Sensor control system, method, and program

Abstract

An input means (81) receives input of the positions and orientations of sensors for capturing moving objects, and the positions of the moving objects. A model construction means (82) constructs Ising model data that models an optimization problem for optimally assigning moving objects to be captured by sensors from the relationship between the position of each moving object, and each region that can be captured as determined by each sensor's position and orientation. An optimization processing means (83) maps the Ising model data to an annealing machine to obtain an execution result that indicates the moving objects to be assigned to sensors. A control means (84) controls the sensors on the basis of the execution result so as to capture the assigned moving objects.

Description

[Technical field]

[0001] The present invention relates to a sensor control system, a sensor control method, and a sensor control program for controlling a sensor that captures a moving object. [Background technology]

[0002] Technologies for capturing moving objects using sensors have been developed. For example, Patent Document 1 describes a system for capturing moving objects using multiple sensors. The system described in Patent Document 1 defines a cost based on the probability that a moving object is present within the sensing range of a sensor, the capacity of the sensor, etc., and determines the sensor to be assigned to each moving object so as to minimize the cost. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] US Patent Application Publication No. 2005 / 0004759 Summary of the Invention [Problem to be solved by the invention]

[0004] The problem of assigning multiple moving objects to multiple sensors to be controlled is a so-called combinatorial optimization problem. Therefore, as the number of sensors and moving objects, and the driving range of the sensor (e.g., the number of driving angle patterns) increase, it becomes difficult to calculate all combination patterns. For example, general methods using algorithms such as the greedy method have issues with the accuracy and speed of the results, making them difficult to use in a realistic time frame (e.g., real time).

[0005] In the system described in Patent Document 1, in order to allocate moving objects to various tracking system resources, optimization is performed using an auction algorithm as well as a genetic algorithm, a branch and bound method, a simulated annealing method, etc. However, there are no examples of applying the simulated annealing method to more detailed sensor resource allocation or optimal control.

[0006] Therefore, an object of the present invention is to provide a sensor control system, a sensor control method, and a sensor control program that can control a plurality of sensors that capture a plurality of moving objects in a realistic time. [Means for solving the problem]

[0007] The sensor control system according to the present invention is characterized by comprising: an input means for receiving input of the position of a sensor that captures a moving object and the direction in which the sensor faces, as well as the position of the moving object; a model construction means for constructing Ising model data that models an optimization problem for optimally allocating moving objects to be captured by the sensor, based on the relationship between the area that can be captured based on the position of the sensor and the direction in which the sensor faces and the position of the moving object; an optimization processing means for mapping the Ising model data to an annealing machine and obtaining an execution result indicating the moving objects to be allocated to the sensor; and a control means for controlling the sensor to capture the assigned moving object based on the execution result.

[0008] The sensor control method according to the present invention is characterized in that it receives input of the position of a sensor that captures a moving object and the direction in which the sensor is facing, as well as the position of the moving object, constructs Ising model data that models an optimization problem for optimally allocating moving objects to be captured by the sensor based on the relationship between the position of the sensor and the direction in which the sensor is facing and the position of the moving object, maps the Ising model data to an annealing machine, obtains an execution result indicating the moving objects to be assigned to the sensor, and controls the sensor to capture the assigned moving object based on the execution result.

[0009] The sensor control program according to the present invention is characterized in that it causes a computer to execute an input process for accepting input of the position of a sensor that captures a moving object and the direction in which the sensor faces, as well as the position of the moving object; a model construction process for constructing Ising model data that models an optimization problem for optimally allocating moving objects to be captured by the sensor, based on the relationship between the position of the sensor and the direction in which the sensor faces and the position of the moving object; an optimization process for mapping the Ising model data to an annealing machine and obtaining an execution result indicating the moving objects to be assigned to the sensor; and a control process for controlling the sensor to capture the assigned moving object based on the execution result. Effect of the Invention

[0010] According to the present invention, a plurality of sensors capturing a plurality of moving objects can be controlled in a realistic time. [Brief description of the drawings]

[0011] [Figure 1] 1 is a block diagram showing a configuration example of an embodiment of a sensor control system; [Diagram 2] 10A and 10B are explanatory diagrams showing an example of an operation of a sensor capturing a moving object; [Diagram 3] FIG. 11 is an explanatory diagram showing an example of a method for deriving a set of moving objects that cannot be captured. [Figure 4] FIG. 1 is an explanatory diagram showing examples of variables used in a QUBO format formula. [Diagram 5] FIG. 1 is an explanatory diagram showing examples of variables used in a QUBO format formula. [Figure 6] FIG. 11 is an explanatory diagram showing an example of a difference amount of a sensor orientation. [Figure 7] FIG. 11 is an explanatory diagram showing a situation in which sensors assigned to a moving object change; [Figure 8] FIG. 11 is an explanatory diagram showing an example of a process of limiting an upper limit of a resource by setting a dummy sensor; [Figure 9] FIG. 11 is an explanatory diagram showing an example of precision points. [Figure 10]FIG. 13 is an explanatory diagram showing an example of a process for capturing an important target with priority. [Figure 11] FIG. 11 is an explanatory diagram showing an example of a process using an important sensor. [Figure 12] 4 is a flowchart showing an example of the operation of the sensor control system. [Figure 13] FIG. 1 is an explanatory diagram showing an example in which the sensor control system is applied to capturing the current position of a marathon runner. [Figure 14] 1 is a block diagram showing an overview of a sensor control system according to the present invention; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] The present invention aims to control a plurality of sensors capturing a plurality of moving objects in a realistic time. The realistic time here means a time that allows the control of the plurality of sensors capturing each moving object to be performed in real time. Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0013] 1 is a block diagram showing an example of the configuration of an embodiment of a sensor control system. The sensor control system 1 of this embodiment includes a plurality of sensors 10, a sensor control device 100, and an annealing machine 200.

[0014] The annealing machine 200 is a dedicated device for finding the ground state of the Hamiltonian of an Ising model (Ising model data), and is a device that executes annealing based on the Ising model generated by the sensor control device 100. Note that the Ising model is a simple model for calculating the spin directions of atoms that constitute a crystal, and is one formulation of a combinatorial optimization problem.

[0015] More specifically, an annealing machine is a device that probabilistically determines the value of a binary variable that minimizes or maximizes an objective function (i.e., Hamiltonian) of an Ising model that has a binary variable as an argument. The binary variable may be realized by classical bits or quantum bits.

[0016] The annealing machine 200 of this embodiment may take any form. The annealing machine 200 may be configured with any hardware that probabilistically determines the value of a binary variable that minimizes or maximizes an objective function with a binary variable as an argument. The annealing machine 200 may be, for example, a non-von Neumann type computer in which the objective function is implemented by hardware in the form of an Ising model. The annealing machine 200 may be a quantum annealing machine or a general annealing machine.

