CN113288058B

CN113288058B - Signal processing method and device

Info

Publication number: CN113288058B
Application number: CN202110584282.4A
Authority: CN
Inventors: 丁玉国; 张闻宇
Original assignee: Beijing Qinglei Technology Co ltd
Current assignee: Beijing Qinglei Technology Co ltd
Priority date: 2021-05-27
Filing date: 2021-05-27
Publication date: 2023-08-01
Anticipated expiration: 2041-05-27
Also published as: CN113288058A

Abstract

The invention discloses a signal processing method and device. Wherein the method comprises the following steps: processing the radar echo signal to obtain a chest cavity motion signal, wherein the radar echo signal is an echo signal generated by reflecting electromagnetic waves emitted by a radar by a target object; filtering the chest cavity motion signal by using a filter to obtain a respiration signal and a heartbeat signal, wherein the respiration signal corresponds to the respiration bandpass frequency of the filter, and the heartbeat signal corresponds to the heartbeat bandpass frequency of the filter; and carrying out joint time-frequency domain processing on the respiratory signals to obtain respiratory frequencies corresponding to the respiratory signals, and carrying out joint time-frequency domain processing and data statistical analysis on the heartbeat signals to obtain heartbeat frequencies corresponding to the heartbeat signals. The invention solves the technical problem that the accuracy of detection is lower because the method for detecting the heartbeat and the respiration by the radar in the related technology only depends on the time domain characteristic or the frequency domain characteristic of the radar signal during analysis.

Description

Signal processing method and device

Technical Field

The invention relates to the field of radar identification, in particular to a signal processing method and device.

Background

The radar emits electromagnetic waves to the surface of the target and receives echoes, and the distance between the target and the radar is represented by the delay of the echoes. According to the types of the transmitted signals, the pulse radar and the continuous wave radar can be divided, and the conventional pulse radar transmits periodic high-frequency pulses, and the continuous wave radar transmits continuous wave signals. The continuous wave radar emits frequency modulation continuous wave signals, so that distance measurement can be realized and speed measurement can be realized. The frequency modulation continuous wave radar emits continuous waves with frequency changing in a sweep frequency period, an echo emitted by an object has a certain frequency difference with an emitted signal, and distance information between a target and the radar can be obtained by measuring the frequency difference.

At present, most of equipment for detecting respiratory heartbeat is wearable belt equipment, and an instrument is required to be in direct contact with the chest cavity or pulse of a human body, so that the comfort of the human body is seriously influenced, and the work and rest quality of the human body can be influenced. Therefore, it is particularly critical to accurately develop a non-contact respiratory heartbeat detection device.

In the respiratory process of a human body, the chest cavity can generate corresponding fluctuation along with the respiratory process, and in the heart beating process, the heart beating can drive the chest cavity to generate corresponding vibration, although the chest cavity vibration caused by the heart beating is weak. Therefore, the fluctuation process of the thoracic cavity can be accurately detected, and the detection of the respiratory heartbeat is critical. It is particularly important to be able to accurately extract respiratory and heartbeat movements from chest movements after detection of the thoracic cavity relief.

The related art uses a doppler radar to detect the respiratory heartbeat frequency of a human body. The method utilizes the number of peaks of the respiration signal and the heartbeat signal in the time domain to estimate the frequency of the respiration and the heartbeat. The main disadvantage of the method is that the frequency detection of respiration and heartbeat is completely dependent on the time domain peak detection result, the detection result is easy to be interfered by noise, and has higher requirements on the quality of signals, thereby having higher requirements on equipment and high cost, and meanwhile, the frequency result judged by the time domain is not fine enough.

The related technology can also utilize a visible light remote sensing system to realize the detection of the respiratory heartbeat frequency of the human body. The method irradiates the thoracic cavity of a human body with visible light, collects the fluctuation process of the thoracic cavity, and judges the breathing and heartbeat frequency in the frequency domain respectively through a band-pass filter. The main disadvantage of this method is that the frequency discrimination of the heartbeat is only performed in the frequency domain, which is easily affected by noise and clutter, and affects the accuracy of detection.

The related art is based on the extraction of heartbeat signals under the background of strong noise of radar echo, and specifically, the radar echo is transmitted to a data preprocessing end in the form of a data frame; preprocessing the radar echo original sequence, filtering the echo of the stationary target, and obtaining an echo signal of a distance unit where the human target is located; judging the random motion of the human body based on the acceleration, and reducing the error of the parameter estimation of the later-stage physical sign; performing physical sign signal separation, and adopting a self-adaptive wavelet scale selection algorithm to realize effective separation of vital sign respiratory signals and heartbeat signals; and carrying out time domain peak searching and downsampling processing on the respiratory signal and the heartbeat signal to finally obtain the respiratory frequency and the heartbeat frequency. The method simply analyzes the time domain of the data and does not consider the frequency domain characteristics thereof. An important factor affecting the accuracy of signal frequency estimation is that it is susceptible to noise, and thus effectively circumventing the effect of noise on signal waveforms is important. In the influence of noise on the waveform of a signal, the noise is an obvious characteristic that the signal has extra wave crests and wave troughs (namely, a burr phenomenon). In this method, a specific respiratory heart rate is obtained by calculating the time difference between adjacent peaks, which is easily affected by the presence of noise to the accuracy of the final result.

In view of the above problems, no effective solution has been proposed at present.

Disclosure of Invention

The embodiment of the invention provides a signal processing method and a signal processing device, which at least solve the technical problem that the accuracy of detection is low because the method for detecting heartbeat and respiration by using a radar in the related technology only depends on the time domain characteristic or the frequency domain characteristic of a radar signal during analysis.

According to an aspect of an embodiment of the present invention, there is provided a signal processing method including: processing a radar echo signal to obtain a chest cavity motion signal, wherein the radar echo signal is an echo signal generated by reflecting electromagnetic waves emitted by a radar by a target object; filtering the chest cavity motion signal by using a filter to obtain a respiratory signal and a heartbeat signal, wherein the respiratory signal corresponds to the respiratory bandpass frequency of the filter, and the heartbeat signal corresponds to the heartbeat bandpass frequency of the filter; and carrying out joint time-frequency domain processing on the respiratory signals to obtain respiratory frequencies corresponding to the respiratory signals, and carrying out joint time-frequency domain processing and data statistical analysis on the heartbeat signals to obtain heartbeat frequencies corresponding to the heartbeat signals.

Optionally, processing the radar echo signal to obtain a chest motion signal includes: performing Fourier transformation on each linear frequency modulation signal in the continuous echo signals according to the time sequence to obtain one-dimensional range profile data of each linear frequency modulation signal; determining signal data of a distance gate with highest energy in each one-dimensional distance image data, and arranging the signal data according to the time sequence of the linear frequency modulation signals to obtain respiratory heartbeat signals in a period of time; and (3) carrying out unwrapping processing on the respiratory heartbeat signal to obtain the chest cavity movement signal.

Optionally, filtering the chest motion signal with a filter to obtain a respiratory signal and a heartbeat signal, including: setting the passband of the filter as respiratory bandpass frequency, and filtering the chest motion signal to obtain the respiratory signal; and setting the passband of the filter as the heartbeat bandpass frequency, and filtering the chest cavity motion signal to obtain the heartbeat signal.

Optionally, performing joint time-frequency domain processing on the respiratory signals to obtain respiratory frequencies corresponding to the respiratory signals includes: determining a respiration effective period number of the respiration signal in a time domain of the respiration signal; determining a respiration time domain estimated effective frequency according to the effective period number and the duration of the respiration signal; performing Fourier transform on the respiratory signal to obtain a respiratory frequency domain distribution signal; and determining the respiratory frequency corresponding to the respiratory signal according to the respiratory time domain estimated effective frequency and the respiratory frequency domain distribution signal.

Optionally, in the time domain of the respiration signal, determining the respiration effective period number of the respiration signal includes: traversing the respiratory signal in the time domain of the respiratory signal to obtain a first peak position sequence with a peak value larger than zero and a first trough position sequence with a peak value smaller than zero; removing peaks with a distance smaller than a first preset threshold value from the first peak position sequence to obtain a first effective peak number, and removing troughs with a distance smaller than the first preset threshold value from the first trough position sequence to obtain a first effective trough number; and determining the number of effective respiratory cycles according to the first effective wave crest number and the first effective wave trough number.

