Sensing system, sensing method, and program

Abstract

The present disclosure provides a sensing system, a sensing method, and a non-transitory computer-readable medium capable of localizing a section of a monitoring target, the sensing system (10) including a data set processing unit (12) configured to obtain time-distance information of vibration signals for each distributed sensing portion, the vibration signals being acquired by a plurality of distributed sensing portions to detect a monitoring target and induced by traffic of moving objects, a frequency density estimation unit (14) configured to estimate a frequency density of the vibration signals detected by the sensing portions based on the time-distance information, a similarity estimation unit (16) configured to estimate a similarity between a pair of sensing portions based on the frequency density, and a localization unit (18) configured to localize a section of the monitoring target based on the similarity.

Description

[Technical field]

[0001] The present disclosure relates to a sensing system, a sensing method, and a non-transitory computer-readable medium. [Background technology]

[0002] In recent years, monitoring systems for infrastructure such as roads have been developed.

[0003] For example, Patent Document 1 (PTL1) discloses a traffic monitoring device including a distributed acoustic sensor (DAS) for acquiring waterfall data and a processor connected to the DAS. The processor is further configured to pre-process the waterfall data, separate the pre-processed waterfall data into a plurality of patches, and process each of the plurality of patches to estimate at least one traffic flow characteristic of the roadway. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] US Patent Application Publication No. 2021 / 0241615 Summary of the Invention [Problem to be solved by the invention]

[0005] If the infrastructure monitored by the monitoring system includes not only regular roads but also bridges or other types of irregular terrain, the monitoring results may be distorted due to the irregular terrain, and therefore localization of sections within the infrastructure based on the monitoring results may be inaccurate.

[0006] It is an object of the present disclosure to provide a sensing system, a sensing method, and a non-transitory computer-readable medium that can be localized. [Means for solving the problem]

[0007] According to one aspect of the present disclosure, a sensing system is provided that includes a dataset processing means for obtaining time-distance information of vibration signals for each distributed sensing part, where the vibration signals are acquired by a plurality of distributed sensing parts to detect a monitored object and are induced by traffic of moving objects; a frequency density estimation means for estimating a frequency density of the vibration signals detected by the sensing parts based on the time-distance information; a similarity estimation means for estimating a similarity between a pair of sensing parts based on the frequency density; and a localization means for localizing a section of the monitored object based on the similarity.

[0008] According to one aspect of the present disclosure, a sensing method is provided that includes obtaining time-distance information of vibration signals for each distributed sensing part, where the vibration signals are acquired by a plurality of distributed sensing parts to detect a monitored object and are induced by traffic of moving objects; estimating a frequency density of the vibration signals detected by the sensing parts based on the time-distance information; estimating a similarity between a pair of sensing parts based on the frequency density; and localizing a section of the monitored object based on the similarity.

[0009] According to one aspect of the present disclosure, a non-transitory computer-readable medium is provided having stored thereon a program for causing a computer to perform the following operations: obtain time-distance information of vibration signals for each distributed sensing portion, where the vibration signals are acquired by a plurality of distributed sensing portions to detect a monitored object and are induced by traffic of moving objects; estimate a frequency density of the vibration signals detected by the sensing portions based on the time-distance information; estimate a similarity between a pair of sensing portions based on the frequency density; and localize a section of the monitored object based on the similarity. Effect of the Invention

[0010] According to the present disclosure, it is possible to provide a sensing system, a sensing method, and a non-transitory computer-readable medium that can be localized. [Brief description of the drawings]

