CN114509749A - Indoor positioning detection system and method - Google Patents
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Abstract
The invention relates to an indoor positioning detection method and system, which comprises a main server and a detection radar connected with the main server, wherein the main server acquires a design drawing, and the design drawing comprises the following functional area distribution in a house: the detection radar comprises a signal generation module, a transmitting module, an ultra-wideband receiving module, an echo signal processing module and a microwave sensor; divide indoor into a plurality of regions through the drawing, place the detection radar at living room house intermediate position, infrared camera and adapter visible light camera are arranged in other functional areas, the detection radar is based on the chaos signal of ultra wide band that chaos circuit produced and is surveyed the signal as the radar, it is high to have distance resolution, advantages such as interference killing feature is strong and detection precision height, can realize wearing the wall to survey, only need arrange a detection radar in whole house, and the radar has integrateed location and has surveyed two kinds of functions with the action, consequently it is with low costs for other indoor detecting system of asylum for the aged.
Description
Technical Field
The invention relates to the technical field of microwave radars, in particular to an indoor positioning detection system and method.
Background
The existing microwave radar system still has certain limitation in the aspect of completely processing human body activities and vital sign signals in real time, and the biggest challenge to the microwave radar sensor is how to provide enough distance detection and displacement monitoring precision at low cost. Several mainstream radar architectures currently include doppler (interferometric) radar, impulse radio ultra wide band (IR-UWB) radar, Frequency Modulated Continuous Wave (FMCW) radar, and stepped frequency modulated continuous wave (SFCW) radar. Doppler radar acquires a phase history based on a single tone Continuous Wave (CW). The displacement sensor has high measurement precision, is widely applied to displacement measurement, but is difficult to detect distance information. Although multiple doppler radars can estimate the position of a moving target in cooperation with an angle of arrival (AOA) algorithm, a single doppler radar has difficulty in spatially distinguishing multiple targets, which limits the main application of the conventional indoor doppler radar sensor to vital sign monitoring and gesture recognition.
Passive sensing, also called Device-free sensing, is a sensing form that does not require active participation of a sensing object in a sensing process, and sensing is usually achieved by detecting the influence of a target on the environment. Two kinds of radars can generally be arranged at general indoor location detecting system, location radar and action detection radar, the effect of location radar is the position of confirming personnel, the life detection radar is used for measuring personnel's action, but arrange the cost of two kinds of radars simultaneously higher to because be two different radars respectively location information and action information input server, consequently location information and action information need correspond, can not directly combine location information and vital sign information together, wasted computational resource.
And the positioning is not accurate enough, and the detection distance can not meet the expected requirement.
Disclosure of Invention
The present invention provides an indoor positioning detection system and method, aiming at the above-mentioned defects in the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows:
an indoor positioning detection method is constructed, wherein the method comprises the following steps:
step 1, generating a chaotic signal, dividing the chaotic signal into two paths, wherein one path is a first detection signal, and the other path is a first reference signal, and transmitting the first detection signal;
step 2, receiving a first echo signal formed by reflection of the first detection signal, determining whether a human body target exists, and executing step 3 if the first echo signal formed by reflection of the human body target is received;
step 3, performing cross-correlation operation on the first echo signal and the first reference signal, calculating the round-trip time of the detection signal reflected by the target, determining the distance between the detection signal and the human body target, and executing step 4;
step 4, generating a local oscillation signal, dividing the local oscillation signal into two paths, wherein one path is a second detection signal, the other path is a second reference signal, mixing the first detection signal and the second detection signal to obtain a mixing signal, sending the mixing signal from the transmitting module, and then executing step 5;
step 5, receiving a second echo signal formed by the reflection of a second detection signal by a personnel target, acquiring amplitude and phase information reflecting the difference between a second reference signal and the second echo signal, and performing short-time Fourier transform through the amplitude and phase change information of the second detection signal to acquire a time-frequency matrix diagram;
and 6, filtering the time-frequency matrix diagram: setting a specific threshold value for each row of non-zero elements in a time-frequency matrix, judging whether the non-zero elements of the row are smaller than the corresponding specific threshold value, if so, setting the non-zero elements to be 0, and if not, reserving the non-zero elements, wherein the specific threshold value is obtained by multiplying the Sum Sum of the maximum value and the minimum value of the non-zero elements of the row by a self-adaptive factor mu;
and 7, performing corresponding feature extraction on the time frequency matrix diagram, realizing human body action identification through a classifier, and judging whether a human body target falls down.
