CN111601253A - Passive passive intelligent tracking and positioning method and system, storage medium and tracking and positioning terminal - Google Patents
Passive passive intelligent tracking and positioning method and system, storage medium and tracking and positioning terminal Download PDFInfo
- Publication number
- CN111601253A CN111601253A CN202010247506.8A CN202010247506A CN111601253A CN 111601253 A CN111601253 A CN 111601253A CN 202010247506 A CN202010247506 A CN 202010247506A CN 111601253 A CN111601253 A CN 111601253A
- Authority
- CN
- China
- Prior art keywords
- signal
- human body
- tracking
- positioning
- passive
- 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.)
- Granted
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/0202—Channel estimation
- H04L25/0204—Channel estimation of multiple channels
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Power Engineering (AREA)
- Radar Systems Or Details Thereof (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention belongs to the technical field of wireless communication and artificial intelligence, and discloses a passive and passive intelligent tracking and positioning method, a system, a storage medium and a tracking and positioning terminal, wherein Wi-Fi equipment acquires CSI information; extracting dynamic propagation path components of each multipath signal by an inter-antenna CSI conjugate multiplication algorithm; estimating the signal parameters of the dynamic propagation path of each multipath signal by a channel parameter estimation algorithm; by combining an artificial intelligence algorithm, each Wi-Fi device estimates the motion trail of the human body independently; and fusing the human body motion tracks obtained by different Wi-Fi equipment in the fourth step by using a multi-equipment motion track fusion algorithm. The invention uses multi-channel parameter combination to track the human motion trail and combines with artificial intelligence algorithm, thus avoiding the problems of low precision and poor robustness in the prior art. The invention uses a plurality of Wi-Fi devices to jointly track the motion trail of the human body, thereby avoiding the problem of lower precision of human body tracking and positioning in the prior art and improving the precision of indoor passive human body tracking and positioning.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence and wireless communication, and particularly relates to a passive and passive intelligent tracking and positioning method, a passive and passive intelligent tracking and positioning system, a storage medium and a tracking and positioning terminal.
Background
In recent years, passive tracking and positioning technology without any equipment carried by a target attracts much attention, aims to track and position indoor human bodies, and has great development potential and wide application prospect in multiple aspects, wherein specific applications include the safety monitoring of old people and patients, smart home and the like. Most of the existing indoor target tracking methods require a target to carry special equipment or wearable equipment, but equipment carrying is unrealistic in some cases. Existing passive trajectory tracking and positioning technologies include ultrasonic, infrared, LED visible, Wi-Fi, and the like. Currently, the research of a tracking and positioning system based on Wi-Fi is always the focus of people, and the system only needs Wi-Fi equipment and is respectively arranged in different environments without additional infrastructure. Human bodies existing in indoor environments can affect the transmission environment of Wi-Fi signals to a certain degree, Channel State Information (CSI) can reflect the change condition of the Wi-Fi signals in a fine-grained mode, and basic motion and position information is extracted through signal reflection on human body targets. Passive tracking positioning is more challenging than active tracking positioning because the signal energy reflected by the human body is typically orders of magnitude weaker than the energy of the direct path signal and is typically superimposed with the signal reflected from walls, furniture, and other nearby clutter. It is difficult to extract useful, accurate positioning information from the reflected signals. An existing passive track-following positioning system, such as a Dynamic-MUSIC system proposed by Yasha Wang et al, university of colorado, performs passive tracking and positioning of a human body by extracting a signal arrival angle (AoA). However, most commercial Wi-Fi network cards only have three antennas and limited bandwidth, and accurate AoA estimation is difficult to achieve, so that it is difficult to accurately perform passive tracking and positioning on a human body in a complex indoor environment. The Indotrack system proposed by Zhantaqing et al of Beijing university realizes measurement of human body movement speed by measuring Doppler frequency shift, and realizes passive tracking and positioning by integrating the human body movement speed with time, but the common Wi-Fi equipment is difficult to realize accurate Doppler frequency shift measurement, so that the measurement precision of the human body movement speed is influenced, and a tracking and positioning result obtained by speed integrating operation generates a large accumulated error. And most of the existing passive track tracking and positioning systems are realized based on 2 Wi-Fi devices, are limited by the number of the Wi-Fi devices, and have lower tracking and positioning precision. In recent years, the development of artificial intelligence technology is rapid, and various technologies are combined with the artificial intelligence technology to generate various changes. Therefore, how to utilize multi-channel parameters (signal arrival angle, signal flight time and Doppler frequency shift) and combine with an artificial intelligence algorithm to realize passive tracking and positioning of a human body so as to reduce the influence of errors of the single-channel parameters on a positioning result; the method has important application value on how to realize passive tracking and positioning of the human body by utilizing a plurality of Wi-Fi devices.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) the existing Dynamic-MUSIC system, Indotack system and the like only carry out passive tracking and positioning on human bodies through single channel parameters (signal arrival angle or Doppler frequency shift), are limited by the estimation precision of the single channel parameters, and have lower tracking and positioning precision and poorer robustness.
