Linear frequency modulation signal parameter design method and system for static object

Abstract

The invention discloses a linear frequency modulation signal parameter design method and system for a static body target, and relates to the field of body target signal processing, and the method comprises the steps: constructing a body target model, and obtaining a scattering time domain echo of the body target model; determining a reference range of a linear frequency modulation signal bandwidth parameter according to the distance between adjacent echo structures in the scattering time domain echo; after the scattering time domain echoes are subjected to matched filtering processing, the maximum value of echo data is selected as a fitness function, optimization iteration is carried out on linear frequency modulation signal bandwidth parameters in combination with an optimization algorithm until iteration is ended, the optimal linear frequency modulation signal bandwidth parameters are obtained, and the linear frequency modulation signals can obtain better gains with smaller bandwidth.

Description

Technical Field

[0001] This application relates to the field of body target signal processing, specifically to a method and system for designing linear frequency modulated signal parameters for static body targets. Background Technology

[0002] The processing of echo signals from solid targets is typically studied from two aspects: waveform design and receiver data processing. Waveform design has always been a hot and challenging area of ​​research for scholars both domestically and internationally. Linear frequency modulated (LFM) signals, as a conventional waveform, are widely used in various industries.

[0003] In the field of waveform design, because linear frequency modulated (LFM) signals have long been considered traditional waveforms with relatively "fixed" parameter selection, domestic and international scholars have primarily conducted extensive research on the combination design of different waveforms and novel waveform designs. Most of this research is based on point targets, while in practical applications, targets are often volumetric targets. Compared to point targets, volumetric targets exhibit significant differences in signal echo structure and overall signal processing performance. Optimizing waveform parameters for volumetric targets is more suitable for real-world application scenarios. Current waveform design technologies do not consider the coupling issue between volumetric targets and LFM signal parameter design.

[0004] Regarding the parameters of linear frequency modulated (LFM) signal processing for volume target echo signals, the choice of center frequency is generally related to propagation loss. As for the selection of bandwidth and pulse width parameters, the gain after matched filtering of the LFM signal is generally considered to be... Where B is the signal bandwidth and T is the signal pulse width. For energy detection, it is generally believed that the larger the values ​​of bandwidth and pulse width, the greater the target echo energy after matching, which is more conducive to detection. Currently, the selection of linear frequency modulated signal parameters for volume target echo signal processing problems usually takes point targets as hypothetical targets, and the parameters of bandwidth and pulse width are usually chosen to be as large as possible. Among them, the larger the bandwidth, the higher the resolution and the greater the energy after matching. However, in actual targets, there are almost no point targets, but volume targets. The basic characteristic of the echo of a volume target is a multi-echo structure, that is, a multi-bright-point model. The larger the bandwidth, the more refined the echo structure after matching filtering, and the higher the bright-point resolution. But for detection, the matching gain of a single bright-point echo is still limited. In the design of linear frequency modulated signal parameters, the potential impact of the volumetric target echo characteristics on detection gain was not taken into account. Summary of the Invention

[0005] This application provides a method and system for designing linear frequency modulated (LFM) signal parameters for static targets, which can obtain the optimal LFM signal bandwidth parameters, enabling the LFM signal to achieve better gain with a smaller bandwidth.

[0006] In a first aspect, embodiments of this application provide a method for designing linear frequency modulated signal parameters for a static volumetric target, the method comprising: Construct a target model and obtain its scattering time-domain echo; The reference range of the linear frequency modulated signal bandwidth parameter is determined based on the distance between adjacent echo structures in the scattered time-domain echo. After performing matched filtering on the scattered time-domain echo, the maximum value of the echo data is selected as the fitness function. The bandwidth parameters of the linear frequency modulated signal are then optimized iteratively using an optimization algorithm until the iteration terminates, thus obtaining the optimal bandwidth parameters of the linear frequency modulated signal.

[0007] In conjunction with the first aspect, in one implementation, constructing a target model and acquiring its scattering time-domain echo includes: The geometric model of the target is drawn using 3D modeling software. The surface of the geometric model is meshed using finite element software to obtain the vertex coordinates and corresponding element number information of the surface elements. Combined with the coordinates of the sound source and the receiver, the target transfer function in the frequency domain of the target is calculated using simulation software. The scattered time-domain echo is calculated using the frequency domain indirect method.

[0008] In conjunction with the first aspect, in one implementation, the calculation of the target transfer function in the frequency domain of the target volume using simulation software includes: Using the coordinates of the sound source and the receiver as input, and employing simulation software, assuming the transmitted signal is a linear frequency modulated signal, the target transfer function in the frequency domain of the bulk target is calculated based on the following formula: , in, The target scattered sound field at the external field point; In the plate element algorithm for the target scattered sound field, the first m The contribution of the scattered sound field generated by each plate; This refers to the displacement of the sound source in the original reference frame. This represents the displacement of the field point in the original reference frame.