[0011] [Figure 1] 1 is an example of a block diagram of a sensing system according to a first embodiment. [Diagram 2] 4 is an example of a flowchart illustrating a method of the sensing system according to the first embodiment. [Figure 3A] 1 shows an example of a wide area localization system including a fiber optic cable according to a second embodiment. [Figure 3B] 1 shows an example of a schematic diagram of a road scene with fiber optic cables. [Figure 4] FIG. 11 is an example of a block diagram of a sensing server according to a second embodiment. [Figure 5A] 1 shows an example of a time-distance chart for each of a series of sensing points on a fiber optic cable attached to a roadway. [Figure 5B] 1 shows an example of a power spectral density. [Figure 5C] 1 shows an example of a cross-correlation matrix obtained for each of the frequency responses of each of the sensing points. [Figure 6] 2 shows an example of data processed in the sensing server 100. [Figure 7] 13 is an example of a flowchart illustrating a method of the sensing system according to the second embodiment. [Figure 8A] An example of the number of events that occurred at each frequency in a bridge section is shown below. [Figure 8B] 8B shows an example of a shift in the frequency distribution corresponding to FIG. 8A. [Figure 9] FIG. 2 is a block diagram of a computer device according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] An embodiment according to the present disclosure will be described below with reference to the drawings. In the following description and drawings, appropriate omissions and simplifications are made in order to clarify the description. Furthermore, throughout the drawings, the same elements are represented by the same symbols (or symbols), and their repeated description is omitted as necessary. Furthermore, in this disclosure, unless otherwise specified, "at least one of A or B (A / B)" may mean either A or B, or both A and B. Similarly, when "at least one" is used for three or more elements, it may mean any one of these elements, or any multiple elements (including all elements). Furthermore, in the description of this disclosure, it should be noted that an element described using a singular form such as "a", "an", "the", or "one" may be a multiple element unless otherwise specified.

[0013] (First embodiment) First, a sensing system 10 according to a first embodiment of the present disclosure will be described with reference to FIG.

[0014] 1, the sensing system 10 includes a data set processing unit 12, a frequency density estimation unit 14, a similarity estimation unit 16, and a localization unit 18. The sensing system 10 may be one or more computers and / or machines. As an example, at least one of the components in the sensing system 10 may be incorporated into a computer as a combination of one or more memories and one or more processors. The computer(s) used as the sensing system 10 may be a server.

[0015] The data set processing unit 12 obtains time-distance information of the vibration signal for each distributed sensing portion. The vibration signal is a time series signal acquired by multiple distributed sensing portions to detect a monitored object and induced by the traffic of a moving object (target object). The distributed sensing portions may be multiple spaced points on a long linear sensor (e.g., optical fiber cable), multiple independent sensors, etc. The distributed sensing portions are laid along a path through which the moving object passes. The moving object may be various objects moving on land, such as motor vehicles (including automobiles, motorcycles, buses, trucks, etc.), trains, trams, bicycles, vehicles not moved by machines, pedestrians (passersby), etc., and the path through which the moving object passes may be a road (including arterial roads and general roads), a railway, a bridge, a pedestrian path, or a bicycle path, etc. The time-distance information indicates the time and position of the vibration, and may be expressed, for example, as time-distance graph information. In addition, in this disclosure, the time-distance graph information may also be referred to as a waterfall dataset.

[0016] The processing of the data set processing unit 12 can be performed using any known technique. For example, the sensing system 10 may obtain raw data sets (vibration signals) measured by multiple distributed sensing parts and pre-process the raw data sets into time-range information. However, the data set processing unit 12 may also obtain time-range information generated by other devices.

[0017] The frequency density estimation unit 14 estimates the frequency density of the vibration signal detected by the sensing portion based on the time-distance information. The frequency density may also be called the spectral density, which indicates the density distribution over all frequencies.

[0018] The similarity estimation unit 16 estimates the similarity between the pair of sensing parts based on the frequency density. For example, the similarity may be calculated using a peak or a graph of the frequency density of the pair of sensing parts. The similarity is a feature of the monitored object, which suggests the difference in physical properties of different sections of the monitored object.

[0019] The localization unit 18 localizes sections of the object based on the similarity, for example, the localization unit 18 may localize sections that have different physical characteristics compared to the rest of the object.

