A method and system for processing indoor object echo characteristics based on acoustic electromagnetic intermodulation

Abstract

The present application proposes a method for processing indoor object echo characteristics based on acoustic electromagnetic intermodulation, including: S1, based on the acoustic electromagnetic intermodulation mechanism, different objects reflect different degrees of amplitude modulation and phase modulation of the acoustic modulated electromagnetic wave signal; S2, extracting the micro-Doppler characteristics and amplitude characteristics of the acoustic modulated electromagnetic wave signal, and making a preliminary judgment and identification of the material of the corresponding object; S3, using the feature processing algorithm to fuse the above preliminary judgment and identification results, and establish a material feature sample library for different objects; S4, importing the material feature sample library into the machine learning model for classification training, so as to realize the recognition and marking of the material properties of different objects. The present application uses the amplitude of the acoustic modulated sideband signal for feature processing, and designs a reliable indoor object echo feature processing algorithm based on acoustic electromagnetic intermodulation, so as to achieve the recognition and marking of key attributes of indoor objects and improve the accuracy of target object judgment.

Description

Technical Field

[0001] The present application relates to the technical field of target detection, and in particular to a method and system for processing echo characteristics of indoor objects based on acoustic and electromagnetic intermodulation. Background Art

[0002] Traditional through-the-wall detection (TWS) technology cannot effectively produce significant echo features. Its main detection target is people who are stationary behind obstacles. That is, it detects various micro-movements caused by human physiological activities to determine whether there are life phenomena, and uses micro-movements to mark echo features. Breathing and heartbeat are important indicators for detecting vital signs, but for stationary objects that cannot produce self-vibration phenomena such as heartbeat and breathing, through-the-wall detection (TWS) technology cannot obtain significant echo features to complete the detection of indoor objects. Therefore, how to effectively make indoor objects produce self-vibration phenomena to assist in the smooth implementation of disaster relief and other operations has become a research hotspot in the detection field.

[0003] Acoustic wave excitation is a method that can effectively make an object produce self-vibration. Unlike the weak signals of breathing and heartbeat, acoustic wave excitation can effectively make an object produce a certain amplitude of vibration and acoustic scattering. At this time, electromagnetic wave signals are used to detect the target, and the scattered electromagnetic signals will produce phase modulation. This scattering effect has prompted the development of a hybrid acoustic electromagnetic sensing technology. However, since the acoustic electromagnetic scattering effect produces modulation sidebands that are very close to the transmitted electromagnetic signal, and there is a lack of mathematical modeling for the scattered electromagnetic field generated by the object excited by the sound source, the characteristics it produces are difficult to process. Summary of the invention

[0004] In order to solve the technical defects existing in the above-mentioned background technology, the present application proposes a method and system for processing indoor object echo characteristics based on acoustic and electromagnetic intermodulation.

[0005] According to the first aspect of the present application, a method for processing indoor object echo characteristics based on acoustic electromagnetic intermodulation is proposed, comprising:

[0006] S1. Based on the mechanism of acoustic electromagnetic intermodulation, different objects reflect different degrees of amplitude modulation and phase modulation of acoustically modulated electromagnetic wave signals;

[0007] S2, extracting the micro-Doppler characteristics and amplitude characteristics of the acoustic modulated electromagnetic wave signal, and making a preliminary judgment and identification on the material of the corresponding object;

[0008] S3, using a feature processing algorithm to fuse the preliminary judgment and recognition results based on the micro-Doppler feature and the amplitude feature, to establish a material feature sample library for different objects; and

[0009] S4. Importing the material feature sample library into a machine learning model for classification training, thereby realizing recognition and labeling of material properties of different objects.

[0010] Preferably, the micro-Doppler feature is manifested as an instantaneous characteristic of the acoustically modulated electromagnetic wave signal, and the micro-Doppler feature specifically includes an instantaneous amplitude, an instantaneous frequency and an instantaneous phase.

[0011] Preferably, the expression of the instantaneous amplitude is specifically:

[0012]

[0013] The expression of the instantaneous frequency is specifically:

[0014]

[0015] The expression of the instantaneous phase is specifically:

[0016]

[0017] Wherein, s(t) is the acoustically modulated electromagnetic wave signal, is the Hilbert transform of the acoustically modulated electromagnetic wave signal.