[0009] In conjunction with the first aspect, in one embodiment, the distance between adjacent echo structures in the scattering time-domain echo is obtained as follows: A sinusoidal signal is selected as the transmitted signal. The center frequency and pulse width of the sinusoidal signal are set. The scattered time-domain echo of the target is calculated using simulation software and the frequency domain indirect method. Based on the distance Δ between adjacent echo structures in the scattered time-domain echo... d Take the minimum value of the distance between adjacent echo structures, min(Δ). d ).

[0010] In conjunction with the first aspect, in one implementation, determining the reference range for the bandwidth parameter of the linear frequency modulated signal includes: If the bandwidth B of the linear frequency modulated signal is taken as the parameter to be optimized, then , and The maximum transmit bandwidth can be selected by the transmitting transducer, and the minimum resolution of the linear frequency modulated signal is greater than the min(Δ). d ).

[0011] In conjunction with the first aspect, in one implementation, after performing matched filtering on the scattered time-domain echo, the maximum value of the echo data is selected as the fitness function, including: The scattered time-domain echo is subjected to matched filtering to obtain a matched-filtered time-domain echo signal. The modulus of the time-domain echo signal is then taken, and the maximum value is used as the fitness function.

[0012] In conjunction with the first aspect, in one implementation, the iterative optimization of the linear frequency modulated signal bandwidth using the combined optimization algorithm includes: The particle swarm optimization algorithm is used to set velocity limits and inertia weights, determine self-learning factors and swarm learning factors, and select the linear frequency modulated signal with the largest bandwidth as the initialization signal under the premise of constant pulse width. The bandwidth parameters of the linear frequency modulated signal are optimized iteratively based on the fitness function.

[0013] In conjunction with the first aspect, in one implementation, the condition for termination of the iteration is: After a preset number of iterations, the fitness function changes by no more than a preset percentage; or, the iteration reaches a preset number of cycles.

[0014] In conjunction with the first aspect, in one embodiment, it further includes: after obtaining the optimal linear frequency modulation signal bandwidth parameters, comparing them with the initialization signal to perform performance analysis, wherein the initialization signal is the linear frequency modulation signal with the maximum bandwidth under the premise that the pulse width remains unchanged.

[0015] Secondly, embodiments of this application provide a system for designing linear frequency modulated signal parameters based on any one of the static volume targets described in the claims, the system comprising: The building module is used to construct the volume target model and obtain the scattering time-domain echo of the volume target model; A selected module is used to determine the reference range of the bandwidth parameter of the linear frequency modulated signal based on the distance between adjacent echo structures in the scattered time-domain echo. The iterative module is used to perform matched filtering on the scattered time-domain echo, select the maximum value of the echo data as the fitness function, and combine it with the optimization algorithm to optimize the bandwidth parameters of the linear frequency modulated signal until the iteration terminates, thereby obtaining the optimal bandwidth parameters of the linear frequency modulated signal.

[0016] The beneficial effects of the technical solutions provided in this application include: After performing matched filtering on the time-domain echoes scattered from the target, the maximum value of the echo data is selected as the fitness function. Combined with the optimization algorithm, the bandwidth parameter of the linear frequency modulated signal is optimized iteratively to adjust the waveform range resolution. When the distance of the target's nearby echo is lower than the waveform range resolution, the energy gains a superposition effect, obtaining the optimal bandwidth parameter of the linear frequency modulated signal. This allows the linear frequency modulated signal to obtain better gain with a smaller bandwidth. The multi-echo structure of the target is considered in a coupled manner. In parameter design, the conventional understanding that "the larger the bandwidth parameter, the better" is broken. This method is simple, effective, easy to operate, and has high computational efficiency. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the method for designing linear frequency modulated signal parameters for a static volume target according to an embodiment of this application. Figure 2 A flowchart illustrating the construction of a target model and the acquisition of its scattering time-domain echo in this application embodiment; Figure 3 This is a schematic diagram of the outer surface unit division of a multi-structure target model according to an embodiment of this application; Figure 4 This is a schematic diagram of the time-domain echo of the CW signal at different incident angles in an embodiment of this application; Figure 5 This is a schematic diagram of the fitness iteration count in an embodiment of this application; Figure 6 This is a diagram showing the matching results and partial schematic of the 0-degree incident angle in an embodiment of this application; Figure 7 This is a diagram showing the matching results and partial schematic of a 40-degree incident angle in an embodiment of this application. Figure 8 This is a diagram showing the matching results and partial schematic of a 90-degree incident angle in an embodiment of this application; Figure 9 This is a schematic diagram of the integration region in an embodiment of this application; Figure 10 This is a schematic diagram of plate scattering in an embodiment of this application; Figure 11 This is a schematic diagram of the target scattered acoustic signal generation process in an embodiment of this application; Figure 12 This is a schematic diagram illustrating the method for updating the particle position in each generation according to an embodiment of this application; Figure 13 This is a schematic diagram of the system for designing linear frequency modulation signal parameters for implementing static volume targets in this application. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0020] In a first aspect, embodiments of this application provide a method for designing linear frequency modulated signal parameters for a static volume target.

