CN114778706A - Indoor object echo characteristic processing method and system based on acoustic-electromagnetic intermodulation - Google Patents
Indoor object echo characteristic processing method and system based on acoustic-electromagnetic intermodulation Download PDFInfo
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Abstract
The application provides an indoor object echo characteristic processing method based on acoustic-electromagnetic intermodulation, which comprises the following steps: s1, enabling different objects to reflect acoustic modulation electromagnetic wave signals with different degrees of amplitude modulation and phase modulation based on an acoustic-electromagnetic intermodulation mechanism; s2, extracting micro Doppler features and amplitude features of the acoustic modulation electromagnetic wave signals, and performing preliminary judgment and identification on the material corresponding to the object; s3, fusing the preliminary judgment and identification results by using a feature processing algorithm, and establishing a material feature sample library about different objects; and S4, importing the material characteristic sample library into a machine learning model for classification training, so as to realize identification and marking of material attributes of different objects. The method and the device utilize the amplitude of the acoustic modulation sideband signal to perform feature processing, design a reliable indoor object echo feature processing algorithm based on acoustic-electromagnetic intermodulation, achieve identification and marking of key attributes of the indoor object, and improve accuracy of target object judgment.
Description
Technical Field
The application relates to the technical field of target detection, in particular to an indoor object echo characteristic processing method and system based on acoustic-electromagnetic intermodulation.
Background
The traditional through-the-wall detection (TWS) technology can not effectively generate obvious echo characteristics, and mainly detects people in a static state after the target is an obstacle, namely, whether life phenomena exist or not is judged by detecting various micro-motions caused by the physiological activities of the human body, and the echo characteristics are marked by the micro-motions. Respiration and heartbeat are important indexes for detecting vital sign signals, but for static objects which cannot generate self-vibration phenomena similar to heartbeat, respiration and the like, a wall-through detection (TWS) technology cannot obtain obvious echo characteristics to complete detection of indoor objects, so that how to effectively enable the indoor objects to generate the self-vibration phenomena assists disaster rescue and other actions to be smoothly carried out becomes a research hotspot in the detection field.
The sound wave excitation is a method capable of effectively enabling an object to generate a self-vibration phenomenon, and different from a weak signal of breathing heartbeat, the sound wave excitation can effectively enable the object to generate vibration and sound scattering with a certain amplitude. When the electromagnetic wave signal is used to detect the target, the scattered electromagnetic signal generates phase modulation, and this scattering effect has prompted the development of a hybrid acoustic-electromagnetic sensing technology. However, the resulting characteristics are difficult to process because the acoustic-electromagnetic scattering effect produces modulation sidebands that are very close relative to the emitted electromagnetic signal, and the scattered electromagnetic field that is generated by exciting the object with the acoustic source lacks mathematical modeling.
Disclosure of Invention
In order to solve the technical defects in the background art, the application provides an indoor object echo characteristic processing method and system based on acoustic-electromagnetic intermodulation.
According to a first aspect of the present application, a method for processing echo characteristics of an indoor object based on acoustic-electromagnetic intermodulation is provided, including:
s1, enabling different objects to reflect acoustic modulation electromagnetic wave signals with different degrees of amplitude modulation and phase modulation based on an acoustic electromagnetic intermodulation mechanism;
s2, extracting micro Doppler features and amplitude features of the acoustic modulation electromagnetic wave signals, and performing preliminary judgment and identification on the material corresponding to the object;
s3, fusing the preliminary judgment and identification results based on the micro Doppler features and the amplitude features by using a feature processing algorithm, and establishing a material feature sample library about different objects; and
and S4, importing the material characteristic sample library into a machine learning model for classification training, so as to realize identification and marking of material attributes of different objects.
Preferably, the micro-doppler signature is indicative of an instantaneous characteristic of the acoustically modulated electromagnetic wave signal, the micro-doppler signature specifically including an instantaneous amplitude, an instantaneous frequency and an instantaneous phase.
