A millimeter wave radar data multi-dimensional feature joint processing method

Abstract

The application discloses a kind of millimeter wave radar data multi-dimensional feature joint processing method, it is related to radar signal processing field, including by the data of the detected object of radar acquisition obtains pulse, the distance dimension imaging is carried out to each pulse of radar acquisition, obtains the one-dimensional range image of observed scene, for each one-dimensional range image, the position of peak point position is extracted, the signal amplitude at the position of peak point position is recorded, defined as RCS, directly carries out difference processing to RCS sequence to obtain RCS sequence difference, accumulates RCS sequence and carries out phase difference to obtain second phase sequence;Respiratory signal is obtained by the band-pass filtering of second phase sequence;Based on RCS sequence difference and respiratory signal are jointly processed, and then apnea is jointly detected to obtain detection result. By the method in the application, it can be aimed at old people in sleep, and robust heart rate estimation is carried out.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of radar signal processing, in particular to a millimeter wave radar data multi-dimensional feature joint processing method. BACKGROUND

[0002] Apnea (especially sleep apnea syndrome) is a common health problem for the elderly, and is closely related to serious complications such as cardiovascular disease and cognitive impairment. Repeated respiratory interruptions at night can cause blood oxygen to drop and heart rate to fluctuate, and may eventually lead to hypertension, arrhythmia, and even sudden death. However, many patients have hidden symptoms, and lack convenient and effective monitoring methods, which greatly hinders the early warning and improvement of the event.

[0003] Existing apnea detection technologies mainly include medical-grade devices and consumer monitoring solutions. Medical-grade devices mainly use polysomnography (PSG), which is highly accurate but expensive and difficult to popularize. Consumer solutions mainly include various portable sleep monitoring devices (HSAT), smart wearable devices (such as smart bands and watches), etc. The main problem with such devices is that they rely on user operation, and improper wearing can lead to data failure. In addition, they have a strong sense of restraint, which may affect natural sleep, and many elderly people are reluctant to use them.

[0004] Millimeter wave radar has advantages such as large detection range, strong privacy, and non-contact, and has gained increasing attention in apnea detection applications. Existing public methods generally analyze waveform features or use neural network methods for apnea detection after obtaining the respiratory waveform. The main problem is that: on the one hand, the respiratory waveform is a type of target feature signal obtained by radar, and using only this information for apnea detection may limit the detection accuracy; on the other hand, neural network methods rely on the completeness of training data and have relatively poor interpretability, and when the detection sensitivity needs to be adjusted in different scenarios, intuitive parameter settings cannot be performed.

[0005] In order to solve these problems, a millimeter wave radar data multi-dimensional feature joint processing method is urgently needed. SUMMARY

[0006] To solve the above problems, the present application proposes a millimeter wave radar data multi-dimensional feature joint processing method, which is used to improve the problems of inconvenient measurement, uncomfortable wearing, and insufficient interpretability in apnea detection for the elderly, and the specific content is as follows:

[0007] A millimeter wave radar data multi-dimensional feature joint processing method, comprising the following steps:

[0008] S1, acquiring data of a detected object by a radar to obtain to-be-processed data, the to-be-processed data comprising pulses, each pulse comprising One sampling point;

[0009] S2. Perform range-dimensional imaging on each pulse acquired by the radar to obtain a one-dimensional range image of the observed scene. ;

[0010] S3, for each Extract the location of the peak point. Record the location of the peak point. The signal amplitude at a given point is defined as the RCS. The set of RCS values ​​at different target value locations constitutes the RCS sequence. ;

[0011] S4, RCS sequence Directly perform differential processing to obtain the RCS sequence difference. ;

[0012] S5, RCS sequence The second phase sequence is obtained by accumulating and performing phase difference.

[0013] The respiratory signal is obtained by bandpass filtering the second phase sequence. ;

[0014] S6, RCS-based sequence differencing and respiratory signals Combined processing is performed to obtain test results for sleep apnea.

[0015] Preferably, in S4, the RCS sequence Directly perform differential processing to obtain the RCS sequence difference. The specific content includes:

[0016] S401, For RCS sequences Smoothing is performed to obtain the smoothed RCS sequence. ;

[0017] S402. The smoothed RCS sequence Differential processing is performed to obtain the RCS sequence difference. ;

[0018] Preferably, for RCS sequences The smoothed expression is obtained by performing smoothing:

[0019] ;

[0020] in: For filtering system, For sampling sequence number, This represents the current radar echo sampling amplitude.

[0021] Preferably, the smoothed RCS sequence The expression for performing the difference processing is:

[0022] .

[0023] Preferably, in S5, the RCS sequence The specific content of obtaining the second phase sequence by accumulating and performing phase difference includes:

[0024] S501, For each , cumulative around Each unit receives the cumulative magnitude. ;

[0025] ;

[0026] in, As an intermediate variable, The number of units to be accumulated. This is the cumulative magnitude;

[0027] S502, Calculation The phase, then for A phase sequence is obtained from several pulses. ;

[0028] S503. For the phase sequence, perform differential processing to obtain the second phase sequence. .

[0029] Preferably, in step S5, the second phase sequence is bandpass filtered to obtain the respiratory signal. The expression for the respiratory signal is:

[0030] ;

[0031] in, These are the filter coefficients of the bandpass filter. This represents the convolution operation.

[0032] Preferably, S6 is based on RCS sequence differencing. and respiratory signals The specific details of the combined processing and subsequent combined detection of apnea are as follows:

[0033] S601, Respiratory signals Threshold judgment and cumulative counting are performed to obtain the cumulative value of respiratory signals. ;

[0034] S602, Differential RCS Sequence threshold judgment and cumulative counting to obtain a cumulative value of the differential RCS sequence

[0035] S603, judging the respiratory waveform branch and the RCS branch to obtain a suspected event of the respiratory waveform branch and the RCS branch and jointly judging to obtain a detection result of the apnea

[0036] preferably, judging the respiratory signal threshold judgment and cumulative counting to obtain a cumulative value of the respiratory signal is expressed as:

[0037]

[0038] wherein: is a respiratory waveform amplitude threshold.

[0039] preferably, judging the differential RCS sequence threshold judgment and cumulative counting to obtain a cumulative value of the differential RCS sequence is expressed as:

[0040]

[0041] preferably, jointly judging and to obtain a detection result of the apnea is as follows:

[0042] S6031, judging the respiratory waveform branch and the RCS branch suspected event:

[0043] if , a suspected apnea event occurs in the respiratory waveform branch, recorded as , otherwise ;

[0044] if , a suspected apnea event occurs in the RCS branch, recorded as , otherwise ;

[0045] wherein: and are counting thresholds of the respiratory waveform branch and the RCS branch respectively;

[0046] S6032, judging the apnea jointly:

[0047] if , the joint counting is increased by 1, i.e. , otherwise ;

[0048] S6033, if​​​ If yes, it is judged that the apnea event occurs, otherwise the above judging process is continued.

[0049] Wherein, is the joint counting threshold.

[0050] Compared with the prior art, the present application has the following beneficial effects:

[0051] The method in the present application collects original data by radar, performs distance dimension imaging, target phase extraction, differential RCS calculation and respiratory waveform extraction, and performs apnea joint detection according to different signal amplitudes and timing characteristics. Through the method in the present application, the occurrence time of apnea can be effectively obtained, and the influence of environmental interference on the output result can be avoided, so that accurate detection results can be obtained.

[0052] The technical method of the present application will be further described in detail below by means of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 is the overall flow chart of the millimeter wave radar data multi-dimensional feature joint processing method of the present application;

[0054] Figure 2 is a typical one-dimensional range image in the embodiment of the present application;

[0055] Figure 3 is a typical smoothed RCS signal (sign amplitude) in the embodiment of the present application;

[0056] Figure 4 is a typical respiratory signal (respiratory waveform) in the embodiment of the present application;

[0057] Figure 5 is the apnea joint judging flow chart in the embodiment of the present application;

[0058] Figure 6 is the apnea detection result using the method of the present application in the embodiment of the present application. DETAILED DESCRIPTION

[0059] The technical method of the present application will be further described in detail below by means of the accompanying drawings and examples.

[0060] The following description of at least one example embodiment is merely illustrative in nature and is in no way limiting on the scope of the application or its applications or uses.

[0061] Techniques, systems, and devices known to those of ordinary skill in the relevant art can not be discussed in detail herein. However, where appropriate, techniques, systems, and devices should be considered as being part of the specification.

[0062] In all of the examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not limiting. Thus, other examples of the exemplary embodiments can have different values.

[0063] Unless otherwise defined, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0064] The present application provides a millimeter wave radar data multi-dimensional feature joint processing method, as shown in Figure 1 , comprising the following steps:

[0065] S1, acquiring data of the detected object by radar to obtain the to-be-processed data, the to-be-processed data including pulses, each pulse containing sampling points.

[0066] S2, performing distance dimension imaging on each pulse acquired by the radar to obtain a one-dimensional range image of the observed scene .

[0067] S3, for each , extracting the position of the peak point position, recording the signal amplitude at the position of the peak point position, defining it as RCS, and the signal amplitude RCS at the position of different target value points constitutes an RCS sequence .

[0068] S4, directly performing difference processing on the RCS sequence to obtain the RCS sequence difference ;

[0069] Further, the specific content of directly performing difference processing on the RCS sequence to obtain the RCS sequence difference in S4 includes.

[0070] S401, performing smoothing processing on the RCS sequence to obtain the smoothed RCS sequence .

[0071] Further, the expression for performing smoothing processing on the RCS sequence to obtain the smoothed RCS sequence is: .

[0072] wherein: is a filter coefficient, is a sample number, is a current radar echo sample amplitude.

[0073] S402, the RCS sequence after smoothing processing is subjected to difference processing to obtain the RCS sequence difference ;

[0074] Further, the RCS sequence after smoothing processing is subjected to difference processing, and the expression is: .

[0075] S5, the RCS sequence is accumulated and phase-difference processed to obtain a second phase sequence, and the second phase sequence is subjected to band-pass filtering to obtain a respiratory signal .

[0076] Further, the specific content of accumulating and phase-difference processing the RCS sequence in S5 to obtain the second phase sequence includes:

[0077] S501, for each , the surrounding units are accumulated to obtain an accumulated amplitude ;

[0078] .

[0079] wherein, is an intermediate variable, is the number of units to be accumulated, is the accumulated amplitude;

[0080] S502, the phase of is calculated, and for pulses, a phase sequence is obtained.

[0081] S503, for the phase sequence, difference processing is performed to obtain a second phase sequence .

[0082] Further, the second phase sequence in S5 is subjected to band-pass filtering to obtain a respiratory signal , and the expression of the respiratory signal is: .

[0083] wherein, is a filter coefficient of the band-pass filter, and the order , the first cutoff frequency and the second cutoff frequency​ i.e. can be calculated by industry standardization methods; represents a convolution operation.

[0084] S6, based on RCS sequence difference and respiratory signal joint processing, and then jointly detect apnea to obtain a detection result.

[0085] Further, in S6, based on RCS sequence difference and respiratory signal joint processing, and then jointly detect apnea to obtain a detection result, the specific content is:

[0086] S301, threshold judgment and cumulative counting are performed on the respiratory signal to obtain a respiratory signal cumulative value .

[0087] S302, threshold judgment and cumulative counting are performed on the difference RCS sequence to obtain a difference RCS sequence cumulative value .

[0088] S303, joint judgment is performed on and , and then jointly detect apnea to obtain a detection result.

[0089] Further, threshold judgment and cumulative counting are performed on the respiratory signal to obtain a respiratory signal cumulative value .

[0090] .

[0091] Wherein: is the respiratory waveform amplitude threshold.

[0092] Further, threshold judgment and cumulative counting are performed on the difference RCS sequence to obtain a difference RCS sequence cumulative value .

[0093] .

[0094] Further, joint judgment is performed on and , and then jointly detect apnea to obtain a detection result, the specific content is:

[0095] S601, respiratory waveform branch and RCS branch suspected event judgment.

[0096] If If yes, a suspected apnea event occurs in the respiratory waveform branch, denoted as , otherwise .

[0097] If yes , a suspected apnea event occurs in the RCS branch, denoted as , otherwise .

[0098] Wherein: and are the counting thresholds of the respiratory waveform branch and the RCS branch, respectively.

[0099] S602, Apnea joint determination:

[0100] If yes , the joint count is incremented by 1, i.e. , otherwise .

[0101] S603, if , it is determined that an apnea event occurs, otherwise the above determination process is continued.

[0102] Wherein, is the joint counting threshold.

[0103] Embodiment one

[0104] S1, collect data of the detected object by radar, assuming that the data contains pulses, each pulse contains sampling points, wherein the typical value is .

[0105] S2, perform distance dimension imaging on each pulse collected by the radar to obtain a one-dimensional range image of the observed scene , as shown in Figure 2 .

[0106] S3, for each , extract the position of its peak point (maximum value point) , record the signal amplitude at the corresponding position, defined as RCS, and the signal amplitudes at the positions of different target value points form an RCS sequence .

[0107] S4, directly perform difference processing on the RCS sequence to obtain the RCS sequence difference ;

[0108] S401, perform smoothing processing on the RCS sequence , as shown in Figure 3 :

[0109] .

[0110] wherein: is a filter coefficient, whose typical value is generally taken as 0.7.

[0111] S402, for , differential processing is performed:

[0112] .

[0113] S5, for the RCS sequence , accumulation and phase difference are performed to obtain a second phase sequence.

[0114] S501, for each , the position of the maximum point thereof is extracted , and the surrounding units are accumulated to obtain a signal , wherein the typical value .

[0115] S502, the phase of is calculated, and for pulses, a phase sequence can be obtained.

[0116] S503, for the phase sequence, differential processing is performed to suppress the influence of low-frequency motion, to obtain a new phase sequence (second phase sequence) .

[0117] As shown in Figure 4 , the phase sequence is band-pass filtered to obtain a respiratory signal , wherein is a filter coefficient of the band-pass filter, and by setting the order , the first cutoff frequency and the second cutoff frequency , it can be calculated by the standardized method in the industry; the normal respiratory rate is generally 5~20 times / minute, so the typical values of and can be taken as 0.2Hz and 0.8Hz, , and the typical value of is 4~8;

[0118] S6, based on the RCS sequence difference and the respiratory signal , joint processing is performed, and then apnea is jointly detected to obtain a detection result, and the apnea joint judgment flow chart is shown in Figure 5 .

[0119] S601, threshold judgment and cumulative counting are performed on the respiratory signal

[0120] .

[0121] wherein: is the respiratory waveform amplitude threshold, which is generally set to 2.

[0122] S602, threshold judgment and cumulative counting are performed on the difference RCS sequence

[0123] .

[0124] S603, joint judgment is performed on and to jointly detect apnea and obtain a detection result, as shown in the following table. Figure 6

[0125] and

[0126] S6031, respiratory waveform branch and RCS branch suspected event judgment:

[0127] If , a suspected apnea event occurs in the respiratory waveform branch, recorded as , otherwise .

[0128] If , a suspected apnea event occurs in the RCS branch, recorded as , otherwise .

[0129] wherein: and are the counting thresholds of the respiratory waveform branch and the RCS branch, respectively, and the typical value is 20.

[0130] S6032, apnea joint judgment: if , the joint count is incremented by 1, i.e. , otherwise .

[0131] S6033, if , it is judged that an apnea event occurs, otherwise the above judgment process is continued, wherein: is the joint counting threshold, and the typical value is 40.

[0132] ​​​​​It should be pointed out finally that the above embodiments are only used to illustrate the technical method of the present application but not to limit it, and although the present application is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical method of the present application can be modified or replaced equivalently, and these modifications or equivalent replacements cannot make the modified technical method deviate from the spirit and scope of the technical method of the present application.

Claims

1. A method for joint processing of multi-dimensional features of millimeter-wave radar data, characterized in that, Includes the following steps: S1. The acquired radar data is processed to obtain data to be processed, the data to be processed including data containing... Each pulse contains 1 pulse. One sampling point; S2. Perform range-dimensional imaging on each pulse acquired by the radar to obtain a one-dimensional range image of the observed scene. ; S3, for each Extract the location of the peak point. Record the location of the peak point. The signal amplitude at a given point is defined as the RCS. The set of RCS values ​​at different target value locations constitutes the RCS sequence. ; S4, RCS sequence Directly perform differential processing to obtain the RCS sequence difference. ; S5, RCS sequence The second phase sequence is obtained by accumulating and performing phase difference. The respiratory signal is obtained by bandpass filtering the second phase sequence. ; S6, RCS-based sequence differencing and respiratory signals Combined processing is performed to obtain test results for sleep apnea; S6 based on RCS sequence differencing and respiratory signals The specific details of the combined processing and subsequent combined detection of sleep apnea are as follows: S601, Respiratory signals Threshold judgment and cumulative counting are performed to obtain the cumulative value of respiratory signals. ; S602, Differential RCS Sequence The cumulative value of the differential RCS sequence is obtained by performing threshold judgment and cumulative counting. ; S603, to and A joint assessment is performed, and then a joint detection is conducted to obtain the detection results for sleep apnea; Respiratory signals Threshold judgment and cumulative counting are performed to obtain the cumulative value of respiratory signals. The expression is: ； in: This is the amplitude threshold for the respiratory waveform; For differential RCS sequences The cumulative value of the differential RCS sequence is obtained by performing threshold judgment and cumulative counting. The expression is: ； right and The specific content of the test results obtained by making a joint judgment and then conducting joint detection of sleep apnea is as follows: S6031, Judgment of suspected events in respiratory waveform branch and RCS branch: like If a suspected apnea event occurs in the respiratory waveform branch, it is recorded as... ,otherwise ; like If a suspected apnea event occurs in the RCS branch, it is recorded as follows: ,otherwise ; in: and These are the counting thresholds for the respiratory waveform branch and the RCS branch, respectively; S6032. Combined assessment of sleep apnea: like Then the combined count is incremented by 1, that is... ,otherwise ; S6033, if If the condition is met, then a respiratory arrest event is determined to have occurred; otherwise, the above judgment process continues. in, This is the joint counting threshold.

2. The method for joint processing of multi-dimensional features of millimeter-wave radar data according to claim 1, characterized in that, S4 RCS sequence Directly perform differential processing to obtain the RCS sequence difference. The specific content includes: S401, For RCS sequences Smoothing is performed to obtain the smoothed RCS sequence. ; S402. The smoothed RCS sequence Differential processing is performed to obtain the RCS sequence difference. .

3. The method for joint processing of multi-dimensional features of millimeter-wave radar data according to claim 2, characterized in that, For RCS sequence The smoothed expression is obtained by performing smoothing: ； in: These are the filter coefficients. For sampling sequence number, This represents the current radar echo sampling amplitude.

4. The method for joint processing of multi-dimensional features of millimeter-wave radar data according to claim 3, characterized in that, Smoothed RCS sequence The expression for performing the difference processing is: 。 5. The method for joint processing of multi-dimensional features of millimeter-wave radar data according to claim 4, characterized in that, S5 for RCS sequences The specific content of obtaining the second phase sequence by accumulating and performing phase difference includes: S501, For each , cumulative around Each unit receives the cumulative magnitude. ; ； in, As an intermediate variable, The number of units to be accumulated. This is the cumulative magnitude; S502, Calculation The phase, then for A phase sequence is obtained from several pulses. ; S503. For the phase sequence, perform differential processing to obtain the second phase sequence. .

6. The method for joint processing of multi-dimensional features of millimeter-wave radar data according to claim 5, characterized in that, In S5, the second phase sequence is bandpass filtered to obtain the respiratory signal. The expression for the respiratory signal is: ； in, These are the filter coefficients of the bandpass filter. This represents the convolution operation.

Images