Signal processing device, signal processing method, and signal processing program

Abstract

To improve a processing speed while maintaining a resolution.SOLUTION: A signal processing device comprises: a data reception unit for acquiring observation data which is received at a measurement position when a transmit wave expanding in a second direction from a plurality of measurement positions in a first direction is transmitted, forming the observation data into a matrix in the first and second directions, and generating a received signal matrix in which the signal intensity is adopted as a position value; a range processing unit for identifying a range in the second direction in the received signal matrix and generating an observation vector which is constituted based on a component in the first direction of the identified range, and setting a sparse vector in an imaging matrix that is composed of the components in the first direction of a range in the second direction that corresponds to the observation vector; a restoration processing unit for performing, for a plurality of ranges in parallel, a restoration process of calculating the components of the sparse vector on the basis of the observation vector and the sparse vector; and a synthesis processing unit for synthesizing the sparse vectors calculated by the restoration process and generating an imaging matrix.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] As an analysis method for an active sensor that outputs sound waves or radio waves, receives reflected waves of the sound waves or radio waves, and analyzes the received signals, the detection results may be visualized (imaging). In this case, a process is performed to convert the received information into an image. For example, Patent Document 1 describes a method for performing 3D imaging of a synthetic aperture radar using compressed sensing. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6456312 Summary of the Invention [Problem to be solved by the invention]

[0004] Compressed sensing processing has a high computational load, and it can take a long time to obtain imaging results, especially when obtaining high-resolution images with small pixel sizes. Therefore, if detection results are required in real time, it becomes necessary to wait for the imaging processing.

[0005] The present disclosure has been made in view of the above, and aims to provide a signal processing device, a signal processing method, and a signal processing program that can improve processing speed while maintaining resolution. [Means for solving the problem]

[0006] A signal processing device according to the present disclosure includes a data receiving unit that acquires observation data received at a plurality of measurement positions in a first direction when an emitted wave spreading in a second direction perpendicular to the first direction is transmitted from the plurality of measurement positions, arranges the observation data in a matrix in the first direction and the second direction, and generates a received signal matrix in which the signal strength is a position value; a range processing unit that identifies a range in the second direction in the received signal matrix, generates an observation vector constructed based on the first-direction components of the identified range, and sets a sparse vector constructed of the first-direction components of the range in the second direction corresponding to the observation vector in an imaging matrix in which the number of divisions in the first direction is greater than that of the received signal matrix and the number of divisions in the second direction is the same as that of the received signal matrix; a restoration processing unit that performs restoration processing in parallel for a plurality of ranges to calculate components of the sparse vector based on the observation vector and the sparse vector; and a synthesis processing unit that synthesizes the sparse vectors calculated by the restoration processing to generate the imaging matrix.

[0007] The signal processing method according to the present disclosure includes a data receiving step of acquiring observation data received at a plurality of measurement positions in a first direction when an emitted wave spreading in a second direction perpendicular to the first direction is transmitted from the plurality of measurement positions, and arranging the observation data in a matrix in the first direction and the second direction to generate a received signal matrix in which the signal strength represents a position value; a range processing step of identifying a range in the second direction in the received signal matrix, generating an observation vector constructed based on the first-direction components of the identified range, and setting a sparse vector constructed of the first-direction components of the range in the second direction corresponding to the observation vector in an imaging matrix in which the number of divisions in the first direction is greater than that of the received signal matrix and the number of divisions in the second direction is the same as that of the received signal matrix; a restoration processing step of performing restoration processing in parallel for a plurality of ranges to calculate components of the sparse vector based on the observation vector and the sparse vector; and a combination processing step of combining the sparse vectors calculated by the restoration processing to generate the imaging matrix.

[0008] A signal processing program according to the present disclosure causes a computer to execute the following steps: a data receiving step of acquiring observation data received at a plurality of measurement positions in a first direction when an emitted wave spreading in a second direction perpendicular to the first direction is transmitted from the plurality of measurement positions, arranging the observation data in a matrix in the first direction and the second direction, and generating a received signal matrix in which the signal strength is a position value; a range processing step of identifying a range in the second direction in the received signal matrix, generating an observation vector constructed based on the first-direction components of the identified range, and setting a sparse vector constructed of the first-direction components of the range in the second direction corresponding to the observation vector in an imaging matrix in which the number of divisions in the first direction is greater than that of the received signal matrix and the number of divisions in the second direction is the same as that of the received signal matrix; a restoration processing step of performing restoration processing in parallel for a plurality of ranges to calculate components of the sparse vector based on the observation vector and the sparse vector; and a combination processing step of combining the sparse vectors calculated by the restoration processing to generate the imaging matrix. [Effects of the Invention]

