CN114384489A - Water mist clutter filtering method for bathroom fall detection radar - Google Patents

Water mist clutter filtering method for bathroom fall detection radar Download PDF

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CN114384489A
CN114384489A CN202210050807.0A CN202210050807A CN114384489A CN 114384489 A CN114384489 A CN 114384489A CN 202210050807 A CN202210050807 A CN 202210050807A CN 114384489 A CN114384489 A CN 114384489A
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杨博
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Changsha Microbrain Intelligent Technology Co ltd
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Abstract

The invention discloses a water mist clutter filtering method of a bathroom fall detection radar, which comprises the following steps: acquiring energy spectrums, three-dimensional coordinate information, speed and signal-to-noise ratio in three-dimensional target point cloud data and range Doppler data information, forming multi-element characteristics, calculating a total deviation coefficient alpha of each frame and the average value of the current N frames by adopting a sliding time window method, and performing iterative inversion on an original echo signal by taking the total deviation coefficient as an influence factor to obtain converged echo data. The invention has the performance of being not influenced by illumination and water mist, protects personal privacy, and effectively inhibits static object clutter and false target interference caused by water mist sprayed by various shower heads.

Description

Water mist clutter filtering method for bathroom fall detection radar
Technical Field
The invention belongs to the technical field of fall detection, and particularly relates to a water mist clutter filtering method for a bathroom fall detection radar.
Background
As the population of china ages, care for the elderly may be inattentive or present unforeseen risks, such as an elderly person falling abnormally in a bathroom. Therefore, many kinds of sensors related to indoor personnel detection and tracking appear in the market, such as ultrasonic, infrared and laser radar, optical cameras and the like, and the sensors are affected by the illumination temperature of the external environment to different degrees to cause the occurrence of false alarms. The millimeter wave radar has the all-weather characteristic, is stronger than other sensors in environmental adaptability, and particularly has irreplaceable natural advantages in the aspects of protecting personal privacy life and the like. However, the normal work of the radar in the bathroom environment is seriously influenced by the clutter of static objects in a narrow space in the bathroom and the false target interference caused by water mist sprayed by shower heads of different types.
Disclosure of Invention
Aiming at the problems, the invention provides a water mist clutter filtering method of a bathroom fall detection radar, which is used for filtering the water mist clutter of the bathroom fall radar based on multi-element characteristic deviation iterative inversion, effectively inhibiting water mist and other static clutter in the work of a millimeter wave radar, and accurately realizing the functions of tracking a human target and detecting fall in an indoor environment.
The invention discloses a water mist clutter filtering method of a bathroom fall detection radar, which comprises the following steps:
configuring initialization parameters by a radar, and sending multiple millimeter wave radar emission electromagnetic waves to acquire original echo data;
carrying out Fourier transform on the original echo data to obtain range-Doppler data of each virtual array element;
carrying out high-precision super-resolution angle measurement on the range-Doppler data to obtain three-dimensional space data of a dynamic target in a monitoring scene;
processing the input signal by using CFAR constant false alarm detection, determining a threshold, and outputting three-dimensional target point cloud data;
acquiring energy spectrums, three-dimensional coordinate information, speed and signal-to-noise ratio in the three-dimensional target point cloud data and the range Doppler data information, forming a multi-element characteristic, calculating a total deviation coefficient alpha of each frame and the current N frame average value by adopting a sliding time window method, and performing iterative inversion on an original echo signal by taking the total deviation coefficient as an influence factor to obtain converged echo data.
Further, tracking and filtering the processed echo data on the target by using a Kalman filtering algorithm to obtain a stable moving target track, identifying the moving posture of the human target according to track information, and judging whether the human target falls down in the bathroom.
Further, the weighting coefficient after the multivariate eigenvalue is compounded is as follows:
Figure BDA0003474216120000021
wherein alpha is the total deviation coefficient after the compounding of the multivariate eigenvalue; epsilon0The weighting factor is a preset range Doppler energy spectrum weighting factor according to the measured data statistical result; alpha is alpha0A coefficient of variation of range-doppler energy; epsilon1、ε2、ε3Weighting factors of preset three-dimensional coordinate information, speed and signal-to-noise ratio; alpha is alpha1、α2、α3Deviation coefficients of three-dimensional coordinate information, speed and signal-to-noise ratio; n is 3.