[0020] Next, an example of the operation of this embodiment will be described with reference to the flowchart of Fig. 2. Details of each process in Fig. 2 have already been described above.

[0021] First, the data set processing unit 12 obtains time-distance information of the vibration signal for each distributed sensing part (step S11). Next, the frequency density estimation unit 14 estimates the frequency density of the vibration signal detected by the sensing part based on the time-distance information obtained by the data set processing unit 12 (step S12).

[0022] The similarity estimation unit 16 estimates the similarity between the pair of sensing parts based on the frequency density estimated by the frequency density estimation unit (step S13). Then, the localization unit 18 localizes the section to be monitored based on the similarity estimated by the similarity estimation unit 16 (step S14).

[0023] Because the sensing system 10 estimates the similarity between a pair of sensed portions based on frequency density, it can account for differences in physical characteristics within a monitored object, such as a road or other type of infrastructure. Thus, by using the similarity data, the sensing system 10 can localize a section of the monitored object by taking into account the differences in physical characteristics within the monitored object. For example, the sensing system 10 can localize a bridge section of a road.

[0024] (Second embodiment) A second embodiment of the present disclosure will be described below with reference to the accompanying drawings. The second embodiment describes one specific example of the first embodiment, but the specific example of the first embodiment is not limited to this embodiment.

[0025] 3A shows an example of a wide-area localization system T including an optical fiber cable F (sensing optical fiber), a distributed acoustic sensor (DAS: Distributed Acoustic Sensor: functioning as a sensing device), and a sensing server 100. In FIG. 3A, an optical fiber cable F buried underground is laid along a road R, particularly along a target lane. The optical fiber cable F is arranged to measure response vibration of the road R due to a moving object on the road, such as a vehicle. The road has two bridges and a rough road section, and one loop of the optical fiber cable F exists on each of the two bridges.

[0026] FIG. 3A also shows the degree of error in the monitoring results induced by road features. Specifically, loop 1 and loop 2 cause errors Δ1 and Δ2, and the bad road section causes an error Δ3. Δ1 and Δ2 correspond to the lengths of loop 1 and loop 2, respectively, and Δ3 corresponds to the length and roughness of the bad road section. Due to these obstacles, the error increases further with the distance from the DAS position. However, the sensing server 100 can localize the road section to a series of sensing points (sensing parts) of the optical fiber cable as shown below.

[0027] FIG. 3B shows an example of a schematic view of a road R having an optical fiber cable F. In FIG. 3B, the optical fiber cable F is installed along the surface below the road R and is used to measure the response vibration of the road R caused by vehicles C1 to C3, which are moving objects passing along the optical fiber cable F. Furthermore, the optical fiber cable F is s a ~s c Each of the sensing parts in the optical fiber cable F is called a sensor.

[0028] 3B, vehicle C1 and vehicle C2 in lane 1 are passing through road R from right to left, and vehicle C3 in lane 2 is moving in the opposite direction to vehicle C1 and vehicle C2. The sensing server 100 can monitor road R and detect each traffic event of the vehicles.

[0029] A vibration signal (e.g., acoustic or vibration data) is induced in the optical fiber cable F by a vehicle (in particular, by an axle of a vehicle passing on a road R having the optical fiber cable). That is, the vibration signal represents a vibration on the road R. For example, in FIG. 3B, a sensor s b detects vibration signals from vehicles C2 and C3 at a certain moment.

[0030] The DAS detects vibration signals in each of a plurality of sensors of the optical fiber cable F. The DAS can detect vibration signals of the road R induced by the axles of a vehicle when the vehicle is passing through any traffic lane of the road R. The vibration signals can be measured at any position on the optical fiber cable F. For example, when the sensing range is 50 km and the spatial resolution is 4 m, the vibration signals at 12,500 locations (sensing channels) can be measured. The DAS transmits the vibration signals as digital data to the sensing server 100 via wired communication. However, the communication between the DAS and the sensing server 100 can be performed by wireless communication.

