster will be labelled “G1” as it contains majority of the targets which were covered in original cluster 201. Cluster 204 indicates another cluster formed which will be labelled “G2”. So, after splitting of clusters, larger group gets identifier of original group and here larger group indicates the majority of the targets covered which were in original cluster.
[57] FIG. 3 illustrates a schematic representation of symmetric and asymmetric clusters, according to an embodiment of the present disclosure. The MOD-DBSCAN can create clusters of any shape or size. The shape can be symmetric or non-symmetric and the size of the cluster is formed in such a way that it covers all the targets in the clusters and the smallest covering shape will be formed. Cluster 301 indicates the noisy target which is not covered in any cluster. Clusters 302 and 303 indicate different shape and size clusters depending upon the number and position of targets.
[58] FIG. 4 illustrates a schematic representation of merging of clusters and maintaining group label, according to an embodiment of the present disclosure. Cluster 401 indicates the cluster formed with group id “G1” in Surface Target Formation Detection System which consists of targets T1, T2 and T3. Cluster 402 indicates the cluster formed with group id “G2” in Surface Target Formation Detection System which consists of targets T4 and T5. 403 indicates the path of motion of the targets which are clustered in clusters 401 and 402, which are moving in the same direction. When targets are moving in the same direction and come in contact with epsilon distance, clusters are merged. Cluster 404 indicates cluster formed which consists of targets T1, T2, T3, T4 and T5 and after merging. This cluster will be labelled “G1” as it contains majority of the targets which were covered in cluster 401. So, after merging of clusters, cluster gets identifier of dominant group and here dominant group indicates the majority of the targets covered which are in merged cluster.
[59] FIG. 5 illustrates a schematic representation of consideration of radial speed and maintaining group label, according to an embodiment of the present disclosure. Cluster 501 indicates cluster which was formed in cluster 203 (shown in FIG. 2). 502 indicates the speed of tracks T1, T2, T3 and T4 in a particular direction. Target T1 is moving fastest while T4 is slowest because formation is taking circular turn. So, we take radial speed into consideration instead of absolute velocity difference so that the formation can also be detected/ maintained in such scenario. Cluster 503 indicates that the group (G1) is maintained as radial speed with respect to each other is taken instead of absolute velocity difference.
[60] FIG. 6 illustrates a schematic representation of calculation of radial speed, according to an embodiment of the present disclosure. 601 indicates the speed of track T2 in a particular direction. 602 indicates the speed of track T1 in a particular direction. 603 indicates the relative velocity of tracks T2 and T1. 604 indicates the line connecting tracks T2 and T1 which is called radial line. 605 indicates the component of relative velocity along the radial line which is called radial component. 606 indicates the component of relative velocity normal to radial line which is called tangential component.
Radial component = v * cos(α)
Tangential component = v * sin(α)
[61] Radial component is given as an input to MOD-DBSCAN algorithm instead of tangential component. When a formation as explained in FIG. 5 is taking circular turn, the radial component of T1 with respect to T2 will be zero but the tangential component will be velocity difference between tracks T1 and T2 which is called absolute velocity difference. So, if a tangential component is considered instead of radial component, tracks T1 and T2 will not be clustered together but if a radial component is taken, T1 and T2 will be clustered together as radial component will be close to zero on taking circular turn. The same case is with all the tracks T1, T2, T3 and T4 with respect to each other.
[62] FIG. 7 illustrates a schematic block diagram for a system 700 for for detecting formation of surface targets, according to an embodiment of the present disclosure. The system 700 includes a computing device 702 including a processor 704 communicably coupled with the memory 706. The memory 706 stores instructions (not shown) executable by the processor 704.
[63] In some embodiments, the processor 704 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, the processor 704 may be configured to fetch and execute computer-readable instructions stored in the memory 706 for facilitating the system 700 to cluster targets. Any reference to a task in the present disclosure may refer to an operation being or that may be performed on data. The memory 706 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium for clustering of targets. The memory 706 may include 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. In some embodiments, the computing device 702 may include an interface 708. The interface 708 may include 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 interface 708 may also provide a communication pathway for one or more components of the computing device 702. Examples of such components include, but are not limited to, the processing engine 710 and a database 750.
[64] In some embodiments, the computing device 702 may include the processing engine 710. The processing engine 710 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine 710. 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 710 may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine 710 may include 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 710. In such examples, the computing device 702 may include 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 computing device 702 and the processing resource. In other examples, the processing engine 710 may be implemented by electronic circuitry.
[65] In some embodiments, the processing engine 710 may include a clustering engine 712, and other engine(s) 714. The clustering engine 712 may be configured to perform one or more functions associated with clustering of targets. The other engine(s) 714 may include engines configured to perform one or more functions ancillary functions associated with the processing engine 710.
[66] FIG. 8 illustrates a schematic flow chart for a method 800 for detecting formation of surface targets, according to an embodiment of the present disclosure. At step 802, the method 800 includes receiving, at the computing device 702, from a database 750 communicably coupled to the computing device 702, information related to the plurality of targets. At step 804, the method 800 further includes classifying, by the computing device 702, targets based on identity of targets into a plurality of hostile/unknown targets. At step 806, the method 800 further includes selecting, by the computing device 702, one or more attributes of interest for the plurality of hostile/unknown targets. At step 808, the method 800 further includes clustering, by the computing device 702, the plurality of hostile/unknown targets into one or more clusters based on the one or more attributes of interest. Each cluster includes two or more hostile/unknown targets having similarities based on the selected one or more attributes. At step 810, the method further includes determining, by the computing device 702, a commonality coefficient for the one or more clusters. The commonality coefficient is indicative of a presence of one or more same targets in the one or more clusters and in corresponding one or more previous clusters formed during a previous iteration. At step 812, the method 800 further includes assigning, by the computing device 702, a cluster a same identification tag as an identification tag during the previous iteration if the commonality coefficient is the cluster is above a predefined threshold value.
[67] FIG. 9 illustrates an exemplary schematic block diagram of a hardware platform for implementation of the system 700. As shown in FIG. 9, a computer system 900 can include an external storage device 910, a bus 920, a main memory 930, a read only memory 940, a mass storage device 950, communication port 960, and a processor 970. A person skilled in the art will appreciate that the computer system may include more than one processor and communication ports. Examples of processor 970 include, but are 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 chip processors or other future processors. Processor 970 may include various modules associated with embodiments of the present invention. Communication port 960 can 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 fibre, a serial port, a parallel port, or other existing or future ports. Communication port 960 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system connects. Memory 930 can be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read-only memory 940 can 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 970. Mass storage 950 may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), 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.
[68] Bus 920 communicatively couples processor(s) 970 with the other memory, storage, and communication blocks. Bus 920 can 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 connects processor 970 to software system.
[69] Optionally, operator and administrative interfaces, e.g., a display, keyboard, and a cursor control device, may also be coupled to bus 920 to support direct operator interaction with a computer system. Other operator and administrative interfaces can be provided through network connections connected through communication port 960. The external storage device 910 can 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.
[70] 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 “comprise” 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.
[71] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions, or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE INVENTION
[72] The present invention provides a system and method for detecting formation of surface targets.
[73] The present invention provides a system and method that is noise resistant.
[74] The present invention provides a system and method that can handle clusters of various shapes and sizes.
[75] The present invention provides a system and method that labels clusters and maintains the clusters.
[76] The present invention provides a system and method that considers radial speed with respect to other targets and maintains the group even when targets move with different speeds.