[0021] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the linear frequency modulated signal parameter design method for a static volume target according to this application. Figure 1 As shown, the design method includes: S1. Construct the target model and obtain its scattering time-domain echo.

[0022] S2. Determine the reference range of the linear frequency modulation signal bandwidth parameter based on the distance between adjacent echo structures in the scattered time-domain echo.

[0023] S3. After performing matched filtering on the scattered time-domain echo, select the maximum value of the echo data as the fitness function, and combine it with the optimization algorithm to optimize the bandwidth parameters of the linear frequency modulated signal until the iteration terminates, thereby obtaining the optimal bandwidth parameters of the linear frequency modulated signal.

[0024] In this embodiment, the reference range of the linear frequency modulated (LFM) signal bandwidth parameter is determined by the distance between adjacent echo structures in the scattered time-domain echo. After performing matched filtering on the scattered time-domain echo of the volume target, the maximum value of the echo data is selected as the fitness function, and the optimal LFM signal bandwidth parameter is obtained by combining it with an optimization algorithm. By considering the coupling of multiple echo structures of the volume target, the conventional understanding that "the larger the bandwidth parameter, the better" is broken in the parameter design, enabling the LFM signal to obtain better gain with a smaller bandwidth.

[0025] Furthermore, such as Figure 2 As shown, in step S1 above, constructing the target model and obtaining its scattering time-domain echo includes: S101. Draw the geometric model of the target object using 3D modeling software. Mesh the surface of the geometric model using finite element software (such as COMSOL) to obtain the vertex coordinates and corresponding element number information of the surface elements. Specifically, mesh the surface of the target object's geometric model using triangular planar elements; then export the vertex coordinates and corresponding element number information of the triangular planar elements on the target object's surface from the finite element software.

[0026] S102. Combining the coordinates of the sound source and the receiver, the target transfer function in the frequency domain of the bulk target is calculated using simulation software. Then, the scattered time-domain echo is calculated using the frequency domain indirect method. In this embodiment, based on the vertex coordinates of the triangular planar element and the corresponding element number information, and simultaneously setting the sound source coordinates and receiver coordinates as input, the simulation software, combined with the theoretical knowledge of the simulation calculation of the scattered time-domain echo of the bulk target in the basic theory, assumes the transmitted signal is a linear frequency modulated signal, and calculates the target transfer function in the frequency domain of the bulk target using the following formula: , in, The target scattered sound field at the external field point; In the plate-based algorithm for the scattered sound field of the target, the contribution of the scattered sound field generated by the m-th plate; This refers to the displacement of the sound source in the original reference frame. This represents the displacement of the field point in the original reference frame.

[0027] Furthermore, in step S2 above, the distance between adjacent echo structures in the scattered time-domain echo is obtained as follows: a sinusoidal signal (cw signal) is selected as the transmitted signal, the center frequency and pulse width of the sinusoidal signal are set, and the scattered time-domain echo of the target is calculated using simulation software and the frequency domain indirect method. According to the scattering time-domain echo The distance Δ between adjacent echo structures d Take the minimum value of the distance between adjacent echo structures, min(Δ). d In this embodiment, it is assumed that the center frequency of the linear frequency modulated signal is... The center frequency of the sinusoidal signal is The pulse width is 1 / .

[0028] In addition to selecting the number of adoptions to be optimized in step 2 above, it is also necessary to determine the reference range of the linear frequency modulation signal bandwidth parameters, including the initial signal parameters and the accuracy of the parameters to be optimized.

[0029] In one embodiment, under the constraint of unique variables, the bandwidth B of the linear frequency modulated (LFM) signal is used as the parameter to be optimized. Changing the LFM signal bandwidth will cause a change in range resolution. The range resolution of the LFM signal is determined by… The bandwidth of the linear frequency modulated signal is calculated. , and The maximum transmit bandwidth can be determined by the selectable transmitter transducer, while simultaneously satisfying... That is, the minimum resolution of the linear frequency modulated signal is greater than the minimum distance between adjacent echo structures min(Δd), so that the energy of adjacent echo structures can be added together after matched filtering of multiple echo structures.

[0030] Considering the gain of a conventional matched filter is Based on this, while keeping the pulse width constant, the linear frequency modulated signal with the largest bandwidth is selected as the initialization signal and also as the comparison signal for subsequent performance analysis. The parameters to be optimized and their reference range are determined, and the precision of the parameters to be optimized is determined according to the computational accuracy and time requirements. Generally, the lower the precision, the higher the computational accuracy, but the longer the total computation time of the algorithm. In this embodiment, a bandwidth precision of 1Hz is recommended.