[0031] Fig. 4 is a block diagram of a sensing server 100, which is an example of a sensing server. Referring to Fig. 4, the sensing server 100 includes a signal acquisition unit 102, a raw data set processing unit 104, a frequency density estimation unit 106, a similarity estimation unit 108, and a localization unit 110. The sensing server 100 is a specific example of the sensing system 10, and may include other units for calculation. Each unit of the sensing system 10 will be described in detail.

[0032] The signal acquisition unit 102 functions as an interface with the sensing server 100 and receives raw vibration signal data (hereinafter referred to as raw data set: X raw In particular, the signal acquisition unit 102 acquires raw signal data at each of a series of sensing points on the optical fiber cable F. FIG. 5A illustrates the X raw 1 shows an example of a time-distance chart of

[0033] The signal acquisition unit 102 is raw to the raw data set processing unit 104. Furthermore, the signal acquisition unit 102 outputs X raw For example, the signal acquisition unit 102 may pre-process X raw Filter the filtered X raw may be output.

[0034] The raw data set processing unit 104 processes the X output by the signal acquisition unit 102 for each of the series of sensing points. raw Next, the raw data set processing unit 104 obtains X raw Remove the amplitude offset from X raw The amplitude offset removed is the DC (Direct Current) component of the measurement signal at each of the sensing points, which is probably caused by the phase drift in the interrogator of the DAS. The standardization is performed to normalize the standard deviation of each channel to 1.

[0035] The raw data set processing unit 104 then uses the standardized signals to derive the X raw The raw dataset processing unit 104 may generate a waterfall trace of the vibration intensity by applying a sum of the absolute intensities over a window of a given length.

[0036] The frequency density estimation unit 106 uses the time distance matrix (generated by the raw data set processing unit 104) to estimate the power spectral density (frequency density) of each of the vibration signals obtained at all the sensing points of the optical fiber cable F. Specifically, the frequency density estimation unit 106 may apply a Fast Fourier Transform (FFT) to each column of each time distance matrix, and use a technique based on the power spectral density to estimate the power spectral density as a dominant structural feature. Furthermore, the frequency density estimation unit 106 can obtain an FFT-based feature vector from each column of each time distance matrix obtained by the raw data set processing unit 104. As a result, the frequency density estimation unit 106 can obtain a distribution of FFT coefficients for estimating the power spectral density. The frequency density estimation unit 106 may standardize the FFT coefficients to obtain the frequency density.

[0037] Frequency density describes the distribution of frequencies in a given vibration signal, indicating the peak frequency for each sensing point, and therefore it is useful to obtain the relationship between different structural frequencies, for example between a bridge and a nearby road surface section.

[0038] FIG. 5B shows an example of power spectral density. In other words, FIG. 5B shows the frequency domain response obtained from each sensing point of the optical fiber cable F. For example, the frequency response at each sensing point on the road infrastructure shows vibrations at natural resonant frequencies. The peak resonant frequencies (peak frequencies) can be used as structural features to identify sections on the infrastructure.

[0039] Returning to Fig. 4, the similarity estimation unit 108 selects a series of sensing points from the time distance matrix, and obtains a cross-correlation matrix calculated from the peak frequency (frequency density) of each series of sensing points. The series of sensing points may be called an interval within the range of the interval length L.

[0040] Specifically, the similarity estimation unit 108 uses the frequency density generated by the frequency density estimation unit 106 to estimate a similarity score between each pair of sensing points for a series of sensing points in the vicinity of the series of sensing points of a predefined length. The similarity score is calculated by a cross-correlation matrix and means a correlation coefficient between two frequency densities of two signals of two sensing points. The similarity estimation unit 108 may also use a threshold parameter within the range of the similarity score to select a frequency density of high similarity in each section. That is, the frequency density of high similarity has a similarity score (index) exceeding a given threshold. As an alternative example, the similarity estimation unit 108 may obtain the highest similarity index between the frequency densities of the series of sensing points within the range of the similarity score, and the series of sensing points form one section of length L. Thus, the similarity estimation unit 108 can select a series of sensing points (channels) with a high similarity index to localize the location of the road.