Further, the calculation formula of the deviation coefficient of the multivariate feature is expressed as follows:
Figure BDA0003474216120000022
Figure BDA0003474216120000023
wherein σ0Is the range-doppler power offset;
Figure BDA0003474216120000024
the current N frame sliding window average value of the distance Doppler energy is obtained; sigma1、σ2、σ3Deviation amount of three-dimensional coordinate information, speed and signal-to-noise ratio; a. theiThe current N frame sliding window average value of three-dimensional coordinate information, speed and signal-to-noise ratio;
the current N frame sliding window average of the multivariate feature is represented as follows:
N＝[powermax*δ]
Figure BDA0003474216120000031
Figure BDA0003474216120000032
wherein the powermaxIs the maximum value of the range-Doppler energy spectrum in the current frame, delta is the adjustment coefficient of the number of the sliding window, and N is the size of the sliding window, wherein A0(j) A range-doppler power spectrum for frame j; when i is 1, Ai(j) Three-dimensional coordinate value of the jth frame; when i is 2, Ai(j) The speed value of the jth frame; when i is 3, Ai(j) Is the signal-to-noise ratio value of the jth frame.
Further, the deviation amount calculation formula of the energy spectrum and other multivariate characteristics is as follows:
Figure BDA0003474216120000033
Figure BDA0003474216120000034
further, the performing iterative inversion on the original echo signal by using the total deviation coefficient as an influence factor to obtain converged echo data includes:
the formula model e (t, z) of the transmitting echo complex signal of the kth virtual array element in the millimeter wave original radar multi-transmitting and multi-receiving array is expressed as follows:
Figure BDA0003474216120000035
wherein A is the amplitude of the transmitted signal; f is the frequency of the radar emission signal; t represents a fast time starting from each frequency modulation; t iscIs a frequency modulation period; k is the frequency modulation slope;
Figure BDA0003474216120000036
as a radarTransmitting a signal initial phase; z is the frequency modulation index of the transmitting antenna;
the sum of the original echo signals of all the transceiving virtual array elements is added to form a signal E (t, z), wherein j represents the reception of the jth virtual array element:
Figure BDA0003474216120000041
constructing a new echo complex signal model E (t, z) through the total deviation coefficient alpha after the composition of the multivariate eigenvalue:
Figure BDA0003474216120000042
repeatedly and circularly constructing a new echo complex signal model until alpha is less than alphaTOr M > MT，
Wherein alpha isTFor a predetermined training error threshold, M is the number of iterations, MTThe maximum number of iterations allowed to be performed.
Compared with the prior art, the invention has the following beneficial effects:
the posture of falling of the human body is detected by adopting a millimeter wave radar processing algorithm, the performance of being free from the influence of illumination and water mist is achieved, and the camera has the advantage of protecting personal privacy life compared with a traditional camera.
The method for extracting the multiple characteristics and performing the echo iterative inversion effectively inhibits static object clutter and false target interference caused by water mist sprayed by various shower heads, and experiments prove that the falling radar has a good detection effect.
The method uses the iterative inversion algorithm based on the multivariate characteristic deviation, has self-adaptive capacity and can cover various complex bathroom scenes.
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FIG. 1 is a flow chart of a water mist clutter filtering method according to the present invention.
Detailed Description
The invention is further described with reference to the accompanying drawings, but the invention is not limited in any way, and any alterations or substitutions based on the teaching of the invention are within the scope of the invention.
Referring to fig. 1, the water mist clutter filtering method of the present invention comprises the following steps:
radar original echo signal: configuring initialization parameters by a radar, and sending multiple millimeter wave radar emission electromagnetic waves to acquire original echo data;
range-doppler data: after Fourier transformation is carried out on the original echo data, distance Doppler data of each virtual array element is obtained;
angle measurement by millimeter wave radar: carrying out high-precision super-resolution angle measurement on the original Doppler data to obtain three-dimensional space data of a dynamic target in a monitoring scene;
CFAR constant false alarm rate detection: CFAR constant false alarm detection firstly processes an input signal, then determines a threshold and outputs three-dimensional target point cloud data;