[0031] Considering the multi-echo structure characteristics (multiple bright spots) of the target, adjusting the bandwidth can adjust the resolution of the linear frequency modulated signal, while reducing the bandwidth can reduce the resolution. This allows multiple adjacent echo structures (i.e., multiple adjacent bright spots) to superimpose their echoes after matched filtering, thus increasing the energy. However, reducing the bandwidth will also reduce the gain of the matched filter. Therefore, a suitable bandwidth parameter needs to be designed so that the energy increment of the multi-echo structure (i.e., multiple bright spots) after matched filtering is greater than the energy reduction caused by reducing the bandwidth. Therefore, the fitness function designed in step S3 above is used to evaluate whether the bandwidth parameter meets the above condition. In step S3 above, after performing matched filtering on the scattered time-domain echo, the maximum value of the echo data is selected as the fitness function, specifically including: First, considering that the energy increment is greater than the energy decrease, matched filtering is performed on the scattered time-domain echo y(t) of the volume target in step S1 to obtain the matched-filtered time-domain echo signal y of the volume target. m (t). Then, the time-domain echo signal y m (t) performs a modulo operation, i.e. ,right The maximum value of the signal is taken as the fitness function.

[0032] In step S3 above, the obtained adaptive function is combined with an optimization algorithm to iteratively optimize the bandwidth parameters of the linear frequency modulated signal. In this embodiment, Particle Swarm Optimization (PSO) is used; in other embodiments, optimization algorithms with the same effect can also be used. It is necessary to set a speed limit, inertia weight, self-learning factor, and swarm learning factor. In this embodiment, the recommended range for the inertia weight is [0.2 1.2]. Considering the accuracy determined in step S2, the positive and negative values ​​of the speed should not exceed 10 times the accuracy. The self-learning factor and swarm learning factor are between 0.5 and 1.5.

[0033] Furthermore, the iteration termination condition in step S3 is: after a preset number of consecutive iterations, the change in the fitness function does not exceed a preset percentage; or, the iteration reaches a preset number of loops. In this embodiment, the iteration is terminated when the change in the fitness function does not exceed 1% after 5 consecutive iterations; or when the number of loops reaches 100.

[0034] Furthermore, in the embodiments, the method for designing the linear frequency modulated signal parameters of a static volume target also includes: S4. Compare the optimal linear frequency modulation signal bandwidth parameters with the initial signal to perform performance analysis.

[0035] The following is a detailed explanation of the linear frequency modulation signal parameter design method for static volume targets, using a specific embodiment as an example, and a performance analysis is performed. The specific process is as follows: Step 1: Construct the geometric model of the target volume and perform mesh generation. Specifically, use 3D modeling software to draw the geometric model of the target volume, and use finite element software (including but not limited to COMSOL) to mesh the surface of the target volume using triangular planar elements. From the finite element software, export the vertex coordinates and corresponding element number information of the triangular planar elements on the surface of the target volume.

[0036] like Figure 3 As shown, in this embodiment, key features such as the cylindrical surface, the front spherical cap surface, the top trapezoidal surface, and the tail horizontal bar structure of the multi-structure target are extracted to establish a three-dimensional geometric model of the multi-structure target. Simultaneously, using 1 / 6 of the minimum wavelength corresponding to the maximum analysis frequency as the maximum mesh size, the outer surface of the multi-structure target model is meshed using triangular elements.

[0037] Step 2: Based on the triangular unit mesh information, sound source coordinates, and receiver coordinates from Step 1, the simulation software is used in conjunction with the theoretical knowledge of the time-domain echo simulation calculation of the scattering of the target in the basic theory. The target transfer function in the frequency domain is calculated using the formula in S102, and then the scattering time-domain echo y(t) of the target is obtained by calculating it through the frequency domain indirect method.

[0038] In this example, a short-pulse-width CW signal is first selected to observe the volume target echo structure constructed in step 1 at a typical observation angle. As shown in Figure 4, from... Figure 4 (a) Figure 4 (b) and Figure 4 As can be seen in (c), the echo structure of a volume target is complex, and the design of the echo processing waveform for a volume target cannot simply refer to the design scheme of a point target.

[0039] Step 3: Select a linear frequency modulated (LFM) signal as the transmitted signal. To reduce computational load, choose a LFM signal center frequency of 5kHz, a pulse width of 40ms, and a sampling rate of 50kHz. Under the constraint of unique variables, let the LFM signal bandwidth B be the optimal parameter. The reference range for the optimal parameter is: B∈[40,4000]. Considering the gain of a conventional matched filter... Based on this, within the range of variable transformation, a linear frequency modulated signal with a maximum bandwidth of 4kHz is selected as the initialization signal (and also as the comparison signal for subsequent performance analysis), which theoretically can achieve the maximum gain effect.

[0040] Step 4: Under the constraint of unique variables in Step 3, the bandwidth of the linear frequency modulated (LFM) signal is taken as the input variable and fed into the particle swarm optimization (PSO) model. The number of iterations is set to 100, the inertia weight is set to 0.8, the self-learning factor is set to 0.5, and the swarm learning factor is set to 0.8. The fitness function is designed. In this embodiment, the maximum time-domain value of the matching result is used as the evaluation criterion for the fitness function, thereby calculating the optimal LFM signal bandwidth. Under the parameter conditions given in Step 3, the number of iterations calculated by the PSO algorithm is shown in Figure 5. As can be seen from Figure 5, the fitness function of the PSO algorithm tends to stabilize after about 23 iterations, that is, the optimal solution has been obtained. The optimal LFM signal bandwidth obtained from the simulation calculation is 389.12Hz.