Preferably, the expression of the instantaneous amplitude is specifically:
the expression of the instantaneous frequency is specifically as follows:
the expression of the instantaneous phase is specifically as follows:
wherein s (t) is the acoustic modulated electromagnetic wave signal,
is a hilbert transform of the acoustic modulated electromagnetic wave signal.
Preferably, in step S2, the amplitude feature of the nth order acoustic modulation sideband signal of the acoustic modulation electromagnetic wave signal is extracted, where an expression of the amplitude feature is specifically:
where pn is the amplitude of the nth harmonic of the acoustic source, kRF is the wave number of the electromagnetic wave in free space, kA is the wave number of the periodic acoustic pressure pA (t), ε r is the relative dielectric constant of the medium, ρ 0 is the density of the medium, cA is the speed of sound in the medium, λ A is the wavelength of the sound in the target material, and λ RF is the wavelength of the electromagnetic wave in free space.
Preferably, the feature processing algorithm specifically adopts support vectorization regression, and the machine learning model adopts a random forest model.
According to a second aspect of the present application, an echo feature processing system for an indoor object based on acoustic-electromagnetic intermodulation is provided, including:
the acoustic modulation electromagnetic wave signal excitation module is configured for enabling different objects to reflect acoustic modulation electromagnetic wave signals with different degrees of amplitude modulation and phase modulation based on an acoustic electromagnetic intermodulation mechanism;
the acoustic electromagnetic echo characteristic extraction and discrimination module is configured to extract micro Doppler characteristics and amplitude characteristics of the acoustic modulation electromagnetic wave signals and perform preliminary judgment and identification on the material corresponding to the object;
a material characteristic sample library creating module configured to fuse preliminary judgment and identification results based on the micro-Doppler characteristic and the amplitude characteristic by using a characteristic processing algorithm, and create a material characteristic sample library about different objects;
and the object material attribute identification and marking module is configured for importing the material characteristic sample library into a machine learning model for classification training, so that identification and marking of different material attributes of the object are realized.
According to a third aspect of the present application, a computer-readable storage medium is proposed, which stores a computer program, which when executed by a processor implements the method for processing echo characteristics of an indoor object based on acoustic-electromagnetic intermodulation as described in the first aspect of the present application.
The application provides an indoor object echo feature processing method and system based on acoustomagnetic intermodulation. In the process of carrying out feature processing, a classification algorithm is used for carrying out material attribute feature identification and marking on the indoor object. Compared with the traditional mode of processing the target echo characteristics based on machine learning, the method and the device increase the characterization degrees of different target characteristic information and improve the accuracy of target object judgment.
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The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and together with the description serve to explain the principles of the application. Other embodiments and many of the intended advantages of embodiments will be readily appreciated as they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale relative to each other. Like reference numerals designate corresponding similar parts.
Fig. 1 is a flowchart of an echo characteristic processing method for an indoor object based on acoustic-electromagnetic intermodulation according to an embodiment of the present application;
FIG. 2 is an experimental scene diagram of acousto-electromagnetic detection of objects of different materials according to an embodiment of the present application;
FIG. 3 is a diagram of a construction scenario of a portable detection device according to an embodiment of the present application;
FIG. 4 is a graph of amplitudes of respective order modulation sideband signals of different material target objects according to one embodiment of the present application;
FIG. 5 is an acoustic electromagnetic modulation echo envelope plot of a plank according to one embodiment of the present application;
FIG. 6 is a diagram illustrating an acousto-electro-magnetic modulation echo spectrum of a wood board according to an embodiment of the present application;
FIG. 7 is a graph of an acousto-electro-magnetically modulated echo envelope for an aluminum plate according to a specific embodiment of the present application;
FIG. 8 is a graph of the acoustomagnetically modulated echo spectrum of an aluminum plate according to one embodiment of the present application;
FIG. 9 is an acousto-electro-magnetically modulated echo envelope plot for a copper plate according to a specific embodiment of the present application;
FIG. 10 is a graph of the acoustomagnetically modulated echo spectrum of a copper plate according to one embodiment of the present application;
FIG. 11 is a graph of echo feature processing results for different materials according to an embodiment of the present application;
fig. 12 is a block diagram of an echo feature processing system for indoor objects based on acoustic electromagnetic intermodulation according to an embodiment of the present application.
