Signal processing device and signal processing method

Abstract

To provide a signal processor and a signal processing method capable of accurately imaging an inspection target.SOLUTION: A signal processor includes an arithmetic section performing: reading processing for reading a predetermined propagation model indicating a propagation path of plane waves when the plane waves transmitted to an interface of an inspection target from a plurality of transmitting and receiving elements propagate through the inside of the inspection target, are reflected at a focal point, and reach the transmitting and receiving elements; acquisition processing for acquiring observation data received by the plurality of transmitting and receiving elements when the plurality of transmitting and receiving elements transmit the plane waves to the inspection target; calculation processing for calculating corresponding data corresponding to an imaging range of the inspection target by grid-free compression sensing on the basis of the acquired observation data; and estimation processing for estimating a position of the focal point of the inspection target on the basis of the read propagation model and the calculated corresponding data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a signal processing device and a signal processing method. [Background technology]

[0002] Compressed sensing is used in various fields of inspection techniques, such as magnetic resonance imaging, etc. In imaging methods using compressed sensing, it is assumed that high intensity distributions in the visualization region are sparse, and it is possible to reconstruct the entire image of an object to be inspected using a small number of observations or to perform imaging of the object with high resolution (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6734270 Summary of the Invention [Problem to be solved by the invention]

[0004] The imaging technique using compressed sensing described above can be applied, for example, to ultrasonic flaw detection of an inspection object. When applying compressed sensing to ultrasonic flaw detection, attempting to image a wide area of ​​the inspection object at once increases the number of pixels and the amount of information to be processed at one time. This may make it impossible to execute due to memory capacity constraints of the processing device. In this case, imaging of a narrow area must be repeated, increasing the inspection man-hours.

[0005] The present disclosure has been made in view of the above, and aims to provide a signal processing device and a signal processing method that are capable of performing processing while suppressing the amount of information processed at one time. [Means for solving the problem]

[0006] The signal processing device according to the present disclosure includes a calculation unit that performs the following operations: a reading process that reads a predetermined propagation model that indicates the propagation path of a plane wave emitted from a plurality of wave transmitting and receiving elements toward an interface of an object to be inspected, the plane wave propagates inside the object to be inspected, is reflected at a focal point, and reaches the wave transmitting and receiving elements; an acquisition process that acquires observation data that is received by the plurality of wave transmitting and receiving elements when the plurality of wave transmitting and receiving elements emit the plane waves toward the object to be inspected; a calculation process that calculates corresponding data corresponding to an imaging range of the object to be inspected by grid-free compressed sensing based on the acquired observation data; and an estimation process that estimates the position of the focal point of the object to be inspected based on the read propagation model and the calculated corresponding data.

[0007] The signal processing method according to the present disclosure includes a reading step of reading a predetermined propagation model that indicates the propagation path of a plane wave transmitted from a plurality of wave transmitting and receiving elements to an interface of an object to be inspected, the plane wave propagating inside the object to be inspected, being reflected at a focal point, and reaching the wave transmitting and receiving elements; an acquisition step of acquiring observation data received by the plurality of wave transmitting and receiving elements when the plurality of wave transmitting and receiving elements transmit the plane waves to the object to be inspected; a calculation step of calculating corresponding data corresponding to an imaging range of the object to be inspected by grid-free compressive sensing based on the acquired observation data; and an estimation step of estimating the position of the focal point of the object to be inspected based on the read propagation model and the calculated corresponding data. [Effects of the Invention]