[0041] Step 5: Substitute the calculated optimal linear frequency modulated signal bandwidth into the formula in S102 above, and compare it with the reference echo. The comparison results are as follows under several typical incident angles: Figure 6 , Figure 7 and Figure 8 As shown. From Figures 6-8 (a) is a schematic diagram of the incident angle matching result, and (b) is a partial schematic diagram of (a). As can be seen from the figures, at different incident angles (0 degrees, 40 degrees and 90 degrees), the bandwidth parameter given in this embodiment, after being substituted into the echo model, has a significantly higher maximum value of the matched filtering result compared to the reference echo. The improvement effect varies at different incident angles, with a maximum improvement of about 2.5 times. The improvement of the matched filtering peak value can effectively improve the detection probability of the volume target.

[0042] Furthermore, the calculation process of the time-domain echo of the scattering of the target in step S102 above is explained by combining the simulation calculation of the time-domain echo of the scattering of the target in the relevant basic theory.

[0043] Kirchhoff approximation method based on physical acoustics: Assuming the frequency of the sound wave is Its angular frequency At this point, let the time factor of the sound field be... And in space There is a sound source. From the source point The emitted sound waves pass through the outer surface of the target After scattering, at the spatial field point The generated scattered sound field is According to the Helmholtz integral formula for sound scattering, the scattered sound field... It can be represented as: (1), in Let the point be the integral moving point on the target surface. n Let d be the surface element at the point of integration. S The unit outward normal vector, Let be the Green's function. In free space, the Green's function can be expressed as: (2), in, For wave number, c This refers to the speed of sound underwater.

[0044] Substituting equation (2) into equation (1), we get: (3), Equation (3) is the Helmholtz integral formula that the target scattered sound field satisfies in free space.

[0045] In high-frequency cases, the Kirchhoff approximation based on physical acoustics can be used to simplify the calculation of equation (3). When the radius of curvature of the scattering surface is much larger than the wavelength of the sound wave, the scattering surface can be divided into a bright region and a shadow region; the region directly illuminated by the incident sound wave is called the bright region, and the region not illuminated by the incident sound wave is called the shadow region. In engineering applications, the Kirchhoff approximation is generally based on the following two basic assumptions: First: The target scattering surface can be divided into bright areas. S 1 and film area S 2. Bright area S 1. Sound wave scattering occurs, creating a shadow area. S 2. It does not produce sound wave scattering.

[0046] Second: Bright area S 1. Each part of the reflecting surface can be regarded as a plane, and the reflection characteristics of the sound wave follow the local plane wave reflection law.

[0047] Under the two assumptions above, it can be concluded that in the bright area S The boundary conditions for surface 1 are: (4), In addition, the target surface The incident sound field at point is: (5), in, A For any amplitude.

[0048] Based on the above two assumptions, and considering both equations (4) and (5), the expression for the scattered sound field using the Kirchhoff approximation method based on physical acoustics can be derived as follows: (6), like Figure 9 The figure shown is a schematic diagram of the integration region of equation (6), which is used to illustrate the further simplification process of equation (6). Figure 9 middle, Let be the position vector of the sound source pointing towards the integral moving point. Let be the position vector of any point in the external field (receiving point) pointing towards the integrating moving point. For the surface element at the point of integration The unit outward normal vector, Loss of position With unit external normal vector Supplementary angle of the included angle For position With unit external normal vector The supplementary angle of the included angle. Thus, first... , Substitution formula (6): (7), At the same time, it should be noted that: (8), Substituting equation (8) into equation (7), we get: (9), Plate-based algorithm for target-scattered sound field: Since the geometry of a typical solid target is irregular, the scattered sound field cannot be directly calculated using equation (9). To solve this problem, the plate element algorithm is used to approximate the calculation of equation (9).

[0049] The basic idea of ​​the plate meta-algorithm is to divide the surface of a complex object into planar plates of sufficiently small size. If the maximum size of all the divided small plates is... As long as the distance between the center of the target body and the distance between the field points in the outer field satisfy the following conditions: At that time, the scattered sound field at the external field point can be expressed as the sum of the scattered sound field contributions from these small planar plates located in the bright region; where, The wavelength of the sound wave is denoted as λ.

[0050] like Figure 10 As shown, this is the first m A schematic diagram of scattering from individual plates. The geometric center of the target is selected. The point is taken as the origin of the original reference frame, and the geometric center of the plate is selected. C The point, used as a reference point for the plate, has a displacement of [missing information] in the original reference frame. .point For any point on the plate, its displacement in the original reference frame is The displacement of the sound source in the original reference frame is The displacement of the external field point (receiving point) in the original reference frame is... Sector Reference Points C Point to any point on the plate Q The position lost And direct the sound source to the point. C The position lost The receiving point points to the point C The position lost Sound source pointing point Q The position lost The receiving point points to the point Q The direction vector is .

