CN112731380A - Intelligent human body monitoring method and monitoring equipment based on millimeter waves - Google Patents
Intelligent human body monitoring method and monitoring equipment based on millimeter waves Download PDFInfo
- Publication number
- CN112731380A CN112731380A CN202011488371.0A CN202011488371A CN112731380A CN 112731380 A CN112731380 A CN 112731380A CN 202011488371 A CN202011488371 A CN 202011488371A CN 112731380 A CN112731380 A CN 112731380A
- Authority
- CN
- China
- Prior art keywords
- millimeter wave
- signal
- monitoring
- data
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 188
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000003062 neural network model Methods 0.000 claims abstract description 28
- 238000012216 screening Methods 0.000 claims abstract description 18
- 238000012806 monitoring device Methods 0.000 claims abstract description 14
- 238000007781 pre-processing Methods 0.000 claims abstract description 11
- 238000003909 pattern recognition Methods 0.000 claims description 23
- 238000001914 filtration Methods 0.000 claims description 21
- 238000012545 processing Methods 0.000 claims description 17
- 238000004891 communication Methods 0.000 claims description 15
- 230000006870 function Effects 0.000 claims description 15
- 230000003068 static effect Effects 0.000 claims description 10
- 230000008030 elimination Effects 0.000 claims description 9
- 238000003379 elimination reaction Methods 0.000 claims description 9
- 230000005021 gait Effects 0.000 claims description 9
- 230000029058 respiratory gaseous exchange Effects 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 6
- 230000036772 blood pressure Effects 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 4
- 230000004888 barrier function Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 claims description 3
- 230000036544 posture Effects 0.000 description 16
- 238000004458 analytical method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 238000001228 spectrum Methods 0.000 description 3
- 238000006073 displacement reaction Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000013075 data extraction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
- G01S13/583—Velocity or trajectory determination systems; Sense-of-movement determination systems using transmission of continuous unmodulated waves, amplitude-, frequency-, or phase-modulated waves and based upon the Doppler effect resulting from movement of targets
- G01S13/584—Velocity or trajectory determination systems; Sense-of-movement determination systems using transmission of continuous unmodulated waves, amplitude-, frequency-, or phase-modulated waves and based upon the Doppler effect resulting from movement of targets adapted for simultaneous range and velocity measurements
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/35—Details of non-pulse systems
- G01S7/352—Receivers
- G01S7/354—Extracting wanted echo-signals
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/415—Identification of targets based on measurements of movement associated with the target
-
- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/417—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section involving the use of neural networks
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Electromagnetism (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention provides a millimeter wave-based human body intelligent monitoring method and monitoring equipment, wherein the method comprises the following steps: arranging at least one millimeter wave monitoring device in the monitoring space, transmitting linear frequency modulation continuous millimeter waves into the monitoring space through the millimeter wave monitoring device, and receiving millimeter wave signals reflected in the monitoring space in real time; preprocessing the millimeter wave monitoring signals, analyzing the signal types of the millimeter wave monitoring signals by adopting a data mode recognition model according to the preprocessed millimeter wave monitoring signals, and screening the millimeter wave monitoring signals related to the target; analyzing and calculating target state information by adopting a neural network model according to the millimeter wave monitoring signals related to the target; sending target state information to a monitoring server or a user terminal in real time, wherein the target state information at least comprises one of the following: multi-target position information, multi-human body posture information, human body physiological information and a plurality of moving object information. The method has the advantages of sensitive target identification, accurate monitoring result, strong anti-interference capability and the like.
Description
Technical Field
The invention relates to the technical field of millimeter wave identification and monitoring, in particular to a human body intelligent monitoring method and monitoring equipment based on millimeter waves.
Background
The millimeter wave refers to an electromagnetic wave with a wavelength of 1-10 mm, and is located in a wavelength range where microwave and far-infrared wave are overlapped, so that the millimeter wave has the characteristics of two wave spectrums. Compared with light waves, the millimeter waves are less influenced by natural light and a thermal radiation source; the device has extremely wide bandwidth, the frequency range of millimeter waves is 30GHz-300GHz, and the device has the characteristics of high precision and high resolution; the beam of the millimeter wave is narrow, and the beam of the millimeter wave is much narrower than that of the microwave under the same antenna size, so that small targets which are closer to each other can be distinguished or the details of the targets can be observed more clearly. The propagation of millimeter waves is much less affected by weather than laser light and can be considered to be all-weather. Compared with microwaves, millimeter wave components are much smaller in size and easier to miniaturize.