(2) The existing Dynamic-MUSIC system, Indotack system and the like only use a small amount of Wi-Fi equipment to perform passive tracking and positioning on human bodies, are limited by the number of the Wi-Fi equipment, and have low tracking and positioning accuracy and poor robustness.
The difficulty in solving the above problems and defects is: how to use multi-channel parameters and an artificial intelligence algorithm to carry out mathematical modeling on the motion trail of the human body and realize passive tracking and positioning of the human body with high accuracy and strong robustness. How to effectively utilize a plurality of Wi-Fi devices to realize passive and passive tracking and positioning of a human body.
The significance of solving the problems and the defects is as follows: the passive tracking and positioning of the human body has very important significance for production and life, and can be widely applied to places such as families, markets, hospitals and the like. The technical problem is solved, and the passive tracking and positioning of the human body are realized by utilizing multi-channel parameters, an artificial intelligence algorithm and a plurality of Wi-Fi nodes, so that the requirements of low cost and high precision can be better met.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a passive and passive intelligent tracking and positioning method, a passive and passive intelligent tracking and positioning system, a storage medium and a tracking and positioning terminal.
The invention is realized in such a way that a passive intelligent tracking and positioning method comprises the following steps:
firstly, collecting CSI information by Wi-Fi equipment;
secondly, extracting the dynamic propagation path component of each multipath signal by a CSI conjugate multiplication algorithm among antennas;
thirdly, estimating the signal parameters of the dynamic propagation path of each multipath signal by a channel parameter estimation algorithm;
fourthly, combining with an artificial intelligence algorithm, and estimating the motion trail of the human body independently by each Wi-Fi device;
and fifthly, fusing the human body motion tracks obtained by different Wi-Fi devices in the fourth step by using a multi-device motion track fusion algorithm.
Further, the first step includes: acquiring the CSI information at the Wi-Fi device, where the CSI information H (i, j) at time i and on the jth (1, …, M) receiving antenna is represented as:
H(i,j)＝[H(i,j,1),H(i,j,2),...,H(i,j,k)]；
wherein k (1, …, N) is the subcarrier number, M is the Wi-Fi receiver antenna number, and N is the Wi-Fi receiver subcarrier number.
Further, the second step includes: selecting a first antenna of the Wi-Fi device as a reference antenna, and respectively carrying out conjugate multiplication on CSI information of other antennas of the same Wi-Fi device and the reference antenna:
C(m)＝H(m)*H*(m0)；
let m be (i, j, k), m0＝(i,j0,k)，j0＝1，(·)*Representing a conjugate operation. After conjugate multiplication, CSI information is divided into static path component and dynamic path component, which are respectively Ps,PdExpressed, then the result of conjugate multiplication is expressed as:
the first term in the formula is a static path component, which is filtered by a band-pass filter with a specific cut-off frequency, and after filtering, c (m) only remains a multipath signal dynamic path component, and the filtered multipath signal dynamic path component is denoted as h (m).
Further, the third step includes: using improved space alternative generalized expectation maximization (SAGE) algorithm to obtain dynamic path components h (m) of multipath signals, and applying a signal parameter matrix theta (theta) of dynamic propagation paths of each multipath signal to [ theta ═ theta1，θ2,θ3,…θl]An estimate is made of l being the number of signal propagation paths, where θl＝[Ωl,τl,υl,al]Angle of arrival omega of signallTime of flight of signal τlDoppler shift of signal upsilonlSignal complex amplitude alAnd (4) forming.
Further, the fourth step includes: the signal parameter matrix theta of the estimated signal propagation paths is [ theta ]1，θ2,θ3,…,θl]To identify the target signal reflection path signal parameter thetaTarAnd using thetaTarAnd tracking and positioning the motion trail of the human body, and after the tracking and positioning are finished, carrying out error check on a tracking and positioning result, such as tracking and positioning errors, and carrying out error correction processing by using an artificial intelligent algorithm.
Further comprising:
(1) in the signal propagation path, the complex amplitude a of the signallThe maximum signal propagation path is used as a target signal reflection path, and the signal parameter is thetaTar＝[ΩTar,τTar,υTar,aTar]；
(2) Solving for signal reflection path distance using kalman filterpThe distance tau of signal reflection path is taken from the measurement value z of Kalman filter systemTar× c, taking the distance change rate-upsilon of signal reflection path as the control quantity u of Kalman filter systemTar× lambda. itWhere c represents the speed of light and λ is the signal wavelength;
signal reflection path distance solved by Kalman filterpThe following relationship exists with the true signal reflection path distance:
range＝rangep+dist；
wherein dist is the linear distance between the receiving end and the transmitting end of the Wi-Fi device, and the human body track coordinates (x, y) are derived from the following formula:
wherein the content of the first and second substances,
ψris the antenna azimuth angle (x) of the Wi-Fi receiverr,yr) Wi-Fi receiver coordinates;
(3) calculating the change rate a of the human body movement speed v and the human body movement speed, wherein (x ', y') is a human body track coordinate before t seconds, and (x, y) is a human body track coordinate at the current moment:
(4) human body movement speed v, human body movement speed change rate a and human body reasonable movement speed v under normal conditionThrReasonable rate of change a of the movement speed of the human bodyThrA comparison is made. If v > vThrOr a > aThrIf the human body movement speed or acceleration is determined to be abnormal, turning to (5), otherwise, turning to (8);
(5) extracting occurrences of v > v using an artificial intelligence algorithm Support Vector Machine (SVM)ThrOr a > aThrThe time range T is used as the abnormal time interval of the human body movement speed/acceleration;
(6) for a signal parameter matrix theta ═ theta in the time range T1，θ2,θ3,…,θl]Selecting the complex amplitude a of the signallThe signal path with the i +1 th maximum is used as the target signal reflection path (i is the initial value1) Turning to (2), recalculating the human motion track in the time range T;
(7) the calculation result of the previous step is called a new track, the human motion track which is not recalculated in the previous step is called an old track, the new track and the old track are spliced, and the operation goes to (3);
(8) and obtaining the human motion track after error correction.
