A signal detection method, device, apparatus and storage medium

Abstract

The application discloses a signal detection method, device and equipment and a storage medium, and is used for improving the accuracy of respiratory signal and heartbeat signal detection. In the application, an echo signal is preprocessed to obtain a target phase signal; and based on the target phase signal and a whole dictionary matrix obtained by pre-construction, a respiratory signal and a heartbeat signal are obtained. The whole dictionary matrix is a discontinuous matrix constructed according to a dictionary matrix corresponding to the respiratory signal and a dictionary matrix corresponding to the heartbeat signal. In the application, the whole dictionary matrix constructed by using the dictionary matrix corresponding to the respiratory signal and the dictionary matrix corresponding to the heartbeat signal is a discontinuous matrix, so that the accuracy of separating the respiratory signal and the heartbeat signal is ensured.

Description

Technical Field

[0001] This invention relates to the field of signal detection technology, and in particular to a signal detection method, apparatus, device, and storage medium. Background Technology

[0002] In the fields of personal health management and sleep quality monitoring, non-invasive and portable monitoring devices are gaining increasing attention. Especially in the home environment, these devices not only need to continuously monitor physiological parameters such as respiration and heart rate without interfering with the user's daily life, but also require high accuracy and reliability. Radar, as an important remote life detection tool, provides a method for non-contact monitoring of vital signs. By processing the echoes reflected from the human body, radar can detect minute displacements caused by heartbeats and respiration.

[0003] However, the displacement caused by a heartbeat is often too weak to be easily masked by the much larger displacement caused by respiration. This makes accurate detection of the heartbeat signal extremely challenging. Furthermore, because vital signs have very similar frequencies, typically around 1 Hz, it is difficult to effectively separate them using ordinary frequency filters. This problem means that existing vital sign detection methods can usually only detect the stronger respiratory signals, resulting in inaccurate detection of the heartbeat signal. This technical difficulty limits the ability of existing radar products used for health monitoring to provide accurate health data. Summary of the Invention

[0004] The purpose of this invention is to provide a signal detection method, apparatus, device, and storage medium to improve the accuracy of respiratory and heartbeat signal detection.

[0005] In a first aspect, embodiments of this application provide a signal detection method, the method comprising:

[0006] The echo signal is acquired; the echo signal is preprocessed to obtain the target phase signal; based on the target phase signal and the acquired pre-constructed overall dictionary matrix, the respiratory signal and heartbeat signal are obtained.

[0007] In this application, a pre-constructed overall dictionary matrix is ​​used to separate radar signals. Since the overall dictionary matrix is ​​a discontinuous matrix, the accuracy of separating respiratory signals and heartbeat signals is guaranteed.

[0008] In some possible embodiments, the echo signal includes a fast time dimension and a slow time dimension. Preprocessing the echo signal to obtain the target phase signal includes: performing coherent accumulation on the fast time dimension of the echo signal to obtain a coherently accumulated signal; performing a phase extraction operation based on the coherently accumulated signal to obtain phase information; and performing correction processing on the phase information to obtain the target phase signal.

[0009] In this application, the signal-to-noise ratio is improved by coherently accumulating the echo signal, thus ensuring the accuracy of the extracted phase information.

[0010] In some possible embodiments, the phase information is corrected to obtain the target phase signal, including: performing linear regression processing on the phase information to obtain the slope and intercept corresponding to the phase information; performing amplitude-phase processing on the phase information based on the slope and intercept to obtain the processed phase signal; and performing normalization processing on the processed phase signal to obtain the target phase signal.

[0011] In this application, phase linear drift caused by the environment can be eliminated by correcting the phase information.

[0012] In some possible embodiments, respiratory signals and heartbeat signals are obtained based on the target phase signal and the acquired pre-constructed overall dictionary matrix, including: acquiring the pre-constructed overall dictionary matrix; obtaining a sparse representation of the target phase signal on the overall dictionary matrix based on the overall dictionary matrix and the target phase signal; obtaining the respiratory signal based on the overall dictionary matrix, and obtaining the heartbeat signal based on the sparse representation.

[0013] In this application, by constructing a discontinuous overall dictionary matrix, the respiratory and heartbeat signals obtained in this application are more accurate.

[0014] In some possible embodiments, obtaining the respiratory signal based on the overall dictionary matrix includes: performing a fast Fourier transform on each column of the overall dictionary matrix to obtain the frequency of each column; determining whether the frequency of each column belongs to a preset respiratory frequency range; if it does, determining the frequency as a first frequency; constructing a first support set based on the first frequency; and obtaining the respiratory signal based on the first support set.

[0015] In this application, the respiratory signal is constructed in the time domain, making the constructed respiratory signal more accurate.

[0016] In some possible embodiments, obtaining the heartbeat signal based on the sparse representation includes: performing a fast Fourier transform on each row of the sparse representation to obtain the frequency of each row; determining whether the frequency of each row belongs to a preset heartbeat frequency range; if it does, determining the frequency as a second frequency; constructing a second support set based on the second frequency; and obtaining the heartbeat signal based on the second support set.

[0017] In this application, the heartbeat signal is constructed in the time domain, making the constructed respiratory signal more accurate.

[0018] In some possible embodiments, before obtaining the respiratory signal and heartbeat signal from the target phase signal and the acquired pre-constructed overall dictionary matrix, the method further includes: updating the pre-constructed overall dictionary matrix to obtain an updated overall dictionary matrix; obtaining the respiratory signal and heartbeat signal based on the target phase signal and the acquired pre-constructed overall dictionary matrix, including: obtaining the respiratory signal and heartbeat signal based on the target phase signal and the updated overall dictionary matrix.

[0019] In this application, by updating the overall dictionary matrix, the overall dictionary matrix is ​​made more accurate, thereby further improving the accuracy of respiratory and heartbeat signals.

[0020] In some possible embodiments, updating the pre-constructed overall dictionary matrix includes: obtaining a sparse representation of the target phase signal on the overall dictionary matrix based on the overall dictionary matrix and the target phase signal; updating the sparse representation using the orthogonal matching pursuit method to obtain the updated sparse representation; and updating the overall dictionary matrix using the singular value decomposition method based on the updated sparse representation to obtain the updated overall dictionary matrix.

[0021] In this application, by updating the overall dictionary matrix, the overall dictionary matrix is ​​made more accurate, thereby further improving the accuracy of respiratory and heartbeat signals.

[0022] In some possible embodiments, after obtaining the respiratory signal and the heartbeat signal, the method further includes: obtaining the respiratory frequency based on the respiratory signal and a preset respiratory frequency range, and obtaining the heartbeat frequency based on the heartbeat signal and a preset heartbeat frequency range.

[0023] In this application, heart rate can be obtained based on heartbeat signals and respiratory rate can be obtained based on respiratory signals, making it easier to understand the user's physical condition more intuitively.

[0024] In some possible embodiments, obtaining the respiratory frequency based on the respiratory signal and a preset respiratory frequency range includes: performing DC filtering on the respiratory signal to obtain a filtered respiratory signal; performing a fast Fourier transform on the filtered respiratory signal to obtain a first respiratory frequency; determining the first respiratory frequency within the preset respiratory frequency range as a second respiratory frequency; and taking the maximum value of the second respiratory frequency as the respiratory frequency.

[0025] In this application, by processing the respiratory signal, the influence of the environment on the respiratory signal is further avoided, thereby ensuring that the obtained respiratory rate is more accurate.

[0026] In some possible embodiments, obtaining the heartbeat frequency based on the heartbeat signal and a preset heartbeat frequency range includes: performing DC filtering on the heartbeat signal to obtain a filtered heartbeat signal; performing a fast Fourier transform on the filtered heartbeat signal to obtain a first heartbeat frequency; determining the first heartbeat frequency within the preset heartbeat frequency range as a second heartbeat frequency; and using the maximum value of the second heartbeat frequency as the heartbeat frequency.