Description of reference numerals: 1. the acoustic modulation electromagnetic wave signal excitation module; 2. an acoustoelectric echo characteristic extraction and discrimination module; 3. a material characteristic sample library creating module; 4. and the object material attribute identification and marking module.
Detailed Description
Features of various aspects and exemplary embodiments of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and the embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element described by the phrase "comprising." does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
According to a first aspect of the application, an indoor object echo feature processing method based on acoustic-electromagnetic intermodulation is provided. Fig. 1 shows a flowchart of an echo characteristic processing method for an indoor object based on acoustic-electromagnetic intermodulation according to an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
and S1, reflecting the acoustic modulation electromagnetic wave signals of different degrees of amplitude modulation and phase modulation by different objects based on an acoustic electromagnetic intermodulation mechanism.
First, as will be appreciated by those skilled in the art, the acousto-electromagnetic intermodulation mechanism is to generate an acoustically modulated electromagnetic wave signal by emitting an acoustic wave stimulus that vibrates an object to produce an acoustic wave signal, and then selecting an electromagnetic signal that modulates the acoustic wave signal, and emitting the electromagnetic signal to acousto-electromagnetically intermodulate the acoustic wave signal.
In a specific embodiment, fig. 2 shows an experimental scene diagram of performing acousto-electromagnetic detection on objects made of different materials according to a specific embodiment of the present application, and as shown in fig. 2, firstly, a wider space is selected, software radio (USRP) equipment is used to control the transceiving of electromagnetic waves, and a mobile phone is used to transmit an audio signal and receive acousto-electromagnetic excitation. The electromagnetic wave receiving and transmitting antennas are all omnidirectional antennas, the vertical distance between the electromagnetic wave receiving and transmitting antennas and the wall is 1m, and the receiving and transmitting antennas are provided with clapboards for blocking direct signals. In this embodiment, the selected target objects are a copper plate, an aluminum plate, and a wood plate, respectively.
Fig. 3 shows a construction scene of the portable detection device according to an embodiment of the present application, and as shown in fig. 3, an electromagnetic wave signal transmitting end is placed in a small darkroom, an ultrasonic transducer for generating a sound wave signal is placed on an outer wall of the darkroom to excite an indoor object, an electromagnetic wave signal receiving end is placed on the same horizontal line, and a PC end is connected to store a reflected sound modulation electromagnetic wave signal.
With continued reference to fig. 1, after step S1,
and S2, extracting the micro Doppler characteristic and the amplitude characteristic of the acoustic modulation electromagnetic wave signal, and preliminarily judging and identifying the material of the corresponding object.
In a specific embodiment, since the differences between the acoustic modulated electromagnetic wave signals reflected by different target objects are mainly reflected by amplitude modulation and phase modulation of different degrees, the micro-doppler characteristic of the acoustic modulated electromagnetic wave signals is studied from these two perspectives, and the micro-doppler characteristic is represented by the transient characteristics of the acoustic modulated electromagnetic wave signals, specifically including the transient amplitude, the transient frequency and the transient phase. And the extracted amplitude characteristic is the amplitude of the nth order acoustic modulation sideband signal of the acoustic modulation electromagnetic wave signal. The contents of this section will be explained below.