At present, cameras are used for monitoring space environments, but along with the fact that people pay more attention to personal privacy, an insensible monitoring technology capable of effectively identifying human bodies and preventing privacy leakage is urgently needed. Based on the characteristics of millimeter waves, the millimeter waves are widely applied to the fields of communication, radar, remote sensing, radio astronomy and the like, some technologies for recognizing human body postures by adopting millimeter waves are already available in the market at present, but the recognition process in the prior art is relatively complex, interference signals, noise signals and the like in millimeter wave signals cannot be effectively eliminated, obstacles cannot be accurately recognized, eliminated or bypassed, the obtained recognition result is not accurate enough, and the reference significance is not large. And the privacy risk of the user is easily revealed by the existing camera technology.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention aims to: aiming at millimeter wave signals collected in a monitoring space, the signals are preprocessed through digital filtering, spatial multipath interference elimination, spatial noise processing and the like, so that interference signals, noise signals, obstacle signals and the like in the signals can be effectively eliminated. Then, analyzing the signal type of the millimeter wave monitoring signal by adopting a data pattern recognition model, screening the millimeter wave monitoring signal related to the target, and eliminating the obstacle of a space static object irrelevant to the target; and analyzing and calculating target state information by adopting a neural network model according to the millimeter wave monitoring signals related to the target. The method has the advantages of sensitive target identification, accurate monitoring result, strong anti-interference capability and the like.
A human body intelligent monitoring method based on millimeter waves comprises the following steps:
arranging at least one millimeter wave monitoring device in the monitoring space, transmitting linear frequency modulation continuous millimeter waves into the monitoring space through the millimeter wave monitoring device, and receiving millimeter wave signals reflected in the monitoring space in real time;
preprocessing the millimeter wave monitoring signals, analyzing the signal types of the millimeter wave monitoring signals by adopting a data mode recognition model according to the preprocessed millimeter wave monitoring signals, and screening the millimeter wave monitoring signals related to the target; analyzing and calculating target state information by adopting a neural network model according to the millimeter wave monitoring signals related to the target;
sending target state information to a monitoring server or a user terminal in real time, wherein the target state information at least comprises one of the following: multi-target position information, multi-human body posture information, human body physiological information and a plurality of moving object information.
Further, the preprocessing the millimeter wave monitoring signal specifically includes: converting the millimeter wave monitoring signal from an analog signal to a digital signal, and performing inverse Fourier transform on the millimeter wave monitoring signal to obtain a transformed time domain digital signal; and sequentially carrying out signal digital filtering, spatial multipath interference elimination and spatial noise processing on the time domain digital signal.
Further, performing signal digital filtering on the time domain digital signal specifically includes:
s101: setting digital filtering parameters, and carrying out anti-interference mean digital filtering on the time domain digital signals of the millimeter wave monitoring signals;
s102: predicting data at the K +1 th moment by the data at the K th moment, and estimating a prediction error at the K +1 th moment by the prediction error at the K th moment;
s103: calculating Kalman gain according to the data at the K moment and the prediction data at the K +1 moment, calculating the optimal estimation value of the data, and calculating the prediction error of the current moment K;
s104: step S102 and step S103 are looped.
Further, the spatial multi-path interference cancellation is performed on the time domain digital signal, which specifically includes:
s201: acquiring a time domain digital signal S of millimeter waves received after transmission at the current moment KKCalculating the weight Q of the current time KK；
S202: acquiring time domain digital signal S of millimeter wave transmitted at K moment and received by K +1K+1Calculating the weight Q at the time K +1K+1；：
S203: generating a multipath interference cancellation amount: Δ S ═ SK·QK-SK+1·QK+1And calculating effective data after interference cancellation: s ═ SK-ΔS；
S204: and (5) looping the steps S201 to S203 until all data converge.
Further, the spatial noise processing is performed on the time domain digital signal, and specifically includes:
carrying out autocorrelation digital noise signal monitoring and cross-correlation digital noise signal monitoring on the time domain digital signal of the millimeter wave monitoring signal, and screening out a digital noise signal;
and calculating the phase difference time domain of the digital noise signal, introducing the digital noise signal into a delayer, introducing the output signal of the delayer and the antecedent noise signal into a multiplier, introducing the output signal of the multiplier into an integrator, introducing the output signal of the integrator into a digital FIR filter, and outputting a digital noise function.
Further, the step of analyzing the signal type of the millimeter wave monitoring signal by using a data pattern recognition model according to the preprocessed millimeter wave monitoring signal, and screening the millimeter wave monitoring signal related to the target specifically includes:
judging whether the effective signals of the preprocessed millimeter wave monitoring signals are annihilated, if so, filtering through a self-adaptive weak signal filter to filter out the effective signals, analyzing the signal types of the effective signals of the millimeter wave monitoring signals by adopting a data pattern recognition model, screening millimeter wave monitoring signals related to a target, and eliminating barriers of space static objects unrelated to the target; if not, the data pattern recognition model is adopted to analyze the signal types of the effective signals of the millimeter wave monitoring signals, millimeter wave monitoring signals related to the target are screened, and the obstacle of the space static object unrelated to the target is eliminated.
Further, the data pattern recognition model includes:
obtaining an effective signal of the millimeter wave monitoring signal as input data of a data pattern recognition model; performing KNN recursion processing on the data, classifying the data by a Bayesian classifier, performing relevance comparison on a data group, extracting relevant data as effective data, establishing a relevant data function, and performing data analysis on the relevant data function.