[0008] According to the present disclosure, it is possible to provide a signal processing device and a signal processing method that are capable of performing processing while suppressing the amount of information processed at one time. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a schematic diagram showing an example of a measurement system including a signal processing device according to this embodiment. [Figure 2] FIG. 2 is a flowchart showing an example of a signal processing method according to this embodiment. [Figure 3] FIG. 3 is a diagram schematically illustrating an example of a propagation path (outgoing path) of a transmission wave in a propagation model. [Figure 4] FIG. 4 is a diagram schematically illustrating an example of a propagation path (return path) of a transmission wave in a propagation model. [Figure 5] FIG. 5 is a flowchart showing an example of an algorithm in the compressed sensing processing step. [Figure 6] FIG. 6 is a diagram schematically illustrating an example of an imaging range of the measurement system. [Figure 7] FIG. 7 is a diagram schematically illustrating an example of monitoring an acoustic image. [Figure 8] FIG. 8 is a diagram schematically illustrating an outline of imaging using a plurality of transmission waves. [Figure 9] FIG. 9 is a diagram showing an example of a transmission level for each refraction angle of a shear wave. [Figure 10] FIG. 10 is a diagram schematically illustrating an outline of imaging using a plurality of transmission waves. [Figure 11] FIG. 11 is a diagram showing an example of a transmission level for each refraction angle of a shear wave. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of a signal processing device and a signal processing method according to the present disclosure will be described with reference to the drawings. Note that the present invention is not limited to these embodiments. Furthermore, the components in the following embodiments include those that are easily replaceable by those skilled in the art, or those that are substantially identical.

[0011] 1 is a schematic diagram showing an example of a measurement system SYS including a signal processing device 100 according to this embodiment. As shown in FIG.

[0012] The sensor 10 has a plurality of wave transmitting and receiving elements 11 that output an emitted wave for detection and receive a reflected wave of the output emitted wave to detect the inspection target 40. The sensor 10 has a plurality of wave transmitting and receiving elements 11 arranged in an array. In this embodiment, the emitted wave is a plane wave, e.g., an acoustic signal such as an ultrasonic wave. In this embodiment, a case where PWI (Plane Wave Imaging) using a plane wave is performed will be described as an example. In this embodiment, the sensor 10 is a wedge-type sensor that emits an emitted wave in an inclined direction with respect to the normal direction (y direction in FIGS. 3 and 4 ) of the interface 41 of the inspection target 40. Note that although the detection uses ultrasonic waves in this embodiment, radio waves or the like may also be used. Furthermore, although the sensor is a wedge-type sensor, other types of sensors may be used as long as they are configured to emit an emitted wave to the interface 41 of the inspection target 40.

[0013] The signal processing device 100 is connected to the sensor 10 via, for example, a pulser receiver 50. The signal processing device 100 processes signals received by the wave transmitting and receiving element 11 to detect the surroundings. The signal processing device 100 has a calculation unit 20 and a storage unit 30. The calculation unit 20 is, for example, a CPU (Central Processing Unit). The calculation unit 20 performs various calculations. The storage unit 30 includes, for example, at least one of a main storage unit such as a RAM (Random Access Memory) or a ROM (Read Only Memory), and an external storage unit such as an HDD (Hard Disk Drive).

[0014] The calculation unit 20 performs a read process to read a predetermined propagation model that indicates the propagation path of a plane wave when the plane wave, transmitted from the multiple wave transmitting and receiving elements 11 to the interface 41 of the inspection object 40, propagates inside the inspection object 40, is reflected at a focal point 42, and reaches the wave transmitting and receiving elements 11. The focal point 42 is, for example, a defect such as a scratch contained inside the inspection object 40. The calculation unit 20 performs an acquisition process to acquire observation data received by the multiple wave transmitting and receiving elements 11 when the multiple wave transmitting and receiving elements 11 transmit plane waves to the inspection object 40. The calculation unit 20 performs an estimation process to estimate the position of the focal point 42 in the inspection object 40 by grid-free compressive sensing (GFCS) based on the read propagation model and the acquired observation data.

[0015] In the estimation process, the calculation unit 20 obtains coefficients of a dual polynomial by solving a predetermined constrained optimization problem based on the observation data, calculates the value of the dual polynomial based on the obtained coefficients of the dual polynomial and a propagation model, and estimates the position of the focal point 42 based on the calculated value of the dual polynomial. The dual polynomial includes a predetermined regularization parameter. The dual polynomial will be described later.

