Method and apparatus for providing a dynamic target impact point sweetener

An apparatus for providing a dynamic target impact point sweetener may include memory and a processor. The memory may store at least a target library indicating respective target parameters for a plurality of known potential targets. The processor may be configured by stored instructions to generate a composite multi-dimensional representation of a target based on radar data received at the apparatus from other aerial vehicles collecting projections over an area in which the target is located and based on radar data collected by an aerial vehicle in which the apparatus is located, identify the target based on the composite multi-dimensional representation, and generate aimpoint data regarding the target based on an identity of the target. The aimpoint data defining the most vulnerable point on the target.

TECHNOLOGICAL FIELD

Embodiments of the present disclosure relate generally to image data processing for tomographic image reconstruction and, more particularly, to the use of tomographic image reconstruction for improving a missile aimpoint such that the missile impacts the most vulnerable point on the target.

BACKGROUND

Missiles of one form or another had been used in combat for centuries prior to the development of guided missile technology in the World War II era. Since then numerous technologies were developed in order to guide missiles to their targets. The use of some form of radiation (e.g., laser or radio waves) has been a common element in many of these guided missiles. However, as advancements in missile guidance have improved, target sophistication continues to improve as well. The cost and complexity of each missile, although being typically only a fraction of the cost and complexity of most targets, makes it ever more imperative that each missile that is fired should be as effective as possible. Accordingly, it becomes increasingly desirable to continue to develop enhancements in missile guidance systems to further the likelihood of success when such weapons are employed.

Missiles with increasingly more sophisticated guidance systems have been developed over the years. In fact, more recently missiles have been developed that can communicate with a central node, such as the platform that launched the missiles, to receive guidance updates in-flight. However, as target sophistication increases, it can be expected that access to the links between launched missiles and a remote central node may be denied in the future. Thus, it may be desirable to develop missile technologies that improve the ability of missiles to not only hit targets, but hit them in the best possible location for strategic impact.

BRIEF SUMMARY

Some embodiments of the present disclosure relate to the provision of an improved guidance system for use with guided missiles. Some example embodiments may be able to provide dynamic determinations to be made regarding preferred points at which to attack a particular target. Moreover, these determinations may be made on a vehicle (e.g., a missile) that is prosecuting an attack on the target. Accordingly, there may be no requirement for the vehicle to be able to communicate with a common platform (e.g., the platform that launched the vehicle) in order to enable such dynamic determinations.

In one example embodiment, an apparatus for providing a dynamic target impact point sweetener is provided. The apparatus may include memory and a processor. The memory may store at least a target library indicating respective target parameters for a plurality of known potential targets. The processor may be configured by stored instructions to generate a composite multi-dimensional representation of a target based on radar data received at the apparatus from other aerial vehicles collecting projections over an area in which the target is located and based on radar data collected by an aerial vehicle in which the apparatus is located, identify the target based on the composite multi-dimensional representation, and generate aimpoint data regarding the target based on an identity of the target. That aimpoint will define the most vulnerable point on the target.

In another example embodiment, a system for providing a dynamic target impact point sweetener is provided. The system may include a plurality of aerial vehicles that are each configured to generate radar data based on projections over an area in which a target is located. One of the plurality of aerial vehicles may be configured to act as a master vehicle and remaining ones of the plurality of aerial vehicles may be configured to act as slave vehicles. The slave vehicles may be configured to provide radar data generated by respective ones of the slave vehicles to the master vehicle. The master vehicle may be configured to generate a composite multi-dimensional representation of the target based on radar data received from the slave vehicles and radar data collected by the master vehicle, identify the target based on the composite multi-dimensional representation, and generate aimpoint data regarding the target based on an identity of the target. That aimpoint will define the most vulnerable point on the target.

In another example embodiment, a method for providing a dynamic target impact point sweetener is provided. The method may include receiving, at a master aerial vehicle, radar data corresponding to a target from at least one slave aerial vehicle in communication with the master vehicle, generating a composite multi-dimensional representation of the target based on radar data received from the at least one slave aerial vehicle and radar data generated by the master aerial vehicle, identifying the target based on the composite multi-dimensional representation, and generating aimpoint data regarding the target based on an identity of the target. That aimpoint will define the most vulnerable point on the target.