[0051] Since the plate is relatively small, the non-uniformity of the incident sound field on the plate due to the emission directivity can be ignored. From equation (9), we can obtain the... m The contribution of the scattered sound field generated by each plate is: (10) in, This is the unit outward normal vector of the plate.

[0052] For a small plate, the outer field point (receiving point) is in the far field. Therefore, the amplitude term within the integral can be approximated as follows: (11), The phase term within the integral can be approximated as follows: (12) Substituting equations (11) and (12) into equation (10), we can obtain: (13) As can be seen from equation (11), for the same plate, only Since it's a variable, the function within the integral is greatly simplified. And... This integral has been studied and calculation results have been provided. Here, we assume that the integral surface of each plate is centered at the geometric center of the plate. C The point is the origin of the coordinate system. A local spatial rectangular coordinate system, wherein The plane is the plane containing the triangular plate. Z The axial direction is parallel to the outward normal direction of the plate. Meanwhile, within the local coordinate system of this plate, let... , The coordinates of the three vertices of the triangular plate are respectively , , Thus, the result of the integral term in equation (13) is: (14), in, , , Substituting equation (14) into equation (13), we can obtain the first... m The contribution of the scattered sound field on each plate is: (15) If the target's bright area The Communist Party of China was divided into M By analyzing the data from each section, the target scattered sound field at the external field point (receiving point) can ultimately be obtained. for: (16) The target transfer function in the frequency domain of the target body is calculated according to equation (16), and then the scattered time-domain echo is calculated using the frequency domain indirect method. The principle of the frequency domain indirect method comes from the time-frequency domain relationship between the signal and the linear time-invariant system in the system. A linear time-invariant system is a system that has superposition properties and whose characteristic behavior does not change with time. First, a linear time-invariant system has superposition properties. If the input of a system is composed of the weighted sum of several signals, then the output of the system will be the weighted sum of the system's response to each of these signals.

[0053] Furthermore, a key characteristic of the unit impulse function is that it can represent a general signal as a linear combination of delayed impulses. Based on this, and combining it with the superposition and time invariance of linear time-invariant systems, the characteristics of a linear time-invariant system can be fully characterized by its unit impulse response.

[0054] If the unit impulse response of a linear time-invariant system is h ( t Then, when the input is a unit impulse function... δ ( t At this point, based on the convolution integral relationship between the system input and output, the system output y(t) is: (17) At this point, the system output is the system's unit impulse response y(t).

[0055] If the system input is any signal x ( t Then, according to the convolution integral relation for linear time-invariant systems, we have: (18) Using convolution operators Equation (18) can also be expressed in the following form: (19) Therefore, the characteristics of a linear time-invariant system can be entirely determined by its impulse response. Of course, for linear time-invariant systems, in addition to the time-domain characteristics of convolution, the frequency-domain characteristics of the system's frequency response can also be used for characterization. In the analysis of linear time-invariant systems, since differential or difference equations and convolution operations in the time domain can be transformed into algebraic operations in the frequency domain, it is often more convenient to perform calculations in the frequency domain than in the time domain for linear time-invariant systems.

[0056] First, the system's input signal x(t), system output response y(t), and time-domain impulse response h(t) are transformed into frequency-domain input X(f), frequency-domain output response Y(f), and frequency-domain transfer function H(f) using Fourier transform: (20) According to the definition of Fourier transform and equation (19), we can obtain: (twenty one), make By changing the order of integration in equation (21), we have: (twenty two), As can be seen from equation (22), for a linear time-invariant system, the time-domain output signal Fourier transform , equal to the time-domain input signal Fourier transform With system impulse response Fourier transform The product of the two. This time-domain and frequency-domain characteristic of linear time-invariant systems provides a method for solving the system's time-domain output signal. Another approach: First, process the input signal in the time domain. With system impulse response Perform a Fourier transform to obtain the spectrum of the input signal. With system frequency domain transfer function Then, the input signal spectrum With system frequency domain transfer function Multiplying them yields the frequency domain output response. Then, by performing an inverse Fourier transform on the multiplication result, the time-domain output signal can be obtained. .

[0057] The frequency domain indirect method is based on this approach to solve for the target's scattered sound field, i.e., the time domain output signal. The scattered acoustic signal is the signal from the target. In practical implementation, the frequency domain indirect method utilizes common methods in linear system signal processing; that is, within the framework of a linear system, the target acoustic scattering problem can be described using acoustic transmission theory. In this case, the target can be considered as a linear time-invariant transmission network, with the incident acoustic signal as the network's input and the scattered acoustic signal as its output. Therefore, the impulse response function and frequency domain response function of the target transmission network are another expression of the target's scattered sound field.

