JP2012042232A - Shape estimation system, center server, shape estimation method, and shape estimation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a shape estimation system that precisely estimates information associated with an object of detection, a center server, a shape estimation method, and a shape estimation program.SOLUTION: Each of a plurality of sensor terminals 201-1 to 201-m includes a detection part 101 which detects the object of detection in a sensing area, and also detects an intensity corresponding to the distance to the object of detection, and a communication part 102 which transmits a detection result obtained by the detection part to the center server 401. The center server includes a communication part 301 which receives the detection results transmitted from the plurality of sensor terminals, and a data processing part 302 which finds estimation equations by a plurality of classes demarcated corresponding to the intensities using the detection results transmitted from the plurality of sensor terminals and sensing area information read out of a storage part 303, and solves simultaneous equations based upon the plurality of estimation equations to compute detection object information representing the object of detection.

Description

  The present invention relates to a shape estimation system, a center server, a shape estimation method, and a shape estimation method for estimating shape information that is information related to a shape of a detection target such as a circumference and size of the detection target based on a detection result of each sensor terminal in the sensor network. The present invention relates to a shape estimation program.

Many sensor terminals (hereinafter also referred to as sensor nodes) with small wireless communication functions are distributed in various locations to form a sensor network, measure the status of various locations, and use the measurement results for various applications. Many attempts have been made (for example, Non-Patent Documents 1 and 2).
Under such circumstances, it is considered to determine the type of the detection target based on the size and shape estimation of the detection target and the estimated information. For example, the size and shape of a vehicle entering a certain area are estimated, and based on the estimated information, the type of vehicle to be detected is determined, or the spread of toxic gas is estimated by detecting toxic gas. There are things to do.
In such a sensor network, if the positions of the sensors are predetermined and deployed, or if the positions where the sensors are disposed can be identified using GPS (Global Positioning System) after deployment, Shape estimation can be performed based on the detection result of the sensor.

  Even if the positions of the sensors arranged in a distributed manner cannot be specified, if the density at which the sensors are arranged (hereinafter referred to as the sensor density) is known, the size of the detection target can be estimated. (For example, Patent Document 1).

Ian. F. Akyildiz et al., “A survey on sensor networks”, IEEE Communication Magazine, No. 40, 8th pp. 102-114, August 2002

Ben. W. Cook et al., “SoC Issues for RF Smart Dust”, Proceedings of the IEEE, No. 94, Vol. 6, pp. 1177-1196, June 2006

JP 2010-60319 A

In the sensor network as described above, when a sensor is arranged at each predetermined position, there is a trouble that the position must be grasped when the sensor is installed. In addition, when each sensor is appropriately deployed and the position of the sensor is autonomously specified and registered by using the GPS or the like attached to the sensor, an additional cost such as the necessity of the GPS for each sensor occurs.
In addition, when estimating the shape of a detection target in a situation where individual sensor positions cannot be specified, for example, if the density at which the individual sensors are arranged (sensor density) is known, the position cannot be specified. The size of the detection target can be estimated. However, since it is difficult to deploy a plurality of sensors uniformly, there is a problem that the estimation accuracy is biased between the case where the detection target is present in a high sensor density region and the case where the detection target is present in a low region. There is.

  The present invention solves the above-described problems, and an object thereof is to provide a shape estimation system, a center server, a shape estimation method, and a shape estimation program that accurately estimate information about a detection target.

  In view of the above-described problems, a shape estimation system according to the present invention is a shape estimation system including a plurality of sensor terminals and one center server, and each of the plurality of sensor terminals is a detection target in a sensing area. And detecting a strength corresponding to the distance to the detection object, and a communication unit for transmitting a detection result by the detection unit to the center server, wherein the center server includes the plurality of Sensing area information related to a sensing area determined in advance according to the intensity of the detection result of the sensor terminal, a detection result by the detection unit, an estimation equation representing a relationship between the sensing area information and the detection object A storage unit for storing; a receiving unit for receiving the detection results transmitted from the plurality of sensor terminals; and the plurality of sensor terminals. The estimation result is obtained for each of a plurality of classes classified according to the intensity using the detection result transmitted from the sensing unit and the sensing area information read from the storage unit, and the simultaneous equations are solved based on the plurality of estimation equations. And a data processing unit for calculating detection object information representing the detection object.