[0016] The calculation unit 20 performs each of the above processes by reading and executing a program (software) from the storage unit 30. The storage unit 30 stores various information such as the calculation contents and programs of the calculation unit 20. The storage unit 30 may also store the processing results detected by the sensor 10, i.e., the results of the exploration.

[0017] In the estimation process, when multiple values ​​of the dual polynomial are calculated for one position, the calculation unit 20 determines the maximum value of the multiple values ​​of the dual polynomial as the value of the dual polynomial at one position.

[0018] In the estimation process, the calculation unit 20 can estimate a position where the value of the calculated dual polynomial exceeds a predetermined threshold as the position of the focal point 42. Furthermore, in the estimation process, the calculation unit 20 may estimate a position corresponding to the maximum value of an envelope of the values ​​of the dual polynomial when the value of the calculated dual polynomial is the value on the vertical axis and the position inside the object of inspection is the value on the horizontal axis as the position of the focal point 42.

[0019] When using observation data in the estimation process, the calculation unit 20 can multiply the data by a coefficient corresponding to the reflection condition of a plane wave.

[0020] The calculation unit 20 may perform a calibration process prior to the estimation process. The calibration process is a process of adjusting a regularization parameter included in the dual polynomial. In the calibration process, the calculation unit 20 estimates the focus positions of the test piece using the same procedure as the estimation process, based on a propagation model and observation data for a test piece in which the positions and number of focal points 42 are preset. Then, the calculation unit 20 adjusts the regularization parameter so that the number of focal points obtained by the estimation corresponds to the number of focal points preset.

[0021] The calculation unit 20 performs each of the above processes by reading and executing a program (software) from the storage unit 30. The storage unit 30 stores various information such as the calculation contents and programs of the calculation unit 20. The storage unit 30 may also store the processing results detected by the sensor 10, i.e., the results of the exploration.

[0022] The memory unit 30 stores signal processing programs that cause the computer to execute the following steps: a reading process that reads a predetermined propagation model that indicates the propagation path of a plane wave when the plane wave emitted from the multiple transmitting and receiving elements 11 to the interface 41 of the object of inspection 40 propagates inside the object of inspection 40, is reflected at the focal point 42, and reaches the transmitting and receiving elements 11; an acquisition process that acquires observation data received by the multiple transmitting and receiving elements 11 when the multiple transmitting and receiving elements 11 emit plane waves to the object of inspection 40; a calculation process that calculates corresponding data corresponding to the imaging range of the object of inspection 40 using grid-free compressed sensing based on the acquired observation data; and an estimation process that estimates the position of the focal point 42 of the object of inspection 40 based on the read propagation model and the calculated corresponding data.

[0023] The measurement process of the measurement system according to this embodiment will be described below. Fig. 2 is a flowchart showing an example of a signal processing method according to this embodiment. As shown in Fig. 2, the signal processing method according to this embodiment includes a reading step S10, an acquisition step S20, a calculation step S30, and an estimation step S40.

[0024] In the reading step S10, the calculation unit 20 reads a predetermined propagation model that indicates the propagation path of a plane wave when the plane wave is transmitted from the multiple wave transmitting and receiving elements 11 to the interface 41 of the test object 40, propagates inside the test object 40, is reflected at a focal point, and reaches the wave transmitting and receiving element 11. In this embodiment, a directional beam can be formed by changing the transmission time of the plane wave for each wave transmitting and receiving element 11. Also, in this embodiment, by superimposing the transmission signals of the multiple wave transmitting and receiving elements 11, the transmission level can be increased compared to when each wave transmitting and receiving element 11 transmits a plane wave individually.

