Physical sign monitoring method and device, equipment and storage medium
By determining the weight of the beamforming process based on constraints in multi-object sign monitoring, the problem of difficulty in obtaining noise power in complex environments is solved, and the signal-to-noise ratio and monitoring accuracy are improved.
Patent Information
- Application Number
- CN202311787394.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-12-22
- Publication Date
- 2025-06-24
AI Technical Summary
The prior art is difficult to obtain real noise power when dealing with complex environments or weak signals, resulting in limited accuracy and efficiency of multi-object sign monitoring.
By acquiring the target signal of the object to be tested and determining the weight of the beamforming process based on the first constraint condition and the second constraint condition, it is ensured that the target signal does not distort after the beamforming and interference is suppressed, while the output signal reaches a local maximum value.
The signal-to-noise ratio of the target signal is improved, the accuracy and efficiency of sign monitoring in complex environments or weak signals are enhanced, and the noise power can be obtained.
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Figure CN120189064A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of health monitoring, and particularly to a body sign monitoring method, device, equipment and storage medium. Background Art
[0002] Using microwave radar for body sign monitoring has the characteristic of realizing body sign monitoring without physical contact with the object to be measured, and has been widely applied in aspects such as home and medical health monitoring, and driver vital status monitoring. Compared with single-object body sign monitoring, multi-object body sign monitoring can greatly improve the monitoring efficiency and has become the research focus in recent years.
[0003] When performing multi-object body sign detection, the related technology usually uses the least squares beamforming algorithm to separate the respiratory signals of different targets. This method is difficult to obtain the true noise power in the face of complex environments or weak signal situations.
[0004] Content of the Application
[0005] The main purpose of the present application is to provide a body sign monitoring method, device, equipment and storage medium, aiming to solve the technical problem that it is difficult to obtain the true noise power in the face of complex environments or weak signal situations in the related technology.
[0006] To achieve the above purpose, the present application provides a body sign monitoring method, and the method includes:
[0007] Obtain n target signals corresponding to each of the p objects to be measured, where the n target signals correspond one-to-one to n signal receiving channels, and both n and p are positive integers greater than or equal to 2;
[0008] Let one of the p objects to be measured be the target object, and the other p - 1 objects to be measured be interference objects;
[0009] Determine n weights for beamforming processing of the n target signals corresponding to the target object based on a first constraint condition and a second constraint condition, where the n weights correspond one-to-one to the n signal receiving channels. The first constraint condition is used to ensure that the n target signals corresponding to the target object are not distorted after beamforming processing, and to suppress the n target signals corresponding to each interference object after beamforming processing; the second constraint condition is used to ensure that the output signal of the n target signals corresponding to the target object after beamforming processing reaches a local maximum value;
[0010] Perform beamforming processing on the n target signals corresponding to the target object based on the n weights to obtain the output signal corresponding to the target object;
[0011] Monitor the physical signs of the target object according to the output signal corresponding to the target object.
[0012] Optionally, the step of determining the n weights for beamforming processing of the n target signals corresponding to the target object based on the first constraint condition and the second constraint condition includes:
[0013] Obtain the azimuth angle corresponding to each object to be measured.
[0014] Determine the n steering vectors of each object to be measured at its corresponding azimuth angle, and the n steering vectors correspond one-to-one to the n signal receiving channels.
[0015] Construct a functional relationship according to the n steering vectors and n unknown weight vectors corresponding to each object to be measured, and the n unknown weight vectors correspond one-to-one to the n signal receiving channels.
[0016] Construct the first constraint condition based on the p functional relationships corresponding to the p objects to be measured. The first constraint condition includes: the function value of the functional relationship corresponding to the target object is equal to the first expected response, and the function value of the functional relationship corresponding to the interference object is equal to the second expected response.
[0017] Construct the second constraint condition based on the functional relationship corresponding to the target object. The second constraint condition includes: the first derivative of the functional relationship corresponding to the target object is 0, and the second derivative of the functional relationship corresponding to the target object is less than 0.
[0018] Solve the n unknown weight vectors based on the first constraint condition and the second constraint condition to determine the n weights.
[0019] Optionally, the functional relationship is as follows:
[0020]
[0021] Where F w (θ j ) is the functional relationship corresponding to the jth object to be measured, w i is the unknown weight vector corresponding to the ith signal receiving channel, α i (θ j ) is the steering vector corresponding to the jth object to be measured at the ith signal receiving channel, θ j is the azimuth angle corresponding to the jth object to be measured, where 1 ≤ i ≤ n, 1 ≤ j ≤ p.
[0022] Optionally, when the mth object to be measured among the p objects to be measured is the target object, the first constraint condition satisfies the following system of equations:
[0023]
[0024] wherein, r d1 is the first expected response, and r d2 is the second expected response.
[0025] Optionally, the first expected response is 1, and the second expected response is 0.
[0026] Optionally, when the m-th target to be measured among the p targets to be measured is the target object, the second constraint condition satisfies the following system of equations:
[0027]
[0028] Optionally, the step of solving the n unknown weight vectors includes:
[0029] Solving the n unknown weight vectors by using Newton's method or the steepest descent method.