Optionally, determining the respiratory frequency corresponding to the respiratory signal according to the respiratory time domain estimated effective frequency and the respiratory frequency domain distribution signal includes: determining a respiration effective frequency range according to the respiration time domain estimated effective frequency and the respiration frequency allowable value; and selecting the highest frequency point in the respiration effective frequency range in the respiration frequency domain distribution signal, and determining the frequency corresponding to the highest frequency point as the respiration frequency.

Optionally, performing joint time-frequency domain processing on the heartbeat signal to obtain a heartbeat frequency corresponding to the heartbeat signal includes: determining the number of heartbeat effective periods of the heartbeat signal in the time domain of the heartbeat signal; determining a heartbeat time domain estimated effective frequency according to the number of the heartbeat effective periods and the duration of the heartbeat signal; performing Fourier transform on the heartbeat signal to obtain a heartbeat frequency domain distribution signal; and determining the heartbeat frequency corresponding to the heartbeat signal according to the effective frequency estimated in the heartbeat time domain and the heartbeat frequency domain distribution signal.

Optionally, before determining the number of effective cardiac cycles of the heartbeat signal in the time domain of the heartbeat signal, the method further includes: performing variance homogenization treatment on the heartbeat signal to obtain a treated heartbeat signal; and executing the step of determining the effective cycle number of the heartbeat signal in the time domain of the heartbeat signal according to the processed heartbeat signal.

Optionally, in the time domain of the heartbeat signal, determining the number of heartbeat effective periods of the heartbeat signal includes: traversing the heartbeat signal in the time domain of the heartbeat signal to obtain a second peak position sequence with a peak value larger than zero and a second trough position sequence with a peak value smaller than zero; removing peaks with a distance smaller than a second preset threshold value from the second peak position sequence to obtain a second effective peak number, and removing troughs with a distance smaller than the second preset threshold value from the second trough position sequence to obtain a second effective trough number; and determining the effective cycle number of the heartbeat according to the second effective wave crest number and the second effective wave trough number.

Optionally, determining, according to the heartbeat time domain estimated effective frequency and the heartbeat frequency domain distribution signal, a heartbeat frequency corresponding to the heartbeat signal includes: determining a heartbeat effective frequency range according to the heartbeat time domain estimated effective frequency and the heartbeat frequency allowable value; selecting all peak frequency points in the heartbeat frequency domain distribution signal, wherein the peak frequency points are in a heartbeat effective frequency range, and recording the peak frequency points as heartbeat frequency points to be selected; screening the heartbeat frequency points to be selected, and determining reserved heartbeat frequency points; carrying out maximum likelihood estimation on the reserved heartbeat frequency points, and determining the probability of each reserved heartbeat frequency point as the heartbeat frequency; and correcting the probability of the reserved heartbeat frequency point through historical data, and determining the heartbeat frequency of the reserved heartbeat frequency point with the maximum probability as the heartbeat frequency.

Optionally, performing maximum likelihood estimation on the reserved heartbeat frequency points, and determining the probability of each reserved heartbeat frequency point as the heartbeat frequency includes: determining a joint probability density function of the heartbeat signals according to the signal model of the heartbeat signals retaining the heartbeat frequency points; determining an objective function satisfied by parameters of the heartbeat signal according to the joint probability density function, wherein the parameters comprise frequency, amplitude and phase; and carrying out transformation solving on the objective function to obtain the probability of reserving the heartbeat frequency point as the heartbeat frequency.

Optionally, correcting the probability of the reserved heartbeat frequency point through historical data, and determining that the heartbeat frequency of the reserved heartbeat frequency point with the largest probability is the heartbeat frequency includes: determining a plurality of heartbeat frequencies of the historical data, wherein the historical data are a plurality of radar echo signals of a first preset time before the radar echo signals and a plurality of heartbeat frequencies corresponding to the radar echo signals; clustering a plurality of heartbeat frequencies of the historical data by taking each reserved heartbeat frequency point as a center, and determining the proportion of the number of heartbeat frequencies successfully clustered with the reserved heartbeat frequency points to the total number of the plurality of heartbeat frequencies; multiplying the probability by the ratio to determine a corrected probability; and taking the heartbeat frequency corresponding to the reserved heartbeat frequency point corresponding to the maximum value of the corrected probability as the final heartbeat frequency.

Optionally, screening the heartbeat frequency points to be selected, and determining to reserve the heartbeat frequency points includes: comparing each heartbeat frequency point to be selected with a plurality of heartbeat frequency points to be selected in a second preset time; if the distance between the heartbeat frequency point to be selected and the frequency point positions of the plurality of heartbeat frequency points to be selected in the second preset time exceeds a third preset threshold value, discarding the heartbeat frequency points to be selected; and if the distance between the heartbeat frequency point to be selected and the frequency point positions of the plurality of heartbeat frequency points to be selected in the second preset time does not exceed a third preset threshold value, taking the heartbeat frequency point to be selected as a reserved heartbeat frequency point.

According to another aspect of the embodiment of the present invention, there is also provided a signal processing apparatus including: the processing module is used for processing the radar echo signals to obtain chest cavity motion signals, wherein the radar echo signals are echo signals generated by reflecting electromagnetic waves emitted by a radar by a target object; the filtering module is used for filtering the chest cavity motion signal by using a filter to obtain a respiration signal and a heartbeat signal, wherein the respiration signal corresponds to the respiration bandpass frequency of the filter, and the heartbeat signal corresponds to the heartbeat bandpass frequency of the filter; and the processing module is used for carrying out joint time-frequency domain processing on the respiratory signals to obtain the respiratory frequency corresponding to the respiratory signals, and carrying out joint time-frequency domain processing and data statistical analysis on the heartbeat signals to obtain the heartbeat frequency corresponding to the heartbeat signals.

According to another aspect of the embodiments of the present invention, there is also provided a processor for executing a program, where the program executes any one of the signal processing methods described above.

According to another aspect of the embodiments of the present invention, there is also provided a computer storage medium including a stored program, where the program, when executed, controls a device in which the computer storage medium is located to perform any one of the signal processing methods described above.

In the embodiment of the invention, a radar echo signal is processed to obtain a chest cavity motion signal, wherein the radar echo signal is an echo signal generated by reflecting electromagnetic waves emitted by a radar by a target object; filtering the chest cavity motion signal by using a filter to obtain a respiration signal and a heartbeat signal, wherein the respiration signal corresponds to the respiration bandpass frequency of the filter, and the heartbeat signal corresponds to the heartbeat bandpass frequency of the filter; and carrying out joint time-frequency domain processing on the respiratory signals to obtain respiratory frequencies corresponding to the respiratory signals, and carrying out joint time-frequency domain processing and data statistical analysis on the heartbeat signals to obtain heartbeat frequencies corresponding to the heartbeat signals. The radar echo signals are processed, the respiration signals and the heartbeat signals are separated through the filter, and the respiration signals and the heartbeat signals are respectively combined and subjected to time-frequency domain analysis, so that the purposes of effectively determining the respiration frequency and the heartbeat frequency in the radar echo signals and guaranteeing the accuracy and the stability are achieved, the technical effects of improving the accuracy of the radar echo signal analysis and the accuracy of the respiration frequency and the heartbeat frequency are achieved, the technical problems that the accuracy of detection is low due to the fact that the radar detection heartbeat and respiration in the related art is achieved, and the time domain characteristic or the frequency domain characteristic of the radar signals are only relied on during analysis are solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:

fig. 1 is a flow chart of a signal processing method according to an embodiment of the present invention;

fig. 2 is a schematic diagram of the main flow of signal processing according to an embodiment of the invention;

fig. 3 is a flowchart of a signal processing method according to an embodiment of the present invention;

fig. 4 is a schematic diagram of a signal processing apparatus according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the invention may be practiced otherwise than as shown or described below. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

According to an embodiment of the present invention, there is provided a method embodiment of a signal processing method, it should be noted that the steps shown in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowchart, in some cases the steps shown or described may be performed in an order different from that herein.