Further, the analyzing and calculating the target state information by using the neural network model according to the millimeter wave monitoring signal related to the target specifically includes:
performing Doppler/micro Doppler operation on the millimeter wave monitoring signal related to the target to acquire spatial polar coordinate data; analyzing and calculating target state information by adopting a neural network model according to the Doppler/micro Doppler calculation result and the space polar coordinate data;
the target state information includes at least one of: target position information, human posture information, human physiological information and object information; the position information at least comprises distance, position distribution and angle, the human posture information at least comprises speed, track, posture, gait and gesture, the human physiological information at least comprises heart rate, respiration and blood pressure, and the object information at least comprises size, shape and moving speed.
Further, the neural network model includes:
s301: obtaining a Doppler/micro Doppler operation result of the millimeter wave monitoring signal as learning data of a neural network model;
s302: learning type data vector X at K timeKIs an input layer;
s303: determining an input weight vector U by the weight value W, and determining a vector function of a hidden layer at the moment K:
SK＝f(UK·XK+W·SK-1)
s304: determining an output weight vector V, and determining a vector function of an output layer at the moment K: o isK＝g(V·SK)；
S305: after completion of the learning of the data at time K, the next learning of the data at time K is performed, and the process loops from step S302 to step S305.
A human intelligent monitoring equipment based on millimeter wave includes:
the microstrip antenna comprises at least one transmitter and one receiver and is used for transmitting linear frequency modulation continuous millimeter waves and receiving reflected millimeter wave signals in real time;
the millimeter wave chip is used for converting the millimeter wave monitoring signal from an analog signal to a digital signal and preprocessing the millimeter wave monitoring signal;
the millimeter wave chip is in communication connection with the microprocessor, and the microprocessor is used for analyzing the signal type of the millimeter wave monitoring signal by adopting a data pattern recognition model according to the preprocessed millimeter wave monitoring signal and screening the millimeter wave monitoring signal related to the target; analyzing and calculating target state information by adopting a neural network model according to the millimeter wave monitoring signals related to the target;
and the communication module is in communication connection with the microprocessor and is used for sending the target state information to the monitoring server or the user side in real time.
Compared with the prior art, the invention has the following advantages:
aiming at millimeter wave signals collected in a monitoring space, the signals are preprocessed by digital filtering, spatial multipath interference elimination, spatial noise processing, spatial stationary object obstacle elimination and the like, so that interference signals, noise signals, obstacle signals and the like in the signals can be effectively eliminated. Then, analyzing the signal type of the millimeter wave monitoring signal by adopting a data pattern recognition model, and screening the millimeter wave monitoring signal related to the target; and analyzing and calculating target state information by adopting a neural network model according to the millimeter wave monitoring signals related to the target. The method has the advantages of sensitive human body identification, accurate monitoring result, strong anti-interference capability and the like.
Drawings
Fig. 1 is a monitoring flow chart of a millimeter wave-based human body intelligent monitoring method according to an embodiment of the present invention;
fig. 2 is a control flow chart of signal digital filtering for a time domain digital signal according to an embodiment of the present invention;
fig. 3 is a control flowchart of spatial multi-path interference cancellation for time domain digital signals according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating a control procedure for performing spatial noise processing on a time-domain digital signal according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating a control procedure for analyzing millimeter wave monitoring signals by using a data pattern recognition model according to a first embodiment of the present invention;
FIG. 6 is a flowchart illustrating an analysis of a data pattern recognition model according to an embodiment of the present invention;
FIG. 7 is a control flow diagram of analyzing target state information using a neural network model according to an embodiment of the present invention;
FIG. 8 is a flow chart illustrating an analysis of a neural network model according to an embodiment of the present invention;
fig. 9 is a communication block diagram of a millimeter wave-based human body intelligent monitoring device according to a second embodiment of the present invention;
fig. 10 is a schematic block diagram of a millimeter wave-based human body intelligent monitoring device according to a second embodiment of the present invention;
fig. 11 is a system block diagram of a millimeter wave-based human body intelligent monitoring system according to a second embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
The first embodiment is as follows:
referring to fig. 1, a human body intelligent monitoring method based on millimeter waves includes the following steps:
at least one millimeter wave monitoring device is arranged in the monitoring space, linear frequency modulation continuous millimeter waves are transmitted into the monitoring space through the millimeter wave monitoring device, and millimeter wave signals reflected in the monitoring space are received in real time. Specifically, the monitoring space is set in advance, and the monitoring space can be a room of a family, a processing workshop of an enterprise, an office of a company and the like; the arrangement quantity of the millimeter wave monitoring devices can be specifically determined according to the size and the shape of the monitoring space, for example, four millimeter wave monitoring devices can be arranged in four corners of a square monitoring space, and an irregular monitoring space can be specifically arranged according to a monitoring visual angle and can be basically monitored comprehensively.
