Surveillance satellite, satellite constellation and integrated data library

Abstract

To provide a flight path prediction method capable of accurately predicting a flight path of a glide projectile.SOLUTION: A model database 350 stores a plurality of flight path models. Starting with launch detection information of a flying object 520 detected by a monitoring satellite 100-0, a ground system 340 references the model database 350, analyzes flying object monitoring information measured by a subsequent monitoring satellite 100-1, and eliminates incompatible flight path models, and then analyzes flying object monitoring information measured by a next subsequent monitoring satellite 100-2 and eliminates incompatible flight path models. This processing is repeated. The ground system 340 determines a flight path model that remains without being eliminated as a tentative flight path prediction model. Based on flying object monitoring information measured by a plurality of subsequent monitoring satellites 100-1, 100-2, etc. that follow the monitoring satellite 100-0, the ground system 340 corrects an amount of deviation from the tentative flight path prediction model and predicts the flight path of the flying object 520 until impact.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a flight path prediction method, a ground system, a flight path model, a flight path prediction device, a flying object tracking system, a flying object countermeasure system, an integrated data library, a surveillance satellite, and a satellite constellation. [Background technology]

[0002] There is a technology for comprehensively monitoring an area at a specific latitude on the entire surface of the Earth using a satellite constellation (for example, Patent Document 1).

[0003] Furthermore, there is technology for predicting the trajectory of projectiles on ballistic trajectories using flight path models. However, with the recent emergence of gliding projectiles, which repeatedly fire intermittently, the variety of flight path models has increased compared to projectiles on ballistic trajectories. Furthermore, there is a problem with gliding projectiles, in that their intermittent firing can cause deviation from the flight path model. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2008-137439 Summary of the Invention [Problem to be solved by the invention]

[0005] The present disclosure aims to provide a flight path prediction method that accurately predicts the flight path of a glide bullet. [Means for solving the problem]

[0006] The monitoring satellite of the present disclosure includes: A flight path prediction method for predicting a flight path of an airborne object by analyzing, with a ground system, airborne object monitoring information acquired by a monitoring satellite of a satellite constellation including a plurality of monitoring satellites each equipped with an infrared monitoring device, the method comprising: The ground system includes: a database storing a plurality of flight path models, which are a plurality of modeled flight paths of the projectile, including launch position coordinates, flight direction, time-series flight distance from launch to impact, and flight altitude profile of the projectile; The ground system includes: The elapsed time after the launch detection measured by the infrared monitoring device of the follow-up monitoring satellite using the plurality of flight path models, starting from the launch detection information of the flying object detected by the infrared monitoring device of the monitoring satellite; The flight distance of the flying object; The flight altitude of the flying object; Analyzing the airborne object monitoring information measured by the follow-up monitoring satellite, including the following, and excluding incompatible flight path models from the plurality of flight path models; repeatedly performing a process of excluding incompatible flight path models from the plurality of flight path models by analyzing the flight path monitoring information measured by the next follow-on monitoring satellite; The remaining flight path model that has not been excluded is determined as the provisional flight path prediction model, a flight path prediction method for predicting the flight path of the flying object up to impact by correcting the deviation amount from the tentative flight path prediction model based on flying object monitoring information measured by a plurality of follow-up monitoring satellites following the monitoring satellite that detected the launch detection information of the flying object; A surveillance satellite in Orbital information of the monitoring satellite; Location information of the response asset; a plurality of flight path models, which are models configured using the launch position coordinates, flight direction, time-series flight distance from launch to impact, and flight altitude profile of the flying object, and which are models of flight paths; The edge server includes a database that stores at least one of the above. [Effects of the Invention]