Preferably, the method for calculating the adaptive factor μ in step 6 is as follows: calculating the maximum value M and the minimum value M of non-zero elements in a column of the matrix, presetting an adaptive factor mu and a threshold value T, wherein mu = M/M, and T = mu1(M + M) setting initial value of μ to μ0Calculating a corresponding threshold value T0And μ0= M/M, according to the threshold value T0Filtering the non-zero elements in the column in a way of being larger than T0Is less than T0Is regarded as 0, then the sum S of the filtered non-zero elements in the column is calculated, and mu is calculated0Step by 0.01 to obtain mu1Calculating a corresponding threshold value T1According to the threshold value T1Filtering the row of non-zero elements, and calculating the sum S of the filtered row of non-zero elementstempCalculating S and StempIf the difference of (d) is not the maximum, mu1And step 0.01, repeating the steps, and outputting mu if the maximum value is reached.
Preferably, the first echo signal is received by an ultra-wideband receiving antenna, and the second echo signal is received by a solid-state microwave sensor.
Preferably, whether the human body target falls down or not is judged according to the speed and the acceleration value of the human body target, if the speed and the acceleration value of the human body target exceed preset threshold values, the human body target is judged to fall down, and if the human body target is judged to fall down, alarm information is sent to a user.
The invention also provides an indoor positioning detection system, which is used for realizing the indoor positioning detection method and is characterized by comprising a main server and a detection radar connected with the main server, wherein the main server acquires a design drawing, and the design drawing comprises the distribution of indoor functional areas of a house:
the detection radar comprises a signal generation module, a transmitting module, an ultra-wideband receiving module, an echo signal processing module and a microwave sensor;
the signal generating module is used for sending a chaotic signal or a mixing signal formed by mixing the chaotic signal and a local oscillator signal, the mixing signal is divided into two paths of signals, namely a detection signal and a reference signal, the detection signal is input into the transmitting module, and the reference signal is input into the echo signal processing module;
the transmitting module is used for transmitting the detection signal;
the ultra-wideband receiving module is used for receiving a first echo signal formed by a detection signal reflected by a person target and inputting the first echo signal into the echo signal processing module;
the microwave sensor is used for receiving a second echo signal formed by reflection of a detection signal personnel target and inputting the second echo signal into the echo signal processing module;
the echo signal processing module is used for acquiring round-trip time data and direction data of a detection signal through a first echo signal and inputting the data into the main server; the second echo signal is used for acquiring amplitude and phase change data of the detection signal and inputting the data into the main server;
the general server determines the position of the human body target through the round trip time data and the direction data, records the position data, obtains the position change of the personnel target within a period of time, and obtains the activity state of the human body target; and the master server performs Fourier transform through the amplitude and phase change information of the second detection signal to obtain the human body action information.
Preferably, the signal generating module includes a first signal generating unit, a second signal generating unit, a mixing unit, a first signal splitting unit and a second signal splitting unit; the first signal generating unit is used for generating chaotic signals and dividing the chaotic signals into two paths of signals through the first signal shunting unit, wherein one path of signals is used as first detection signals and input into the frequency mixing unit, and the other path of signals is used as first reference signals and input into the echo signal processing module; the second signal generating unit is used for generating a local oscillator signal and dividing the local oscillator signal into two paths of signals through the second signal branching unit, wherein one path of signals is used as a second detection signal and input to the frequency mixing unit, and the other path of signals is used as a second reference signal and input to the echo signal processing module; the frequency mixing unit is used for mixing the first detection signal and the second detection signal to obtain a frequency mixing signal, and inputting the frequency mixing signal to the transmitting module.
Preferably, the echo signal processor is configured to perform a cross-correlation operation on the first echo signal and the first reference signal, and calculate a round-trip time of the probe signal reflected by the target; and the second reference signal is used for interfering the second echo signal, detecting the interfered signal and transmitting the detected amplitude and phase change information to the master server.
Preferably, the system further comprises a demodulator, a power amplifier and a low noise amplifier, wherein the power amplifier is used for performing power amplification on the mixed signal, and the mixed signal is sent out through the transmitting module; the detection signal irradiates a human target and is reflected to return to form an echo signal, the first echo signal received by the ultra-wideband receiving module is amplified by the low-noise amplifier and then input into the demodulator, and the first echo signal is demodulated by the demodulator and then input into the echo signal processing module.
Preferably, the total server still is connected with the discernment radar that is used for discerning human vital sign, the discernment radar launches identification signal, identification signal forms third echo signal through personnel's target reflection, the third echo signal is received to the discernment radar to through the short-time Fourier transform to third echo signal obtain the time frequency matrix chart, carry out corresponding feature extraction to the time frequency matrix chart, acquire the breathing and the heartbeat information of personnel's target.