[0017] Here, the QUBO (Quadratic Unconstrained Binary Optimization) model, which can be transformed one-to-one with the Ising model, is a formulation of the combinatorial optimization problem. Therefore, a combinatorial optimization problem that has been modeled as a QUBO model can be solved by an annealing machine. Therefore, in the following explanation, a case where an Ising model to be optimized by the annealing machine 200 is expressed in the QUBO format will be explained.

[0018] The sensor 10 is a sensor for capturing a moving object using multiple resources in the sensor control system 1 of this embodiment. A plurality of sensors 10 of this embodiment are present and are connected to the sensor control device 100 in a communicable manner (for example, wireless communication, etc.).

[0019] The sensor 10 of this embodiment is assumed to be a directional sensor, and captures an assigned moving target based on the control of the sensor control device 100. The sensor 10 of this embodiment is assumed to be capable of changing the capture direction by rotating around a specific axis. Furthermore, it is assumed that the resources available for the sensor 10 of this embodiment to capture moving objects (the maximum number of moving objects that can be captured) are fixed.

[0020] The position of the sensor 10 may be fixed or may not be fixed. For example, the sensor 10 may be attached to a device such as a vehicle or a drone, and the position of the sensor 10 may be changed according to the movement of a moving object to be captured.

[0021] Fig. 2 is an explanatory diagram showing an example of an operation of a sensor capturing a moving object. The example shown in Fig. 2 shows an operation of capturing a plurality of moving objects 20 using a plurality of sensors 10. The range shown by the dashed line in Fig. 2 is the range in which the sensor 10 can capture the moving object 20.

[0022] The example shown in FIG. 2 indicates that in state S1, five moving objects 20 are captured by three sensors 10. The sensor control system 1 of this embodiment controls the sensors 10 so as to minimize the number of moving objects 20 that cannot be captured (i.e., minimize the number of moving objects 20 that are not captured). Then, by appropriately determining and controlling each sensor to be assigned to capture the moving objects, it becomes possible to capture more moving objects (seven moving objects 20) with three sensors 10, as shown in state S2. The example shown in FIG. 2 indicates that many moving objects can be captured by changing the angle of the sensor 10.

[0023] In the following description, the moving object is an incoming object (e.g., a missile or a drone). In this case, the form of the sensor 10 is, for example, a radar. The moving object is not limited to an incoming object, and may be, for example, a person or a mobile terminal. When the moving object is a person, the form of the sensor 10 used is, for example, a camera. When the moving object is a mobile terminal, the form of the sensor 10 used is, for example, a base station antenna.

[0024] The sensor control device 100 includes a device control unit 110, a memory unit 120, an input unit 130, a target coordinate estimation unit 140, a sensor control optimization unit 150, a sensor control unit 160, a new target detection unit 170, and an output unit 180.

[0025] The device control unit 110 controls various functions of the sensor control device 100 .

[0026] The storage unit 120 stores various types of information used for processing by the sensor control device 100. The storage unit 120 of this embodiment also stores a sensor coordinate and specification database 121 and a current target coordinate database 122.

[0027] The sensor coordinate and specifications database 121 is a database that stores various pieces of specification information such as the position of the sensor 10 and parameter settings. For example, when the position of the sensor 10 changes, the sensor coordinate and specifications database 121 may successively update the position of the sensor 10 after the change. The position of the sensor 10 after the change may be obtained, for example, from a GPS (Global Positioning System) or may be obtained directly from a device equipped with the sensor 10.

[0028] Furthermore, when the direction in which the sensor 10 faces (hereinafter simply referred to as the direction of the sensor 10) is changed by control, the sensor coordinate and specifications database 121 may successively update the changed direction of the sensor 10. The changed direction of the sensor 10 may be obtained from the sensor control unit 160 or the sensor control optimization unit 150, which will be described later, or may be obtained directly from each sensor 10.

[0029] The current target coordinate database 122 stores information indicating the position of a moving object at a current time t that the sensor 10 is attempting to capture (hereinafter, sometimes referred to as a current target coordinate). Since a moving object is, as the name suggests, an object that moves, strictly speaking, it is difficult to grasp its current position. Therefore, the current target coordinate database 122 may store, as the current target coordinate, information indicating the position of the moving object at the most recent captured time t-1, or information indicating the position of the moving object at the current time t estimated by the target coordinate estimation unit 140 described later.

[0030] The input unit 130 accepts input of various information used to control the sensor 10. For example, when the position of the sensor 10 changes, the input unit 130 may accept an input of the position of the sensor 10 after the change from the sensor 10 or a device equipped with the sensor 10. Furthermore, the input unit 130 may accept an input of information indicating the current state of the sensor 10, such as the current orientation, directly from the sensor 10, or may acquire and accept the information from information stored in the storage unit 120. Note that the input unit 130 may be included in a sensor control optimization unit 150 described later.

[0031] The target coordinate estimation unit 140 estimates the position of the moving object at the current time t (i.e., the current target coordinates). The method by which the target coordinate estimation unit 140 estimates the current target coordinates is arbitrary. For example, the target coordinate estimation unit 140 may identify the speed of each moving object based on multiple observations of the moving object, and estimate the current target coordinates of the moving object based on the identified speed and the observed position of the moving object.

[0032] For example, at time t a Let the position of the moving object in P a , the speed is V a At this time, the target coordinate estimation unit 140 calculates the target coordinate b Position P of a moving object in b , P b =P b +V a *(t b -t a Furthermore, the target coordinate estimation unit 140 may estimate the position of the moving object by specifying the acceleration of the moving object based on multiple observations of each moving object and taking the specified acceleration into account.

[0033] The sensor control optimization unit 150 performs a process of determining a sensor to be assigned to capture a moving object. Therefore, a device that implements the sensor control optimization unit 150 can be called an assignment determination device. In other words, the sensor control optimization unit 150 may be realized as a standalone device. The sensor control optimization unit 150 includes an Ising model data construction unit 151 and an annealing processing unit 152.

[0034] The Ising model data construction unit 151 acquires information indicating the state of the sensor 10 and information indicating the position of the moving object. Specifically, the Ising model data construction unit 151 receives input of information indicating the position and orientation of the sensor 10 at the capture time t, and information indicating the position of the moving object. The information indicating the position of the moving object at the capture time t is, for example, the current target coordinates.

[0035] The Ising model data construction unit 151 may acquire information indicating the position and orientation of the sensor 10 and information indicating the position of a moving object from the sensor coordinate and specifications database 121 and the current target coordinate database 122 stored in the storage unit 120. In addition, the Ising model data construction unit 151 may acquire the current target coordinates from the target coordinate estimation unit 140, or may acquire information indicating the position and orientation of the sensor 10 directly from the sensor 10.

[0036] Next, the Ising model data construction unit 151 constructs Ising model data (hereinafter sometimes simply referred to as a model) that models an optimization problem for optimally allocating moving objects to be captured by the sensor 10, based on the relationship between the area that can be captured based on the position of the sensor 10 and the direction in which the sensor 10 faces, and the position of the moving objects.