Preprocessing the millimeter wave monitoring signals, analyzing the signal types of the millimeter wave monitoring signals by adopting a data mode recognition model according to the preprocessed millimeter wave monitoring signals, and screening the millimeter wave monitoring signals related to the target; and analyzing and calculating target state information by adopting a neural network model according to the millimeter wave monitoring signals related to the target. Specifically, the pretreatment method comprises the following steps: converting the millimeter wave monitoring signal from an analog signal to a digital signal, and performing inverse Fourier transform on the millimeter wave monitoring signal to obtain a transformed time domain digital signal; and sequentially carrying out signal digital filtering, spatial multipath interference elimination and spatial noise processing on the time domain digital signal.
Sending target state information to a monitoring server or a user terminal in real time, wherein the target state information at least comprises one of the following: multi-target position information, multi-human body posture information, human body physiological information and a plurality of moving object information. Specifically, a plurality of millimeter wave monitoring devices can access a local server or a cloud server through a wireless local area network or an operator network, various monitoring information in a monitoring space is input and connected into the server through signal communication, and the server is connected with a user operation and maintenance platform and user mobile equipment. The position information at least comprises distance, position distribution and angle, the human posture information at least comprises speed, track, posture, gait and gesture, the human physiological information at least comprises heart rate, respiration and blood pressure, and the object information at least comprises size, shape and moving speed.
According to the intelligent human body monitoring method based on the millimeter waves, the millimeter wave signals are preprocessed through digital filtering, spatial multipath interference elimination, spatial noise processing and the like, so that interference signals, noise signals, obstacle signals and the like in the signals can be effectively eliminated. And then, analyzing the signal type of the millimeter wave monitoring signal by adopting a data pattern recognition model, screening the millimeter wave monitoring signal related to the target, and eliminating the obstacle of the space static object unrelated to the target. And finally, analyzing and calculating target state information by adopting a neural network model according to the millimeter wave monitoring signals related to the target, and analyzing and calculating physiological parameters of human body posture, gait, posture, number of people, motion trail, human heart rate, respiration and the like by monitoring and analyzing various states of the millimeter waves acting on the human body/object, such as power spectrum, human body displacement, motion speed, motion trail, human body micro-fluctuation, human body gait and the like. The method has the advantages of sensitive human body identification, accurate monitoring result, strong anti-interference capability and the like.
Referring to fig. 2, the method for digitally filtering the time domain digital signal of the millimeter wave monitoring signal is as follows:
s101: setting digital filtering parameters, and carrying out anti-interference mean digital filtering on the time domain digital signals of the millimeter wave monitoring signals;
s102: predicting data at the K +1 th moment by the data at the K th moment, and estimating a prediction error at the K +1 th moment by the prediction error at the K th moment;
s103: calculating Kalman gain according to the data at the K moment and the prediction data at the K +1 moment, calculating the optimal estimation value of the data, and calculating the prediction error of the current moment K;
s104: step S102 and step S103 are looped.
In this way, the interference signal in the millimeter wave monitoring signal can be preliminarily filtered.
Referring to fig. 3, the method for performing spatial multi-path interference cancellation on the time domain digital signal of the millimeter wave monitoring signal is as follows:
s201: obtaining K times at the current momentMillimeter wave time domain digital signal S received after transmissionKCalculating the weight Q of the current time KK；
S202: acquiring time domain digital signal S of millimeter wave transmitted at K moment and received by K +1K+1Calculating the weight Q at the time K +1K+1；：
S203: generating a multipath interference cancellation amount: Δ S ═ SK·QK-SK+1·QK+1And calculating effective data after interference cancellation: s ═ SK-ΔS；
S204: and (5) looping the steps S201 to S203 until all data converge.
Thus, the interference signal in the millimeter wave monitoring signal can be effectively eliminated.
Referring to fig. 4, the method of performing spatial noise processing on the time domain digital signal of the millimeter wave monitoring signal is as follows:
carrying out autocorrelation digital noise signal monitoring and cross-correlation digital noise signal monitoring on the time domain digital signal of the millimeter wave monitoring signal, and screening out a digital noise signal;
and calculating the phase difference time domain of the digital noise signal, introducing the digital noise signal into a delayer, introducing the output signal of the delayer and the antecedent noise signal into a multiplier, introducing the output signal of the multiplier into an integrator, introducing the output signal of the integrator into a digital FIR filter, and outputting a digital noise function.
In this way, the noise signal in the millimeter wave monitor signal can be effectively eliminated.
In the millimeter wave-based human body intelligent monitoring method, for millimeter wave signals collected in a monitoring space, the millimeter wave signals are preprocessed through the digital filtering, the spatial multipath interference elimination, the spatial noise processing and the like in sequence, so that interference signals, noise signals, obstacle signals and the like in the millimeter wave monitoring signals can be effectively eliminated. And then powerful data support is provided for subsequent target identification and target state analysis, the sensitivity and the agility of target identification are ensured, the accuracy and the reliability of a target monitoring result are improved, and the anti-interference capability is strong.