DETAILED DESCRIPTION

As discussed above, continued efforts are being made to define enhancements to guided missile technology. As an example, U.S. Pat. No. 7,642,953 to Cheng et al., which shares a common assignee with the present application, describes the use of three dimensional (3D) tomographic image reconstruction to develop an accurate image of a target. According to U.S. Pat. No. 7,642,953, the contents of which are incorporated herein by reference, a central node is in communication with a plurality of vehicles to collect radar data about an object. The central node, which may be for example, the platform that launched the vehicles, uses radar return data corresponding to the object from each of the vehicles (e.g., missiles) to generate the 3D image of the object.

Example embodiments contemplate that it may not always be possible, or perhaps even desirable to communicate with the central node. For example, the communication environment may be hostile enough to make sharing the radar return data with the central node difficult or even impossible. Example embodiments may therefore enable the sharing of radar return data among the vehicles themselves, without reliance on the central node. Moreover, some example embodiments may enable each of the vehicles to have the ability to generate the 3D image using data collected locally and combined with data provided from other vehicles. Thus, one or more of the vehicles themselves may generate a composite 3D image of the object (e.g., target) so that an accurate assessment of the target may be confirmed. The one or more vehicles may then share the composite 3D image of the object with other vehicles so that, for example, prosecution of an attack on the object may occur with improved confidence in the target's identity.

Some example embodiments may further enable each of the vehicles to have the ability to act as a master vehicle that actually does the composite 3D image generation using data provided from the other vehicles. Although one vehicle may act as the master vehicle, any of the slaves may take over the role of master, if desired or needed.

In some example embodiments, the composite 3D image may be compared to a target image library to determine a vulnerable region (or sweet spot) on the object. For example, the target image library may store data on various classes of targets (or specific hull numbers in some instances) that indicates the location of the engine room, the control center, ammunition storage or other vulnerable areas on the object. The master vehicle may, as an alternative or addition to providing the composite 3D image to other vehicles, provide information indicative of the location of the vulnerable region of the object to the slaves so that the vehicles can each more specifically target the vulnerable region. As such, example embodiments of the present invention may enable the vehicles themselves to dynamically employ a target impact point sweetener to more effectively attack a target, even in the absence of communication with the launching platform or some other remote controlling node.

FIG. 1illustrates a system for creating a three-dimensional (3D) image of an object according to an example embodiment. The system ofFIG. 1illustrates a plurality of vehicles (e.g., vehicles100,102and104) that may be associated with a launch platform106or other central node. The launch platform106or central node may be a manned aircraft or even a ship or other ground-based platform. In the example ofFIG. 1, the vehicles100,102and104are capable of obtaining radar data of an object (e.g., target object108) on the surface of the earth110. The surface of the earth could be either ground or water. It should also be appreciated that although three vehicles and one launch platform are shown inFIG. 1, any number of vehicles and launch platforms could be employed in some embodiments (e.g., including fewer or more vehicles and perhaps more launch platforms).

In an example embodiment, the vehicles100,102and104may be any combination of different types of missiles, unmanned aerial vehicles (UAVs) or aircraft that may be capable of obtaining radar data pertaining to the target object108. As such, for example, the vehicles100,102and104may each include hardware (e.g., antennas and corresponding processing equipment) for projecting beams or cones of electromagnetic radiation from corresponding radar systems on each of the vehicles100,102and104onto the target object108and then collecting the data that returns from those beams or cones. In this example, vehicle100projects cone112, vehicle102projects cone114, and vehicle104projects cone116.

In response to these cones being projected, the different vehicles may each collect the signals that return from a corresponding one of the cones112,114and116to generate respective different partial views of the target object108. Conventional computer assisted tomography that is used for medical purposes to image a part of the body, typically employs a plurality of fixed sensors to receive return data from respective different angles that correspond to the positioning of the sensors at a given time. Similarly, example embodiments may provide a system that can generate a plurality of different views of the target object108based on the return data received. However, due to movement of the vehicles100,102and104, the projected cones112,114and116are in relatively constant motion to generate respective different views of the target object108. Thus, the effects of a medical tomographic image may be reproduced with far fewer sensors since the moving sensors provide for different views over time. Each of the vehicles100,102and104may collect its own data that is reflective of the views it has generated over time while receiving radar data corresponding to the target object108. The radar data may be generated responsive to active transmissions by one or more of the vehicles100,102and104(or even the launch platform106). Each of these respective partial images that are generated by the vehicles100,102and104may then be fed to a single master vehicle (e.g., vehicle100). The master vehicle, which is one of the vehicles instead of being the launch platform106, may more easily communicate with the other vehicles since it is typically closer in proximity to the other vehicles. In an example embodiment, the vehicles100,102and104may communicate with each other using communication links118. The master vehicle may then generate a composite 3D image of the object based on the radar data received from each of the other vehicles (which may be considered to be slave vehicles).