[0007] According to the present disclosure, a flight path prediction method can be provided that accurately predicts the flight path of a glide bullet. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 10 is a diagram according to the first embodiment, showing a flight path prediction method 335. [Figure 2] FIG. 1 is a diagram of the first embodiment, showing an example of the configuration of a satellite constellation forming system 600. [Figure 3] FIG. 1 is a diagram of the first embodiment, showing an example of the configuration of a satellite 620 of the satellite constellation forming system 600. [Figure 4] FIG. 10 is a diagram according to the first embodiment, showing an example of a flight path model of a ballistic flying object 521 as viewed from a certain altitude. [Figure 5] FIG. 10 is a diagram according to the first embodiment, showing an example of a flight path model of a ballistic flying object 521 in the distance direction and height direction. [Figure 6] FIG. 10 is a diagram of the first embodiment, showing a model of the flight path of a flying object that is intermittently ejected. [Figure 7] FIG. 1 is a diagram according to the first embodiment, showing the hardware configuration of a flight path prediction device 490. [Figure 8] FIG. 1 is a diagram of the first embodiment, showing a flying object tracking system 360. [Figure 9] FIG. 3 is a diagram of the first embodiment, showing a flying object countermeasure system 370. [Figure 10] FIG. 10 is a diagram of the second embodiment, showing the layout of the integrated data library. [Figure 11] FIG. 10 is a diagram of the second embodiment, showing the hardware configuration of the integrated data library. [Figure 12] FIG. 10 is a diagram of the second embodiment, showing a satellite equipped with an edge server. [Figure 13] FIG. 10 is a diagram of the second embodiment, showing a satellite equipped with an artificial intelligence computer. [Figure 14] FIG. 10 is a diagram of the second embodiment, showing a satellite constellation having a circular communication network and a mesh communication network. DETAILED DESCRIPTION OF THE INVENTION

[0009] In the description of the embodiments and drawings, the same elements and corresponding elements are denoted by the same reference numerals. The description of elements denoted by the same reference numerals will be omitted or simplified as appropriate. In the following embodiments, "unit" may be read as "circuit," "step," "procedure," "process," or "circuitry" as appropriate.

[0010] Embodiment 1 ***Configuration Description*** 1 is a diagram showing a flight path prediction method 335. In the flight path prediction method 335, a satellite constellation 610 is composed of monitoring satellites 100 equipped with infrared monitoring devices. The infrared monitoring devices are a first infrared monitoring device 101 and a second infrared monitoring device 102, as described below. The flight path prediction method 335 of the first embodiment is a path prediction method in which the flight object monitoring information of the flight object 520 acquired by the satellite constellation 610 is analyzed by a ground system 340, and a path prediction of the flight object 520 is performed. Details will be described later.

[0011] 2 and 3, an example of satellites 620 and ground equipment 700 in a satellite constellation forming system 600 that forms a satellite constellation 610 will be described. The satellite constellation forming system 600 is an integrated satellite constellation. The satellite constellation forming system 600 may be simply referred to as a satellite constellation.

[0012] Fig. 2 shows an example configuration of a satellite constellation forming system 600. The satellite constellation forming system 600 includes a computer. While Fig. 2 shows the configuration of one computer, in reality, a computer is provided for each of the multiple satellites 620 that make up the satellite constellation 610 and for each of the ground facilities 700 that communicate with the satellites 620. The computers provided for each of the multiple satellites 620 and for each of the ground facilities 700 that communicate with the satellites 620 work together to realize the functions of the satellite constellation forming system 600. An example configuration of a computer that realizes the functions of the satellite constellation forming system 600 will be described below.

[0013] The satellite constellation forming system 600 includes a satellite 620 and a ground facility 700. The satellite 620 includes a communication device 622 that communicates with a communication device 950 of the ground facility 700. In Fig. 2, the communication device 622 is illustrated as one of the components included in the satellite 620.

[0014] The satellite constellation forming system 600 includes a processor 910, as well as other hardware such as a memory 921, an auxiliary storage device 922, an input interface 930, an output interface 940, and a communication device 950. The processor 910 is connected to the other hardware via signal lines and controls the other hardware.

[0015] The satellite constellation forming system 600 includes, as a functional element, a satellite constellation forming unit 911. The functions of the satellite constellation forming unit 911 are realized by hardware or software. The satellite constellation forming unit 911 controls the formation of the satellite constellation 610 while communicating with the satellites 620.

[0016] 3 shows an example of the configuration of a satellite 620 of the satellite constellation forming system 600. The satellite 620 includes a satellite control device 621, a communication device 622, a propulsion device 623, an attitude control device 624, a power supply device 625, and a monitoring device 626. The satellite 620 may include other components that realize various functions, but FIG. 3 will explain the satellite control device 621, the communication device 622, the propulsion device 623, the attitude control device 624, the power supply device 625, and the monitoring device 626. The satellite 620 in FIG. 3 is an example of a monitoring satellite 100.