  In the above-described shape estimation system, the estimation equation includes a sensor density indicating a density at which the detection unit exists as an unknown, and the data processing unit calculates the sensor density by solving the simultaneous equations. Features.

  The above-described shape estimation system is characterized in that the detection target information includes information indicating the size of the detection target and information indicating a circumference.

  In the shape estimation system described above, the estimation equation is

であり、但し、ｉはクラス、Ｎ（ｉ）はクラスｉの検出結果を得た前記センサ端末の数、Ｌｓ（ｉ）は、クラスｉに対応するセンシングエリアＳＡの周長、Ｓｓ（ｉ）は、クラスｉに対応するセンシングエリアＳＡの大きさ、λはセンサ密度、Ｌは前記検出対象物の周長、Ｓは前記検出対象物Ｘの大きさであることを特徴とする。

Where i is the class, N (i) is the number of sensor terminals that have obtained the detection result of class i, Ls (i) is the circumference of the sensing area SA corresponding to class i, and Ss (i) Is the size of the sensing area SA corresponding to class i, λ is the sensor density, L is the circumference of the detection object, and S is the size of the detection object X.

  In view of the above-described problems, the center server according to the present invention includes sensing area information relating to a sensing area determined in advance according to the intensity of detection results from a plurality of sensor terminals, detection results from the detection unit, and the sensing area information. And a storage unit that stores an estimation equation that represents the relationship between the detection object, a reception unit that receives the detection results transmitted from the plurality of sensor terminals, and the transmission unit that is transmitted from the plurality of sensor terminals. The detection result is obtained by obtaining the estimation equation for each of a plurality of classes classified according to the intensity using the detection result and sensing area information read from the storage unit, and solving the simultaneous equations based on the plurality of estimation equations. And a data processing unit for calculating detected object information representing the object.

  In view of the above-described problems, the shape estimation method according to the present invention detects a detection object in the sensing area and detects an intensity according to the distance from the detection object. A detection result indicating the detected result is transmitted to a center server, the center server receives the detection result transmitted from the plurality of sensor terminals, and the detection result transmitted from the plurality of sensor terminals; The sensing result and the sensing for each of a plurality of classes classified according to the intensity using sensing area information related to a sensing area determined in advance according to the intensity of the detection result by the plurality of sensor terminals. By obtaining an estimation equation representing the relationship between the area information and the detection object, and solving the simultaneous equations based on the plurality of estimation equations, Calculating a detection target information representing the serial detection object and characterized.

  In view of the above-described problems, the shape estimation program according to the present invention uses a detection unit that detects a detection target in a sensing area and detects an intensity according to a distance from the detection target. Communication means for transmitting a detection result by the means, sensing area information regarding a sensing area determined in advance according to the intensity of the detection result, and a relationship between the detection result by the detection means, the sensing area information, and the detection object Storage means for storing an estimation equation representing the detection result, receiving means for receiving the detection result, and a plurality of classes classified according to the intensity using the detection result and the sensing area information read from the storage means Obtaining the estimation equation and solving the simultaneous equations based on the plurality of estimation equations. Characterized in that it is a shape estimation program for functioning as a data processing means for calculating a detection object information representing an object.

  According to the present invention, information about a detection target can be estimated with high accuracy.

It is a block diagram which shows an example of a structure of the shape estimation system which concerns on embodiment of this invention. It is a figure for demonstrating an example of the sensing area of the sensor terminal which concerns on embodiment of this invention. It is a flowchart for demonstrating an example of the shape estimation method which concerns on embodiment of this invention.

Hereinafter, a shape estimation system 1 according to an embodiment of the present invention will be described with reference to FIG.
FIG. 1 is a schematic block diagram showing the configuration of a shape estimation system 1 according to this embodiment. The shape estimation system 1 includes a center server 401 and m sensor terminals 201-1 to 201-m as sensor terminals that detect the presence or absence of a detection target, and estimates the shape information of the detection target. Here, the shape information (detection target information) relates to the shape of the detection target, such as the area (size) of the detection target, the length of the outer periphery of the detection target, and the number of irregularities of the detection target. Information. Hereinafter, the area of the detection target in the shape estimation system 1 is represented as an area S, and the outer circumference is represented as a circumferential length L.