[0027] In this application, by processing the heartbeat signal, the influence of the environment on the heartbeat signal is further avoided, thereby ensuring that the obtained heart rate is more accurate.

[0028] In some possible embodiments, the preset overall dictionary matrix is ​​constructed according to the following method: obtaining the dictionary matrix corresponding to the respiratory signal based on a preset respiratory frequency range and a preset number of samples; obtaining the dictionary matrix corresponding to the heartbeat signal based on a preset heartbeat frequency range and a preset number of samples; obtaining the dictionary matrix corresponding to the interference signal based on a preset interference frequency range and a preset number of samples; and obtaining the overall dictionary matrix based on the dictionary matrix corresponding to the respiratory signal, the dictionary matrix corresponding to the heartbeat signal, and the dictionary matrix corresponding to the interference signal.

[0029] In this application, a discontinuous overall dictionary matrix is ​​constructed based on respiratory signals, heartbeat signals, and interference signals to ensure the accuracy of echo signal separation.

[0030] In some possible embodiments, after obtaining the overall dictionary matrix, the method further includes: determining the breathing phase and heartbeat phase using an accumulator; obtaining the total phase based on the breathing phase, heartbeat phase, and a preset interference phase; constructing a training sample set based on the total phase; updating the overall dictionary matrix using the training sample set, and using the updated overall dictionary matrix as the overall dictionary matrix.

[0031] In this application, the overall dictionary matrix is ​​updated to make it more accurate.

[0032] In some possible embodiments, before acquiring the echo signal, the method further includes: transmitting a radar signal; the radar signal is used to form an echo signal after being reflected by the human body.

[0033] Secondly, embodiments of this application also provide a signal detection device, the device comprising:

[0034] The receiving module acquires the echo signal; the preprocessing module preprocesses the echo signal to obtain the target phase signal.

[0035] The signal separation module is used to obtain respiratory signals and heartbeat signals based on the target phase signal and the acquired pre-constructed overall dictionary matrix. The overall dictionary matrix is ​​a discontinuous matrix constructed based on the dictionary matrices corresponding to the respiratory signals and the heartbeat signals.

[0036] In some possible embodiments, the preprocessing module is specifically used to: coherently accumulate the fast time dimension of the echo signal to obtain a coherently accumulated signal; perform phase extraction based on the coherently accumulated signal to obtain phase information; and perform correction processing on the phase information to obtain the target phase signal.

[0037] In some possible embodiments, the preprocessing module is specifically used to: perform linear regression processing on the phase information to obtain the slope and intercept corresponding to the phase information; perform amplitude-phase processing on the phase information based on the slope and intercept to obtain the processed phase signal; and perform normalization processing on the processed phase signal to obtain the target phase signal.

[0038] In some possible embodiments, the signal separation module is specifically used to: obtain a pre-constructed overall dictionary matrix; obtain a sparse representation of the target phase signal on the overall dictionary matrix based on the overall dictionary matrix and the target phase signal; obtain a respiratory signal based on the overall dictionary matrix, and obtain a heartbeat signal based on the sparse representation.

[0039] In some possible embodiments, the signal separation module is specifically used to: perform a fast Fourier transform on each column of the overall dictionary matrix to obtain the frequency of each column; determine whether the frequency of each column belongs to a preset respiratory frequency range; if it does, determine the frequency as a first frequency; construct a first support set based on the first frequency; and obtain a respiratory signal based on the first support set.

[0040] In some possible embodiments, the signal separation module is specifically used to: perform a fast Fourier transform on each row of the sparse representation to obtain the frequency of each row; determine whether the frequency of each row belongs to a preset heartbeat frequency range; if it does, determine the frequency as a second frequency; construct a second support set based on the second frequency; and obtain the heartbeat signal based on the second support set.

[0041] In some possible embodiments, the signal separation module is further configured to: update the pre-built overall dictionary matrix to obtain an updated overall dictionary matrix; and obtain respiratory signals and heartbeat signals based on the target phase signal and the obtained pre-built overall dictionary matrix, including: obtaining respiratory signals and heartbeat signals based on the target phase signal and the updated overall dictionary matrix.

[0042] In some possible embodiments, the signal separation module is specifically used to: obtain a sparse representation of the target phase signal on the overall dictionary matrix based on the overall dictionary matrix and the target phase signal; update the sparse representation using the orthogonal matching pursuit method to obtain the updated sparse representation; and update the overall dictionary matrix using the singular value decomposition method based on the updated sparse representation to obtain the updated overall dictionary matrix.

[0043] In some possible embodiments, the signal separation module is further configured to: obtain a respiratory frequency based on a respiratory signal and a preset respiratory frequency range, and obtain a heart rate based on a heart rate signal and a preset heart rate range.

[0044] In some possible embodiments, the signal separation module is specifically used to: perform DC filtering on the respiratory signal to obtain a filtered respiratory signal; perform a fast Fourier transform on the filtered respiratory signal to obtain a first respiratory frequency; determine the first respiratory frequency within a preset respiratory frequency range as a second respiratory frequency; and take the maximum value of the second respiratory frequency as the respiratory frequency.

[0045] In some possible embodiments, the signal separation module is specifically used to: perform DC filtering on the heartbeat signal to obtain a filtered heartbeat signal; perform a fast Fourier transform on the filtered heartbeat signal to obtain a first heartbeat frequency; determine the first heartbeat frequency within a preset heartbeat frequency range as a second heartbeat frequency; and take the maximum value of the second heartbeat frequency as the heartbeat frequency.

[0046] In some possible embodiments, the signal separation module is specifically used to: obtain a dictionary matrix corresponding to the respiratory signal based on a preset respiratory frequency range and a preset number of samples; obtain a dictionary matrix corresponding to the heartbeat signal based on a preset heartbeat frequency range and a preset number of samples; obtain a dictionary matrix corresponding to the interference signal based on a preset interference frequency range and a preset number of samples; and obtain an overall dictionary matrix based on the dictionary matrix corresponding to the respiratory signal, the dictionary matrix corresponding to the heartbeat signal, and the dictionary matrix corresponding to the interference signal.

[0047] In some possible embodiments, the signal separation module is also used to: determine the respiratory phase and heartbeat phase using an accumulator; and obtain the total phase based on the respiratory phase, heartbeat phase, and a preset interference phase.

[0048] A training sample set is constructed based on the total phase; the training sample set is used to update the overall dictionary matrix, and the updated overall dictionary matrix is ​​used as the overall dictionary matrix.

[0049] In some possible embodiments, the receiving module is also used to: transmit radar signals; the radar signals are used to form echo signals after being reflected by the human body.

[0050] Thirdly, another embodiment of this application also provides an electronic device, including at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the methods provided in the first aspect embodiment of this application.

[0051] Fourthly, another embodiment of this application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for causing a computer to perform any of the methods provided in the first aspect of this application.

[0052] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of a non-invasive monitoring device for a signal detection method provided in an embodiment of this application.

[0054] Figure 2 A schematic diagram of the echoes of respiratory and heartbeat signals provided in an embodiment of this application for a signal detection method;

[0055] Figure 3 This is a schematic diagram of the overall process of a signal detection method provided in an embodiment of this application;

[0056] Figure 4 This application provides a schematic flowchart of a signal detection method for preprocessing echo signals according to an embodiment of the present application.

[0057] Figure 5 This is a schematic flowchart illustrating the phase information correction process of a signal detection method provided in an embodiment of this application.