Fig. 1 is a flowchart of a signal processing method according to an embodiment of the present invention, as shown in fig. 1, the method includes the steps of:

step S102, processing a radar echo signal to obtain a chest cavity motion signal, wherein the radar echo signal is an echo signal generated by reflecting electromagnetic waves emitted by a radar by a target object;

step S104, filtering the chest cavity motion signal by using a filter to obtain a respiration signal and a heartbeat signal, wherein the respiration signal corresponds to the respiration bandpass frequency of the filter, and the heartbeat signal corresponds to the heartbeat bandpass frequency of the filter;

step S106, carrying out joint time-frequency domain processing on the respiratory signals to obtain respiratory frequencies corresponding to the respiratory signals, and carrying out joint time-frequency domain processing and data statistical analysis on the heartbeat signals to obtain the heartbeat frequencies corresponding to the heartbeat signals.

Through the steps, the radar echo signals are processed to obtain chest cavity motion signals, wherein the radar echo signals are echo signals generated by reflecting electromagnetic waves emitted by a radar by a target object; filtering the chest cavity motion signal by using a filter to obtain a respiration signal and a heartbeat signal, wherein the respiration signal corresponds to the respiration bandpass frequency of the filter, and the heartbeat signal corresponds to the heartbeat bandpass frequency of the filter; the method comprises the steps of carrying out joint time-frequency domain processing on respiratory signals to obtain respiratory frequencies corresponding to the respiratory signals, carrying out joint time-frequency domain processing and data statistics analysis on heartbeat signals to obtain the heartbeat frequencies corresponding to the heartbeat signals, separating the respiratory signals and the heartbeat signals through a filter by processing radar echo signals, and carrying out joint time-frequency domain analysis on the respiratory signals and the heartbeat signals respectively, so that the purposes of effectively determining the respiratory frequencies and the heartbeat frequencies in the radar echo signals and guaranteeing accuracy and stability are achieved, the accuracy of radar echo signal analysis is improved, the technical effects of improving the accuracy of the respiratory frequencies and the heartbeat frequencies are achieved, and the technical problems that the radar heart beat measurement and respiration methods in related technologies only depend on the time domain characteristics or the frequency domain characteristics of the radar signals during analysis, and the detection accuracy is low are solved.

The radar echo signal is an echo generated by transmitting an electromagnetic wave to a target object through a radar, and the target object may be a person, and the radar may be a pulse radar or a continuous wave radar.

The radar echo signals are processed to obtain the chest cavity motion signals, and the signals with higher energy can be screened from the radar echo signals to serve as the chest cavity motion signals, so that subsequent filtering and operation processing are facilitated. The method can also be an operation of correcting the radar echo signal by denoising and the like, so that the accuracy and the effective rate of the radar echo signal are improved, and the accuracy of signal processing is further improved.

And filtering the chest cavity motion signal by using a filter to obtain a respiratory signal and a heartbeat signal. The radar echo signal is a superposition signal of a respiration signal and a heartbeat signal of a target object, and the respiration signal and the heartbeat signal in the radar echo signal are separated through a filter to obtain the respiration signal and the heartbeat signal for the convenience of processing and analysis.

Specifically, the breathing signal can be obtained by setting the breathing band-pass frequency of the filter corresponding to the breathing signal in the filter, filtering the radar echo signal, and the heartbeat signal can be obtained by setting the heartbeat band-pass frequency of the filter corresponding to the heartbeat signal in the filter and filtering the radar echo signal. Thereby effectively separating the respiratory signal and the heartbeat signal in the radar echo signal.

And respectively carrying out joint time-frequency domain processing on the respiratory signals to obtain respiratory frequencies corresponding to the respiratory signals, and carrying out joint time-frequency domain processing on the heartbeat signals to obtain heartbeat frequencies corresponding to the heartbeat signals. Compared with the mode of only carrying out time domain analysis or frequency domain analysis on signals in the prior art, the method is more accurate, and the effectiveness and the accuracy of the respiratory frequency and the heartbeat frequency are improved. Therefore, the technical effect of improving the accuracy of radar echo signal analysis and the accuracy of determining the respiratory frequency and the heartbeat frequency is achieved, and the technical problem that the accuracy of detection is low due to the fact that the method for measuring the heartbeat and the respiration by the radar in the related technology only depends on the time domain characteristic or the frequency domain characteristic of the radar signal during analysis is solved.

Optionally, processing the radar echo signal to obtain a chest motion signal includes: performing Fourier transformation on each linear frequency modulation signal in the continuous echo signals according to the time sequence to obtain one-dimensional range profile data of each linear frequency modulation signal; determining signal data of a distance gate with highest energy in each one-dimensional distance image data, and arranging the signal data according to the time sequence of the linear frequency modulation signals to obtain respiratory heartbeat signals in a period of time; and (5) carrying out unwrapping processing on the respiratory heartbeat signal to obtain a chest motion signal.

The step of processing the radar echo signal may be performed by:

(1) Fourier transforming each chirp signal chirp of the acquired radar echo to obtain a one-dimensional range (r) of the echo signal: namely, the linear frequency modulation signal of the echo signal is subjected to Fourier transformation to obtain one-dimensional range profile data of the echo signal.

Wherein chirp is a received radar echo chirp signal, r is a range gate label, j is an imaginary symbol, and τ is an integral variable;

(2) The distance gate with the highest energy is selected as R in the one-dimensional range (R), and is the signal data of the human echo at the current moment:

P＝max(abs(range(r)))，

R＝find(abs(range(r))＝＝P)，

data＝range(R)，

where abs () represents an absolute value, max () represents a maximum value, P is a maximum value in the distance-dimensional signal, and find () represents a position where a signal having energy P in the distance-dimensional signal is obtained.

The main purpose of this step is to select the echo signal of the range gate with the highest energy in the one-dimensional range profile.

(3) And (3) respectively carrying out the steps (1) - (2) on each chirp according to the time sequence to obtain a data signal at each moment, and smoothly arranging the obtained data signals according to the time to obtain a respiratory heartbeat signal (t) in a period of time:

signal(t)＝[data1 data2...datan]，

Wherein, data1 … datan represents data signals obtained from chirp signals at time 1 to time n, respectively.

(4) Determination of phase of signal (t) _signal (t) and performing unwrapping to obtain a signal phase _data (t)：

phase _signal (t)＝angle(signal(t))，

phase _data (t)＝unwrap(phase _signal (t))，

Where angle () is the phase signal, and unwrap () is the unwrap of the phase signal. Phase unwrapping is a technique for normalizing a phase signal and is well known in the art.

Optionally, filtering the chest motion signal with a filter to obtain a respiratory signal and a heartbeat signal, including: setting the passband of the filter as respiratory bandpass frequency, and filtering the chest motion signal to obtain a respiratory signal; and setting the passband of the filter as the heartbeat bandpass frequency, and filtering the chest cavity motion signal to obtain a heartbeat signal.

Since the respiratory rate of the human body is usually 0.1-0.8Hz, the respiratory bandpass frequency may be 0.1-0.8Hz in this embodiment. The heart beat frequency of the human body is usually 0.7-2.5Hz, so the heart beat band pass frequency can be 0.7-2.5Hz in the embodiment. So as to process the heartbeat signal and the respiratory signal according to the radar echo signal.

Optionally, performing joint time-frequency domain processing on the respiratory signal to obtain a respiratory frequency corresponding to the respiratory signal includes: determining the number of effective respiratory cycles of the respiratory signal in the time domain of the respiratory signal; determining a respiration time domain estimated effective frequency according to the respiration effective period number and the duration of the respiration signal; carrying out Fourier transform on the breathing signals to obtain breathing frequency domain distribution signals; and determining the respiratory frequency corresponding to the respiratory signal according to the respiratory time domain estimated effective frequency and the respiratory frequency domain distribution signal.