[0041] FIG. 5C shows an example of a cross-correlation matrix obtained for each of the frequency responses of each of the sensing points in the predetermined frequency range of interest corresponding to each structural section. For example, bridge frequencies below 5 Hz are used to obtain cross-correlation that distinguishes the bridge from other nearby sections of the bridge. A high (e.g., highest) index similarity between nearby sensing points represents frequency responses of a particular section on the road infrastructure that match each other. In FIG. 5C, the vertical and horizontal sides of the matrix indicate the distance from a given sensing point.

[0042] The localization unit 110 determines the location of each of the sections corresponding to the sensing portion having a high similarity index (e.g., the highest similarity index) above a given threshold. The located sections are useful for localizing the identified sections to sections of a layout of optical fiber cables F attached to a road infrastructure. The fiber layout may be a map of fiber attachments corresponding to physical locations in each of the road sections having a specific physical distance. The fiber layout map may be used to localize the optical fiber cable sensing channels identified by the above process to the road sections. Thus, the localization unit 110 can use the map information to localize the actual location of sections of wide-area infrastructure, such as road infrastructure with bridges and tunnels, to monitor traffic and infrastructure characteristics.

[0043] Figure 6 shows an example of data processed in the sensing server 100. Figure 6(A) shows an example snapshot of a normalized waterfall data set (time-distance graph) in a situation where a vehicle is moving away and coming towards the sensing server 100, and the vibration intensity (vibration signal) of the optical fiber cable F is visualized, with each line in Figure 6(A) showing each trajectory of the vehicle. The vibration intensity is commensurate with the type of vehicle passing on the road.

[0044] Furthermore, in Fig. 6(A), the dashed frame represents a bridge section on the roadway, and the vibration intensity is unknown because the vibration of the bridge section is large. The raw data set processing unit 104 can obtain the time-distance information shown in Fig. 6(A).

[0045] Fig. 6(B) shows an example snap of FFT-based features of the normalized data shown in Fig. 6(A) in each column of each of the time-distance graph matrices. In Fig. 6(B), the dashed boxes represent the bridge sections present on the roadway corresponding to the dashed boxes in Fig. 6(A). The frequency density estimation unit 106 can obtain the FFT-based features shown in Fig. 6(B) by using the time-distance matrix.

[0046] Figure 6(C) shows an example snapshot of identified bridge presence corresponding to the FFT-based features shown in Figure 6(B). In Figure 6(C), the dashed boxes represent bridge sections present on the roadway corresponding to the dashed boxes in Figures 6(A) and 6(B). The localization unit 110 determines the location of each of the sections with high similarity index to localize (identify) the bridge sections shown in Figure 6(C).

[0047] Next, an example of the operation of the sensing server 100 will be described with reference to the flowchart of Fig. 7. The details of each process in Fig. 7 have already been described.

[0048] First, the signal acquisition unit 102 acquires a raw data set (X raw ) and outputs it to the raw data set processing unit 104 (step S21). Next, the raw data set processing unit 104 raw (step S22). Specifically, the raw data set processing unit 104 pre-processes X raw Remove the amplitude offset from X raw The raw data set processing unit 104 then uses the standardized signals to calculate X for each of a series of sensing points on the fiber optic cable F. raw Calculate the time distance matrix of

[0049] Then, the frequency density estimation unit 106 estimates (step S23) the power spectral density of each of the vibration signals obtained at all the sensing points of the optical fiber cable F. In particular, the frequency density estimation unit 106 can obtain the distribution of FFT coefficients for estimating the power spectral density.

[0050] Next, the similarity estimation unit 108 selects a series of sensing points from the time distance matrix, and obtains a cross-correlation matrix calculated from the peak frequency of each sensing point (step S24).Then, the similarity estimation unit 108 selects a series of sensing points (channels) with high similarity index to localize the road position (step S25).