[0018] Preferably, in step S2, the amplitude feature of the n-th order acoustically modulated sideband signal of the acoustically modulated electromagnetic wave signal is extracted, and the expression of the amplitude feature is specifically:

[0019]

[0020] Where pn is the amplitude of the nth harmonic of the sound source, kRF is the wave number of the electromagnetic wave in free space, kA is the wave number of the periodic sound pressure pA(t), εr is the relative dielectric constant of the medium, ρ0 is the density of the medium, cA is the speed of sound in the medium, λA is the wavelength of the sound in the target material, and λRF is the wavelength of the electromagnetic wave in free space.

[0021] Preferably, the feature processing algorithm specifically adopts support vector regression, and the machine learning model adopts a random forest model.

[0022] According to the second aspect of the present application, a system for processing indoor object echo characteristics based on acoustic electromagnetic intermodulation is proposed, comprising:

[0023] An acoustically modulated electromagnetic wave signal excitation module is configured to cause different objects to reflect acoustically modulated electromagnetic wave signals with different degrees of amplitude modulation and phase modulation based on an acoustic-electromagnetic intermodulation mechanism;

[0024] An acoustic electromagnetic echo feature extraction and identification module is configured to extract the micro-Doppler feature and amplitude feature of the acoustic modulated electromagnetic wave signal, and to make a preliminary judgment and identification on the material of the corresponding object;

[0025] A material feature sample library creation module is configured to use a feature processing algorithm to fuse the preliminary judgment and recognition results based on the micro-Doppler feature and the amplitude feature to establish a material feature sample library for different objects;

[0026] The object material property recognition and marking module is configured to import the material feature sample library into the machine learning model for classification training, thereby realizing the recognition and marking of the material properties of different objects.

[0027] According to a third aspect of the present application, a computer-readable storage medium is proposed, which stores a computer program. When the computer program is executed by a processor, it implements the indoor object echo feature processing method based on acoustic and electromagnetic intermodulation as described in the first aspect of the present application.

[0028] The present application proposes a method and system for processing indoor object echo features based on acoustic electromagnetic intermodulation. According to the characteristics of acoustic electromagnetic echoes, starting from amplitude modulation and phase modulation, the Hilbert transform method is used to extract the instantaneous characteristics of the acoustic modulated electromagnetic wave signal, establish a single feature, and design a reliable echo feature processing algorithm and system based on acoustic electromagnetic intermodulation. In the process of feature processing, a classification algorithm will be used to identify and mark the material attribute features of indoor objects. Compared with the traditional method of processing target echo features based on machine learning, the present application increases the degree of representation of different target feature information and improves the accuracy of target object judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated into and constitute a part of this specification. The accompanying drawings illustrate the embodiments and are used together with the description to explain the principles of the present application. It will be easy to recognize other embodiments and many expected advantages of the embodiments because they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale with each other. The same reference numerals refer to corresponding similar parts.

[0030] Figure 1 It is a flow chart of a method for processing indoor object echo characteristics based on acoustic and electromagnetic intermodulation according to an embodiment of the present application;

[0031] Figure 2 This is a diagram of an experimental scene for performing acoustic and electromagnetic detection on objects of different materials according to a specific embodiment of the present application;

[0032] Figure 3It is a scene diagram of a portable detection device according to a specific embodiment of the present application;

[0033] Figure 4 is a diagram of the amplitude of each order of acoustic modulation sideband signals of target objects of different materials according to a specific embodiment of the present application;

[0034] Figure 5 is an acousto-electromagnetic modulation echo envelope diagram of a wooden board according to a specific embodiment of the present application;

[0035] Figure 6 This is an acousto-electromagnetic modulation echo spectrum diagram of a wooden board according to a specific embodiment of the present application;

[0036] Figure 7 is an acousto-electromagnetic modulation echo envelope diagram of an aluminum plate according to a specific embodiment of the present application;

[0037] Figure 8 This is an acousto-electromagnetic modulation echo spectrum diagram of an aluminum plate according to a specific embodiment of the present application;

[0038] Fig. 9 is an acousto-electromagnetic modulation echo envelope diagram of a copper plate according to a specific embodiment of the present application;

[0039] Fig.10 This is an acousto-electromagnetic modulation echo spectrum diagram of a copper plate according to a specific embodiment of the present application;

[0040] Fig.11 is a graph showing the echo feature processing results of different materials according to a specific embodiment of the present application;

[0041] Fig.12 It is a block diagram of an indoor object echo feature processing system based on acoustic and electromagnetic intermodulation according to an embodiment of the present application.