[0025] In the measurement process of the measurement system according to this embodiment, the propagation time τ from the time of transmission until the transmitted wave i reaches the pixel l corresponding to an arbitrary focus and the reflected wave from there returns to the kth wave transmitting / receiving element 11 (kth element) is ilkThe propagation model A(k) read in the propagation model reading step S10 is a propagation time τ ilk The propagation model is pre-created offline.

[0026] Fig. 3 is a diagram schematically illustrating an example of a propagation path (outgoing path) of a transmission wave in a propagation model. Fig. 3 shows the path from the transmitter of sensor 10 to focal point 42. Fig. 4 is a diagram schematically illustrating an example of a propagation path (return path) of a transmission wave in a propagation model. Fig. 4 shows the path from focal point 42 to the receiver of sensor 10.

[0027] Propagation time τ ilk is the propagation path d of the outgoing route shown in Figure 3 (1) , d (2) , and the return propagation path d shown in Fig. 4 (3) , d (4) The velocity of sound in the sensor 10 can be calculated by dividing v by the velocity of sound. w , the speed of sound within the inspection object 40 is v s Then, the propagation time τ ilk is written as follows:

number

[0028] Propagation path d on the outbound route (1) , d (2) is the coordinate [x VIRT y VIRT ] T , the coordinate x of the intersection of the plane wave front with the interface 41 at the time of emission start b , the angle of incidence θ at the interface 41 W Using this, write as follows:

number

[0029] Return propagation path d (3) , d (4) is the reflected wave at the kth element position [xk y k ] T is the path that reaches the destination in the shortest time, and is obtained by optimizing the coordinates on the interface 41. That is, the return propagation path d (3) , d (4) is the optimized interface coordinate [x VIRT y VIRT ] T Use it to write as follows:

number

[0030] Coordinate x on interface 41 b to 0 <x b ≦x w The difference in the height direction between the propagation path shown in Figure 4 and the target element is △H k The coordinate where is minimized is x VIRT,k In Figures 3 and 4, the optimization is performed as follows: VIRT,k = 0, the above relationship holds true for sensors 10 of any shape if they can be optimized using the shortest time route.

number

[0031] In the PWI that transmits a plane wave, the plane wave is formed only within a range of width corresponding to the array aperture length of the sensor 10. Therefore, within the range of interface coordinates shown in FIG.

number

[0032] In the above optimization algorithm, x bTherefore, while the accuracy of the model is improved by making the grid interval smaller, the calculation time increases. In order to ensure accuracy within a realistic calculation time, in this embodiment, after optimization, ΔH k The grid spacing can be determined by using the following as an index of accuracy.

number

[0033] Next, in the observation data acquisition step S20, the calculation unit 20 acquires observation data received by the plurality of wave transmitting and receiving elements 11 when the plurality of wave transmitting and receiving elements 11 transmit plane waves to the inspection object 40.

[0034] Here, the angle of the beam transmitted from the transmitting and receiving element 11 is θ i , where the number of elements of the wave transmitting and receiving element 11 is M, a complex received signal vector including an echo of a defect inside the inspection object 40 is

number

[0035] The real signal can be directly observed by the transmitting and receiving element 11, but by performing processing such as Hilbert transform and down-conversion on it, a complex signal η i (k) is obtained.

[0036] Next, in a generating step S30, the calculation unit 20 generates imaging data of the inspection object 40 using the GFCS based on the acquired observation data.

[0037] FIG. 5 is a diagram schematically illustrating an example of an imaging range by a measurement system. As shown in FIG. 5, at measurement time k, a portion of the inspection object 40 becomes an imaging range 40R. FIG. 6 is a diagram illustrating an example of a case where the imaging range is divided by pixels. When the imaging range 40R is divided by pixels as shown in FIG. 6, the number of pixels is set to L. Furthermore, the number of pixels in the range that can be imaged with instantaneous values ​​at measurement time k is set to L(k).