[0009] According to the present disclosure, it is possible to improve processing speed while maintaining resolution. [Brief explanation of the drawings]

[0010] [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 schematic diagram illustrating the measurement process of the measurement system according to this embodiment. [Figure 3] FIG. 3 is an explanatory diagram for explaining an example of signal processing. [Figure 4] FIG. 4 is a flowchart showing an example of processing in the signal processing device. [Figure 5] FIG. 5 is a flowchart showing an example of processing in the signal processing device. [Figure 6]FIG. 6 is an explanatory diagram for explaining another example of signal processing. [Figure 7] FIG. 7 is a flowchart showing another example of the processing in the signal processing device. [Figure 8] FIG. 8 is a flowchart showing another example of the processing in the signal processing device. [Figure 9] FIG. 9 is a schematic diagram illustrating another measurement process of the measurement system according to this embodiment. [Figure 10] FIG. 10 is a flowchart showing another example of the processing in the signal processing device. [Figure 11] FIG. 11 is a flowchart showing another example of the processing in the signal processing device. [Figure 12] FIG. 12 is a schematic diagram showing a measurement system including a signal processing device according to a modified example. [Figure 13] FIG. 13 is a diagram showing a measurement system according to a modified example. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of 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. In the following description, an example will be described in which a first direction is the azimuth direction and a second direction perpendicular to the first direction is the range direction.

[0012] 1 is a schematic diagram showing an example of a measurement system 10 including a signal processing device according to this embodiment. As shown in FIG. 1, the measurement system 10 includes an ultrasonic sensor 12, a signal processing device 14, a control unit 16, and a storage unit 18.

[0013] The ultrasonic sensor 12 is an active sensor that outputs sound waves for detection and receives reflections of the output sound waves to perform detection. The ultrasonic sensor 12 includes a transmitter 32 and a receiver 34. The transmitter 32 outputs ultrasonic waves. In this embodiment, the transmitter 32 outputs a pulse signal. The receiver 34 receives the reflected waves that are output from the transmitter 32, reflected off something, and reach the ultrasonic sensor 12.

[0014] In the ultrasonic sensor 12, the transmitter 32 and receiver 34 may be the same, or the transmitter 32 and receiver 34 may be arranged in an array to transmit and receive in all directions. In this embodiment, ultrasonic waves are used for detection, but radio waves or the like may also be used. When a highly directional inspection wave is used as a sensor, the direction in which the inspection wave is output may be moved, i.e., swept, to detect the target area.

[0015] The signal processing device 14 is connected to the ultrasonic sensor 12 via, for example, a pulser receiver 28. The signal processing device 14 processes signals received by the ultrasonic sensor 12 to detect the surroundings. The signal processing device 14 includes a first calculation unit 22, a second calculation unit 24, and a memory unit 26. The first calculation unit 22 is, for example, a central processing unit (CPU). The second calculation unit 24 is capable of performing more parallel calculations than the first calculation unit 22, and is capable of performing dozens of parallel calculations. In this embodiment, the second calculation unit 24 is, for example, a graphics processing unit (GPU). The memory unit 26 stores various information such as the calculation contents and programs of the first calculation unit 22 and the second calculation unit 24. The memory unit 26 includes, for example, at least one of a main memory such as a random access memory (RAM), a read-only memory (ROM), and an external memory such as an HDD (hard disk drive).

[0016] In this embodiment, the memory unit 26 stores therein a signal processing program that causes a computer to execute the following steps: a data receiving step of acquiring observation data received at measurement positions when transmitted waves that spread in a range direction perpendicular to the azimuth direction are transmitted from multiple measurement positions in the azimuth direction, arranging the observation data in a matrix in the azimuth direction and the range direction, and generating a received signal matrix in which the signal intensity represents a position value; a range processing step of identifying a range in the range direction in the received signal matrix, generating an observation vector based on the azimuth direction components of the identified range, and setting a sparse vector composed of the azimuth direction components of the range in the range direction corresponding to the observation vector in an imaging matrix in which the number of divisions in the azimuth direction is greater than that of the received signal matrix and the number of divisions in the range direction is the same as that of the received signal matrix; a restoration processing step of performing restoration processing in parallel for multiple ranges to calculate the components of the sparse vector based on the observation vector and the sparse vector; and a combination processing step of combining the sparse vectors calculated by the restoration processing to generate an imaging matrix.