[0042] Explanation of the accompanying drawings: 1. Acoustic modulated electromagnetic wave signal excitation module; 2. Acoustic electromagnetic echo feature extraction and discrimination module; 3. Material feature sample library creation module; 4. Object material property recognition and labeling module. DETAILED DESCRIPTION

[0043] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and Examples. It should be understood that the specific embodiments described herein are only configured to explain the present application and are not configured to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0044] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0045] According to the first aspect of the present application, a method for processing indoor object echo characteristics based on acoustic and electromagnetic intermodulation is proposed. Figure 1 FIG. 4 shows a flow chart of a method for processing indoor object echo characteristics based on acoustic electromagnetic intermodulation according to an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0046] S1. Based on the mechanism of acoustic-electromagnetic intermodulation, different objects reflect acoustically modulated electromagnetic wave signals with different degrees of amplitude modulation and phase modulation.

[0047] First of all, those skilled in the art should understand that the mechanism of acoustic-electromagnetic intermodulation is to generate an acoustic wave signal by emitting acoustic wave excitation to make the object vibrate, and then select an electromagnetic signal that can modulate the acoustic wave signal, and emit the electromagnetic signal to perform acoustic-electromagnetic intermodulation on the acoustic wave signal, thereby generating an acoustic modulated electromagnetic wave signal.

[0048] In a specific embodiment, Figure 2 FIG. 1 shows an experimental scene diagram of acoustic and electromagnetic detection of objects of different materials according to a specific embodiment of the present application, such as Figure 2 As shown, first, a relatively open space is selected, and a software radio (USRP) device is used to control the transmission and reception of electromagnetic waves, and the mobile phone is used to transmit audio signals to receive acoustic electromagnetic excitation. The electromagnetic wave transmitting and receiving antennas are all omnidirectional antennas, and the vertical distance from the wall is 1m. The transmitting and receiving antennas are equipped with partitions to block direct signals. In this embodiment, the target objects selected are copper plates, aluminum plates and wooden boards.

[0049] Figure 3 A diagram showing a portable detection device construction scenario according to a specific embodiment of the present application is shown in FIG. Figure 3As shown, an electromagnetic wave signal transmitter is placed in a small darkroom, an ultrasonic transducer that generates sound wave signals is placed on the outer wall of the darkroom to stimulate objects in the room, and an electromagnetic wave signal receiver is placed on the same horizontal line and connected to a PC to store the reflected sound modulated electromagnetic wave signal.

[0050] Continue to refer to Figure 1 , after step S1,

[0051] S2. Extract the micro-Doppler characteristics and amplitude characteristics of the acoustically modulated electromagnetic wave signal, and make a preliminary judgment and identification on the material of the corresponding object.

[0052] In a specific embodiment, since the difference between the acoustic modulated electromagnetic wave signals reflected by different target objects is mainly reflected in different degrees of amplitude modulation and phase modulation, the micro-Doppler characteristics of the acoustic modulated electromagnetic wave signals are studied from these two perspectives. The micro-Doppler characteristics are manifested as the instantaneous characteristics of the acoustic modulated electromagnetic wave signals, specifically including instantaneous amplitude, instantaneous frequency and instantaneous phase. The extracted amplitude feature is the amplitude of the nth order acoustic modulated sideband signal of the acoustic modulated electromagnetic wave signal. This part of the content will be explained below.

[0053] Definition 1: Instantaneous characteristics of acoustically modulated electromagnetic wave signals

[0054] When obtaining the instantaneous characteristics, the Hilbert transform of the acoustic modulated electromagnetic wave signal is first obtained to obtain its corresponding analytical signal, and then the characteristic parameters to be selected are extracted from the instantaneous characteristics. This method is simple, and the amount of calculation is relatively small, and it is easy to implement. For the acoustic modulated electromagnetic wave signal s(t), the calculation of its Hilbert transform is:

[0055]

[0056] in, is s(t) and The convolution can also be expressed as:

[0057]

[0058] Relative s(t) and As the real and imaginary parts respectively, the expression of the analytical signal is:

[0059]

[0060] According to the definition of Hilbert transform, the transformed signal The spectrum characteristics are:

[0061]

[0062] Where S(ω) is the spectrum of the acoustically modulated electromagnetic wave signal s(t), so the spectrum of the analytical signal z(t) is:

[0063]

[0064] It can be seen that the spectrum of the analytical signal has a unilateral characteristic, and its Fourier transform is twice the Fourier transform of the original received signal, so the instantaneous characteristics of the signal can be extracted.

