CN114002663A - Millimeter wave radar-based presence or absence detection method - Google Patents
Millimeter wave radar-based presence or absence detection method Download PDFInfo
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
The invention relates to a millimeter wave radar application technology, and discloses a method for detecting the existence of people based on a millimeter wave radar, which can conveniently and accurately judge whether people exist indoors and improve the detection effect. The method comprises the steps of carrying out relevant signal processing operation on echo signals of a millimeter wave radar to obtain distance dimensional information, carrying out static clutter elimination, carrying out phase extraction, phase expansion, trend elimination and Fourier transform on data subjected to static clutter elimination to obtain the distribution condition of signals, carrying out signal processing on the data subjected to static clutter elimination to obtain point cloud data, carrying out analysis processing and threshold value judgment on the obtained amplitude-frequency signal data, and judging whether people exist in the current environment by combining point cloud data judgment.
Description
Technical Field
The invention relates to a millimeter wave radar application technology, in particular to a method for detecting the existence of people based on a millimeter wave radar.
Background
The monitoring device for monitoring indoor personnel in the past mainly comprises a passive infrared sensor, an optical camera and the like; however, these devices are easily interfered by external environment, resulting in poor monitoring effect, and in addition, the camera monitoring is adopted to be not beneficial to the privacy protection of users.
The millimeter wave radar is an emerging technology in recent two years as life perception, and has a wider application prospect in scenes such as automobile cabs, meeting rooms, family bedrooms, living rooms, specific confidential places and the like because the millimeter wave radar has a wider coverage area, good environmental applicability and higher accuracy and stability compared with other monitoring devices.
However, in these application scenarios, when the person is still, the millimeter wave radar hardly generates a point cloud, and it is impossible to determine whether there is a person in the scene according to the point information.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the millimeter wave radar-based method for detecting the existence of the people is provided, whether people exist indoors can be judged conveniently and accurately, and the detection effect is improved.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a method for detecting the existence of people based on a millimeter wave radar comprises the following steps:
s1, the millimeter wave radar transmits electromagnetic wave signals to a space to be monitored, echo signals are collected and processed, and static clutter elimination processing is carried out on the processed data;
s2, performing arc tangent calculation on the data subjected to static clutter elimination to obtain phase information, and performing unfolding and trend removing operation on the phase; meanwhile, signal processing is carried out on the data subjected to static clutter elimination processing to obtain point cloud data, whether a person exists in a detected scene is judged according to whether a plurality of frames continuously have the point cloud number, and a first preliminary judgment result is obtained;
s3, performing low-pass filtering processing on the data after the phase expansion and trend removing operation to obtain low-frequency data;
s4, carrying out Fourier transform on the filtered low-frequency data to obtain power data of signals in a frequency domain, carrying out normalization operation on the data, comparing the data with the background noise data, and setting a threshold value to distinguish noise and people to obtain a second primary judgment result;
and S5, integrating the first preliminary judgment result and the second preliminary judgment result, and finally judging whether people exist in the scene.
As a further optimization, in step S1, the collecting echo signals and performing signal processing, and performing static clutter elimination processing on the processed data specifically includes:
and performing distance dimension Fourier transform on the echo signal to obtain information on the distance dimension, selecting a frame of radar echo data only containing an indoor background, performing Fourier transform, and performing difference on the obtained information on the distance dimension and the background data, thereby eliminating the static clutter generated by a large object indoors.
As a further optimization, in step S2, the performing arctangent calculation on the obtained data after the static clutter removal processing to obtain phase information, and performing phase unwrapping and detrending operations specifically include:
extracting phase data from the distance dimension information according to the relationship that the distance in the millimeter wave radar is in direct proportion to the phase of the data after the static clutter is eliminated;
carrying out phase unwrapping processing on the extracted phase data to obtain a true phase value;
and (3) carrying out segmentation and trend removing processing on the expanded phase data: and constructing a linear function through adjacent extreme values of the obtained phase real values, solving a predicted value corresponding to each index, making a difference between the phase real value and the predicted value, accumulating data of a certain number of frames, and performing Fourier transform to obtain the signal intensity distribution condition in the frequency domain range.