In an example embodiment, the vehicles100,102and104may coordinate (e.g., under control of the master vehicle) to obtain the collection of certain views to provide improved image construction. For example, if a particular aspect or angle of the target object108is not accurately represented based on the image data received (e.g., as determined by the master vehicle responsive to generation of the 3D composite image), the master vehicle may identify the missing aspect or angle to one or more of the slaves and the slaves (and/or the master) may maneuver accordingly to attempt to obtain a view of the target object108that corresponds to the missing aspect or angle.

Example embodiments of the present invention enable the use of radar images to examine (e.g., with the corresponding cones112,114and116) an area of uncertainty (AOU)119around the target object108in order to enable generation of a relatively complete image of the AOU119and the target object108therein. The vehicles100,102and104may fly around the target object108, which may itself also be moving. Thus, the AOU119may be moving. Moreover, in some cases, as indicated above, coordination of the flight paths of the vehicles100,102and104may be accomplished via the communication links118to provide for control over the formation and/or movement of the vehicles100,102and104to improve the quality and/or completeness of the images received therefrom. As such, a relatively accurate composite 3D image of the target object108may be generated over time to enable identification of the target object108.

The master vehicle (e.g., vehicle100) may receive radar data from each of the other vehicles and combine the received radar data with the radar data collected locally at the master vehicle in order to generate a composite 3D image of the target object108. In some embodiments, the composite 3D image may be a three dimensional image of the corresponding object on the ground or on the water. The composite 3D image may, in some cases, also include data indicative of some internal features of the target object108in instances where sufficient power is able to be used to project the cones in a fashion that permits the transmitted waves to penetrate (at least to some degree) the target object108. The composite 3D image (with or without data indicative of internal features) may then be compared to a target library to determine an accurate model and/or identity of the target object108as described in greater detail below. Once the target object108has been identified (or its identity confirmed), aimpoint data may be generated and shared with the other vehicles based on the class or identity of the target object108. The aimpoint data may then be used by the vehicles to guide prosecution of an attack on the target object108based on vulnerabilities of the target object108as determined by the identity or classification of the target object108. This aimpoint data will define the most vulnerable point on the target.

Accordingly, example embodiments may provide for observation of a target to be performed by a plurality of vehicles in which at least one of the vehicles is capable of guiding the observation and also performing tomographic reconstruction of a composite 3D image of the target using data received from the vehicles. The corresponding one of the vehicles may also be configured to identify the target based on the composite 3D image and share information determined based on the identity (e.g., the composite 3D image itself and/or aimpoint data for the identified target) with the other vehicles.

FIG. 2illustrates a block diagram of hardware that may be employed on the master vehicle or any vehicle that can function as the master vehicle. It should be appreciated that, as indicated above, in some embodiments all vehicles have the capability of functioning as the master vehicle. Thus, each vehicle may, in some embodiments, include the structure described inFIG. 2.

As shown inFIG. 2, the vehicles100,102and104may include a data processing system200to process data received responsive to locally received radar returns or return data received by other vehicles and generate the composite 3D image of an object. The data processing system200may include a communication bus202or other communication fabric to provide communication between the various components of the data processing system200. The data processing system200components may include a processor204, a memory206, a communication unit208and an input/output unit210.