[0058] Figure 6A A phase diagram of the echo signal containing respiratory and heartbeat information provided in an embodiment of this application for a signal detection method;

[0059] Figure 6B A schematic diagram of a signal after coherent accumulation of a fast time dimension, provided in an embodiment of this application;

[0060] Figure 6C A schematic diagram of the target phase signal after amplitude and phase error correction and normalization, provided in an embodiment of this application, for a signal detection method;

[0061] Figure 7 A schematic flowchart illustrating the process of obtaining respiratory and heartbeat signals using a signal detection method provided in this application embodiment;

[0062] Figure 8 A schematic diagram illustrating the process of constructing an overall dictionary matrix for a signal detection method provided in this application embodiment;

[0063] Figure 9A A schematic diagram illustrating the process of training the overall dictionary matrix for a signal detection method provided in this application embodiment;

[0064] Figure 9B A schematic diagram of measured respiratory signals, heartbeat signals, and radar phase signals for a signal detection method provided in an embodiment of this application;

[0065] Figure 9C A schematic diagram of three sets of simulated signals for a signal detection method provided in an embodiment of this application;

[0066] Figure 10 A schematic diagram illustrating the process of updating a pre-constructed overall dictionary matrix using a training sample set, as provided in an embodiment of this application;

[0067] Figure 11 A schematic diagram illustrating the process of obtaining respiratory signals based on an overall dictionary matrix, as provided in an embodiment of this application;

[0068] Figure 12 A schematic flowchart illustrating a signal detection method for obtaining heartbeat signals based on sparse representation, provided in an embodiment of this application;

[0069] Figure 13 A schematic flowchart illustrating a signal detection method provided in this application for obtaining respiratory frequency based on respiratory signal and a preset respiratory frequency range;

[0070] Figure 14 A schematic diagram illustrating the process of obtaining the heartbeat frequency based on the heartbeat signal and a preset heartbeat frequency range, as provided in an embodiment of this application;

[0071] Figure 15A This is a schematic diagram of the target phase signal of a signal detection method provided in an embodiment of this application;

[0072] Figure 15B The application provides a signal detection method for the purposes of this application. Figure 3 A schematic diagram of the respiratory signals obtained from the steps shown;

[0073] Figure 15CThe application provides a signal detection method for the purposes of this application. Figure 3 A schematic diagram of the heartbeat signal obtained from the steps shown;

[0074] Figure 16 A schematic flowchart of a signal detection method provided in an embodiment of this application;

[0075] Figure 17 A schematic diagram illustrating the process of updating a pre-constructed overall dictionary matrix in a signal detection method provided in an embodiment of this application;

[0076] Figure 18 This is a schematic diagram of the overall process of a signal detection method provided in an embodiment of this application;

[0077] Figure 19 A schematic diagram of an apparatus for a signal detection method provided in an embodiment of this application;

[0078] Figure 20 This is a schematic diagram of an electronic device for a signal detection method provided in an embodiment of this application. Detailed Implementation

[0079] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0080] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0081] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0082] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0083] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0084] The inventors' research has found that non-invasive and portable monitoring devices are gaining increasing importance in the fields of personal health management and sleep quality monitoring, especially in home environments, such as... Figure 1 As shown, such devices not only need to continuously monitor physiological parameters such as respiration and heart rate without interfering with the user's daily life, but also require high precision and reliability. Radar, as an important long-range life detection tool, provides a method for non-contact monitoring of vital signs. By processing the echoes reflected from the human body, radar can detect minute displacements caused by heartbeat and respiration.

[0085] However, the displacement produced by the heartbeat is usually too weak to be easily masked by the larger displacement of respiration, such as Figure 2 As shown, this situation makes accurate detection of heartbeat signals extremely challenging. Furthermore, because the frequencies of vital signs are very close, typically around 1 Hz, it is difficult to effectively separate them using ordinary frequency filters. This problem means that existing vital sign detection methods can usually only detect strong respiratory signals, resulting in inaccurate detection of heartbeat signals. This technical difficulty limits the ability of existing radar products used for health monitoring to provide accurate health data.

[0086] To address the aforementioned problems, this application provides a signal detection method, apparatus, device, and storage medium to solve these problems. The inventive concept of this application can be summarized as follows: acquiring an echo signal; preprocessing the echo signal to obtain a target phase signal; and obtaining a respiratory signal and a heartbeat signal based on the target phase signal and a pre-constructed overall dictionary matrix. The overall dictionary matrix is ​​a discontinuous matrix constructed based on the dictionary matrices corresponding to the respiratory signal and the heartbeat signal. In this application, the overall dictionary matrix constructed using the dictionary matrices corresponding to the respiratory signal and the heartbeat signal are discontinuous in frequency. Since the frequency difference between the respiratory signal and the heartbeat signal is significant, the respiratory signal and the heartbeat signal can be better separated based on their respective frequencies, thereby ensuring the accuracy of the separation.

[0087] The signal detection method provided in this application is applicable to various terminal devices, including but not limited to computers, laptops, smartphones, tablets, smartwatches, smart bracelets, wireless headphones, vehicle terminals, and other devices with radar signal detection capabilities.

[0088] To facilitate a further understanding of the signal detection method provided in the embodiments of this application, the following detailed description of the signal detection method provided in the embodiments of this application is provided in conjunction with the accompanying drawings:

[0089] like Figure 3The diagram shown is a schematic flowchart of a signal detection method provided in an embodiment of this application, wherein:

[0090] In step 301: Receive the echo signal.

[0091] In this application, when the entity executing a signal detection method has the capability to transmit radar signals, the echo signal is the signal formed after the radar signal is reflected by the human body after the entity transmits the radar signal; when the entity executing the signal detection method does not have the capability to transmit radar signals, the echo signal is the signal formed after the radar signal is transmitted by a device capable of transmitting radar signals and then emitted by the human body.

[0092] In this application, the radar signal can be used for execution. Figure 3 The device used in the step can transmit the signal, or it can be a separate radar device; this application does not limit this.

[0093] To ensure the accuracy of signal detection, when using the signal detection method provided in this application, the user can place the device transmitting the radar signal directly facing the chest cavity. For example, if the user is lying flat on a bed and the device transmitting the radar signal is a millimeter-wave radar device, the millimeter-wave radar device can be placed on the ceiling directly facing the chest cavity. Figure 3 If the device used in the steps is a user's mobile phone, the millimeter-wave radar sends radar signals, and the mobile phone receives the generated echo signals. If the user's mobile phone has a radar transmitter installed, and the user is sleeping on their side, the phone can be hung on the wall directly opposite their chest. It should be noted that this application does not limit the location of the device transmitting radar signals; the above are merely two embodiments.

[0094] It should be noted that the above description only uses millimeter-wave radar as an example and does not limit the type of radar. The radar equipment in this application can be ultra-wideband radar (UWB radar), millimeter-wave radar, etc.

[0095] To conserve computational resources while ensuring signal detection accuracy, it can be executed periodically. Figure 3 The steps shown are executed each time. Figure 3 The echo signal used in the steps shown is the echo signal received in this cycle.

[0096] For example: if the preset cycle is 10 minutes, then it will be executed once every ten minutes. Figure 3 The steps are shown.

[0097] In step 302: the echo signal is preprocessed to obtain the target phase signal.

[0098] In this application, in order to ensure the accuracy and stability of signal detection, the echo signal needs to be preprocessed after it is obtained to improve the signal-to-noise ratio.

[0099] In some possible embodiments, the echo signal includes a fast time dimension and a slow time dimension, and the preprocessing of the echo signal can be specifically implemented as follows: Figure 4 The steps shown are as follows:

[0100] In step 401: coherent accumulation is performed on the fast time dimension of the echo signal to obtain the coherently accumulated signal.

[0101] In this application, the echo signal can be represented as: S(r,t), where r represents the fast time dimension and t represents the slow time dimension.