  The m sensor terminals 201-1 to 201-m each detect a detection target in the sensing area and detect a strength corresponding to the distance between the terminal and the detection target. 101, a communication unit 102 that transmits information indicating a detection result by the detection unit 101 (hereinafter, detection result information) to the center server 401, and a power supply unit 103 that supplies power. Each sensor terminal 201-1 to 201-m is assigned a unique identification number (hereinafter referred to as a terminal ID) and stored in a storage unit (not shown) of the terminal itself. When transmitting the detection result information, the communication unit 102 associates the terminal ID and transmits it to the center server 401.

The detection unit 101 determines that the sensing area X is in the sensing area SA when the sensing area SA having a predetermined area is set as a detectable range and a part of the detection object X is included in the sensing area SA. To detect. When the detection unit 101 detects the detection target X in the sensing area SA, the detection unit 101 obtains an intensity M corresponding to the distance from the detection target X as a sensing result. In the present embodiment, it is assumed that the intensity M is 1 when the distance becomes 0, and the intensity M approaches 0 as the distance from the detection object X and the detection unit 101 increases.
In this embodiment, it is assumed that all the detection units 101 mounted on the sensor terminals 201-1 to 201-m have the same characteristics, the characteristics are known, and the strength corresponding to the distance is determined in advance based on the characteristics. deep.

For example, a sensor having characteristics as shown in FIG. 2 can be used as an example of the detection unit 101. FIG. 2 is a diagram for explaining an example of characteristics of the detection unit 101 according to the present embodiment.
As illustrated in FIG. 2, the detection unit 101 obtains the detection result of the intensity M1 when the detection target object X is within the sensing area Ss1 having the radius r1 from the device itself. That is, the detection unit 101 obtains the detection result of the intensity M1 when the distance from the detection target object X is less than r1.
Moreover, the detection part 101 obtains the detection result of intensity | strength M2, when the detection target object X exists in the range (except the range of sensing area Ss1) of sensing area Ss2 of radius r2 from an own apparatus. That is, the detection unit 101 obtains the detection result of the intensity M2 when the distance to the detection target X is not less than r1 and less than r2.
Furthermore, the detection unit 101 obtains the detection result of the intensity M3 when the detection target object X is within the range of the sensing area Ss3 having a radius r3 (excluding the ranges of the sensing areas Ss1 and Ss2) from the device itself. That is, the detection unit 101 obtains the detection result of the intensity M3 when the distance to the detection target X is not less than r2 and less than r3.
The detection unit 101 cannot detect the detection target object X because the detection target object X does not exist in the sensing area SA when the detection target object X is located at a radius r3 or more away from the device itself. . The range of radius r3 from this device is the sensing area SA in this embodiment.

  In the present embodiment, the intensity M (i) indicates a level when the detection unit 101 obtains a numerical value within a certain range as a detection result, and a plurality of classes classified according to the intensity. Equal to C (i). However, the present invention is not limited to this, and the sensing area As (i) shown in FIG. 1 is defined for each class C (i), and is within a range determined in advance as the class C (i). The intensity M may be detected by the detection unit 101.

If the detection unit 101 has directivity, the intensity for each distance corresponding to the direction is determined in advance based on characteristics (properties) whose intensity varies depending on the direction.
Hereinafter, the sensor terminal 201 is also described as a general term for the m sensor terminals 201-1 to 201-m.

The center server 401 is a computer terminal that includes a communication unit 301, a data processing unit 302, a storage unit 303, an input / output unit 304, and a power supply unit 305.
The communication unit 301 performs communication processing for receiving detection result information transmitted from the sensor terminals 201-1 to 201-m. The communication unit 301 stores the received detection result information in the storage unit 303 for each terminal ID.
The data processing unit 302 is a calculation processing unit for the estimated value of the shape information of the detection target object that calculates the estimated value of the shape information of the detection target object X based on the detection result information received by the communication unit 301.
The data processing unit 302 obtains an estimation equation represented by the following expression (1) for each intensity {i = 1, 2, 3,...}, And solves the linear simultaneous equations based on these estimation equations, thereby detecting objects of detection. As an estimated value of the shape information of the object X, the size (area) S, the circumference L, and the sensor density λ of the detection object X are calculated.