[0017] The satellite control device 621 is a computer that controls the propulsion device 623 and the attitude control device 624, and includes a processing circuit. Specifically, the satellite control device 621 controls the propulsion device 623 and the attitude control device 624 in accordance with various commands transmitted from the ground facility 700. The communication device 622 is a device that communicates with the ground facility 700. Alternatively, the communication device 622 is a device that communicates with satellites 620 before and after in the same orbital plane, or with satellites 620 in adjacent orbital planes. Specifically, the communication device 622 transmits various data related to its own satellite to the ground facility 700 or other satellites 620. In addition, the communication device 622 receives various commands transmitted from the ground facility 700. The propulsion device 623 is a device that provides thrust to the satellite 620, changing the speed of the satellite 620. The attitude control device 624 is a device for controlling attitude elements such as the attitude of the satellite 620, the angular velocity of the satellite 620, and the line of sight (LOS). The attitude control device 624 changes each attitude element to a desired direction. Alternatively, the attitude control device 624 maintains each attitude element in a desired direction. The attitude control device 624 includes an attitude sensor, an actuator, and a controller. The attitude sensor is a device such as a gyroscope, an Earth sensor, a sun sensor, a star tracker, a thruster, and a magnetic sensor. The actuator is a device such as an attitude control thruster, a momentum wheel, a reaction wheel, and a control moment gyro. The controller controls the actuator according to measurement data from the attitude sensor or various commands from the ground equipment 700. The power supply unit 625 includes devices such as solar cells, batteries, and a power control device, and supplies power to each device on board the satellite 620. The monitoring device 626 is a device for monitoring objects. Specifically, the monitoring device 626 is a device for monitoring or observing objects such as space objects, flying objects, or land, sea, and air vehicles. The monitoring device 626 is also called an observation device. For example, the monitoring device 626 is an infrared monitoring device that uses infrared rays to detect the temperature rise caused by atmospheric friction when a flying object enters the atmosphere. The monitoring device 626 detects the temperature of the plume or the flying object itself at the time of launch. Alternatively, the monitoring device 626 may be an information gathering device using light waves or radio waves. The monitoring device 626 may be a device that detects objects using an optical system. The monitoring device 626 uses an optical system to photograph objects flying at an altitude different from the orbital altitude of the observation satellite. Specifically, the monitoring device 626 may be a visible optical sensor.

[0018] The processing circuit provided in the satellite control device 621 will now be described. The processing circuit may be dedicated hardware or a processor that executes a program stored in memory. In the processing circuit, some functions may be realized by dedicated hardware and the remaining functions may be realized by software or firmware. In other words, the processing circuit may be realized by hardware, software, firmware, or a combination of these. Specifically, the dedicated hardware may be a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination of these. ASIC is an abbreviation for Application Specific Integrated Circuit. FPGA is an abbreviation for Field Programmable Gate Array.

[0019] <Method of forming a satellite constellation> The following describes a satellite constellation 610 formed by the satellite constellation system 600. The satellite constellation 610 is formed by a ground facility 700 controlling satellites 620.

[0020] 1, in the flight path prediction method 335, airborne object monitoring information acquired by a monitoring satellite 100 of a satellite constellation 610 including multiple monitoring satellites 100 equipped with infrared monitoring devices is analyzed by a ground system 340. Here, the infrared monitoring devices are a first infrared monitoring device 101 and a second infrared monitoring device 102, as will be described later. The ground system 340 includes a model database 350 that stores multiple flight path models, which are multiple modeled flight paths for the flying object, including the launch position coordinates, flight direction, time-series flight distance from launch to impact, and flight altitude profile of the flying object 520. The ground system 340 starts from the launch detection information of the flying object 520 detected by the infrared monitoring device (101, 102) of the monitoring satellite 100-0 and uses a plurality of flight path models in the model database 350 to analyze the flying object monitoring information measured by the follow-on monitoring satellite 100-1, including the elapsed time after launch detection measured by the infrared monitoring device (101, 102) equipped on the follow-on monitoring satellite 100-1, the flight distance of the flying object 520, and the flight altitude of the flying object 520. The ground system 340 then excludes incompatible flight path models from the multiple flight path models in the model database 350, and analyzes the airborne object monitoring information measured by the next follow-on monitoring satellite 100-2. Through this analysis, the ground system 340 repeatedly performs a process of eliminating incompatible trajectory models from among the multiple trajectory models. The ground system 340 determines the trajectory model that remains after repeated processing without being excluded as the tentative trajectory prediction model. The ground system 340 corrects the deviation from the provisional flight path model based on the flight path monitoring information measured by multiple follow-on monitoring satellites 100-1, 100-2, etc. that follow the monitoring satellite 100-0 that detected the launch detection information of the flight path 520, and predicts the flight path of the flight path 520 until impact.

[0021] 4 shows an example of a flight path model of a ballistic flying object 521 as viewed from a certain altitude. In FIG. 4, the launch area and impact area of ​​the ballistic flying object 521 are shown. 5 shows an example of a flight path model in the distance and height directions of a ballistic flying object 521. In FIG. 5, the horizontal axis represents the flight distance of the ballistic flying object 521, and the vertical axis represents the altitude of the ballistic flying object 521. Figure 6 shows a model of the flight path of an intermittently jetting projectile. Figure 6 corresponds to Figure 5. In Figure 6, the horizontal axis represents the flight distance of the intermittently jetting projectile, and the vertical axis represents the altitude of the intermittently jetting projectile.

[0022] If the flying object 520 that poses a security threat is a ballistic flying object, the launch area where it is expected to be launched and the impact area where it is expected to land can be assumed in advance, as shown in Figure 3. Therefore, it is possible to set a typical flight path model, including the distance from the launch area to the impact area, the flight direction, the arrival time, and the trajectory and arrival altitude in the case of ballistic flight. In recent years, the emergence of gliding projectiles, which repeatedly eject projectiles intermittently, has led to an increase in the variety of flight path models compared to ballistic missiles. However, for gliding missiles, it is possible to assume a flight path model to the impact area as a flight model in which the missile glides through the upper atmosphere after the initial thrust at launch has ended. Even if there is deviation from the flight path model due to intermittent thrust, the amount of change is small in both the vertical and horizontal directions when compared to the overall profile of the predicted flight path from launch to impact. Therefore, by assuming a typical flight path model as a provisional flight path and then correcting it using measurement information from monitoring satellites and actual trajectory measurement results, a highly accurate predicted flight path can be generated.

[0023] Referring to Figure 1, a plurality of monitoring satellites 100 are equipped with a first infrared monitoring device 101 pointing toward the center of the earth and a second infrared monitoring device 102 pointing toward the edge of the earth. The first infrared monitoring device 101 detects the high temperature spray that accompanies the launch of the projectile, which is to be the starting point of the trajectory model. The second infrared monitoring device 102 detects the main body of the flying object, whose temperature has risen after the end of the jet, against the background of space, and serves as reference information for the flight path model during flight.

[0024] See Figure 1. The high-temperature spray called a plume that accompanies the thrust of the propulsion device at launch spreads over a wide area at a high temperature. Therefore, the plume that is monitored by the infrared monitoring device 101, which is oriented toward the center of the earth, can be detected and distinguished from infrared radiation from the earth's surface. On the other hand, the temperature of the flying object itself, which has risen after the end of the injection, does not reach as high a temperature as the spray, and the monitoring target is limited to the dimensions of the flying object itself. Therefore, the infrared monitoring device 101, which is oriented toward the center of the earth, has the problem that it is buried in infrared radiation information from the earth's surface, called clutter, making it difficult to identify. Therefore, a monitoring method oriented toward the edge of the earth, called limb observation, using the second infrared monitoring device 102, has the effect of monitoring the flying object against the background of space, making it possible to identify the flying object as a bright spot against a low-temperature background.

[0025] FIG. 7 shows the hardware configuration of the trajectory prediction device 490. The trajectory prediction device 490 is a computer. The trajectory prediction device 490 includes a processor 910A. In addition to the processor 910A, the trajectory prediction device 490 includes other hardware such as a memory 921A, an auxiliary storage device 922A, an input interface 930A, an output interface 940A, and a communication device 950A. The processor 910A is connected to the other hardware via signal lines and controls the other hardware. The functions of the processor 910A, the memory 921A, the auxiliary storage device 922A, the input interface 930A, the output interface 940A, and the communication device 950A are similar to the functions of the processor 910, the memory 921, the auxiliary storage device 922, the input interface 930A, the output interface 940, and the communication device 950 described in FIG. 2, and therefore will not be described again.

[0026] The trajectory prediction device 490 includes, as a functional element, a trajectory model selection unit 351. The function of the trajectory model selection unit 351 is realized by a program. Furthermore, the auxiliary storage device 922A realizes the model database 350.

[0027] Model database 350 of flight path prediction device 490 stores multiple flight path models, which are multiple modeled flight paths for a flying object, including the flight object's launch position coordinates, flight direction, time-series flight distance from launch to impact, and flight altitude profile. Flight path model selection unit 351 uses the flight object monitoring information detected by monitoring satellite 100 as a starting point and compares the flight object monitoring information measured by the follow-up monitoring satellite with the multiple flight path models, and selects the flight path model with the least deviation from the multiple flight path models stored in model database 350.

[0028] The trajectory model selection unit 351 compares the selected trajectory model with the airborne object monitoring information measured by the monitoring satellite 100, corrects the deviation amount, and generates a corrected trajectory prediction result.

[0029] 8 shows a missile tracking system 360. The missile tracking system 360 includes a satellite constellation 610 and a ground system 340. The missile tracking system 360 performs launch detection and tracking of a missile 520. The ground system 340 includes a flight path prediction device 490.

[0030] 9 shows a missile response system 370. The missile response system 370 includes a satellite constellation 610, a ground system 340, and response assets 332. The ground system 340 includes a flight path prediction device 490, and transmits missile information to response assets 332 in the vicinity of the predicted flight path by referring to the prediction results of the flight path prediction device 490.

[0031] ***Effects of the First Embodiment*** According to the flight path prediction method, ground system, flight path model, flight path prediction device, flying object tracking system, flying object countermeasure system, and ground system of embodiment 1, the flight path of a glide bullet can be predicted with high accuracy.

[0032] Embodiment 2 A second embodiment will be described with reference to Figures 10 to 14. In the second embodiment, an integrated data library 380 having a database 381, a monitoring satellite 100 equipped with an edge server 390 having the database 381, a monitoring satellite 100 equipped with an artificial intelligence computer 391, and a satellite constellation forming a hybrid constellation will be described.

[0033] <Integrated Data Library 380> In recent years, the diversification of threats and the diversification of surveillance, communication, and response systems has increased the need for Joint All Domain Command & Control (JADC2), in which various ground centers operate using a common database. Ground centers can also be read as domains. Commonly used databases can be used as a Unified Data Library (UDL) in a cloud or edge computing environment, making it possible to share information among various ground centers. Furthermore, the concept of a space data center using satellite IoT has been proposed, and it will also be possible to share information in a space data center.

[0034] <Integrated Data Library> FIG. 10 shows an integrated data library 380 according to the second embodiment. The integrated data library 380 is a library that is referenced by at least one of the monitoring satellite 100 and the ground system 340 in the flight path prediction method of the first embodiment. The integrated data library 380 includes the following components, as shown in FIG. 11 : Orbital information of the monitoring satellite 100; Location information of response asset 332; Multiple flight path models; The system includes a database 381 that stores at least one of the above. Here, the multiple flight path models are models constructed using the launch position coordinates, flight direction, time-series flight distance from launch to impact, and flight altitude profile of the flying object 520, and are multiple models in which the flight path is modeled. As shown in FIG. 10, the integrated data library 380 is located on the ground, but the integrated data library 380 may also be located on a satellite.

[0035] 11 shows the hardware configuration of the integrated data library 380. The integrated data library 380 is a computer. The integrated data library 380 includes a CPU 382, ​​a communication device 383, and a storage device 384. The storage device 384 implements the database 381.

[0036] <Cloud Computing: Satellite equipped with Edge Server 390> As the amount of information increases with the advancement of the information society, the increase in power consumption and heat dissipation measures have become issues. In particular, in centralized systems, the increase in power consumption and heat dissipation measures for supercomputers and large-scale data centers have become serious issues. Meanwhile, in space, heat can be released into deep space through radiative cooling. Therefore, it is possible to deploy a supercomputer or data center to realize a cloud environment on the satellite constellation side, perform calculations in orbit, and then transmit only the necessary data to users on Earth. This will contribute to the achievement of the SDGs on Earth by maintaining a cloud environment and reducing greenhouse gas emissions.

[0037] Edge Computing Edge computing, which places edge servers on the IoT side, is attracting attention as a method for realizing a distributed architecture. Conventional IoT typically uses a centralized system where data collected by sensors is sent to the cloud via the internet for analysis. In contrast, edge computing uses a system where data processing is distributed to the device itself or to edge servers installed between the device and the cloud, achieving real-time, low-load data processing. Furthermore, with the increasing volume of information that accompanies the advancement of the information society, the increase in power consumption and heat dissipation measures have become issues. In particular, in centralized systems, the increasing power consumption and heat dissipation measures of supercomputers and large-scale data centers have become serious issues. On the other hand, in outer space, heat can be released into deep space through radiative cooling, so it is reasonable to treat satellites as devices in IoT, place edge servers on the satellite constellation side, and transmit only necessary data to the ground after performing distributed computing processing in orbit. A hybrid constellation has the effect of realizing low latency and centralized data management by exchanging information with a cloud equipped with a data center in ground equipment 700 via a circular or mesh communication network.

[0038] In FIG. 10, the monitoring satellite 100 in the flight path prediction method of the first embodiment may be configured to include an edge server 390 having a database 381 . 12 shows a configuration in which a monitoring satellite 100 is equipped with an edge server 390 having a database 381. Note that the monitoring device of the monitoring satellite 100 is omitted in FIG. 12. The hardware configuration of the edge server 390 is the same as that of the integrated data library 380 in FIG. 11.

[0039] <Artificial Intelligence Computer 391> Below, we will explain about artificial intelligence, which is sometimes written as AI. Artificial intelligence neural networks can be divided into supervised learning, in which the network is optimized for a problem by inputting a teacher signal (correct answer), and unsupervised learning, in which a teacher signal is not required. By training the system in advance with flight models for various types of projectiles and propellants, and using them as training models for multiple typical flight patterns, it becomes easier and faster to infer from the actual measurement data obtained by detecting launches and acquiring trajectory information. As a result of this inference, the projectile's trajectory and landing position can be predicted. However, in order to predict the flight path of a flying object whose flight direction is unknown during the launch detection stage, it is necessary for subsequent monitoring satellites to track and monitor the flying object.Furthermore, in order to transmit the launch detection information to the subsequent monitoring satellites, the launch detection information must be transmitted via a communication network formed by a constellation of communication satellites. In a communications network using a communications satellite constellation, the flight positions of the communications satellites change constantly, so it is necessary to search for the optimal communications route and determine the ID of the communications satellite that will exchange information about the satellite, as well as the time of transmission and reception. This situation is also true for the exchange of information about satellites between monitoring satellites and communications satellites. When optimal route search is performed by a ground system, it is necessary to send commands to the monitoring satellite and communication satellite, including the time to send and receive information about the flying object and the satellite ID. However, the communication network for sending these commands poses a challenge. Therefore, it would be rational for communication satellites to be equipped with AI-based analytical devices, search for optimal routes in orbit, and generate and communicate commands in orbit to communication satellites that make up the communication route. An effective method for searching for the optimal route in orbit is to use an algorithm known as the Dijkstra algorithm. While the weighting for each route remains constant in the static Dijkstra algorithm, in a communications network formed by a communications satellite constellation, the weighting for each route changes over time as the satellites' flight positions change. Therefore, for each communications satellite that performs an optimal route search while updating its orbital information, the communications satellite that receives the satellite information performs an optimal route search and transmits the satellite information to the next communications satellite, repeating this process.

[0040] In addition, there are known two methods for route search: breadth-first search and depth-first search. For launch detection information, breadth-first search prioritizes transmitting information about the missile to the communication network as quickly as possible, and once tracking is repeated by subsequent satellites and the flight direction can be roughly estimated, it is rational to perform depth-first search.

[0041] In the flying object tracking system, the flying object is tracked and monitored by repeating the above-mentioned machine learning flight path prediction and Dijkstra's algorithm route search, and the final landing position is inferred.

[0042] Furthermore, after repeated tracking of flying objects, machine learning is performed on past tracking results, and deep learning is performed on examples of flying object behavior that differ from the multiple flying object models used as training models. This makes it possible to improve the accuracy and speed up predictions of flying object trajectories.

[0043] Since there are differences between the flight direction and distance of projectiles launched from mobile launch platforms (TELs) rather than fixed launch platforms and typical flight models, it is effective to complement the trajectory model using deep learning on actual measurement data.

[0044] 13 shows a configuration in which a monitoring satellite 100 is equipped with an artificial intelligence computer 391. A monitoring satellite 100 equipped with an edge server 390 having a database 381 may also be configured to be equipped with the artificial intelligence computer 391. The artificial intelligence computer 391 autonomously determines a transmission destination for the flying object information by referencing the database 381, and transmits the flying object information to the determined transmission destination. The artificial intelligence computer has the effects described above in <Artificial Intelligence Computer>.

[0045] Figure 14 shows a satellite constellation 20 according to embodiment 2. In embodiment 2, the satellite constellation shown in Figure 14 is configured as the satellite constellation 20 formed by a monitoring satellite 100 and a communication satellite 200 that constitute an airborne object tracking system that performs airborne object launch detection and airborne object tracking using the flight path prediction method described in embodiment 1. The satellite constellation in Figure 14 is as follows. A plurality of communication satellites 200, each equipped with a communication device for communicating with satellites in front and behind it in the direction of travel in the same orbital plane, form a communication constellation of a circular communication network 21. and, A monitoring satellite 100 equipped with communication devices for communicating with satellites before and after it flies between a plurality of communication satellites 200 that form a communication constellation of a circular communication network 21. In Figure 14, the same orbital plane 23 is shown as a representative example of the same orbital plane. The surveillance satellite 100 and a plurality of communication satellites 200 that form a communication constellation reconstruct a circular communication network 21, or reconstruct a mesh communication network 22 with adjacent orbits, forming a hybrid constellation for surveillance and communication.

[0046] A hybrid constellation is a constellation that achieves multiple missions, including both non-communication missions such as observation and positioning, and communication missions. This constellation is formed when communication satellites that form a communication network simultaneously carry mission equipment other than communication, such as observation and positioning, or when satellites other than communication satellites, such as observation satellites and positioning satellites, simultaneously carry communication equipment that serves as part of the communication network. Information can be exchanged between ground users and the hybrid constellation via a circular communication network 21 or a mesh communication network 22. This also has the advantage of enabling low-latency centralized data management between each satellite that makes up the hybrid constellation and distributed computing, considered the Internet of Things (IoT). Some of the functions of cloud data centers, which have traditionally been installed on the ground, are installed on the satellite as a space data center. Then, certain processing is performed in orbit, and only the processing results are transmitted to the ground, thereby contributing to reducing the burden on ground processing. For example, it is rational to search for the shortest route when orbital information of individual communication satellites that make up a communication satellite constellation is collected at a space data center and transmitted via a circular or mesh communication network formed by the communication satellite constellation. According to conventional technology, orbital information was collected and transmitted from the satellite to the ground, where it was analyzed and evaluated and then transmitted back to the satellite. However, by autonomously handling this processing in space, the amount of data can be reduced and the burden on ground processing can be eased. [Explanation of symbols]

[0047] 21 circular communication network, 22 mesh communication network, 23 orbit, 100 monitoring satellite, 101 first infrared monitoring device, 102 second infrared monitoring device, 200 communication satellite, 332 countermeasure asset, 335 flight path prediction method, 340 ground system, 342 CPU, 343 communication device, 344 storage device, 350 model database, 351 flight path model selection unit, 360 flight path tracking system, 370 flight path countermeasure system, 380 integrated data library, 381 database, 390 edge server, 391 artificial intelligence computer, 490 flight path prediction device, 520 flight path prediction device, 610 satellite constellation, 620 satellite, 621 satellite control device, 622 communication device, 623 propulsion device, 624 attitude control device, 625 power supply device, 626 monitoring device, 700 Ground equipment, 910 processor, 911 satellite constellation formation unit, 921 memory, 922 auxiliary storage device, 930 input interface, 940 output interface, 950 communication device, 910A processor, 921A memory, 922A auxiliary storage device, 930A input interface, 940A output interface, 950A communication device.

Claims

1. A flight path prediction method for predicting a flight path of an airborne object by analyzing, with a ground system, airborne object monitoring information acquired by a monitoring satellite of a satellite constellation including a plurality of monitoring satellites each equipped with an infrared monitoring device, the method comprising: The ground system includes: a database storing a plurality of flight path models, which are a plurality of modeled flight paths of the projectile, including launch position coordinates, flight direction, time-series flight distance from launch to impact, and flight altitude profile of the projectile; The ground system includes: The elapsed time after the launch detection measured by the infrared monitoring device of the follow-up monitoring satellite using the plurality of flight path models, starting from the launch detection information of the flying object detected by the infrared monitoring device of the monitoring satellite; The flight distance of the flying object; The flight altitude of the flying object; Analyzing the airborne object monitoring information measured by the follow-up monitoring satellite, including the following, and excluding incompatible flight path models from the plurality of flight path models; repeatedly performing a process of excluding incompatible flight path models from the plurality of flight path models by analyzing the flight path monitoring information measured by the next follow-on monitoring satellite; The remaining flight path model that has not been excluded is determined as the provisional flight path prediction model, a flight path prediction method for predicting the flight path of the flying object up to impact by correcting the deviation amount from the tentative flight path prediction model based on flying object monitoring information measured by a plurality of follow-up monitoring satellites following the monitoring satellite that detected the launch detection information of the flying object; A surveillance satellite in Orbital information of the monitoring satellite; Location information of the response asset; a plurality of flight path models, which are models configured using the launch position coordinates, flight direction, time-series flight distance from launch to impact, and flight altitude profile of the flying object, and which are models of flight paths; A monitoring satellite comprising an edge server having a database storing at least one of the above.

2. The monitoring satellite including the edge server includes:

2. A monitoring satellite according to claim 1, comprising an artificial intelligence computer that autonomously determines a destination of the aerial vehicle information to be transmitted by referring to the database, and transmits the aerial vehicle information to the determined destination.

3. A flight path prediction method for predicting a flight path of an airborne object by analyzing, with a ground system, airborne object monitoring information acquired by a monitoring satellite of a satellite constellation including a plurality of monitoring satellites each equipped with an infrared monitoring device, the method comprising: The ground system includes: a database storing a plurality of flight path models, which are a plurality of modeled flight paths of the projectile, including launch position coordinates, flight direction, time-series flight distance from launch to impact, and flight altitude profile of the projectile; The ground system includes: The elapsed time after the launch detection measured by the infrared monitoring device of the follow-up monitoring satellite using the plurality of flight path models, starting from the launch detection information of the flying object detected by the infrared monitoring device of the monitoring satellite; The flight distance of the flying object; The flight altitude of the flying object; Analyzing the airborne object monitoring information measured by the follow-up monitoring satellite, including the following, and excluding incompatible flight path models from the plurality of flight path models; repeatedly performing a process of excluding incompatible flight path models from the plurality of flight path models by analyzing the flight path monitoring information measured by the next follow-on monitoring satellite; The remaining flight path model that has not been excluded is determined as the provisional flight path prediction model, a flight path prediction method for predicting the flight path of the flying object up to impact by correcting the deviation amount from the tentative flight path prediction model based on flying object monitoring information measured by a plurality of follow-up monitoring satellites following the monitoring satellite that detected the launch detection information of the flying object; A satellite constellation formed by a monitoring satellite and a plurality of communication satellites that constitute an airborne object tracking system that performs airborne object launch detection and airborne object tracking using A plurality of satellites each having a communication device for communicating with a satellite in front of or behind the satellite in the same orbital plane form a communication constellation of a circular communication network, and, a monitoring satellite having a communication device for communicating with a preceding and succeeding satellite flies between the plurality of communication satellites forming the communication constellation; The surveillance satellite and the plurality of communication satellites forming the communication constellation reconstruct the circular communication network or reconstruct a mesh communication network with adjacent orbits, forming a hybrid constellation for surveillance and communication.

4. A flight path prediction method for predicting a flight path of an airborne object by analyzing, with a ground system, airborne object monitoring information acquired by a monitoring satellite of a satellite constellation including a plurality of monitoring satellites each equipped with an infrared monitoring device, the method comprising: The ground system includes: a database storing a plurality of flight path models, which are a plurality of modeled flight paths of the projectile, including launch position coordinates, flight direction, time-series flight distance from launch to impact, and flight altitude profile of the projectile; The ground system includes: The elapsed time after the launch detection measured by the infrared monitoring device of the follow-up monitoring satellite using the plurality of flight path models, starting from the launch detection information of the flying object detected by the infrared monitoring device of the monitoring satellite; The flight distance of the flying object; The flight altitude of the flying object; Analyzing the airborne object monitoring information measured by the follow-up monitoring satellite, including the following, and excluding incompatible flight path models from the plurality of flight path models; repeatedly performing a process of excluding incompatible flight path models from the plurality of flight path models by analyzing the flight path monitoring information measured by the next follow-on monitoring satellite; The remaining flight path model that has not been excluded is determined as the provisional flight path prediction model, a flight path prediction method for predicting the flight path of the flying object up to impact by correcting the deviation amount from the tentative flight path prediction model based on flying object monitoring information measured by a plurality of follow-up monitoring satellites following the monitoring satellite that detected the launch detection information of the flying object; a database referenced by at least one of the monitoring satellite and the ground system in Orbital information of the monitoring satellite; Location information of the response asset; a plurality of flight path models, which are models configured using the launch position coordinates, flight direction, time-series flight distance from launch to impact, and flight altitude profile of the flying object, and which are models of flight paths; A database that stores at least one of the following: An integrated data library comprising a computer having:

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