CN116702015B - Signal processing method, device, unmanned aerial vehicle and computer readable storage medium - Google Patents

Signal processing method, device, unmanned aerial vehicle and computer readable storage medium Download PDF

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CN116702015B
CN116702015B CN202310993104.6A CN202310993104A CN116702015B CN 116702015 B CN116702015 B CN 116702015B CN 202310993104 A CN202310993104 A CN 202310993104A CN 116702015 B CN116702015 B CN 116702015B
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object sub
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CN116702015A (en
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陈彦
张宾宾
张东恒
胡洋
孙启彬
吴曼青
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University of Science and Technology of China USTC
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/414—Discriminating targets with respect to background clutter
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2131—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on a transform domain processing, e.g. wavelet transform
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

The invention provides a signal processing method, a signal processing device, an unmanned aerial vehicle and a computer readable storage medium, which can be applied to the technical field of signal processing. The method comprises the following steps: classifying the original received signals to obtain a still object sub-signal and a biological object sub-signal, wherein the original received signals are signals received after the detection equipment detects objects in a detection range, the still object sub-signal comprises a plurality of still object sub-signal elements, and the objects comprise a still object and a biological object; calculating linear coefficients among a plurality of stationary object sub-signal elements; obtaining the motion state of the detection equipment according to the linear coefficient; under the condition that the motion state represents that the motion direction of the detection equipment is changed, calculating a compensation coefficient between a stationary object sub-signal and a biological object sub-signal; and updating the original received signal according to the compensation coefficient to obtain a biological characteristic signal representing the vital sign of the biological object.

Description

Signal processing method, device, unmanned aerial vehicle and computer readable storage medium
Technical Field
The present invention relates to the field of signal processing technologies, and in particular, to a signal processing method, a signal processing device, an unmanned aerial vehicle, and a computer readable storage medium.
Background
In a fire, when a person inhales toxic gases, the person quickly falls into a coma. If the comatose can not be found and treated in time, the life is threatened greatly. Therefore, it is critical to timely find the unconscious people in the fire disaster for the fire rescue. Due to the complex environment of a fire, unmanned aerial vehicles are often used for environment detection.
However, respiratory micro-movements of the comatose can be overwhelmed by the intense and complex unmanned movements, which makes it difficult to distinguish between the detected signals of the comatose and the detected signals of stationary objects, making it more difficult to obtain the vital sign conditions of the comatose.
Disclosure of Invention
In view of the above, the present invention provides a signal processing method, apparatus, unmanned aerial vehicle, and computer-readable storage medium.
According to a first aspect of the present invention, there is provided a signal processing method comprising:
classifying the original received signals to obtain a still object sub-signal and a biological object sub-signal, wherein the original received signals are signals received after the detection equipment detects objects in a detection range, the still object sub-signal comprises a plurality of still object sub-signal elements, and the objects comprise a still object and a biological object. A linear coefficient between a plurality of stationary object sub-signal elements is calculated. And obtaining the motion state of the detection equipment according to the linear coefficient. Under the condition that the motion state represents the change of the motion direction of the detection equipment, calculating a compensation coefficient between the stationary object sub-signal and the biological object sub-signal. And updating the original received signal according to the compensation coefficient to obtain a biological characteristic signal representing the vital sign of the biological object.
According to an embodiment of the present invention, classifying the original received signal to obtain a still object sub-signal and a biological object sub-signal includes:
and performing two-dimensional Fourier transform on the original received signal to obtain a spatial signal, wherein the spatial signal comprises a plurality of spatial signal elements. And determining a stationary object sub-signal element and a biological object sub-signal element from the spatial signal according to the correlation information among the plurality of spatial signal elements in the spatial signal. And determining a still object sub-signal and a biological object sub-signal respectively according to the still object sub-signal element and the biological object sub-signal element.
According to an embodiment of the present invention, determining a stationary object sub-signal element and a biological object sub-signal element from a spatial signal based on correlation information between a plurality of spatial signal elements in the spatial signal comprises:
and extracting phases corresponding to the spatial signal elements from the spatial signal according to a first preset condition. And calculating correlation information among a plurality of spatial signal elements in the spatial signal according to the phases corresponding to the spatial signal elements in the spatial signal. And determining a stationary object sub-signal element and a biological object sub-signal element from the spatial signal based on correlation information between the plurality of spatial signal elements.