なお、Ｎ（ｉ）は、強度（クラス）ｉの検出結果を得たセンサ端末の数である。Ｌｓ（ｉ）は、強度（クラス）ｉに対応するセンシングエリアＳＡの周長であり、Ｓｓ（ｉ）は、強度（クラス）ｉに対応するセンシングエリアＳＡの大きさ（面積）である。λは、センサ密度である。Ｌは、検出対象物Ｘの周長であり、Ｓは、検出対象物Ｘの大きさ（面積）である。
また、センシングエリアＳＡの周長Ｌｓ（ｉ）と大きさＳｓ（ｉ）は、検出部１０１が指向性を有する場合、保持している「距離ｒ（ｉ）に対応する検出部１０１からの範囲の大きさと周長」である。無指向性の場合は、保持している「距離ｒ（ｉ）に対応する強度Ｍ（ｉ）」データを用いて、２πｒ（ｉ）、π｛ｒ（ｉ）｝^２により与えておくこともできる。

N (i) is the number of sensor terminals that have obtained the detection result of intensity (class) i. Ls (i) is the circumference of the sensing area SA corresponding to the intensity (class) i, and Ss (i) is the size (area) of the sensing area SA corresponding to the intensity (class) i. λ is the sensor density. L is the circumference of the detection object X, and S is the size (area) of the detection object X.
In addition, the circumferential length Ls (i) and the size Ss (i) of the sensing area SA are stored in the range from the detection unit 101 corresponding to the “distance r (i)” when the detection unit 101 has directivity. Size and circumference ". In the case of non-directionality, 2πr (i), π {r (i)} ² may be given using the stored “intensity M (i) corresponding to distance r (i)” data. it can.

The storage unit 303 stores information (parameters) used for the calculation processing of the estimated value of the shape information by the data processing unit 302. As parameters stored in the storage unit 303, the circumference Ls (i), the area Ss (i), and the intensity M (i) of the sensing area SA of the detection unit 101 mounted on each of the sensor terminals 201-1 to 201-m. Are input via the input / output unit 304 according to the shape information of the estimation target. For example, the parameters of the sensing area SA are determined in advance for each intensity M (i) according to the example described with reference to FIG.
The input / output unit 304 includes, for example, an input unit that receives input of information from a user using a keyboard, mouse, touch panel, buttons, keys, and the like, and an output unit that performs display on a display such as an LCD (Liquid Crystal Display). I / O device.
The power supply unit 305 supplies power for driving each unit of the center server device 401.

In the present embodiment, the sensor server 401 includes the area S and the circumference L of the detection target X, and the installation positions of the sensor terminals 201-1 to 201-m (that is, the sensor density of the sensor terminals 201-1 to 201-m). The estimated value of λ) is calculated.
The known information includes sensor characteristics of the detection unit 101 of the sensor terminal 201 (for example, areas Ss (1), Ss (2), Ss (3) of the sensing area SA corresponding to the distance to the detection target X). The intensity M (1), M (2), M (3)) for each. The circumferential length Ls (i) {i = 1, 2, 3} of the sensing area SA is also known. In the case of non-directionality, instead of the area Ss (i) and the circumferential length Ls (i) of the sensing area SA, the radius r (i) is known and the area Ss (i) and the circumferential length Ls of the sensing area SA are known. (I) can also be calculated.

  Hereinafter, with reference to FIG. 3, the area S and circumference L of these detection objects X and the installation positions of the sensor terminals 201-1 to 201-m (that is, the sensor density of the sensor terminals 201-1 to 201-m). An example of an estimation method for estimating the shape information about the detection target X by the sensor server 401 when both λ) are unknown will be described. FIG. 3 is a flowchart for explaining an example of the shape estimation method according to the present embodiment.