Further, the fifth step includes:
(1) calculating human motion track confidence ξ obtained by the ith station Wi-Fi deviceiWherein u is1u2Is a constant value, u, related to the environmenti,TarAnd Ωi,TarThe signal Doppler frequency shift and the signal arrival angle of a target signal reflection path of the ith Wi-Fi device are obtained;
(2) the confidence ξ of the human motion track coordinateiObtaining the human body motion track dynamic weight u of the ith station Wi-Fi equipmentiWherein R is the total number of Wi-Fi devices participating in human motion trajectory tracking:
(3) using dynamic weights uiCalculating the human body motion track (x) obtained by each Wi-Fi device in the step fouri,yi) And carrying out dynamic weighted fusion to obtain a fused motion track (x, y):
it is another object of the present invention to provide a program storage medium for receiving user input, the stored computer program causing an electronic device to perform the steps comprising:
firstly, collecting CSI information by Wi-Fi equipment;
secondly, extracting the dynamic propagation path component of each multipath signal by a CSI conjugate multiplication algorithm among antennas;
thirdly, estimating the signal parameters of the dynamic propagation path of each multipath signal by a channel parameter estimation algorithm;
fourthly, combining with an artificial intelligence algorithm, and estimating the motion trail of the human body independently by each Wi-Fi device;
and fifthly, fusing the human body motion tracks obtained by different Wi-Fi devices in the fourth step by using a multi-device motion track fusion algorithm.
Another object of the present invention is to provide a passive tracking and positioning system implementing the passive tracking and positioning method, the passive tracking and positioning system comprising:
the information acquisition module is used for realizing acquisition of CSI information by the Wi-Fi equipment;
the signal dynamic propagation path extraction module is used for realizing that a CSI dynamic signal path extraction algorithm extracts the dynamic propagation path of each multi-path signal;
the signal parameter estimation module is used for estimating the signal parameters of the dynamic propagation paths of the multipath signals by a channel parameter estimation algorithm;
the motion trail estimation module is used for independently estimating the motion trail of the human body by each Wi-Fi device;
and the motion track fusion module is used for fusing the human motion tracks obtained by different Wi-Fi devices by using a multi-device motion track fusion algorithm.
The invention also aims to provide an intelligent tracking and positioning terminal, wherein the passive tracking and positioning system is installed on the tracking and positioning terminal, and the tracking and positioning terminal is installed on an intelligent medical control platform, a security monitoring control platform, an emergency rescue platform or an intelligent household application platform.
By combining all the technical schemes, the invention has the advantages and positive effects that: because the invention uses multi-channel parameters (signal arrival angle AoA, signal flight time ToF and signal Doppler frequency shift DFS) and combines with artificial intelligence algorithm to jointly track the motion trail of the human body, the precision is high, and the problem that the passive tracking positioning precision of the traditional method is limited by the single-channel parameter estimation precision is solved. The invention uses a multi-device motion track fusion algorithm to fuse the motion track tracking results obtained by a plurality of Wi-Fi devices, has high precision and overcomes the problem of lower precision of the traditional method for tracking and positioning the human body by only using 2 Wi-Fi devices.
The invention aims to solve the problem that the passive tracking and positioning precision of the prior passive human body tracking and positioning technology is limited by the single-channel parameter estimation precision, and uses multi-channel parameters (signal arrival angle AoA, signal flight time ToF and signal Doppler frequency shift DFS) and combines an artificial intelligence algorithm to jointly track the motion trail of the human body. The invention solves the problem that the prior art only uses 2 Wi-Fi devices to perform human body tracking and positioning with lower precision, and adopts a multi-device motion track fusion algorithm to perform dynamic weighted fusion on motion track tracking results obtained by a plurality of Wi-Fi devices, thereby further improving the track tracking precision.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly described below, and it is apparent that the drawings described below are only some embodiments of the present application, and for a person of ordinary skill in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a passive tracking and positioning method according to an embodiment of the present invention.
Fig. 2 is a flowchart of a human motion trajectory estimation module according to an embodiment of the present invention.
FIG. 3 is a flow chart of a multi-device trajectory fusion algorithm module provided by an embodiment of the present invention.