According to an embodiment of the present invention, calculating a linear coefficient between stationary object sub-signal elements in a stationary object sub-signal includes:
the phase of the stationary object sub-signal element is determined. And calculating a linear coefficient between the plurality of still object sub-signal elements according to the respective phases of the plurality of still object sub-signal elements.
According to an embodiment of the invention, the biological object sub-signal comprises a plurality of biological object sub-signal elements. Wherein calculating the compensation coefficient between the stationary object sub-signal and the biological object sub-signal comprises:
the phase of the biological object sub-signal element is determined. The energy of the biological object sub-signal element is calculated according to the phase of the biological object sub-signal element. And calculating the energy of the sub-signal element of the static object according to the phase of the sub-signal element of the static object. And calculating the similarity between the element energy of the stationary object sub-signal and the element energy of the biological object sub-signal to obtain an element similarity result. And determining a target biological object sub-signal element and a target static object sub-signal element from the biological object sub-signal elements and the static object sub-signal elements according to the element similarity result with the highest similarity in the element similarity results. And obtaining a compensation coefficient according to the ratio between the target biological object sub-signal element and the target stationary object sub-signal element.
According to an embodiment of the invention, the original received signal comprises a signal delay comprising a probing device delay.
According to an embodiment of the present invention, updating an original received signal according to a compensation coefficient, obtaining a biometric signal includes: the biometric signal is calculated using the following formula:
y new(t) representing the biometric signal, y h(t) Representing the original received signal, the received signal,representing the static compensation signal,representing the amplitude of the kth stationary object sub-signal, A h Represents the energy of the target biological object element, eta represents the compensation coefficient,representing the time delay of the detection device->Representing biological object delay->Representing the kth stationary object sub-signal delay caused by the detection device,/for>Representing the initial time delay of the biological object, +.>Representing the initiation of the kth stationary objectTime delay j represents complex unit, f c Representing frequency, e represents an exponential function.
A second aspect of the present invention provides a signal processing apparatus comprising: the device comprises a classification module, a first calculation module, a first acquisition module, a second calculation module and a second acquisition module.
The classification module is used for classifying the original received signals to obtain a static object sub-signal and a biological object sub-signal, wherein the original received signals are signals received after the detection equipment detects the object in the detection range, the static object sub-signal comprises a plurality of static object sub-signal elements, and the object comprises a static object and a biological object. And the first calculation module is used for calculating the linear coefficients among the plurality of stator object sub-signal elements. The first obtaining module is used for obtaining the running state of the detection equipment according to the linear coefficient. And the second calculation module is used for calculating the compensation coefficient between the stationary object sub-signal and the biological object sub-signal under the condition that the motion state represents the change of the motion direction of the detection equipment. And a second obtaining module for updating the original received signal according to the compensation coefficient to obtain a biological characteristic signal representing vital signs of the biological object.
A third aspect of the invention provides a drone comprising:
and the detection equipment is used for detecting the object in the detection range to obtain an original received signal. And the signal processing device is used for processing the original received signal obtained by the detection equipment according to a signal processing method to obtain a biological characteristic signal representing vital signs of the biological object.
A fourth aspect of the invention also provides a computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the above method.
According to the signal processing method, the signal processing device, the unmanned aerial vehicle and the computer readable storage medium, the original received signals are classified to obtain the stationary object sub-signals and the biological object sub-signals, and the linear coefficients among a plurality of stationary object sub-signal elements are calculated to obtain the motion state of the detection equipment. And calculating a compensation coefficient between the stationary object sub-signal and the biological object sub-signal when the motion state of the detection equipment changes, and updating the original received signal according to the compensation coefficient so as to obtain a biological characteristic signal. Therefore, the technical problem of inaccurate distinction between the stationary object sub-signals and the biological object sub-signals in the related technology is at least partially overcome, and the accurate acquisition of the biological characteristic signals is realized.