Defining one: instantaneous characteristics of acoustically modulated electromagnetic wave signals
When the instantaneous characteristic is obtained, the Hilbert transform of the acoustic modulation electromagnetic wave signal is obtained to obtain a corresponding analytic signal, and then the characteristic parameter to be selected is extracted from the instantaneous characteristic. For an acoustically modulated electromagnetic wave signal s (t), the hilbert transform is calculated as:
wherein, the first and the second end of the pipe are connected with each other,
is s (t) and
can also be expressed as:
s (t) and
the expressions of the analytic signals are respectively used as a real part and an imaginary part, and the expressions are as follows:
the transformed signal being defined according to a Hilbert transform
The spectrum characteristics of (A) are as follows:
where S (ω) is the spectrum of the acoustic modulated electromagnetic wave signal S (t), the spectrum of the analytic signal z (t) can be obtained as:
it can be seen that the spectrum of the analytic signal has a single-sided characteristic, and its fourier transform is twice that of the original received signal, so that the instantaneous characteristic of the signal can be extracted.
The instantaneous amplitude is:
the instantaneous frequency is:
the instantaneous phase is:
after extracting the instantaneous characteristics of the acoustic modulation electromagnetic wave signal, especially the instantaneous amplitude and the instantaneous phase, the corresponding inversion and identification can be realized according to the reflection characteristics of different objects to the acoustic modulation electromagnetic wave signal, namely, the initial judgment and identification are carried out, and an initial judgment and identification result is obtained.
Defining two: amplitude of nth order sound modulation sideband signal
Assuming that the transmitted electromagnetic signal is a linearly polarized plane wave propagating along the z-axis, the active maxwell equation can be written as:
wherein the constitutive relation of the non-magnetic medium is as follows:
where ε 0 is the dielectric constant of free space,. epsilon.r is the dielectric constant of the medium,. epsilon. (t) is the periodic sound pressure,. rho.0 is the density of the medium,. cA is the speed of sound in the medium,. sigma.y. (tA) is the vector of the E field in the y-axis direction that varies slowly with the time t of the sound wave,. mu.0Is the free space permeability.
The difference of the electromagnetic signal with respect to time can be simplified to a vector form, and thus the wave equation of the electromagnetic wave propagation in the target object of acoustic vibration can be expressed as:
where kRF is the electromagnetic wave number in free space.
Provided that the angular frequency and wavenumber of the periodic sound pressure pa (t) are ω a and kA, respectively, it can be expressed as:
wherein pn is the amplitude of the nth harmonic of the sound source, and the received electromagnetic wave signal subjected to the acoustic wave modulation obtained by solving the wave equation of the electromagnetic wave can be expressed by the following formula:
where FACoust,0 and FACoust, n are Fourier coefficients.
Therefore, with respect to the electromagnetic wave transmission signal, the amplitude of the nth order sound modulation sideband signal is:
where λ a is the wavelength of sound in the target material and λ RF is the wavelength of the electromagnetic wave in free space. Since the target vibration is generated by acoustic excitation, the vibration frequency is the same as the acoustic frequency, so the vibration-induced modulation is the same as the acoustic wave-induced modulation in the target.
It can be seen from the amplitude expression of the nth-order acoustically modulated sideband signal that the amplitude of the acoustically modulated sideband signal is influenced by the relative dielectric constant, density, sound velocity and other parameters of the material. And according to the obtained expression, the material of the corresponding target object can also be identified by inversion, namely, the initial judgment and identification are carried out, and another initial judgment and identification result is obtained.
Fig. 4 shows an amplitude diagram of each order of the acoustic modulation sideband signals of target objects made of different materials according to an embodiment of the present application, and as shown in fig. 4, in the simulation, the amplitudes of each order of the sideband signals, i.e., aacust, n, of the reflected acoustic modulation electromagnetic wave signals generated by the targets made of three materials, i.e., copper, aluminum and wood, are subjected to simulation analysis and substituted into different parameters, such as relative dielectric constant, density and acoustic velocity, of each material, and it can be observed that different parameter characteristics corresponding to different materials all exist in the amplitudes of the sideband signals.