[0030] Optionally, before the step of obtaining the n target signals corresponding to each target to be measured among the p targets to be measured, the method further includes:
[0031] Obtaining the distance of each target to be measured;
[0032] Obtaining the n input signals corresponding to each target to be measured, where the n input signals correspond one-to-one to the n signal receiving channels, and the input signals are signals obtained by performing mixing processing on the echo signals received by the signal receiving channels;
[0033] Performing windowing and range dimension fast Fourier transform (FFT) on each input signal to obtain the frequency domain signal corresponding to each input signal;
[0034] Extracting the n frequency domain signals corresponding to each target to be measured according to the distance of each target to be measured to obtain the n target signals corresponding to each target to be measured.
[0035] Optionally, the step of performing physical sign monitoring on the target object according to the output signal corresponding to the target object includes:
[0036] Performing DC removal, phase extraction, and phase unwrapping on the output signal corresponding to the target object to obtain the phase corresponding to the target object;
[0037] Performing slow time dimension windowing and FFT processing on multiple frames of the phase corresponding to the target object to obtain the physical sign change condition of the target object.
[0038] This application also provides a physical sign monitoring device, and the device includes:
[0039] An acquisition module, configured to acquire n target signals corresponding to each of p objects to be measured, where the n target signals correspond one-to-one to n signal receiving channels, and n and p are both positive integers greater than or equal to 2;
[0040] A physical sign monitoring module, configured to set 1 object to be measured among the p objects to be measured as a target object, and the other p - 1 objects to be measured as interference objects; determine n weights for performing beamforming processing on the n target signals corresponding to the target object based on a first constraint condition and a second constraint condition, where the n weights correspond one-to-one to the n signal receiving channels, and the first constraint condition is used to ensure that the n target signals corresponding to the target object are not distorted after beamforming processing, and to ensure that the n target signals corresponding to each interference object are suppressed after beamforming processing; the second constraint condition is used to ensure that the output signal after beamforming processing of the n target signals corresponding to the target object reaches a local maximum value; perform beamforming processing on the n target signals corresponding to the target object based on the n weights to obtain an output signal corresponding to the target object; and monitor the physical signs of the target object according to the output signal corresponding to the target object.
[0041] Optionally, the acquisition module is further configured to acquire the azimuth angle corresponding to each object to be measured; the physical sign monitoring module is further configured to determine n steering vectors of each object to be measured at its corresponding azimuth angle, where the n steering vectors correspond one-to-one to the n signal receiving channels; construct a functional relationship according to the n steering vectors and n unknown weight vectors corresponding to each object to be measured, where the n unknown weight vectors correspond one-to-one to the n signal receiving channels; construct the first constraint condition based on the p functional relationships corresponding to the p objects to be measured, and the first constraint condition includes: the function value of the functional relationship corresponding to the target object is equal to a first desired response, and the function value of the functional relationship corresponding to the interference object is equal to a second desired response; construct the second constraint condition based on the functional relationship corresponding to the target object, and the second constraint condition includes: the first derivative of the functional relationship corresponding to the target object is 0, and the second derivative of the functional relationship corresponding to the target object is less than 0; solve the n unknown weight vectors based on the first constraint condition and the second constraint condition to determine the n weights;
[0042] And / or the obtaining module is further configured to obtain the distance of each object to be measured; obtain the n input signals corresponding to each object to be measured, where the n input signals correspond one-to-one to the n signal receiving channels, and the input signal is a signal obtained by performing mixing processing on the echo signal received by the signal receiving channel; the physical sign monitoring module is further configured to perform windowing and range dimension fast Fourier transform (FFT) on each input signal to obtain the frequency domain signal corresponding to each input signal; extract the n frequency domain signals corresponding to each object to be measured according to the distance of each object to be measured to obtain the n target signals corresponding to each object to be measured.
[0043] And / or the physical sign monitoring module is further configured to perform DC removal, phase extraction, and phase unwrapping processing on the output signal corresponding to the target object to obtain the phase corresponding to the target object; perform slow-time dimension windowing and FFT processing on multiple frames of phases corresponding to the target object to obtain the physical sign change condition of the target object.
[0044] The present application further provides a physical sign monitoring device, where the physical sign monitoring device includes: a memory, a processor, and a physical sign monitoring program stored on the memory and executable on the processor, and when the physical sign monitoring program is executed by the processor, the steps of the physical sign monitoring method described above are implemented.
[0045] The present application further proposes a storage medium, where a physical sign monitoring program is stored on the storage medium, and when the physical sign monitoring program is executed by a processor, the steps of the physical sign monitoring method described above are implemented.
[0046] A method, device, equipment and storage medium for physical sign monitoring provided by the present application. Compared with the related art where it is difficult to obtain the true noise power in the face of complex environments or weak signal scenarios, in the present application, n target signals corresponding to each of the p objects to be measured are obtained. The n target signals correspond one-to-one to n signal receiving channels, where both n and p are positive integers greater than or equal to 2. Let one of the p objects to be measured be the target object, and the other p - 1 objects to be measured be interference objects. Based on the first constraint condition and the second constraint condition, n weights for beamforming processing of the n target signals corresponding to the target object are determined. The n weights correspond one-to-one to the n signal receiving channels, where the first constraint condition is used to ensure that the n target signals corresponding to the target object are not distorted after beamforming processing and to suppress the n target signals corresponding to each interference object after beamforming processing. The second constraint condition is used to make the output signal of the n target signals corresponding to the target object reach a local maximum value. Based on the n weights, beamforming processing is performed on the n target signals corresponding to the target object to obtain the output signal corresponding to the target object. Physical sign monitoring of the target object is performed according to the output signal corresponding to the target object. By optimizing the constraint conditions of the least squares beamforming method in the present application, while the interference in the output signal is suppressed, the target signal still has a relatively high signal-to-noise ratio. Specifically, new constraint conditions are added using the first-order and second-order derivatives, making the output response in the target direction a local maximum value, thereby improving the signal-to-noise ratio of the output signal. Compared with the related art, it can not only separate the respiratory signals of different targets based on the maximum signal-to-noise ratio criterion, but also obtain the true noise power in the face of complex environments or weak signal scenarios. BRIEF DESCRIPTION OF THE DRAWINGS
[0047] The drawings herein are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application and used together with the specification to explain the principles of the present application. To more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the following will briefly introduce the drawings required for use in the description of the embodiments or the related art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.
[0048] Figure 1 It is a schematic diagram of the overall process of physical sign monitoring related to the present application;
[0049] Figure 2 It is a schematic diagram of the process of the physical sign monitoring method of the present application;
[0050] Figure 3Schematic diagram of the process for determining the weights of the beamforming algorithm of the present application;
[0051] Figure 4 Schematic diagram of the structure of the body sign monitoring device of the present application;
[0052] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the solution of the embodiment of the present application.
[0053] The realization of the purpose, functional features and advantages of the present application will be further described with reference to the accompanying drawings in combination with the embodiments. Detailed implementation manners
[0054] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0055] Before introducing the embodiments of the present application, the terms related to the present application will be introduced first:
[0056] Body sign monitoring: The body sign monitoring involved in the present application refers to the body sign monitoring using a microwave radar (for example, a millimeter-wave radar). Due to the ultra-high precision of the short wave, the microwave radar can detect movements as short as millimeters. For example, when a person is breathing quietly, the microwave radar can accurately identify the tiny movements such as the heartbeat and breathing at the chest and abdomen positions of the human body. Due to these tiny movements, the reflected signal of the microwave radar is phase-modulated, and the modulation includes components of the tiny movements, such as the movements caused by the heartbeat and breathing. Therefore, by transmitting multiple frequency-modulated signals through the microwave radar and demodulating, integrating, amplifying, filtering and other processing on the echo signals reflected by the human body, the body sign parameters of the measured object can be obtained, for example, the heartbeat frequency or the breathing frequency, etc.
[0057] It should be noted that the microwave radar involved in the present application refers to a multiple-input multiple-output (MIMO) radar. This MIMO radar uses multiple transmitting antennas and receiving antennas at the transmitting end and the receiving end respectively. Among them, the multiple receiving antennas can be understood as multiple signal receiving channels.
[0058] Beamforming: A signal processing technology. In array signal processing, the signals collected by multiple signal receiving channels are often weighted and combined to fuse the data of multiple channels to synthesize a composite signal with a specific direction response, which can achieve signal enhancement in a specific direction and suppress signal interference in other directions to improve the signal-to-noise ratio of the received signal. This weighted combination processing is called beamforming.
[0059] Steering vector: It is a vector used to guide and indicate direction. In this application, the n steering vectors corresponding to a certain object to be measured can be determined respectively according to the array information of n signal receiving channels and the azimuth angle of the object to be measured. Among them, the n signal receiving channels correspond to n receiving antennas.
[0060] Fast Fourier transform (FFT) processing: It is a technology that converts a time-domain signal into a frequency-domain signal. Further, range-dimension FFT: It is a signal processing technology used to process radar data. By performing FFT on the received signal, the signal in the time domain can be converted into a signal in the frequency domain. In a millimeter-wave radar, range-dimension FFT can be used to convert the received echo signal into the distance information of the target object.
[0061] Windowing: Usually, the collected signal may not be an integer number of cycles. The signal with a non-integer number of cycles usually shows different characteristics from the original signal with an integer number of continuous cycles, thus affecting data analysis. Therefore, in actual signal processing, windowing can be used to present a continuous waveform as much as possible, thereby reducing the error generated when performing FFT on a non-integer number of cycles.
[0062] DC removal: A signal usually contains a DC component and an AC component. For many applications, the DC component is useless and may even interfere with signal processing. Therefore, the DC component in the signal can be removed during signal processing.
[0063] Phase extraction: Since the signal output after beamforming processing is a complex signal, and more accurate position information of the target lies in the phase of the complex signal, it is necessary to extract the phase in the complex signal to perform physical sign monitoring on the target object based on this phase.
[0064] Phase unwrapping: Phase unwrapping is a signal processing technology that eliminates the phase discontinuity by continuously adding or subtracting integer multiples of 2π to the phase jumps, so as to accurately analyze and process the signal. The purpose of phase unwrapping is to merge phase jumps. A phase jump refers to a discontinuous mutation in the phase of a signal, where the phase value suddenly jumps from one cycle to another, making the phase information unable to be directly and correctly interpreted and used.
[0065] As an example, the solution of this application can be applied to the scenario of multi-object physical sign monitoring, where the physical signs can be respiration or heartbeat, etc., or other physical signs, and this application does not make any limitations in this regard.
[0066] As an example, the method for determining the weights of the beamforming algorithm involved in the solution of this application can also be applied to other scenarios that require the use of the beamforming algorithm, and this application does not make any limitations in this regard.
[0067] Taking the monitoring scenario including the object to be measured 1, the object to be measured 2, channel 1, and channel 2 as an example, the overall process of vital sign monitoring will be introduced. Refer to Figure 1 , the overall process of vital sign monitoring includes:
[0068] Step S110, obtaining the echo signal and prior information of the object to be measured.
[0069] Specifically, the echo signal of the object to be measured can be obtained from the signal receiving channel of the radar.
[0070] It can be understood that in the scenario including the object to be measured 1, the object to be measured 2, channel 1, and channel 2, channel 1 can receive the echo signal 1 of the object to be measured 1 and the echo signal 1 of the object to be measured 2, and channel 2 can receive the echo signal 2 of the object to be measured 1 and the echo signal 2 of the object to be measured 2. Based on this, the echo signal 1 of the object to be measured 1 and the echo signal 1 of the object to be measured 2 can be obtained from channel 1, and the echo signal 2 of the object to be measured 1 and the echo signal 2 of the object to be measured 2 can be obtained from channel 2.
[0071] Among them, the prior information of the object to be measured includes distance and azimuth angle. For example, the distance 1 and azimuth angle 1 of the object to be measured 1, and the distance 2 and azimuth angle 2 of the object to be detected 2.
[0072] Optionally, the prior information can be detected in advance by a sensor, or can be set in advance by a user or a staff member, and the present application does not limit this.
[0073] Step S120, performing mixing processing on each echo signal to obtain a corresponding input signal.
[0074] Among them, the mixing processing refers to the processing of mixing the radar echo signal with the transmitted signal to obtain a beat signal.
[0075] Step S130, windowing each input signal, and then performing distance-dimensional FFT processing to obtain a corresponding frequency-domain signal.
[0076] Step S140, extracting based on the distance of the object to be measured in each frequency-domain signal to obtain a corresponding target signal.
[0077] Step S150, performing beamforming processing on the target signal corresponding to each object to be measured to obtain an output signal for each object to be measured.
[0078] That is, weighted summation processing is performed on multiple target signals corresponding to each object to be measured. For example, weighted summation processing is performed on target signal 1 and target signal 2 corresponding to object to be measured 1 to obtain the output signal of object to be measured 1; weighted summation processing is performed on target signal 1 and target signal 2 corresponding to object to be measured 2 to obtain the output signal of object to be measured 2.
[0079] Step S160: Perform DC removal, phase extraction, and phase unwrapping on each output signal to obtain the phase corresponding to each object to be measured.
[0080] As an example, filtering processing can also be performed on the output signal to filter other interference signals in the scene.
[0081] Step S170: Obtain the phases obtained from multiple frames, window the phases obtained from multiple frames along the slow time dimension, and perform FFT processing to obtain the physical sign change conditions of each object to be measured.
[0082] The physical sign change conditions can be, for example, respiratory rate, heart rate, etc.
[0083] Based on the above scenario, the background involved in this application is as follows:
[0084] Regarding the above step S150, considering that in the monitoring scenario of multiple objects, the mutual interference between different objects is relatively serious, the beamforming algorithm adopted needs to have the function of suppressing interference in a specific direction. Among them, the least squares beamforming algorithm is a relatively simple and easy-to-implement method, and this algorithm calculates the weights of different channels based on the principle of "minimizing the mean square error between the true response and the desired response of the beamformer".
[0085] Specifically, the formula expression of the above principle is:
[0086]
[0087] where w H is the conjugate transpose matrix of the weight vector, A = [α(θ1), α(θ2), …, α(θ j ), …, α(θ p )] is the steering vector matrix of p directions to be constrained, that is, the steering vector matrix corresponding to p objects to be measured, α(θ j ) is the steering vector corresponding to the jth object to be measured, r d is a 1×p dimensional desired response, θ j is the azimuth angle corresponding to the jth object to be measured. Among them, each object to be measured corresponds to n steering vectors, n is the number of signal reception channels. For example, the steering vector α(θ j ) corresponding to the jth object to be measured = [α1(θ j ), α2(θj ), …, α n (θ j )].
[0088] Based on the above expression, by selecting an appropriate weight vector w, the actual response w of the beamformer can be made to H A approach the desired response r d .
[0089] The above problem can be equivalently solved as a system of equations based on the least squares criterion as follows:
[0090]
[0091] Typically, the output of the desired algorithm can ensure that the signal is not distorted and suppresses interference signals in other directions to the greatest extent.