As a further optimization, in step S2, the signal processing is performed on the data after the static clutter removal processing to obtain point cloud data, and whether a person exists in the detected scene is determined according to whether a plurality of frames continuously have a point cloud number, which specifically includes:
and (3) firstly judging distance dimension constant false alarm of the data subjected to static clutter elimination processing, screening out possible target position data, then carrying out speed dimension Fourier transform and then carrying out angle calculation, finally obtaining point cloud data through speed dimension and angle dimension constant false alarm, and judging whether a detected scene exists according to whether a plurality of frames continuously have point cloud number.
As a further optimization, in step S3, the low-pass filtering process is performed on the data after the phase unwrapping and detrending operations, and specifically includes: and carrying out SG (SavitzkyGolay) filtering on the data after the phase unwrapping and the detrending, and removing high-frequency noise to obtain a low-frequency signal.
As a further optimization, in step S4, the normalizing the data, comparing the normalized data with the background noise data, and setting a threshold to distinguish noise from human specifically includes:
and carrying out normalization processing on the phase data after Fourier transformation, counting the signal distribution condition of the bottom noise under the unmanned condition and the signal distribution condition under the manned condition, respectively calculating the mean value of the two signal intensities, further calculating the ratio of the signal intensity mean values under the manned condition and the unmanned condition, judging that the manned condition exists when the ratio is greater than a certain threshold value, and otherwise, judging that the unmanned condition exists.
As a further optimization, in step S5, the step of integrating the first preliminary determination result and the second preliminary determination result to finally determine whether there is a person in the scene includes:
if the first preliminary judgment result and the second preliminary judgment result both judge that someone exists, finally judging that someone exists in the scene;
if the first preliminary judgment result and the second preliminary judgment result are both judged to be unmanned, the situation that no people exist in the scene is finally judged;
if one of the first preliminary judgment result and the second preliminary judgment result is that people are judged, and the other one is that no people are judged, the repeated detection method is not less than m times, if at least one of the first preliminary judgment result and the second preliminary judgment result is that people are judged, the situation that people exist in the scene is finally judged, and if the situation that people exist in the scene is judged for more than n times, the situation that no people exist in the scene is finally judged.
The invention has the beneficial effects that:
the method comprehensively considers the judgment result of the existence of the person obtained by analyzing and processing the point cloud data and the judgment result of the existence of the person obtained by analyzing and comparing the signal intensity distribution conditions, can conveniently and accurately judge whether the person exists indoors, has low code complexity and high calculation real-time performance, can be applied to many scenes, and achieves better detection effect.
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Fig. 1 is a flowchart of a method for detecting presence or absence of a person based on a millimeter wave radar in an embodiment of the present invention.
Detailed Description
The invention aims to provide a method for detecting the existence of people based on a millimeter wave radar, which can conveniently and accurately judge whether people exist indoors or not and improve the detection effect. The method mainly comprises the following steps:
step 1, the millimeter wave radar continuously transmits electromagnetic wave signals to a space to be detected, collects echo signals and performs signal processing, and performs static clutter elimination processing on the processed data, so that the influence of clutter signals generated by large indoor static objects on target echo signals can be reduced;
and 2, performing arc tangent calculation on the data subjected to the static clutter elimination processing to obtain phase information, and performing unfolding and trend removing operation on the phase. Meanwhile, signal processing is carried out on the data subjected to static clutter elimination processing to obtain point cloud data, whether a person exists in a detected scene is judged according to whether a plurality of frames continuously have the point cloud number, and a first preliminary judgment result is obtained;
step 3, performing low-pass filtering processing on the data after the phase is unfolded and detrended to obtain low-frequency data;
step 4, carrying out Fourier transform on the filtered data to obtain power data of signals in a frequency domain, carrying out normalization operation on the data, comparing the data with the background noise data, and setting a threshold value to distinguish noise and people to obtain a second primary judgment result;
and 5, integrating the first preliminary judgment result and the second preliminary judgment result to judge whether a person exists in the scene.
Specifically, the distance dimension information is obtained by performing relevant signal processing operation on echo signals of the millimeter wave radar, static clutter elimination is performed, the distribution condition of the signals is obtained by performing phase extraction, phase expansion, trend removal and Fourier transform on data subjected to static clutter elimination, meanwhile, distance, speed and angle signal processing is performed on the data subjected to static clutter elimination to obtain point cloud data, and the existence of people in the current environment can be judged by performing analysis processing and threshold judgment on the obtained amplitude-frequency signal data and combining judgment on the point cloud data.