Referring to fig. 5, the method for analyzing the millimeter wave monitoring signal by using the data pattern recognition model is as follows:
judging whether the effective signals of the preprocessed millimeter wave monitoring signals are annihilated, if so, filtering through a self-adaptive weak signal filter to filter out the effective signals, analyzing the signal types of the effective signals of the millimeter wave monitoring signals by adopting a data pattern recognition model, screening millimeter wave monitoring signals related to a target, and eliminating barriers of space static objects unrelated to the target; if not, the data pattern recognition model is adopted to analyze the signal types of the effective signals of the millimeter wave monitoring signals, millimeter wave monitoring signals related to the target are screened, and the obstacle of the space static object unrelated to the target is eliminated.
Referring to fig. 6, the process of recognizing the data pattern recognition model is as follows:
obtaining an effective signal of the millimeter wave monitoring signal as input data of a data pattern recognition model; performing KNN recursion processing on the data, classifying the data by a Bayesian classifier, performing relevance comparison on a data group, extracting relevant data as effective data, establishing a relevant data function, and performing data analysis on the relevant data function.
Therefore, the monitoring target can be effectively identified sensitively and swiftly, and the identification accuracy is high. After the monitored target is identified, the obstacle of the space static object is eliminated, the interference factors in the millimeter wave monitoring signal can be further eliminated by adopting the modes of passing, bypassing and the like, the accuracy and the reliability of the target monitoring result are improved, and the anti-interference capability is strong.
Referring to fig. 7, the method for analyzing and calculating the target state information by using the neural network model is as follows:
performing Doppler/micro Doppler operation on the millimeter wave monitoring signal related to the target to acquire spatial polar coordinate data; analyzing and calculating target state information by adopting a neural network model according to the Doppler/micro Doppler calculation result and the space polar coordinate data;
the target state information includes at least one of: multi-target position information, multi-person posture information, human body physiological information and a plurality of moving object information; the position information at least comprises distance, position distribution and angle, the human posture information at least comprises speed, track, posture, gait and gesture, the human physiological information at least comprises heart rate, respiration and blood pressure, and the object information at least comprises size and shape.
Referring to fig. 8, the process of the neural network model analysis is as follows:
s301: obtaining a Doppler/micro Doppler operation result of the millimeter wave monitoring signal as learning data of a neural network model;
s302: learning type data vector X at K timeKIs an input layer;
s303: determining an input weight vector U by the weight value W, and determining a vector function of a hidden layer at the moment K:
SK＝f(UK·XK+W·SK-1)
s304: determining an output weight vector V, and determining a vector function of an output layer at the moment K: o isK＝g(V·SK)；
S305: after completion of the learning of the data at time K, the next learning of the data at time K is performed, and the process loops from step S302 to step S305.
Therefore, the target can be accurately and efficiently monitored, the state information of the monitored target is analyzed, the target monitoring result is accurate, and the anti-interference capability is strong.
In specific implementation, the neural network model may be an RNN neural network model.
Example two:
referring to fig. 9, a human intelligent monitoring equipment based on millimeter wave includes:
the microstrip antenna comprises at least one transmitter and one receiver and is used for transmitting linear frequency modulation continuous millimeter waves and receiving reflected millimeter wave signals in real time. Specifically, one or more transmitting and one or more receiving microstrip antennas transmit chirp continuous millimeter waves to a detection space. The millimeter waves are projected to a detected object or a human body to generate a certain reflected signal, and the reflected signal is received by the microstrip antenna.
The millimeter wave chip is used for converting the millimeter wave monitoring signal from an analog signal to a digital signal and preprocessing the millimeter wave monitoring signal. Specifically, the millimeter wave chip provides a linear frequency modulation continuous millimeter wave with a certain transmission power to the microstrip antenna, and receives a reflected signal obtained by the microstrip antenna. And the digital-to-analog conversion circuit in the chip is responsible for converting the analog signal into the digital signal. The built-in SOC of the chip preprocesses the received transmission signal.
The millimeter wave chip is in communication connection with the microprocessor, and the microprocessor is used for analyzing the signal type of the millimeter wave monitoring signal by adopting a data pattern recognition model according to the preprocessed millimeter wave monitoring signal and screening the millimeter wave monitoring signal related to the target; and analyzing and calculating target state information by adopting a neural network model according to the millimeter wave monitoring signals related to the target. Specifically, the microprocessor receives a preprocessing signal transmitted by the millimeter wave chip and performs specific algorithm processing, so as to obtain a series of target parameters such as the size, distance, angle, position distribution, speed, track, shape, posture, gait, gesture, heart rate, respiration, blood pressure and the like of a detected object or a human body.
And the communication module is in communication connection with the microprocessor and is used for sending the target state information to the monitoring server or the user side in real time. Specifically, the communication module receives a result parameter signal output by the microprocessor and transmits the result parameter signal to a local area network, a wireless local area network or an operator wireless network. And the local or cloud server receives the result sent by the communication module.
During specific implementation, the monitoring equipment can directly adopt a millimeter wave sensor, and during specific implementation, the neural network model can adopt an RNN neural network model.
Referring to fig. 10, a microstrip antenna ant of the millimeter wave sensor, a radio frequency transceiving millimeter wave driving chip SRchip, a signal preprocessing SOC unit Signalpre-proc, data extraction speed, angle, distance, and other various related quantities.