Fig. 4 is a comparison graph of the simulation results of tracking and positioning errors of two existing indoor passive human body tracking and positioning methods provided by the embodiment of the invention.
FIG. 5 is a comparison graph of simulation results of different numbers of Wi-Fi devices influencing a trajectory tracking result when an experimental environment is unchanged, according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a passive and passive intelligent tracking and positioning method, a system, a storage medium and a tracking and positioning terminal, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the passive tracking intelligent positioning method provided by the present invention includes the following steps:
s101: collecting CSI information through existing Wi-Fi equipment;
s102: extracting dynamic propagation paths of each multi-path signal through a CSI dynamic signal path extraction algorithm;
s103: estimating the signal parameters of the dynamic propagation path of each multipath signal by a channel parameter estimation algorithm;
s104: by combining an artificial intelligence algorithm, each Wi-Fi device estimates the motion trail of the human body independently;
s105: and fusing the human body motion tracks obtained by different Wi-Fi devices by using a multi-device motion track fusion algorithm.
The passive intelligent tracking and positioning system provided by the invention comprises:
the information acquisition module is used for realizing acquisition of CSI information by the Wi-Fi equipment;
the signal dynamic propagation path extraction module is used for realizing that a CSI dynamic signal path extraction algorithm extracts the dynamic propagation path of each multi-path signal;
the signal parameter estimation module is used for estimating the signal parameters of the dynamic propagation paths of the multipath signals by a channel parameter estimation algorithm;
the motion trail estimation module is used for independently estimating the motion trail of the human body by each Wi-Fi device;
and the motion track fusion module is used for fusing the human motion tracks obtained by different Wi-Fi devices by using a multi-device motion track fusion algorithm.
The technical solution of the present invention is further described below with reference to the accompanying drawings.
The passive intelligent tracking and positioning method provided by the invention specifically comprises the following steps:
step one, obtaining CSI information at a Wi-Fi device, where at time i, CSI information H (i, j) on a jth (1, …, M) receiving antenna may be represented as:
H(i,j)＝[H(i,j,1),H(i,j,2),...,H(i,j,k)]；
wherein k (1, …, N) is the subcarrier number, M is the Wi-Fi receiver antenna number, and N is the Wi-Fi receiver subcarrier number.
Selecting a first antenna of the Wi-Fi device as a reference antenna, and respectively performing conjugate multiplication on CSI information of other antennas of the same Wi-Fi device and the reference antenna:
C(m)＝H(m)*H*(m0)；
let m be (i, j, k), m0＝(i,j0,k)，j0＝1，(·)*Representing a conjugate operation. After conjugate multiplication, CSI information is divided into static path component and dynamic path component, which are respectively Ps,PdExpressed, the result of conjugate multiplication can be expressed as:
the first term in the above equation is a static path component, which can be filtered by a band-pass filter with a specific cut-off frequency, c (m) after filtering only remains a dynamic path component of the multipath signal, and the dynamic path component of the multipath signal after filtering is denoted as h (m).
Step three, using the obtained multipath signal dynamic path component h (m) to apply an improved space alternative generalized expectation maximization (SAGE) algorithm to obtain a signal parameter matrix theta ═ theta of the dynamic propagation path of each multipath signal1，θ2,θ3,…θl]An estimate is made of l being the number of signal propagation paths, where θl＝[Ωl,τl,υl,al]Angle of arrival omega of signallTime of flight of signal τlDoppler shift of signal upsilonlSignal complex amplitude alAnd (4) forming.
Step four, the signal parameter matrix theta of the estimated signal propagation paths is [ theta ]1，θ2,θ3,…,θl]To identify the target signal reflection path signal parameter thetaTarAnd using thetaTarAnd tracking and positioning the motion trail of the human body, and after the tracking and positioning are finished, carrying out error check on a tracking and positioning result, such as tracking and positioning errors, and carrying out error correction processing by using an artificial intelligent algorithm. As shown in fig. 2, the specific implementation is as follows:
(1) in the signal propagation path, the complex amplitude a of the signallThe maximum signal path is used as the target signal reflection path, and the signal parameter is thetaTar＝[ΩTar,τTar,υTar,aTar]。
(2) Solving for signal reflection path distance using kalman filterpThe distance tau of signal reflection path is taken from the measurement value z of Kalman filter systemTar× c, taking the distance change rate-upsilon of signal reflection path as the control quantity u of Kalman filter systemTar× λ, where c represents the speed of light and λ is the signal wavelength.
Signal reflection path distance solved by Kalman filterpThe following relationship exists with the true signal reflection path distance:
range＝rangep+dist；
and dist is the linear distance between the receiving end and the transmitting end of the Wi-Fi equipment. At this time, the coordinates (x, y) of the body trajectory obtained by the Wi-Fi device can be derived by the following formula:
wherein the content of the first and second substances,
ψrantenna side for Wi-Fi receiverOrientation angle (x)r,yr) Coordinates of a Wi-Fi receiver.
