A signal processing method, system and related components of a microwave micro-motion sensor

Abstract

The application discloses a signal processing method, system and related components of a microwave micro-motion sensor, and the signal processing method comprises the following steps: acquiring an initial digital signal corresponding to a transmission signal; performing wavelet decomposition on the initial digital signal to obtain a plurality of decomposed components; removing the decomposed components corresponding to interference signals from all the decomposed components; reconstructing the remaining decomposed components to obtain a reconstructed signal; performing FFT operation on the reconstructed signal to obtain an FFT operation result; using a multi-channel complex band-pass filter to obtain a plurality of complex numbers corresponding to a plurality of preset FFT indexes from the FFT operation result; and determining whether the complex numbers exceed corresponding threshold values, and if so, determining that there is a living body in the current detection range. The application removes interference quantities from the initial digital signal by wavelet decomposition, and the obtained reconstructed signal is subjected to subsequent calculation, so that the detection of whether there is a living body in the current detection range is more accurate and is not affected by the interference signals.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of microwave micro-motion sensor, in particular to a signal processing method and system of microwave micro-motion sensor and related components. BACKGROUND

[0002] Compared with traditional pyroelectric infrared detection technology, microwave detection technology has the advantages of hidden installation and being not affected by environmental temperature, and is widely used in various industries, mainly for detecting whether there is a person in the area, and then realizing the purpose of sensing light and sensing whether the area is occupied.

[0003] In frequency selection, considering that the lower the frequency, the less likely the signal is reflected, and the higher the frequency, the higher the requirement for the production and manufacturing process of the equipment, 5.725GHz-5.875GHz is usually selected as the frequency range of microwave detection.

[0004] However, the frequency is close to the frequency of WiFi signal, and mutual influence exists when coexisting, if the microwave detection is applied in the WiFi environment, the detection result will be disturbed by the WiFi signal, and the accuracy of the detection result is reduced.

[0005] Therefore, how to provide a solution to the above technical problems is a problem that needs to be solved by the technical personnel in the field at present. SUMMARY

[0006] Therefore, the purpose of the present application is to provide a signal processing method and system of microwave micro-motion sensor for reducing interference and related components. The specific scheme is as follows:

[0007] A signal processing method of a microwave micro-motion sensor, comprising:

[0008] obtaining an initial digital signal corresponding to a transmitted signal;

[0009] wavelet-decomposing the initial digital signal to obtain a plurality of decomposed components, removing the decomposed components corresponding to interference signals from all the decomposed components, reconstructing using the remaining decomposed components to obtain a reconstructed signal;

[0010] performing FFT operation on the reconstructed signal to obtain an FFT operation result;

[0011] using a multi-channel complex band-pass filter to obtain a plurality of complex numbers corresponding to a plurality of preset FFT indexes from the FFT operation result;

[0012] determining whether the complex number exceeds the corresponding threshold value, if yes, determining that there is a living body in the current detection range.

[0013] Preferably, before the judging whether the complex number exceeds the corresponding threshold, the method further comprises:

[0014] filtering the complex numbers by using a multi-channel complex adaptive filter, wherein parameters of the multi-channel complex adaptive filter are matched with the micro-motion feature of the living body.

[0015] Preferably, the micro-motion feature is a breathing feature of the living body.

[0016] Preferably, before the judging whether the complex number exceeds the corresponding threshold, the method further comprises:

[0017] performing periodic multi-channel complex autocorrelation processing on the complex numbers.

[0018] Preferably, the process of obtaining the initial digital signal corresponding to the transmission signal comprises:

[0019] obtaining a transmission signal and a reflection signal corresponding to the transmission signal;

[0020] multiplying the transmission signal and the reflection signal and passing the product through a band-pass filter to obtain an intermediate frequency signal;

[0021] performing ADC sampling on the intermediate frequency signal to obtain an initial digital signal.

[0022] Preferably, the process of judging whether the complex number exceeds the corresponding threshold, if yes, determining that the living body exists in the current detection range, comprises:

[0023] judging whether the complex number exceeds the corresponding threshold;

[0024] if yes, judging whether the FFT index corresponding to the complex number that exceeds the corresponding threshold is only a boundary index in the plurality of preset FFT indexes;

[0025] if yes, determining a detection distance corresponding to the boundary index;

[0026] judging whether the detection distance is located in the current detection range;

[0027] if yes, determining that the living body exists in the current detection range.