FIG. 5 is an acoustic electromagnetic modulation echo envelope plot of a plank according to one embodiment of the present application;
FIG. 6 is a graph of the acoustomagnetic modulation echo spectrum of a wooden board according to one embodiment of the present application;
FIG. 7 is a graph of an acousto-electro-magnetically modulated echo envelope for an aluminum plate according to a specific embodiment of the present application;
FIG. 8 is a graph of the acoustomagnetically modulated echo spectrum of an aluminum plate according to one embodiment of the present application;
FIG. 9 is an acousto-electro-magnetically modulated echo envelope plot for a copper plate according to a specific embodiment of the present application;
FIG. 10 is a graph of the acoustomagnetically modulated echo spectrum of a copper plate according to one embodiment of the present application;
as shown in fig. 5-10, by extracting the envelopes of the electromagnetic wave signals reflected by the three targets in the experiment of fig. 2 and transforming the envelopes to obtain the frequency spectrums of the electromagnetic wave signals, it can be found through observation that the amplitude variations and the frequency spectrum characteristics of the envelopes of the electromagnetic waves reflected by the targets of the three materials under the excitation of the acoustic electromagnetic waves are different, that is, different objects carry characteristic spike signals at the amplitudes through the amplitude modulation generated by the acoustic electromagnetic intermodulation.
With continued reference to fig. 1, after step S2,
and S3, fusing the preliminary judgment and identification results based on the micro Doppler characteristics and the amplitude characteristics by using a characteristic processing algorithm, and establishing a material characteristic sample library related to different objects.
In a specific embodiment, in combination with the above, the preliminary judgment and identification result based on the micro doppler feature is a result obtained by performing inverse calculation according to the instantaneous characteristics of the extracted acoustic modulation electromagnetic wave signal in the definition one; and the preliminary judgment and identification result based on the amplitude characteristics is a result obtained by carrying out inversion calculation according to the obtained amplitude expression of the nth order acoustic modulation sideband signal in the second definition. In this embodiment, a Support Vectorization Regression (SVR) feature processing algorithm is specifically adopted to fuse the two primary judgment recognition results.
Fig. 11 shows a graph of echo feature processing results of different materials according to a specific embodiment of the present application, as shown in fig. 11, in this embodiment, feature processing is performed on acoustic modulation electromagnetic echo signals of four different objects, namely, wood board, copper, aluminum, and stainless steel, as can be seen from fig. 11, indoor object recognition is performed by using amplitude modulation extraction features generated in echo signals received by the portable detection device shown in fig. 3, and curves of different materials which are fitted by a feature processing algorithm have great differences. And subsequently, the material attribute identification of the indoor object can be realized by establishing an indoor object material characteristic sample library.
With continued reference to fig. 1, after step S3,
and S4, importing the material characteristic sample library into a machine learning model for classification training, thereby realizing the identification and marking of material attributes of different objects.
In a specific embodiment, the machine learning model adopts a high-performance random forest model to realize material attribute feature identification and marking of indoor objects.
According to a second aspect of the application, an indoor object echo feature processing system based on acoustic-electromagnetic intermodulation is provided, and the system is built based on the indoor object echo feature processing method based on acoustic-electromagnetic intermodulation of the first aspect of the application. Fig. 12 is a block diagram illustrating an echo feature processing system for indoor objects based on acoustic electromagnetic intermodulation according to an embodiment of the present application, where, as shown in fig. 12, the system includes:
the acoustic modulation electromagnetic wave signal excitation module 1 is configured to enable different objects to reflect acoustic modulation 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 2 is configured to extract micro Doppler features and amplitude features of acoustic modulation electromagnetic wave signals, and perform preliminary judgment and identification on the material of a corresponding object;
the material characteristic sample library creating module 3 is configured to fuse preliminary judgment identification results based on micro Doppler characteristics and amplitude characteristics by using a characteristic processing algorithm, and create a material characteristic sample library related to different objects;
and the object material attribute identification and marking module 4 is configured to introduce the material characteristic sample library into the machine learning model for classification training, so that identification and marking of material attributes of different objects are realized.
According to a third aspect of the present application, a computer-readable storage medium is proposed, which stores a computer program, which when executed by a processor, implements the method for processing echo characteristics of an indoor object based on acoustic-electromagnetic intermodulation as in the first aspect of the present application.