[0038] Here, the observation data obtained by the measurement system according to this embodiment

number

[0039] Fig. 7 is a flowchart showing an example of an algorithm in the generation step S30. As shown in Fig. 7, in the generation step S30, the calculation unit 20 acquires observation data (step S301), obtains a solution to a semi-definite programming problem based on the acquired observation data (step S302), and calculates corresponding data corresponding to the imaging range of the inspection object 40 (see Fig. 5) (step S303).

[0040] In step S302, the calculation unit 20 calculates a constrained optimization problem (SDP)

number

[0041] After finding a solution to the semidefinite programming problem, the calculation unit 20 calculates the observed data at time k as

number

[0042] where Toep(u) is a vector

number

[0043] In addition, λ (>0) in Equation 9 is a regularization parameter. The user can set the regularization parameter to any value. Qualitatively, the larger the regularization parameter λ, the sparser the solution obtained. The method for adjusting the regularization parameter will be described later.

[0044] In step S303, the calculation unit 20 uses the solution of the above equation 9 to calculate

number

number

[0045] The SPD shown in the above equation (9) is an optimization problem that mimics the Lasso problem that should be solved.

number

[0046] where, ||η i || A is the vector η i represents the atomic norm for . Because the atomic norm is like an L1 norm defined by an infinite number of unknowns, this optimization problem cannot be solved directly. Therefore, this optimization problem can be solved by approximating it with the SDP of equation 9. The SDP of equation 9 can be solved, for example, by a known solver.

[0047] Next, in an estimation step S40, the calculation unit 20 estimates the position of the focal point 42 of the inspection object 40 based on the read propagation model and the generated imaging data.

[0048] 8 is a flowchart showing an example of an algorithm in the estimation step S40. As shown in FIG. 8, in the estimation step S40, the calculation unit 20 calculates the value of the dual polynomial based on the propagation model read in step S10 and the corresponding data calculated in step S30 (step S401). The calculation unit 20 estimates the position of the focal point 42 of the inspection object 40 based on the calculated value of the dual polynomial (step S402).

[0049] In step S401, the calculation unit 20 calculates the coefficients Q(k) of the dual polynomial shown in Equation 13 and the propagation model corresponding to the imaging range 40R (see FIG. 5) at the measurement time k.

number

number

number

[0050] In numbers 16 and 17

number

[0051] The value of the dual polynomial calculated in step S402 is the overlap of the imaging range 40R at each measurement time. Fig. 9 is a graph showing an example of the value of the dual polynomial for each pixel in the imaging range 40R. The vertical axis of the graph in Fig. 9 represents the magnitude of the value of the dual polynomial, and the horizontal axis represents each pixel in the imaging range 40R.

[0052] 9, multiple values ​​of the dual polynomial are calculated for pixels in the overlapping portion of the imaging range 40R. In actual measurements, a strong plane wave is not necessarily incident on the entire imaging range 40R, and therefore values ​​that differ from those originally assumed will be included.

[0053] Therefore, when multiple dual polynomial values ​​are calculated for one pixel (position) in the estimation process, the calculation unit 20 can estimate the coordinates of a pixel for which the value of at least one dual polynomial exceeds a predetermined threshold as the coordinates of the focal point 42. In the example shown in FIG. 9, a threshold value α is set. FIG. 9 also shows an enlarged view of the portion exceeding the threshold value α. The calculation unit 20 can estimate the coordinates of a pixel that exceeds the threshold value α as the coordinates of the focal point 42. In this example, usability is high when there are many focal points 42. For example, when there are many expected focal points 42, usability is increased by setting the value of the threshold value α to a low value.

[0054] Furthermore, when multiple dual polynomial values ​​are calculated for one pixel (position), the calculation unit 20 can set the maximum value of the multiple dual polynomial values ​​as the value of the dual polynomial at that pixel. In this case, the calculation unit 20 calculates a curve QL connecting the coordinates of the maximum values ​​of the dual polynomials for each pixel. The calculation unit 20 can estimate the coordinates corresponding to the peak value (maximum value) of the calculated curve QL as the position of the focal point 42. This example is highly useful when the number of focal points 42 is small.

[0055] When multiple focal points 42 are included, if the reflection conditions are the same for each focal point 42, the profile of the value of the dual polynomial can be evaluated correctly, and the above-mentioned methods can be used appropriately. However, in reality, the reflection conditions at the focal point 42 may differ depending on the attenuation over distance of the transmitted wave from the wave transmitting and receiving element 11 and the reflected wave at the focal point 42, the state of the focal point 42, etc. Therefore, for the above-mentioned observation data (actual measured values), the reflection conditions depending on the state of the focal point 42 and the attenuation over distance are analytically or experimentally determined in advance, and by multiplying the reflection conditions by a coefficient corresponding to the determined reflection conditions, an algorithm that takes into account the influence of the actual environment can be applied.

[0056] The calculation unit 20 may perform an adjustment process to adjust λ (regularization parameter) in Equation 9. The user can set the regularization parameter to any value. Qualitatively, the larger the regularization parameter λ, the sparser the solution obtained. The calculation unit 20 can perform the adjustment process by using a test piece.

[0057] Fig. 10 is a diagram showing an example of a test piece 51. As shown in Fig. 10, the positions and number of artificial defects 52 are set in advance in the test piece 51. Test pieces 51 are prepared in advance for each material, such as metal, polymer, fluid, etc. The calculation unit 20 estimates the positions of the artificial defects 52 in the test piece 51 using the same procedures as the calculation process and estimation process described above, based on the propagation model and observation data in the test piece 51. Then, the calculation unit 20 adjusts the regularization parameter so that the number of artificial defects 52 obtained by this estimation corresponds to the number of artificial defects 52 set in advance.

[0058] FIG. 11 is a graph showing an example of the value of the dual polynomial for each regularization parameter. As shown in FIG. 11, for example, when the regularization parameter is λ1, the number of peaks in the value of the dual polynomial is greater than the number of preset artificial defects 52. In FIG. 11, the peaks other than those of the pixels corresponding to the preset artificial defects 52 are indicated by arrows. In contrast, when the regularization parameter is λ2, the number of peaks in the value of the dual polynomial is the same as the number of preset artificial defects 52. Therefore, when the inspection object 40 is made of a material corresponding to the test piece 51 (for example, the same or similar material as the test piece), the position of the focal point 42 can be appropriately estimated by setting the regularization parameter to λ2.

[0059] As described above, in the present disclosure, the signal processing device according to the first aspect includes a calculation unit 20 that performs the following operations: a reading process that reads a predetermined propagation model that indicates the propagation path of a plane wave when the plane wave transmitted from the plurality of transmitting and receiving elements 11 to the interface 41 of the object of inspection 40 propagates inside the object of inspection 40, is reflected at the focal point 42, and reaches the transmitting and receiving elements 11; an acquisition process that acquires observation data received by the plurality of transmitting and receiving elements 11 when the plurality of transmitting and receiving elements 11 transmits plane waves to the object of inspection 40; a calculation process that calculates corresponding data corresponding to the imaging range of the object of inspection 40 by grid-free compressive sensing based on the acquired observation data; and an estimation process that estimates the position of the focal point 42 of the object of inspection 40 based on the read propagation model and the calculated corresponding data.

[0060] Therefore, the inspection object 40 is probed using a plane wave emitted from the transmitting / receiving element 11 to the interface 41 of the inspection object 40, and based on the acquired observation data, the GFCS calculates corresponding data corresponding to the imaging range of the inspection object 40, and based on the calculated corresponding data and the propagation model, the position of the focus 42 of the inspection object 40 is estimated, making it possible to reduce the amount of information processed at one time and perform processing.