As shown in FIG. 3, the input / output unit 304 of the center server device 401 responds to an operator's operation with the radii r (1), r (2) r (3) (omnidirectional) of the sensing area SA that is known information. ) Or sensor characteristics (for example, the area Ss (1), Ss (2), Ss (3) and the circumference of the sensing area corresponding to the intensity M (1), M (2), M (3) Ls (1), Ls (2), and Ls (3)} are received as estimated value parameters (step ST101) and written to the storage unit 303 (step ST102).
In addition, when the detection part 101 has directivity, the intensity | strength according to a direction and distance is input as a sensor characteristic.

  When the input / output unit 304 receives an input of the detection processing execution instruction from the operator, the input / output unit 304 transmits a sensing processing execution request to the sensor terminal 201 via the communication unit 301 (step ST103). In the sensor terminal 201-k (where k is an integer from 1 to m), the detection unit 101 performs detection processing (sensing) of the detection target X (step ST104), and the detection result is converted to the sensing result J (k). To the center server device 401 (step S105). If the sensing result J (k)> 0, it indicates that “the detection target has been detected”, and the sensing result J (k) = 0 indicates that “the detection target has not been detected”. If the sensing result J (k) = 1, the intensity M is maximum, and for example, the relationship 1> intensity M1> M2> M3> 0 is established.

  In the center server device 401, the communication unit 301 receives the sensing result J (k) from the sensor node device 201 (step S106), and the data processing unit 302 calculates the number N (i) of detection sensors that have detected the detection target X. , Intensity M (1), M (2),... Are calculated (step S107). That is, the data processing unit 302 calculates the sum of the number of sensors detected for each intensity, and generates an estimation equation for each class C (i) corresponding to each intensity.

Specifically, the data processing unit 302 determines whether or not the value of the sensing result J (k) is larger than the intensity M (1) (step ST108). Here, if the sensing result J (k)> the intensity M (1) (step ST108—YES), the data processing unit 302 adds 1 to the number of class N sensors N (1) (step ST109).
On the other hand, when the sensing result J (k) ≦ the intensity M (1) (step ST108—NO), the data processing unit 302 determines whether or not the value of the sensing result J (k) is larger than the intensity M (2). Judgment is made (step ST110). Here, when the sensing result J (k)> the intensity M (2) (step ST110-YES), the data processing unit 302 adds 1 to the number of class N sensors N (2) (step ST111).

On the other hand, when the sensing result J (k) ≦ the intensity M (2) (step ST110—NO), the data processing unit 302 determines whether or not the value of the sensing result J (k) is larger than the intensity M (3). Judgment is made (step ST112). Here, when the sensing result J (k)> the intensity M (3) (step ST112—YES), the data processing unit 302 adds 1 to the number of class N sensors N (3) (step ST113).
On the other hand, when the sensing result J (k) ≦ intensity M (3) (step ST112—NO), the data processing unit 302 adds 1 to the number of class N sensors N (4) (step ST114).

The data processing unit 302 classifies the sensing results J (k) from all the sensor terminals 201-1 to 201-m. After classifying all the sensor terminals 201-1 to 201-m (step ST115-YES), the data processing unit 302 reads information indicating parameters and estimation equations from the storage unit 303, and sets each class. An estimation equation indicating the number N (i) of sensors generated in step ST116 is generated (step ST116).
Specifically, the data processing unit 302 stores the circumference Ls (i), the area Ss (i) of the sensing area SA, and the estimation equation shown in the above equation (1) as parameters stored in the storage unit 303. Read from the unit 303. Then, the data processing unit 302 uses the parameters Ls (i) and Ss (i) that have been read and the number N (i) of detection sensors for the detection target X calculated for each class, and the estimation equation shown in Expression (1). For each class {i = 1, 2, 3,...}, And solving the linear simultaneous equations based on these estimation equations, the size of the detection object X is obtained as an estimated value of the shape information of the detection object X. (Area) S, circumference L, and sensor density λ are calculated (step ST117).