the iterative inversion algorithm of multivariate characteristic deviation: and calculating the total deviation coefficient alpha of each frame and the average value of the current N frames by sliding a time window according to the multivariate characteristics of energy spectrum, three-dimensional coordinate information, speed, signal-to-noise ratio and the like in the target point cloud and the Doppler data information. The invention carries out iterative inversion on the original echo signal by taking the total deviation coefficient as an influence factor to obtain new echo data, and the new echo data are repeatedly circulated for many times until alpha is more than alphaTOr M > MTWherein α isTFor a predetermined training error threshold, M is the number of iterations, MTTo allow a maximum number of iterations to be performed.
Tracking and falling detection of human body targets: and tracking and filtering the target by using a Kalman filtering algorithm after a multivariate characteristic deviation iterative inversion algorithm to finally obtain a stable moving target track, and identifying the posture of the human target according to the target height and the broadening in the track information to accurately judge whether the human target falls down in the bathroom.
The above step 5 is described in detail based on the multivariate characteristic deviation iterative inversion algorithm as follows: and 2, the range Doppler data and the target point cloud data formed in the steps 3 and 4 have multiple characteristic quantities such as the energy spectrum, three-dimensional coordinate information, speed, signal-to-noise ratio and the like of the target. After collecting multiple groups of measured data, the statistical result of the target characteristic change is as follows:
the radar data characteristic distribution characteristics of the human body target are as follows:
1) the azimuth direction and the distance direction of the three-dimensional coordinate information are movably changed in the detection range, and the height direction is uniform about 0 to 1.8 m;
2) the velocity distribution varies from 0 to 5m/s, depending on the state of motion of the person, approaching 0 at rest;
3) when people are static, the energy is smaller and close to 0, and the energy fluctuates when people move;
4) the signal-to-noise ratio is high for human targets.
The radar data characteristic distribution characteristics of the water mist clutter of various shower heads are as follows:
1) the azimuth direction, the distance direction and the height direction of the three-dimensional coordinate information are fixed positions;
2) the speed is relatively constant and uniform and is related to the water flow spraying speed;
3) the energy distribution is uniform, and no obvious fluctuation exists for a long time;
4) the signal to noise ratio is low.
The multivariate characteristic deviation iterative inversion algorithm has self-adaptive capacity, and a weight factor epsilon is assigned to the multivariate characteristic through the characteristic quantity of a large number of sample analysis. And then calculating the deviation amount of each characteristic of the average value of each frame and the current N frames by adopting a sliding time window, and combining each characteristic weight factor to obtain a final total deviation coefficient alpha.
The specific calculation formula is shown as follows, and the weighting coefficient after the multi-element characteristic value is compounded is as follows:
Figure BDA0003474216120000061
wherein alpha is the total deviation coefficient after the compounding of the multivariate eigenvalue; epsilon0The weighting factor is a preset range Doppler energy spectrum weighting factor according to the measured data statistical result; alpha is alpha0A coefficient of variation of range-doppler energy; epsiloniFor other multivariate characteristics (three-dimensional coordinate information)Speed, signal-to-noise ratio) is preset; alpha is alphaiDeviation coefficients for other multivariate features (three-dimensional coordinate information, speed, signal-to-noise ratio); and N is the number of other multivariate feature quantities.
The derivation formula of various multivariate characteristic deviation values is expressed as follows:
Figure BDA0003474216120000071
Figure BDA0003474216120000072
wherein σ0Is the range-doppler power offset;
Figure BDA0003474216120000073
is the average value of the range-doppler power sliding window; sigmaiDeviation amount of other multivariate characteristics (three-dimensional coordinate information, speed, signal-to-noise ratio);
Figure BDA0003474216120000074
the average value of the sliding window of the current N frames of other multivariate characteristics (three-dimensional coordinate information, speed and signal-to-noise ratio);
the average state value of the sliding window of the current N frames is expressed as follows, the size N of the sliding window is influenced by the maximum value of the energy of the target point cloud, the maximum value of the energy of the target point cloud is larger, the target detection is stable, and the number of the sliding windows can be properly reduced due to the fact that clutter is relatively weaker; otherwise, the target energy is weak, the clutter is relatively strong, and the number of the sliding windows can be properly increased:
N＝[powermax*δ]
Figure BDA0003474216120000075
Figure BDA0003474216120000076