Drawings
The foregoing and other objects, features and advantages of the invention will be apparent from the following description of embodiments of the invention with reference to the accompanying drawings, in which:
fig. 1 shows a flow chart of a signal processing method according to an embodiment of the invention;
FIG. 2 shows a schematic diagram of the results of detection by a target detector according to an embodiment of the invention;
FIG. 3 is a schematic diagram showing the result of determining stationary object sub-signal elements and biological object sub-signal elements from a spatial signal based on correlation information between a plurality of spatial signal elements in the spatial signal according to an embodiment of the present invention;
FIG. 4 is a diagram showing the result of updating an original received signal according to a compensation factor to obtain a biometric signal according to an embodiment of the present invention;
fig. 5 shows a block diagram of a signal processing apparatus according to an embodiment of the present invention;
fig. 6 shows a schematic representation of a drone according to an embodiment of the present invention;
fig. 7 shows a block diagram of an electronic device adapted to implement a signal processing method according to an embodiment of the invention.
Detailed Description
Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. It should be understood that the description is only illustrative and is not intended to limit the scope of the invention. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the present invention.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
In the technical scheme of the invention, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing, applying and the like of the personal information of the user all accord with the regulations of related laws and regulations, necessary security measures are adopted, and the public order harmony is not violated.
In the technical scheme of the invention, the processes of data acquisition, collection, storage, use, processing, transmission, provision, disclosure, application and the like all conform to the regulations of related laws and regulations, necessary security measures are adopted, and the public order harmony is not violated.
In fire rescue, it is very necessary to rescue trapped people in time. But the high temperature, toxic gas and confusing fire scene bring great difficulty and danger to search and rescue. Therefore, it is very necessary to develop a mechanical fire based search and rescue system. The search and rescue system based on the unmanned aerial vehicle has good flexibility and is widely applied. The unmanned aerial vehicle's search and rescue system often is equipped with vision camera and laser radar, can realize good performance, but because the dense smoke density that the conflagration produced is great, can't be applied to the conflagration scene. While recent advances in millimeter wave radar have led to its widespread use in many sensing applications. Millimeter wave radar has some unique characteristics, such as being non-invasive and capable of operating under dense smoke and high temperature conditions. Therefore, it is a natural idea to give a search and rescue system based on an unmanned aerial vehicle millimeter wave perception capability.
However, it is not easy to build such a system, and the main challenge is how to distinguish between reflected signals of trapped persons and reflected signals of stationary objects in complex combustion scenes. In particular, the reflection of stationary objects in the environment is typically much stronger than the reflection of humans, which makes it difficult to detect humans from the original radar echoes. Since human respiration affects the propagation of millimeter wave signals, the reflected signal from stationary objects does not change over time. Therefore, the existing rescue radar system widely utilizes the signal propagation distinction between human respiration and stationary objects for trapped person detection. However, in a fire, the respiratory micro-movements of the comatose can be overwhelmed by the intense and complex movements of the unmanned aerial vehicle, which makes it difficult to distinguish between stationary comatose and stationary objects, and the vital sign conditions of the comatose cannot be perceived.
In view of this, an embodiment of the present invention provides a signal processing method, which classifies an original received signal to obtain a still object sub-signal and a biological object sub-signal, where the original received signal is a signal received after a detection device detects an object in a detection range, the still object sub-signal includes a plurality of still object sub-signal elements, and the object includes a still object and a biological object. Calculating linear coefficients among a plurality of stationary object sub-signal elements; and obtaining the motion state of the detection equipment according to the linear coefficient. Under the condition that the motion state represents the change of the motion direction of the detection equipment, calculating a compensation coefficient between the stationary object sub-signal and the biological object sub-signal. And updating the original received signal according to the compensation coefficient to obtain a biological characteristic signal representing the vital sign of the biological object.
Fig. 1 shows a flow chart of a signal processing method according to an embodiment of the invention.
As shown in fig. 1, the signal processing method of this embodiment includes operations S110 to S150.
In operation S110, the original received signal is classified to obtain a stationary object sub-signal and a biological object sub-signal.
In operation S120, a linear coefficient between a plurality of stationary object sub-signal elements is calculated.
In operation S130, a motion state of the detection device is obtained according to the linear coefficient.
In operation S140, in case that the motion state represents a change in the motion direction of the detection device, a compensation coefficient between the stationary object sub-signal and the biological object sub-signal is calculated.
In operation S150, the original received signal is updated according to the compensation coefficient, resulting in a biometric signal characterizing vital signs of the biological subject.
According to an embodiment of the present invention, the original received signal is a signal received after the detection device detects an object within a detection range, the still object sub-signal includes a plurality of still object sub-signal elements, and the object includes a still object and a biological object.
Taking a continuous frequency modulated signal as an example, the original received signal at time t can be expressed as shown in equation (1) according to an embodiment of the present invention.
(1)
Where y (t) is the original received signal, A is the energy of the reflected signal, j represents the complex unit, f c Representing frequency, e represents an exponential function. τ represents the delay of signal propagation as shown in equation (2).
(2)
Where d is the radial distance between the radar and the object and c is the speed of light.
According to an embodiment of the invention, the linear coefficients are indicative of the relative phase relationship between the plurality of stationary object sub-signal elements. The linear coefficient is not changed, and the position of the detection device relative to the static object is not changed; the linear coefficient changes and the position of the detection device relative to the stationary object changes.
According to an embodiment of the invention, the movement state of the detection device may be whether the direction of movement has changed.
According to an embodiment of the invention, the compensation coefficient characterizes a parametric relationship between the stationary object sub-signal and the biological object sub-signal. For example, the ratio of the still object sub-signal phase to the biological object sub-signal phase is 0.5, and the compensation coefficient between the still object sub-signal and the biological object sub-signal may be 0.5. For example, the energy ratio of the still object sub-signal to the biological object sub-signal is 0.8, and the compensation coefficient between the still object sub-signal and the biological object sub-signal may be 0.8.
According to an embodiment of the present invention, classifying the original received signal to obtain a still object sub-signal and a biological object sub-signal includes:
and performing two-dimensional Fourier transform on the original received signal to obtain a spatial signal, wherein the spatial signal comprises a plurality of spatial signal elements. And determining a stationary object sub-signal element and a biological object sub-signal element from the spatial signal according to the correlation information among the plurality of spatial signal elements in the spatial signal. And respectively determining a still object sub-signal and a biological object sub-signal according to the still object sub-signal element and the biological object sub-signal element.
According to the embodiment of the invention, after the original received signal Y (t) is subjected to two-dimensional fourier transform, a spatial domain signal Y is obtained, as shown in formula (3).
(3)
Wherein g A 、g D Is the number of points at which the angle, distance of the signal may exist. θ gA Expressed in g A Angle of point gamma gD Expressed in g D The distance between the points is 1 and 2 … … T time points in total.
According to an embodiment of the present invention, determining a stationary object sub-signal element and a biological object sub-signal element from a spatial signal based on correlation information between a plurality of spatial signal elements in the spatial signal comprises:
and extracting phases corresponding to the spatial signal elements from the spatial signal according to a first preset condition. And calculating correlation information among a plurality of spatial signal elements in the spatial signal according to the phases corresponding to the spatial signal elements in the spatial signal. And determining the stationary object sub-signal element and the biological object sub-signal element from the spatial signal according to the correlation information among the plurality of spatial signal elements.
According to an embodiment of the present invention, the first preset condition may be that the energy of the possible signal is greater than or equal to a threshold value, and the phase corresponding to the spatial signal element is extracted from the signal in the spatial signal YWhere D represents the number of extracted signal points.
And calculating the correlation information among a plurality of spatial signal elements in the spatial signal according to the phases corresponding to the spatial signal elements in the spatial signal, as shown in a formula (4).
(4)
Wherein,ρ s1,s2 representing phase P S1 And phase P S2 And pearson coefficients therebetween. n represents the point of time at which,the average value of the phase of signal point 1 at the points of time 1 to n is shown. The signal with the smallest correlation with other signals can be determined as the biological object sub-signal.
According to an embodiment of the present invention, calculating a linear coefficient between stationary object sub-signal elements in a stationary object sub-signal includes:
the phase of the stationary object sub-signal element is determined. A linear coefficient between the plurality of still object sub-signal elements is calculated based on the respective phases of the plurality of still object sub-signal elements.
Calculating the respective phases of two stationary object sub-signal elements as P S1 、P S2 Linear coefficient η between two stationary object sub-signal elements s1,s2 As shown in equation (5).
(5)
Whether the movement direction of the detecting device is changed is determined according to whether the linear coefficient is changed.
According to an embodiment of the invention, the biological object sub-signal comprises a plurality of biological object sub-signal elements. Wherein calculating the compensation coefficient between the stationary object sub-signal and the biological object sub-signal comprises: the phase of the biological object sub-signal element is determined. The energy of the biological object sub-signal element is calculated according to the phase of the biological object sub-signal element. And calculating the energy of the sub-signal element of the static object according to the phase of the sub-signal element of the static object. And calculating the similarity between the element energy of the stationary object sub-signal and the element energy of the biological object sub-signal to obtain an element similarity result. And determining a target biological object sub-signal element and a target static object sub-signal element from the biological object sub-signal elements and the static object sub-signal elements according to the element similarity result with the highest similarity in the element similarity results. And obtaining a compensation coefficient according to the ratio between the target biological object sub-signal element and the target static object sub-signal element.
The phase PS of the stationary object sub-signal element and the phase Ph of the biological object sub-signal element are fourier transformed and then their energies are calculated as shown in equation (6).
（6）
Wherein As represents a still object sub-signal elementEnergy, A h Representing biological object sub-signal element energy. The FFT represents the fourier transform. Based on the still object sub-signal element energy and the biological object sub-signal element energy, as and A are obtained h Energy distribution in the frequency domain.
And calculating the similarity between the element energy of the stationary object sub-signal and the element energy of the biological object sub-signal to obtain an element similarity result. And then selecting a window with highest frequency energy similarity through a sliding window, and determining a target biological object sub-signal element and a target static object sub-signal element as shown in a formula (7). And obtaining a compensation coefficient according to the ratio between the target biological object sub-signal element and the target stationary object sub-signal element.
(7)
Wherein m represents the mth sliding window. By calculating the frequency energy of the target biological object sub-signal element in the mth sliding windowFrequency energy of the target stator signal element +.>The ratio results in a compensation coefficient as shown in equation (8).
(8)
If the influence of the biological object and the detection device on the original received signal is taken into account, the original received signal is as shown in formula (9).
（9）
Wherein,representing the time delay of the detection device,/->Representing the time delay of the biological object,representing an initial time delay of the biological object.
According to the embodiment of the invention, the original received signal is updated according to the compensation coefficient to obtain the biological characteristic signal. The biological characteristic signal can be calculated by multiplying the phase of the stationary object sub-signal element by the compensation coefficient as shown in the following formula (10).
（10）
Further calculation results in equation (11).
（11）
y new(t) Representing the biometric signal, y h(t) Representing the said original received signal(s),representing the still compensation signal, < >>Representing the amplitude of the kth stationary object sub-signal, A h Represents the target biological object element energy, eta represents the compensation coefficient,/->Representing the kth stationary object sub-signal delay caused by the detection device,/for>Representing the initial time delay of the kth stationary object.
From equation (11), the time delay of the detection device in the original received signal is eliminated, and the biological characteristic signal is obtained.
According to the embodiment of the invention, the frequency modulation continuous wave radar is fixed on the unmanned aerial vehicle, the signal is transmitted and received by adopting frequency modulation continuous waves, the initial frequency is 77GHz, the number of frequency points is 128, and the bandwidth is 3GHz. The unmanned aerial vehicle enters a fire scene to start searching, and detects an object existing on the scene, and the phase corresponding to the airspace signal element is extracted from the airspace signal according to a first preset condition through the target detector, so that a result obtained through detection of the target detector is obtained. As shown in fig. 2, however, it is not possible to directly see whether a biological Object exists among three objects, object1, object2, object3 in the figure, due to the movement of the drone. The stationary object sub-signal element and the biological object sub-signal element can be determined from the spatial signal according to the correlation information among a plurality of spatial signal elements in the spatial signal. The correlation analysis can be performed on the plurality of spatial signal elements through the pearson coefficients, so that correlation information among the plurality of spatial signal elements is obtained. Through the stationary object sub-signal element, a stationary object in the detection site can be obtained. The biological object in the detection site can be obtained through the biological object sub-signal element. As shown in fig. 3, the target object 301 may be determined as a biological object, that is, a victim, and the target object 302 and the target object 303 may be determined as still objects, wherein the target object 301 may be understood as object1 in fig. 2, the target object 302 may be understood as object2 in fig. 2, and the target object 303 may be understood as object3 in fig. 2. In order to monitor vital signs of a biological object, the original received signal is updated according to the compensation coefficient to obtain a biological characteristic signal. As shown in fig. 4, the original received signal can eliminate the motion of the unmanned aerial vehicle through the algorithm of the invention, and the respiratory signal of the comatose can be obtained. Wherein the comatose is a biological subject and the respiratory signal of the comatose is a biological characteristic signal. In conclusion, the invention can search and monitor life of the comatose in the fire disaster in the field experiment, and verifies the effectiveness of the invention in searching and monitoring breath of the comatose in the indoor fire disaster environment.
Based on the signal processing method, the invention also provides a signal processing device. The device will be described in detail below in connection with fig. 5.
Fig. 5 shows a block diagram of a signal processing apparatus according to an embodiment of the present invention.
As shown in fig. 5, a signal processing apparatus 500 of this embodiment includes a classification module 510, a first calculation module 520, a first obtaining module 530, a second calculation module 540, and a second obtaining module 550.
The classification module 510 is configured to classify the original received signal to obtain a stationary object sub-signal and a biological object sub-signal. In an embodiment, the classification module 510 may be configured to perform the operation S110 described above, which is not described herein.
The first calculation module 520 is configured to calculate a linear coefficient between a plurality of sub-signal elements of the stator object. In an embodiment, the first computing module 520 may be configured to perform the operation S120 described above, which is not described herein.
A first obtaining module 530 is configured to obtain the operation state of the detecting device according to the linear coefficient. In an embodiment, the first obtaining module 530 may be used to perform the operation S130 described above, which is not described herein.
The second calculating module 540 is configured to calculate a compensation coefficient between the stationary object sub-signal and the biological object sub-signal in a case where the motion state represents a change in the motion direction of the detecting device. In an embodiment, the second computing module 540 may be used to perform the operation S140 described above, which is not described herein.
A second obtaining module 550 is configured to update the original received signal according to the compensation coefficient, and obtain a biometric signal characterizing a vital sign of the biological object. In an embodiment, the second obtaining module 550 may be used to perform the operation S150 described above, which is not described herein.
Any of the classification module 510, the first calculation module 520, the first obtaining module 530, the second calculation module 540, and the second obtaining module 550 may be combined in one module to be implemented, or any of the modules may be split into a plurality of modules according to an embodiment of the present invention. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to an embodiment of the invention, at least one of the classification module 510, the first calculation module 520, the first obtaining module 530, the second calculation module 540, and the second obtaining module 550 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or as hardware or firmware in any other reasonable way of integrating or packaging the circuitry, or as any one of or a suitable combination of three of software, hardware, and firmware. Alternatively, at least one of the classification module 510, the first calculation module 520, the first obtaining module 530, the second calculation module 540, and the second obtaining module 550 may be at least partially implemented as a computer program module, which when executed, may perform the respective functions.
There is also provided, in accordance with an embodiment of the present invention, a drone including: detection device and signal processing means.
And the detection equipment is used for detecting the object in the detection range to obtain an original received signal. And the signal processing device is used for processing the original received signal obtained by the detection equipment according to the signal processing method to obtain a biological characteristic signal representing the vital sign of the biological object.
Fig. 6 shows a schematic representation of a drone according to an embodiment of the present invention.
As shown in fig. 6, the unmanned aerial vehicle 600 according to the embodiment of the invention includes a detection device 601, a signal processing apparatus 602.
The detection device 601 is configured to detect an object in a detection range, so as to obtain an original received signal.
The signal processing device 602 is configured to process the original received signal obtained by the detection device 601 according to the signal processing method provided by the embodiment of the present invention, and obtain a biological characteristic signal representing a vital sign of a biological object.
The detection device 601 may be a millimeter wave radar and the signal processing means 602 may be a processor of the drone.
Fig. 7 shows a block diagram of an electronic device adapted to implement a signal processing method according to an embodiment of the invention.
As shown in fig. 7, the electronic device 700 according to the embodiment of the present invention further includes a processor 701 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. The processor 701 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 701 may also include on-board memory for caching purposes. The processor 701 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flow according to an embodiment of the invention.
In the RAM 703, various programs and data necessary for the operation of the electronic apparatus 700 are stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. The processor 701 performs various operations of the method flow according to an embodiment of the present invention by executing programs in the ROM 702 and/or the RAM 703. Note that the program may be stored in one or more memories other than the ROM 702 and the RAM 703. The processor 701 may also perform various operations of the method flow according to embodiments of the present invention by executing programs stored in the one or more memories.
According to an embodiment of the invention, the electronic device 700 may further comprise an input/output (I/O) interface 705, the input/output (I/O) interface 705 also being connected to the bus 704. The electronic device 700 may also include one or more of the following components connected to an input/output (I/O) interface 705: an input section 706 including a keyboard, a mouse, and the like; an output portion 707 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. The drive 710 is also connected to an input/output (I/O) interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read therefrom is mounted into the storage section 708 as necessary.
The present invention also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present invention.
According to embodiments of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to an embodiment of the invention, the computer-readable storage medium may include ROM 702 and/or RAM 703 and/or one or more memories other than ROM 702 and RAM 703 described above.
Embodiments of the present invention also include a computer program product comprising a computer program containing program code for performing the method shown in the flowcharts. The program code means for causing a computer system to carry out the signal processing method provided by the embodiment of the invention when the computer program product is run on the computer system.
The above-described functions defined in the system/apparatus of the embodiment of the present invention are performed when the computer program is executed by the processor 701. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the invention.
In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed over a network medium in the form of signals, downloaded and installed via the communication section 709, and/or installed from the removable medium 711. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. The above-described functions defined in the system of the embodiment of the present invention are performed when the computer program is executed by the processor 701. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the invention.
According to embodiments of the present invention, program code for carrying out computer programs provided by embodiments of the present invention may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or in assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that the features recited in the various embodiments of the invention and/or in the claims may be combined in various combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the invention. In particular, the features recited in the various embodiments of the invention and/or in the claims can be combined in various combinations and/or combinations without departing from the spirit and teachings of the invention. All such combinations and/or combinations fall within the scope of the invention.
The embodiments of the present invention are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the invention is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the invention, and such alternatives and modifications are intended to fall within the scope of the invention.

Claims (8)

1. A signal processing method, comprising:
classifying an original received signal to obtain a static object sub-signal and a biological object sub-signal, wherein the original received signal is a signal received by a detection device after detecting an object in a detection range, the static object sub-signal comprises a plurality of static object sub-signal elements, and the object comprises a static object and a biological object;
calculating linear coefficients among the plurality of stationary object sub-signal elements, wherein the linear coefficients are used for representing relative phase relations among the plurality of stationary object sub-signal elements;
obtaining the motion state of the detection equipment according to the linear coefficient;
calculating a compensation coefficient between the stationary object sub-signal and the biological object sub-signal under the condition that the motion state represents the change of the motion direction of the detection equipment, wherein the compensation coefficient represents the parameter relationship between the stationary object sub-signal and the biological object sub-signal; and
updating the original received signal according to the compensation coefficient to obtain a biological characteristic signal representing vital signs of the biological object;
the original received signal comprises a signal delay, and the signal delay comprises a detection device delay;
the updating the original received signal according to the compensation coefficient, the obtaining a biometric signal characterizing vital signs of the biological subject comprises: the biometric signal is calculated using the following formula:
，
y new(t) representing the biometric signal, y h(t) Representing the said original received signal(s),representing the still compensation signal, < >>Representing the kth still objectAmplitude of sub-signal, A h Represents the target biological object element energy, eta represents the compensation coefficient, and->Representing the time delay of the detection device,/->Representing the time delay of the biological object,representing the kth stationary object sub-signal delay caused by said detecting device,/for>Representing the initial time delay of the biological object, +.>Represents the initial time delay of the kth stationary object, j represents complex units, f c Representing frequency, e represents an exponential function.
2. The method of claim 1, wherein classifying the original received signal to obtain a still object sub-signal and a biological object sub-signal comprises:
performing two-dimensional Fourier transform on the original received signal to obtain a spatial signal, wherein the spatial signal comprises a plurality of spatial signal elements;
determining the stationary object sub-signal element and the biological object sub-signal element from the spatial signal according to correlation information among a plurality of spatial signal elements in the spatial signal; and
and respectively determining the still object sub-signal and the biological object sub-signal according to the still object sub-signal element and the biological object sub-signal element.
3. The method of claim 2, wherein said determining said still object sub-signal element and said biological object sub-signal element from said spatial signal based on correlation information between a plurality of said spatial signal elements in said spatial signal comprises:
extracting phases corresponding to the spatial signal elements from the spatial signal according to a first preset condition;
calculating correlation information among a plurality of spatial signal elements in the spatial signal according to phases corresponding to the spatial signal elements in the spatial signal; and
and determining the stationary object sub-signal element and the biological object sub-signal element from the spatial domain signal according to correlation information among a plurality of spatial domain signal elements.
4. The method of claim 1, wherein said calculating linear coefficients between a plurality of said still object sub-signal elements comprises:
determining the phase of the stationary object sub-signal element; and
and calculating the linear coefficient among the plurality of the stationary object sub-signal elements according to the respective phases of the plurality of the stationary object sub-signal elements.
5. The method of claim 1, wherein the biological object sub-signal comprises a plurality of biological object sub-signal elements;
wherein said calculating a compensation coefficient between said still object sub-signal and said biological object sub-signal comprises:
determining the phase of the biological object sub-signal element;
calculating the energy of the biological object sub-signal element according to the phase of the biological object sub-signal element;
calculating the energy of the sub-signal element of the static object according to the phase of the sub-signal element of the static object;
calculating the similarity between the still object sub-signal element energy and the biological object sub-signal element energy to obtain an element similarity result;
determining a target biological object sub-signal element and a target static object sub-signal element from a plurality of biological object sub-signal elements and a plurality of static object sub-signal elements according to an element similarity result with highest similarity in the element similarity results; and
and obtaining the compensation coefficient according to the ratio between the target biological object sub-signal element and the target static object sub-signal element.
6. A signal processing apparatus, comprising:
the device comprises a classification module, a detection module and a detection module, wherein the classification module is used for classifying an original received signal to obtain a still object sub-signal and a biological object sub-signal, the original received signal is a signal received by the detection device after the detection of an object in a detection range, the still object sub-signal comprises a plurality of still object sub-signal elements, and the object comprises a still object and a biological object;
a first calculation module, configured to calculate a linear coefficient between a plurality of the stationary object sub-signal elements, where the linear coefficient is a characteristic of a relative phase relationship between the plurality of stationary object sub-signal elements;
the first obtaining module is used for obtaining the motion state of the detection equipment according to the linear coefficient;
the second calculation module is used for calculating a compensation coefficient between the static object sub-signal and the biological object sub-signal under the condition that the motion state represents the change of the motion direction of the detection equipment, and the compensation coefficient represents the parameter relationship between the static object sub-signal and the biological object sub-signal; and
the second obtaining module is used for updating the original received signal according to the compensation coefficient to obtain a biological characteristic signal representing vital signs of the biological object;
the original received signal comprises a signal delay, and the signal delay comprises a detection device delay;
the updating the original received signal according to the compensation coefficient, the obtaining a biometric signal characterizing vital signs of the biological subject comprises: the biometric signal is calculated using the following formula:
，
y new(t) representing the biometric signal, y h(t) Representing the said original received signal(s),representing the still compensation signal, < >>Representing the amplitude of the kth stationary object sub-signal, A h Represents the target biological object element energy, eta represents the compensation coefficient, and->Representing the time delay of the detection device,/->Representing the time delay of the biological object,representing the kth stationary object sub-signal delay caused by said detecting device,/for>Representing the initial time delay of the biological object, +.>Represents the initial time delay of the kth stationary object, j represents complex units, f c Representing frequency, e represents an exponential function.
7. An unmanned aerial vehicle, comprising:
the detection equipment is used for detecting the object in the detection range to obtain an original received signal;
signal processing means for processing the raw received signal obtained by the detection device according to the signal processing method of any of claims 1 to 5, resulting in a biometric signal characterizing the vital sign of the biological subject.
8. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method according to any of claims 1 to 5.
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