[0017] The first calculation unit 22 includes a data receiving unit 42, a range processing unit 44, and a synthesis processing unit 48. The second calculation unit 24 includes a restoration processing unit 46.

[0018] The data receiving unit 42 acquires information on the received signal acquired by the ultrasonic sensor 12, and performs demodulation, integration processing, etc., and in the first embodiment, the synthetic aperture length L A A received signal matrix is ​​created by organizing the measurement results in the azimuth direction and the range direction within the range. The data receiving unit 42 may perform demodulation processing, beamforming processing, pulse compression processing, range curvature correction, etc. on the received signal.

[0019] The range processing unit 44 selects a region to be processed from the matrix of pixels that make up the acoustic image (imaging image) created by processing the received signal matrix. The matrix of pixels that make up the imaging image is an observation matrix in which the number of pixels in the azimuth direction and range direction is preset by user specification or the like. The imaging image is created by processing the information of each pixel in the observation matrix based on the information of the received signal matrix. Here, the range to be processed is pixels that are at the same position in the range direction.

[0020] The restoration processing unit 46 executes processing based on the range selected by the range processing unit 44 and a known observation matrix, and calculates range components. The restoration processing unit 46 uses, for example, ADMM (Alternating Direction Method of Multipliers). The restoration processing unit 46 restores data in the range of the observation matrix using information on the received signal and the range of the observation matrix. In this embodiment, the restoration processing unit 46 performs restoration processing in parallel for multiple ranges using the second calculation unit 24 configured with a GPU.

[0021] The synthesis processing unit 48 accumulates information on each pixel in the range of the imaging image acquired by the restoration processing unit 46, and creates an image of the synthetic aperture length.

[0022] The control unit 16 and the storage unit 18 are mechanisms for controlling the movement of the ultrasonic sensor 12. The control unit 16 controls the movement of the ultrasonic sensor 12 by reading and executing a program (software) from the storage unit 18. The storage unit 18 stores various information such as the calculation contents and programs of the control unit 16. The storage unit 18 may store the processing results detected by the signal processing device 14, i.e., the results of the exploration.

[0023] FIG. 2 is a schematic diagram illustrating the measurement process of the measurement system according to this embodiment. As shown in FIG. 2, the measurement system 10 is configured with a plurality of ultrasonic sensors 12 arranged side by side in the azimuth direction x. FIG. 2 shows a case where there are M ultrasonic sensors 12, and the ultrasonic sensors 12 are distinguished as #1, #2, ..., #i, ..., #M. In the example shown in FIG. 2, the measurement target R of the measurement system 10 is metal, underwater, etc. In the measurement system 10, a certain ultrasonic sensor 12 transmits a transmission signal ξ(t) to the inside of the measurement target R, and the ultrasonic sensor 12 itself receives a signal reflected by a reflection source l inside the measurement target R and receives a reception signal ξ(t). i Received as (t).

[0024] 2, the measurement system 10 uses a so-called linear scan measurement method to detect a reflector l inside the measurement target R, which is surrounded by the azimuth direction and the range direction orthogonal to the azimuth direction. In FIG. 2, the search area is shown on one plane, but the search range is a three-dimensional area surrounded by the area reached by the transmitted wave.

[0025] When the linear scan measurement method is performed based on the above measurement principle, the signal processing device 14 receives the received signal ζ, which is a real signal, from the ultrasonic sensor 12. i The signal processor 14 receives the real received signal ζ i (t) is down-converted to obtain a complex baseband signal.

[0026] In the measurement system 10, the received signal (real number) at the i-th measurement position is expressed as follows:

number

[0027] Also, the round-trip time τ of the signal to the reflection source l il’ (i=1,2,···,M, l´=1,2,···,L´) is the position [X i 0] T and the position of the reflector [x l´ r l´ ]T Using

number

[0028] The complex baseband signal obtained by down-converting this received signal is

number

[0029] The received signal at each measurement position is expressed as a vector shown in [Equation 4] below.

number

number

[0030] Fig. 3 is an explanatory diagram for explaining an example of signal processing. The left side of Fig. 3 shows an example of a received signal matrix 130. In the received signal matrix 130, the horizontal direction is the azimuth direction (measurement position) and the vertical direction is the range direction (time). The right side of Fig. 3 shows an imaging matrix 132 estimated based on the received signal matrix 130. In the imaging matrix 132, the horizontal direction is the azimuth direction and the vertical direction is the range direction. In the imaging matrix 132, each section represents one pixel of the acoustic image.