The invention has the beneficial effects that: the indoor is divided into a plurality of areas by a drawing, a detection radar is placed in the middle of a house in a living room, an infrared camera and a pickup visible light camera are arranged in other functional areas, the detection radar is based on an ultra-wideband chaotic signal generated by a chaotic circuit as a radar detection signal, and has the advantages of high distance resolution (reaching the centimeter level), strong anti-jamming capability, high detection precision and the like, through-wall detection can be realized, only one detection radar needs to be arranged in the whole house, and the radar integrates two functions of positioning detection and motion detection, so the cost is low compared with other indoor detection systems in the old care houses, the radar firstly carries out position detection, the motion function can be started only when a person is detected to be positioned indoors, the infrared camera and the pickup are in a normally closed state, and the corresponding infrared camera and pickup can be started only when the person is detected to be positioned in a certain monitoring area, the infrared camera, the sound pick-up, the detection radar and the identification radar are used for carrying out health detection on the old people in different aspects, including breathing, heartbeat, body temperature, cough frequency and whether the old people fall down or not, the method is comprehensive, the real-time body condition of the old people can be obtained to the maximum extent, the care difficulty of parents of the old people is reduced, meanwhile, the method can also be applied to a nursing home, the health of a plurality of old people can be monitored simultaneously, the cost of the nursing home is reduced, and the possibility of conflict between the old people and a nursing worker is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be further described with reference to the accompanying drawings and embodiments, wherein the drawings in the following description are only part of the embodiments of the present invention, and for those skilled in the art, other drawings can be obtained without inventive efforts according to the accompanying drawings:
FIG. 1 is a block diagram of an indoor positioning detection system according to a preferred embodiment of the present invention;
fig. 2 is a flow chart of the adaptive factor μ calculation of the indoor positioning detection method according to the preferred embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without inventive step, are within the scope of the present invention.
As shown in fig. 1, the indoor positioning detection system of the preferred embodiment of the present invention includes a main server and a detection radar connected to the main server, wherein the main server obtains a design drawing, and the design drawing includes distribution of indoor functional areas of a house:
the detection radar comprises a signal generation module, a transmitting module, an ultra-wideband receiving module, an echo signal processing module and a microwave sensor;
the signal generating module is used for sending out a chaotic signal or a mixing signal formed by mixing the chaotic signal and a local oscillator signal, the mixing signal is divided into a detection signal and a reference signal, the detection signal is input into the transmitting module, and the reference signal is input into the echo signal processing module;
the transmitting module is used for transmitting the detection signal;
the ultra-wideband receiving module is used for receiving a first echo signal formed by a detection signal reflected by a person target and inputting the first echo signal into the echo signal processing module;
the microwave sensor is used for receiving a second echo signal formed by reflection of the detection signal personnel target and inputting the second echo signal into the echo signal processing module;
the echo signal processing module is used for acquiring round-trip time data and direction data of the detection signal through the first echo signal and inputting the data into the main server; the second echo signal is used for acquiring the amplitude and phase change data of the detection signal and inputting the data into the main server;
the main server determines the position of the human body target through the round trip time data and the direction data, records the position data, obtains the position change of the personnel target within a period of time, and obtains the activity state of the human body target; and the main server performs Fourier transform through the amplitude and phase change information of the second detection signal to obtain the action information of the human body.
The indoor is divided into a plurality of areas by a drawing, a detection radar is placed in the middle of a house in a living room, an infrared camera and a pickup visible light camera are arranged in other functional areas, the detection radar is based on an ultra wide band chaotic signal generated by a chaotic circuit as a radar detection signal, the detection radar has the advantages of high distance resolution (reaching centimeter level), strong anti-jamming capability, high detection precision and the like, wall-through detection can be realized, only one detection radar needs to be arranged in the whole house, and the radar integrates two functions of positioning detection and vital sign detection, so the cost is low compared with other indoor detection systems in the old care houses, the radar firstly carries out position detection, the action detection function can be started only when a person is detected to be positioned indoors, the infrared camera and the pickup are in a normally closed state, and the corresponding infrared camera and pickup can be started only when the person is detected to be positioned in a certain monitoring area, the infrared camera, the sound pick-up, the detection radar and the identification radar are used for carrying out health detection on the old people in different aspects, including breathing, heartbeat, body temperature, cough frequency and whether the old people fall down or not, the method is comprehensive, the real-time body condition of the old people can be obtained to the maximum extent, the care difficulty of parents of the old people is reduced, meanwhile, the method can also be applied to a nursing home, the health of a plurality of old people can be monitored simultaneously, the cost of the nursing home is reduced, and the possibility of conflict between the old people and a nursing worker is reduced.