[0061] The signal processing device according to the second aspect is the signal processing device according to the first aspect, in which the calculation unit 20 calculates the coefficients of the dual polynomial as corresponding data by solving a predetermined constrained optimization problem based on the observation data in the calculation process, and in the estimation process, obtains the value of the dual polynomial based on the obtained coefficients of the dual polynomial and a propagation model, and estimates the position of the focal point 42 based on the obtained value of the dual polynomial. Therefore, the position of the focal point 42 can be appropriately estimated by the GFSC.

[0062] The signal processing device according to the third aspect is the signal processing device according to the second aspect, in which, when multiple dual polynomial values ​​are calculated for one position in the estimation process, the calculation unit 20 estimates the position where at least one of the dual polynomial values ​​exceeds a predetermined threshold as the position of the focal point 42. Therefore, the position of the focal point 42 can be appropriately estimated.

[0063] A signal processing device according to a fourth aspect is the signal processing device according to the second or third aspect, in which, when multiple dual polynomial values ​​are calculated for one position in the estimation process, the calculation unit 20 sets the maximum value of the multiple dual polynomial values ​​as the value of the dual polynomial at one position, thereby making it possible to determine a more effective dual polynomial value.

[0064] In the signal processing device according to the fifth aspect, in the signal processing device according to the fourth aspect, the calculation unit 20 estimates, in the estimation process, a position corresponding to a maximum value of a curve passing through coordinates indicating the values ​​of the dual polynomial, where the values ​​of the calculated dual polynomial are the values ​​on the vertical axis and the positions inside the inspection object 40 are the values ​​on the horizontal axis, as the position of the focal point 42. Therefore, the position of the focal point 42 can be appropriately estimated.

[0065] A signal processing device according to a sixth aspect is the signal processing device according to any one of the second to fifth aspects, in which the calculation unit 20 multiplies the observed data by a coefficient corresponding to the reflection condition of a plane wave when using the observed data in the estimation process, thereby making it possible to apply an algorithm that takes into account the influence of the actual environment.

[0066] A signal processing device according to a seventh aspect is the signal processing device according to any one of the second to sixth aspects, in which the dual polynomial includes a predetermined regularization parameter, and the calculation unit 20 estimates the positions of the artificial defects 52 in the test piece 51 in which the positions and number of the artificial defects 52 are set in advance, using the same procedures as the calculation process and estimation process based on a propagation model and observation data for the test piece 51, and performs an adjustment process to adjust the regularization parameter so that the number of the artificial defects 52 obtained by the estimation corresponds to the preset number of the artificial defects 52. Therefore, the regularization parameter can be appropriately set, and the position of the focal point 42 can be estimated with high accuracy.

[0067] The signal processing method according to the eighth aspect includes a reading step S10 for reading a predetermined propagation model that indicates the propagation path of a plane wave transmitted from a plurality of transmitting and receiving elements 11 to an interface 41 of an object to be inspected 40, which propagates inside the object to be inspected 40, is reflected at a focal point 42, and reaches the transmitting and receiving elements 11; an acquisition step S20 for acquiring observation data received by the plurality of transmitting and receiving elements 11 when the plurality of transmitting and receiving elements 11 transmits plane waves to the object to be inspected 40; a calculation step S30 for calculating corresponding data corresponding to the imaging range of the object to be inspected 40 by grid-free compressive sensing based on the acquired observation data; and an estimation step S40 for estimating the position of the focal point 42 of the object to be inspected 40 based on the read propagation model and the calculated corresponding data.