[0058] Assume the impulse response function of the target transfer network is ,in It's a time delay. It is the loss of the sound source. It is the positional loss of the field point (receiving point). If the target's position is lost, then the frequency domain response function of the target channel, commonly known as the target transfer function, is: Furthermore, its impulse response function is a Fourier transform of the target channel's impulse response function, and the transform pair is the time delay. and frequency .

[0059] like Figure 11 The diagram shown illustrates the generation process of the target's scattered acoustic signal. For incident sound signal, Let be the impulse response function. This is the target-scattered acoustic signal. Accordingly, Let be the spectral function of the incident sound signal. Let be the target transfer function in the frequency domain. Let be the spectral function of the target scattered acoustic signal.

[0060] In the time domain, the transient radiated sound pressure signal of the structure can be represented by the convolution of the excitation signal and the impulse response function of the structure's radiated sound field: (twenty three), in, This represents the convolution operation. Based on the fundamental principle of the correspondence between the time-domain and frequency-domain responses of linear systems in signal processing, the scattered sound pressure signal in the target frequency domain can be obtained as follows: (twenty four), Since the time-domain and frequency-domain signals of the target-scattered sound wave are Fourier transform pairs, we have: (25), If target acoustic scattering can be viewed as the response of a linear system to an externally applied acoustic signal, then the frequency domain distribution of the target-scattered sound field at the external field point (receiving point) can be obtained by multiplying the spectrum of the incident acoustic signal by the target transfer function. Furthermore, based on the characteristics of a linear system, the time-domain signal of the target-scattered sound field at the external field point (receiving point) (i.e., the scattered time-domain echo) can be obtained from its frequency domain response through the inverse Fourier transform.

[0061] The particle swarm optimization algorithm in step S3 will be further explained below.

[0062] Particle swarm optimization (PSO) is a type of evolutionary algorithm, similar to simulated annealing. It starts from random solutions, searches for the optimal solution through generation selection, and evaluates the quality of the solution through fitness. However, it has simpler rules than genetic algorithms. It does not have the crossover and mutation operations of genetic algorithms. Instead, it searches for the global optimum by following the currently found optimal value.

[0063] like Figure 12 The diagram illustrates how particle positions are updated in each generation; where x represents the particle's initial position, v represents the particle's velocity, and p represents the optimal position found by the particle. The Particle Swarm Optimization (PSO) algorithm first initializes the particle swarm as a group of random particles (random solutions), and then iteratively finds the optimal solution. In each iteration, a particle updates itself by tracking two extrema; one extremum is the optimal solution found by the particle itself, called the individual extremum; the other extremum is the optimal solution found by the entire swarm so far, which is the global extremum. Alternatively, instead of the entire swarm, only a portion of the swarm can be used as the particle's neighbors; in this case, the extremum among all neighbors is the local extremum.

[0064] Suppose there is a D-dimensional target search space with N particles forming a community, where the i-th particle... X i Represented as a D-dimensional vector: (26) The speed of the i-th particle V i It is also a D-dimensional vector, denoted as: (27) The optimal position found so far by the i-th particle is called the individual extreme value. p best , denoted as: (28) The optimal location found so far by the entire particle swarm is the global extremum. gbest , denoted as: (29) Once these two optimal values ​​are found, the particle updates its velocity and position according to the following formula: (30) Where c1 and c2 are learning factors, also known as acceleration constants; r1 and r2 are uniform random numbers within [0,1], used to increase the randomness of particle flight; w is the inertial weight; vid is the particle velocity, and vid∈(-vmax,vmax), where vmax is a constant set by the user to limit the particle velocity, and vid consists of the following three parts: (1) The “Inertia” or “Momentum” part reflects the particle’s “Habit” of motion, which means that the particle has a tendency to maintain its previous velocity.

[0065] (2) The “Cognition” section reflects the particle’s memory or remembrance of its own historical experience, representing the particle’s tendency to approach its best historical position.

[0066] (3) The “Social” section reflects the collective historical experience of cooperation and knowledge sharing among particles, representing the tendency of particles to approach the best position in the collective or neighborhood history.

[0067] Secondly, such as Figure 13 As shown, this application provides a linear frequency modulated signal parameter design system for static volume targets, which can be used to implement the above-described embodiment of the linear frequency modulated signal parameter design method for static volume targets. In this embodiment, the design system includes a construction module, a selection module, and an iteration module.

[0068] The module is used to construct the volume target model and obtain the scattering time-domain echo of the volume target model.

[0069] A selected module is used to determine the reference range of the linear frequency modulated signal bandwidth parameter based on the distance between adjacent echo structures in the scattered time-domain echo.

[0070] The iterative module is used to perform matched filtering on the scattered time-domain echo, select the maximum value of the echo data as the fitness function, and combine it with the optimization algorithm to optimize the bandwidth parameters of the linear frequency modulated signal until the iteration terminates, thereby obtaining the optimal bandwidth parameters of the linear frequency modulated signal.

[0071] The functions of each module in the above design system correspond to the steps in the above design method embodiments, and their functions and implementation processes will not be described in detail here.