wherein the powermaxThe maximum value of the range Doppler energy spectrum in the current frame is delta, the sliding window number adjustment coefficient is delta, when clutter is relatively strong, the delta value is set to be large, and when the clutter is relatively weak, the delta value is set to be small; n is the size of the sliding window, wherein A0(j) A range-doppler power spectrum for frame j; when i is 1, Ai(j) Three-dimensional coordinate value of the jth frame; when i is 2, Ai(j) The speed value of the jth frame; when i is 3, Ai(j) Is the signal-to-noise ratio value of the jth frame.
The energy spectrum and other multivariate eigenvalue deviation are derived as follows:
Figure BDA0003474216120000077
Figure BDA0003474216120000078
iterative inversion is needed after the total deviation coefficient is solved, and new echo data are reconstructed from the original echo data and the total deviation coefficient.
The specific steps are as follows:
the formula model e (t, z) of the transmitting echo complex signal of the kth virtual array element in the millimeter wave original radar multi-transmitting and multi-receiving array is expressed as follows:
Figure BDA0003474216120000081
wherein A is the amplitude of the transmitted signal; f is the frequency of the radar emission signal; t represents a fast time starting from each frequency modulation; t iscIs a frequency modulation period; k is the frequency modulation slope;
Figure BDA0003474216120000082
a signal initial phase is transmitted for the radar; z is the fm index of the transmit antenna.
Wherein, the sum of the original echo signals of all the transmitting and receiving virtual array elements is superposed signal E (t, z), where j represents the reception of the jth virtual array element:
Figure BDA0003474216120000083
and constructing a new echo complex signal model E' (t, z) by the weighting coefficient alpha after the multi-element characteristic value composition:
Figure BDA0003474216120000084
repeatedly and circularly constructing a new echo complex signal model until alpha is less than alphaTOr M > MT(wherein, α)TFor a predetermined training error threshold, M is the number of iterations, MTAnd the maximum iteration times are allowed), performing iterative inversion to obtain converged echo data, continuously performing Fourier transform, angle measurement and CFAR detection radar signal processing, tracking and filtering the target through a Kalman filtering algorithm, finally obtaining a stable moving target track, identifying the moving posture of the human target according to track information, and accurately judging whether the human target falls down in the bathroom.
Compared with the prior art, the invention has the following beneficial effects:
the posture of falling of the human body is detected by adopting a millimeter wave radar processing algorithm, the performance of being free from the influence of illumination and water mist is achieved, and the camera has the advantage of protecting personal privacy life compared with a traditional camera.
The method for extracting the multiple characteristics and performing the echo iterative inversion effectively inhibits static object clutter and false target interference caused by water mist sprayed by various shower heads, and experiments prove that the falling radar has a good detection effect.
The method uses the iterative inversion algorithm based on the multivariate characteristic deviation, has self-adaptive capacity and can cover various complex bathroom scenes.
The word "preferred" is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as "preferred" is not necessarily to be construed as advantageous over other aspects or designs. Rather, use of the word "preferred" is intended to present concepts in a concrete fashion. The term "or" as used in this application is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless specified otherwise or clear from context, "X employs A or B" is intended to include either of the permutations as a matter of course. That is, if X employs A; b is used as X; or X employs both A and B, then "X employs A or B" is satisfied in any of the foregoing examples.
Also, although the disclosure has been shown and described with respect to one or an implementation, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The present disclosure includes all such modifications and alterations, and is limited only by the scope of the appended claims. In particular regard to the various functions performed by the above described components (e.g., elements, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the herein illustrated exemplary implementations of the disclosure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or other features of the other implementations as may be desired and advantageous for a given or particular application. Furthermore, to the extent that the terms "includes," has, "" contains, "or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term" comprising.
Each functional unit in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or a plurality of or more than one unit are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium. The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Each apparatus or system described above may execute the storage method in the corresponding method embodiment.
In summary, the above-mentioned embodiment is an implementation manner of the present invention, but the implementation manner of the present invention is not limited by the above-mentioned embodiment, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be regarded as equivalent replacements within the protection scope of the present invention.