Specifically, the data processing unit 302 calculates the estimated value of the shape information from the total number of sensors N (i) for each class C (i) by the following simultaneous equations based on the equation (1). To do.
Number of sensors N (1) = λ {1 / 2π · L·Ls (1) + S + Ss (1)} (2)
Number of sensors N (2) = λ {1 / 2π · L·Ls (2) + S + Ss (2)} Equation (3)
Number of sensors N (3) = λ {1 / 2π · L·Ls (3) + S + Ss (3)} Equation (4)

  Next, the data processing unit 302 outputs the calculated size (area) S, circumference L, and sensor density λ of the detection object X to the input / output unit 304 as shape information, and the output unit of the input / output unit 304 has a shape. Information is output (step S118).

As described above, the data processing unit 302 can generate a plurality of estimation equations for each class classified according to the intensity M as a detection result by the detection unit 101. As a result, by preparing at least the number of unknowns for this estimation equation, the unknowns can be calculated using simultaneous equations.
Therefore, as described above, even if the estimation equation for calculating the circumference L or the size S of the detection target X includes the sensor density λ, three or more classes are prepared and estimated. By generating a plurality of equations for each class, an unknown number of sensor densities λ can be calculated. Therefore, even if the position of the sensor terminal is not specified and the sensor density λ is unknown, the shape information of the detection target X can be calculated.

Therefore, even if the position is not grasped at the time of sensor installation, it is not necessary to identify and register the sensor position autonomously with GPS or the like attached to the sensor after appropriately deploying each sensor. Additional costs such as the need for GPS for the sensors can be reduced.
In addition, when estimating the shape of a detection object in a situation where individual sensor positions cannot be specified, for example, the sensor density at which the individual sensors are arranged is unknown, and a plurality of sensors are uniformly deployed. Even in the absence of the sensor, the sensor density λ can be estimated, so that it is possible to reduce the bias caused by the case where the detection target exists in the high sensor density region and the low detection region.

  As described above, as described above, the data processing unit 302 uses information on the perimeter Ls, the area Ss of the sensing area of the sensor node devices 201-1 to 201-m, and the number N (i) of sensors that detect the detection target X. Based on the above equation, it is possible to calculate the estimated value of the shape information regarding the detection target object X to be estimated. The shape estimation system 1 is not a sensor having a function of measuring the area S of the detection portion of the detection target, but a sensor that detects the intensity M (i) according to the presence and absence of the detection target X and the distance. Since the estimated value of the shape information of the detection object can be calculated by applying to −1 to 201-m, the size of the detection object X can be calculated by solving simultaneous equations even if the sensor density is unknown. The perimeter can be calculated. In addition, since the estimated value of the circumference or area of the detection target can be directly calculated without using the determination function when estimating the shape information of the detection target, the shape of the detection target can be narrowed down in advance or The application is not limited to a simple determination function with a short processing time for calculating the estimated value, and can be widely applied and applied.

The above-described center server 401 and sensor terminals 201-1 to 201-m have a computer system therein. The operation processes of the communication unit 301 of the center server 401, the data processing unit 302, the detection units 101 of the sensor terminals 201-1 to 201-m, and the communication unit 102 are stored in a computer-readable recording medium in the form of a program. The computer system reads out and executes this program, and the above processing is performed. The “computer system” herein includes a CPU, various memories, an OS, and hardware such as peripheral devices.
Further, the “computer system” includes a homepage providing environment (or display environment) if a WWW system is used.

Further, a program for realizing each step shown in FIG. 3 is recorded on a computer-readable recording medium, and a program for realizing the function of the center server device 401 shown in FIG. A process of calculating an estimated value of the shape information of the detection target may be performed by recording the program on the medium, causing the computer system to read and execute the program recorded on the recording medium.
The “computer-readable recording medium” means a flexible disk, a magneto-optical disk, a ROM, a writable nonvolatile memory such as a flash memory, a portable medium such as a CD-ROM, a hard disk built in a computer system, etc. This is a storage device.

Further, the “computer-readable recording medium” means a volatile memory (for example, DRAM (Dynamic DRAM) in a computer system that becomes a server or a client when a program is transmitted through a network such as the Internet or a communication line such as a telephone line. Random Access Memory)), etc., which hold programs for a certain period of time.
The program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the “transmission medium” for transmitting the program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line.
The program may be for realizing a part of the functions described above. Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, and what is called a difference file (difference program) may be sufficient.

  DESCRIPTION OF SYMBOLS 101 ... Detection part, 102 ... Communication part, 103 ... Power supply part, 201_1-202_m ... Sensor terminal, 301 ... Communication part, 302 ... Data processing part, 303 ... Memory | storage , 304 ... I / O unit, 305 ... Power supply unit, 402 ... Center server

Claims (7)

A shape estimation system comprising a plurality of sensor terminals and one center server,
Each of the plurality of sensor terminals is
A detection unit that detects a detection object in the sensing area and detects an intensity according to a distance from the detection object;
A communication unit that transmits a detection result by the detection unit to the center server,
The center server is
Sensing area information related to a sensing area determined in advance according to the intensity of detection results from the plurality of sensor terminals, and an estimation equation representing a relationship between a detection result by the detection unit, the sensing area information, and the detection object A storage unit for storing
A receiving unit for receiving the detection results transmitted from the plurality of sensor terminals;
Using the detection results transmitted from the plurality of sensor terminals and sensing area information read from the storage unit, the estimation equation is obtained for each of a plurality of classes divided according to the intensity, and the plurality of estimation equations A data processing unit that calculates detection object information representing the detection object by solving simultaneous equations based on the equation;
A shape estimation system characterized by comprising: The estimation equation is
Including the sensing density indicating the density of the detection unit as an unknown,
The data processing unit
The shape estimation system according to claim 1, wherein the sensing density is calculated by solving the simultaneous equations. The detection object information is:
The shape estimation system according to claim 1, comprising information representing a size of the detection target and information representing a circumference. The estimation equation is

And
Where i is the class, N (i) is the number of sensor terminals that have obtained the detection result of class i, Ls (i) is the circumference of the sensing area SA corresponding to class i, and Ss (i) is the class The size of the sensing area SA corresponding to i, λ is the sensor density, L is the circumference of the detection object, and S is the size of the detection object X. The shape estimation system according to claim 1. Sensing area information relating to a sensing area determined in advance according to the intensity of detection results by a plurality of sensor terminals, a detection result by the detection unit, an estimation equation representing a relationship between the sensing area information and the detection object, A storage unit for storing
A receiving unit for receiving the detection results transmitted from the plurality of sensor terminals;
Using the detection results transmitted from the plurality of sensor terminals and sensing area information read from the storage unit, the estimation equation is obtained for each of a plurality of classes divided according to the intensity, and the plurality of estimation equations A data processing unit that calculates detection object information representing the detection object by solving simultaneous equations based on the equation;
A center server characterized by comprising: Each of the plurality of sensor terminals
While detecting the detection object in the sensing area, the intensity according to the distance from the detection object is detected,
Sending a detection result indicating the detected result to the center server;
The center server is
Receiving the detection results transmitted from the plurality of sensor terminals;
The detection results transmitted from the plurality of sensor terminals and the sensing area information related to the sensing area determined in advance according to the intensity of the detection results by the plurality of sensor terminals are classified according to the intensity. For each of a plurality of classes, an estimation equation representing a relationship between the detection result, the sensing area information, and the detection object is obtained, and the detection object is obtained by solving simultaneous equations based on the plurality of estimation equations. A shape estimation method characterized by calculating detection object information to be represented. Computer
A detecting means for detecting a detection object in the sensing area and detecting an intensity according to a distance from the detection object;
A communication means for transmitting a detection result by the detection means;
A memory for storing sensing area information related to a sensing area determined in advance according to the intensity of the detection result, a detection result by the detection means, and an estimation equation representing a relationship between the sensing area information and the detection object. means,
Receiving means for receiving the detection result;
By obtaining the estimation equation for each of a plurality of classes classified according to the intensity using the sensing area information read from the detection result and the storage means, by solving the simultaneous equations based on the plurality of estimation equations, Data processing means for calculating detection object information representing the detection object;
Shape estimation program to function as

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