[0072] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0073] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0074] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0075] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0076] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0077] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0078] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for designing linear frequency modulated signal parameters for a static volume target, characterized in that, The method includes: Construct a target model and obtain its scattering time-domain echo; The reference range of the linear frequency modulated signal bandwidth parameter is determined based on the distance between adjacent echo structures in the scattered time-domain echo. After performing matched filtering on the scattered time-domain echo, the maximum value of the echo data is selected as the fitness function. The bandwidth parameters of the linear frequency modulated signal are then optimized iteratively using an optimization algorithm until the iteration terminates, thus obtaining the optimal bandwidth parameters of the linear frequency modulated signal.

2. The method for designing linear frequency modulated signal parameters for a static body target as described in claim 1, characterized in that, The construction of the target model and the acquisition of its scattering time-domain echo include: The geometric model of the target is drawn using 3D modeling software. The surface of the geometric model is meshed using finite element software to obtain the vertex coordinates and corresponding element number information of the surface elements. Combined with the coordinates of the sound source and the receiver, the target transfer function in the frequency domain of the target is calculated using simulation software. The scattered time-domain echo is calculated using the frequency domain indirect method.

3. The method for designing linear frequency modulated signal parameters for a static target as described in claim 2, characterized in that, The calculation of the target transfer function in the frequency domain of the target using simulation software includes: Using the coordinates of the sound source and the receiver as input, and employing simulation software, assuming the transmitted signal is a linear frequency modulated signal, the target transfer function in the frequency domain of the bulk target is calculated based on the following formula: ， in, The target scattered sound field at the external field point; In the plate element algorithm for the target scattered sound field, the first m The contribution of the scattered sound field generated by each plate; The position of the sound source in the original reference frame. This represents the position of the field point in the original reference frame.

4. The method for designing linear frequency modulated signal parameters for a static body target as described in claim 1, characterized in that, The method for obtaining the distance between adjacent echo structures in the scattered time-domain echo is as follows: A sinusoidal signal is selected as the transmitted signal. The center frequency and pulse width of the sinusoidal signal are set. The scattered time-domain echo of the target is calculated using simulation software and the frequency domain indirect method. Based on the distance Δ between adjacent echo structures in the scattered time-domain echo... d Take the minimum value of the distance between adjacent echo structures, min(Δ). d ).

5. The method for designing linear frequency modulated signal parameters for a static volume target as described in claim 4, characterized in that, Determine the reference range for the bandwidth parameters of the linear frequency modulated signal, including: If the bandwidth B of the linear frequency modulated signal is taken as the parameter to be optimized, then , and The maximum transmit bandwidth can be selected by the transmitting transducer, and the minimum resolution of the linear frequency modulated signal is greater than the min(Δ). d ).

6. The method for designing linear frequency modulated signal parameters for a static body target as described in claim 4, characterized in that, After performing matched filtering on the scattered time-domain echo, the maximum value of the echo data is selected as the fitness function, including: The scattered time-domain echo is subjected to matched filtering to obtain a matched-filtered time-domain echo signal. The modulus of the time-domain echo signal is then taken, and the maximum value is used as the fitness function.

7. The method for designing linear frequency modulated signal parameters for a static body target as described in claim 6, characterized in that, The optimization iterative process of combining optimization algorithms to optimize the bandwidth of linear frequency modulated signals includes: The particle swarm optimization algorithm is used to set velocity limits and inertia weights, determine self-learning factors and swarm learning factors, and select the linear frequency modulated signal with the largest bandwidth as the initialization signal under the premise of constant pulse width. The bandwidth parameters of the linear frequency modulated signal are optimized iteratively based on the fitness function.

8. The method for designing linear frequency modulated signal parameters for a static body target as described in claim 1, characterized in that, The condition for termination of the iteration is: After a preset number of iterations, the fitness function changes by no more than a preset percentage; or, the iteration reaches a preset number of cycles.

9. The method for designing linear frequency modulated signal parameters for a static volume target as described in claim 1, characterized in that, Also includes: After obtaining the optimal linear frequency modulation signal bandwidth parameters, a performance analysis is performed by comparing them with the initial signal, where the initial signal is the linear frequency modulation signal with the maximum bandwidth under the premise that the pulse width remains unchanged.

10. A system for designing linear frequency modulated signal parameters for a static volume target based on any one of claims 1-9, characterized in that, The system includes: The building module is used to construct the volume target model and obtain the scattering time-domain echo of the volume target model; A selected module is used to determine the reference range of the bandwidth parameter of the linear frequency modulated signal based on the distance between adjacent echo structures in the scattered time-domain echo. The iterative module is used to perform matched filtering on the scattered time-domain echo, select the maximum value of the echo data as the fitness function, and combine it with the optimization algorithm to optimize the bandwidth parameters of the linear frequency modulated signal until the iteration terminates, thereby obtaining the optimal bandwidth parameters of the linear frequency modulated signal.

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