[0102] In some possible embodiments, coherent accumulation of the echo signal in the fast time dimension can improve the signal-to-noise ratio. Equation 1 can be used when performing coherent accumulation of the fast time dimension in the echo signal, where:

[0103]

[0104] Where R is the number of fast time dimensions in the received echo signal, and S(r,t) is the received echo signal, S acc (t) represents the signal after coherent accumulation, r is the fast time dimension, and t is the slow time dimension.

[0105] For example, if the period is 10 minutes, a signal detection was performed at 12:10, and the current event is 12:20, then a signal detection needs to be performed here. The echo signals participating in this signal detection are the echo signals received between 12:10 and 12:20.

[0106] In step 402: a phase extraction operation is performed on the signal after coherent accumulation to obtain phase information.

[0107] In this application, after coherent accumulation of the echo signal, a phase extraction operation is required to obtain the phase information corresponding to the echo signal.

[0108] In some possible embodiments, when performing phase extraction on the coherently accumulated signal, Formula 2 can be used, wherein:

[0109]

[0110] in, For phase information, S acc (t) represents the signal after coherent accumulation, and the arg(·) function is used to extract the phase angle of the complex signal.

[0111] In step 403: the phase information is corrected to obtain the target phase signal.

[0112] In this application, in order to eliminate linear drift caused by the system or by the environment, the phase information needs to be corrected after it is obtained.

[0113] In some possible embodiments, the phase information is corrected, specifically as follows: Figure 5 The steps shown are as follows:

[0114] In step 501: linear regression is performed on the phase information to obtain the slope and intercept corresponding to the phase information.

[0115] In this application, when performing linear regression processing on the phase information, Formula 3 can be used to obtain the slope corresponding to the phase information, and Formula 5 can be used to obtain the intercept corresponding to the phase information, wherein:

[0116]

[0117] Where: a is the slope, and N is the number of slow time dimensions in the received echo signal. This represents the mean of the slow time dimension in the received echo signal. for The mean.

[0118]

[0119] Where b is the intercept and a is the slope. This represents the mean of the slow time dimension in the received echo signal. This represents the average value of the phase information.

[0120] In step 502: the phase information is processed based on the slope and intercept to obtain the processed phase signal.

[0121] After obtaining the slope and intercept, the phase information can be processed according to the linear trend. In specific implementation, Formula 5 can be used, where:

[0122]

[0123] in, The processed phase signal, Here, b represents the phase information, a represents the intercept, a represents the slope, and t represents the slow time dimension.

[0124] In step 503: the processed phase signal is normalized to obtain the target phase signal.

[0125] In this application, in order to further ensure the accuracy of signal detection, the processed phase signal needs to be normalized after it is obtained.

[0126] In some possible embodiments, the normalization of the processed phase signal can be performed using Formula 6, wherein:

[0127]

[0128] in, For the target phase signal, The processed phase signal, The maximum value in the processed phase signal. This is the minimum value in the processed phase signal.

[0129] For example: the phase of the acquired echo signal containing breathing and heartbeat information, such as... Figure 6A As shown, the signal after coherent accumulation of the fast time dimension is as follows: Figure 6B As shown, from Figure 6B It can be seen that the signal-to-noise ratio has been significantly improved. The target phase signal after amplitude and phase error correction and normalization is as follows: Figure 6C As shown, with Figure 6B In contrast, the tendency for linear drift was effectively eliminated.

[0130] In step 303: Based on the target phase signal and the acquired pre-constructed overall dictionary matrix, the respiratory signal and heartbeat signal are obtained.

[0131] In this application, the overall dictionary matrix is ​​divided into three parts, representing the respiratory signal, heartbeat signal, and interference signal, respectively. The three parts of the overall dictionary matrix in this application are discontinuous and have different frequencies. The discontinuity of the three parts of the overall dictionary matrix can better separate the respiratory signal, heartbeat signal, and interference signal.

[0132] In some possible embodiments, respiratory and heartbeat signals are obtained based on the target phase signal and the acquired pre-constructed overall dictionary matrix. Specifically, this can be implemented as follows: Figure 7 The steps shown are as follows:

[0133] Step 701: Obtain the pre-built overall dictionary matrix.

[0134] In some possible embodiments, constructing the overall dictionary matrix can be specifically implemented as follows: Figure 8 The steps shown are as follows:

[0135] In step 801: Based on a preset respiratory rate range and a preset number of samples, a dictionary matrix corresponding to the respiratory signal is obtained. In this application, a preset respiratory rate range can be preset, and the lower limit of the preset respiratory rate range is set to... Set the upper limit to Set the preset sampling number for breathing to k. B The dictionary matrix corresponding to the respiratory signal is shown in Formula 7:

[0136]

[0137] Among them, D B Let be the dictionary matrix corresponding to the respiratory signals, containing the basis vectors. in It is obtained by sampling within a preset respiratory rate range based on a frequency interval, where the frequency interval is obtained based on the preset sampling number corresponding to respiration according to Formula 8, wherein:

[0138]

[0139] Where, Δf B For frequency intervals, This is the upper limit of the preset respiratory rate range. k is the lower limit of the preset respiratory rate range. B The preset number of samples corresponding to breathing.

[0140] For example: if the preset number of samples is 11, the upper limit of the preset breathing frequency range is 2 Hz, and the lower limit is 0.5 Hz, then the frequency interval is 0.15 Hz.

[0141] In step 802: Based on the preset heartbeat frequency range and the preset sampling number pair, the dictionary matrix corresponding to the heartbeat signal is obtained.

[0142] In this application, a preset heart rate range can be set in advance, and the lower limit of the preset heart rate range is set to... Set the upper limit to Set the preset sampling number corresponding to the heartbeat to k. H The dictionary matrix corresponding to the heartbeat signal is shown in Formula 9:

[0143]

[0144] Among them, D H This is the dictionary matrix corresponding to the heartbeat signal, containing the basis vectors. in It is obtained by sampling within a preset heartbeat frequency range based on frequency intervals, where the frequency intervals are obtained based on Formula 10 and a preset number of samples, wherein:

[0145]

[0146] Where, Δf H For frequency intervals, To set the upper limit of the preset heart rate range, k is the lower limit of the preset heart rate range. H This is the preset number of samples corresponding to the heartbeat.

[0147] For example, if the preset number of samples is 11, the upper limit of the preset heartbeat frequency range is 2 Hz, and the lower limit is 0.5 Hz, then the frequency interval is 0.15 Hz.

[0148] In step 803: Based on the preset interference frequency range and the preset sampling number, the dictionary matrix corresponding to the interference signal is obtained.

[0149] In this application, a preset interference frequency range can be set in advance, and the lower limit of the preset interference frequency range is set to... Set the upper limit to Set the preset sampling number corresponding to the interference to k. N The dictionary matrix corresponding to the interference signal is shown in Formula 11:

[0150]

[0151] Where, d N Let be the dictionary matrix corresponding to the interference signal, containing the basis vectors. in It is obtained by sampling within a preset interference frequency range based on frequency intervals, where the frequency intervals are obtained based on Formula 12 and a preset number of samples, wherein:

[0152]

[0153] Where, Δf N For frequency intervals, To set the upper limit of the preset interference frequency range, k is the lower limit of the preset interference frequency range. N The preset number of samples corresponding to the interference.

[0154] For example, if the preset number of samples is 11, the upper limit of the preset interference frequency range is 2 Hz, and the lower limit is 0.5 Hz, then the frequency interval is 0.15 Hz.

[0155] In step 804: Based on the dictionary matrix corresponding to the respiratory signal, the dictionary matrix corresponding to the heartbeat signal, and the dictionary matrix corresponding to the interference signal, the overall dictionary matrix is ​​obtained.

[0156] In this application, the overall dictionary matrix can be regarded as a combination of the dictionary matrices corresponding to the respiratory signal, heartbeat signal, and interference signal, respectively. Therefore, the overall dictionary matrix in this application can be represented by Formula 13, where:

[0157] D = D B +D H +D H ,(Formula 13)

[0158] Where D is the overall dictionary matrix, D B D is the dictionary matrix corresponding to the respiratory signals. H D is the dictionary matrix corresponding to the heartbeat signal. N This is the dictionary matrix corresponding to the interference signal.

[0159] In some possible implementations, to make the overall dictionary matrix more consistent with the actual situation, it can be trained after obtaining the overall dictionary matrix. Specifically, this can be implemented as follows: Figure 9A The steps shown are as follows:

[0160] In step 901: the respiratory phase and heartbeat phase are determined by an accumulator, and the total phase is obtained based on the respiratory phase, heartbeat phase and preset interference phase.

[0161] Since the frequency of breathing and heartbeat are determined by an individual's respiratory rate and heart rate per minute, respectively, the respiratory rate is as follows:

[0162] As shown in Equation 14, the heart rate is as shown in Equation 15:

[0163]

[0164] Among them, f B Breathing rate, bpm B The total number of breaths an individual takes in one minute.

[0165]

[0166] Among them, f H Heart rate, bpm H The total number of heartbeats per minute for an individual.

[0167] In this application, an accumulator can be used to simulate the displacement of an individual's chest cavity caused by respiration and heartbeat. The accumulator is used at each sampling time point t. i If the phase is accumulated, the displacement caused by respiration is shown in Equation 16, and the displacement caused by heartbeat is shown in Equation 17:

[0168] d B (t i ) = A B ·εB (t i )·sin(acc B (t i )), (Formula 16)

[0169] Where, d B (t i ) for in t i Displacement caused by breathing, A B ε is the preset respiratory displacement amplitude. B (t i ) is the preset respiratory amplitude variation factor, acc B (t i ) for in t i The phase accumulation value corresponding to the breath accumulated by the time accumulator.

[0170] d H (t i ) = A H ·ε H (t i )·sin(acc H (t i )), (Formula 17)

[0171] Where, d H (t i ) for in t i Displacement caused by heartbeat, A H ε is the preset heart rate displacement amplitude. H (t i ) is the preset heart rate amplitude variation factor, acc H (t i ) for in t i The phase accumulation value corresponding to the heartbeat accumulated by the time accumulator.

[0172] In some possible embodiments, acc B (t i The update method is shown in Formula 18:

[0173]

[0174] Among them, acc B (t i ) for in t i The phase accumulation value corresponding to the breath accumulated by the time accumulator, acc B (t i-1 f is the phase accumulation value corresponding to the last breath accumulated by the accumulator. B τ is the respiratory rate. B (t i f is the preset time variation factor corresponding to respiration.s This is the sampling frequency of the radar.

[0175] In some possible embodiments, acc H (t i The update method is shown in Formula 19:

[0176]

[0177] Among them, acc H (t i ) for in t i The phase accumulation value corresponding to the breath accumulated by the time accumulator, acc H (t i-1 f is the phase accumulation value corresponding to the last breath accumulated by the accumulator. H τ is the respiratory rate. H (t i f is the preset time variation factor corresponding to respiration. s This is the sampling frequency of the radar.

[0178] Where, τ B (t i ) and τ H (t i It is used to simulate the time difference between breathing and heartbeat.

[0179] In summary, the phase change caused by the displacement during respiration, i.e., the respiratory phase, can be obtained as shown in Formula 20, where:

[0180]

[0181] in, The breathing phase is λ, the radar wavelength is d. B (t) represents the displacement caused by respiration.

[0182] The phase change caused by the displacement generated by the heartbeat, i.e., the heartbeat phase, is shown in Formula 21, where:

[0183]

[0184] in, Let λ be the heartbeat phase, λ be the radar wavelength, and d be the... H (t) represents the displacement caused by the heartbeat.

[0185] In this application, in order to make the phase more closely resemble the real situation, an interference term is added. The total phase in this application is shown in Formula 22:

[0186]

[0187] in, For total phase, This is the respiratory phase. The phase of the heartbeat. This is a distractor.

[0188] For example: Figure 9B The middle part shows three sets of measured respiratory signals, heartbeat signals, and radar phase signals. Figure 9C Based on the three sets of simulation signals generated in step 901, a comparison with the real signals shows that the simulation signals can well simulate the phase changes caused by breathing and heartbeat, and also exhibit interference and fluctuations similar to the real signals.

[0189] In step 902: a training sample set is constructed based on the total phase.

[0190] In this application, in order to ensure the accuracy of the constructed overall dictionary matrix, the constructed overall dictionary matrix can be updated. In order to enrich the training sample set, a preset number of steps 901 can be performed to obtain a preset number of total phases, and then the training sample set is constructed based on the preset number of total phases.

[0191] In step 903: the training sample set is used to update the overall dictionary matrix, and the updated overall dictionary matrix is ​​used as the overall dictionary matrix.

[0192] In some possible embodiments, the pre-constructed overall dictionary matrix is ​​updated using the training sample set, specifically as follows: Figure 10 The steps shown are as follows:

[0193] In step 1001: Based on the overall dictionary matrix and the target phase matrix, the sparse representation of the target phase matrix on the overall dictionary matrix is ​​obtained.

[0194] The target phase matrix is ​​constructed based on the total phase in the training sample set.

[0195] Formula 23 is used to obtain the sparse representation of the target phase matrix on the global dictionary matrix:

[0196] Φ = DX, (Formula 23)

[0197] Where Φ is the target phase matrix, D is the global dictionary matrix, and X is the sparse representation of the target phase matrix on the global dictionary matrix.

[0198] Since the sparse representation X is a k×m matrix, where k = k B +k H +k N k B k is the preset number of samples corresponding to breathing. H k is the preset number of samples corresponding to the heartbeat. NTo represent the preset number of samples corresponding to the interference, where m is the total number of phases in the training sample set, Equation 23 can be expanded based on Equations 7, 9, and 11 to obtain Equation 24:

[0199]

[0200] Wherein, basis vectors in It is a basis vector obtained by sampling within a preset respiratory rate range based on frequency intervals. in It is a basis vector obtained by sampling within a preset heartbeat frequency range based on frequency intervals. in It is obtained by sampling within a preset interference frequency range based on frequency intervals.

[0201] Based on formulas 7, 9, and 11, formula 24 can be rewritten as formula 25:

[0202]

[0203] Among them, X B D is the dictionary matrix corresponding to the respiratory signals. B sparse representation of X H D is the dictionary matrix corresponding to the heartbeat signal. H sparse representation of X N D is the dictionary matrix corresponding to the interference signal. N sparse representation.

[0204] In step 1002: The sparse representation is updated using the orthogonal matching pursuit method to obtain the updated sparse representation.

[0205] As can be seen from Equation 25, the target phase signal is decomposed into a linear combination of respiratory signal, heartbeat signal, and interference signal. In this application, the convergence of training the overall dictionary matrix can be determined by setting the number of iterations. Updating the overall dictionary matrix once represents one iteration.

[0206] For example, if the number of iterations is set to τ, the convergence condition is as shown in Equation 26:

[0207]

[0208] Where, x i Let x be a row vector representing the sparse representation of X, and let D represent the coefficients of the global dictionary matrix D; ||x|| i ||0 is x i The l0 norm represents the number of non-zero elements, and τ is the number of iterations.

[0209] In some possible embodiments, the constrained optimization problem in Equation 26 can be transformed into an unconstrained optimization problem using the Lagrange multiplier method, as shown in Equation 27:

[0210]

[0211] Where λ is the regularization parameter, ||x i ||1 is x i The l1 norm, using x i The l1 norm is used to replace the l0 norm to simplify the calculation. Based on Equation 27, the sparse representation X can be updated by orthogonal matching pursuit.

[0212] In step 1003: Based on the updated sparse representation, the singular value decomposition method is used to update the overall dictionary matrix, resulting in the updated overall dictionary matrix.