Referring to fig. 11, the millimeter wave-based human body intelligent monitoring system to which the above millimeter wave-based human body intelligent monitoring device can be applied comprises a plurality of millimeter wave sensors, a monitoring server, a user mobile device and a user operation and maintenance platform, wherein the plurality of millimeter wave sensors, the user mobile device and the user operation and maintenance platform are respectively in communication connection with the monitoring server.
The human body intelligent monitoring equipment based on the millimeter waves can move into a monitoring system, and the system can firstly carry out pretreatment on millimeter wave signals, such as digital filtering, spatial multipath interference elimination and spatial noise processing, and can effectively eliminate interference signals, noise signals, obstacle signals and the like in the signals. Then, the signal type of the millimeter wave monitoring signal is analyzed, the millimeter wave monitoring signal related to the target is screened, and the obstacle of the space static object unrelated to the target is eliminated. And finally, analyzing and calculating target state information according to the millimeter wave monitoring signals related to the target, and analyzing and calculating physiological parameters of human body posture, gait, posture, number of people, motion trail, human heart rate, respiration and the like by monitoring and analyzing various states of the millimeter waves acting on the human body/object, such as power spectrum, human body displacement, moving speed, moving trail, human body micro-fluctuation, human body gait and the like. The method has the advantages of sensitive human body identification, accurate monitoring result, strong anti-interference capability and the like.
Finally, the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting, although the present invention is described in detail with reference to the embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the protection scope of the present invention.
Claims (10)
1. A human body intelligent monitoring method based on millimeter waves is characterized by comprising the following steps:
arranging at least one millimeter wave monitoring device in the monitoring space, transmitting linear frequency modulation continuous millimeter waves into the monitoring space through the millimeter wave monitoring device, and receiving millimeter wave signals reflected in the monitoring space in real time;
preprocessing the millimeter wave monitoring signals, analyzing the signal types of the millimeter wave monitoring signals by adopting a data mode recognition model according to the preprocessed millimeter wave monitoring signals, and screening the millimeter wave monitoring signals related to the target; analyzing and calculating target state information by adopting a neural network model according to the millimeter wave monitoring signals related to the target;
sending target state information to a monitoring server or a user terminal in real time, wherein the target state information at least comprises one of the following: multi-target position information, multi-human body posture information, human body physiological information and a plurality of moving object information.
2. The intelligent human body monitoring method based on millimeter waves according to claim 1, wherein the preprocessing of millimeter wave monitoring signals specifically comprises: converting the millimeter wave monitoring signal from an analog signal to a digital signal, and performing inverse Fourier transform on the millimeter wave monitoring signal to obtain a transformed time domain digital signal; and sequentially carrying out signal digital filtering, spatial multipath interference elimination and spatial noise processing on the time domain digital signal.
3. The intelligent human body monitoring method based on millimeter waves according to claim 2, wherein the signal digital filtering is performed on the time domain digital signal, and specifically comprises:
s101: setting digital filtering parameters, and carrying out anti-interference mean digital filtering on the time domain digital signals of the millimeter wave monitoring signals;
s102: predicting data at the K +1 th moment by the data at the K th moment, and estimating a prediction error at the K +1 th moment by the prediction error at the K th moment;
s103: calculating Kalman gain according to the data at the K moment and the prediction data at the K +1 moment, calculating the optimal estimation value of the data, and calculating the prediction error of the current moment K;
s104: step S102 and step S103 are looped.
4. The intelligent human body monitoring method based on millimeter waves according to claim 2, wherein the spatial multi-path interference cancellation is performed on the time domain digital signal, specifically comprising:
s201: acquiring a time domain digital signal S of millimeter waves received after transmission at the current moment KKCalculating the weight Q of the current time KK；
S202: acquiring time domain digital signal S of millimeter wave transmitted at K moment and received by K +1K+1Calculating the weight Q at the time K +1K+1；：
S203: generating a multipath interference cancellation amount: Δ S ═ SK·QK-SK+1·QK+1And calculating effective data after interference cancellation: s ═ SK-ΔS；
S204: and (5) looping the steps S201 to S203 until all data converge.
5. The intelligent human body monitoring method based on millimeter waves according to claim 2, wherein the spatial noise processing is performed on the time domain digital signal, and specifically comprises:
carrying out autocorrelation digital noise signal monitoring and cross-correlation digital noise signal monitoring on the time domain digital signal of the millimeter wave monitoring signal, and screening out a digital noise signal;
and calculating the phase difference time domain of the digital noise signal, introducing the digital noise signal into a delayer, introducing the output signal of the delayer and the antecedent noise signal into a multiplier, introducing the output signal of the multiplier into an integrator, introducing the output signal of the integrator into a digital FIR filter, and outputting a digital noise function.