The processor204may be embodied in a number of different ways. For example, the processor204may be embodied as various processing means such as a processing element, a coprocessor, a controller or various other hardware processing devices including integrated circuits such as, for example, an ASIC (application specific integrated circuit), an FPGA (field programmable gate array), a hardware accelerator, or the like. In an exemplary embodiment, the processor204may be configured to execute instructions stored in a memory device (e.g., memory206) or otherwise accessible to the processor204. By executing stored instructions or operating in accordance with hard coded instructions, the processor204may control the operation of the data processing system200by directing functionality of the data processing system200associated with implementing composite 3D image generation and target identification described herein according to the respective configuration provided to the data processing system200by the processor204and/or the instructions stored in memory206for configuring the processor204. As such, whether configured by hardware or software methods, or by a combination thereof, the processor204may represent an entity capable of performing operations according to embodiments of the present invention while configured accordingly.

The memory206may include, for example, volatile and/or non-volatile memory. The memory206may be configured to store information, instructions and/or the like. For example, the memory206could be configured to buffer data for processing by the processor204or prior to transmission or responsive to reception. Additionally or alternatively, the memory206could be configured to store instructions for execution by the processor204. The memory206may be an integrated part of the data processing system200or may be a removable memory device.

In some embodiments, the communication unit208may include hardware, and in some cases also software for configuring the hardware, for enabling the data processing system200to interface with other devices and users, if applicable. Thus, for example, if the data processing system200is embodied as the master vehicle, the data processing system200may include circuitry and/or components to enable inter-device interface (e.g., via the communication links118). As such, the communication unit208may include wired and/or wireless interface circuitry such as an antenna (or antennas) and corresponding transmit and receive circuitry to enable wireless communication with other devices over a radio access technology.

In an example embodiment, the input/output unit210may provide for connection to any other modules that may be used in connection with the data processing system200. Thus, for example, the input/output unit210may provide for an interface with a radar system for generating transmissions and receiving and/or processing return data. The input/output unit210may also provide for any other interface needed with other components to provide, receive, process, store, or otherwise manipulate data that may be generated or used within the data processing system200.

In an example embodiment, the processor204and/or the memory206may comprise portions of processing circuitry configured to cause the data processing system200to perform functionality according to the configuration either hardwired into the processor204or provided by the execution of instructions stored in the memory206. As such, the data processing system200may be configured to control processes associated with composite 3D image reconstruction and target identification along with the provision of aimpoint data to other vehicles as described herein. Thus, for example, the data processing system200may represent an apparatus that may be configured (e.g., by execution of stored instructions) to generate a composite three dimensional representation of a target based on radar data received at the apparatus from other aerial vehicles generating projections over an area in which the target is located (e.g., the AOU119) and based on radar data generated by an aerial vehicle in which the apparatus is located. The apparatus may be further configured to identify the target based on the composite three dimensional representation, and generate aimpoint data regarding the target based on an identity of the target. This aimpoint data will define the most vulnerable point on the target. In an example embodiment, the apparatus may include memory storing at least an updateable target library indicating respective target parameters for a plurality of known potential targets. The processor204may be further configured to communicate the aimpoint data from the apparatus, acting as a master vehicle, to at least one of the other aerial vehicles acting as a slave vehicle. In some embodiments, identifying the target may include comparing the composite three dimensional representation of the target to a plurality of known representations of targets in the target library to determine the identity of the target based on a degree of matching between the composite three dimensional representation and one of the known representations. In some cases, generating aimpoint data may include utilizing characteristics regarding vulnerable locations within the target based on the identity of the target to generate coordinates for an aimpoint for attacking the target. In an example embodiment, the processor204may be further configured to shift the apparatus from a master vehicle status to a slave vehicle status thereby causing the apparatus to stop generating the composite three dimensional representation, identifying the target and generating the aimpoint data and instead causing the apparatus to provide radar data generated by the aerial vehicle to one of the other aerial vehicles acting as a master vehicle and receive aimpoint data from the master vehicle. In some embodiments, the processor204may be further configured to define a transit time by which the processor204is to identify the target and during which the aerial vehicles collect radar data regarding the target as described below.