The application provides an indoor object echo feature processing method and system based on acoustomagnetic intermodulation. In the process of carrying out feature processing, a classification algorithm is used for carrying out material attribute feature identification and marking on the indoor object. Compared with the traditional mode of processing the target echo characteristics based on machine learning, the method and the device increase the characterization degrees of different target characteristic information and improve the accuracy of target object judgment.
In the embodiments of the present application, it should be understood that the disclosed technical contents may be implemented in other ways. The above-described embodiments of the apparatus/system/method are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be an indirect coupling or communication connection through some interfaces, units or modules, and may be electrical or in other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.
It is apparent that various modifications and variations can be made to the embodiments of the present application by those skilled in the art without departing from the spirit and scope of the 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 than those listed in a claim. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims shall not be construed as limiting the scope.
Claims (7)
1. An indoor object echo feature processing method based on acoustic-electromagnetic intermodulation is characterized by comprising the following steps:
s1, enabling different objects to reflect acoustic modulation electromagnetic wave signals with different degrees of amplitude modulation and phase modulation based on an acoustic-electromagnetic intermodulation mechanism;
s2, extracting micro Doppler features and amplitude features of the acoustic modulation electromagnetic wave signals, and performing preliminary judgment and identification on the material corresponding to the object;
s3, fusing the preliminary judgment and identification results based on the micro Doppler characteristics and the amplitude characteristics by using a characteristic processing algorithm, and establishing a material characteristic sample library related to different objects; and
and S4, importing the material characteristic sample library into a machine learning model for classification training, so as to realize identification and marking of material attributes of different objects.
2. The method according to claim 1, wherein the micro-doppler signature is indicative of the instantaneous behavior of the acoustically modulated electromagnetic wave signal, the micro-doppler signature comprising in particular an instantaneous amplitude, an instantaneous frequency and an instantaneous phase.
3. The method according to claim 2, characterized in that said instantaneous amplitude is expressed in particular by:
the expression of the instantaneous frequency is specifically as follows:
the expression of the instantaneous phase is specifically:
wherein s (t) is the acoustically modulated electromagnetic wave signal,
is a hilbert transform of the acoustic modulated electromagnetic wave signal.
4. The method according to claim 1, wherein in the step S2, the extracted amplitude characteristic is the amplitude of the nth order acoustically modulated sideband signal of the acoustically modulated electromagnetic wave signal, and the expression specifically is as follows:
wherein p isnIs the amplitude, k, of the nth harmonic of the sound sourceRFIs the wave number, k, of the electromagnetic wave in free spaceAIs a periodic sound pressure pA(t) wave number, εrIs the relative dielectric constant, ρ, of the medium0Is the density of the medium, cAIs the speed of sound in the medium, λAIs the wavelength, lambda, of sound in the target materialRFIs the wavelength of the electromagnetic wave in free space.
5. The method according to claim 2, wherein the feature processing algorithm specifically employs support vectorization regression, and the machine learning model employs a random forest model.
6. An indoor object echo characteristic processing system based on acoustic-electromagnetic intermodulation, characterized by comprising:
the acoustic modulation electromagnetic wave signal excitation module is configured for enabling different objects to reflect acoustic modulation electromagnetic wave signals with different degrees of amplitude modulation and phase modulation based on an acoustic electromagnetic intermodulation mechanism;
the acoustic electromagnetic echo characteristic extraction and discrimination module is configured to extract micro Doppler characteristics and amplitude characteristics of the acoustic modulation electromagnetic wave signals and perform preliminary judgment and identification on the material corresponding to the object;
a material characteristic sample library creating module configured to fuse preliminary judgment recognition results based on the micro-doppler characteristic and the amplitude characteristic by using a characteristic processing algorithm, and create a material characteristic sample library about different objects;
and the object material attribute identification and marking module is configured for importing the material characteristic sample library into a machine learning model for classification training, so that identification and marking of different material attributes of the object are realized.
7. A computer-readable storage medium storing a computer program which, when executed by a processor, implements the method of any one of claims 1-5.
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