[0068] Therefore, the inspection object 40 is probed using a plane wave emitted from the transmitting / receiving element 11 to the interface 41 of the inspection object 40, and based on the acquired observation data, the GFCS calculates corresponding data corresponding to the imaging range of the inspection object 40, and based on the calculated corresponding data and the propagation model, the position of the focus 42 of the inspection object 40 is estimated, making it possible to reduce the amount of information processed at one time and perform processing. [Explanation of symbols]

[0069] 10 sensors 11 Transmitting and receiving element 20 Arithmetic section 30 Storage section 40 Inspection subjects 40R imaging range 41 Interface 42 focus 50 Pulsar Receiver 51 Test Piece 52 Artificial Defects 100 Signal processing device QL curve SYS Measurement System 10 Sensors 11 Transmitting and receiving element 20 Arithmetic section 30 Storage section 40 Inspection subjects 41 Interface 50 Pulsar Receiver 100 Signal processing device SYS Measurement System

Claims

1. a reading process of reading a predetermined propagation model that indicates a propagation path of a plane wave transmitted from a plurality of wave transmitting and receiving elements to an interface of an object to be inspected, the plane wave propagating through the object to be inspected, reflected at a focal point, and reaching the wave transmitting and receiving elements; an acquisition process for acquiring observation data received by the plurality of wave transmitting and receiving elements when the plurality of wave transmitting and receiving elements transmit the plane waves to the test object; a calculation process of calculating corresponding data corresponding to an imaging range of the inspection object by grid-free compressed sensing based on the acquired observation data; a calculation unit that performs an estimation process of estimating a position of the focus of the inspection object based on the read propagation model and the calculated correspondence data, The calculation unit In the calculation process, coefficients of a dual polynomial are calculated as the corresponding data by solving a predetermined constrained optimization problem based on the observation data; In the estimation process, a value of the dual polynomial is calculated based on the acquired coefficients of the dual polynomial and the propagation model, and the position of the focal point is estimated based on the calculated value of the dual polynomial. Signal processing device.

2. When a plurality of values ​​of the dual polynomial are calculated for one position in the estimation process, the calculation unit estimates a position where at least one value of the dual polynomial exceeds a predetermined threshold as the position of the focal point. The signal processing device according to claim 1 .

3. When a plurality of values ​​of the dual polynomial are calculated for one position in the estimation process, the calculation unit determines the maximum value of the plurality of values ​​of the dual polynomial as the value of the dual polynomial at the one position. The signal processing device according to claim 1 .

4. In the estimation process, the calculation unit estimates, as the position of the focus, a position corresponding to a maximum value of a curve passing through coordinates indicating the value of the dual polynomial, where the calculated value of the dual polynomial is the value on the vertical axis and the position inside the object of inspection is the value on the horizontal axis. The signal processing device according to claim 3 .

5. When the calculation unit uses the observation data in the estimation process, the calculation unit multiplies the observation data by a coefficient corresponding to a reflection condition of the plane wave. The signal processing device according to claim 1 .

6. the dual polynomial includes a predetermined regularization parameter; The calculation unit estimates the positions of the foci of the test piece in the same procedure as the calculation process and the estimation process based on the propagation model and the observation data for the test piece in which the positions and number of foci are set in advance, and performs an adjustment process of adjusting the regularization parameter so that the number of foci obtained by the estimation corresponds to the number of foci set in advance. The signal processing device according to claim 1 .

7. a reading step of reading a predetermined propagation model that indicates a propagation path of a plane wave transmitted from a plurality of wave transmitting and receiving elements to an interface of an object to be inspected, the plane wave propagating through the object to be inspected, reflected at a focal point, and reaching the wave transmitting and receiving elements; an acquisition step of acquiring observation data received by the plurality of wave transmitting and receiving elements when the plurality of wave transmitting and receiving elements transmit the plane wave to the test object; a calculation step of calculating corresponding data corresponding to an imaging range of the inspection object by grid-free compressed sensing based on the acquired observation data; an estimation step of estimating a position of the focal point of the inspection object based on the read propagation model and the calculated corresponding data; Including, In the calculation step, coefficients of a dual polynomial are calculated as the corresponding data by solving a predetermined constrained optimization problem based on the observation data; In the estimation step, a value of the dual polynomial is calculated based on the acquired coefficients of the dual polynomial and the propagation model, and the position of the focal point is estimated based on the calculated value of the dual polynomial. Signal processing methods.

Images