In the time domain of the respiration signals, the respiration effective period number and the respiration time domain estimated effective frequency of the respiration signals are determined, and in the frequency domain, the respiration frequency corresponding to the respiration signals is determined according to the respiration time domain estimated effective frequency and the respiration frequency domain distribution signals. Therefore, the respiratory signals are analyzed in the time domain and the frequency domain to obtain the respiratory frequency corresponding to the respiratory signals, and compared with the mode of only carrying out time domain analysis or frequency domain analysis on the signals in the prior art, the method is more accurate, and the effectiveness and the accuracy of respiratory frequency determination are improved.

Therefore, the technical effect of improving the accuracy of radar echo signal analysis and the accuracy of respiratory frequency determination is achieved, and the technical problem that the accuracy of detection is low due to the fact that the method for measuring heart beat and respiration by the radar in the related technology only depends on the time domain characteristic or the frequency domain characteristic of the radar signal during analysis is solved.

Optionally, in the time domain of the respiration signal, determining the number of respiration effective periods of the respiration signal comprises: in the time domain of the respiratory signal, a first peak position sequence with a peak value larger than zero and a first trough position sequence with a peak value smaller than zero are obtained by traversing the respiratory signal; removing peaks with a distance smaller than a first preset threshold value from the first peak position sequence to obtain a first effective peak number, and removing troughs with a distance smaller than the first preset threshold value from the first trough position sequence to obtain a first effective trough number; and determining the number of effective respiratory cycles according to the first effective wave crest number and the first effective wave trough number.

In the time domain of the respiratory signal, a first crest sequence and a first trough sequence are obtained, and screening is carried out through a first preset threshold value, so that false crests and troughs possibly existing in the respiratory signal are removed, a first effective crest number and a first effective trough number are obtained, and the respiratory effective period number is determined according to the first effective crest number and the first effective trough number. Judging whether the wave peaks are generated due to noise or clutter or not by judging whether the distance between adjacent wave peaks meets a first preset threshold value or not, and eliminating wave peaks generated by interference. And the number of active periods of the respiratory signal is obtained.

Optionally, determining the respiratory frequency corresponding to the respiratory signal according to the respiratory time domain estimated effective frequency and the respiratory frequency domain distribution signal includes: determining a respiration effective frequency range according to the respiration time domain estimated effective frequency and the respiration frequency allowable value; and selecting the highest frequency points with the largest number in the respiration effective frequency range in the respiration frequency domain distribution signal, and determining the frequency corresponding to the highest frequency points as the respiration frequency.

The respiratory rate allowable value and the respiratory effective rate may form an effective rate range of a respiratory signal, specifically, the time length of the signal is T, the respiratory effective period number is N, the respiratory effective rate is N/T, the respiratory rate allowable value may be 0.1Hz, and the effective rate range is [0.1Hz, N/t+0.1Hz ]. The frequencies of the respiration signals falling in the effective frequency range are all possible final respiration frequencies, and the frequency of the highest frequency point with the largest occurrence number is selected to be used as the final respiration frequency, so that the final respiration frequency is obtained by joint time-frequency domain processing according to the respiration signals.

If there are N valid respiratory cycles in the time T in the time domain discrimination, the upper limit of the respiratory frequency discriminated by the time domain can be obtained as N/T. Only because the heart cycle is estimated by counting the number of peaks and troughs, the heart cycle is easily affected by noise, and the noise can cause extra peaks and troughs, so that the counted number of effective cycles is larger than the actual number of breathing cycles. The respiratory signal is subjected to Fourier transformation to obtain the frequency domain distribution of the respiratory signal, and the respiratory frequency N/T estimated by time domain detection is used as the upper limit to find the highest frequency point of the frequency spectrum energy as the final respiratory frequency. The range of one frequency is determined by time domain detection, and the final frequency can be refined by frequency domain detection.

Optionally, performing the joint time-frequency domain processing and the data statistical analysis on the heartbeat signal to obtain the heartbeat frequency corresponding to the heartbeat signal includes: determining the effective cycle number of the heartbeat signal in the time domain of the heartbeat signal; determining the effective frequency of the heartbeat time domain estimation according to the number of the effective heartbeat periods and the duration of the heartbeat signal; performing Fourier transform on the heartbeat signal to obtain a heartbeat frequency domain distribution signal; and determining the heartbeat frequency corresponding to the heartbeat signal according to the effective frequency of the heartbeat time domain estimation and the heartbeat frequency domain distribution signal.

In the time domain of the heartbeat signal, determining the effective cycle number of the heartbeat signal and the effective frequency estimated by the heartbeat time domain, and in the frequency domain, determining the heartbeat frequency corresponding to the heartbeat signal according to the effective frequency estimated by the heartbeat time domain and the heartbeat frequency domain distribution signal. Therefore, the heartbeat signals are analyzed in the time domain and the frequency domain to obtain the heartbeat frequencies corresponding to the heartbeat signals, and compared with the mode of only carrying out time domain analysis or frequency domain analysis on the signals in the prior art, the method is more accurate, and the effectiveness and the accuracy of heartbeat frequency determination are improved.

Therefore, the technical effect of improving the accuracy of radar echo signal analysis and the accuracy of heartbeat frequency determination is achieved, and the technical problem that the accuracy of detection is low due to the fact that the method for detecting the heartbeat and the heartbeat frequency by the radar in the related technology only depends on the time domain characteristic or the frequency domain characteristic of the radar signal during analysis is solved.

Optionally, before determining the number of effective cardiac cycles of the heartbeat signal in the time domain of the heartbeat signal, the method further includes: performing variance homogenization treatment on the heartbeat signal to obtain a treated heartbeat signal; and executing the time domain of the heartbeat signal according to the processed heartbeat signal, and determining the effective heartbeat cycle number of the heartbeat signal.

It should be noted that, because the frequency of the respiratory signal is 0.1-0.8Hz, the frequency of the heartbeat signal is 0.7-2.5Hz, and the frequency of the respiratory signal and the frequency of the heartbeat signal are partially overlapped, in this embodiment, before determining the number of effective cycles of the heartbeat signal in the time domain of the heartbeat signal, the variance homogenization treatment can be further performed on the heartbeat signal, for example, 1s is used as a window, the variance of the signal in the window is calculated, and the window is drawn on the signal, so as to ensure that the processed signal variance is kept at a similar size. Thereby eliminating the influence of the rising edge and the falling edge of the respiratory signal in the radar echo signal on the waveform of the heartbeat signal.

Optionally, determining the number of heartbeat effective cycles of the heartbeat signal in the time domain of the heartbeat signal includes: obtaining a second peak position sequence with a peak value larger than zero and a second trough position sequence with a peak value smaller than zero by traversing the heartbeat signal in the time domain of the heartbeat signal; removing peaks with the distance smaller than a second preset threshold value from the second peak position sequence to obtain a second effective peak number, and removing troughs with the distance smaller than the second preset threshold value from the second trough position sequence to obtain a second effective trough number; and determining the effective cardiac cycle number according to the second effective wave crest number and the second effective wave trough number.

Obtaining a second peak sequence and a second trough sequence in the time domain of the heartbeat signal, screening through a second preset threshold value, removing peaks and troughs possibly having errors in the heartbeat signal, obtaining a second effective peak number and a second effective trough number, and determining the effective cycle number of the heartbeat according to the second effective peak number and the second effective trough number. Judging whether the wave peaks are generated due to noise or clutter or not by judging whether the distance between adjacent wave peaks meets a second preset threshold value or not, and eliminating wave peaks generated by interference. And the number of effective heart cycles of the heart beat signal is obtained.

Optionally, determining the heartbeat frequency corresponding to the heartbeat signal according to the heartbeat time domain estimated effective frequency and the heartbeat frequency domain distribution signal includes: determining a heartbeat effective frequency range according to the heartbeat time domain estimated effective frequency and the heartbeat frequency allowable value; selecting all peak frequency points in the heartbeat frequency domain distribution signal within the effective frequency range of the heartbeat, and marking the peak frequency points as heartbeat frequency points to be selected; screening the heartbeat frequency points to be selected, and determining reserved heartbeat frequency points; carrying out maximum likelihood estimation on the reserved heartbeat frequency points, and determining the probability of each reserved heartbeat frequency point as the heartbeat frequency; and correcting the probability of the reserved heartbeat frequency point through historical data, and determining the heartbeat frequency of the reserved heartbeat frequency point with the maximum probability as the heartbeat frequency.