[0031] Here, the pixel l=1, 2, ..., L(L x L y ) where there is a reflector l such as a metal defect or target. The received signal model is as follows:

number

[0032] Number of pixels in the acoustic image L (number of pixels in the azimuth direction L x and the number of pixels in the range direction L y ) is a parameter that can be set arbitrarily by the user. Therefore, it is unrelated to the true value L' of the number of reflection sources. Assuming the sparsity of the acoustic image, the signal s representing the reflection intensity at the position of pixel l is l (t) is approximately 0.

[0033] For example, if the number of pixels is 1000 x 1000, L = 10 6 Therefore, restoring an acoustic image all at once requires a significant amount of processing time. Therefore, in this embodiment, restoration is performed row by row in the range direction. The range r of the restoration target can be expressed as r = 0.5tν using the time t in the range direction and the sound speed ν, so it can be seen that there is a one-to-one correspondence between time and range. The coefficient 0.5 is a value used to obtain the time required for a one-way trip between the measurement position and the pixel position.

[0034] Here, the observation vector obtained by extracting only the waveform of a specific range r from the received signal η(t) is

number

[0035] The reflection intensity at range r corresponding to the observation vector is expressed as a sparse vector:

number

[0036] In this case, the correspondence between the observation vector and the sparse vector is

number

[0037] Also, A(r) is

number

number

[0038] A sparse vector s(r) can be estimated by applying a known compressed sensing restoration algorithm (e.g., Alternating Direction Method of Multipliers) to the above [Equation 9]. Pixel region 134a of matrix 130 shown on the left side of Fig. 3 indicates the observation vector η(r) in range r. Pixel region 136a of matrix 132 shown on the right side of Fig. 3 indicates the sparse vector s(r) corresponding to the observation vector η(r) in range r.

[0039] In this way, the restoration process is performed using the observation vector η(r) for each range r and the observation matrix A(r) as a representative value of the received signal matrix η(t). In this case, it is possible to perform the restoration process in parallel for a plurality of ranges r. In this embodiment, the restoration process is performed in parallel for a plurality of ranges r by the restoration processing unit 46 of the second calculation unit 24, which is configured by a GPU.

[0040] Next, an example of processing in the signal processing device 14 will be described with reference to Figures 4 and 5. Figures 4 and 5 are flowcharts showing an example of processing in the signal processing device 14.

[0041] As shown in FIG. 4, first, before the measurement by the ultrasonic sensor 12, an observation matrix

number

[0042] The process shown in FIG. 5 is a processing flow when ADMM (Alternating Direction Method of Multipliers) is adopted as the restoration algorithm.

[0043] In the signal processing device 14, the data receiving unit 42 receives the signal (real number) from the ultrasonic sensor 12.

number

[0044] The range processing unit 44 generates observation vectors by allocating data for each range r from the received signal matrix based on, for example, the number of pixels set by the user, and sets sparse vectors (step S202). The range processing unit 44 transfers the observation vector and sparse vector data to the second calculation unit 24 (step S203). In the second calculation unit 24, the restoration processing unit 46 performs restoration processing in parallel for multiple ranges r based on the transferred observation vector and sparse vector data and the observation matrix data stored in the storage unit 26 (step S204). The restoration processing unit 46 transfers the restored sparse vector data to the first calculation unit 22 (step S205). In the first calculation unit 22, the synthesis processing unit 48 generates an imaging matrix (imaging image) based on the restored sparse vector data (step S206), and the process ends.

[0045] As described above, the signal processing device 14 of this embodiment includes a data receiving unit 42 that acquires observation data received at measurement positions when transmitted waves that spread in the range direction perpendicular to the azimuth direction are transmitted from multiple measurement positions in the azimuth direction, arranges the observation data in a matrix in the azimuth direction and the range direction, and generates a received signal matrix in which the signal intensity is the position value; a range processing unit 44 that identifies the range direction range in the received signal matrix, generates an observation vector based on the azimuth direction components of the identified range, and sets a sparse vector composed of the azimuth direction components of the range in the range direction corresponding to the observation vector in an imaging matrix in which the number of divisions in the azimuth direction is greater than that of the received signal matrix and the number of divisions in the range direction is the same as that of the received signal matrix; a restoration processing unit 46 that performs restoration processing in parallel for multiple ranges to calculate the components of the sparse vector based on the observation vector and the sparse vector; and a combination processing unit 48 that combines the sparse vectors calculated by the restoration processing to generate an imaging matrix.