Example (b):
the flow of the method for detecting the existence of people based on the millimeter wave radar provided by the embodiment is shown in fig. 1, and the method comprises the following steps:
step 1, the millimeter wave radar continuously transmits electromagnetic wave signals to a space to be detected, collects echo signals and performs data processing, and performs static clutter elimination processing on the processed data, so that the influence of clutter signals generated by large indoor static objects on target echo signals can be reduced;
wherein, carry out signal processing and static clutter's elimination to echo signal and handle mainly includes:
fourier transform is carried out on the reflected signals to obtain information on a distance dimension; in order to improve the effective degree of static clutter elimination, a frame of data in an unmanned state in the environment when the radar is started is selected as background data, each frame of data collected by the radar in the subsequent process is different from the background data, and echo interference of a large object in the environment is eliminated in the maximum amplitude.
And 2, performing arc tangent calculation on the data accumulated with a certain number of frames after static clutter elimination to obtain phase information, then expanding the phase to obtain a real phase value, and performing trend removing operation to avoid phase drift. Meanwhile, signal processing of a distance dimension, a speed dimension and an angle dimension is carried out on the data subjected to static clutter elimination processing to obtain point cloud data, and whether a person exists in a detected scene is judged according to whether a plurality of frames continuously have the point cloud number;
step 3, carrying out SG low-pass filtering processing on the data after the phase expansion and trend removing operation to eliminate high-frequency noise and obtain low-frequency data, wherein filtering parameters of SG filtering are obtained according to a large amount of experimental test data;
step 4, carrying out Fourier transform on the filtered data to obtain power data of signals in a frequency domain, carrying out normalization operation on the data, comparing the data with the background noise data, and setting a threshold value to distinguish noise and people;
the concrete method comprises the following steps:
and (3) carrying out normalization processing on the phase data after Fourier transformation, counting the signal distribution condition of the bottom noise under the unmanned condition and the signal distribution condition under the manned condition, respectively calculating the mean value of the two signal intensities, and further calculating the ratio of the mean values of the signal intensities under the manned condition and the unmanned condition, wherein when the ratio is greater than a certain threshold value, the currently accumulated data can be judged to be manned, otherwise, the data is judged to be unmanned, and the threshold value is obtained to be 0.25 by experimental tests and combined experience.
Step 5, judging whether a person exists in the scene or not according to the combination of point cloud data in the step 2 and threshold judgment in the step 4;
if the point cloud data obtained after signal processing in the step 2 judges that a person exists and the threshold value judges that the person exists in the detected scene in the step 4, if the two conditions are judged that the person does not exist, the detected scene is judged to be unmanned, if one condition judges that the person exists and the other condition judges that the person does not exist, the test is repeated for 9 times, if at least one condition appears for not less than 7 times in the 9 times, the detected scene is judged to be unmanned, and if not, the detected scene is judged to be unmanned.
Based on the above, after the person is determined to be in the scene, a solid foundation can be laid for subsequent applications (such as light control, respiration and heart rate calculation, and the like).
Claims (7)
1. A method for detecting the existence of people based on a millimeter wave radar is characterized by comprising the following steps:
s1, the millimeter wave radar transmits electromagnetic wave signals to a space to be monitored, echo signals are collected and processed, and static clutter elimination processing is carried out on the processed data;
s2, performing arc tangent calculation on the data subjected to static clutter elimination to obtain phase information, and performing unfolding and trend removing operation on the phase; meanwhile, signal processing is carried out on the data subjected to static clutter elimination processing to obtain point cloud data, whether a person exists in a detected scene is judged according to whether a plurality of frames continuously have the point cloud number, and a first preliminary judgment result is obtained;
s3, performing low-pass filtering processing on the data after the phase expansion and trend removing operation to obtain low-frequency data;
s4, carrying out Fourier transform on the filtered low-frequency data to obtain power data of signals in a frequency domain, carrying out normalization operation on the data, comparing the data with the background noise data, and setting a threshold value to distinguish noise and people to obtain a second primary judgment result;
and S5, integrating the first preliminary judgment result and the second preliminary judgment result, and finally judging whether people exist in the scene.