[0213] After obtaining the updated sparse representation, we can get Formula 28:

[0214]

[0215] Among them, D fixed It is a matrix representing respiratory signals based on a pre-defined global dictionary matrix, X. fixed It is a matrix representing heartbeat signals based on a pre-defined overall dictionary matrix. d k x k Based on the overall dictionary matrix, D fixed X fixed Based on Equation 28, the problem of minimizing the reconstruction error can be rewritten as Equation 29 using the obtained interference signal:

[0216]

[0217] Based on Equation 29, singular value decomposition can be used to determine the residual E. i Performing singular value decomposition yields Formula 30:

[0218] U∑V T =SVD(E i ), (Formula 30)

[0219] Where U is the first positive definite matrix, V is the second positive definite matrix, and V T Let ∑ be the transpose of the second positive definite matrix, and let ∑ be a diagonal matrix.

[0220] In some possible embodiments, the optimal rank is determined by the first column of U and the first element of ∑ and V. T The first column constitutes the formula, thus we can obtain formulas 31 and 32:

[0221] dk =u1, (Formula 31)

[0222]

[0223] Where u1 is the first column of U, and σ1 is the first singular value of ∑, which is also the largest singular value in ∑. For V T The first column.

[0224] After obtaining d k and x k Then, formulas 31 and 32 can be substituted into formula 24 to obtain the updated overall dictionary matrix.

[0225] Step 702: Based on the overall dictionary matrix and the target phase signal, obtain the sparse representation of the target phase signal on the overall dictionary matrix.

[0226] The specific implementation method of this step is the same as that of step 1001, and will not be repeated here.

[0227] Step 703: Obtain the respiratory signal based on the overall dictionary matrix and the heartbeat signal based on the sparse representation.

[0228] In some possible embodiments, the respiratory signal is obtained based on the overall dictionary matrix, specifically as follows: Figure 11 The steps shown are as follows:

[0229] In step 1101: Perform a fast Fourier transform on each column of the overall dictionary matrix to obtain the frequency of each column.

[0230] For example: Let the i-th column in the overall dictionary matrix be denoted as d. i (t), performing a Fast Fourier Transform on the i-th column yields the frequency corresponding to the i-th column:

[0231] In step 1102: For each column of frequency, determine whether the frequency falls within the preset respiratory rate range; if it does, then determine the frequency as the first frequency.

[0232] In this application, after obtaining the frequency of each column, the frequency corresponding to the respiratory signal can be filtered out by preset respiratory frequency range.

[0233] In step 1103: Construct a first support set based on the first frequency.

[0234] In this application, Formula 33 can be used to construct the first support set:

[0235]

[0236] Among them, Ω BAs the first support set, This is the upper limit of the preset respiratory rate range. This is the lower limit of the preset respiratory rate range. Let i be the i-th respiratory rate.

[0237] In step 1104: the respiratory signal is obtained based on the first support set.

[0238] After obtaining the first support set, the corresponding columns need to be found from the overall dictionary matrix based on the first support set to construct the respiratory signal, as shown in Equation 34:

[0239]

[0240] in, This is a breathing signal. For the corresponding columns determined from the overall dictionary matrix based on the first support set, The corresponding rows are determined from the sparse representation based on the first support set.

[0241] For example: if the first support set includes: column 1, column 2, and column 3, then... These are the first, second, and third columns of the overall dictionary matrix. These represent the first, second, and third lines in a sparse representation.

[0242] In other possible embodiments, the heartbeat signal is obtained based on a sparse representation, specifically as follows: Figure 12 The steps shown are as follows:

[0243] In step 1201: Perform a Fast Fourier Transform on each row of the sparse representation to obtain the frequency of each row.

[0244] For example: let x denote the i-th row in the sparse representation. i (t), performing a Fast Fourier Transform on the i-th row yields the frequency corresponding to the i-th row as follows:

[0245] In step 1202: For the frequency of each row, determine whether the frequency belongs to the preset heart rate range; if it does, then determine the frequency as the second frequency.

[0246] In this application, after obtaining the frequency of each row, the frequency corresponding to the heartbeat signal can be filtered out by preset heartbeat frequency range.

[0247] In step 1203: Construct a second support set based on the second frequency.

[0248] In this application, Formula 35 can be used to construct the second support set:

[0249]

[0250] Among them, Ω H For the second support set, To set the upper limit of the preset heart rate range, This is the lower limit of the preset heart rate range. Let be the i-th heartbeat frequency.

[0251] In step 1204: the heartbeat signal is obtained based on the second support set.

[0252] After obtaining the second support set, it is necessary to find the corresponding rows from the sparse representation based on the second support set to construct the heartbeat signal, as shown in Equation 36:

[0253]

[0254] in, This is a heartbeat signal. For the corresponding columns determined from the overall dictionary matrix based on the second support set, The corresponding rows are determined from the sparse representation based on the second support set.

[0255] For example: if the second support set includes: row 1, row 2, and row 3, then... These are the first, second, and third columns of the overall dictionary matrix. These represent the first, second, and third lines in a sparse representation.

[0256] In some possible embodiments, after determining the respiratory and heartbeat signals, in order to allow the user to more intuitively understand their physical condition, therefore, after implementing... Figure 3 After the steps shown, the respiratory rate can be obtained based on the respiratory signal and a preset respiratory rate range, and the heart rate can be obtained based on the heart rate signal and a preset heart rate range.

[0257] In some possible embodiments, the respiratory rate is obtained based on the respiratory signal and a preset respiratory rate range, specifically as follows: Figure 13 The steps shown are as follows:

[0258] In step 1301: the respiratory signal is subjected to DC filtering to obtain the filtered respiratory signal.

[0259] DC filtering of the respiratory signal can be performed using formula 37, where:

[0260]

[0261] in, It is the filtered respiratory signal. It is a breathing signal, and N is the number of slow time dimensions.

[0262] In step 1302: Perform a fast Fourier transform on the filtered respiratory signal to obtain the first respiratory frequency.

[0263] The fast Fourier transform of the filtered respiratory signal can be performed using Equation 38, where:

[0264]

[0265] in, It is the first respiratory rate. It is the filtered respiratory signal.

[0266] In step 1303: the first respiratory rate within the preset respiratory rate range is determined as the second respiratory rate.

[0267] In this application, This is the upper limit of the preset respiratory rate range. To set the lower limit of the preset respiratory rate range, based on and The second respiratory rate within the preset respiratory rate range can then be determined, and this second respiratory rate is denoted as...

[0268] In step 1304: the maximum value of the second respiratory rate is taken as the respiratory rate.

[0269] In this application, considering that a higher frequency indicates a higher intensity of characterization, the maximum value is selected as the respiratory frequency in the second respiratory frequency.

[0270] To simplify the calculation process, steps 1302-1304 can be simplified to formula 38:

[0271]

[0272] Among them, f B Respiratory rate, This is the lower limit of the preset respiratory rate range. It is the filtered respiratory signal.

[0273] In some possible embodiments, the heart rate is obtained based on the heart rate signal and a preset heart rate frequency range, specifically as follows: Figure 14 The steps shown are as follows:

[0274] In step 1401: the heartbeat signal is subjected to DC filtering to obtain the filtered heartbeat signal.

[0275] DC filtering of the heartbeat signal can be performed using formula 39, where:

[0276]

[0277] in, It is the filtered heartbeat signal. It is a heartbeat signal, and N is the number of slow time dimensions.

[0278] In step 1402: Perform a fast Fourier transform on the filtered heartbeat signal to obtain the first heartbeat frequency.

[0279] The Fast Fourier Transform (FFT) of the filtered heartbeat signal can be performed using Equation 40, where:

[0280]

[0281] in, It is the first heartbeat frequency. It is the filtered heartbeat signal.

[0282] In step 1403: the first heartbeat frequency within the preset heartbeat frequency range is determined as the second heartbeat frequency.

[0283] In this application, To set the upper limit of the preset heart rate range, To set the lower limit of the preset heart rate range, based on and The second heart rate within the preset heart rate range can then be determined, and this second heart rate is denoted as...

[0284] In step 1404: the maximum value of the second heartbeat frequency is taken as the heartbeat frequency.

[0285] In this application, considering that a higher frequency indicates a higher intensity, the maximum value is selected as the heartbeat frequency in the second heartbeat frequency.

[0286] To simplify the calculation process, steps 1302-1304 can be simplified to formula 38:

[0287]

[0288] Among them, f H Heart rate To set the upper limit of the preset heart rate range, This is the lower limit of the preset heart rate range. It is the filtered heartbeat signal.

[0289] For example: target phase signal such as Figure 15A As shown, this application adopts Figure 3 The respiratory signals obtained from the steps shown are as follows: Figure 15B As shown, the obtained heartbeat signal is as follows Figure 15C As shown.

[0290] pass Figures 15A-15C It can be seen that in Figure 15A The heartbeat information is almost completely masked by the breathing information; only the waveform of the breath can be seen. Figure 15B and Figure 15C The respiratory and heartbeat signals processed by the method of this application show that the respiratory and heartbeat signals are well reconstructed and the waveforms are relatively smooth without obvious interference components.

[0291] In this application, the construction of the overall dictionary matrix is ​​divided into three parts, representing breathing, heartbeat, and interference respectively. Since different signals have different frequencies, the discontinuity of the three parts of the overall dictionary matrix allows breathing, heartbeat, and interference to be better separated.

[0292] To further improve the accuracy of signal detection, this application also provides a signal detection method, such as... Figure 16 As shown:

[0293] Step 1601: Send radar signals and receive echo signals; the echo signals are formed after the radar signals are reflected by the human body.

[0294] The specific implementation method of this step is the same as that of step 301, and will not be repeated here.

[0295] Step 1602: Preprocess the echo signal to obtain the target phase signal.

[0296] The specific implementation method of this step is the same as that of step 302, and will not be repeated here.

[0297] Step 1603: Obtain the pre-built overall dictionary matrix and update the pre-built overall dictionary matrix to obtain the updated overall dictionary matrix.

[0298] In some possible embodiments, the pre-built overall dictionary matrix is ​​updated, specifically as follows: Figure 17 The steps shown are as follows:

[0299] In step 1701: Based on the overall dictionary matrix and the target phase signal, the sparse representation of the target phase signal on the overall dictionary matrix is ​​obtained.

[0300] Formula 39 is used to obtain the sparse representation of the target phase signal on the global dictionary matrix:

[0301]

[0302] in, Let X be the target phase signal, D be the global dictionary matrix, and X be the sparse representation of the target phase signal on the global dictionary matrix.

[0303] In step 1702: the sparse representation is updated using the orthogonal matching pursuit method to obtain the updated sparse representation.

[0304] The specific implementation method of this step is the same as that of step 1002, at which point τ equals 1.

[0305] In step 1703: Based on the updated sparse representation, the singular value decomposition method is used to update the overall dictionary matrix, resulting in the updated overall dictionary matrix.

[0306] The specific implementation method of this step is the same as that of step 1003, and will not be repeated here.

[0307] Step 1604: Based on the target phase signal and the updated global dictionary matrix, obtain the respiratory signal and heartbeat signal.

[0308] The specific implementation method of this step is the same as that of step 303, and will not be repeated here.

[0309] Step 1605: Obtain the respiratory rate based on the respiratory signal and the preset respiratory rate range, and obtain the heart rate based on the heartbeat signal and the preset heartbeat rate range.

[0310] The specific implementation method of this step is the same as that of step 304, and will not be repeated here.

[0311] To facilitate further understanding, the overall flow of a signal detection method provided in the embodiments of this application is described below, such as... Figure 18 As shown, where:

[0312] In step 1801: a radar signal is sent and an echo signal is received.

[0313] In step 1802: coherently accumulate the fast time dimension of the echo signal to obtain the coherently accumulated signal.

[0314] In step 1803: a phase extraction operation is performed on the signal after coherent accumulation to obtain phase information.

[0315] In step 1804: the phase information is corrected to obtain the target phase signal.

[0316] In step 1805: Obtain the overall dictionary matrix updated based on the training sample set.

[0317] In step 1806: the overall dictionary matrix is ​​updated to obtain the updated overall dictionary matrix.

[0318] In step 1807: Based on the target phase signal and the updated global dictionary matrix, the respiratory signal and heartbeat signal are obtained.

[0319] In step 18051: Obtain the pre-constructed overall dictionary matrix and training sample set.

[0320] In step 18052: Obtain the training sample set and construct the target phase matrix based on the total phase in the training sample set.

[0321] In step 18053: Based on the overall dictionary matrix and the target phase matrix, the sparse representation of the target phase matrix on the overall dictionary matrix is ​​obtained.

[0322] In step 18054: the sparse representation is updated using the orthogonal matching pursuit method to obtain the updated sparse representation.

[0323] In step 18055: Based on the updated sparse representation, the singular value decomposition method is used to update the overall dictionary matrix, resulting in the updated overall dictionary matrix.

[0324] In step 1808: the respiratory rate is obtained based on the respiratory signal and a preset respiratory rate range, and the heart rate is obtained based on the heart rate signal and a preset heart rate range.

[0325] Based on the same inventive concept, after introducing a signal detection method provided by an embodiment of this application, as follows... Figure 19 As shown, a terminal device control device 1900 provided in an embodiment of this application will be described below. The device includes:

[0326] Receiver module 19001 is used to acquire echo signals;

[0327] Preprocessing module 19002 is used to preprocess the echo signal to obtain the target phase signal;

[0328] The signal separation module 19003 is used to obtain the respiratory signal and the heartbeat signal based on the target phase signal and the acquired pre-constructed overall dictionary matrix.

[0329] Corresponding to the above embodiments, this application also provides an electronic device. Figure 20 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 2000 may include: a processor 2001, a memory 2002, and a communication unit 2003. The electronic device also includes a radar component (not shown in the figure), which has the ability to transmit and receive radar signals.

[0330] These components communicate via one or more buses. Those skilled in the art will understand that the structure of the electronic device shown in the figure does not constitute a limitation on the embodiments of the present invention. It can be a bus topology or a star topology, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0331] The communication unit 2003 is used to establish a communication channel, enabling the electronic device to communicate with other devices. It can receive user data sent by other devices or send user data to other devices.

[0332] The processor 2001 serves as the control center of the electronic device, connecting various parts of the device via interfaces and lines. It executes software programs and / or modules stored in the memory 2002 and retrieves data stored in the memory to perform various functions and / or process data. The processor may be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 2001 may consist only of a central processing unit (CPU). In this embodiment, the CPU may have a single processing core or include multiple processing cores.

[0333] The memory 2002 is used to store the execution instructions of the processor 2001. The memory 2002 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0334] When the execution instructions in memory 2002 are executed by processor 2001, the electronic device 2000 is able to perform operations. Figure 3 Some or all of the steps in the illustrated embodiments.

[0335] In a specific implementation, the present invention also provides a computer storage medium, wherein the computer storage medium may store a program, which, when executed, may include some or all of the steps of the various embodiments of the signal detection method provided by the present invention. The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0336] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments of the present invention.

[0337] The same or similar parts between the various embodiments in this specification can be referred to mutually. In particular, the device embodiments and terminal embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

Claims

1. A signal detection method, characterized in that, The method includes: Acquire echo signal; The echo signal is preprocessed to obtain the target phase signal; The echo signal includes a fast time dimension and a slow time dimension. The preprocessing of the echo signal to obtain the target phase signal includes: performing coherent accumulation on the fast time dimension of the echo signal to obtain a coherently accumulated signal; performing phase extraction on the coherently accumulated signal to obtain phase information; and performing correction processing on the phase information to obtain the target phase signal. Based on the target phase signal and the pre-constructed overall dictionary matrix, the respiratory signal and heartbeat signal are obtained; the overall dictionary matrix is ​​a discontinuous matrix constructed based on the dictionary matrix corresponding to the respiratory signal and the dictionary matrix corresponding to the heartbeat signal. The step of obtaining the respiratory signal and heartbeat signal based on the target phase signal and a pre-constructed overall dictionary matrix includes: acquiring the pre-constructed overall dictionary matrix; obtaining a sparse representation of the target phase signal on the overall dictionary matrix based on the overall dictionary matrix and the target phase signal; obtaining the respiratory signal based on the overall dictionary matrix, and obtaining the heartbeat signal based on the sparse representation. Wherein: obtaining the respiratory signal based on the overall dictionary matrix includes: performing a fast Fourier transform on each column of the overall dictionary matrix to obtain the frequency of each column; determining whether the frequency of each column belongs to a preset respiratory frequency range; if it does, determining the frequency as a first frequency; constructing a first support set based on the first frequency; and obtaining the respiratory signal based on the first support set. The step of obtaining the heartbeat signal based on the sparse representation includes: performing a fast Fourier transform on each row of the sparse representation to obtain the frequency of each row; determining whether the frequency of each row belongs to a preset heartbeat frequency range; if it does, determining the frequency as a second frequency; constructing a second support set based on the second frequency; and obtaining the heartbeat signal based on the second support set. The overall dictionary matrix includes: D = D B +D H +D N ; Where D is the overall dictionary matrix, D B Let D be the dictionary matrix corresponding to the respiratory signal. H Let D be the dictionary matrix corresponding to the heartbeat signal. N This is the dictionary matrix corresponding to the interference signal.

2. The method according to claim 1, characterized in that, The step of correcting the phase information to obtain the target phase signal includes: The phase information is subjected to linear regression to obtain the slope and intercept corresponding to the phase information; The phase information is subjected to amplitude-phase processing based on the slope and intercept to obtain the processed phase signal; The processed phase signal is normalized to obtain the target phase signal.

3. The method according to claim 1, characterized in that, Before obtaining the respiratory signal and heartbeat signal by comparing the target phase signal with a pre-constructed overall dictionary matrix, the method further includes: The pre-constructed overall dictionary matrix is ​​updated to obtain the updated overall dictionary matrix; The process of obtaining respiratory and heartbeat signals based on the target phase signal and the acquired pre-constructed overall dictionary matrix includes: Based on the target phase signal and the updated overall dictionary matrix, the respiratory signal and heartbeat signal are obtained.

4. The method according to claim 3, characterized in that, The update process for the pre-constructed overall dictionary matrix includes: Based on the overall dictionary matrix and the target phase signal, a sparse representation of the target phase signal on the overall dictionary matrix is ​​obtained; The sparse representation is updated using the orthogonal matching pursuit method to obtain the updated sparse representation; Based on the updated sparse representation, the singular value decomposition method is used to update the overall dictionary matrix, resulting in the updated overall dictionary matrix.

5. The method according to claim 1, characterized in that, After obtaining the respiratory and heartbeat signals, the method further includes: The respiratory frequency is obtained based on the respiratory signal and a preset respiratory frequency range, and the heart rate is obtained based on the heart rate signal and a preset heart rate range.

6. The method according to claim 5, characterized in that, The step of obtaining the respiratory frequency based on the respiratory signal and a preset respiratory frequency range includes: The respiratory signal is subjected to DC filtering to obtain a filtered respiratory signal; The filtered respiratory signal is subjected to a fast Fourier transform to obtain the first respiratory frequency; The first respiratory frequency within the preset respiratory frequency range is determined as the second respiratory frequency; The maximum value among the second respiratory rates is taken as the respiratory rate.

7. The method according to claim 5, characterized in that, The step of obtaining the heart rate based on the heart rate signal and a preset heart rate frequency range includes: The heartbeat signal is subjected to DC filtering to obtain a filtered heartbeat signal; The filtered heartbeat signal is subjected to a fast Fourier transform to obtain the first heartbeat frequency; The first heartbeat frequency within the preset heartbeat frequency range is determined as the second heartbeat frequency; The maximum value among the second heartbeat frequencies is taken as the heartbeat frequency.

8. The method according to any one of claims 1-7, characterized in that, The pre-built global dictionary matrix is ​​constructed according to the following method: Based on a preset respiratory rate range and a preset number of samples, a dictionary matrix corresponding to the respiratory signal is obtained; Based on the preset heartbeat frequency range and the preset number of samples, the dictionary matrix corresponding to the heartbeat signal is obtained; Based on the preset interference frequency range and preset sampling number, the dictionary matrix corresponding to the interference signal is obtained; The overall dictionary matrix is ​​obtained based on the dictionary matrix corresponding to the respiratory signal, the dictionary matrix corresponding to the heartbeat signal, and the dictionary matrix corresponding to the interference signal.

9. The method according to claim 8, characterized in that, After obtaining the overall dictionary matrix, the method further includes: The respiratory phase and heartbeat phase are determined by an accumulator; the total phase is obtained based on the respiratory phase, the heartbeat phase, and a preset interference phase. A training sample set is constructed based on the total phase; The training sample set is used to update the overall dictionary matrix, and the updated overall dictionary matrix is ​​used as the overall dictionary matrix.

10. The method according to claim 1, characterized in that, Before acquiring the echo signal, the method further includes: The radar signal is transmitted; the radar signal is used to form an echo signal after being reflected by the human body.

11. A signal detection device, characterized in that, The device includes: The receiving module acquires the echo signal; The preprocessing module is used to preprocess the echo signal to obtain the target phase signal; The preprocessing module is specifically used for: coherently accumulating the fast time dimension of the echo signal to obtain a coherently accumulated signal; performing phase extraction based on the coherently accumulated signal to obtain phase information; and performing correction processing on the phase information to obtain the target phase signal. The signal separation module is used to obtain respiratory signals and heartbeat signals based on the target phase signal and the acquired pre-constructed overall dictionary matrix; the overall dictionary matrix is ​​a discontinuous matrix constructed based on the dictionary matrix corresponding to the respiratory signal and the dictionary matrix corresponding to the heartbeat signal. The signal separation module is specifically used for: acquiring a pre-constructed overall dictionary matrix; obtaining a sparse representation of the target phase signal on the overall dictionary matrix based on the overall dictionary matrix and the target phase signal; obtaining the respiratory signal based on the overall dictionary matrix, and obtaining the heartbeat signal based on the sparse representation; The signal separation module is specifically used for: performing a fast Fourier transform on each column of the overall dictionary matrix to obtain the frequency of each column; determining whether the frequency of each column belongs to a preset respiratory frequency range; if it does, determining the frequency as a first frequency; constructing a first support set based on the first frequency; and obtaining a respiratory signal based on the first support set. The signal separation module is specifically used for: performing a fast Fourier transform on each row of the sparse representation to obtain the frequency of each row; determining whether the frequency of each row belongs to a preset heartbeat frequency range; if it does, determining the frequency as a second frequency; constructing a second support set based on the second frequency; and obtaining a heartbeat signal based on the second support set. The overall dictionary matrix includes: D = D B +D H +D N ; Where D is the overall dictionary matrix, D B Let D be the dictionary matrix corresponding to the respiratory signal. H Let D be the dictionary matrix corresponding to the heartbeat signal. N This is the dictionary matrix corresponding to the interference signal.

12. An electronic device, characterized in that, It includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to perform the method of any one of claims 1-10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1-10.

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