In some embodiments, the processor204may be configured to control (e.g., via execution of corresponding instructions) various functional components used to define a processing unit300(seeFIG. 3) that may carry out example embodiments. However, in some cases, the processing unit300may actually be embodied by the processor204(e.g., by execution of instructions for performing the corresponding functions of the processing unit300).FIG. 3illustrates various components of the processing unit300according to an example embodiment. The processing unit300may include a filter302, a token maker304, a token bank306, a sinogram unit308and an optimization algorithm unit310. The filter302may be a local filter that filters radar data collected by the processing unit300before the radar data is processed by the processor204for composite 3D image reconstruction, target identification and/or aimpoint data provision. As used herein a portion of the radar data collected for an object by the processing unit300may be referred to as a projection. In an example embodiment, the filter302may receive projections312and store filtered projections314along with a token316in a batch buffer317for communication to the master vehicle (or for use locally for composite 3D image reconstruction and target identification along with the provision of aimpoint data when the vehicle employing the processing unit300is the master vehicle). The data stored in the batch buffer317may be communicated via the communication links118to the master vehicle or used locally if the processing unit300is associated with a vehicle acting as the master vehicle.

The batch buffer317may be configured to store multiple tokens and associated filter projections for transmission in batches. In some cases, the batch buffer317may provide for matching the data rates between the communication links employed and generation of filtered projections314by filter302. The token maker304may generate tokens (e.g., token316) that are grouped for batch communication to the master vehicle. Generally speaking, a token may be generated by the token maker304after the radar system completes one cycle of data collection that is processed by the filter302. A cycle may refer to the minimum amount of time needed to collect and process one portion of radar data or one projection including conditioning and filtering, as appropriate. Each token may include an identification of the corresponding vehicle on which the processing unit300is located, a location of the vehicle (e.g., at the time the corresponding radar data is generated), a time stamp of when the radar data was generated, etc. Other information that may be included in the token may include sensor operating parameters or any other information that may be used to identify what portions of the radar data are provided (and therefore what other portions may be needed to produce a complete composite 3D image).

The sinogram unit308may be an example of a sampling unit that may determine what samples or portions of radar data are to be collected. In some cases, the sinogram unit308may use tokens stored in the token bank306to generate a pictorial representation of samples in a sampling process for a three dimensional tomographic reconstruction process. The sinogram unit308may be configured to identify different portions or projections of radar data that have been collected by the processing unit300(and other processing units for the processing unit of the master vehicle) based on the tokens. One or more sinograms326may be stored in the sinogram unit308based on the operation of the sinogram unit308as described herein.

The optimization algorithm unit310may be configured to receive the sinograms326and cue data328as inputs to generate control instructions330. The cue data328may include information regarding the current location of the object on which data is being collected (e.g., from the master vehicle) and/or estimated feature data regarding the object. The control instructions330may be used by the sinogram unit308to update portions of the radar data collected for use in reconstructing the composite 3D image of the object and may include instructions for flight control and/or sensor control in some cases. The flight control instructions may be used to direct the vehicle to locations that may enable collection of data corresponding to missing portions of the radar data.

In an example embodiment, the processor204(ofFIG. 2) may control operation of the processing unit300for collection of radar data and may then, in the master vehicle, use the radar data collected from various different processing units of each of the vehicles to generate a composite 3D image of the object (e.g., the target object108). The composite 3D image of the object may then be used to identify (or confirm the identification of) the object based on comparison of the composite 3D image to known target data. In an example embodiment, the memory206may store a target library including image data for various different potential targets. In some cases, a publication such as Jane's Fighting Ships or other source may be used to provide data that can be used for comparison. As such, the processor204may be configured to determine the class or type of target that the object corresponds to and, in some cases, even perhaps the hull number of certain distinctive targets. The disclosure of commonly owned U.S. Patent Application Publication No. 2008/0308670 to Meyer et al., which is incorporated herein by reference, describes an example of the use of a target image library for comparing target image data to that of stored image data to determine a specific target.

Example embodiments of the present disclosure may utilize the identification of a specific target (e.g., target object108) to determine vulnerabilities of the corresponding target. As discussed above, the location of the engine room, the control center, ammunition storage or other vulnerable areas on the target object108may be determined based on known information about the corresponding identified object. This information may then be used to generate aimpoint data that may be provided from the master vehicle to other vehicles. In some examples, the aimpoint data may include coordinate data such as global positioning system (GPS) coordinates that indicate the location of a vulnerable location on the target object108. As such, the aimpoint data may identify a “sweet spot” for hitting the specific identified target. In some embodiments, the aimpoint data may be accompanied with or otherwise include the composite 3D image data as well.