The above-mentioned heart beat frequency allowable value and heart beat effective frequency can be formed into effective frequency range of heart beat signal, and the time length of said signal is T, and the number of heart beat effective period is N _h The effective frequency of the heartbeat is N _h T, the allowable value of the heartbeat frequency can be 0.7Hz, the effective frequency range is [0.7Hz, N _h /T+0.1Hz]. The frequencies of the heartbeat signal falling in the effective frequency range are possible to be the final heartbeat frequency. The frequency obtained in the time domain reduces the limited area of the heartbeat frequency selection range, reduces the influence of noise points to a certain extent, improves the accuracy of the heartbeat frequency points, and selects the frequency points which are possibly the heartbeat.

Optionally, performing maximum likelihood estimation on the reserved heartbeat frequency points, and determining the probability of each reserved heartbeat frequency point as the heartbeat frequency includes: determining a joint probability density function of the heartbeat signals according to a signal model of the heartbeat signals retaining the heartbeat frequency points; determining an objective function satisfied by parameters of the heartbeat signal according to the joint probability density function, wherein the parameters comprise frequency, amplitude and phase; and carrying out transformation solving on the objective function to obtain the probability of reserving the heartbeat frequency point as the heartbeat frequency.

The specific steps of the maximum likelihood estimation (MLE, maximum Likelihood Estimate, also called maximum likelihood estimation) are as follows:

assuming that the heartbeat signal is a sinusoidal signal, the signal model at this time is:

x[n]＝Acos(2πfn+φ)+w[n]，n＝0，1，...，N-1 (1)

wherein x [ n ]]Is the heartbeat signal, A is the amplitude of the heartbeat, f is the heartbeat frequency, phi is the initial phase of the heartbeat signal, w [ n ]]Is noise, wherein the noise is assumed to be 0 mean and sigma variance ² Is a gaussian white noise of (c).

Since the noise is Gaussian white noise, a joint probability density function of the heartbeat signal x [ n ] is obtained at this time:

at this time, it is necessary to find a parameter that maximizes the probability density, i.e., the required parameter satisfies the following objective function:

due to pi and sigma ² For a fixed constant, the objective function may be converted into the following form:

at this time, the parameter is converted, and the expression (4) is rewritten by the expression (5),

the objective function at this time is available as:

order theThen the objective function can be written as:

J(α ₁ ，α ₂ ，f)＝(x-α ₁ c-α ₂ s) ^T (x-α ₁ c-α ₂ s) (7)

order theThen the objective function can be written as:

J(α ₁ ，α ₂ ，f)＝(x-Hα) ^T (x-Hα) (8)

the parameter α is biased, and the bias is set to 0:

bringing formula (9) to formula (8) yields:

J(α ₁ ，α ₂ ，f)＝x ^T (1-H(H ^T H) ^-1 H ^T )x (10)

the objective function can now be written as:

at this time, the expansion (11) equation is available at which the objective function is:

the parameter estimates available are:

and judging the reserved heartbeat frequency points to be selected through maximum likelihood estimation, and respectively judging the possibility that each heartbeat frequency point to be selected is the heartbeat frequency.

Optionally, correcting the probability of the reserved heartbeat frequency point through the historical data, and determining that the heartbeat frequency of the reserved heartbeat frequency point with the highest probability is the heartbeat frequency includes: determining a plurality of heartbeat frequencies of historical data, wherein the historical data is a plurality of radar echo signals of a first preset time before the radar echo signals and a plurality of heartbeat frequencies corresponding to the radar echo signals; clustering a plurality of heartbeat frequencies of the historical data by taking each reserved heartbeat frequency point as a center, and determining the proportion of the number of heartbeat frequencies successfully clustered with the reserved heartbeat frequency points to the total number of the plurality of heartbeat frequencies; multiplying the probability by the proportion to determine the corrected probability; and taking the heartbeat frequency corresponding to the reserved heartbeat frequency point corresponding to the maximum value of the corrected probability as the final heartbeat frequency.

Correcting the judgment result of the MLE by using the statistical information of the historical data: and (3) carrying out statistical analysis on the historical heartbeat frequency selected in the first preset time (such as the previous 30 s), clustering by taking the reserved heartbeat frequency points as the center through a clustering method, wherein the number of the historical frequency points distributed to each reserved heartbeat frequency point is the discrimination result of the historical data on the reserved heartbeat frequency points, and the final heartbeat frequency is obtained by integrating the discrimination result of the historical frequency points and the discrimination result of the MLE. And correcting the judgment of the MLE through statistical analysis of the historical data, and comprehensively considering the historical data and the MLE estimation result to select the heartbeat frequency.

Optionally, screening the heartbeat frequency points to be selected, and determining the reserved heartbeat frequency points includes: comparing each heartbeat frequency point to be selected with a plurality of heartbeat frequency points to be selected in a second preset time; if the distance between the to-be-selected heartbeat frequency point and the frequency point positions of the plurality of to-be-selected heartbeat frequency points in the second preset time exceeds a third preset threshold value, discarding the to-be-selected heartbeat frequency point; and if the distance between the to-be-selected heartbeat frequency point and the frequency point positions of the plurality of to-be-selected heartbeat frequency points in the second preset time does not exceed a third preset threshold value, taking the to-be-selected heartbeat frequency point as a reserved heartbeat frequency point.

Screening the heartbeat frequency points to be selected, determining reserved heartbeat frequency points, and judging the continuity of the heartbeat frequency points to be selected: comparing each to-be-selected heartbeat frequency point with a to-be-selected heartbeat frequency point in a second preset time (within the previous 5 s), if the to-be-selected heartbeat frequency point has a larger distance from the to-be-selected heartbeat frequency point in the previous 5s to the frequency point position, namely, the distance between the to-be-selected heartbeat frequency point and the frequency point position of a plurality of to-be-selected heartbeat frequency points in the second preset time exceeds a third preset threshold value, discarding the frequency point, and if the to-be-selected heartbeat frequency points with small difference between the frequency points, namely, the distance between the to-be-selected heartbeat frequency point and the frequency point position of a plurality of to-be-selected heartbeat frequency points in the second preset time does not exceed the third preset threshold value, reserving the frequency point.

Thereby eliminating noise points in the heartbeat frequency points to be selected. For heartbeat signals, the heartbeat frequency is typically continuously variable over time, i.e. the frequency of the heartbeat signal does not mutate. And respectively comparing the frequency difference between the frequency point at the current moment and the frequency point to be selected in the previous 5s, if the frequency point with a smaller frequency difference exists, indicating that the frequency point to be selected is continuously changed along with time, namely the heartbeat frequency is possible, and if the frequency difference is too large, the frequency to be selected is suddenly changed, namely the frequency to be selected is possibly caused by noise and needs to be discarded.

It should be noted that the present embodiment also provides an alternative implementation manner, and the detailed description of this implementation manner is provided below.

The embodiment provides a respiratory heartbeat frequency estimation method based on a maximum likelihood estimation theory and data statistical analysis, and belongs to the technical field of radar target identification.

The purpose of this embodiment is to provide a respiratory heartbeat frequency estimation method based on maximum likelihood estimation theory and data statistics analysis, aiming at the accurate detection problem of non-contact respiratory heartbeat, the obtained echo signals of the frequency-modulated continuous wave radar are separated by using a band-pass filter, and then the respiratory heartbeat signals are extracted by adopting methods such as joint time-frequency domain analysis, estimation theory, statistics analysis and the like.

Fig. 2 is a schematic diagram of a main flow of signal processing according to an embodiment of the present invention, and as shown in fig. 2, the respiratory heartbeat detection method based on the estimation theory proposed in this embodiment is mainly divided into a data preprocessing portion, a respiratory detection portion and a heartbeat detection portion, where the data preprocessing portion includes the following steps:

(1) Fourier transforming each chirp (chirp signal) of the acquired radar echo to obtain a one-dimensional range (r) of the echo signal:

P＝max(abs(range(r)))

R＝find(abs(range(r))＝＝P)

data＝range(R)

signal(t)＝[data1 data2...datan]

phase _signal (t)＝angle(signal(t))

phase _data (t)＝unwrap(phase _signal (t))

Wherein angle () is a function for solving a phase signal, which can be implemented by an arctan function, and unwrap () is a function for unwrapping the phase signal.