[0046] Furthermore, the signal processing method according to this embodiment includes a data receiving step of acquiring observation data received at measurement positions when transmitted waves that spread in a range direction perpendicular to the azimuth direction are transmitted from multiple measurement positions in the azimuth direction, arranging the observation data in a matrix in the azimuth direction and the range direction, and generating a received signal matrix in which the signal intensity represents a position value; a range processing step of identifying a range in the range direction in the received signal matrix, generating an observation vector based on the azimuth direction components of the identified range, and setting a sparse vector composed of the azimuth direction components of the range in the range direction corresponding to the observation vector in an imaging matrix in which the number of divisions in the azimuth direction is greater than that of the received signal matrix and the number of divisions in the range direction is the same as that of the received signal matrix; a restoration processing step of performing restoration processing in parallel for multiple ranges to calculate the components of the sparse vector based on the observation vector and the sparse vector; and a combination processing step of combining the sparse vectors calculated by the restoration processing to generate an imaging matrix.

[0047] In addition, the signal processing program according to this embodiment causes a computer to execute the following steps: a data receiving step of acquiring observation data received at measurement positions when transmitted waves that spread in a range direction perpendicular to the azimuth direction are transmitted from multiple measurement positions in the azimuth direction, arranging the observation data in a matrix in the azimuth direction and the range direction, and generating a received signal matrix in which the signal intensity represents a position value; a range processing step of identifying a range in the range direction in the received signal matrix, generating an observation vector based on the azimuth direction components of the identified range, and setting a sparse vector composed of the azimuth direction components of the range in the range direction corresponding to the observation vector in an imaging matrix in which the number of divisions in the azimuth direction is greater than that of the received signal matrix and the number of divisions in the range direction is the same as that of the received signal matrix; a restoration processing step of performing restoration processing in parallel for multiple ranges to calculate the components of the sparse vector based on the observation vector and the sparse vector; and a combination processing step of combining the sparse vectors calculated by the restoration processing to generate an imaging matrix.

[0048] With this configuration, by setting ranges for the matrix of an imaging image and performing compressed sensing restoration on a range-by-range basis, processing can be performed faster and more accurately than when the entire image is restored at once. In this case, by performing restoration processing on multiple ranges in parallel, processing speed can be improved compared to when composite processing is performed serially for each range. This allows processing speed to be improved while maintaining resolution.

[0049] In the signal processing device 14 according to this embodiment, the data receiving unit 42, range processing unit 44, and synthesis processing unit 48 are provided in the first calculation unit 22, and the restoration processing unit 46 is provided in the second calculation unit 24, which is capable of executing more parallel calculations than the first calculation unit 22. Therefore, the restoration processing unit 46 can efficiently perform parallel processing for a plurality of ranges.

[0050] In the signal processing device 14 according to this embodiment, the first calculation unit 22 is a CPU, and the second calculation unit 24 is a GPU. Therefore, the second calculation unit 24 configured by a GPU can efficiently perform parallel processing for a plurality of ranges.

[0051] In the signal processing device 14 according to this embodiment, the range processing unit 44 sets the number of divisions in at least one of the azimuth direction and the range direction based on information input from the outside. Therefore, the allowable number of divisions that can be set by, for example, a user can be increased.

[0052] In the signal processing device 14 according to this embodiment, the range processing unit 44 sets a range corresponding to one row in the range direction of the imaging matrix. Therefore, the processing amount for each range can be reduced, and the processing speed can be improved.

[0053] In the signal processing device 14 according to this embodiment, the observation data is data received at the measurement position in response to an emitted wave transmitted at the measurement position. Therefore, when performing the so-called linear scan measurement method, the processing speed can be improved while maintaining the resolution.

[0054] Fig. 6 is an explanatory diagram for explaining another example of signal processing. As shown in Fig. 6, the range processing unit 44 can set a range r corresponding to multiple rows in the range direction of the imaging matrix. The number of rows included in the range r can be set by, for example, a user. Below, a case where the number of rows included in the range r is K will be described. Note that when K=1, the same as in the above embodiment will be applied.