6. The intelligent human body monitoring method based on millimeter waves according to claim 1, wherein the step of analyzing the signal type of the millimeter wave monitoring signals by adopting a data pattern recognition model according to the preprocessed millimeter wave monitoring signals and screening the millimeter wave monitoring signals related to the target specifically comprises the steps of:
judging whether the effective signals of the preprocessed millimeter wave monitoring signals are annihilated, if so, filtering through a self-adaptive weak signal filter to filter out the effective signals, analyzing the signal types of the effective signals of the millimeter wave monitoring signals by adopting a data pattern recognition model, screening millimeter wave monitoring signals related to a target, and eliminating barriers of space static objects unrelated to the target; if not, the data pattern recognition model is adopted to analyze the signal types of the effective signals of the millimeter wave monitoring signals, millimeter wave monitoring signals related to the target are screened, and the obstacle of the space static object unrelated to the target is eliminated.
7. The intelligent human body monitoring method based on millimeter waves according to claim 6, wherein the data pattern recognition model comprises:
obtaining an effective signal of the millimeter wave monitoring signal as input data of a data pattern recognition model; performing KNN recursion processing on the data, classifying the data by a Bayesian classifier, performing relevance comparison on a data group, extracting relevant data as effective data, establishing a relevant data function, and performing data analysis on the relevant data function.
8. The intelligent human body monitoring method based on millimeter waves according to claim 1, wherein the analyzing and calculating target state information by using a neural network model according to the millimeter wave monitoring signals related to the target specifically comprises:
performing Doppler/micro Doppler operation on the millimeter wave monitoring signal related to the target to acquire spatial polar coordinate data; analyzing and calculating target state information by adopting a neural network model according to the Doppler/micro Doppler calculation result and the space polar coordinate data;
the target state information includes at least one of: multi-target position information, multi-person posture information, human body physiological information and a plurality of moving object information; the position information at least comprises distance, position distribution and angle, the human posture information at least comprises speed, track, posture, gait and gesture, the human physiological information at least comprises heart rate, respiration and blood pressure, and the object information at least comprises size, shape and moving speed.
9. The intelligent human body monitoring method based on millimeter waves according to claim 8, wherein the neural network model comprises:
s301: obtaining a Doppler/micro Doppler operation result of the millimeter wave monitoring signal as learning data of a neural network model;
s302: learning type data vector X at K timeKIs an input layer;
s303: determining an input weight vector U by the weight value W, and determining a vector function of a hidden layer at the moment K:
SK＝f(UK·XK+W·SK-1)
s304: determining an output weight vector V, and determining a vector function of an output layer at the moment K: o isK＝g(V·SK)；
S305: after completion of the learning of the data at time K, the next learning of the data at time K is performed, and the process loops from step S302 to step S305.
10. The utility model provides a human intelligent monitoring equipment based on millimeter wave which characterized in that includes:
the microstrip antenna comprises at least one transmitter and one receiver and is used for transmitting linear frequency modulation continuous millimeter waves and receiving reflected millimeter wave signals in real time;
the millimeter wave chip is used for converting the millimeter wave monitoring signal from an analog signal to a digital signal and preprocessing the millimeter wave monitoring signal;
the millimeter wave chip is in communication connection with the microprocessor, and the microprocessor is used for analyzing the signal type of the millimeter wave monitoring signal by adopting a data pattern recognition model according to the preprocessed millimeter wave monitoring signal and screening the millimeter wave monitoring signal related to the target; analyzing and calculating target state information by adopting a neural network model according to the millimeter wave monitoring signals related to the target;
and the communication module is in communication connection with the microprocessor and is used for sending the target state information to the monitoring server or the user side in real time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011488371.0A CN112731380A (en) | 2020-12-16 | 2020-12-16 | Intelligent human body monitoring method and monitoring equipment based on millimeter waves |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011488371.0A CN112731380A (en) | 2020-12-16 | 2020-12-16 | Intelligent human body monitoring method and monitoring equipment based on millimeter waves |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112731380A true CN112731380A (en) | 2021-04-30 |
Family
ID=75602487
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011488371.0A Pending CN112731380A (en) | 2020-12-16 | 2020-12-16 | Intelligent human body monitoring method and monitoring equipment based on millimeter waves |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112731380A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113645269A (en) * | 2021-06-29 | 2021-11-12 | 北京金茂绿建科技有限公司 | Millimeter wave sensor data transmission method and device, electronic equipment and storage medium |
| CN113786180A (en) * | 2021-10-25 | 2021-12-14 | 中国科学技术大学 | Human body blood pressure detection method based on millimeter wave radar signal |
| CN113985393A (en) * | 2021-10-25 | 2022-01-28 | 南京慧尔视智能科技有限公司 | Target detection method, device and system |
| CN116449330A (en) * | 2023-06-20 | 2023-07-18 | 精华隆智慧感知科技（深圳）股份有限公司 | Indoor people number estimation method and device, computer equipment and storage medium |
| CN117031136A (en) * | 2023-09-22 | 2023-11-10 | 北京中成康富科技股份有限公司 | Detection method, device, equipment and storage medium of millimeter wave therapeutic instrument |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111175741A (en) * | 2020-01-10 | 2020-05-19 | 浙江大学 | Single-frequency continuous millimeter wave Doppler sensing microwave wall safety space early warning method |
| US20200166611A1 (en) * | 2018-11-22 | 2020-05-28 | Jomoo Kitchen & Bath Co., Ltd | Detection method, detection device, terminal and detection system |
| CN111481184A (en) * | 2020-04-24 | 2020-08-04 | 华侨大学 | Multi-target respiration heart rate monitoring method and system based on millimeter wave radar technology |
-
2020
- 2020-12-16 CN CN202011488371.0A patent/CN112731380A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200166611A1 (en) * | 2018-11-22 | 2020-05-28 | Jomoo Kitchen & Bath Co., Ltd | Detection method, detection device, terminal and detection system |
| CN111175741A (en) * | 2020-01-10 | 2020-05-19 | 浙江大学 | Single-frequency continuous millimeter wave Doppler sensing microwave wall safety space early warning method |
| CN111481184A (en) * | 2020-04-24 | 2020-08-04 | 华侨大学 | Multi-target respiration heart rate monitoring method and system based on millimeter wave radar technology |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113645269A (en) * | 2021-06-29 | 2021-11-12 | 北京金茂绿建科技有限公司 | Millimeter wave sensor data transmission method and device, electronic equipment and storage medium |
| CN113786180A (en) * | 2021-10-25 | 2021-12-14 | 中国科学技术大学 | Human body blood pressure detection method based on millimeter wave radar signal |
| CN113985393A (en) * | 2021-10-25 | 2022-01-28 | 南京慧尔视智能科技有限公司 | Target detection method, device and system |
| CN113786180B (en) * | 2021-10-25 | 2023-10-20 | 中国科学技术大学 | Human blood pressure detection device based on millimeter wave radar signal |
| CN113985393B (en) * | 2021-10-25 | 2024-04-16 | 南京慧尔视智能科技有限公司 | Target detection method, device and system |
| CN116449330A (en) * | 2023-06-20 | 2023-07-18 | 精华隆智慧感知科技（深圳）股份有限公司 | Indoor people number estimation method and device, computer equipment and storage medium |
| CN116449330B (en) * | 2023-06-20 | 2023-10-13 | 精华隆智慧感知科技（深圳）股份有限公司 | Indoor people number estimation method and device, computer equipment and storage medium |
| CN117031136A (en) * | 2023-09-22 | 2023-11-10 | 北京中成康富科技股份有限公司 | Detection method, device, equipment and storage medium of millimeter wave therapeutic instrument |
| CN117031136B (en) * | 2023-09-22 | 2024-03-29 | 北京中成康富科技股份有限公司 | Detection method, device, equipment and storage medium of millimeter wave therapeutic instrument |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112731380A (en) | Intelligent human body monitoring method and monitoring equipment based on millimeter waves | |
| CN112998668B (en) | Millimeter wave-based non-contact far-field multi-human-body respiration heart rate monitoring method | |
| US5247303A (en) | Data quality and ambiguity resolution in a doppler radar system | |
| Rovňáková et al. | TOA estimation and data association for through-wall tracking of moving targets | |
| CN112634566B (en) | Intelligent electronic fence construction method and system based on millimeter waves | |
| Cui et al. | 3D detection and tracking for on-road vehicles with a monovision camera and dual low-cost 4D mmWave radars | |
| Kocur et al. | Imaging method: an efficient algorithm for moving target tracking by UWB radar | |
| KR20220141748A (en) | Method and computer readable storage medium for extracting target information from radar signal | |
| CN116125458A (en) | Personnel positioning method based on millimeter wave radar | |
| Kocur et al. | Through wall tracking of moving targets by M-sequence UWB radar | |
| CN114325623B (en) | Human body limb movement information measurement method based on millimeter wave radar | |
| Kocur et al. | Imaging method: A strong tool for moving target tracking by a multistatic UWB radar system | |
| Iqbal et al. | Indoor motion classification using passive RF sensing incorporating deep learning | |
| KR102322480B1 (en) | Position Detecting System and Method of Ultra Wide Band Using the Optimized Detection Threshold at Objects Size and Motion Strenth | |
| Khawaja et al. | UWB radar based beyond wall sensing and tracking for ambient assisted living | |
| CN112924928B (en) | Indoor Wi-Fi multi-person detection method based on path separation | |
| Vaca-Rubio et al. | User localization using rf sensing: A performance comparison between lis and mmwave radars | |
| CN114063628A (en) | Unmanned aerial vehicle disaster relief system and method based on deep learning and radar detection | |
| Zande | 3D Point Cloud Object Detection for Millimeter Wave Radar: a Synthesis Study | |
| Kriukova et al. | Motion Zone Estimation for Adaptive Radar-System Parameters Selection | |
| Bouhlel et al. | FMCW radar system for transponder identification | |
| CN117572423B (en) | Doppler aperture same transceiver array radar | |
| Li et al. | Indoor Multi-Human Device-Free Tracking System Using Multi-Radar Cooperative Sensing | |
| Cheredachuk et al. | Adaptive Algorithm for Radar-System Parameters Tuning by means of Motion Zone Estimation | |
| Streck et al. | Comparison of two different radar concepts for pedestrian protection on bus stops |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |