CN116942125A - UWB vital sign signal detection method based on NGO-VMD algorithm - Google Patents
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Abstract
The invention provides a UWB vital sign signal detection method based on an NGO-VMD algorithm, and relates to the technical field of signal detection. The method comprises the steps of transmitting Gaussian pulse signals through an ultra-wideband radar module and receiving UWB radar echo signals reflected by a target human body so as to monitor vital signs of the target human body, wherein the method comprises the following steps: preprocessing the UWB radar echo signals to obtain echo signals containing vital signs; processing echo signals containing vital signs through an NGO-VMD algorithm, and decomposing the echo signals to obtain a plurality of modal components; dividing the modal components according to a pre-acquired reference respiratory signal frequency range and a reference heartbeat signal frequency range to obtain a respiratory signal and a heartbeat signal. The method realizes the detection of the vital sign signals, overcomes the unsafe of contact detection, can also reduce signal noise interference, and effectively improves the accuracy and timeliness of the extraction of the vital sign signals.
Description
Technical Field
The invention relates to the technical field of signal detection, in particular to a UWB vital sign signal detection method based on an NGO-VMD algorithm.
Background
Radars are mainly classified into ultra wideband radars and continuous wave radars from the working principle in the field of biological detection. The ultra-wideband radar life detection can achieve centimeter-level distance measurement precision, has strong capability of penetrating through obstacles, has strong capability of suppressing clutter under complex background, and can be used for positioning and imaging targets, but has some difficulties such as difficulty in improving pulse power and limited detection range; in the case of multi-target detection, confusion may occur. Continuous wave radar life detection has the following advantages: the system has simple structure, low equipment cost and easy realization; the detection principle is simpler, the starting is early, the technology is relatively mature, however, the continuous wave radar has some defects, the attenuation anti-interference capability is poor easily when the electromagnetic wave propagates, and the Doppler frequency shift can be caused by the micro motion of any object, so that the extraction of the breathing and heartbeat information is interfered.
UWB (ultra wide band) radar is a new system radar which is developed most rapidly at present, and because the system works in a wider frequency bandwidth, the system has the characteristics of higher data transmission rate, higher resolution and strong penetrability, so that the UWB radar is widely applied in the fields of positioning, detection, communication, biomedical treatment and the like.
In terms of time-frequency analysis of human body signs, lanbo Liu et al in the United states used ultra-wideband radar to acquire signal echoes of human body signs and Hilbert transform (Hilbert-HuangTransform, HHT) to perform time-frequency analysis of the echoes. The Russian team proposes an orthogonal two-channel processing algorithm to obtain a time-frequency analysis chart of respiration and heartbeat, and an experiment for monitoring a human body target by a pulse radar comprises five modes. These methods are difficult to meet the requirements of weak vital sign signal extraction, and the accuracy and timeliness of vital sign signal extraction are not ideal.
Disclosure of Invention
The invention aims to provide a UWB vital sign signal detection method based on an NGO-VMD algorithm to solve the problem of vital sign signal detection.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the invention provides a UWB vital sign signal detection method based on NGO-VMD algorithm, which is used for transmitting Gaussian pulse signals through an ultra-wideband radar module and receiving UWB radar echo signals reflected by a target human body in a preset test environment so as to monitor vital signs of the target human body, and comprises the following steps:
preprocessing the UWB radar echo signals to obtain echo signals containing vital signs;
processing echo signals containing vital signs through an NGO-VMD algorithm, and decomposing the echo signals to obtain a plurality of modal components;
dividing the modal components according to a pre-acquired reference respiratory signal frequency range and a reference heartbeat signal frequency range to obtain a respiratory signal and a heartbeat signal.
Optionally, the preprocessing operation includes a coherent background noise removal operation, a clutter removal operation, and a maximum distance gate selection operation in this order.
Optionally, removing the coherent background noise comprises:
in a preset test environment, measuring when no target human body exists, so as to acquire a first echo signal;
subtracting the first echo signal from the UWB radar echo signal to obtain a second echo signal for removing coherent background noise, wherein the second echo signal is used for clutter removal operation.
Optionally, the clutter removal operation includes: and filtering clutter signals in the second echo signals by adopting a filter bank through a moving target detection method to obtain third echo signals without clutter signals, wherein the third echo signals are used for carrying out maximum distance gate selection operation.
Optionally, the maximum distance door selection operation includes: and extracting a body surface vibration signal of the target human body from the third echo signal by adopting a range gate selection algorithm, and taking the extracted body surface vibration signal as an echo signal containing vital signs.
Optionally, the processing, by the NGO-VMD algorithm, the echo signal containing the vital sign, and decomposing the echo signal to obtain a plurality of modal components, including:
setting parameter ranges of NGO parameters, mode number K and punishment parameters alpha;
performing iterative optimization of the NGO algorithm to obtain an optimal parameter combination optimized by the NGO;
and inputting the obtained optimal parameter combination into a VMD algorithm to decompose the body surface vibration signals so as to obtain a plurality of modal components.
Optionally, the reference respiratory signal frequency ranges from 0.13Hz to 0.4Hz and the reference heartbeat signal frequency ranges from 0.83Hz to 3.3Hz.
The beneficial effects of the invention include:
the UWB vital sign signal detection method based on the NGO-VMD algorithm is used for transmitting Gaussian pulse signals through an ultra-wideband radar module and receiving UWB radar echo signals reflected by a target human body in a preset test environment so as to monitor vital signs of the target human body, and comprises the following steps: preprocessing the UWB radar echo signals to obtain echo signals containing vital signs; processing echo signals containing vital signs through an NGO-VMD algorithm, and decomposing the echo signals to obtain a plurality of modal components; dividing the modal components according to a pre-acquired reference respiratory signal frequency range and a reference heartbeat signal frequency range to obtain a respiratory signal and a heartbeat signal. The method realizes the detection of the vital sign signals, overcomes the unsafe of contact detection, can also reduce signal noise interference, and effectively improves the accuracy and timeliness of the extraction of the vital sign signals.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 shows a flow chart of a UWB vital sign signal detection method based on an NGO-VMD algorithm according to an embodiment of the present invention;
FIG. 2 is a block diagram showing a moving object detection method according to an embodiment of the present invention;
fig. 3 shows a schematic flow chart of an NGO-VMD algorithm according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a UWB vital sign signal detection method based on an NGO-VMD algorithm, which aims to solve the problem of vital sign signal detection.
Fig. 1 shows a flow chart of a UWB vital sign signal detection method based on an NGO-VMD algorithm according to an embodiment of the present invention. As shown in fig. 1, the method for detecting UWB vital signs based on NGO-VMD algorithm provided by the present invention is used for transmitting gaussian pulse signals and receiving UWB radar echo signals reflected by a target human body through an ultra wideband radar module in a preset test environment, so as to monitor vital signs of the target human body, and the method includes:
step 101, preprocessing the UWB radar echo signals to obtain echo signals containing vital signs.
Optionally, the preprocessing operation includes a coherent background noise removal operation, a clutter removal operation, and a maximum distance gate selection operation in this order.
The operation of removing coherent background noise includes: in a preset test environment, measuring when no target human body exists, so as to acquire a first echo signal; subtracting the first echo signal from the UWB radar echo signal to obtain a second echo signal for removing coherent background noise, wherein the second echo signal is used for clutter removal operation.
Specifically, in the experimental process, noise inevitably affects non-contact detection of human body signs of the UWB radar sensor, so that in pretreatment, coherent background noise is firstly removed, and a simple background subtraction method is adopted, so that the method is convenient and effective. The simple background subtraction method comprises the following steps: 1. in the experimental environment (i.e. preset test environment), the measurement is carried out when no target human body exists, and an echo signal A is obtained b (i.e., the first echo signal described above). 2. When the target human body is still in the experimental environment, measuring again to obtain an echo signal A 0 (i.e., UWB radar echo signals to be processed). 3. And (3) directly subtracting the echo signal in the step (1) from the echo signal in the step (2) to obtain the signal data A with the background noise removed. As shown in the following formula (1), wherein A, A 0 、A b The data form of (a) is M×N matrix, M is slow time sampling point, and N is fast time sampling point.
A＝A 0 -A b (1)
The clutter removal operation includes: and filtering clutter signals in the second echo signals by adopting a filter bank through a moving target detection method to obtain third echo signals without clutter signals, wherein the third echo signals are used for carrying out maximum distance gate selection operation.
Specifically, due to the complexity of the measurement environment, the presence of clutter cannot be eliminated in the experimental means, and clutter filtering needs to be performed in the subsequent signal processing process so as to extract weaker human respiratory signals and heartbeat signals. Clutter is mainly composed of low-frequency direct current or slowly-changing linear trends generated after reflection of multiple objects in the human body or environment. Firstly, clutter is analyzed, and as different reflection signals are generated by scanning different parts of a target by an ultra-wideband radar sensor, amplitude, phase and the like of echo are modulated, and the clutter can be expressed as shown in the following formula (2).
Wherein S is A To cause noise of amplitude variation, S r To cause phase-changing noise, f is the sampling frequency, B is the bandwidth, P r Is radar generation power, L r Is the scattering rate of radar emission, N r The normalized accumulation of noise within the bandwidth is represented, G being the gain.
The clutter power spectral density function can be expressed as shown in equation (3).
Wherein,,is the variance of the noise, W is the filter factor. From equation (3), the clutter power spectrum center frequency is around zero frequency, and the feature information frequency required for the experiment is also low frequency, and aliasing occurs in the frequency domain with the clutter. Therefore, clutter filtering is required before echo data is processed. Considering that the ultra-wideband radar sensor has direct current clutter and low frequency clutter in the non-contact monitoring echo signal of the human body, the invention adopts moving target detection to achieve the purpose of filtering the clutter, and a block diagram of the moving target detection method is shown in figure 2. The clutter power spectrum center frequency is around zero frequency, the feature information frequency required by the experiment is also low frequency, and aliasing is generated with the clutter in the frequency domain. Therefore, clutter filtering is required before echo data is processed. In consideration of the fact that direct current clutter and low frequency clutter exist in non-contact monitoring echo signals of an ultra-wideband radar sensor on a human body, a moving target detection technology is selected, and a filter unit is adopted to respectively filter out clutter and target information, so that the signal detection technology of a moving target and a fixed target can be distinguished.
The key characteristic of the moving target detection method for suppressing static clutter or low-frequency clutter is that the phase change between the successive pulse signals is not large, and the static clutter or low-frequency clutter can be filtered by the method shown in the block diagram 2 only by two pulse signals. In the invention, the echo signal form is shown as the formula (4), and the mathematical expression of clutter filtering by the moving target detection method is shown as the formula (5).
The maximum distance door selection operation includes: and extracting a body surface vibration signal of the target human body from the third echo signal by adopting a range gate selection algorithm, and taking the extracted body surface vibration signal as an echo signal containing vital signs.
Specifically, after clutter filtering, the next step is to extract the body surface vibration signal, i.e. extract the slow time signal carrying life information from the received signal. In the invention, the body surface vibration signal extraction method is a distance gate selection algorithm, and a slow time signal corresponding to the maximum energy position on a distance gate (a fast time sampling point) is extracted as a body surface vibration signal. The preprocessed signal data is in the form of an MxN matrix, and each column of data in N is subjected to square sum addition, namely, the column with the largest square sum in the slow time direction is taken as the optimal distance gate. Can be expressed as the formula:
and 102, processing echo signals containing vital signs through an NGO-VMD algorithm, and decomposing to obtain a plurality of modal components.
Optionally, the processing, by the NGO-VMD algorithm, the echo signal containing the vital sign, and decomposing the echo signal to obtain a plurality of modal components, including: setting a north eagle optimization algorithm (NGO) parameter and a parameter range of a modal number K and a punishment parameter alpha; performing iterative optimization of the NGO algorithm to obtain an optimal parameter combination optimized by the NGO; and inputting the obtained optimal parameter combination into a Variational Modal Decomposition (VMD) algorithm to decompose the body surface vibration signals so as to obtain a plurality of modal components.
As shown in fig. 3, after preprocessing, the echo signal at this time reflects the real-time life condition of the human body target, and includes low-frequency information and high-frequency information, which include complete inching information of the human body target, and parameter ranges of NGO parameters, the number of modes K and punishment parameters α are set first; then, performing iterative optimization of an NGO algorithm to obtain an optimal parameter combination optimized by the NGO, inputting the obtained parameter into the VMD to decompose body surface vibration signals, and stripping signals with different frequencies in an original signal by a variation modal decomposition algorithm to decompose the signals into a plurality of modal components and a residual quantity; and finally obtaining a respiratory signal and a heartbeat signal according to the frequency division.
Specifically, a northern hawk optimization algorithm (NGO) is an intelligent optimization algorithm for simulating northern hawk recognition and hunting, and first, an initial population number is created, wherein the initial population number comprises information such as the northern hawk number used for optimizing, the sample length used for iteration each time, the number of problem variables and the like, and then, an objective function value used for judging an optimizing result is set. The algorithm is divided into two phases when iterating: the mathematical model of the hunting object identification stage and the pursuit stage is as follows:
the first stage: prey identification phase (exploration phase)
At this stage, the northern hawk randomly selects a prey and catches it, and the search space randomly selects the prey, so that the exploratory capacity of the algorithm is greatly enhanced, and the objective is to determine the optimal area, and the mathematical expression is as follows:
P i ＝X k ，i＝1，2，...，N，k＝1，2，...，i-1，i+1，...N (7)
wherein P is i Is the position of the hunting object selected by the hawk in the north;is the objective function value, i.e. fitness value, N is the sample length, k is a value of [1, N]Random natural numbers of (a); />Is the new state of the ith northern hawk; />Is its new state in the j-th dimension,/->Is the adaptation value corresponding to the value, and r is the value of [0,1 ]]I is 1 or 2, both of which are random numbers that are intended to produce random NGO behavior during searching and updating.
And a second stage: chasing stage (development stage)
When the hunting is attempted to run away after the hawk attacks the hunting in the north, the hunting is assumed to be in the radial attack position in the case of pursuit, and the mathematical expression at this stage is as follows:
wherein T is the current iteration number and T is the maximum iteration number;is a new state of the ith northern eagle in the second hunting stage;/>is a new state of the ith northern eagle in the j dimension in the hunting stage of the second section;is the fitness value in the new state.
The VMD algorithm needs to set proper mode number K and punishment parameter alpha before decomposing signals, and the NGO algorithm has higher convergence accuracy and good stability, so that the VMD algorithm is used for parameter optimization.
A variational modal decomposition algorithm (VMD) is a non-recursive adaptive decomposition algorithm, which can self-determine the number of modal decomposition and reduce the time sequence with high complexity and strong nonlinearity. The core idea of the algorithm is to construct and solve the variational problem, and obtain the optimal solution through iteration, so as to determine the center frequency and bandwidth of each inherent modal component to extract each modal component, wherein most of each modal is near the center frequency, and the sum of the bandwidths of the components is minimum. Unlike the definition of the natural mode function of the empirical mode decomposition algorithm, the natural mode function of the variant mode decomposition algorithm is an amplitude-modulated frequency-modulated signal, as shown in equation (13).
Wherein u is k (t) intrinsic mode function IMF, A k (t) is u k Envelope magnitude of (t).
The VMD algorithm aims at finding out K natural mode functions, wherein each natural mode function has a limited bandwidth with different center frequencies, and the self-adaptive decomposition of signals is realized. The variational modal decomposition algorithm comprises two major parts, namely constructing a variational problem and solving the variational problem. The whole VMD process is to iterate the center frequency and bandwidth of each modal component continuously to realize the self-adaptive decomposition of each frequency band of the signal, the basic principle can be converted into the solution of the variation problem, and the constructed constraint variation problem is expressed as:
wherein: u (u) k A first IMF component decomposed for the VMD; omega k Instantaneous frequency for the kth IMF component; delta (t) is a dirac function;is a hilbert transform.
To solve the optimal solution of the variation problem of (14), a quadratic penalty parameter alpha and a Lagrange multiplier lambda are introduced, and the constructed augmentation Lagrange function is as follows:
step 103, dividing the plurality of modal components according to a pre-acquired reference respiratory signal frequency range and a reference heartbeat signal frequency range to obtain a respiratory signal and a heartbeat signal.
According to researches, the respiratory frequency range (i.e. the frequency range of the reference respiratory signal) of the human body is about 0.13Hz to 0.4Hz, and the heartbeat frequency range (i.e. the frequency range of the reference heartbeat signal) is about 0.83Hz to 3.3Hz, so that respiratory signal components and heartbeat signal components can be divided according to the frequency.
In summary, the ultra-wideband radar module is used for vital sign monitoring by transmitting a gaussian pulse signal and receiving an echo signal reflected by a target. In real life, because the measuring environment is complex, the echo signals obtained by non-contact monitoring are not as pure as those obtained by contact type signals, a series of signal preprocessing operations are required to be carried out on the radar echo signals, including removing coherent background noise, removing clutter, selecting a maximum distance gate, and inhibiting various factors affecting vital sign monitoring caused by experimental environment, so that echo signals containing vital signs are obtained for analysis of a next vital sign extraction algorithm. In the method provided by the embodiment of the invention, for example, a background subtraction method is adopted to remove coherent background noise, a moving target detection method is used to inhibit static clutter or low-frequency clutter, a matrix with the data form of M× (N-1) of signals filtered by the moving target detection method is used to extract body surface vibration signals; when vital sign information is extracted by adopting an NGO-VMD algorithm, a Variation Modal Decomposition (VMD) algorithm needs to set a proper modal number K and punishment parameters alpha, so that the vital sign information is optimized by adopting a northern hawk optimization algorithm (NGO), and the obtained parameters are input into the VMD to decompose body surface vibration signals to obtain a series of modal components; and finally, dividing modal components according to different frequencies of the respiratory signal and the heartbeat signal, and recombining the modalities to obtain the respiratory signal and the heartbeat signal. The method realizes the detection of the vital sign signals, overcomes the unsafe of contact detection, can also reduce signal noise interference, and effectively improves the accuracy and timeliness of the extraction of the vital sign signals.
The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement the same, but not limit the scope of the present invention, and all equivalent changes or modifications made according to the spirit of the present invention should be included in the scope of the present invention.
Claims (7)
1. The method is used for transmitting Gaussian pulse signals through an ultra-wideband radar module and receiving UWB radar echo signals reflected by a target human body in a preset test environment so as to monitor vital signs of the target human body, and comprises the following steps:
preprocessing the UWB radar echo signals to obtain echo signals containing vital signs;
processing the echo signals containing vital signs through an NGO-VMD algorithm, and decomposing the echo signals to obtain a plurality of modal components;
dividing the modal components according to a pre-acquired reference respiratory signal frequency range and a reference heartbeat signal frequency range to obtain a respiratory signal and a heartbeat signal.
2. The method for detecting UWB vital sign signals based on the NGO-VMD algorithm of claim 1, wherein the preprocessing operation sequentially comprises a coherent background noise removal operation, a clutter removal operation, and a maximum distance gate selection operation.
3. The NGO-VMD algorithm based UWB vital sign signal detection method of claim 2 wherein the removing of the coherent background noise operation comprises:
in the preset test environment, measuring is carried out when no target human body exists, so as to obtain a first echo signal;
subtracting the first echo signal from the UWB radar echo signal to obtain a second echo signal for removing coherent background noise, wherein the second echo signal is used for performing clutter removal operation.
4. The NGO-VMD algorithm-based UWB vital sign signal detection method of claim 3, wherein the clutter removal operation comprises: and filtering clutter signals in the second echo signals by adopting a filter bank through a moving target detection method to obtain third echo signals without clutter signals, wherein the third echo signals are used for carrying out the maximum distance gate selection operation.
5. The NGO-VMD algorithm based UWB vital sign signal detection method of claim 4 wherein the maximum distance gate selection operation comprises: and extracting a body surface vibration signal of the target human body from the third echo signal by adopting a range gate selection algorithm, and taking the extracted body surface vibration signal as the echo signal containing vital signs.
6. The NGO-VMD algorithm-based UWB vital sign signal detection method of claim 5, wherein the processing, by the NGO-VMD algorithm, the echo signal containing vital signs to obtain a plurality of modal components includes:
setting parameter ranges of NGO parameters, mode number K and punishment parameters alpha;
performing iterative optimization of the NGO algorithm to obtain an optimal parameter combination optimized by the NGO;
and inputting the obtained optimal parameter combination into a VMD algorithm to decompose the body surface vibration signal so as to obtain a plurality of modal components.
7. The NGO-VMD algorithm-based UWB vital sign signal detection method of claim 1, wherein the reference respiratory signal frequency range is 0.13Hz to 0.4Hz and the reference heartbeat signal frequency range is 0.83Hz to 3.3Hz.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117421561A (en) * | 2023-12-18 | 2024-01-19 | 中国海洋大学 | Turbulence denoising method and system based on parameter optimization VMD (virtual machine direction detector) combined wavelet |
| CN117724094A (en) * | 2024-02-07 | 2024-03-19 | 浙江大华技术股份有限公司 | Vital sign detection method |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117421561A (en) * | 2023-12-18 | 2024-01-19 | 中国海洋大学 | Turbulence denoising method and system based on parameter optimization VMD (virtual machine direction detector) combined wavelet |
| CN117421561B (en) * | 2023-12-18 | 2024-03-12 | 中国海洋大学 | Turbulence denoising method and system based on parameter optimization VMD (virtual machine direction detector) combined wavelet |
| CN117724094A (en) * | 2024-02-07 | 2024-03-19 | 浙江大华技术股份有限公司 | Vital sign detection method |
| CN117724094B (en) * | 2024-02-07 | 2024-05-07 | 浙江大华技术股份有限公司 | Vital sign detection method |
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