[0055] In this case, the observation vector is

number

[0056] where:

number

number

number

number

[0057] Next, another example of processing in the signal processing device 14 will be described with reference to Fig. 7 and Fig. 8. Fig. 7 and Fig. 8 are flowcharts showing another example of processing in the signal processing device 14. As shown in Fig. 7, before measurement is performed by the ultrasonic sensor 12, an observation matrix is ​​set in advance (step S301), and an observation matrix with K rows obtained by aggregating multiple rows (hereinafter referred to as K rows) is acquired (step S302). Then, the acquired observation matrix with K rows is stored in the storage unit 26 (step S303).

[0058] As shown in FIG. 8, in the signal processing device 14, the data receiving unit 42 acquires a received signal (real number) by the ultrasonic sensor 12, and generates a received signal matrix using complex baseband signals by down-converting the acquired received signal (step S401).

[0059] The range processing unit 44 generates an observation vector by allocating data for each range r from the received signal matrix based on, for example, the number of pixels set by the user, and sets a sparse vector (step S402).The range processing unit 44 generates an observation vector and a sparse vector that aggregate K rows of data based on the observation vector and the sparse vector (step S403), and transfers the observation vector and the sparse vector to the second calculation unit 24 (step S404).

[0060] In the second calculation unit 24, the restoration processing unit 46 performs restoration processing in parallel for multiple ranges r based on the transferred observation vector and sparse vector data, i.e., the observation vector and sparse vector data in which K rows of data for one range r are aggregated, and the observation matrix data for K rows stored in the storage unit 26 (step S405). The restoration processing unit 46 transfers the restored sparse vector data to the first calculation unit 22 (step S406). In the first calculation unit 22, the synthesis processing unit 48 generates an imaging matrix (imaging image) based on the restored sparse vector data (step S407), and ends the processing.

[0061] As described above, in the signal processing device 14 according to this embodiment, the range processing unit 44 sets ranges corresponding to multiple rows in the range direction of the imaging matrix. This configuration makes it possible to better reflect the correlation of pixel values ​​of multiple rows aggregated into one range compared to when one range contains data for one row, thereby improving restoration accuracy.

[0062] FIG. 9 is a schematic diagram illustrating another measurement process of the measurement system according to this embodiment. As shown in FIG. 9, the measurement system 10 is similar to the above-described embodiment in that a plurality of ultrasonic sensors 12 are arranged in the azimuth direction x. The measurement system 10 includes a plurality of ultrasonic sensors 12 (#i, i=1, 2, ..., M T ) to the inside of the measurement object R, the signal reflected by the reflection source l inside the measurement object R is detected by the plurality of ultrasonic sensors 12 (#j, j=1, 2, ..., M R 9, the measurement system 10 detects a reflection source l within the measurement target R, which is surrounded by the azimuth direction and the range direction orthogonal to the azimuth direction, by a so-called full matrix capture (FMC: synonymous with multistatic) measurement method.

[0063] Below, a case where a transmission signal from one ultrasonic sensor 12 is received by all ultrasonic sensors 12 will be described. In this case,

number

[0064] Here, the received signal η received by each ultrasonic sensor 12 is i (t)

number

number

number

[0065] where:

number

number

number

[0066] Here, time τ ilj represents the time required for a round trip from when a transmission signal is transmitted from the ultrasonic sensor 12 of the transmission source, reflected by pixel #l, and received by each ultrasonic sensor 12. The time required for the outward journey is τ il Let the return journey time be τ lj Then,

number

[0067] In this way, in the FMC measurement method, the dimension of the received signal matrix is ​​M 2 Therefore, the dimension of the received signal matrix becomes larger than that in the linear scan measurement method. For example, when the number of ultrasonic sensors 12 is 64, the dimension of the received signal matrix becomes 64. 2 =4096.