[0051] Finally, the localization unit 110 localizes the identified section using a section of the layout of the optical fiber cable F (step S26).

[0052] In the related art, a fiber optic cable acquires vibration signals (acoustic or vibration data), and the fiber optic cable is attached to a monitored object such as a road infrastructure. A sensing device acquires environmental vibrations around the object to be monitored by analyzing the Rayleigh backscattering of pulsed light transmitted in the fiber optic cable.

[0053] The length of the fiber optic cable laid along the road is longer than the actual physical length of the road. Sensing applications utilizing embedded fiber optic cables may require calibration of the sensing point on the fiber optic cable with the target object (e.g., bridge, tunnel, and / or road structure) to ensure that the sensing point is close to the target object.

[0054] The visible and invisible track sections of the optical fiber cable are used to classify bridge sections on the road and localize points on the optical fiber cable. On the other hand, when only visible or invisible track sections exist, it is difficult to localize points on the fiber cable. For example, when the time-distance waterfall graph of the vibration signal only has visible track sections, it is difficult to localize reference points and it is difficult to localize the road infrastructure overall.

[0055] In the present disclosure, the sensing server 100 can estimate the similarity between a pair of sensing parts based on frequency density, and localize the section to be monitored based on the similarity. Thus, the sensing server 100 can localize the exact section of the road infrastructure.

[0056] In addition, the raw data set processing unit 104 can generate a time distance matrix of the vibration signal as time distance information by removing bias components from the vibration signal and standardizing the amplitude of the vibration signal. Therefore, the sensing server 100 can calculate the similarity with simple preparation and reduce the process required for localization.

[0057] Furthermore, the frequency density estimation unit 106 can estimate the frequency density of the vibration signal of the sensing part by applying a Fast Fourier Transformation (FFT) to the columns of the time distance matrix. Therefore, the sensing server 100 can calculate the similarity using a known technique, and the calculation method is easy to implement.

[0058] In addition, the similarity estimation unit 108 can calculate the cross-correlation matrix between the frequency densities of the sensing parts to estimate the similarity. Therefore, the sensing server 100 can calculate the similarity with simple preparation and reduce the process required for localization.

[0059] Furthermore, the localization unit 110 may localize the intervals corresponding to the sensing parts having similarity indices above a given threshold. Thus, the sensing server 100 can more accurately identify the intervals, which may have different physical characteristics compared with the rest of the monitored object.

[0060] In addition, the localization unit 110 may use map information to localize the actual location of the section. Thus, the sensing server 100 can more accurately identify the section by referring to the actual map information.

[0061] Furthermore, the wide area localization system T may include an optical fiber cable F including a sensing portion, which is attached to the road. Thus, the sensing method disclosed in the second embodiment may be applied to monitoring of road infrastructure. For example, the sensing server 100 may monitor a bridge section in the road infrastructure, and the natural frequency of the bridge may be monitored by analyzing the statistical distribution of several events of vehicle passing on the bridge section. The statistical distribution is the mean value and standard deviation of the frequency, which may be generated by the frequency density estimation unit 106 based on the spectral density of a series of sensing points. The deviation or variance of the frequency distribution may be used as an index of a change in structural characteristics that is likely to be infrastructure damage. By using the actual position information of the section of the optical fiber cable F localized by the localization unit 110, the sensing server 100 may obtain the statistical distribution (natural frequency) and analyze them to estimate the natural frequency and ultimately the change in structural characteristics in the specific section. The specific section may be, for example, but not limited to, a bridge or tunnel section. In this way, the sensing server 100 may detect the degree of damage in the specific section.

[0062] Figure 8A shows an example histogram of the number of events that occurred at each frequency on a bridge section. In Figure 8A, the horizontal axis shows frequency in Hertz, and the vertical axis shows the number of times a vehicle passed over the bridge section (number of sample events). Figure 8A shows that the frequency of events changes as the bridge section goes from an initial undamaged state to a damaged state.