[0037] First, the Ising model data construction unit 151 derives a set of moving objects that cannot be captured from the position and orientation of the sensor 10 and the relationship between the positions of the moving objects. Hereinafter, each sensor 10 is represented by n, the orientation of the sensor 10 by d, and a moving object by α. The set of moving objects that cannot be captured is represented by T n,d (T￣ denotes a superscript bar.)

[0038] In this embodiment, it is assumed that the capture range of each sensor 10 is determined in advance based on the position and orientation of the sensor itself. The capture range is determined with respect to distance and direction. For example, if the distance from the sensor is β 1 [m] or more, β2 [m] or less, the front direction of the sensor 10 is set as the reference direction of 0 degrees, -γ 1 [degree] or more γ 2 [degrees] or less (γ 1 ,γ 2 >0), etc.

[0039] In this case, the Ising model data construction unit 151 specifies the capture range of the sensor 10 for each posture that the sensor 10 can take, based on the information indicating the capture range and the position of the sensor 10. Then, the Ising model data construction unit 151 specifies moving objects that are not included in the specified capture range among the moving objects to be captured, and derives a set of moving objects that cannot be captured.

[0040] Fig. 3 is an explanatory diagram showing an example of a method for deriving a set of moving objects that cannot be captured. In the example shown in Fig. 3, moving objects 20a to 20g exist as moving objects to be captured, and an area 40 is a captureable range for the direction d of the sensor 10. In this case, the Ising model data construction unit 151 identifies the moving objects 20a, 20b, 20d, and 20g that do not exist within the range of the area 40 as moving objects that cannot be captured, and derives a set of these moving objects as a set of moving objects that cannot be captured.

[0041] Next, the Ising model data construction unit 151 constructs Ising model data that models an optimization problem for optimally allocating moving objects to be captured by each sensor. Note that in this embodiment, an optimization problem modeled in a QUBO format that can be converted into Ising model data is exemplified.

[0042] 4 and 5 are explanatory diagrams showing examples of variables used in QUBO-formatted formulas. As shown in FIG. 4, a variable indicating whether the n-th sensor is facing the direction d or not (i.e., a variable representing the sensor's attitude) is denoted by s n,d ∈{0,1}. s n ,d When the value of is 1, it indicates that the nth sensor is pointing in the direction d, and s n,dWhen the value of is 0, it indicates that the nth sensor is not facing the direction d.

[0043] Also, as shown in Fig. 5, we define a variable x that indicates whether the n-th sensor captures the moving object α or not. n,α ∈{0,1}. x n,α When the value of x is 1, it indicates that the nth sensor captures the moving object α, and n,α When the value of is 0, it indicates that the n-th sensor does not capture the moving object α.

[0044] In this embodiment, the Ising model data construction unit 151 constructs a model (mathematical formula) in which an objective function is expressed in QUBO format to minimize the number of moving objects not assigned to each sensor (i.e., to minimize the number of moving objects that are not captured and are assigned to each sensor), with the constraint that the number of moving objects to be captured by each sensor does not exceed a predetermined upper limit. The above-mentioned upper limit is, for example, the resource that the sensor 10 can use to capture moving objects (the maximum number of moving objects that can be captured). The objective function to minimize the number of moving objects that are not captured is expressed by the following formula 1.

[0045]

number

[0046] In Equation 1, T num is the number of moving objects, and S num is the number of sensors. Also, z n,α is an auxiliary variable that represents any one of 0 to the number of sensors. Thus, the value in the parentheses in Equation 1 indicates that the number of sensors capturing a moving object ranges from 1 to S num It will be 0 if there are individuals and 1 if there are zero individuals.

[0047] Since it is considered inefficient to capture one moving object with a large number of sensors, the number of sensors capturing one moving object may be limited to 1 or 2. In this case, the objective function for minimizing the number of missed captures can be expressed by the following formula 2, which has the advantage of eliminating the need to use the auxiliary variable z.

[0048]

number

[0049] In this embodiment, since it is assumed that each sensor 10 faces only one direction, the constraint function indicating that each sensor 10 faces only one direction is expressed by the following formula 3. In formula 3, C 1 represents a constant, and D num represents the number of orientations that the sensor can face. This corresponds to each sensor facing one direction in Figure 4.

[0050]

number

[0051] Since it is necessary to suppress the allocation of a moving object that cannot be captured to a sensor, the constraint function representing the suppression of the allocation of a moving object that cannot be captured to a sensor is expressed by the following Equation 4. Note that C 2 also represents a constant. n,d As described above, represents a set of moving objects that cannot be captured by each sensor. n,d and a variable x indicating whether a moving object is assigned to a sensor. n,α It becomes possible to associate with.

[0052]

number

[0053] Furthermore, the constraint function that indicates that the number of moving objects to be captured by each sensor should not exceed a predetermined upper limit is expressed by the following Equation 5. 3 represents a constant, and capa represents an upper limit. Also, y n,m is an auxiliary variable that represents any number between 0 and the upper limit. This corresponds to the total sum in the vertical direction of the table in FIG. 5 being limited to within the upper limit.

[0054]

number

[0055] The Ising model data construction unit 151 may construct a model obtained by the sum of at least the objective function shown in Equation 1 or 2 and the constraint function shown in Equation 5. This makes it possible to construct an objective function that minimizes the number of moving objects that cannot be assigned to each sensor, with the constraint that the number of moving objects to be captured by each sensor does not exceed a predetermined upper limit.

[0056] Furthermore, in addition to the objective function shown above, the Ising model data construction unit 151 may construct a model obtained by adding the constraint functions shown in Equation 3 and Equation 4. This makes it possible to construct an objective function that, in addition to the above constraints, restricts each sensor 10 to face only one direction and restricts the allocation of a moving object that cannot be captured to a sensor.

[0057] In addition, during optimization, consideration may be given to suppressing the degree of change in the sensor's orientation. This is because a swiveling sensor generally cannot sense while changing its orientation, and therefore a smaller change allows for more efficient capture of a moving object. The constraint function representing suppression of the degree of change in the sensor's orientation is expressed by the following Equation 6. C in Equation 6 4 represents a constant, and P n,d indicates the difference amount from the previous sensor orientation.

[0058]

number

[0059] Fig. 6 is an explanatory diagram showing an example of the difference amount of the sensor orientation. The example shown in Fig. 6 shows that the penalty value increases as the angle from the previous sensor orientation increases.

[0060] In addition, during optimization, consideration may be given to suppressing the degree of change in the sensor assigned to a moving object. When a moving object moves out of the area that the sensor can capture, it is necessary to switch the assignment to another sensor. However, since sensing is often not possible during this switching (handover), the less switching, the more efficiently the moving object can be captured.

[0061] The constraint function representing the restriction on the degree of change of the sensor assigned to the moving object is expressed by the following Equation 7. 5 and C 6 represents a constant, and px n, α is the value of x indicating whether the moving object α was assigned to the sensor n in the previous time. n,α represents the predicted time during which the sensor n can continuously capture the moving object α (hereinafter, referred to as tracking duration).