The breath detection portion includes the steps of:

(1) For signal phase _data (t) band-pass filtering, wherein the respiratory frequency is generally between 0.1 and 0.8Hz, and the set respiratory filter passband is set to obtain a filtered signal data _breath (t)：

data _breath (t)＝filter(phase _data (t)，bp)

Wherein, filter () is a filter, bp is the pass band range of the filter, and at this time, the pass band range of the breathing filter is 0.1-0.8Hz.

(2) Judging the number of respiratory signal peaks in the time domain of the signal: traversing signal data _breath (t) obtaining a peak position sequence peak having a peak value greater than 0 _breath (n), and a sequence of trough positions with peaks less than 0 valley _breath (n)。

(3) Screening the wave crest position sequence to obtain the effective period number N of the mutual information number _b : traversing peak position sequence peak _breath (N) removing peaks with too close distance before and after, wherein the number of the remaining peaks is the number N1 of effective peaks, and traversing the trough position sequence valley in the same way _breath (N) eliminating the wave trough with too short distance before and after, wherein the number of the rest wave trough is the effective wave trough number N2, and the effective period number is as follows:

N _b ＝(N1+N2)/2

(4) Judging the frequency of the respiratory signal in the frequency domain of the signal:signal data _breath And (T) the time length is T, and the number of effective respiratory periods obtained in the step (3) in the T time is N, so that the effective respiratory frequency is N/T. For signal data _breath (t) performing Fourier transform to obtain frequency domain distribution _breath (f) Distributing the signal frequency in the frequency domain _breath (f) In which the upper limit of the effective respiratory frequency is [0.1Hz, N/T+0.1Hz]Searching the highest frequency point in the range, namely the final frequency f of respiration _breath 。

The heartbeat detection section includes the steps of:

(1) For signal phase _data (t) band-pass filtering, typically with a heartbeat frequency of 0.7-2.5Hz, to set the passband of the heartbeat filter to obtain a filtered signal data _heart (t)：

data _heart (t)＝filter(phase _data (t)，bp2)

Wherein bp2 is the passband of the filter, and the passband of the heartbeat filter is 0.7-2.5Hz.

(2) Performing variance homogenization treatment on the heartbeat signal: and (3) taking 1s as a window, calculating the variance of the signal in the window, and windowing the signal to ensure that the processed signal variance is kept at a similar size.

(3) Judging the number of peaks and troughs of the heartbeat signal in the time domain of the signal: traversing signal data _heart (t) obtaining a peak position sequence peak having a peak value greater than 0 _heart (n), and a sequence of valley positions valley with a valley value less than 0 _heart (n)。

(4) Screening the wave crest and wave trough position sequences to obtain the number N of effective cardiac cycles _h : traversing peak position sequence peak _heart (N) removing peaks with too close front and rear distances, wherein the number of the remaining peaks is the number N1 of the effective ground peaks; traversing a sequence of peak positions valley _heart (N) eliminating the wave trough with too short distance before and after, wherein the number of the rest wave trough is the number N2 of effective ground wave trough, and the number of effective period is as follows:

N _h ＝(N1+N2)/2

(5) Judging the heartbeat frequency point to be selected of the heartbeat signal in the frequency domain of the signal: signal signaldata _heart The time length of (T) is T, and the number of heartbeat effective periods in the T time is N obtained in the step (4) _h The effective frequency of the heartbeat is N _h and/T. For signal data _heart (t) performing Fourier transform to obtain frequency domain distribution frequency of heartbeat signals _heart (f) Distributing the signal frequency in the frequency domain _heart (f) At the effective cycle frequency N of heartbeat _h T is the upper limit, in the range [0.7, N _h /T+0.3Hz]Searching all peak frequency points, and recording the peak frequency points as heartbeat frequency points to be selected.

(6) Carrying out continuity judgment on the heartbeat frequency points to be selected: each heartbeat frequency point to be selected is compared with the heartbeat frequency point to be selected in the previous 5s, if the heartbeat frequency point to be selected and the heartbeat frequency point to be selected in the previous 5s are in a larger distance, the heartbeat frequency point is discarded, and if the heartbeat frequency point to be selected is in a small difference, the heartbeat frequency point to be selected is reserved.

(7) And carrying out maximum likelihood estimation on the reserved heartbeat frequency points to be selected: and respectively judging each reserved heartbeat frequency point to be selected by using the MLE, and judging the possibility that each heartbeat frequency point to be selected becomes the heartbeat frequency.

(8) Correcting the judgment result of the MLE by using the statistical information of the historical data: and (3) carrying out statistical analysis on the historical heartbeat frequency selected in the previous 30s, clustering by taking the reserved heartbeat frequency points as the center through a clustering method, wherein the number of the historical frequency points distributed to each reserved heartbeat frequency point is the discrimination result of the historical data on the reserved heartbeat frequency points, and obtaining the final heartbeat frequency by integrating the discrimination result of the historical frequency points and the discrimination result of the MLE.

The method is different from the existing non-contact respiration heartbeat detection method, and the method firstly utilizes a filter to separate a respiration frequency band from a heartbeat frequency band, and combines a time-frequency domain to estimate the frequency of a respiration signal and a heartbeat frequency point to be selected of the heartbeat signal, and selects the heartbeat frequency after continuous discrimination, maximum likelihood discrimination and historical statistical analysis are carried out on the heartbeat frequency point to be selected. Compared with the method in the related art, the method of the embodiment has finer detection results, can effectively improve the accuracy of the results by utilizing the combined time-frequency domain analysis, and can effectively reduce the influence of noise and clutter by estimating the heartbeat frequency by utilizing the estimation theory; compared with the method in the related art, the method in the embodiment utilizes the combined time-frequency domain analysis and estimation theory method to judge the respiratory heartbeat frequency, so that the influence of noise and clutter can be reduced, and the accuracy is improved. The method of the present embodiment is finer than the method of the related art, and at the same time, the method of the present embodiment extracts the respiratory signal and the heartbeat signal, which can further reduce the influence of phenomena such as burrs, and improve the accuracy.

Fig. 3 is a flowchart of a signal processing method according to an embodiment of the present invention, and as shown in fig. 3, the respiratory heartbeat frequency estimation method based on the maximum likelihood estimation theory and the data statistical analysis according to the present embodiment includes the following data preprocessing step, a respiratory detection step, and a heartbeat detection step, where the data preprocessing step includes:

(1) Fourier transforming each chirp of the acquired radar echo to obtain a one-dimensional range (r) of the echo signal:

P＝max(abs(range(r)))

R＝find(abs(range(r))＝＝P)

data＝range(R)

signal(t)＝[data1 data2...datan]

phase _signal (t)＝angle(signal(t))

phase _data (t)＝unwrap(phase _signal (t))

The breath detection portion includes the steps of:

data _breath (t)＝filter(phase _data (t)，bp)

Wherein, filter () is a filter, bp is the pass band range of the filter, and at this time, the pass band range of the breathing filter is 0.1-0.8Hz. Filter algorithms are well known in the art.

The main purpose of this step is to obtain a signal within the breathing passband.

(3) Screening the wave crest position sequence to obtain the effective period of the respiratory signalNumber N _b : traversing peak position sequence peak _breath (N) removing peaks with too close distance before and after, wherein the number of the remaining peaks is the number N1 of effective peaks, and traversing the trough position sequence valley in the same way _breath (N) removing the wave trough with too close distance before and after, the number of the rest wave trough is the effective wave trough number N2, and the effective period number is

N _b ＝(N1+N2)/2

The main purpose of this step is to judge whether the wave crest is due to noise or clutter by judging the distance between adjacent wave crests, and to eliminate the wave crest generated by interference.

(4) Judging the frequency of the respiratory signal in the frequency domain of the signal: signal data _breath And (T) the time length is T, and the number of effective respiratory periods obtained in the step (3) in the T time is N, so that the effective respiratory frequency is N/T. For signal data _breath (t) performing Fourier transform to obtain frequency domain distribution _breath (f) Distributing the signal frequency in the frequency domain _breath (f) In which the upper limit of the effective respiratory frequency is [0.1Hz, N/T+0.1Hz]Searching the highest frequency point in the range, namely the final frequency f of respiration _breath 。

If there are N valid respiratory cycles in the time T in the time domain discrimination, the upper limit of the respiratory frequency discriminated by the time domain can be obtained as N/T. Only because the heart cycle is estimated by counting the number of peaks and troughs, the heart cycle is easily influenced by noise, and the noise can cause extra peaks and troughs, so that the counted number of effective breathing cycles is larger than the actual number of effective breathing cycles. The respiratory signal is subjected to Fourier transformation to obtain the frequency domain distribution of the respiratory signal, and the highest frequency point is found out by taking the respiratory frequency N/T detected in the time domain as the upper limit, so as to be used as the final respiratory frequency.

The method determines a frequency range through time domain detection, and can refine the final frequency through frequency domain detection.

The heartbeat detection section includes the steps of:

(1) For signal phase _data (t) band-pass filtering, typically with a heartbeat frequency of 0.7-2.5Hz, set as heartbeatThe passband of the filter, the obtained filtered signal is data _heart (t)：

data _heart (t)＝filter(phase _data (t)，bp2)

The main purpose of this step is to obtain a signal within the passband of the heartbeat.

The main purpose of this step is to eliminate the effect of the rising and falling edges of the respiratory signal on the waveform of the heartbeat signal.

N _h ＝(N1+N2)/2

(5) Judging the heartbeat frequency point to be selected of the heartbeat signal in the frequency domain of the signal: signal data _heart The time length of (T) is T, and the effective period number N in the T time is obtained in the step (4) _h The effective frequency of the heartbeat is N _h and/T. For signal data _heart (t) Fourier transforming to obtain heartFrequency domain distribution of a hop signal _heart (f) Distributing the signal frequency in the frequency domain _heart (f) At the effective cycle frequency N of heartbeat _h T is the upper limit, in the range [0.7, N _h /T+0.3Hz]Searching all peak frequency points, and recording the peak frequency points as heartbeat frequency points to be selected.

The main purpose of this step is to reduce the influence of noise points to a certain extent by narrowing the limited area of the frequency selection range of the heartbeat frequency obtained in the time domain, improve the accuracy of the heartbeat frequency points, and select the frequency points which are likely to be the heartbeat.

The main purpose of this step is to eliminate noise points. For heartbeat signals, the heartbeat frequency is typically continuously variable over time, i.e. the frequency of the heartbeat signal does not mutate. And respectively comparing the frequency difference between the frequency point at the current moment and the frequency point to be selected in the previous 5s, if the frequency point with a smaller frequency difference exists, indicating that the frequency point to be selected is continuously changed along with time, namely the heartbeat frequency is possible, and if the frequency difference is too large, the frequency to be selected is suddenly changed, namely the frequency to be selected is possibly caused by noise and needs to be discarded.

The main purpose of the step is to judge the reserved heartbeat frequency points to be selected through maximum likelihood estimation, and respectively judge the possibility that each heartbeat frequency point to be selected is the heartbeat frequency.

The main purpose of the step is to correct the discrimination of the MLE through statistical analysis of the historical data, comprehensively consider the historical data and the MLE estimation result and select the heartbeat frequency.

The maximum likelihood estimation method is used in the heartbeat breath detection process, and is described herein.

x[n]＝Acos(2πfn+φ)+w[n]，n＝0，1，...，N-1 (1)

the objective function at this time is available as:

order theThen the objective function can be written as:

J(α ₁ ，α ₂ ，f)＝(x-α ₁ c-α ₂ s) ^T (x-α ₁ c-α ₂ s) (7)

order theThen the objective function can be written as:

J(α ₁ ，α ₂ ，f)＝(x-Hα) ^T (x-Hα) (8)

the parameter α is biased, and the bias is set to 0:

bringing formula (9) to formula (8) yields:

J(α ₁ ，α ₂ ，f)＝x ^T (1-H(H ^T H) ^-1 H ^T )x (10)

the objective function can now be written as:

the parameter estimates available are:

the embodiment relates to a respiratory heartbeat frequency estimation method based on a maximum likelihood estimation theory and data statistical analysis, and belongs to the technical field of radar target identification. The method comprises the steps of obtaining a respiratory heartbeat signal from an original radar echo through data preprocessing, extracting the respiratory signal and the heartbeat signal from the respiratory heartbeat signal by utilizing a band-pass filter, and carrying out combined time-frequency domain analysis on the respiratory signal to select respiratory frequency; and selecting a heartbeat frequency point to be selected from the heartbeat signal through combined time-frequency domain analysis, and selecting the heartbeat frequency through continuous discrimination, maximum likelihood estimation discrimination and historical data statistical analysis of the heartbeat frequency point to be selected. The method has higher accuracy aiming at non-contact respiratory heartbeat detection.

Fig. 4 is a schematic diagram of a signal processing apparatus according to an embodiment of the present invention, and as shown in fig. 4, according to another aspect of an embodiment of the present invention, there is further provided a signal processing apparatus including: a processing module 42, a filtering module 44 and a processing module 46, which are described in detail below.

The processing module 42 is configured to process the radar echo signal to obtain a chest motion signal, where the radar echo signal is an echo signal generated by reflecting an electromagnetic wave emitted by the radar by a target object; the filtering module 44 is connected to the processing module 42, and is configured to filter the chest motion signal by using a filter to obtain a respiratory signal and a heartbeat signal, where the respiratory signal corresponds to a respiratory bandpass frequency of the filter, and the heartbeat signal corresponds to a heartbeat bandpass frequency of the filter; the processing module 46 is connected to the filtering module 44, and is configured to perform joint time-frequency domain processing on the respiratory signal to obtain a respiratory frequency corresponding to the respiratory signal, and perform joint time-frequency domain processing and data statistics analysis on the heartbeat signal to obtain a heartbeat frequency corresponding to the heartbeat signal.

By the device, the radar echo signal is processed to obtain the chest cavity motion signal, wherein the radar echo signal is generated by reflecting electromagnetic waves emitted by the radar by a target object; filtering the chest cavity motion signal by using a filter to obtain a respiration signal and a heartbeat signal, wherein the respiration signal corresponds to the respiration bandpass frequency of the filter, and the heartbeat signal corresponds to the heartbeat bandpass frequency of the filter; the method comprises the steps of carrying out joint time-frequency domain processing on respiratory signals to obtain respiratory frequencies corresponding to the respiratory signals, carrying out joint time-frequency domain processing and data statistics analysis on heartbeat signals to obtain the heartbeat frequencies corresponding to the heartbeat signals, separating the respiratory signals and the heartbeat signals through a filter by processing radar echo signals, and carrying out joint time-frequency domain analysis on the respiratory signals and the heartbeat signals respectively, so that the purposes of effectively determining the respiratory frequencies and the heartbeat frequencies in the radar echo signals and guaranteeing accuracy and stability are achieved, the accuracy of radar echo signal analysis is improved, the technical effects of accuracy of determining the respiratory frequencies and the heartbeat frequencies are improved, and the technical problems that the accuracy of detection is low due to the fact that the radar detection method in the related technology only depends on time domain characteristics or frequency domain characteristics of the radar signals in analysis are solved.

According to another aspect of the embodiment of the present invention, there is also provided a processor, configured to execute a program, where the program executes any one of the signal processing methods described above.

According to another aspect of the embodiments of the present invention, there is also provided a computer storage medium, the computer storage medium including a stored program, wherein the program when run controls a device in which the computer storage medium is located to perform the signal processing method of any one of the above.

The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.