Claims (6)

1. A water mist clutter filtering method of a bathroom fall detection radar is characterized by comprising the following steps:
configuring initialization parameters by a radar, and sending multiple millimeter wave radar emission electromagnetic waves to acquire original echo data;
carrying out Fourier transform on the original echo data to obtain range-Doppler data of each virtual array element;
carrying out high-precision super-resolution angle measurement on the range-Doppler data to obtain three-dimensional space data of a dynamic target in a monitoring scene;
processing the input signal by using CFAR constant false alarm detection, determining a threshold, and outputting three-dimensional target point cloud data;
acquiring energy spectrums, three-dimensional coordinate information, speed and signal-to-noise ratio in the three-dimensional target point cloud data and the range Doppler data information, forming a multi-element characteristic, calculating a total deviation coefficient alpha of each frame and the current N frame average value by adopting a sliding time window method, and performing iterative inversion on an original echo signal by taking the total deviation coefficient as an influence factor to obtain converged echo data.
2. The method for filtering the water mist clutter of the bathroom fall detection radar as claimed in claim 1, wherein the processed echo data is subjected to target tracking filtering by using a kalman filtering algorithm to obtain a stable moving target track, and then the moving posture of the human target is identified according to track information to judge whether the human target falls down in the bathroom.
3. The method for water mist clutter filtering of a bathroom fall detection radar as claimed in claim 1, wherein the overall deviation coefficient α is:
Figure FDA0003474216110000011
wherein alpha is the total deviation coefficient after the compounding of the multivariate eigenvalue; epsilon0The weighting factor is a preset range Doppler energy spectrum weighting factor according to the measured data statistical result; alpha is alpha0A coefficient of variation of range-doppler energy; epsilon1、ε2、ε3Weighting factors of preset three-dimensional coordinate information, speed and signal-to-noise ratio; alpha is alpha1、α2、α3Deviation coefficients of three-dimensional coordinate information, speed and signal-to-noise ratio; n is 3.
4. The method for filtering water mist clutter of a bathroom fall detection radar as claimed in claim 1, wherein the calculation formula of the deviation coefficient of the multivariate feature is expressed as follows:
Figure FDA0003474216110000021
Figure FDA0003474216110000022
wherein σ0Is the range-doppler power offset;
Figure FDA0003474216110000023
the current N frame sliding window average value of the distance Doppler energy is obtained; sigma1、σ2、σ3Deviation amount of three-dimensional coordinate information, speed and signal-to-noise ratio;
Figure FDA0003474216110000024
for three-dimensional coordinate informationThe current N frame sliding window average value of speed and signal-to-noise ratio;
the current N frame sliding window average of the multivariate feature is represented as follows:
N＝[powermax*δ]
Figure FDA0003474216110000025
Figure FDA0003474216110000026
wherein the powermaxIs the maximum value of the range-Doppler energy spectrum in the current frame, delta is the adjustment coefficient of the number of the sliding window, and N is the size of the sliding window, wherein A0(j) A range-doppler power spectrum for frame j; when i is 1, Ai(j) Three-dimensional coordinate value of the jth frame; when i is 2, Ai(j) The speed value of the jth frame; when i is 3, Ai(j) Is the signal-to-noise ratio value of the jth frame.
5. The method for water mist clutter filtering of a bathroom fall detection radar as claimed in claim 1, wherein the deviation amount of the energy spectrum and other multivariate features is calculated as follows:
Figure FDA0003474216110000031
Figure FDA0003474216110000032
6. the method for filtering water mist clutter of a bathroom fall detection radar according to claim 1, wherein the iteratively inverting the original echo signal by using the total deviation coefficient as an influence factor to obtain converged echo data comprises:
the formula model e (t, z) of the transmitting echo complex signal of the kth virtual array element in the millimeter wave original radar multi-transmitting and multi-receiving array is expressed as follows:
Figure FDA0003474216110000036
wherein A is the amplitude of the transmitted signal; f is the frequency of the radar emission signal; t represents a fast time starting from each frequency modulation; t iscIs a frequency modulation period; k is the frequency modulation slope;
Figure FDA0003474216110000033
a signal initial phase is transmitted for the radar; z is the frequency modulation index of the transmitting antenna;
the sum of the original echo signals of all the transceiving virtual array elements is added to form a signal E (t, z), wherein j represents the reception of the jth virtual array element:
Figure FDA0003474216110000034
constructing a new echo complex signal model E (t, z) by the total deviation coefficient alpha:
Figure FDA0003474216110000035
repeatedly and circularly constructing a new echo complex signal model until alpha is less than alphaTOr M > MT，
Wherein alpha isTFor a predetermined training error threshold, M is the number of iterations, MTThe maximum number of iterations allowed to be performed.
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CN114814778A (en) * 2022-06-29 2022-07-29 长沙莫之比智能科技有限公司 Carrier speed calculation method based on millimeter wave radar
CN115120205A (en) * 2022-08-29 2022-09-30 长沙莫之比智能科技有限公司 Human body heartbeat signal iterative enhancement method based on millimeter wave radar range and azimuth spectrum

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114814778A (en) * 2022-06-29 2022-07-29 长沙莫之比智能科技有限公司 Carrier speed calculation method based on millimeter wave radar
CN115120205A (en) * 2022-08-29 2022-09-30 长沙莫之比智能科技有限公司 Human body heartbeat signal iterative enhancement method based on millimeter wave radar range and azimuth spectrum

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