[0068] In order to handle such a huge matrix, in this embodiment,

number

number

[0069] So,

number

number

[0070] In this case, the observation vector is

number

[0071] When multiple rows of data are aggregated in one range, the observation vector of range r shown in [Equation 31] is

number

[0072] where:

number

[0073] Next, another example of the processing in the signal processing device 14 will be described with reference to FIGS. 10 and 11. FIGS. 10 and 11 are flowcharts showing another example of the processing in the signal processing device 14. As shown in FIG. 10, before measurement is performed by the ultrasonic sensor 12, a widthwise matrix Φ (see Equation 27) is set in advance (step S501). The set widthwise matrix Φ is stored in the storage unit 26. An observation matrix is ​​also set (step S502), and a new observation matrix is ​​set by multiplying the set observation matrix by the widthwise matrix Φ (step S503). When multiple rows of data are aggregated in one range r, an observation matrix (shown in Equation 33) with K rows that aggregates multiple rows (hereinafter referred to as K rows) is obtained (step S504). When one range r includes one row of data, step S504 does not need to be performed. Thereafter, the set widthwise matrix Φ and the new observation matrix are stored in the storage unit 26 (step S505).

[0074] As shown in FIG. 11, in the signal processing device 14, the data receiving unit 42 acquires a received signal (real number) by the ultrasonic sensor 12 and down-converts the acquired received signal to generate a received signal matrix using complex baseband signals (step S601).

[0075] The range processing unit 44 generates an observation vector by allocating data for each range r from the received signal matrix based on, for example, the number of pixels set by the user, and sets a sparse vector (step S602). The range processing unit 44 calculates a new observation vector by multiplying the observation vector by the horizontally long matrix Φ stored in the storage unit 26 (step S603). When multiple rows of data are aggregated into one range r, an observation vector is generated by aggregating K rows of data from the calculated new observation vector, and a sparse vector is similarly generated by aggregating K rows of data (step S604). When one range r contains only one row of data, step S604 does not need to be performed. The range processing unit 44 transfers the observation vector and sparse vector to the second calculation unit 24 (step S605).

[0076] In the second calculation unit 24, the restoration processing unit 46 performs restoration processing in parallel for multiple ranges r based on the transferred observation vectors and sparse vectors and the observation matrix stored in the storage unit 26 (step S606). The restoration processing unit 46 transfers data of the restored sparse vectors to the first calculation unit 22 (step S607). In the first calculation unit 22, the synthesis processing unit 48 generates an imaging matrix (imaging image) based on the data of the restored sparse vectors (step S608), and the process ends.

[0077] In this way, in the signal processing device 14 according to this embodiment, the observation data is data received at all measurement positions for an emitted wave transmitted at one measurement position. With this configuration, the restoration process can be performed with the same amount of calculation and processing time as in the linear scan measurement method, even in the FMC measurement method.

[0078] The technical scope of the present invention is not limited to the above-described embodiment, and appropriate modifications can be made without departing from the spirit of the present invention. For example, the above-described embodiment has been described with reference to an example in which an ultrasonic sensor 12 is disposed at each of a plurality of measurement positions, but the present invention is not limited to this.

[0079] FIG. 12 is a schematic diagram showing a measurement system 10A according to a modified example. As shown in FIG. 12, the measurement system 10A includes a drive unit 20 in addition to the components of the measurement system 10 (see FIG. 1) described above. Other components of the measurement system 10A are the same as those of the measurement system 10 described above. The drive unit 20 functions as a power source for moving the ultrasonic sensor 12. The specific configuration of the drive unit 20 depends on the operating mode of the ultrasonic sensor 12. For example, if the driving body is a vehicle that travels on land, the drive unit 20 includes multiple wheels and a prime mover that drives some or all of the multiple wheels. If the ultrasonic sensor 12 is a vehicle that travels through the air, the drive unit is a propeller and a drive source that drives the propeller. If the ultrasonic sensor 12 is a vehicle that travels underwater, the drive unit is a screw and a prime mover that drives the screw. The specific configuration of the drive unit 20 illustrated here is merely an example and is not limited thereto. The drive unit 20 only needs to function as a power source that enables the ultrasonic sensor 12 to travel.

[0080] FIG. 13 is a diagram showing a measurement system 10A according to a modified example. The measurement system 10A shown in FIG. 12 is configured to perform a surrounding detection process while moving an ultrasonic sensor 12 in an azimuth direction x using a driving unit 20. The ultrasonic sensor 12 moves in the azimuth direction x, transmitting a transmission signal to the measurement object R at each of positions #1, #2, ..., #i, ..., #M, and receiving a signal reflected by a reflection source l of the measurement object R as a received signal. The ultrasonic sensor 12 emits a transmission signal with a spread width of angle θ, and the emitted range is the search range. This configuration allows the number of ultrasonic sensors 12 to be reduced. Note that a configuration in which multiple ultrasonic sensors 12 are moved in the azimuth direction x to perform a multistatic measurement method may also be used. [Explanation of symbols]