In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims

1. A signal processing method, comprising:

processing a radar echo signal to obtain a chest cavity motion signal, wherein the radar echo signal is an echo signal generated by reflecting electromagnetic waves emitted by a radar by a target object;

filtering the chest cavity motion signal by using a filter to obtain a respiratory signal and a heartbeat signal, wherein the respiratory signal corresponds to the respiratory bandpass frequency of the filter, and the heartbeat signal corresponds to the heartbeat bandpass frequency of the filter;

carrying out joint time-frequency domain processing on the respiratory signals to obtain respiratory frequencies corresponding to the respiratory signals, and carrying out joint time-frequency domain processing and data statistical analysis on the heartbeat signals to obtain heartbeat frequencies corresponding to the heartbeat signals;

the processing the heartbeat signal in a combined time-frequency domain to obtain the heartbeat frequency corresponding to the heartbeat signal comprises the following steps:

Determining the number of heartbeat effective periods of the heartbeat signal in the time domain of the heartbeat signal;

determining a heartbeat time domain estimated effective frequency according to the number of the heartbeat effective periods and the duration of the heartbeat signal;

performing Fourier transform on the heartbeat signal to obtain a heartbeat frequency domain distribution signal;

determining a heartbeat effective frequency range according to the heartbeat time domain estimated effective frequency and the heartbeat frequency allowable value;

selecting all peak frequency points in the heartbeat frequency domain distribution signal, wherein the peak frequency points are in the heartbeat effective frequency range, and recording the peak frequency points as heartbeat frequency points to be selected;

screening the heartbeat frequency points to be selected, and determining reserved heartbeat frequency points;

carrying out maximum likelihood estimation on the reserved heartbeat frequency points, and determining the probability of each reserved heartbeat frequency point as the heartbeat frequency;

and correcting the probability of the reserved heartbeat frequency point through historical data, and determining the heartbeat frequency of the reserved heartbeat frequency point with the maximum probability as the heartbeat frequency.

2. The method of claim 1, wherein processing the radar echo signal to obtain a chest motion signal comprises:

performing Fourier transformation on each linear frequency modulation signal in the continuous echo signals according to the time sequence to obtain one-dimensional range profile data of each linear frequency modulation signal;

Determining signal data of a distance gate with highest energy in each one-dimensional distance image data, and arranging the signal data according to the time sequence of the linear frequency modulation signals to obtain respiratory heartbeat signals in a period of time;

and (3) carrying out unwrapping processing on the respiratory heartbeat signal to obtain the chest cavity movement signal.

3. The method of claim 2, wherein filtering the chest motion signal with a filter to obtain a respiration signal and a heartbeat signal comprises:

setting the passband of the filter as respiratory bandpass frequency, and filtering the chest motion signal to obtain the respiratory signal;

and setting the passband of the filter as the heartbeat bandpass frequency, and filtering the chest cavity motion signal to obtain the heartbeat signal.

4. The method of claim 1, wherein performing joint time-frequency domain processing on the respiratory signals to obtain respiratory frequencies corresponding to the respiratory signals comprises:

determining a respiration effective period number of the respiration signal in a time domain of the respiration signal;

determining a respiration time domain estimated effective frequency according to the respiration effective period number and the duration of the respiration signal;

Performing Fourier transform on the respiratory signal to obtain a respiratory frequency domain distribution signal;

and determining the respiratory frequency corresponding to the respiratory signal according to the respiratory time domain estimated effective frequency and the respiratory frequency domain distribution signal.

5. The method of claim 4, wherein determining the number of breath valid periods of the respiratory signal in the time domain of the respiratory signal comprises:

traversing the respiratory signal in the time domain of the respiratory signal to obtain a first peak position sequence with a peak value larger than zero and a first trough position sequence with a peak value smaller than zero;

removing peaks with a distance smaller than a first preset threshold value from the first peak position sequence to obtain a first effective peak number, and removing troughs with a distance smaller than the first preset threshold value from the first trough position sequence to obtain a first effective trough number;

and determining the number of effective respiratory cycles according to the first effective wave crest number and the first effective wave trough number.

6. The method of claim 4, wherein determining the respiratory frequency corresponding to the respiratory signal from the respiratory time domain estimated effective frequency and the respiratory frequency domain distribution signal comprises:

Determining a respiration effective frequency range according to the respiration time domain estimated effective frequency and the respiration frequency allowable value;

and selecting the highest frequency point in the respiration effective frequency range in the respiration frequency domain distribution signal, and determining the frequency corresponding to the highest frequency point as the respiration frequency.

7. The method of claim 1, wherein prior to determining the number of heartbeat active cycles of the heartbeat signal in the time domain of the heartbeat signal, further comprising:

performing variance homogenization treatment on the heartbeat signal to obtain a treated heartbeat signal;

and executing the step of determining the effective cycle number of the heartbeat signal in the time domain of the heartbeat signal according to the processed heartbeat signal.

8. The method of claim 1, wherein determining the number of heartbeat active cycles of the heartbeat signal in the time domain of the heartbeat signal comprises:

traversing the heartbeat signal in the time domain of the heartbeat signal to obtain a second peak position sequence with a peak value larger than zero and a second trough position sequence with a peak value smaller than zero;

removing peaks with a distance smaller than a second preset threshold value from the second peak position sequence to obtain a second effective peak number, and removing troughs with a distance smaller than the second preset threshold value from the second trough position sequence to obtain a second effective trough number;

And determining the effective cycle number of the heartbeat according to the second effective wave crest number and the second effective wave trough number.

9. The method of claim 1, wherein performing maximum likelihood estimation on the retained beat frequency points, determining a probability for each retained beat frequency point as a beat frequency comprises:

determining a joint probability density function of the heartbeat signals according to the signal model of the heartbeat signals retaining the heartbeat frequency points;

determining an objective function satisfied by parameters of the heartbeat signal according to the joint probability density function, wherein the parameters comprise frequency, amplitude and phase;

and carrying out transformation solving on the objective function to obtain the probability of reserving the heartbeat frequency point as the heartbeat frequency.

10. The method of claim 1, wherein correcting the probability of the reserved heart beat frequency point by historical data, determining the heart beat frequency of the reserved heart beat frequency point with the highest probability as the heart beat frequency comprises:

determining a plurality of heartbeat frequencies of the historical data, wherein the historical data are a plurality of radar echo signals of a first preset time before the radar echo signals and a plurality of heartbeat frequencies corresponding to the radar echo signals;

Clustering a plurality of heartbeat frequencies of the historical data by taking each reserved heartbeat frequency point as a center, and determining the proportion of the number of heartbeat frequencies successfully clustered with the reserved heartbeat frequency points to the total number of the plurality of heartbeat frequencies;

multiplying the probability by the ratio to determine a corrected probability;

and taking the heartbeat frequency corresponding to the reserved heartbeat frequency point corresponding to the maximum value of the corrected probability as the final heartbeat frequency.

11. The method of claim 1, wherein screening the candidate heartbeat frequency points and determining a reserved heartbeat frequency point comprises:

comparing each heartbeat frequency point to be selected with a plurality of heartbeat frequency points to be selected in a second preset time;

if the distance between the heartbeat frequency point to be selected and the frequency point positions of the plurality of heartbeat frequency points to be selected in the second preset time exceeds a third preset threshold value, discarding the heartbeat frequency points to be selected;

and if the distance between the heartbeat frequency point to be selected and the frequency point positions of the plurality of heartbeat frequency points to be selected in the second preset time does not exceed a third preset threshold value, taking the heartbeat frequency point to be selected as a reserved heartbeat frequency point.

12. A signal processing apparatus, comprising:

the processing module is used for processing the radar echo signals to obtain chest cavity motion signals, wherein the radar echo signals are echo signals generated by reflecting electromagnetic waves emitted by a radar by a target object;

the filtering module is used for filtering the chest cavity motion signal to obtain a respiratory signal and a heartbeat signal, wherein the respiratory signal corresponds to the respiratory bandpass frequency of the filter, and the heartbeat signal corresponds to the heartbeat bandpass frequency of the filter;

the processing module is used for carrying out joint time-frequency domain processing on the respiratory signals to obtain respiratory frequencies corresponding to the respiratory signals, and carrying out joint time-frequency domain processing and data statistical analysis on the heartbeat signals to obtain heartbeat frequencies corresponding to the heartbeat signals;

13. A processor for running a program, wherein the program when run performs the signal processing method of any one of claims 1 to 11.