As shown in fig. 1, the signal generating module includes a first signal generating unit, a second signal generating unit, a mixing unit, a first signal splitting unit, and a second signal splitting unit; the first signal generating unit is used for generating chaotic signals and dividing the chaotic signals into two paths of signals through the first signal shunting unit, wherein one path of signals is used as first detection signals and input into the frequency mixing unit, and the other path of signals is used as first reference signals and input into the echo signal processing module; the second signal generating unit is used for generating a local oscillator signal and dividing the local oscillator signal into two paths of signals through the second signal branching unit, wherein one path of signals is used as a second detection signal and input to the frequency mixing unit, and the other path of signals is used as a second reference signal and input to the echo signal processing module; the frequency mixing unit is used for mixing the first detection signal and the second detection signal to obtain a frequency mixing signal and inputting the frequency mixing signal to the transmitting module;
the receiving module receives a first echo signal formed by reflection of a person target, the echo signal processing module collects the first echo signal, performs cross-correlation operation on the first echo signal and a first reference signal, and calculates the round trip time of a detection signal reflected by the target, so as to obtain distance information of the human body target; the microwave sensor receives a second echo signal formed by reflection of a person target, the echo signal processing module collects and stores the second echo signal, amplitude and phase information reflecting the difference between a second reference signal and the second echo signal is obtained, and the amplitude and phase signals are subjected to Fourier transform, so that the motion information of a human body can be obtained.
As shown in fig. 1, the echo signal processor is configured to perform a cross-correlation operation on the first echo signal and the first reference signal, and calculate a round-trip time of the probe signal reflected by the target; the second reference signal is used for interfering the second echo signal, detecting the interfered signal and transmitting the detected amplitude and phase change information to the main server; in view of the low correlation of chaotic signals and the single-frequency resonance of the solid-state microwave sensor, the systems of the two channels are basically not affected, and the detection of positions and actions can be realized simultaneously.
As shown in fig. 1, the wireless communication device further includes a demodulator, a power amplifier and a low noise amplifier, wherein the power amplifier is used for performing power amplification on the mixed signal, and the mixed signal is sent out through the transmitting module; the detection signal irradiates a human target and is reflected to return to form an echo signal, the first echo signal received by the ultra-wideband receiving module is amplified by the low-noise amplifier and then input into the demodulator, and the first echo signal is demodulated by the demodulator and then input into the echo signal processing module.
The main server is also connected with an identification radar for identifying human vital signs, the identification radar transmits an identification signal, the identification signal is reflected by a personnel target to form a third echo signal, the identification radar receives the third echo signal, a time-frequency matrix diagram is obtained through short-time Fourier transform of the third echo signal, corresponding feature extraction is carried out on the time-frequency matrix diagram, and finally human vital sign identification is realized through a classifier; through gather the vital sign that judges that radar data can be intelligent target in detection range, in case take place to breathe the unusual condition of heartbeat, can in time feed back information to family or nursing staff, have that detection range is wide, the precision is high, the reaction is in time and detect convenient advantage.
As shown in fig. 1, a general server acquires distance and azimuth information of a person target, and determines a detection area where the person target is located through a design drawing; and when the master server judges that the personnel target is in an abnormal state and needs to be notified to the user, sending out the detection area information of the personnel target and the vital sign information of the personnel target together.
As shown in fig. 1, the main server divides a plurality of monitoring areas according to a design drawing, an infrared camera and a sound pickup which are both connected with the main server are arranged in any monitoring area, and the infrared camera is used for shooting an infrared image of the monitoring area where a human body target is located and transmitting the infrared image to the main server; the sound pickup is used for receiving sound, measuring sound pressure, converting the measurement result of the sound pressure into a voice signal and transmitting the voice signal to the main server; the main server is used for processing the human body target and the infrared image to obtain body temperature information of the human body target; the voice recognition system is also used for carrying out feature extraction on the voice signals to obtain the voice information of the human body target; converting the received sound signal into a voice signal in a PCM format for recording; the voice recognizer 122 detects the voice signal end points in the PCM format, eliminates non-cough signals, uses the rest signals as candidate cough signals, extracts features of the candidate cough signals according to frames, and converts the features into a feature vector sequence.
The invention also provides an indoor positioning detection method based on the indoor positioning detection system, which comprises the following steps:
step 1, generating a chaotic signal, dividing the chaotic signal into two paths, wherein one path is a first detection signal, and the other path is a first reference signal, and transmitting the first detection signal;
step 2, receiving a first echo signal formed by reflection of the first detection signal, determining whether a human body target exists, and executing step 3 if the first echo signal formed by reflection of the human body target is received;
step 3, performing cross-correlation operation on the first echo signal and the first reference signal, calculating the round-trip time of the detection signal reflected by the target, determining the distance between the detection signal and the human body target, and executing step 4;
step 4, generating a local oscillation signal, dividing the local oscillation signal into two paths, wherein one path is a second detection signal, the other path is a second reference signal, mixing the first detection signal and the second detection signal to obtain a mixing signal, sending the mixing signal from the transmitting module, and then executing step 5;
step 5, receiving a second echo signal formed by a second detection signal through the reflection of a personnel target, acquiring amplitude and phase information reflecting the difference between a second reference signal and the second echo signal, and performing short-time Fourier transform through the amplitude and phase change information of the second detection signal to acquire a time-frequency matrix chart;
step 5, filtering the time-frequency diagram: setting a specific threshold value for each row of non-zero elements in the time-frequency matrix, judging whether the non-zero elements of the row are smaller than the corresponding specific threshold value, if so, setting the non-zero elements to be 0, and if not, keeping the non-zero elements, wherein the specific threshold value is obtained by multiplying the Sum Sum of the maximum and minimum values of the non-zero elements of the row by a self-adaptive factor mu.
And 6, carrying out corresponding feature extraction on the time frequency matrix diagram, realizing human body action identification through a classifier, and judging whether a human body target falls down.
The time-frequency matrix diagram reflects the change of frequency along with time, and can represent frequency information in local time, particularly micro Doppler frequency generated by human body motion. The time frequency matrix diagram of the human body action is used as the input of the neural network, and the classification of different actions can be effectively realized by using the micro Doppler frequency information of different actions. A time frequency matrix image obtained through short-time Fourier transform contains a lot of noise and background clutter, so that the micro Doppler characteristic with weak energy is blurred, and the classification accuracy is low. Therefore, before being input into the neural network, the time-frequency matrix image needs to be subjected to noise reduction processing, and the human body motion micro-doppler characteristics in the time-frequency matrix image are enhanced. The traditional denoising method mainly comprises hard threshold filtering, principal component analysis and the like. These methods have a certain limitation in removing the time-frequency matrix noise. The hard threshold filtering removes noise and background clutter by setting a threshold, and retains the micro-doppler characteristics of motion. For a time frequency matrix diagram obtained in a complex wall-through environment, noise and background clutter energy in the time frequency matrix diagram are different, and the effect of hard threshold filtering is poor. Meanwhile, the energy components in the time-frequency matrix diagram do not have low-rank sparsity, so the processing effect of the principal component analysis method is poor.
As shown in FIG. 2, the present invention employs an adaptive threshold method for filtering, wherein the filtering method is to set a non-zero element in each column of the time-frequency matrixAnd setting the nonzero element smaller than the threshold value to be 0 and simultaneously reserving other nonzero elements by proper threshold values to realize the filtering purpose. Compared with the traditional hard threshold filtering method, the self-adaptive threshold filtering method is more accurate, and the specific method comprises the following steps: calculating the maximum value M and the minimum value M of non-zero elements in a column of the matrix, presetting an adaptive factor mu and a threshold value T, wherein mu = M/M, and T = mu1(M + M) setting initial value of μ to μ0Calculating a corresponding threshold value T0And μ0= M/M, according to the threshold value T0Filtering the non-zero elements in the column in a way of being larger than T0Is less than T0Is regarded as 0, then the sum S of the filtered non-zero elements in the column is calculated, and mu is calculated0Step by 0.01 to obtain mu1Calculating a corresponding threshold value T1According to the threshold value T1Filtering the row of non-zero elements, and calculating the sum S of the filtered row of non-zero elementstempCalculating S and StempIf the difference of (d) is not the maximum, mu1And step 0.01, repeating the steps, and outputting mu and T if the maximum value is reached.
At this time, the non-zero element of the column matrix may be filtered, and its expression is:
unlike hard threshold filtering, the threshold filtering of the present invention regards a time-frequency matrix diagram as a two-dimensional time-frequency matrix composed of different frequency components, and makes full use of the non-zero element value information at each position in the matrix. Since the frequency components contained in the time-frequency matrix diagram are complex, it is not appropriate to set a single global threshold for filtering. Therefore, the current threshold needs to be adaptively set according to the frequency components at different moments, so as to eliminate noise and background clutter at the current moment. By adaptively setting a corresponding threshold for the frequency component at each time, global filtering can be achieved. Noise and background clutter in the filtered time frequency matrix image are effectively suppressed, and micro Doppler characteristics of human body actions are highlighted.
The infrared camera shoots an infrared image of a monitoring area where a human body target is located and transmits the infrared image to the main server, the sound pickup receives sound and measures sound pressure, and a measurement result of the sound pressure is converted into a voice signal and transmitted to the main server; the main server processes the infrared image of the human body target to obtain the body temperature information of the human body target, and meanwhile, performs feature extraction on the voice signal to obtain the voice information of the human body target.
If the general server judges whether the respiration, heartbeat or body temperature information of the human body target is in an abnormal state, if the respiration, heartbeat or body temperature information of the human body target is judged to be abnormal, alarm information is sent to a user; comparing the extracted sound signal with the sound prestored in the cough, and sending alarm information to a user if the duration or frequency of the cough sent by the human body target is detected to be abnormal; the main server realizes human body action recognition through the recognition radar, and sends alarm information to the user if the human body target falls down.
The scheme of the invention achieves more accurate detection in the aspect of home old people detection, the system can detect the position state of the old people in each room of the whole house by one set of radar detection system when the system is at home, the influence of detection signals can not be weakened by the blocking of a room partition wall, the activity state of a human body can be automatically identified, the detection accuracy is higher, and a larger monitoring range is realized.
The ultra-wideband radar of the chaotic signal is adopted for imaging and displaying of the target, the ultra-wideband radar has good distance resolution and large bandwidth range of the chaotic signal with side lobe compression capacity, and the ultra-wideband chaotic signal-based wall-penetrating radar detection system is realized. The distribution of the indoor functional areas of the house acquired by the main server system can accurately acquire rooms and areas where the old people are located through a monitoring or display system, and the monitoring effect on the large-scale old people state of a large-scale old age care system or a hospital is better.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (9)
1. An indoor positioning detection method is characterized by comprising the following steps:
step 1, generating a chaotic signal, dividing the chaotic signal into two paths, wherein one path is a first detection signal, and the other path is a first reference signal, and transmitting the first detection signal;
step 2, receiving a first echo signal formed by reflection of the first detection signal, determining whether a human body target exists, and executing step 3 if the first echo signal formed by reflection of the human body target is received;
step 3, performing cross-correlation operation on the first echo signal and a first module reference signal, calculating the round-trip time of the detection signal reflected by a target, determining the distance between the detection signal and the human body target, and executing step 4;
step 4, generating a local oscillator signal, dividing the local oscillator signal into two paths, wherein one path is a second detection signal, the other path is a second reference signal, performing frequency mixing on the first detection signal and the second detection signal to obtain a frequency mixing signal, sending the frequency mixing signal from a transmitting module, and then executing step 5;
step 5, receiving a second echo signal formed by the reflection of a second detection signal by a personnel target, acquiring amplitude and phase information reflecting the difference between a second reference signal and the second echo signal, and performing short-time Fourier transform through the amplitude and phase change information of the second detection signal to acquire a time-frequency matrix diagram;
step 6, filtering the time frequency matrix diagram: setting a specific threshold value for each row of non-zero elements in a time-frequency matrix, judging whether the non-zero elements of the row are smaller than the corresponding specific threshold value, if so, setting the non-zero elements to be 0, and if not, reserving the non-zero elements, wherein the specific threshold value is obtained by multiplying the Sum Sum of the maximum value and the minimum value of the non-zero elements of the row by a self-adaptive factor mu;
and 7, performing corresponding feature extraction on the time frequency matrix diagram, realizing human body action identification through a classifier, and judging whether a human body target falls down.
2. The indoor positioning detection method according to claim 1, wherein the adaptive factor μ in the step 6 is calculated by: calculating the maximum value M and the minimum value M of non-zero elements in a column of the matrix, presetting an adaptive factor mu and a threshold value T, wherein mu = M/M, and T = mu1(M + M) setting initial value of μ to μ0Calculating a corresponding threshold value T0And μ0= M/M, according to the threshold value T0Filtering the non-zero elements in the column in a way of being larger than T0Is less than T0Is regarded as 0, then the sum S of the filtered non-zero elements in the column is calculated, and mu is calculated0Step by 0.01 to obtain mu1Calculating a corresponding threshold value T1According to the threshold value T1Filtering the row of non-zero elements, and calculating the sum S of the filtered row of non-zero elementstempCalculating S and StempIf the difference of (d) is not the maximum, mu1And step 0.01, repeating the steps, and outputting mu if the maximum value is reached.
3. The indoor location detection method of claim 1, wherein the first echo signal is received by an ultra-wideband receiving antenna and the second echo signal is received by a solid-state microwave sensor.
4. The indoor positioning detection method according to claim 1, wherein whether the human body target falls down is judged according to the speed and the acceleration value of the human body target, if the speed and the acceleration value of the human body target exceed preset thresholds, the human body target is judged to fall down, and if the human body target is judged to fall down, alarm information is sent to a user.
5. An indoor positioning detection system based on the indoor positioning detection method of any one of claims 1 to 4, comprising a main server and a detection radar connected with the main server, wherein the main server obtains a design drawing, and the design drawing comprises indoor functional area distribution of a house:
the detection radar comprises a signal generation module, a transmitting module, an ultra-wideband receiving module, an echo signal processing module and a microwave sensor;
the signal generating module is used for sending a chaotic signal or a mixing signal formed by mixing the chaotic signal and a local oscillator signal, the mixing signal is divided into two paths of signals, namely a detection signal and a reference signal, the detection signal is input into the transmitting module, and the reference signal is input into the echo signal processing module;
the transmitting module is used for transmitting the detection signal;
the ultra-wideband receiving module is used for receiving a first echo signal formed by a detection signal reflected by a person target and inputting the first echo signal into the echo signal processing module;
the microwave sensor is used for receiving a second echo signal formed by reflection of a detection signal personnel target and inputting the second echo signal into the echo signal processing module;
the echo signal processing module is used for acquiring round-trip time data and direction data of a detection signal through a first echo signal and inputting the data into the main server; the second echo signal is used for acquiring amplitude and phase change data of the detection signal and inputting the data into the main server;
the main server determines the position of the human body target through the round trip time data and the direction data, records the position data, obtains the position change of the personnel target within a period of time, and obtains the activity state of the human body target; and the general server performs Fourier transform through the amplitude and phase change information of the second detection signal to obtain the human body action information.
6. The indoor positioning detection system according to claim 5, wherein the signal generation module includes a first signal generation unit, a second signal generation unit, a mixing unit, a first signal splitting unit, and a second signal splitting unit; the first signal generating unit is used for generating chaotic signals and dividing the chaotic signals into two paths of signals through the first signal shunting unit, wherein one path of signals is used as first detection signals and input into the frequency mixing unit, and the other path of signals is used as first reference signals and input into the echo signal processing module; the second signal generating unit is used for generating a local oscillation signal, and the local oscillation signal is divided into two paths of signals by the second signal dividing unit, wherein one path of signals is used as a second detection signal and input into the frequency mixing unit, and the other path of signals is used as a second reference signal and input into the echo signal processing module; the frequency mixing unit is used for mixing the first detection signal and the second detection signal to obtain a frequency mixing signal, and inputting the frequency mixing signal to the transmitting module.
7. The indoor positioning detection system according to claim 6, wherein the echo signal processor is configured to perform a cross-correlation operation on the first echo signal and the first reference signal, and calculate a round-trip time of the probe signal reflected by the target; and the second reference signal is used for interfering the second echo signal, detecting the interfered signal and transmitting the detected amplitude and phase change information to the master server.
8. The indoor positioning detection system of claim 5, further comprising a demodulator, a power amplifier and a low noise amplifier, wherein the power amplifier is used for power amplifying the mixed signal, and the mixed signal is emitted through the transmitting module; the detection signal irradiates a human target and is reflected to return to form an echo signal, the first echo signal received by the ultra-wideband receiving module is amplified by the low-noise amplifier and then input into the demodulator, and the first echo signal is demodulated by the demodulator and then input into the echo signal processing module.
9. The indoor positioning detection system according to claim 5, wherein the main server is further connected with a recognition radar for recognizing human vital signs, the recognition radar transmits a recognition signal, the recognition signal is reflected by the human target to form a third echo signal, the recognition radar receives the third echo signal, and obtains a time frequency matrix diagram through short-time Fourier transform of the third echo signal, and performs corresponding feature extraction on the time frequency matrix diagram to obtain breathing and heartbeat information of the human target.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115529204A (en) * | 2022-10-08 | 2022-12-27 | 东莞理工学院 | Indoor control system and method |
| WO2024037385A1 (en) * | 2022-08-17 | 2024-02-22 | 华为技术有限公司 | Communication method and electronic device |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101339246A (en) * | 2008-08-08 | 2009-01-07 | 太原理工大学 | Chaos signal radar automobile anti-collision system and its method |
| CN101363914A (en) * | 2008-08-28 | 2009-02-11 | 太原理工大学 | Chaos colidar anti-collision system for automobile and method thereof |
| CN102608617A (en) * | 2012-03-09 | 2012-07-25 | 太原理工大学 | Chaotic laser-based ultra wide band throughwall radar detection device |
| CN104605886A (en) * | 2015-02-10 | 2015-05-13 | 中国科学院声学研究所 | Stridor detecting device and method |
| CN104678390A (en) * | 2015-03-10 | 2015-06-03 | 太原理工大学 | Ultra-wideband direct chaotic speed-measuring and ranging radar device based on heterodyne correlation method |
| CN104765031A (en) * | 2015-03-02 | 2015-07-08 | 太原理工大学 | Ultra-wide bandwidth microwave chaos life detection radar device |
| CN108876724A (en) * | 2017-05-10 | 2018-11-23 | 核工业北京地质研究院 | SAR images filter method based on self-adaptation three-dimensional Block- matching |
| CN110286368A (en) * | 2019-07-10 | 2019-09-27 | 北京理工大学 | A kind of Falls Among Old People detection method based on ULTRA-WIDEBAND RADAR |
| US20200064444A1 (en) * | 2015-07-17 | 2020-02-27 | Origin Wireless, Inc. | Method, apparatus, and system for human identification based on human radio biometric information |
| US20210247483A1 (en) * | 2015-07-17 | 2021-08-12 | Fengyu Wang | Method, apparatus, and system for wireless vital monitoring using high frequency signals |
| CN113384250A (en) * | 2021-05-26 | 2021-09-14 | 上海交通大学 | Low-power-consumption realization method of millimeter wave radar system for vital sign detection |
| CN113925475A (en) * | 2021-10-16 | 2022-01-14 | 谢俊 | Non-contact human health monitoring device and method |
-
2022
- 2022-04-19 CN CN202210409559.4A patent/CN114509749A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101339246A (en) * | 2008-08-08 | 2009-01-07 | 太原理工大学 | Chaos signal radar automobile anti-collision system and its method |
| CN101363914A (en) * | 2008-08-28 | 2009-02-11 | 太原理工大学 | Chaos colidar anti-collision system for automobile and method thereof |
| CN102608617A (en) * | 2012-03-09 | 2012-07-25 | 太原理工大学 | Chaotic laser-based ultra wide band throughwall radar detection device |
| CN104605886A (en) * | 2015-02-10 | 2015-05-13 | 中国科学院声学研究所 | Stridor detecting device and method |
| CN104765031A (en) * | 2015-03-02 | 2015-07-08 | 太原理工大学 | Ultra-wide bandwidth microwave chaos life detection radar device |
| CN104678390A (en) * | 2015-03-10 | 2015-06-03 | 太原理工大学 | Ultra-wideband direct chaotic speed-measuring and ranging radar device based on heterodyne correlation method |
| US20200064444A1 (en) * | 2015-07-17 | 2020-02-27 | Origin Wireless, Inc. | Method, apparatus, and system for human identification based on human radio biometric information |
| US20210247483A1 (en) * | 2015-07-17 | 2021-08-12 | Fengyu Wang | Method, apparatus, and system for wireless vital monitoring using high frequency signals |
| CN108876724A (en) * | 2017-05-10 | 2018-11-23 | 核工业北京地质研究院 | SAR images filter method based on self-adaptation three-dimensional Block- matching |
| CN110286368A (en) * | 2019-07-10 | 2019-09-27 | 北京理工大学 | A kind of Falls Among Old People detection method based on ULTRA-WIDEBAND RADAR |
| CN113384250A (en) * | 2021-05-26 | 2021-09-14 | 上海交通大学 | Low-power-consumption realization method of millimeter wave radar system for vital sign detection |
| CN113925475A (en) * | 2021-10-16 | 2022-01-14 | 谢俊 | Non-contact human health monitoring device and method |
Non-Patent Citations (3)
| Title |
|---|
| PIN-HENG CHEN等: "A Portable Real-Time Digital Noise Radar System for Through-the-Wall Imaging", 《IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING》 * |
| 王凡: "基于特征增强和浅层神经网络的穿墙雷达人体动作识别", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑(月刊)》 * |
| 郭超逸: "基于混沌相关和相位检测的生命探测雷达", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑(月刊)》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024037385A1 (en) * | 2022-08-17 | 2024-02-22 | 华为技术有限公司 | Communication method and electronic device |
| CN115529204A (en) * | 2022-10-08 | 2022-12-27 | 东莞理工学院 | Indoor control system and method |
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