[0028] Preferably, the process of determining the detection distance corresponding to the boundary index comprises:

[0029] respectively calculating absolute values of all the complex numbers;

[0030] The quadratic curve interpolation is performed on all the absolute values and the corresponding FFT indexes to obtain an interpolation result; the interpolation result includes the relationship between the FFT index and the detection distance;

[0031] The detection distance corresponding to the boundary index is determined according to the difference result.

[0032] Correspondingly, the application further discloses a signal processing system of a microwave micro-motion sensor, which comprises:

[0033] An acquisition module is configured to acquire an initial digital signal corresponding to a transmission signal;

[0034] A wavelet processing module is configured to perform wavelet decomposition on the initial digital signal to obtain a plurality of decomposed components, remove the decomposed components corresponding to interference signals from all the decomposed components, and perform reconstruction on the remaining decomposed components to obtain a reconstructed signal;

[0035] An FFT processing module is configured to perform FFT operation on the reconstructed signal to obtain an FFT operation result;

[0036] A first filtering module is configured to use a multi-channel complex band-pass filter to obtain a plurality of complex numbers corresponding to a plurality of preset FFT indexes from the FFT operation result;

[0037] A judging module is configured to judge whether the complex numbers exceed corresponding threshold values, and if yes, determine that there is a living body in a current detection range.

[0038] Correspondingly, the application further discloses a signal processing device of a microwave micro-motion sensor, which comprises:

[0039] A memory is configured to store a computer program;

[0040] A processor is configured to execute the computer program to implement the steps of the signal processing method of the microwave micro-motion sensor according to any one of the preceding embodiments.

[0041] Correspondingly, the application further discloses a readable storage medium, wherein the readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the signal processing method of the microwave micro-motion sensor according to any one of the preceding embodiments.

[0042] The application discloses a signal processing method of a microwave micro-motion sensor, and comprises the following steps: acquiring an initial digital signal corresponding to a transmitting signal; performing wavelet decomposition on the initial digital signal to obtain a plurality of decomposed components; removing the decomposed components corresponding to interference signals from all the decomposed components; reconstructing by using the remaining decomposed components to obtain a reconstructed signal; performing FFT operation on the reconstructed signal to obtain an FFT operation result; obtaining a plurality of complex numbers corresponding to a plurality of preset FFT indexes from the FFT operation result by using a multi-channel complex band-pass filter; and determining whether the complex numbers exceed corresponding threshold values, and if yes, determining that there is a living body in a current detection range. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only constitute a part of the embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on the provided drawings.

[0044] Figure 1 The step flow chart of the signal processing method of the microwave micro-motion sensor in the embodiment of the present application;

[0045] Figure 2 The step flow chart of the specific signal processing method of the microwave micro-motion sensor in the embodiment of the present application;

[0046] Figure 3 The structure distribution diagram of the signal processing system of the microwave micro-motion sensor in the embodiment of the present application. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0048] The current microwave detection usually selects 5.725GHz-5.875GHz as the frequency range of the microwave detection, but the frequency is close to the frequency of the WiFi signal, and the two coexist and influence each other. If the microwave detection is applied in the WiFi environment, the detection result will be interfered by the WiFi signal, and the accuracy of the detection result is reduced.

[0049] The application removes the interference quantity from the initial digital signal by wavelet decomposition, and the reconstructed signal obtained is used for subsequent calculation, so that the detection of whether there is a living body in the current detection range is more accurate.

[0050] The embodiment of the application discloses a signal processing method of a microwave micro-motion sensor, referring to Figure 1 as shown, comprising:

[0051] S1: acquiring an initial digital signal corresponding to a transmission signal;

[0052] It can be understood that the process of acquiring the initial digital signal corresponding to the transmission signal in step S1 specifically comprises:

[0053] acquiring the transmission signal and the reflection signal corresponding thereto;

[0054] multiplying the transmission signal and the reflection signal and passing through a band-pass filter to obtain an intermediate frequency signal;

[0055] performing ADC sampling on the intermediate frequency signal to obtain the initial digital signal.

[0056] S2: performing wavelet decomposition on the initial digital signal to obtain a plurality of decomposed components, removing the decomposed component corresponding to the interference signal from all the decomposed components, and reconstructing by using the remaining decomposed components to obtain a reconstructed signal;

[0057] It can be understood that the interference signal herein mainly refers to a WiFi signal that interferes with the microwave micro-motion sensing signal, and after wavelet decomposition of the initial digital signal, the decomposed component corresponding to the interference signal can be removed from the plurality of decomposed components because the specific non-continuous frequency component of the interference signal is different from the microwave micro-motion sensing signal, and then the remaining decomposed components are reconstructed, so that the influence of the interference signal in the reconstructed signal is greatly reduced.

[0058] S3: performing FFT operation on the reconstructed signal to obtain an FFT operation result;

[0059] S4: using a multi-channel complex band-pass filter to obtain a plurality of complex numbers corresponding to a plurality of preset FFT indexes from the FFT operation result;

[0060] It can be understood that the FFT operation result includes complex numbers corresponding to the FFT indexes from small to large, and the FFT indexes from small to large represent the distances from near to far, and the numerical value of the corresponding complex number represents the strength of the reflection energy of the corresponding distance. Only the complex numbers corresponding to the required FFT indexes, that is, the complex numbers corresponding to the preset FFT indexes of the current detection range, are taken, for example, using a bandwidth of 150MHz, and the resolution is about 1m, only the distance of 6m needs to be detected, the preset FFT index is 0-6, and the complex numbers are filtered through a multi-channel complex band-pass filter, the number of channels of the filter is the same as the number of preset FFT indexes, and the static signal and other high-frequency interference are filtered out, and the complex number signal similar to the action frequency change of the living body is reserved.

[0061] S5: determining whether there is a complex number exceeding the corresponding threshold value, and if so, determining that there is a living body in the current detection range.

[0062] It can be understood that the living body here is generally a human body, and the corresponding action frequency includes micro-motions such as breathing.

[0063] The embodiment of the application discloses a signal processing method of a microwave micro-motion sensor, including: obtaining an initial digital signal corresponding to a transmitted signal; wavelet-decomposing the initial digital signal to obtain a plurality of decomposed components, removing a decomposed component corresponding to an interference signal from all the decomposed components, reconstructing by using the remaining decomposed components to obtain a reconstructed signal; performing FFT operation on the reconstructed signal to obtain an FFT operation result; obtaining a plurality of complex numbers corresponding to a plurality of preset FFT indexes from the FFT operation result by using a multi-channel complex band-pass filter; and determining whether there is a complex number exceeding the corresponding threshold value, and if so, determining that there is a living body in the current detection range. The application removes the interference quantity from the initial digital signal by wavelet decomposition, and the obtained reconstructed signal is subjected to subsequent calculation, so that the detection of whether there is a living body in the current detection range is more accurate.

[0064] The embodiment of the application discloses a specific signal processing method of a microwave micro-motion sensor, and compared with the previous embodiment, the technical solution is further described and optimized. Specifically:

[0065] Before determining whether there is a complex number exceeding the corresponding threshold value, the method further includes:

[0066] Filtering the plurality of complex numbers by using a multi-channel complex adaptive filter; and the parameters of the multi-channel complex adaptive filter are matched with the micro-motion characteristics of the living body.

[0067] It can be understood that the micro-motion characteristics include but are not limited to the breathing characteristics of the living body.

[0068] It can be understood that in the traditional 24G, 60G radar detection, the amplitude of the complex signal change after the multi-channel complex band-pass filter is enough to identify the human body in sleep, but in the 5.8G frequency band, the electromagnetic environment is complex, the resolution is low, and other moving target interference is mixed, so the original judgment method cannot be used to judge the stationary human body. Therefore, a filter matched with the current measured micro-motion characteristics of the living body needs to be added to filter out or attenuate other signals except the micro-motion characteristics, and the filter and the multi-channel complex adaptive filter.

[0069] Further, before judging whether there is a complex number exceeding the corresponding threshold after filtering a plurality of complex numbers by the multi-channel complex adaptive filter, the method further includes:

[0070] Periodically performing multi-channel complex autocorrelation processing on the plurality of complex numbers.

[0071] It can be understood that autocorrelation processing can make a periodic signal appear a peak value in the processing result, and the size of the peak value can be used to determine whether there is a stationary living body in the current detection range. Generally, data of a period of time is continuously stored, and the length of the period of time generally exceeds two continuous breathing periods of a normal person. The data of each channel in the period of time constitutes a data block. After multi-channel complex autocorrelation calculation, if the amplitude peak value of a certain channel exceeds the set threshold, it is determined that there is a living body in the current detection range.

[0072] Further, considering that the available bandwidth of the current signal is narrow and the distance resolution is low, and the penetration of the 5.8G signal is strong, it is difficult to accurately determine whether the living body is located in the current detection range. If the target distance needs to be measured more accurately, the absolute value size of the plurality of complex numbers filtered according to the FFT operation result can be calculated, and the quadratic curve difference of the FFT index is calculated, so that a more accurate distance determination is obtained. Specifically, the process of determining whether there is a complex number exceeding the corresponding threshold, if yes, determining that there is a living body in the current detection range, includes:

[0073] determining whether there is a complex number exceeding the corresponding threshold;

[0074] if yes, determining whether the FFT index corresponding to the complex number exceeding the corresponding threshold is only a boundary index in the plurality of preset FFT indexes;

[0075] if yes, determining the detection distance corresponding to the boundary index;

[0076] determining whether the detection distance is located in the current detection range;

[0077] if yes, determining that there is a living body in the current detection range.

[0078] Specifically, for example, the preset FFT index corresponding to the current detection range is 5-19, and the boundary index is 5 and 19. If only the complex number corresponding to the FFT index of 5 and / or 9 exceeds the corresponding threshold, and the complex numbers of the FFT index of 6-18 do not exceed the threshold, the living body is located at the edge of the detection range, and it is necessary to determine more accurately, that is, it is necessary to further determine whether the living body is located in the detection range according to the detection distance corresponding to the boundary index.

[0079] Further, the process of determining the detection distance corresponding to the boundary index comprises:

[0080] respectively taking absolute values of all complex numbers;

[0081] using all absolute values and corresponding FFT indexes to perform quadratic curve interpolation to obtain an interpolation result; the interpolation result comprises the relationship between the FFT index and the detection distance;

[0082] determining the detection distance corresponding to the boundary index according to the difference result.

[0083] In combination with the above content, the specific step flow of the signal processing method in the embodiment is as shown in Figure 2 , comprising:

[0084] S11: obtaining an initial digital signal corresponding to a transmitted signal;

[0085] S12: performing wavelet decomposition on the initial digital signal to obtain a plurality of decomposed components, removing the decomposed component corresponding to the interference signal from all decomposed components, and using the remaining decomposed components to reconstruct to obtain a reconstructed signal;

[0086] S13: performing FFT operation on the reconstructed signal to obtain an FFT operation result;

[0087] S14: using a multi-channel complex band-pass filter to obtain a plurality of complex numbers corresponding to a plurality of preset FFT indexes from the FFT operation result;

[0088] S15: filtering the plurality of complex numbers using a multi-channel complex adaptive filter;

[0089] S16: performing periodic multi-channel complex autocorrelation processing on the plurality of complex numbers;

[0090] S17: determining whether there is a complex number exceeding the corresponding threshold; if not, determining that there is no living body in the current detection range; if yes, performing step S18;

[0091] S18: if yes, determining whether the FFT index corresponding to the complex number exceeding the corresponding threshold is only the boundary index in the plurality of preset FFT indexes; if yes, performing step S19, and if not, determining that there is a living body in the current detection range;

[0092] S19: If yes, determining that the detection distance corresponding to the boundary index;

[0093] S20: Judging whether the detection distance is located in the current detection range; if yes, determining that there is a living body in the current detection range, and if no, determining that there is no living body in the current detection range.

[0094] In the embodiment, the method of combining the adaptive filter with the autocorrelation greatly improves the signal-to-noise ratio of the microwave micro-motion sensing signal, so that the detection result is more reliable; meanwhile, the quadratic curve interpolation method is used to improve the accuracy of distance measurement, so that the target beyond the current detection range is accurately filtered out.

[0095] Correspondingly, the embodiment of the application further discloses a signal processing system of a microwave micro-motion sensor, as shown in Figure 3 The signal processing system comprises:

[0096] An acquisition module 1 is configured to acquire an initial digital signal corresponding to a transmitted signal;

[0097] A wavelet processing module 2 is configured to perform wavelet decomposition on the initial digital signal to obtain a plurality of decomposed components, remove the decomposed components corresponding to interference signals from all the decomposed components, and reconstruct the remaining decomposed components to obtain a reconstructed signal;

[0098] An FFT processing module 3 is configured to perform FFT operation on the reconstructed signal to obtain an FFT operation result;

[0099] A first filtering module 4 is configured to use a multi-channel complex band-pass filter to obtain a plurality of complex numbers corresponding to a plurality of preset FFT indexes from the FFT operation result;

[0100] A judgment module 5 is configured to judge whether the complex numbers exceed corresponding threshold values, and if yes, determine that there is a living body in the current detection range.

[0101] In the embodiment of the application, the wavelet decomposition is used to remove the interference quantity from the initial digital signal, and the reconstructed signal obtained is used for subsequent calculation, so that the detection of whether there is a living body in the current detection range is more accurate.

[0102] In some specific embodiments, the signal processing system further comprises an adaptive module 6 configured to filter a plurality of the complex numbers using a multi-channel complex adaptive filter before judging whether the complex numbers exceed corresponding threshold values; and the parameters of the multi-channel complex adaptive filter are matched with the micro-motion characteristics of the living body.

[0103] In some specific embodiments, the micro-motion feature is specifically a breathing feature of the living body.

[0104] In some specific embodiments, the signal processing system further comprises a self-correlation module 7, configured to perform periodic multi-channel complex self-correlation processing on the plurality of complex numbers after the plurality of complex numbers are filtered by the multi-channel complex adaptive filter and before the determination of whether there is a complex number exceeding the corresponding threshold.

[0105] In some specific embodiments, the acquisition module 1 comprises:

[0106] The receiving unit is configured to acquire the transmission signal and the corresponding reflection signal thereof;

[0107] The processing unit is configured to multiply the transmission signal and the reflection signal thereof and pass through a band-pass filter to obtain an intermediate frequency signal;

[0108] The sampling unit is configured to perform ADC sampling on the intermediate frequency signal to obtain an initial digital signal.

[0109] In some specific embodiments, the determination module 5 comprises:

[0110] The first determination unit is configured to determine whether there is a complex number exceeding the corresponding threshold; if so, the second determination unit is triggered;

[0111] The second determination unit is configured to determine whether the FFT index corresponding to the complex number exceeding the corresponding threshold is only a boundary index in the plurality of preset FFT indexes; if so, the distance calculation unit and the third determination unit are triggered;

[0112] The distance calculation unit is configured to determine the detection distance corresponding to the boundary index.

[0113] The third determination unit is configured to determine whether the detection distance is located in the current detection range; if so, it is determined that there is a living body in the current detection range.

[0114] In some specific embodiments, the distance calculation unit comprises:

[0115] The absolute value sub-unit is configured to calculate the absolute value of all the complex numbers respectively;

[0116] The interpolation sub-unit is configured to perform quadratic curve interpolation on all the absolute values and the corresponding FFT indexes to obtain an interpolation result; the interpolation result comprises the relationship between the FFT index and the detection distance.

[0117] The determination sub-unit is configured to determine the detection distance corresponding to the boundary index according to the difference result.

[0118] Correspondingly, the application also discloses a signal processing device of a microwave micro-motion sensor, which comprises:

[0119] a memory for storing a computer program;

[0120] a processor for executing the computer program to realize the steps of the signal processing method of the microwave micro-motion sensor according to any one of the above.

[0121] Correspondingly, the application also discloses a readable storage medium, wherein the readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the signal processing method of the microwave micro-motion sensor according to any one of the above.

[0122] In the above embodiment, the specific content of the signal processing method of the microwave micro-motion sensor can be referred to the related description in the above embodiment, and will not be repeated here.

[0123] In the above embodiment, the signal processing device and the readable storage medium of the microwave micro-motion sensor have the same technical effects as the signal processing method of the microwave micro-motion sensor in the above embodiment, and will not be repeated here.

[0124] Finally, it should be noted that in this document, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0125] The above describes in detail the signal processing method, system and related components of the microwave micro-motion sensor provided by the application. The principle and implementation mode of the application are described by applying specific examples in this document. The above description of the embodiments is only used to help understand the method of the application and its core idea; meanwhile, for the general technical personnel in the field, according to the idea of the application, the specific implementation mode and application range will be changed; and the above description of the application should not be understood as the limitation of the application.

Claims

1. A signal processing method for a microwave micro-motion sensor, characterized in that: include: Obtaining an initial digital signal corresponding to the transmitted signal; Performing wavelet decomposition on the initial digital signal to obtain a plurality of decomposed components, removing the decomposed components corresponding to the interference signal from all the decomposed components, and reconstructing using the remaining decomposed components to obtain a reconstructed signal; Performing an FFT operation on the reconstructed signal to obtain an FFT operation result; Using a multi-channel complex bandpass filter, a plurality of complex numbers corresponding to a plurality of preset FFT indices are obtained from the FFT operation result; Filtering the plurality of complex numbers using a multi-channel complex adaptive filter; parameters of the multi-channel complex adaptive filter are matched with micro-motion characteristics of the living body; After filtering is completed, it is determined whether the complex number exceeds the corresponding threshold. If so, it is determined that there is a living organism within the current detection range; The process of determining whether the plurality of numbers exceeds a corresponding threshold, and if so, determining whether a living organism exists within the current detection range, includes: Determine whether the plurality exceeds a corresponding threshold; If so, determining whether the FFT index corresponding to the plurality of numbers exceeding the corresponding threshold is only a boundary index among the plurality of preset FFT indexes; If so, determine the detection distance corresponding to the boundary index; Determining whether the detection distance is within the current detection range; If so, it is determined that there is a living organism within the current detection range; The process of determining the detection distance corresponding to the boundary index includes: Find the absolute values ​​of all the complex numbers respectively; Performing quadratic curve interpolation using all the absolute values ​​and the corresponding FFT indexes to obtain an interpolation result; the interpolation result includes a relationship between the FFT index and the detection distance; The detection distance corresponding to the boundary index is determined according to the interpolation result.

2. The signal processing method according to claim 1, wherein: The micro-motion feature is specifically a breathing feature of the living organism.

3. The signal processing method according to claim 1, wherein: After filtering the plurality of complex numbers using the multi-channel complex adaptive filter and before determining whether any complex number exceeds a corresponding threshold, the method further includes: Performing periodic multi-channel complex autocorrelation processing on the plurality of complex numbers.

4. The signal processing method according to claim 1, wherein: The process of obtaining the initial digital signal corresponding to the transmitted signal specifically includes: Obtaining a transmitted signal and its corresponding reflected signal; Multiplying the transmitted signal and the reflected signal and passing the resultant signal through a bandpass filter to obtain an intermediate frequency signal; Perform ADC sampling on the intermediate frequency signal to obtain an initial digital signal.

5. A signal processing system for a microwave micro-motion sensor, characterized in that: include: An acquisition module, used to acquire an initial digital signal corresponding to the transmitted signal; a wavelet processing module configured to perform wavelet decomposition on the initial digital signal to obtain a plurality of decomposed components, remove the decomposed components corresponding to the interference signal from all the decomposed components, and reconstruct the signal using the remaining decomposed components to obtain a reconstructed signal; An FFT processing module is used to perform an FFT operation on the reconstructed signal to obtain an FFT operation result; A first filtering module is configured to obtain a plurality of complex numbers corresponding to a plurality of preset FFT indices from the FFT operation result using a multi-channel complex bandpass filter; An adaptive module, configured to filter the plurality of complex numbers using a multi-channel complex adaptive filter, wherein the parameters of the multi-channel complex adaptive filter are matched with the micro-motion characteristics of the living body; A judgment module, configured to judge whether the plurality of numbers exceeds a corresponding threshold after filtering, and if so, to determine that a living organism exists within the current detection range; Wherein, the judgment module includes: The first judgment unit is configured to judge whether the plurality of numbers exceeds a corresponding threshold; if so, triggering the second judgment unit; A second judgment unit is configured to judge whether the FFT index corresponding to the plurality of numbers exceeding the corresponding threshold is only a boundary index among the plurality of preset FFT indexes; if so, triggering the distance calculation unit and the third judgment unit; A distance calculation unit, configured to determine a detection distance corresponding to the boundary index; a third judging unit, configured to judge whether the detection distance is within a current detection range; if so, to determine that a living being exists within the current detection range; The distance calculation unit is specifically used to: Find the absolute values ​​of all the complex numbers respectively; Performing quadratic curve interpolation using all the absolute values ​​and the corresponding FFT indexes to obtain an interpolation result; the interpolation result includes a relationship between the FFT index and the detection distance; The detection distance corresponding to the boundary index is determined according to the interpolation result.

6. A signal processing device for a microwave micro-motion sensor, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the signal processing method of the microwave micro-motion sensor according to any one of claims 1 to 4 when executing the computer program.

7. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the signal processing method of the microwave micro-motion sensor according to any one of claims 1 to 4.

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