[0065] The instantaneous amplitude is:

[0066]

[0067] The instantaneous frequency is:

[0068]

[0069] The instantaneous phase is:

[0070]

[0071] After extracting the instantaneous characteristics of the acoustic modulated electromagnetic wave signal, especially the instantaneous amplitude and instantaneous phase, the corresponding inversion and recognition can be realized according to the reflection characteristics of different objects to the acoustic modulated electromagnetic wave signal, that is, preliminary judgment and recognition, to obtain a preliminary judgment and recognition result.

[0072] Definition 2: Amplitude of the nth-order acoustic modulation sideband signal

[0073] Assuming that the transmitted electromagnetic signal is a linearly polarized plane wave propagating along the z-axis, the active Maxwell equations can be written as:

[0074]

[0075] The constitutive relation of non-magnetic medium is:

[0076]

[0077]

[0078] Among them, ε0 is the dielectric constant of free space, εr is the dielectric constant of the medium, pA(t) is the periodic sound pressure, ρ0 is the density of the medium, cA is the speed of sound in the medium, ∑y(tA) is the vector of the ∑ field in the y-axis direction that changes slowly with the sound wave time t, and μ0 is the magnetic permeability of free space.

[0079] The difference of the electromagnetic signal with respect to time can be simplified to a vector form, so the wave equation for the propagation of electromagnetic waves in the acoustically vibrating target object can be expressed as:

[0080]

[0081] Where kRF is the wave number of the electromagnetic wave in free space.

[0082] If the angular frequency and wave number of the periodic sound pressure pA(t) are ωA and kA respectively, it can be expressed as:

[0083]

[0084] Where pn is the amplitude of the nth harmonic of the sound source. By solving the electromagnetic wave equation, the received electromagnetic wave signal modulated by the sound wave can be expressed as follows:

[0085]

[0086] where FAcoust,0 and FAcoust,n are Fourier coefficients.

[0087] Therefore, relative to the electromagnetic wave transmission signal, the amplitude of the nth order acoustic modulation sideband signal is:

[0088]

[0089] Where λA is the wavelength of the sound in the target material and λRF is the wavelength of the electromagnetic wave in free space. Since the target vibration is excited by the sound and the vibration frequency is the same as the sound frequency, the modulation caused by the vibration in the target is the same as the modulation caused by the sound wave.

[0090] From the amplitude expression of the nth order acoustic modulation sideband signal, it can be seen that the amplitude of the acoustic modulation sideband signal is affected by the relative dielectric constant, density, sound velocity and other parameters of the material. According to the obtained expression, the material of the corresponding target object can also be inversely identified, that is, preliminary judgment and recognition, and another preliminary judgment and recognition result can be obtained.

[0091] Figure 4 FIG. 4 shows the amplitude diagram of each order of acoustic modulation sideband signals of target objects of different materials according to a specific embodiment of the present application, such as Figure 4 As shown, in the simulation, the amplitudes of the sideband signals of each order of the reflected sound modulated electromagnetic wave signals generated by the targets of copper, aluminum and wood, namely AAcoust,n, are simulated and analyzed, and the different relative dielectric constants, densities, sound speeds and other parameters of various materials are substituted. It can be observed that the different parameter characteristics corresponding to different materials are all present in the amplitude of the sideband signal.

[0092] Figure 5 is an acousto-electromagnetic modulation echo envelope diagram of a wooden board according to a specific embodiment of the present application;

[0093] Figure 6 This is an acousto-electromagnetic modulation echo spectrum diagram of a wooden board according to a specific embodiment of the present application;

[0094] Figure 7 is an acousto-electromagnetic modulation echo envelope diagram of an aluminum plate according to a specific embodiment of the present application;

[0095] Figure 8 This is an acousto-electromagnetic modulation echo spectrum diagram of an aluminum plate according to a specific embodiment of the present application;

[0096] Fig. 9 is an acousto-electromagnetic modulation echo envelope diagram of a copper plate according to a specific embodiment of the present application;

[0097] Fig.10 This is an acousto-electromagnetic modulation echo spectrum diagram of a copper plate according to a specific embodiment of the present application;

[0098] like Figure 5-10 As shown, by extracting Figure 2 In the experiment, the electromagnetic wave signal envelopes reflected by the three targets were transformed to obtain the electromagnetic wave signal spectrum. It can be observed that the amplitude changes and spectral characteristics of the electromagnetic wave envelopes reflected by the three material targets under acoustic and electromagnetic excitation are different, that is, the amplitude modulation generated by the acoustic and electromagnetic intermodulation of different objects carries characteristic peak signals at the amplitude.

[0099] Continue to refer to Figure 1 , after step S2,

[0100] S3. Use feature processing algorithms to fuse the preliminary identification results based on micro-Doppler features and amplitude features, and establish a material feature sample library for different objects.

[0101] In a specific embodiment, in combination with the above content, the preliminary judgment and recognition result based on the micro-Doppler feature is the result inverted and calculated according to the instantaneous characteristics of the extracted acoustic modulated electromagnetic wave signal in Definition 1; the preliminary judgment and recognition result based on the amplitude feature is the result inverted and calculated according to the amplitude expression of the obtained n-th order acoustic modulated sideband signal in Definition 2. In this embodiment, the support vector regression (SVR) feature processing algorithm is specifically used to fuse the above two preliminary judgment and recognition results.

[0102] Fig.11 FIG. 4 shows the echo feature processing result of different materials according to a specific embodiment of the present application, such as Fig.11 As shown in the figure, in this embodiment, the acoustic modulated electromagnetic echo signals of four different objects, namely wood, copper, aluminum and stainless steel, are subjected to feature processing. Fig.11 It can be seen that Figure 3 The amplitude modulation feature extraction generated in the echo signal received by the portable detection device shown in the figure is used to identify indoor objects. The curves fitted by the feature processing algorithm for different materials are quite different. Subsequently, by establishing a sample library of indoor object material features, the material attribute recognition of indoor objects can be realized.

[0103] Continue to refer to Figure 1 , after step S3,

[0104] S4. Import the material feature sample library into the machine learning model for classification training, so as to realize the recognition and labeling of material properties of different objects.

[0105] In a specific embodiment, the machine learning model uses a high-performance random forest model to achieve material attribute feature recognition and labeling of indoor objects.

[0106] According to the second aspect of the present application, a system for processing indoor object echo characteristics based on acoustic electromagnetic intermodulation is proposed. The system is built based on the method for processing indoor object echo characteristics based on acoustic electromagnetic intermodulation of the first aspect of the present application. Fig.12 FIG. 4 shows a block diagram of an indoor object echo feature processing system based on acoustic electromagnetic intermodulation according to an embodiment of the present application. Fig.12 As shown, the system includes:

[0107] The acoustic modulated electromagnetic wave signal excitation module 1 is configured to make different objects reflect the acoustic modulated electromagnetic wave signals with different degrees of amplitude modulation and phase modulation based on the acoustic and electromagnetic intermodulation mechanism;

[0108] The acoustic electromagnetic echo feature extraction and identification module 2 is configured to extract the micro-Doppler feature and amplitude feature of the acoustic modulated electromagnetic wave signal, and make a preliminary judgment and identification on the material of the corresponding object;

[0109] The material feature sample library creation module 3 is configured to use a feature processing algorithm to fuse the preliminary judgment and recognition results based on the micro-Doppler feature and the amplitude feature to establish a material feature sample library for different objects;

[0110] The object material property recognition and marking module 4 is configured to import the material feature sample library into the machine learning model for classification training, thereby realizing the recognition and marking of the material properties of different objects.

[0111] According to the third aspect of the present application, a computer-readable storage medium is proposed, which stores a computer program. When the computer program is executed by a processor, it implements the indoor object echo feature processing method based on acoustic electromagnetic intermodulation as described in the first aspect of the present application.

[0112] The present application proposes a method and system for processing indoor object echo features based on acoustic electromagnetic intermodulation. According to the characteristics of acoustic electromagnetic echoes, starting from amplitude modulation and phase modulation, the Hilbert transform method is used to extract the instantaneous characteristics of the acoustic modulated electromagnetic wave signal, establish a single feature, and design a reliable echo feature processing algorithm and system based on acoustic electromagnetic intermodulation. In the process of feature processing, a classification algorithm will be used to identify and mark the material attribute features of indoor objects. Compared with the traditional method of processing target echo features based on machine learning, the present application increases the degree of representation of different target feature information and improves the accuracy of target object judgment.

[0113] In the embodiments of the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device / system / method embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0114] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0115] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0117] Obviously, those skilled in the art can make various modifications and changes to the embodiments of the present application without departing from the spirit and scope of the present application. In this way, if these modifications and changes are within the scope of the claims of the present application and their equivalents, the present application is also intended to cover these modifications and changes. The word "comprising" does not exclude the presence of other elements or steps not listed in the claims. The simple fact that certain measures are recorded in mutually different dependent claims does not indicate that the combination of these measures cannot be used to profit. Any figure mark in the claims should not be considered to limit the scope.

Claims

1. A method for processing indoor object echo characteristics based on acoustic electromagnetic intermodulation, characterized in that: include: S1. Based on the mechanism of acoustic electromagnetic intermodulation, different objects reflect different degrees of amplitude modulation and phase modulation of acoustically modulated electromagnetic wave signals; S2. Extracting the micro-Doppler feature and amplitude feature of the acoustic modulated electromagnetic wave signal. The micro-Doppler feature is manifested as the instantaneous characteristic of the acoustic modulated electromagnetic wave signal. The micro-Doppler feature specifically includes instantaneous amplitude, instantaneous frequency and instantaneous phase. The instantaneous amplitude is specifically expressed as: The expression of the instantaneous frequency is specifically: The expression of the instantaneous phase is specifically: Wherein, s(t) is the acoustically modulated electromagnetic wave signal, is the Hilbert transform of the acoustically modulated electromagnetic wave signal, and the extracted amplitude feature is the amplitude of the n-th order acoustically modulated sideband signal of the acoustically modulated electromagnetic wave signal, and its specific expression is: Among them, p n is the amplitude of the nth harmonic of the sound source, k RF is the wave number of the electromagnetic wave in free space, k A is the periodic sound pressure p A (t) wave number, ε r is the relative dielectric constant of the medium, ρ0 is the density of the medium, c A is the speed of sound in the medium, λ A is the wavelength of sound in the target material, λ RF It is the wavelength of electromagnetic waves in free space, which makes a preliminary judgment and identification on the material of the corresponding object; S3, using a feature processing algorithm to fuse the preliminary judgment and recognition results based on the micro-Doppler feature and the amplitude feature, to establish a material feature sample library for different objects; and S4. Importing the material feature sample library into a machine learning model for classification training, thereby realizing recognition and labeling of material properties of different objects.

2. The method according to claim 1, characterized in that The feature processing algorithm specifically adopts support vectorized regression, and the machine learning model adopts a random forest model.

3. A system for processing indoor object echo characteristics based on acoustic electromagnetic intermodulation, characterized in that: include: An acoustically modulated electromagnetic wave signal excitation module is configured to cause different objects to reflect acoustically modulated electromagnetic wave signals with different degrees of amplitude modulation and phase modulation based on an acoustic-electromagnetic intermodulation mechanism; The acoustic electromagnetic echo feature extraction and discrimination module is configured to extract the micro-Doppler feature and amplitude feature of the acoustic modulated electromagnetic wave signal. The micro-Doppler feature is manifested as the instantaneous characteristic of the acoustic modulated electromagnetic wave signal. The micro-Doppler feature specifically includes instantaneous amplitude, instantaneous frequency and instantaneous phase. The expression of the instantaneous amplitude is specifically: The expression of the instantaneous frequency is specifically: The expression of the instantaneous phase is specifically: Wherein, s(t) is the acoustically modulated electromagnetic wave signal, is the Hilbert transform of the acoustically modulated electromagnetic wave signal, and the extracted amplitude feature is the amplitude of the n-th order acoustically modulated sideband signal of the acoustically modulated electromagnetic wave signal, and its specific expression is: Among them, p n is the amplitude of the nth harmonic of the sound source, k RF is the wave number of the electromagnetic wave in free space, k A is the periodic sound pressure p A (t) wave number, ε r is the relative dielectric constant of the medium, ρ0 is the density of the medium, c A is the speed of sound in the medium, λ A is the wavelength of sound in the target material, λ RF It is the wavelength of electromagnetic waves in free space, which makes a preliminary judgment and identification on the material of the corresponding object; A material feature sample library creation module is configured to use a feature processing algorithm to fuse the preliminary judgment and recognition results based on the micro-Doppler feature and the amplitude feature to establish a material feature sample library for different objects; The object material property recognition and marking module is configured to import the material feature sample library into the machine learning model for classification training, thereby realizing the recognition and marking of the material properties of different objects.

4. A computer-readable storage medium storing a computer program, wherein the computer program implements the method according to any one of claims 1 to 2 when executed by a processor.

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