(3) Calculating the change rate a of the human body motion speed v and the human body speed, wherein (x ', y') is the human body track coordinate before t seconds, and (x, y) is the human body track coordinate at the current moment:
(4) the human body movement speed v, the human body speed change rate a and the reasonable human body movement speed v under the normal conditionThrReasonable rate of change of speed of human body aThrA comparison is made. If v > vThrOr a > aThrIf the speed or acceleration of the human body is abnormal, the step goes to (5), otherwise, the step goes to (8).
(5) Extracting v > v using artificial intelligence algorithm Support Vector Machine (SVM)ThrOr a > aThrAnd the time range T is used as the abnormal time interval of the human body movement speed/acceleration.
(6) For a signal parameter matrix theta ═ theta in the time range T1，θ2,θ3,…,θl]Selecting the complex amplitude a of the signallAnd (3) taking the signal path with the (i + 1) th maximum as a target signal reflection path (i is the initial value of 1), turning to the step (2), and recalculating the human motion track in the time range T.
(7) The calculation result of the previous step is called a new track, the human motion track which is not recalculated in the previous step is called an old track, and the new track and the old track are spliced. Go to (3).
(8) And obtaining the human motion track (x, y) after error correction.
And step five, fusing the human body motion trail obtained by each Wi-Fi device in the step four by using a self-adaptive weighted fusion algorithm to obtain a final human body motion trail result. The specific implementation of this step is as follows:
(1) calculating human motion track confidence ξ obtained by the ith station Wi-Fi deviceiWherein u is1u2Is a constant value, u, related to the environmenti,TarAnd Ωi,TarIs as followsSignal Doppler shift and signal arrival angle of target signal reflection path of i Wi-Fi equipment:
(2) the confidence ξ of the human motion track coordinateiObtaining the dynamic weight u of the human body motion track of the ith station Wi-Fi equipmentiWherein R is the total number of Wi-Fi devices participating in human motion tracking and positioning:
(3) using dynamic weights uiHuman motion trajectory (x) calculated for each Wi-Fi devicei,yi) And carrying out dynamic weighted fusion to obtain a fused motion track (x, y):
the technical effects of the present invention will be described in detail with reference to simulations.
Firstly, simulation conditions: four pieces of Wi-Fi equipment are arranged in an indoor space with a visual distance of 10m by 10m, each piece of Wi-Fi equipment is provided with three omnidirectional antennas, CSI is collected, and passive tracking and positioning of an indoor human body are carried out.
Secondly, simulating contents and results:
As seen in FIG. 4, the positioning error of the method of the invention is obviously superior to that of a Dynamic-MUSIC, Indotack indoor human passive tracking and positioning system, and compared with the existing passive tracking and positioning mode, the precision is obviously improved.
And 2, simulating the positioning errors of different amounts of used Wi-Fi equipment by using the method of the invention, wherein the experimental environment is unchanged, and the result is shown in figure 5.
As seen in fig. 5, passive tracking location performance is related to the number of Wi-Fi devices used, the more Wi-Fi devices used, the smaller the location error.
The technical effects of the present invention will be described in detail with reference to experiments.
The system performance is evaluated through a series of experimental simulations, experimental design and verification are carried out on the provided passive and intelligent tracking and positioning of the indoor human body, and the experimental result is analyzed in detail.
Through experimental comparison, the scheme of the invention is compared with the existing Dynamic-MUSIC and Indotack indoor human passive tracking and positioning scheme, and the positioning errors are counted, wherein the positioning errors are within 0.75m under 90% of the scheme of the invention, within 1.3m under 90% of the Indotack track tracking scheme, and within 1.7m under 90% of the Dynamic-MUSIC track tracking scheme. Therefore, the scheme of the patent is obviously superior to a Dynamic-MUSIC and Indotrack indoor human passive tracking and positioning scheme.
When the scheme of the invention uses 2 pieces of Wi-Fi equipment to perform passive tracking and positioning, the positioning error is mostly within 0.81m, 3 pieces of Wi-Fi equipment are used, the positioning error is mostly within 0.65m, 4 pieces of Wi-Fi equipment are used, and the positioning error is mostly within 0.53 m. The greater the number of Wi-Fi devices used, the higher the positioning accuracy. Therefore, compared with the traditional track tracking scheme, the scheme of the invention can utilize a plurality of Wi-Fi devices to perform passive tracking and positioning on the human body, and obviously improves the positioning precision.
Through the simulation and the experiment, the scheme of the invention effectively solves the problem that the human body passive tracking and positioning technology only uses 2 Wi-Fi devices to perform human body tracking and positioning with lower precision, and effectively solves the problem that the human body passive tracking and positioning precision is limited by single-channel parameter estimation precision.
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is defined by the appended claims.
Claims (10)
1. A passive intelligent tracking and positioning method is characterized by comprising the following steps:
firstly, collecting CSI information by Wi-Fi equipment;
secondly, extracting the dynamic propagation path component of each multipath signal by a CSI conjugate multiplication algorithm among antennas;
thirdly, estimating the signal parameters of the dynamic propagation path of each multipath signal by a channel parameter estimation algorithm;
fourthly, combining with an artificial intelligence algorithm, and estimating the motion trail of the human body independently by each Wi-Fi device;
and fifthly, fusing the human body motion tracks obtained by different Wi-Fi devices in the fourth step by using a multi-device motion track fusion algorithm.