[0062]

number

[0063] The tracking duration is calculated by estimating the position of the moving object α at each of a plurality of future time points and determining whether the moving object α at the estimated position can be captured by the sensor n. For example, it is assumed that the attitude of the sensor 10 is controlled every second. It is also assumed that the moving object α at the positions estimated from 1 second to t seconds later is in a position that can be captured by the sensor n. It is also assumed that the moving object α at the position estimated for t+1 seconds later is in a position that cannot be captured by the sensor 10. In this case, the DB n,α=t.

[0064] Fig. 7 is an explanatory diagram showing a situation in which the sensors assigned to the moving objects change. In the example shown in Fig. 7, it is assumed that the sensor 10a captures the moving object 20a, and the sensor 10b captures the moving object 20b. It is assumed that the moving objects 20a and 20b are each moving toward the lower right of the figure.

[0065] In this case, at time t, the sensor 10a can capture the moving object 20a in both the area 41a and the area 42a. Similarly, the sensor 10b can capture the moving object 20b in both the area 41b and the area 42b. However, if the area 41a and the area 41b are captured at time t, at time t+2, both the moving object 20a and the moving object 20b will be out of the captureable area, so it is necessary to switch the sensor or change the direction.

[0066] On the other hand, if the area 42a and the area 42b have been captured at time t, the moving objects 20a and 20b are still included in the captureable area at time t+2, so there is no need to switch sensors or change the direction. Therefore, it is possible to capture the moving objects efficiently.

[0067] Furthermore, in this embodiment, the accuracy of capturing a moving object can be improved by allowing the use of multiple sensor resources (specifically, the number of times the sensor emits radio waves) to capture a moving object.

[0068] In general, the distribution of targets to be captured can be modeled as an ellipse, narrow in the sensor's azimuth direction and wide in the distance direction. Therefore, capturing a moving object with another sensor has the advantage that the elliptical distributions overlap, reducing the error range. Furthermore, increasing the number of times the sensor emits radio waves to capture the target increases the number of samplings, which has the advantage of reducing the position error.

[0069] Therefore, in this embodiment, the combination of sensor resources is optimized so that the number of missed captures can be minimized while the tracking error of a moving object can be reduced. As a prerequisite for such optimization, it is assumed that each sensor 10 can use a plurality of predetermined resources.

[0070] Specifically, the Ising model data construction unit 151 of this embodiment constructs a model (mathematical formula) in which an objective function is expressed in QUBO format to minimize the number of missed captures of moving objects assigned to each sensor so that the number of sensors to capture one moving object and the number of resources used by each sensor for capture do not exceed a predetermined upper limit, with the constraint that the number of moving objects to be captured by each sensor does not exceed a predetermined upper limit. This makes it possible to minimize the number of missed captures while suppressing the use of unnecessary resources.

[0071] For example, assume that the upper limit of the number of sensors to be assigned to one moving object is 3, and the upper limit of the number of resources each sensor uses for capture is 2. In this case, the objective function for minimizing the number of missed captures is expressed by the following formula 8. That is, the above formula 1 or formula 2 can be rewritten as the following formula 8.

[0072]

number

[0073] In the formula 8, dmy1 represents the first dummy sensor, and dmy2 represents the second dummy sensor. dmy1,α is a variable indicating whether the first dummy sensor dmy1 captures the moving object α or not, and x dmy2,α is a variable indicating whether the second dummy sensor dmy2 captures the moving object α or not.

[0074] This dummy sensor is a non-existent sensor for adjusting the number of sensors used to capture one object so as not to exceed the upper limit. Fig. 8 is an explanatory diagram showing an example of a process for setting a dummy sensor to limit the upper limit of the sensors.

[0075] In the example shown in FIG. 8, a case where each sensor can use a maximum of two resources is illustrated. The horizontal direction in FIG. 8 indicates the sensor resources, and the vertical direction indicates the moving objects. The objective function illustrated in Equation 8 corresponds to the upper limit of the number of resources each sensor uses to capture one moving object, in the horizontal direction (e.g., part P1) of the table illustrated in FIG. 8. Therefore, for example, when the upper limit of the number of sensors to be assigned to one moving object is 3, and the upper limit of the number of resources each sensor uses to capture is 2, optimizing the objective function indicates optimizing the total of the resources (i.e., the total of each row) of the sensors (including the dummy sensors DMY1 and DMY2) capturing each moving object in the table illustrated in FIG. 8 to be 6.

[0076] At this time, the Ising model data construction unit 151 may construct a model including a constraint on the amount of resources used by one sensor for the same moving object. Specifically, the Ising model data construction unit 151 may construct Ising model data including a constraint that suppresses the use of more than a predetermined number of resources. This makes it possible to suppress the use of unnecessary resources. In this case, the number of resources used by each sensor when capturing one moving object needs to be smaller than a predetermined number. For example, a constraint function indicating that one sensor does not use three or more resources for the same moving object is expressed by the following formula 9.

[0077]

number

[0078] 8, Equation 9 indicates that, for example, in part P2, the number of resources that one sensor allocates to one moving object is 0, 1, or 2. In addition, when the upper limit of the number of sensors that can be allocated to one moving object is 3 and the upper limit of the number of resources that each sensor uses for capture is 2, the constraint function is expressed as the sum of Equation 8 and Equation 9 with appropriate coefficients.

[0079] In addition, with regard to the constraint function representing that the number of moving objects to be captured by each sensor should not exceed a predetermined upper limit, it is possible to rewrite the above-mentioned equation 5 as the following equation 10.

[0080]

number

[0081] 8, the formula 10 corresponds to, for example, the total resource used by one sensor for capturing part P3 not exceeding a defined upper limit. Note that the dummy sensor is a sensor for adjusting the number of moving objects to be captured by each sensor so that it does not exceed a defined upper limit, and therefore no upper limit is set on the resource.

[0082] It is known that the closer the moving object is, or the more resources are used to capture it, the higher the tracking accuracy becomes, and the more resources are used to improve the tracking accuracy, the further the moving object is from the sensor. It is also known that the more perpendicular the angle between the radio waves radiated by the sensor's orientation is, the higher the tracking accuracy becomes.

[0083] Therefore, in this embodiment, a value indicating the tracking accuracy according to the number of resources of a sensor capturing a moving object, the distance of the moving object, and the orientation between the sensors (hereinafter referred to as an accuracy point) is defined, and this value may be used in the optimization process.

[0084] Fig. 9 is an explanatory diagram showing an example of accuracy points calculated according to the number of sensor resources and the distance of a moving object. The example shown in Fig. 9 is an example of accuracy points where the number of resources consumed by one sensor is 1 or 2, and the distance is also defined in two ranges (closer or farther than a predetermined distance). Fig. 9 shows that the tracking accuracy becomes higher as the moving object is closer, and also becomes higher when it is captured by two resources.

[0085] Note that the value of the accuracy point is an example. The number of resources is not limited to two, and the distance division is not limited to two. The accuracy point may be defined by a function indicating the relationship between the number of resources and the distance, instead of the table format as shown in FIG. 9.

[0086] In addition, the tracking accuracy according to the direction between the sensors is most effective when the angles of the radio waves are perpendicular to each other. Therefore, when the accuracy point calculated according to the number of sensor resources and the distance to the moving object is taken as the basic point ap, when capturing a moving object with two sensors s1 and s2, the accuracy point considering the direction between the sensors is calculated, for example, by the following formula 11.

[0087] Accuracy points = (ap s1 +ap s2 )*(1+sin(θ s1s2 )) (Equation 11)

[0088] In Equation 11, a s1 and ap s2 indicate the base points of the sensors s1 and s2, respectively, and θ s1s2 indicates the angle between the radio wave from the sensor s1 and the radio wave from the sensor s2. The accuracy point illustrated in Equation 11 is s1s2 is maximum when it is at a right angle.

[0089] Furthermore, when a moving object is captured by three sensors s1, s2, and s3, the accuracy point taking into consideration the orientation between the sensors is calculated, for example, by Equation 12 shown below.

[0090] Accuracy points = (ap s1 +ap s2 +ap s3 )* (1+0.5*(sin(θ s1s2 )+sin(θ s2s3 )+sin(θ s1s3 ))) (Formula 12)

[0091] In Equation 12, a s1 , a.p. s2 and ap s3 respectively indicate the base points of the sensors s1, s2 and s3. s1s2 indicates the angle between the radio waves from the sensor s1 and the sensor s2, and θ s2s3 indicates the angle between the radio waves from the sensor s2 and the sensor s3, and θ s1s3 indicates the angle between the radio wave from the sensor s1 and the radio wave from the sensor s3. The accuracy point illustrated in the example of Equation 12 is maximized when each angle is 120 degrees.

[0092] In the above example, the case where the number of sensors is two or three is illustrated, but the same applies to the case where the number of sensors is four or more.

[0093] Therefore, the Ising model data construction unit 151 may construct a model including a constraint that the sum of accuracy points calculated as a value indicating tracking accuracy that increases as the moving object is captured by a plurality of resources and that increases as the moving object is closer becomes larger. Furthermore, the Ising model data construction unit 151 may construct a model including a constraint that the sum of accuracy points indicating tracking accuracy that is defined to increase as the angle between radio waves irradiated by the sensor orientation becomes perpendicular becomes larger.

[0094] In addition, the accuracy point may be defined as a value that takes into account both of the above tracking accuracies (i.e., a tracking accuracy that is higher the more the object is captured by multiple resources and the closer the moving object is, and further, a value indicating a tracking accuracy that is defined so that the angle between the radio waves emitted by the sensor is perpendicular, the higher the tracking accuracy).

[0095] For example, when the upper limit of the number of resources that each sensor uses to capture one moving object is 3, the objective function for increasing the sum of accuracy points is expressed by the following formula 13. In formula 13, s2 is other than s1 and includes the first dummy sensor dmy1. Also, s3 is other than s1 and s2 and includes the first dummy sensor dmy1 and the second dummy sensor dmy2.

[0096]

number

[0097] The method for optimizing the tracking accuracy by using the accuracy points has been described above. In the present embodiment, a method for optimizing the allocation in consideration of a moving object that should be captured with priority will be described.

[0098] Among multiple moving objects, there may be a moving object that is desired to be captured by a sensor for as long as possible. Hereinafter, such a moving object will be referred to as an important target. Furthermore, a sensor that is predetermined as a sensor that should capture the important target in comparison with other sensors will be referred to as an important sensor. An important sensor is, for example, a sensor with higher tracking accuracy and performance than other sensors.

[0099] Fig. 10 is an explanatory diagram showing an example of a process for capturing an important target with priority. In the example shown in Fig. 10, four moving objects 20 are included in the area that the sensor 10 can capture, and the moving objects 20 marked with stars are important targets. In this case, since the important targets should be captured with priority, it is shown that the moving objects 20 marked with two stars have been selected as important targets.

[0100] Fig. 11 is an explanatory diagram showing an example of processing using an important sensor. In the example shown in Fig. 11, it is assumed that the sensor 10x is an important sensor and the sensor 10y is a normal sensor. In addition, in the example shown in Fig. 11, it is assumed that four moving objects 20 are included in the area that can be captured by both the sensor 10x and the sensor 10y, and the moving object 20 marked with a star is an important target.

[0101] In this case, since the important sensor 10x should capture the important target with priority, the two moving objects 20 marked with stars, which are important targets, are assigned to the sensor 10x, and the remaining two moving objects 20 are assigned to the sensor 10y.

[0102] In order to realize such optimization, the Ising model data construction unit 151 may construct a model in which an objective function includes a weighted formula that reduces the value of the objective function as more important targets are assigned to the sensor, so that moving objects that should be captured with priority (i.e., important targets) can be preferentially assigned to the sensor.

[0103] An objective function including a weighted equation that has the effect of preferentially allocating important targets to sensors would be, for example, the equation in parentheses in Equation 2 above changed to Equation 14 below for important targets.

[0104]

number

[0105] C target is a constant that is determined in advance by an administrator or the like according to the degree to which important targets are to be prioritized for allocation. In the case of important targets, the formula in the parentheses of the formula 1 shown above is changed to (1 + C target ) may be changed to a weighted formula.

[0106] Furthermore, in order to assign a moving object that should be captured preferentially (i.e., an important target) to an important sensor, the Ising model data construction unit 151 may construct a model including, in an objective function, a formula having an effect of reducing the value of the objective function when an important target is assigned to an important sensor.

[0107] An objective function including an equation having the effect of preferentially allocating important targets to important sensors is, for example, the equation in parentheses in Equation 2 above, plus Equation 15 below for important targets.

[0108]

number

[0109] C sensor is a constant determined in advance by an administrator or the like according to the level of incentive given when an important sensor is assigned an important target. In the case of important targets, in the same manner as in the case of formula 2, formula 15 may be added to the formula for the part of formula 1 where the sum related to the target is calculated.

[0110] In the above explanation, C target and C sensor The example shows the case where is a constant. target and C sensor does not need to be fixed, but may be a fixed or continuously changing value for a moving object. target and C sen sor By continuously changing the priority, it becomes possible to continuously change the priority.

[0111] The objective function and constraints used when constructing a model have been described above. The Ising model data construction unit 151 can construct a model by adding any of the above-mentioned Ising models (Hamiltonians) according to the constraints to be specified.

[0112] The annealing processing unit 152 maps the modeled optimization problem (i.e., Ising model data) to the annealing machine 200 to obtain an optimal solution. As a result, the annealing processing unit 152 obtains an execution result indicating a moving object to be assigned to the sensor 10. Note that a method of mapping an Ising model to an annealing machine to obtain a solution is widely known, and therefore a detailed description thereof will be omitted.

[0113] The sensor control unit 160 controls the sensor 10 so as to capture the assigned moving object based on the optimization result (execution result) by the sensor control optimization unit 150. Specifically, the sensor control unit 160 changes the orientation of the sensor 10 so as to capture the moving object assigned to the sensor 10. Note that the method of controlling the sensor 10 is widely known, and a detailed description thereof will be omitted here.

[0114] The new target detection unit 170 detects a new moving object. For example, if the sensor control system 1 is equipped with a sensor (not shown, hereinafter referred to as a new detection sensor) for detecting a new moving object, the new target detection unit 170 may acquire a detection result by the new detection sensor. The new detection sensor may be installed, for example, so as to comprehensively capture the capture space regarding the presence or absence of a new moving object.

[0115] Also, without using a new detection sensor, the existing sensor 10 may be given the role of detecting a new moving object. Specifically, at least one or more sensors among the multiple sensors 10 may be installed at a position where the boundary between the capture space and the outside of the capture space can be captured, and when a moving object straddling the boundary is detected, the new target detection unit 170 may detect the moving object as a new moving object. Thereafter, the new moving object detected by the new target detection unit 170 is added to the objects to be captured.

[0116] The output unit 180 outputs the execution result by the annealing machine 200. The output unit 180 may display, for example, each sensor and a moving object assigned to each sensor as a capture target according to their respective positions and orientations in a manner as exemplified in FIG. 3. That is, the output unit 180 may display the position and orientation of each sensor, as well as the area that each sensor can capture according to the state of the sensor, in association with the moving object assigned to each sensor as a capture target. In addition, the output unit 180 may output, for example, a log showing the optimization result.

[0117] The device control unit 110, the input unit 130, the target coordinate estimation unit 140, the sensor control optimization unit 150 (more specifically, the Ising model data construction unit 151 and the annealing processing unit 152), the sensor control unit 160, the new target detection unit 170, and the output unit 180 are realized by a computer processor (e.g., a CPU (Central Processing Unit)) that operates according to a program (sensor control program).

[0118] For example, a program may be stored in the storage unit 120 of the sensor control device 100, and the processor may read the program and operate, in accordance with the program, as the device control unit 110, the input unit 130, the target coordinate estimation unit 140, the sensor control optimization unit 150 (more specifically, the Ising model data construction unit 151 and the annealing processing unit 152), the sensor control unit 160, the new target detection unit 170, and the output unit 180. Furthermore, the functions of the sensor control device 100 may be provided in a SaaS (Software as a Service) format.

[0119] Also, the device control unit 110, the input unit 130, the target coordinate estimation unit 140, the sensor control optimization unit 150 (more specifically, the Ising model data construction unit 151 and the annealing processing unit 152), the sensor control unit 160, the new target detection unit 170, and the output unit 180 may each be realized by dedicated hardware. Also, a part or all of the components of each device may be realized by a general-purpose or dedicated circuit, processor, etc., or a combination of these. These may be configured by a single chip, or may be configured by multiple chips connected via a bus. A part or all of the components of each device may be realized by a combination of the above-mentioned circuits, etc., and a program.

[0120] Furthermore, when some or all of the components of the sensor control device 100 are realized by multiple information processing devices, circuits, etc., the multiple information processing devices, circuits, etc. may be centrally or distributed. For example, the information processing devices, circuits, etc. may be realized as a client-server system, a cloud computing system, or the like, each connected via a communication network.

[0121] Next, the operation of this embodiment will be described. FIG. 12 is a flowchart showing an example of the operation of the sensor control system 1. The input unit 130 accepts input of the position and orientation of the sensor 10, and the position of the moving object (step S11). The Ising model data construction unit 151 constructs Ising model data that models an optimization problem for optimally allocating the moving object to be captured by the sensor 10 from the relationship of the input information (step S12). The Ising model data construction unit 151 maps the Ising model data to the annealing machine 200 and obtains an execution result indicating the moving object to be allocated to the sensor 10 (step S13). Then, the sensor control unit 160 controls the sensor 10 to capture the allocated moving object based on the execution result (step S14).

[0122] As described above, in this embodiment, the input unit 130 receives input of the position and orientation of the sensor 10 and the position of the moving object, and the Ising model data construction unit 151 constructs Ising model data that models an optimization problem for optimally allocating moving objects to be captured by the sensor 10 from the relationship between the area that can be captured by the position and orientation of the sensor 10 and the position of the moving object. Then, the Ising model data construction unit 151 maps the Ising model data to the annealing machine 200 to obtain an execution result indicating the moving object to be allocated to the sensor 10, and the sensor control unit 160 controls the sensor 10 to capture the allocated moving object based on the execution result. Thus, multiple sensors that capture multiple moving objects can be controlled in a realistic time.

[0123] In the above embodiment, the sensor control system 1 of the present embodiment captures a flying object (e.g., a missile or a drone) that is a moving object. In addition, the sensor control system 1 of the present embodiment can also be used to capture the current position of a marathon runner, for example.

[0124] Fig. 13 is an explanatory diagram showing an example in which the sensor control system of this embodiment is applied to capture the current position of a marathon runner. The example shown in Fig. 13 shows a situation in which a camera 10p is installed along the road and captures a runner, which is a moving object 20. The example shown in Fig. 13 also shows that the bib number, ranking, name, etc. of the captured runner are displayed.

[0125] Generally, times are measured in marathon races by placing receivers on the course and having each runner carry a timing chip (such as an RS tag (Runners ShareTag)) that is assigned unique identification information. However, registering runner information on the timing chip is time-consuming, and distributing and carrying the timing chips to the runners is also a hassle.

[0126] To solve this problem, a method of capturing runners using facial recognition with multiple cameras is conceivable. However, the number of runners that can be photographed by each camera is limited due to factors such as the processing power of the facial recognition process. Also, to maintain the quality of the video, it is preferable to minimize the panning movement of the camera that is photographing. Furthermore, to reduce the complexity of the authentication process, it is preferable to also reduce the handover process (handing over the runner between cameras).

[0127] To address these issues, the sensor control system 1 of the present embodiment can be applied. Specifically, the Ising model data construction unit 151 may construct a model that minimizes the number of runners that are not captured by each camera, with the constraint that the number of runners assigned to each camera does not exceed a predetermined upper limit.

[0128] Furthermore, by considering the constraint function shown in Equation 6 above, it is possible to add a constraint that suppresses the degree to which the camera orientation changes, and by considering the constraint function shown in Equation 7 above, it is also possible to add a constraint that suppresses the degree to which the camera assigned to the runner changes.

[0129] Furthermore, the sensor control system 1 of this embodiment can be applied not only to capturing the current positions of marathon runners, but also to a service that provides commemorative photos of runners during a race. Specifically, in a marathon event, cameras are installed at various points to take pictures of runners, and there is also a service that provides the runners with the images taken at a later date.

[0130] However, in competitions in which many runners participate, it is difficult to check the bib numbers and other information on the captured images in real time, so images are generally provided with a time lag. On the other hand, by using the sensor control system 1 of this embodiment, multiple cameras capturing multiple runners can be controlled in a realistic time, making it possible to provide images that identify individual runners in real time after the race ends.

[0131] Next, an overview of the present invention will be described. Fig. 14 is a block diagram showing an overview of a sensor control system according to the present invention. The sensor control system 80 according to the present invention includes an input means 81 (e.g., input unit 130) that accepts input of the position and direction of a sensor (e.g., sensor 10) that captures a moving object (e.g., a flying object, a person, a mobile terminal, etc.), and the position of the moving object (e.g., a target coordinate position), a model construction means 82 (e.g., Ising model data construction unit 151) that constructs Ising model data that models an optimization problem for optimally allocating a moving object to be captured by the sensor from the relationship between the area that can be captured by the position of the sensor and the direction of the sensor and the position of the moving object, an optimization processing means 83 (e.g., annealing processing unit 152) that maps the Ising model data to an annealing machine and obtains an execution result indicating the moving object to be assigned to the sensor, and a control means 84 (e.g., sensor control unit 160) that controls the sensor to capture the assigned moving object based on the execution result.

[0132] Such a configuration makes it possible to control multiple sensors capturing multiple moving objects in a realistic time.

[0133] In addition, the model construction means 82 may construct Ising model data that represents an objective function (e.g., the above formulas 8 and 10) that minimizes the number of moving objects that are not captured and are assigned to each sensor so that the total number of resources used by all sensors to capture one moving object does not exceed a predetermined upper limit, with the constraint that the number of moving objects to be captured by the sensors does not exceed a predetermined upper limit.

[0134] Furthermore, the model construction means 82 may construct Ising model data including a constraint (eg, the above formula 9) that prevents one sensor from using more than a predetermined number of resources for the same moving object.

[0135] In addition, the model construction means 82 may construct Ising model data that includes a constraint (e.g., Equation 13) that the sum of accuracy points calculated as a value indicating tracking accuracy, which becomes higher as the moving object is captured by multiple resources and becomes higher as the moving object is closer to the sensor, becomes larger.

[0136] In addition, the model construction means 82 may construct Ising model data that includes in a constraint (e.g., Equation 11, Equation 12) a larger sum of accuracy points indicating tracking accuracy that is defined so that the more perpendicular the angle between the radio waves irradiated by the sensor orientation is, the higher the sum of accuracy points becomes.

[0137] In addition, the model construction means 82 may construct Ising model data in which the objective function includes a weighted formula (e.g., formula 14) that reduces the value of the objective function as the sensor is assigned to an important target, which is a moving object that should be captured with priority.

[0138] In addition, the model construction means 82 may construct Ising model data in which an objective function includes a formula (e.g., formula 15) that has the effect of reducing the value of the objective function when an important target is assigned to an important sensor that is a sensor that is predetermined as a sensor that should capture the important target.

[0139] Furthermore, the model construction means 82 may construct model data in which the constraints and objective functions are expressed in the QUBO format.

[0140] The sensor control system 80 may also include a set derivation means (e.g., the Ising model data construction unit 151) that derives a set of moving objects that cannot be captured from the relationship between the position of the sensor, the direction in which the sensor faces, and the positions of the moving objects. The model construction means 82 may then construct a model including a constraint that suppresses the allocation of moving objects included in the set to the sensor.

[0141] A part or all of the above-described embodiments can be described as, but is not limited to, the following supplementary notes.

[0142] (Note 1) An input means for receiving an input of a position of a sensor capturing a moving object, a direction in which the sensor is facing, and a position of the moving object; a model construction means for constructing Ising model data that models an optimization problem for optimally allocating moving objects to be captured by the sensor based on a relationship between a region that can be captured by the sensor based on a position of the sensor and a direction in which the sensor faces, and a position of the moving object; an optimization processing means for mapping the Ising model data to an annealing machine to obtain an execution result indicating a moving object to be assigned to the sensor; and a control means for controlling the sensor so as to capture the assigned moving object based on the execution result. A sensor control system comprising:

[0143] (Appendix 2) The model construction means constructs Ising model data representing an objective function that minimizes the number of moving objects that are not captured by each sensor, so that the total number of resources used by the sensors to capture one moving object does not exceed a predetermined upper limit, with the constraint that the number of moving objects that the sensors are to capture does not exceed a predetermined upper limit. 2. The sensor control system of claim 1.

[0144] (Appendix 3) The model construction means constructs Ising model data including constraints that prevent one sensor from using more than a predetermined number of resources for the same moving object. 3. The sensor control system of claim 1 or 2.

[0145] (Appendix 4) The model construction means constructs Ising model data including a constraint that the sum of accuracy points calculated as a value indicating tracking accuracy, which becomes higher as the moving object is captured by multiple resources and becomes higher as the moving object is closer to the sensor, becomes larger. 4. A sensor control system according to any one of claims 1 to 3.

[0146] (Appendix 5) The model construction means constructs Ising model data including a constraint that the sum of accuracy points indicating the tracking accuracy defined so that the angle between the radio waves irradiated by the sensor orientation becomes higher as the angle becomes perpendicular to each other is larger. 5. The sensor control system of claim 4.

[0147] (Appendix 6) The model construction means constructs Ising model data including an objective function having a weighted formula that reduces the value of the objective function as the sensor is assigned to an important target, which is a moving object that should be captured with priority. 6. A sensor control system according to any one of claims 1 to 5.

[0148] (Appendix 7) The model construction means constructs Ising model data including in an objective function a mathematical expression having an effect of reducing the value of the objective function when the important target is assigned to an important sensor that is a sensor that is predetermined as a sensor that should capture the important target. 7. The sensor control system of claim 6.

[0149] (Appendix 8) The model construction means constructs model data that expresses constraints and objective functions in QUBO format. 8. A sensor control system according to any one of claims 1 to 7.

[0150] (Appendix 9) A set deriving means for deriving a set of moving objects that cannot be captured from the relationship between the position of the sensor, the direction in which the sensor faces, and the position of the moving object, A model constructing means constructs a model including constraints that constrain the assignment of moving objects included in the set to sensors. 9. A sensor control system according to any one of claims 1 to 8.

[0151] (Appendix 10) An output means is provided for displaying the position and orientation of each sensor, as well as the area that each sensor can capture depending on the state of the sensor, and the moving object assigned to each sensor as a capture target in association with each other. 10. A sensor control system according to any one of claims 1 to 9.

[0152] (Appendix 11) An input means for receiving an input of a position of a sensor capturing a moving object, a direction in which the sensor is facing, and a position of the moving object; a model construction means for constructing Ising model data that models an optimization problem for optimally allocating moving objects to be captured by the sensor based on a relationship between a region that can be captured by the sensor based on a position of the sensor and a direction in which the sensor faces, and a position of the moving object; and an optimization processing means for mapping the Ising model data to an annealing machine and obtaining an execution result indicating a moving object to be assigned to the sensor. An allocation determination device comprising:

[0153] (Appendix 12) Accepting input of a position of a sensor capturing a moving object, a direction in which the sensor is facing, and a position of the moving object; constructing Ising model data that models an optimization problem for optimally allocating moving objects to be captured by the sensor based on a relationship between a region that can be captured by the sensor based on the position of the sensor and the direction in which the sensor faces and the position of the moving object; Mapping the Ising model data to an annealing machine to obtain an execution result indicating a moving object to be assigned to the sensor; Based on the execution result, the sensor is controlled to capture the assigned moving object. A sensor control method comprising:

[0154] (Appendix 13) Construct Ising model data that represents an objective function that minimizes the number of moving objects that are not captured by each sensor so that the total number of resources used by the sensors to capture one moving object does not exceed a predetermined upper limit, with the constraint that the number of moving objects that the sensors are to capture does not exceed a predetermined upper limit. 13. The sensor control method of claim 12.

[0155] (Appendix 14) To the computer, an input process for receiving an input of a position of a sensor capturing a moving object, a direction in which the sensor is facing, and the position of the moving object; A model construction process for constructing Ising model data that models an optimization problem for optimally allocating moving objects to be captured by the sensor based on a relationship between a region that can be captured by the sensor based on a position of the sensor and a direction in which the sensor is facing and a position of the moving object; An optimization process of mapping the Ising model data to an annealing machine to obtain an execution result indicating a moving object to be assigned to the sensor; and A control process for controlling the sensor so as to capture the assigned moving object based on the execution result. A storage medium that stores a sensor control program for executing the above.

[0156] (Appendix 15) To the computer, In the model construction process, Ising model data is constructed that represents an objective function that minimizes the number of moving objects that are not captured by each sensor, so that the total number of resources used by all sensors to capture one moving object does not exceed a predetermined upper limit, with the constraint that the number of moving objects that the sensors are to capture does not exceed a predetermined upper limit. 15. The storage medium of claim 14, storing a sensor control program for:

[0157] (Appendix 16) To the computer, an input process for receiving an input of a position of a sensor capturing a moving object, a direction in which the sensor is facing, and the position of the moving object; A model construction process for constructing Ising model data that models an optimization problem for optimally allocating moving objects to be captured by the sensor based on a relationship between a region that can be captured by the sensor based on a position of the sensor and a direction in which the sensor is facing and a position of the moving object; An optimization process of mapping the Ising model data to an annealing machine to obtain an execution result indicating a moving object to be assigned to the sensor; and A control process for controlling the sensor so as to capture the assigned moving object based on the execution result. A sensor control program for executing the above.

[0158] (Appendix 17) To the computer, In the model construction process, Ising model data is constructed that represents an objective function that minimizes the number of moving objects that are not captured by each sensor, so that the total number of resources used by all sensors to capture one moving object does not exceed a predetermined upper limit, with the constraint that the number of moving objects that the sensors are to capture does not exceed a predetermined upper limit. 17. The sensor control program according to claim 16. [Explanation of symbols]

[0159] 10 Sensors 20 moving objects 100 Sensor control device 110 Device control section 120 Storage section 121 Sensor coordinate and specifications database 122 Current target coordinate database 130 Input section 140 Target coordinate estimation section 150 Sensor Control Optimization Department 151 Ising Model Data Construction Department 152 Annealing Processing Section 160 Sensor control unit 170 New Target Detection Unit 180 Output section 200 Annealing Machine

Claims

1. Input means for receiving the position of the moving object, the position and orientation of the sensor for capturing the moving object, and the input of the position of the moving object; Model construction means for constructing Ising model data that models an optimization problem for optimally allocating the moving object to be captured by the sensor from the relationship between the area that can be captured by the position and orientation of the sensor and the position of the moving object; Optimization processing means for mapping the Ising model data to an annealing machine to obtain an execution result indicating the moving object to be assigned to the sensor; A sensor control system comprising control means for controlling the sensor to capture the assigned moving object based on the execution result. The sensor control system is characterized in that.

2. The model construction means constructs Ising model data representing an objective function that minimizes the number of missed captures of the moving object assigned to each sensor so that the number of moving objects to be captured by the sensor does not exceed a predetermined upper limit and the total number of resources used for capture by the entire sensor for one moving object does not exceed a predetermined upper limit. The sensor control system according to claim 1.

3. The model construction means constructs Ising model data including a constraint for suppressing one sensor from using more resources than a predetermined number for the same moving object. The sensor control system according to claim 1 or claim 2.

4. The model construction means constructs Ising model data including a constraint that the sum of accuracy points calculated as a value indicating tracking accuracy, which becomes higher as the moving object is captured by more resources and becomes higher as the moving object is closer to the sensor, becomes larger. The sensor control system according to any one of claims 1 to 3.

5. The model construction means constructs Ising model data including a constraint that the sum of accuracy points indicating tracking accuracy, which is defined to become higher as the angle between the radio waves irradiated by the orientation of the sensor becomes orthogonal, becomes larger. The sensor control system according to claim 4.

6. The model construction means constructs Ising model data including a mathematical formula with weighted values in the objective function that reduces the value of the objective function as the important target, which is the moving object to be preferentially captured, is assigned to the sensor. The sensor control system according to any one of claims 1 to 5.

7. The model construction means constructs an Ising model data that includes a mathematical formula having an effect of reducing the value of the objective function when an important target is assigned to an important sensor, which is a sensor predetermined as a sensor for capturing an important target, in the objective function. The sensor control system according to claim 6.

8. The model construction means constructs model data representing the constraints and the objective function in QUBO format. The sensor control system according to any one of claims 1 to 7.

9. The position of a sensor that captures a moving object and the orientation of the sensor, and accepts an input of the position of the moving object. Constructs Ising model data that models an optimization problem for optimally assigning a moving object to be captured by the sensor from the relationship between the area that can be captured by the position and orientation of the sensor and the position of the moving object. Maps the Ising model data to an annealing machine to obtain an execution result indicating the moving object assigned to the sensor. Based on the execution result, controls the sensor to capture the assigned moving object. A sensor control method characterized by the above.

10. On a computer, An input process that accepts the position of a sensor that captures a moving object and the orientation of the sensor, and an input of the position of the moving object. A model construction process that constructs Ising model data that models an optimization problem for optimally assigning a moving object to be captured by the sensor from the relationship between the area that can be captured by the position and orientation of the sensor and the position of the moving object. An optimization process that maps the Ising model data to an annealing machine to obtain an execution result indicating the moving object assigned to the sensor, and A control process that controls the sensor to capture the assigned moving object based on the execution result. A sensor control program for causing the above to be executed.

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