[0063] FIG. 8B shows an example of a frequency distribution shift corresponding to FIG. 8A. In FIG. 8B, the horizontal axis shows frequency in Hertz, and the vertical axis shows the density of frequency density. Also, the statistical distribution of bridge frequencies in undamaged and damaged states is plotted as a density distribution. As shown in FIG. 8B, when the bridge section goes from an initial undamaged state to a damaged state, the graph of the density distribution shifts and the standard deviation of the density distribution increases. The sensing server 100 may calculate the density distribution shown in FIG. 8B, perform an analysis to estimate the change in structural characteristics in the bridge section, and output the estimation result (the results of FIG. 8A and / or FIG. 8B may be output as necessary). In this way, the sensing server 100 can be applied as a monitoring system to observe the change in structural characteristics after accurate localization. However, the above-mentioned method of observing the change in structural characteristics is only an example and is not limited thereto.

[0064] Within the scope of the entire disclosure (including the scope of claims) of the present disclosure, modifications and adjustments of each embodiment and each example are possible based on the basic technical concept of the present disclosure. Therefore, the present embodiment should be considered to be illustrative in all respects and not restrictive.

[0065] For example, in the second embodiment, the frequency density estimation unit 106 uses a technique based on FFT and power spectral density. However, the specific calculation method used is not limited to these. Other units of the sensing server 100 may use alternative calculation methods other than those mentioned above.

[0066] The disclosures of the above-mentioned Patent Document 1 are incorporated herein by reference. Based on the basic technical concept of the present disclosure, modifications and adjustments of each embodiment and each example are possible within the scope of the overall disclosure of the present disclosure (including the scope of claims). Therefore, the present embodiment should be considered to be illustrative in all respects and not restrictive.

[0067] Next, with reference to FIG. 9, a configuration example of the sensing system (monitoring system) described in the above-mentioned embodiments will be described below.

[0068] The sensing system, including examples of both the sensing system 10 and the sensing server 100, may be implemented on a computer system as shown in Fig. 9. Referring to Fig. 9, a computer system 90, such as a server, includes a communication interface 91, a memory 92, and a processor 93. Additionally, the computer system 90 may include a display device 94.

[0069] The communications interface 91 (e.g., a network interface controller (NIC)) may be configured to communicatively connect to a sensor(s) provided in the infrastructure. For example, as shown in FIG. 3B, the sensor(s) may be provided below the ground (i.e., a road lane). Additionally, the communications interface 91 may communicate with other computer(s) and / or machine(s) to receive and / or transmit data related to the computations of the computer system 90.

[0070] The memory 92 stores a program 95 (program instructions) that enables the computer system 90 to function as the sensing system 10 or the sensing server 100. The memory 92 includes, for example, a semiconductor memory (e.g., a random access memory (RAM), a read-only memory (ROM), an electrically erasable and programmable ROM (EEPROM), and / or a storage device including at least one of a hard disk drive (HDD), a solid-state drive (SSD), a compact disk (CD), a digital versatile disk (DVD), and the like. From another perspective, the memory 92 is formed by a volatile memory and / or a non-volatile memory. The memory 92 may include a storage device disposed separately from the processor 93. In this case, the processor 93 may access the memory 92 via an I / O interface (not shown).

[0071] The processor 93 is configured to read a program 95 (program instructions) from the memory 92 and execute the program 95 (program instructions) to realize the functions and processes of the above-described embodiments. The processor 93 may be, for example, a microprocessor, an MPU (microprocessing unit), or a CPU (central processing unit). Furthermore, the processor 93 may include multiple processors. In this case, each of the processors executes one or more programs including a set of instructions to cause the computer to perform the algorithm described above with reference to the drawings.

[0072] The display device 94 displays the results of the sensing. The display device 94 may be, for example, a liquid crystal display or a touch panel. However, the computer system 90 may include a speaker that notifies the results of the sensing in addition to or instead of the display device 94.