2. The millimeter wave radar-based presence/absence detection method according to claim 1,
in step S1, the collecting echo signals and performing signal processing, and performing static clutter cancellation processing on the processed data specifically include:
and performing distance dimension Fourier transform on the echo signal to obtain information on the distance dimension, selecting a frame of radar echo data only containing an indoor background, performing Fourier transform, and performing difference on the obtained information on the distance dimension and the background data, thereby eliminating the static clutter generated by a large object indoors.
3. The millimeter wave radar-based presence/absence detection method according to claim 1,
in step S2, the performing arctangent calculation on the obtained data after the static clutter removal processing to obtain phase information, and performing phase unwrapping and detrending operations specifically include:
extracting phase data from the distance dimension information according to the relationship that the distance in the millimeter wave radar is in direct proportion to the phase of the data after the static clutter is eliminated;
carrying out phase unwrapping processing on the extracted phase data to obtain a true phase value;
and (3) carrying out segmentation and trend removing processing on the expanded phase data: and constructing a linear function through adjacent extreme values of the obtained phase real values, solving a predicted value corresponding to each index, making a difference between the phase real value and the predicted value, accumulating data of a certain number of frames, and performing Fourier transform to obtain the signal intensity distribution condition in the frequency domain range.
4. The millimeter wave radar-based presence/absence detection method according to claim 3,
in step S2, the signal processing is performed on the data after the static clutter removal processing to obtain point cloud data, and whether a person exists in the detected scene is determined according to whether a plurality of frames continuously have a point cloud number, which specifically includes:
and (3) firstly judging distance dimension constant false alarm of the data subjected to static clutter elimination processing, screening out possible target position data, then carrying out speed dimension Fourier transform and then carrying out angle calculation, finally obtaining point cloud data through speed dimension and angle dimension constant false alarm, and judging whether a detected scene exists according to whether a plurality of frames continuously have point cloud number.
5. The millimeter wave radar-based presence/absence detection method according to claim 1,
in step S3, the low-pass filtering process is performed on the data after the phase unwrapping and detrending operations, and specifically includes: and carrying out SG filtering on the data after the phase unwrapping and the detrending, removing high-frequency noise and obtaining a low-frequency signal.
6. The millimeter wave radar-based presence/absence detection method according to claim 1,
in step S4, the normalizing the data, comparing the normalized data with the background noise data, and setting a threshold to distinguish noise from human specifically includes:
and carrying out normalization processing on the phase data after Fourier transformation, counting the signal distribution condition of the bottom noise under the unmanned condition and the signal distribution condition under the manned condition, respectively calculating the mean value of the two signal intensities, further calculating the ratio of the signal intensity mean values under the manned condition and the unmanned condition, judging that the manned condition exists when the ratio is greater than a certain threshold value, and otherwise, judging that the unmanned condition exists.
7. The millimeter wave radar-based presence/absence detection method according to any one of claims 1 to 6,
in step S5, the step of integrating the first preliminary determination result and the second preliminary determination result to finally determine whether there is a person in the scene specifically includes:
if the first preliminary judgment result and the second preliminary judgment result both judge that someone exists, finally judging that someone exists in the scene;
if the first preliminary judgment result and the second preliminary judgment result are both judged to be unmanned, the situation that no people exist in the scene is finally judged;
if one of the first preliminary judgment result and the second preliminary judgment result is that people are judged, and the other one is that no people are judged, the repeated detection method is not less than m times, if at least one of the first preliminary judgment result and the second preliminary judgment result is that people are judged, the situation that people exist in the scene is finally judged, and if the situation that people exist in the scene is judged for more than n times, the situation that no people exist in the scene is finally judged.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114966672A (en) * | 2022-06-09 | 2022-08-30 | 深圳大学 | Intelligent security monitoring processing method and system based on optical and microwave vision |
| CN115345908A (en) * | 2022-10-18 | 2022-11-15 | 四川启睿克科技有限公司 | Human body posture recognition method based on millimeter wave radar |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114966672A (en) * | 2022-06-09 | 2022-08-30 | 深圳大学 | Intelligent security monitoring processing method and system based on optical and microwave vision |
| CN115345908A (en) * | 2022-10-18 | 2022-11-15 | 四川启睿克科技有限公司 | Human body posture recognition method based on millimeter wave radar |
| CN115345908B (en) * | 2022-10-18 | 2023-03-07 | 四川启睿克科技有限公司 | Human body posture recognition method based on millimeter wave radar |
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