2. The passive tracking positioning method of claim 1, wherein said first step comprises: acquiring the CSI information at the Wi-Fi device, where the CSI information H (i, j) at time i and on the jth (1, …, M) receiving antenna is represented as:
H(i,j)＝[H(i,j,1),H(i,j,2),...,H(i,j,k)]；
wherein k (1, …, N) is the subcarrier number, M is the Wi-Fi receiver antenna number, and N is the Wi-Fi receiver subcarrier number.
3. The passive tracking positioning method of claim 1, wherein said second step comprises: selecting a first antenna of the Wi-Fi device as a reference antenna, and respectively carrying out conjugate multiplication on CSI information of other antennas of the same Wi-Fi device and the reference antenna:
C(m)＝H(m)*H*(m0)；
let m be (i, j, k), m0＝(i,j0,k)，j0＝1，(·)*Representing conjugate operation, dividing CSI information into static path component and dynamic path component after conjugate multiplication, respectively using Ps,PdExpressed, then the result of conjugate multiplication is expressed as:
the first term in the formula is a static path component, which is filtered by a band-pass filter with a specific cut-off frequency, and after filtering, c (m) only remains a multipath signal dynamic path component, and the filtered multipath signal dynamic path component is denoted as h (m).
4. The passive tracking positioning method of claim 1, wherein said third step comprises: using improved space alternative generalized expectation maximization (SAGE) algorithm to obtain dynamic path components h (m) of multipath signals, and applying a signal parameter matrix theta (theta) of dynamic propagation paths of each multipath signal to [ theta ═ theta1，θ2,θ3,…θl]An estimate is made of l being the number of signal propagation paths, where θl＝[Ωl,τl,υl,al]Angle of arrival omega of signallTime of flight of signal τlDoppler shift of signal upsilonlSignal complex amplitude alAnd (4) forming.
5. The passive tracking positioning method of claim 1, wherein said fourth step comprises: the signal parameter matrix theta of the estimated signal propagation paths is [ theta ]1，θ2,θ3,…,θl]To identify the target signal reflection path signal parameter thetaTarAnd using thetaTarAnd tracking and positioning the motion trail of the human body, and after the tracking and positioning are finished, carrying out error check on a tracking and positioning result, such as tracking and positioning errors, and carrying out error correction processing by using an artificial intelligent algorithm.
6. The passive tracking positioning method of claim 5, further comprising:
(1) in the signal propagation path, the complex amplitude a of the signallThe maximum signal propagation path is used as a target signal reflection path, and the signal parameter is thetaTar＝[ΩTar,τTar,υTar,aTar]；
(2) Solving for signal reflection path distance using kalman filterpThe distance tau of signal reflection path is taken from the measurement value z of Kalman filter systemTar× c, taking the distance change rate-upsilon of signal reflection path as the control quantity u of Kalman filter systemTar× λ, where c represents the speed of light and λ is the signal wavelength;
signal reflection path distance solved by Kalman filterpThe following relationship exists with the true signal reflection path distance:
range＝rangep+dist；
wherein dist is the linear distance between the receiving end and the transmitting end of the Wi-Fi device, and the human body track coordinates (x, y) are derived from the following formula:
wherein the content of the first and second substances,
ψris the antenna azimuth angle (x) of the Wi-Fi receiverr,yr) Wi-Fi receiver coordinates;
(3) calculating the change rate a of the human body movement speed v and the human body movement speed, wherein (x ', y') is a human body track coordinate before t seconds, and (x, y) is a human body track coordinate at the current moment:
(4) human body movement speed v, human body movement speed change rate a and human body reasonable movement speed v under normal conditionThrReasonable rate of change a of the movement speed of the human bodyThrMaking a comparison if v > vThrOr a > aThrIf the human body movement speed or acceleration is determined to be abnormal, turning to (5), otherwise, turning to (8);
(5) extracting occurrences of v > v using an artificial intelligence algorithm Support Vector Machine (SVM)ThrOr a > aThrThe time range T is used as the abnormal time interval of the human body movement speed/acceleration;
(6) for a signal parameter matrix theta ═ theta in the time range T1，θ2,θ3,…,θl]Selecting the complex amplitude a of the signallTaking the signal path with the (i + 1) th maximum as a target signal reflection path (i is the initial value of 1), turning to the step (2), and recalculating the human motion track within the time range T;
(7) the calculation result of the previous step is called a new track, the human motion track which is not recalculated in the previous step is called an old track, the new track and the old track are spliced, and the operation goes to (3);
(8) and obtaining the human motion track after error correction.