[0073] The program 95 includes program instructions (program modules) for executing the processing of each unit of the sensing system in the above-described embodiments.

[0074] The program includes instructions (or software code) that, when loaded into a computer, causes the computer to perform one or more of the functions described in the embodiments. The program may be stored in a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, the non-transitory computer-readable medium or a tangible storage medium may include random access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technology, CD-ROM, digital versatile disk (DVD), Blu-ray disk (®) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device, or other magnetic storage device. The program may be transmitted on a temporary computer-readable medium or a communication medium. By way of example and not limitation, the temporary computer-readable medium or a communication medium may include electrical, optical, acoustic, or other forms of propagated signals.

[0075] Within the scope of the claims of the present disclosure, various combinations and selections of various disclosed elements (including each element of each example, each element of each drawing, etc.) are possible. In other words, the present disclosure naturally includes various variations and modifications that may be made by a person skilled in the art according to the overall disclosure including the claims and technical concepts. [Explanation of symbols]

[0076] 10. Sensing System 12 Dataset Processing Unit 14 Frequency Density Estimation Unit 16 Similarity Estimation Unit 18 Localization Unit T Wide-area localization system 100 Sensing Server 102 Signal Acquisition Unit 104 Raw Dataset Processing Unit 106 Frequency Density Estimation Unit 108 Similarity Estimation Unit 110 Localization Unit 90 Computer Systems 91 Communication Interface 92 Memory 93 Processor 94 Display device 95 Programs

Claims

1. a data set processing means for obtaining time-distance information of vibration signals for each distributed sensing portion, the vibration signals being acquired by the plurality of distributed sensing portions for detecting monitored objects and induced by traffic of moving objects; a frequency density estimation means for estimating a frequency density of the vibration signal detected by the sensing portion based on the time-distance information; a similarity estimation means for estimating a similarity between the pair of sensing parts based on the frequency density; a localization means for localizing the section of the monitoring target based on the similarity; A sensing system comprising:

2. The data set processing means generates a time distance matrix of the vibration signal as the time distance information by removing a bias component from the vibration signal and standardizing an amplitude of the vibration signal. The sensing system of claim 1 .

3. The frequency density estimation means estimates the frequency density of the vibration signal of the sensing portion by applying a Fast Fourier Transformation (FFT) to columns of the time distance matrix. The sensing system of claim 2 .

4. the similarity estimation means calculates a cross-correlation matrix between the frequency densities of the sensing portion to estimate the similarity; The sensing system according to claim 1 .

5. The localization means localizes the interval corresponding to the sensing portion having a similarity index exceeding a given threshold. The sensing system according to claim 1 .

6. The localization means localizes the actual location of the section using map information. The sensing system according to claim 1 .

7. the sensing system analyzing structural characteristics in the section using the actual location of the section and the frequency density of the section; The sensing system according to claim 6 .

8. an optical fiber cable including the sensing portion attached to a road; The sensing system of claim 1 , further comprising:

9. Obtaining time-distance information of vibration signals for each distributed sensing portion, the vibration signals being acquired by the plurality of distributed sensing portions to detect a monitoring target and induced by a traffic of a moving object; estimating a frequency density of the vibration signal detected by a sensing portion based on the time-distance information; estimating a similarity between the pair of sensing portions based on the frequency density; localizing an interval of the monitoring target based on the similarity; A sensing method comprising:

10. On the computer, Obtaining time-distance information of vibration signals for each distributed sensing portion, the vibration signals being acquired by the plurality of distributed sensing portions to detect a monitoring target and induced by a traffic of a moving object; estimating a frequency density of the vibration signal detected by a sensing portion based on the time-distance information; estimating a similarity between the pair of sensing portions based on the frequency density; localizing an interval of the monitoring target based on the similarity; A non-transitory computer-readable medium storing a program for executing the above.

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