[0092] Based on this, in the related art, the weights of beamforming are usually determined in the following manner:
[0093] First, let one of the p objects to be measured be the target object, and the other objects to be measured be interference objects. Let the desired response corresponding to the target object be r d1 ; the desired response corresponding to the interference object be r d2 .
[0094] Exemplarily, r d1 = 1, r d2 = 0. Based on this, when taking the first object to be measured as the target object, it can be equivalently solved as a system of equations as follows:
[0095]
[0096] Based on this, the weights for beamforming processing of the first object to be measured can be obtained.
[0097] Similarly, the second object to be measured can be taken as the target object, and the others as interference objects, and the weights for beamforming processing of the second object to be measured can be obtained according to the above method. By analogy, the weights corresponding to all objects to be measured can be obtained.
[0098] The related art only makes constraints on the responses of the beamformer in the target direction and the interference direction. Although the beamforming weights obtained thereby can ensure that the signal in the target direction is not distorted, higher gains may be obtained in other directions other than the target and interference, resulting in an inability to ensure a high signal-to-noise ratio in the target direction.
[0099] Based on this, the present application provides a vital sign monitoring method, aiming to optimize the method for determining the weights of the beamforming algorithm. Specifically, on the basis of the related art, the present application adds a constraint condition to determine the weights, so as to ensure that the beamforming output signal corresponding to the target object reaches a local maximum while not being distorted, thereby improving the signal-to-noise ratio of the signal corresponding to the target object.
[0100] The vital sign detection solution involved in the present application will be introduced below.
[0101] Refer to Figure 2 , an embodiment of the present application provides a vital sign monitoring method, and the method includes:
[0102] Step S210, obtaining n target signals corresponding to each of the p objects to be measured;
[0103] Among them, the n target signals correspond one-to-one to n signal receiving channels, and both n and p are positive integers greater than or equal to 2.
[0104] As an example, the vital sign monitoring method can be applied to a vital sign monitoring device. Exemplarily, as Figure 4 shown; it can also be applied to a vital sign monitoring device, exemplarily, as Figure 5 shown.
[0105] As an example, before the step of obtaining n target signals corresponding to each of the p objects to be measured, the method further includes:
[0106] Step A1, obtaining the distance of each object to be measured;
[0107] Step A2, obtaining n input signals corresponding to each of the p objects to be measured, the n input signals corresponding one-to-one to the n signal receiving channels, and the input signals being signals obtained by mixing the echo signals received by the signal receiving channels;
[0108] Step A3, performing windowing and range dimension FFT on each input signal to obtain the frequency domain signal corresponding to each input signal;
[0109] Step A4, extracting the n frequency domain signals corresponding to each of the p objects to be measured according to the distance of each object to be measured to obtain the n target signals corresponding to each of the p objects to be measured.
[0110] It can be understood that steps A1 to A4 correspond to steps S110 to S140 above and will not be elaborated.
[0111] Step S220, designating 1 of the p objects to be measured as the target object and the other p - 1 objects to be measured as interference objects;
[0112] Step S230: Determine n weights for beamforming processing of the n target signals corresponding to the target object based on the first constraint condition and the second constraint condition;
[0113] Among them, the n weights correspond one-to-one to the n signal receiving channels.
[0114] Among them, the first constraint condition is used to ensure that the n target signals corresponding to the target object are not distorted after beamforming processing, and to suppress the n target signals corresponding to each interfering object after beamforming processing; the second constraint condition is used to ensure that the output signal of the n target signals corresponding to the target object reaches a local maximum value after beamforming processing;
[0115] As an example, the step of determining n weights for beamforming processing of the n target signals corresponding to the target object based on the first constraint condition and the second constraint condition includes:
[0116] Step B1: Obtain the azimuth angle corresponding to each object to be measured;
[0117] Step B2: Determine n steering vectors of each object to be measured at its corresponding azimuth angle, and the n steering vectors correspond one-to-one to the n signal receiving channels;
[0118] It can be understood that in this application, the n steering vectors form an n-dimensional steering vector, and each steering vector in the n steering vectors is a component of the n-dimensional steering vector.
[0119] Step B3: Construct a functional relationship according to the n steering vectors and n unknown weight vectors corresponding to each object to be measured, and the n unknown weight vectors correspond one-to-one to the n signal receiving channels;
[0120] The functional relationship is as follows:
[0121]
[0122] Among them, F w (θ j ) is the functional relationship corresponding to the jth object to be measured, w i is the unknown weight vector corresponding to the ith signal receiving channel, α i (θ j ) is the steering vector corresponding to the jth object to be measured at the ith signal receiving channel, θ j is the azimuth angle corresponding to the jth object to be measured, where 1 ≤ i ≤ n, 1 ≤ j ≤ p.
[0123] Step B4: Construct the first constraint condition based on the p functional relationships corresponding to the p objects to be measured. The first constraint condition includes: the function value of the functional relationship corresponding to the target object is equal to the first expected response, and the function value of the functional relationship corresponding to the interference object is equal to the second expected response;
[0124] As an example, when the m-th object to be measured among the p objects to be measured is the target object, the first constraint condition satisfies the following system of equations:
[0125]
[0126] where r d1 is the first expected response, and r d2 is the second expected response.
[0127] In other words, the above system of equations means that the function value of the functional relationship corresponding to the m-th object to be measured is equal to the first expected response r d1 , and the function values of the functional relationships corresponding to all other objects to be measured are equal to the second expected response r d2 .
[0128] It can be understood that the values of the first expected response and the second expected response should be based on "the weights determined based on the first expected response and the second expected response can ensure that the signal of the target object is not distorted and the signal of the interference object is suppressed after beamforming processing".
[0129] As an example, the first expected response r d1 is 1, and the second expected response r d2 is 0. Based on this example, the first constraint condition satisfies the following system of equations:
[0130]
[0131] Based on this, it can be ensured that the output signal corresponding to the m-th object to be measured is not distorted, and the signals of other objects to be measured are suppressed.
[0132] Step B5: Construct the second constraint condition based on the functional relationship corresponding to the target object. The second constraint condition includes: the first derivative of the functional relationship corresponding to the target object is 0, and the second derivative of the functional relationship corresponding to the target object is less than 0;
[0133] As an example, when the m-th object to be measured among the p objects to be measured is the target object, the second constraint condition satisfies the following system of equations:
[0134]
[0135] Based on the first-order derivative and the second-order derivative, the output signal corresponding to the m-th object to be measured can reach a local maximum value.
[0136] Step B6, solve for the n unknown weight vectors based on the first constraint condition and the second constraint condition to determine the n weights.
[0137] As an example, the Newton method or the steepest descent method can be used to solve for the n unknown weight vectors.
[0138] Step S240, perform beamforming processing on the n target signals corresponding to the target object based on the n weights to obtain the output signal corresponding to the target object;
[0139] Step S250, perform physical sign monitoring on the target object according to the output signal corresponding to the target object.
[0140] As an example, the step of performing physical sign monitoring on the target object according to the output signal corresponding to the target object includes:
[0141] Step C1, perform DC removal, phase extraction, and phase unwrapping processing on the output signal corresponding to the target object to obtain the phase corresponding to the target object;
[0142] Step C2, perform windowing in the slow-time dimension and FFT processing on multiple frames of the phase corresponding to the target object to obtain the physical sign change condition of the target object.
[0143] It can be understood that steps C1 and C2 correspond to steps S160 and S170 above and will not be elaborated further.
[0144] It can be understood that the above only introduces the physical sign monitoring of the m-th object to be measured.
[0145] Similarly, the physical sign monitoring of each of the other objects to be measured can also be implemented in the above manner and will not be elaborated further.
[0146] A vital sign monitoring method, device, equipment and storage medium provided by the present application. Compared with the related art where it is difficult to obtain the true noise power in the face of complex environments or weak signal scenarios, in the present application, n target signals corresponding to each of the p objects to be measured are obtained, and the n target signals correspond one-to-one to n signal receiving channels, where n and p are both positive integers greater than or equal to 2; one of the p objects to be measured is set as the target object, and the other p - 1 objects to be measured are set as interfering objects; based on the first constraint condition and the second constraint condition, n weights for performing beamforming processing on the n target signals corresponding to the target object are determined, and the n weights correspond one-to-one to the n signal receiving channels, where the first constraint condition is used to ensure that the n target signals corresponding to the target object are not distorted after beamforming processing and to ensure that the n target signals corresponding to each interfering object are suppressed after beamforming processing; the second constraint condition is used to ensure that the output signal after beamforming processing of the n target signals corresponding to the target object reaches a local maximum value; based on the n weights, beamforming processing is performed on the n target signals corresponding to the target object to obtain the output signal corresponding to the target object; and vital sign monitoring of the target object is performed according to the output signal corresponding to the target object. By optimizing the constraint conditions of the least squares beamforming method, the present application suppresses interference in the output signal while still maintaining a relatively high signal-to-noise ratio for the target signal. Specifically, new constraint conditions are added using the first and second derivatives to make the output response in the target direction a local maximum value, thereby improving the signal-to-noise ratio of the output signal. Compared with the related art, it can not only separate the respiratory signals of different targets based on the maximum signal-to-noise ratio criterion, but also obtain the true noise power in the face of complex environments or weak signal scenarios.
[0147] The present application also provides a vital sign monitoring device. Refer to Figure 4 , the vital sign monitoring device includes:
[0148] An acquisition module 410, configured to acquire n target signals corresponding to each of the p objects to be measured, where the n target signals correspond one-to-one to n signal receiving channels, and n and p are both positive integers greater than or equal to 2;
[0149] The sign monitoring module 420 is configured to set one of the p objects to be measured as the target object, and the other p - 1 objects to be measured as interfering objects; determine n weights for beamforming processing of the n target signals corresponding to the target object based on a first constraint condition and a second constraint condition, where the n weights correspond one-to-one to the n signal receiving channels. The first constraint condition is used to ensure that the n target signals corresponding to the target object are not distorted after beamforming processing, and to ensure that the n target signals corresponding to each interfering object are suppressed after beamforming processing. The second constraint condition is used to ensure that the output signal of the n target signals corresponding to the target object after beamforming processing reaches a local maximum value; perform beamforming processing on the n target signals corresponding to the target object based on the n weights to obtain the output signal corresponding to the target object; and monitor the signs of the target object according to the output signal corresponding to the target object.
[0150] In a possible implementation manner of the present application, the obtaining module 410 is further configured to obtain the azimuth angle corresponding to each object to be measured; the sign monitoring module 420 is further configured to determine n steering vectors of each object to be measured at its corresponding azimuth angle, where the n steering vectors correspond one-to-one to the n signal receiving channels; construct a functional relationship according to the n steering vectors and n unknown weight vectors corresponding to each object to be measured, where the n unknown weight vectors correspond one-to-one to the n signal receiving channels; construct the first constraint condition based on the p functional relationships corresponding to the p objects to be measured, where the first constraint condition includes: the function value of the functional relationship corresponding to the target object is equal to a first desired response, and the function value of the functional relationship corresponding to the interfering object is equal to a second desired response; construct the second constraint condition based on the functional relationship corresponding to the target object, where the second constraint condition includes: the first derivative of the functional relationship corresponding to the target object is 0, and the second derivative of the functional relationship corresponding to the target object is less than 0; solve the n unknown weight vectors based on the first constraint condition and the second constraint condition to determine the n weights.
[0151] And / or, the obtaining module 410 is further configured to obtain the distance of each object to be measured; obtain n input signals corresponding to each object to be measured, where the n input signals correspond one-to-one to the n signal receiving channels, and the input signal is a signal obtained by performing mixing processing on the echo signal received by the signal receiving channel; the physical sign monitoring module 420 is further configured to perform windowing and range dimension FFT on each input signal to obtain a frequency domain signal corresponding to each input signal; extract the n frequency domain signals corresponding to each object to be measured according to the distance of each object to be measured to obtain n target signals corresponding to each object to be measured.
[0152] And / or, the physical sign monitoring module 420 is further configured to perform DC removal, phase extraction, and phase unwrapping processing on the output signal corresponding to the target object to obtain the phase corresponding to the target object; perform windowing and FFT processing on multiple frames of phases corresponding to the target object in the slow time dimension to obtain the physical sign change condition of the target object.
[0153] The specific implementation manners of the physical sign monitoring device of this application are basically the same as those of the embodiments of the above physical sign monitoring method, and will not be elaborated here.
[0154] Refer to Figure 5 , Figure 5 is a schematic structural diagram of a device in the hardware operating environment involved in the solution of the embodiment of this application.
[0155] As Figure 5 shown, the physical sign monitoring device may include: a processor 1001, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to implement connection communication between the processor 1001 and the memory 1005.
[0156] Optionally, the physical sign monitoring device may further include a user interface, a network interface, a camera, an RF (Radio Frequency) circuit, sensors, a WiFi module, and so on. The user interface may include a display device (Display) and an input sub-module such as a keyboard (Keyboard). Optionally, the user interface may further include a standard wired interface and a wireless interface. The network interface may include a standard wired interface and a wireless interface (such as a WI-FI interface).
[0157] Those skilled in the art can understand that Figure 5 the structural diagram of the physical sign monitoring device shown in
[0158] does not constitute a limitation on the physical sign monitoring device, and may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements. Figure 5As shown in the figure, the memory 1005 as a storage medium may include an operating system, a network communication module, and a vital sign monitoring program. The operating system is a program that manages and controls the hardware and software resources of the vital sign monitoring device, and supports the operation of the vital sign monitoring program and other software and / or programs. The network communication module is used to implement the communication between the internal modules of the memory 1005, as well as the communication between other hardware and software in the vital sign monitoring device.
[0159] In Figure 5 In the vital sign monitoring device shown in the figure, the processor 1001 is used to execute the vital sign monitoring program stored in the memory 1005 to implement the steps of the vital sign monitoring method described in any one of the above.
[0160] The specific implementation manner of the vital sign monitoring device of the present application is basically the same as each embodiment of the above vital sign monitoring method, and will not be described in detail here.
[0161] The embodiment of the present application provides a storage medium, and the storage medium stores one or more programs, and the one or more programs can also be executed by one or more processors to implement the steps of the vital sign monitoring method described in any one of the above.
[0162] The specific implementation manner of the storage medium of the present application is basically the same as each embodiment of the above vital sign monitoring method, and will not be described in detail here.
[0163] The present application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the steps of the above vital sign monitoring method.
[0164] The specific implementation manner of the computer program product of the present application is basically the same as each embodiment of the above vital sign monitoring method, and will not be described in detail here.
[0165] It should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.
[0166] In the embodiments of the present application, prefix words such as "first" and "second" are only used to distinguish different described objects, and have no limiting effect on the position, order, priority, quantity or content of the described objects.
[0167] The serial numbers of the above embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.
[0168] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a hardware platform, or can be implemented by hardware. However, in many cases, the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the current technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as Read Only Memory (ROM) / Random Access Memory (RAM), magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present application.
[0169] The above are only the preferred embodiments of the present application, and do not limit the scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the protection scope of the present application.
Claims
1. A physical sign monitoring method, characterized in that, The method includes: Obtaining n target signals corresponding to each of p objects to be measured, where the n target signals correspond one-to-one to n signal receiving channels, and both n and p are positive integers greater than or equal to 2; Designating 1 object to be measured among the p objects to be measured as the target object, and the other p - 1 objects to be measured as interfering objects; Determining n weights for beamforming the n target signals corresponding to the target object based on a first constraint condition and a second constraint condition, where the n weights correspond one-to-one to the n signal receiving channels. The first constraint condition is used to ensure that the n target signals corresponding to the target object are not distorted after beamforming and to suppress the n target signals corresponding to each interfering object after beamforming. The second constraint condition is used to make the output signal of the n target signals corresponding to the target object reach a local maximum value; Performing beamforming on the n target signals corresponding to the target object based on the n weights to obtain the output signal corresponding to the target object; Monitoring the physical signs of the target object according to the output signal corresponding to the target object.
2. The physical sign monitoring method according to claim 1, characterized in that The step of determining n weights for beamforming the n target signals corresponding to the target object based on the first constraint condition and the second constraint condition includes: Obtaining the azimuth angle corresponding to each object to be measured; Determining n steering vectors of each object to be measured at its corresponding azimuth angle, where the n steering vectors correspond one-to-one to the n signal receiving channels; Constructing a functional relationship according to the n steering vectors and n unknown weight vectors corresponding to each object to be measured, where the n unknown weight vectors correspond one-to-one to the n signal receiving channels; Constructing the first constraint condition based on the p functional relationships corresponding to the p objects to be measured. The first constraint condition includes: the function value of the functional relationship corresponding to the target object is equal to a first desired response, and the function value of the functional relationship corresponding to the interfering object is equal to a second desired response; Constructing the second constraint condition based on the functional relationship corresponding to the target object. The second constraint condition includes: the first derivative of the functional relationship corresponding to the target object is 0, and the second derivative of the functional relationship corresponding to the target object is less than 0; Solving the n unknown weight vectors based on the first constraint condition and the second constraint condition to determine the n weights.
3. The physical sign monitoring method according to claim 2, wherein The first desired response is 1, and the second desired response is 0.
4. The physical sign monitoring method according to claim 2, wherein The step of solving the n unknown weight vectors includes: Solving the n unknown weight vectors using the Newton method or the steepest descent method.
5. The physical sign monitoring method according to any one of claims 1 to 4, characterized in that Before the step of obtaining n target signals corresponding to each of p objects to be measured, the method further includes: Obtaining the distance of each object to be measured; Obtain the n input signals corresponding to each object to be measured. The n input signals correspond one-to-one to the n signal receiving channels. The input signal is a signal obtained by performing mixing processing on the echo signal received by the signal receiving channel; Perform windowing and range-dimension fast Fourier transform (FFT) on each input signal to obtain the frequency-domain signal corresponding to each input signal; Extract the n frequency-domain signals corresponding to each object to be measured according to the distance of each object to be measured, to obtain the n target signals corresponding to each object to be measured.
6. The physical sign monitoring method according to any one of claims 1 to 4, characterized in that, The step of performing physical sign monitoring on the target object according to the output signal corresponding to the target object includes: Perform DC removal, phase extraction, and phase unwrapping processing on the output signal corresponding to the target object to obtain the phase corresponding to the target object; Perform windowing in the slow-time dimension and FFT processing on multiple frames of the phase corresponding to the target object to obtain the physical sign change condition of the target object.
7. A vital sign monitoring device, characterized in that, The device includes: An acquisition module, configured to acquire the n target signals corresponding to each of the p objects to be measured. The n target signals correspond one-to-one to the n signal receiving channels, where n and p are both positive integers greater than or equal to 2; A physical sign monitoring module, configured to set 1 object to be measured among the p objects to be measured as the target object, and the other p - 1 objects to be measured as interference objects; determine n weights for performing beamforming processing on the n target signals corresponding to the target object based on a first constraint condition and a second constraint condition. The n weights correspond one-to-one to the n signal receiving channels, where the first constraint condition is used to ensure that the n target signals corresponding to the target object are not distorted after beamforming processing, and to ensure that the n target signals corresponding to each interference object are suppressed after beamforming processing; the second constraint condition is used to ensure that the output signal of the n target signals corresponding to the target object after beamforming processing reaches a local maximum value; perform beamforming processing on the n target signals corresponding to the target object based on the n weights to obtain the output signal corresponding to the target object; and perform physical sign monitoring on the target object according to the output signal corresponding to the target object.
8. A physical sign monitoring device, characterized in that, The physical sign monitoring device includes: a memory, a processor, and a physical sign monitoring program stored on the memory and executable on the processor. When the physical sign monitoring program is executed by the processor, the steps of the physical sign monitoring method according to any one of claims 1 to 6 are implemented.
9. A storage medium, characterized in that, A physical sign monitoring program is stored on the storage medium. When the physical sign monitoring program is executed by the processor, the steps of the physical sign monitoring method according to any one of claims 1 to 6 are implemented.