In some example embodiments, the processor204may control operation of a target classifier230configured to identify or classify targets based on a comparison of the composite 3D image to known target data from a target library232(e.g., stored in the memory206). The identity or classification of the target may then be used by an aimpoint generator234to generate aimpoint data as described above. Since each vehicle may be able to operate as the master, each vehicle may have a target library232on board. However, in some embodiments, only the master vehicle may actually employ the target library232. The target library232may be updateable via a software modification.

As indicated previously, in some cases each of the vehicles (e.g., vehicles100,102and104) may have the capability of acting as a master vehicle. The launch platform106may specifically identify one of the vehicles as the master vehicle in some cases. However, in other cases, other criteria may be used for selection of the master vehicle and in some cases that selection may be made automatically by the vehicles themselves and a vehicle determining that it is or should be the master vehicle may announce to the other vehicles via the communication links118. Criteria such as order of launch (e.g., first or last launched), order of arrival on scene or acquisition of the target (e.g., first or last to acquire contact with the target), processing or communication capability (e.g., if different vehicles are employed and have differing capabilities), battery or power level, device state (e.g., normal condition, interference level, damage status, etc.) and other factors may all be used to set specific conditions under which a particular vehicle may declare itself to be the master vehicle or relinquish master vehicle status to a more qualified vehicle. For example, if a particular vehicle is first on scene and declares itself to be the master vehicle initially, the particular vehicle may later transfer (or request another device to declare itself master) the master vehicle status to another vehicle in response to the particular vehicle taking damage (or losing communication completely), or having a low battery level which might limit its processing capabilities.

In some embodiments, a transit time may be defined for the vehicles100,102and104to define the time that is allotted to collect data on the object to build an image of the object. The transit time may be programmed into each of the vehicles at launch, or may be communicated to the vehicles by the master vehicle. In some embodiments, the master vehicle may process data received to generate the composite 3D image as soon as sufficient data is available to generate the image and make a match to a corresponding image in the target library232. However, if sufficient data has not yet been received by the time the transit time expires, the master vehicle may process available data and estimate the identity of the object based on the available data at the time of expiration of the transit time. Some embodiments may enable the master vehicle to extend the transit time one or more times for predefined intervals under certain conditions. However, some embodiments may define a maximum transit time (or minimum transit time) to ensure that a strike can be initiated within certain predetermined parameters.

After the master vehicle provides aimpoint data to the vehicles, attack on the target object108may be authorized either by virtue of the aimpoint data being provided or by separate message providing such authorization. In some cases, one or more of the vehicles that have been provided with the aimpoint data (e.g., those vehicles that are missiles) may simply attack the target based on the aimpoint data. However, in some other examples, the vehicles may continue to generate radar data on the target as they approach the target to further confirm (or even modify) the aimpoint data based on perceivable differences between the aimpoint data provided and the current position of the target (e.g., due to evasive maneuvers or other factors that may change target location or orientation). In both cases, the aimpoint data will define the most vulnerable point on the target.

FIG. 4illustrates a process flow for operation of a master vehicle according to an example embodiment. As indicated at operation400, the master vehicle may establish communication with slave vehicles. The master vehicle may receive radar data corresponding to a target that is collected by slave vehicles at operation402. The master vehicle may also locally generate radar data corresponding to the target at operation404. At operation406, the locally generated radar data and the received radar data may be processed to generate a composite 3D image of the target. The composite 3D image of the target may be used to identify the target at operation408after which time aimpoint data may be generated based on the identity of the target at operation410. The aimpoint data may be provided to the slave vehicles at operation412. At operation414, the master vehicle and/or the slave vehicles may attack the target based on the aimpoint data.

FIG. 5illustrates a process flow for operation of a slave vehicle according to an example embodiment. At operation500, the slave vehicle may establish communication with the master vehicle. The slave vehicle may transmit, to the master vehicle, radar data corresponding to a target that is collected by the slave vehicle at operation502. The slave vehicle may monitor periodically for continued communication with the master vehicle at operation504. If communication is lost (or if an instruction is received to switch to or assume master status), the slave vehicle may assume the role of master vehicle at operation506and shift to operation according toFIG. 4. However, if communication is maintained, the slave vehicle may continue to provide radar data to the master vehicle until the transit time is reached or until aimpoint data is received from the master vehicle at operation508. At operation510, the slave vehicle may attack the target based on the aimpoint data. That aimpoint data will define the most vulnerable point on the target.