[0081] 10,10A measurement system 12 Ultrasonic Sensor 14 Signal Processing Device 16 Control Unit 18,26 Storage part 20 Drive unit 22 1st calculation section 24 2nd calculation section 28 Pulsar Receiver 32 Transmitter 34 Receiving unit 42 Data receiving unit 44 Range processing unit 46 Restoration processing section 48 Synthesis Processing Unit 130 Received signal matrix 132 Imaging Matrix 134a, 136a pixel area

Claims

1. a data receiving unit that acquires observation data received at a plurality of measurement positions when an emission wave spreading in a second direction perpendicular to the first direction is emitted from the measurement positions, arranges the observation data in a matrix in the first direction and the second direction, and generates a received signal matrix in which the signal strength is a position value; a range processing unit that identifies a range in the second direction in the received signal matrix, generates an observation vector configured based on components in the first direction of the identified range, and sets a sparse vector configured of components in the first direction of the range in the second direction corresponding to the observation vector in an imaging matrix in which the number of divisions in the first direction is larger than that of the received signal matrix and the number of divisions in the second direction is the same as that of the received signal matrix; a restoration processing unit that performs restoration processing for calculating components of the sparse vector based on the observation vector and the sparse vector in parallel for a plurality of the ranges; a synthesis processing unit that synthesizes the sparse vectors calculated by the restoration processing to generate the imaging matrix; A signal processing device comprising:

2. the data receiving unit, the range processing unit, and the synthesis processing unit are provided in a first calculation unit, The restoration processing unit is provided in a second calculation unit that can execute more parallel calculations than the first calculation unit. The signal processing device according to claim 1 .

3. the first calculation unit is a CPU, The second processing unit is a GPU. The signal processing device according to claim 2 .

4. The range processing unit sets the number of divisions in at least one of the first direction and the second direction based on information input from an external device. The signal processing device according to any one of claims 1 to 3.

5. The range processing unit sets the range corresponding to one row in the imaging matrix in the second direction. The signal processing device according to any one of claims 1 to 4.

6. The range processing unit sets the ranges corresponding to a plurality of rows of the imaging matrix in the second direction. The signal processing device according to any one of claims 1 to 5.

7. The observation data is data received at the measurement position regarding the transmitted wave transmitted at the measurement position. The signal processing device according to any one of claims 1 to 6.

8. The observation data is data received at a plurality of measurement positions in response to the transmitted wave transmitted at one of the measurement positions. The signal processing device according to any one of claims 1 to 6.

9. a data receiving step of acquiring observation data received at a plurality of measurement positions in a first direction when an emission wave spreading in a second direction perpendicular to the first direction is transmitted from the measurement positions, and forming a matrix of the observation data in the first direction and the second direction to generate a received signal matrix in which the signal strength is a position value; a range processing step of specifying a range in the second direction in the received signal matrix, generating an observation vector configured based on the components in the first direction of the specified range, and setting a sparse vector configured of the components in the first direction of the range in the second direction corresponding to the observation vector in an imaging matrix in which the number of divisions in the first direction is larger than that of the received signal matrix and the number of divisions in the second direction is the same as that of the received signal matrix; a restoration processing step of performing restoration processing for calculating components of the sparse vector based on the observation vector and the sparse vector in parallel for a plurality of the ranges; a synthesis processing step of synthesizing the sparse vectors calculated by the restoration processing to generate the imaging matrix; A signal processing method comprising:

10. a data receiving step of acquiring observation data received at a plurality of measurement positions in a first direction when an emission wave spreading in a second direction perpendicular to the first direction is transmitted from the measurement positions, and forming a matrix of the observation data in the first direction and the second direction to generate a received signal matrix in which the signal strength is a position value; a range processing step of specifying a range in the second direction in the received signal matrix, generating an observation vector configured based on the components in the first direction of the specified range, and setting a sparse vector configured of the components in the first direction of the range in the second direction corresponding to the observation vector in an imaging matrix in which the number of divisions in the first direction is larger than that of the received signal matrix and the number of divisions in the second direction is the same as that of the received signal matrix; a restoration processing step of performing restoration processing for calculating components of the sparse vector based on the observation vector and the sparse vector in parallel for a plurality of the ranges; a synthesis processing step of synthesizing the sparse vectors calculated by the restoration processing to generate the imaging matrix; A signal processing program that causes a computer to execute the above.

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