7. The passive tracking positioning method of claim 1, wherein said fifth step comprises:
(1) calculating human motion track confidence ξ obtained by the ith station Wi-Fi deviceiWherein u is1u2Is a constant value, u, related to the environmenti,TarAnd Ωi,TarThe signal Doppler frequency shift and the signal arrival angle of a target signal reflection path of the ith Wi-Fi device are obtained;
(2) the confidence ξ of the human motion track coordinateiObtaining the human body motion track dynamic weight u of the ith station Wi-Fi equipmentiWherein R is the total number of Wi-Fi devices participating in human motion trajectory tracking:
(3) using dynamic weights uiCalculating the human body motion track (x) obtained by each Wi-Fi device in the step fouri,yi) And carrying out dynamic weighted fusion to obtain a fused motion track (x, y):
8. a program storage medium for receiving user input, the stored computer program causing an electronic device to perform the steps comprising:
firstly, collecting CSI information by Wi-Fi equipment;
secondly, extracting the dynamic propagation path component of each multipath signal by a CSI conjugate multiplication algorithm among antennas;
thirdly, estimating the signal parameters of the dynamic propagation path of each multipath signal by a channel parameter estimation algorithm;
fourthly, combining with an artificial intelligence algorithm, and estimating the motion trail of the human body independently by each Wi-Fi device;
and fifthly, fusing the human body motion tracks obtained by different Wi-Fi devices in the fourth step by using a multi-device motion track fusion algorithm.
9. A passive tracking and positioning system for implementing the passive intelligent tracking and positioning method according to any one of claims 1 to 7, wherein the passive tracking and positioning system comprises:
the information acquisition module is used for realizing acquisition of CSI information by the Wi-Fi equipment;
the signal dynamic propagation path extraction module is used for realizing that a CSI dynamic signal path extraction algorithm extracts the dynamic propagation path of each multi-path signal;
the signal parameter estimation module is used for estimating the signal parameters of the dynamic propagation paths of the multipath signals by a channel parameter estimation algorithm;
the motion trail estimation module is used for independently estimating the motion trail of the human body by each Wi-Fi device;
and the motion track fusion module is used for fusing the human motion tracks obtained by different Wi-Fi devices by using a multi-device motion track fusion algorithm.
10. A tracking and positioning terminal, characterized in that the passive and passive tracking and positioning system of claim 9 is installed on the tracking and positioning terminal, and the tracking and positioning terminal is installed on an intelligent medical control platform, a security monitoring control platform, an emergency rescue platform or an intelligent household application platform.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010247506.8A CN111601253B (en) | 2020-03-31 | 2020-03-31 | Passive passive intelligent tracking and positioning method and system, storage medium and tracking and positioning terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010247506.8A CN111601253B (en) | 2020-03-31 | 2020-03-31 | Passive passive intelligent tracking and positioning method and system, storage medium and tracking and positioning terminal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111601253A true CN111601253A (en) | 2020-08-28 |
| CN111601253B CN111601253B (en) | 2021-08-10 |
Family
ID=72190362
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010247506.8A Active CN111601253B (en) | 2020-03-31 | 2020-03-31 | Passive passive intelligent tracking and positioning method and system, storage medium and tracking and positioning terminal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111601253B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112330924A (en) * | 2020-11-05 | 2021-02-05 | 北京理工大学 | Falling event detection method based on channel state information in indoor environment |
| CN112926639A (en) * | 2021-02-20 | 2021-06-08 | 浙江大华技术股份有限公司 | Trajectory tracking method and device, storage medium and electronic device |
| CN113453180A (en) * | 2021-06-06 | 2021-09-28 | 西安电子科技大学 | Intelligent detection method and system for human body tumble and information data processing terminal |
| US20220078050A1 (en) * | 2018-12-17 | 2022-03-10 | U-Blox Ag | Estimating one or more characteristics of a communications channel |
| WO2022088036A1 (en) * | 2020-10-30 | 2022-05-05 | 网络通信与安全紫金山实验室 | Terminal positioning method and apparatus, and computer device and storage medium |
| CN116347357A (en) * | 2023-04-17 | 2023-06-27 | 天津大学 | Indoor wireless passive positioning method for embedding motion limitation into neural network |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105933867A (en) * | 2016-06-23 | 2016-09-07 | 长沙学院 | Passive real-time indoor two-anchor-point positioning method based on channel state information |
| CN106452687A (en) * | 2016-11-18 | 2017-02-22 | 清华大学 | System and method for recognizing motion direction in motion sensing game |
| CN107171749A (en) * | 2017-07-17 | 2017-09-15 | 北京大学 | The method for determining the Doppler frequency shift for the wireless signal that moving object is directly reflected |
| CN108957399A (en) * | 2018-07-05 | 2018-12-07 | 北京航空航天大学 | A method of motion detection is realized using LTE signaling channel status information |
| CN109633534A (en) * | 2019-01-18 | 2019-04-16 | 重庆邮电大学 | A kind of micro-doppler estimation method for passive target motion tracking |
| CN110809240A (en) * | 2019-11-05 | 2020-02-18 | 重庆邮电大学 | Indoor target passive tracking method based on WiFi multi-dimensional parameter characteristics |
| CN110855382A (en) * | 2019-12-02 | 2020-02-28 | 北京邮电大学 | Method and device for detecting human body activity data based on CSI (channel State information), and electronic equipment |
-
2020
- 2020-03-31 CN CN202010247506.8A patent/CN111601253B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105933867A (en) * | 2016-06-23 | 2016-09-07 | 长沙学院 | Passive real-time indoor two-anchor-point positioning method based on channel state information |
| CN106452687A (en) * | 2016-11-18 | 2017-02-22 | 清华大学 | System and method for recognizing motion direction in motion sensing game |
| CN107171749A (en) * | 2017-07-17 | 2017-09-15 | 北京大学 | The method for determining the Doppler frequency shift for the wireless signal that moving object is directly reflected |
| CN108957399A (en) * | 2018-07-05 | 2018-12-07 | 北京航空航天大学 | A method of motion detection is realized using LTE signaling channel status information |
| CN109633534A (en) * | 2019-01-18 | 2019-04-16 | 重庆邮电大学 | A kind of micro-doppler estimation method for passive target motion tracking |
| CN110809240A (en) * | 2019-11-05 | 2020-02-18 | 重庆邮电大学 | Indoor target passive tracking method based on WiFi multi-dimensional parameter characteristics |
| CN110855382A (en) * | 2019-12-02 | 2020-02-28 | 北京邮电大学 | Method and device for detecting human body activity data based on CSI (channel State information), and electronic equipment |
Non-Patent Citations (1)
| Title |
|---|
| 冷媛: "基于CSI的入侵检测与室内定位技术研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220078050A1 (en) * | 2018-12-17 | 2022-03-10 | U-Blox Ag | Estimating one or more characteristics of a communications channel |
| US11601307B2 (en) * | 2018-12-17 | 2023-03-07 | U-Blox Ag | Estimating one or more characteristics of a communications channel |
| WO2022088036A1 (en) * | 2020-10-30 | 2022-05-05 | 网络通信与安全紫金山实验室 | Terminal positioning method and apparatus, and computer device and storage medium |
| CN112330924A (en) * | 2020-11-05 | 2021-02-05 | 北京理工大学 | Falling event detection method based on channel state information in indoor environment |
| CN112926639A (en) * | 2021-02-20 | 2021-06-08 | 浙江大华技术股份有限公司 | Trajectory tracking method and device, storage medium and electronic device |
| CN113453180A (en) * | 2021-06-06 | 2021-09-28 | 西安电子科技大学 | Intelligent detection method and system for human body tumble and information data processing terminal |
| CN116347357A (en) * | 2023-04-17 | 2023-06-27 | 天津大学 | Indoor wireless passive positioning method for embedding motion limitation into neural network |
| CN116347357B (en) * | 2023-04-17 | 2023-10-20 | 天津大学 | Indoor wireless passive positioning method for embedding motion limitation into neural network |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111601253B (en) | 2021-08-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111601253B (en) | Passive passive intelligent tracking and positioning method and system, storage medium and tracking and positioning terminal | |
| Li et al. | Toward location-enabled IoT (LE-IoT): IoT positioning techniques, error sources, and error mitigation | |
| Yang et al. | A novel NLOS error compensation method based IMU for UWB indoor positioning system | |
| CN108089183B (en) | Detection and tracking integrated method for asynchronous multi-base radar system | |
| CN112070894B (en) | Real environment navigation multipath real-time simulation method, device, medium and electronic equipment | |
| CN108318856A (en) | The target positioning of fast accurate and tracking under a kind of heterogeneous network | |
| CN103116688A (en) | Multi-source abnormal sensor target track correlation method for airborne avionic system | |
| CN106226732B (en) | The indoor wireless positioning and tracing method filtered based on TOF and iteration without mark | |
| Geng et al. | Indoor tracking with RFID systems | |
| CN105093198A (en) | Flight path fusion method for networking detection of distributed external radiation source radars | |
| CN112162244B (en) | Event trigger target tracking method under related noise and random packet loss environment | |
| CN106323272B (en) | A kind of method and electronic equipment obtaining track initiation track | |
| Liang et al. | Multi-Bernoulli filter for target tracking with multi-static Doppler only measurement | |
| Li et al. | Research on a factor graph-based robust UWB positioning algorithm in NLOS environments | |
| Aernouts et al. | Combining TDoA and AoA with a particle filter in an outdoor LoRaWAN network | |
| Li et al. | An indoor location algorithm based on Kalman filter fusion of ultra-wide band and inertial measurement unit | |
| CN113347709B (en) | Indoor positioning method and system based on UWB | |
| Kasmi et al. | Algorithms and position optimization for a decentralized localization platform based on resource-constrained devices | |
| CN113189541B (en) | Positioning method, device and equipment | |
| CN109752690A (en) | Elimination algorithm, system, device and the storage medium of unmanned plane positioning NLOS | |
| CN116047495B (en) | State transformation fusion filtering tracking method for three-coordinate radar | |
| CN102595309A (en) | Wall through tracking method based on wireless sensor network | |
| CN112346010A (en) | Dual-machine passive positioning method based on scale difference and time difference | |
| Sun et al. | Multi-robot range-only SLAM by active sensor nodes for urban search and rescue | |
| CN113840231B (en) | Multi-network auxiliary positioning method for distributed large-scale multi-antenna system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |