CN112924928A - Indoor Wi-Fi multi-person detection method based on path separation - Google Patents
Indoor Wi-Fi multi-person detection method based on path separation Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0257—Hybrid positioning
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract
The invention discloses an indoor Wi-Fi multi-person detection method based on path separation, which comprises the following steps: 1) constructing a Wi-Fi signal system, wherein the Wi-Fi signal system comprises a signal transmitting device and a signal receiving device, the signal transmitting device and the signal receiving device are provided with an antenna array, and the signal transmitting device and the signal receiving device collect a Channel State Information (CSI) matrix and a received signal; 2) constructing a multi-dimensional parameter joint probability model according to the channel state information CSI matrix obtained in the step 1) and the received signal, and searching and obtaining multi-dimensional parameters of single paths with different strengths according to the multi-dimensional parameter joint probability model; 3) extracting all reflection path parameters and the number of paths by using the multidimensional parameter of the single path with the maximum intensity; 4) and carrying out indoor Wi-Fi multi-user detection in a dynamic and static mixed state according to all extracted reflection path parameters and the number of paths.
Description
Technical Field
The invention belongs to the field of wireless sensing, and relates to an indoor Wi-Fi multi-person detection method based on path separation.
Background
Indoor human detection is the basis for many practical applications such as security monitoring, behavioral analysis and elderly care. A variety of location and tracking technologies have emerged, including ultrasound, infrared, video cameras, LED visible light, and the like. They have the limitations of narrow signal coverage and difficult deployment. Other solutions often require special equipment to be carried by the target person, which is inconvenient or even infeasible in the case of aging, burglary, etc. While wireless device based solutions have particular advantages in these respects, such systems receive and process Wi-Fi signals reflected from nearby objects to obtain basic information about the location and velocity of nearby reflectors. However, device-less tracking is particularly challenging compared to device-based tracking, as tracking uses much weaker reflected signals. This problem becomes more difficult with normal Wi-Fi devices because of their limited number of antennas, small bandwidth, and severe hardware noise.
In recent years, research into indoor positioning using wireless signals has been significantly advanced. Even with commercially available Wi-Fi chipsets, advanced systems can achieve accuracy of tens of centimeters. However, existing proposals are directed to enterprise networks where multiple Wi-Fi access points can combine their information to co-locate a user. Today, however, most homes and small businesses have only one Wi-Fi access point. Therefore, a large number of wireless networks are excluded from accurate indoor positioning. Developing a technology that can locate users and objects using a single Wi-Fi access point will enable a range of important applications.
Most of existing Wi-Fi-based positioning and activity recognition methods are based on single information such as an arrival angle, flight time and Doppler shift, overlapping of results is likely to occur by using the single-dimensional information, and specific and required accurate results are difficult to distinguish. Wi-Fi positioning and tracking accuracy is limited by the number of antennas, which determines the angular accuracy, and the frequency bandwidth, which determines the resolution of the signal arrival time. For the fingerprint-based method, when the environment changes, the target features also change, and a large amount of data needs to be collected again for training. In addition, most of the existing positioning work can only position a single dynamic target, and a better result cannot be generated under the scene of multiple targets and static targets. The invention achieves the aim of detecting a plurality of targets in a dynamic and static mixed state by establishing a multidimensional information combination model and analyzing more-dimensional information of the wireless signal.
In summary, the drawbacks of the current stage method mainly include:
1. fingerprint-based methods require a lot of training and have low environmental suitability;
2. the method using single-dimensional information has low detection precision, and the method based on flight time needs precise synchronization of hardware equipment;
3. only a single dynamic target can be detected, and the detection of a static target and a plurality of targets cannot be accurately carried out.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an indoor Wi-Fi multi-person detection method based on path separation, which can be used for Wi-Fi indoor multi-person detection and has strong environmental adaptability and high detection precision.
In order to achieve the purpose, the indoor Wi-Fi multi-person detection method based on the path separation comprises the following steps:
1) constructing a Wi-Fi signal system, wherein the Wi-Fi signal system comprises a signal transmitting device and a signal receiving device, the signal transmitting device and the signal receiving device are provided with an antenna array, and the signal transmitting device and the signal receiving device collect a Channel State Information (CSI) matrix and a received signal;
2) constructing a multi-dimensional parameter joint probability model according to the channel state information CSI matrix obtained in the step 1) and the received signal, and searching and obtaining multi-dimensional parameters of single paths with different strengths according to the multi-dimensional parameter joint probability model;
3) extracting all reflection path parameters and the number of paths by using the multidimensional parameter of the single path with the maximum intensity;
4) and carrying out multi-person detection on the indoor Wi-Fi in a dynamic and static mixed state according to all the extracted reflection path parameters and the number of paths.
The specific operation of the step 2) is as follows:
2.1) respectively constructing angle state matrix expressions of an arrival angle AoA and an emission angle AoD to obtain correct arrival angle and emission angle values, and then constructing a joint angle state matrix according to the obtained correct arrival angle and emission angle values;
2.2) eliminating Doppler frequency shift DFS in the joint angle state matrix and reversing signal delay ToF, and then correlating the joint angle state matrix with the transmitted signal to calculate the correlation between the joint angle state matrix and the transmitted signal;
2.3) constructing a four-dimensional joint evaluator, and calculating multi-dimensional parameters of single paths with different strengths by using the four-dimensional joint evaluator.
The specific operation process of the step 3) is as follows:
3.1) constructing a path signal through the multidimensional parameters of the single path with the maximum strength;
3.2) removing the path signal constructed in the step 3.1) from the received signal, and then taking the residual signal as a new mixed original received signal;
3.3) judging whether the intensity of the current new mixed original received signal is less than or equal to a preset noise signal intensity threshold, when the intensity of the current new mixed original received signal is less than or equal to the preset noise signal intensity threshold, extracting all reflection path parameters and path quantity, otherwise, turning to the step 3.1).
In the step 1), the array elements of the antenna array are of equidistant structures and are all half wavelengths of signals.
The arrival angle and the transmitting angle in the step 2.1) are respectively the included angles between the signal and the receiving antenna array and between the signal and the transmitting antenna array.
The doppler shift DFS and the signal delay ToF in step 2.2) are signal frequency shifts caused by propagation delay and propagation path change of the signal, respectively.
The invention has the following beneficial effects:
in the specific operation of the indoor Wi-Fi multi-user detection method based on the path separation, a multi-dimensional combined parameter probability model is established by utilizing complex multi-paths and combining information of four dimensions of signals, path parameters are extracted in a high dimension, the resolution of a path is improved, the problem of low resolution caused by single dimension in the traditional method is solved, in addition, targets and environmental conditions are analyzed by combining all path information to extract path information of an indoor environment, and then the extracted path information of the indoor environment is compared with a path of a scene where the targets exist to accurately identify a plurality of targets in a moving state and a static state.
Drawings
FIG. 1a is a phase lag diagram generated by the angle of arrival of an antenna array;
FIG. 1b is a phase lag diagram generated by the antenna array transmission angle;
FIG. 2 is a diagram illustrating the variation of Doppler shift;
FIG. 3 is a flow chart of the system detection of the present invention;
FIG. 4 is a multi-dimensional parametric model structure diagram of the present invention;
FIG. 5a is a schematic diagram of a single-path one-dimensional parameter AoA search;
FIG. 5b is a schematic diagram of the search results of a single-path two-dimensional space AoA-AoD;
FIG. 6 is a flow chart of path splitting;
FIG. 7a is a diagram of a single parameter AoA search result for Path 1;
fig. 7b is a diagram illustrating the search result of the single-parameter AoA of path 2.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
the invention discloses an indoor Wi-Fi multi-person detection method based on path separation, which comprises the following steps:
1) and constructing a Wi-Fi signal receiving and transmitting system, wherein the Wi-Fi signal receiving and transmitting system comprises a transmitting end and a receiving end, and the transmitting end and the receiving end are respectively provided with three antennas with half-wavelength intervals and used for collecting a channel state information CSI matrix H and a received signal Y.
2) And constructing a joint probability model of four-dimensional parameters according to the channel state information CSI matrix H and the received signals, wherein the four-dimensional parameters comprise an arrival angle, a transmitting angle, flight time and Doppler frequency shift.
The arrival angle AoA and the transmission angle AoD respectively represent the included angles between the signal and the receiving and transmitting antenna arrays, as shown in fig. 1. And using the steering vector to represent the relation of phase lag of the signals on different antennas, as shown in formula (1), wherein M represents the number of antennas, θ represents the angle between the signals and the antenna array, and d is the distance between the antennas:
a(θ)＝[1,Φ(θ)1,…,Φ(θ)M-1]T (1)
respectively using a (theta) and
representing the receiving and transmitting antenna steering vector matrixes, and equations (2) and (3) are angle state matrix expressions of AoA and AoD respectively, wherein H is an initial channel state matrix:
Ha(θ)＝Ha(θ) (2)
using joint angle state matrix HdAnd reconstructing the time domain receiving signal by the transmitting signal S:
the received signal is shifted in frequency and delayed with respect to the transmitted signal S, and in order to estimate the shift and delay, the shift is cancelled and the signal delay is inverted in the received signal y and then correlated with the transmitted signal S, the doppler shift γ causing a phase shift of 2 γ t with respect to the received time domain signal. Thus, by multiplying the signal by e-j2πγtTo eliminate the frequencyAnd (6) moving.
The invention estimates the delay by means of inverting the signal delay, correlates the received signal with the delayed version S (t-tau) of the transmitted signal, and then calculates the correlation, namely:
so far, in combination with the above steps, four parameters are set in total, and a four-dimensional joint evaluation function is constructed, where T is a signal duration, and the four-dimensional joint evaluation function is:
wherein the content of the first and second substances,
θ, γ, τ are the arrival angle, emission angle, time of flight, and doppler shift, respectively.
The parameter estimation process is a search process of four parameters, namely four parameters
The estimated expression function of (a) is:
when the four parameters all obtain correct values in the searching process, a peak value appears in the evaluation function, and when the parameters are estimated, a plurality of parameters can be selected according to the actual environment and the precision requirement to form estimation models with different dimensions.
Step 3) multipath extraction, which specifically comprises the following steps:
the received signal Y is formed by superimposing signals of multiple paths, L is the number of paths, and is expressed by equation (8):
dividing the original received signal into two parts of information superposition, i.e. the path s with the strongest signal strength1And noise W1，W1Including all the remaining paths and the ambient noise, as shown in equation (9):
obtaining a single path s according to step 2)1Parameter v of1Reconstructing the path s by equation (10)1β is the signal path loss factor:
removing signal s from received signal Y1The residual signal Y1As a new mixed received signal, equation (11):
repeating step 3), recalculating the channel state matrix according to equation (7):
H1＝Y1(t)/S (12)
iterative solution of the second strongest path s in turnlUntil the noise is less than the threshold, at which point all paths and noise are considered to be separated, resulting in all path parameters v ═ v1,v2,…,vL]And the number of paths L is obtained simultaneously. The path splitting flow is shown in fig. 6. Taking a single parameter AoA search as an example, fig. 7 shows AoA search results for two paths.
4) Carry out indoor multi-target detection, specifically do:
the extraction of the target information is realized through the separation of the environment path and the target path, firstly, the unmanned scene data is collected through the step 1), and the step 2) and the step 3) are executed to extract all environment path parameters and reconstruct the path signal. Then, test data of a scene where the targets exist is collected through the step 1), the environment signals obtained in the step 4.1) are removed from the received signals, and then reflected signal information related to all the targets is obtained through the step 3), wherein the reflected signal information includes dynamic targets and static targets, the number of dynamic people is preliminarily and roughly estimated according to the total peak number of Doppler-ToF relational graphs of all paths, and the dynamic and static targets and the environment can be more accurately distinguished by adding information of other dimensions. By comparing the analysis results of the two state data in the same environment, the multi-dimensional information of all targets can be extracted, and multi-target detection is realized.
Aiming at the conditions that the existing indoor detection technology is harsh in multi-user detection conditions and poor in precision, no advance training is needed, the resolution limit of any single dimension is not changed, such as increasing the number of antennas, improving the bandwidth, synchronizing the time of equipment and the like, a multi-dimensional information combination model is established by using complex multi-paths, multi-dimensional information of different paths is extracted, and the Wi-Fi indoor multi-user detection can be realized by comparing the path conditions of the unmanned environment.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (7)
1. An indoor Wi-Fi multi-person detection method based on path separation is characterized by comprising the following steps:
1) constructing a Wi-Fi signal system, wherein the Wi-Fi signal system comprises a signal transmitting device and a signal receiving device, the signal transmitting device and the signal receiving device are provided with an antenna array, and the signal transmitting device and the signal receiving device collect a Channel State Information (CSI) matrix and a received signal;
2) constructing a multi-dimensional parameter joint probability model according to the channel state information CSI matrix obtained in the step 1) and the received signal, and searching and obtaining multi-dimensional parameters of single paths with different strengths according to the multi-dimensional parameter joint probability model;
3) extracting all reflection path parameters and the number of paths by using the multidimensional parameter of the single path with the maximum intensity;
4) and carrying out multi-person detection on the indoor Wi-Fi in a dynamic and static mixed state according to all the extracted reflection path parameters and the number of paths.
2. The indoor Wi-Fi multi-person detection method based on path separation of claim 1, wherein the specific operations of step 2) are:
2.1) respectively constructing angle state matrix expressions of an arrival angle AoA and an emission angle AoD to obtain correct arrival angle and emission angle values, and then constructing a joint angle state matrix according to the obtained correct arrival angle and emission angle values;
2.2) eliminating Doppler frequency shift DFS in the joint angle state matrix and reversing signal delay ToF, and then correlating the joint angle state matrix with the transmitted signal to calculate the correlation between the joint angle state matrix and the transmitted signal;
2.3) constructing a four-dimensional joint evaluator, and calculating multi-dimensional parameters of single paths with different strengths by using the four-dimensional joint evaluator.
3. The indoor Wi-Fi multi-person detection method based on path separation as claimed in claim 1, wherein the specific operation procedure of step 3) is as follows:
3.1) constructing a path signal through the multidimensional parameters of the single path with the maximum strength;
3.2) removing the path signal constructed in the step 3.1) from the received signal, and then taking the residual signal as a new mixed original received signal;
3.3) judging whether the intensity of the current new mixed original received signal is less than or equal to a preset noise signal intensity threshold, when the intensity of the current new mixed original received signal is less than or equal to the preset noise signal intensity threshold, extracting all reflection path parameters and path quantity, otherwise, turning to the step 3.1).
4. The indoor Wi-Fi multi-person detection method based on path separation of claim 1, wherein the specific operations of step 4) are:
4.1) collecting data in the unmanned environment, and executing the step 2) and the step 3), extracting all environment path parameters:
4.2) collecting the test data of the target existing scene, removing the environment signal obtained in the step 4.1) from the received signal, and performing the step 2) and the step 3) again to obtain the multi-dimensional information related to all targets so as to detect the dynamic targets and the static targets.
5. The indoor Wi-Fi multi-person detection method based on path separation as claimed in claim 1, wherein array elements of the antenna array in step 1) are of an equidistant structure and are all half wavelengths of signals.
6. The indoor Wi-Fi multi-person detection method based on path separation as claimed in claim 1, wherein the arrival angle and the transmission angle in step 2.1) are included angles between the signal and the receiving antenna array and the transmitting antenna array, respectively.
7. The method as claimed in claim 1, wherein the doppler shift DFS and the signal delay ToF in step 2.2) are the propagation delay of the signal and the frequency shift of the signal caused by the propagation path change, respectively.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106658590A (en) * | 2016-12-28 | 2017-05-10 | 南京航空航天大学 | Design and implementation of multi-person indoor environment state monitoring system based on WiFi channel state information |
| WO2018160141A1 (en) * | 2017-03-03 | 2018-09-07 | Nanyang Technological University | Apparatus and method for localisation and/or tracking |
| CN108519580A (en) * | 2018-04-19 | 2018-09-11 | 广西欣歌拉科技有限公司 | The contactless positioning of multiple target and tracing system |
| CN109738861A (en) * | 2018-12-12 | 2019-05-10 | 重庆邮电大学 | A kind of three-dimensional combined estimation method based on Wi-Fi channel state information |
| CN110049551A (en) * | 2019-04-26 | 2019-07-23 | 中国科学技术大学 | Signal tracing method based on commercial wireless WiFi equipment |
| CN110366108A (en) * | 2019-07-09 | 2019-10-22 | 南京邮电大学 | Indoor orientation method based on channel state information and received signal strength |
| CN110381440A (en) * | 2019-06-16 | 2019-10-25 | 西安电子科技大学 | The fingerprint indoor orientation method of joint RSS and CSI based on deep learning |
| 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 |
| CN111866709A (en) * | 2020-06-29 | 2020-10-30 | 重庆邮电大学 | Indoor Wi-Fi positioning error bound estimation method for moving target |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112924928B (en) * | 2021-01-26 | 2023-05-02 | 西安交通大学 | Indoor Wi-Fi multi-person detection method based on path separation |
-
2021
- 2021-01-26 CN CN202110105100.0A patent/CN112924928B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106658590A (en) * | 2016-12-28 | 2017-05-10 | 南京航空航天大学 | Design and implementation of multi-person indoor environment state monitoring system based on WiFi channel state information |
| WO2018160141A1 (en) * | 2017-03-03 | 2018-09-07 | Nanyang Technological University | Apparatus and method for localisation and/or tracking |
| CN108519580A (en) * | 2018-04-19 | 2018-09-11 | 广西欣歌拉科技有限公司 | The contactless positioning of multiple target and tracing system |
| CN109738861A (en) * | 2018-12-12 | 2019-05-10 | 重庆邮电大学 | A kind of three-dimensional combined estimation method based on Wi-Fi channel state information |
| CN110049551A (en) * | 2019-04-26 | 2019-07-23 | 中国科学技术大学 | Signal tracing method based on commercial wireless WiFi equipment |
| CN110381440A (en) * | 2019-06-16 | 2019-10-25 | 西安电子科技大学 | The fingerprint indoor orientation method of joint RSS and CSI based on deep learning |
| CN110366108A (en) * | 2019-07-09 | 2019-10-22 | 南京邮电大学 | Indoor orientation method based on channel state information and received signal strength |
| 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 |
| CN111866709A (en) * | 2020-06-29 | 2020-10-30 | 重庆邮电大学 | Indoor Wi-Fi positioning error bound estimation method for moving target |
Non-Patent Citations (3)
| Title |
|---|
| 吴习芊等: "基于WiFi信号的多维参数估计与三维轨迹跟踪算法", 《信号处理》 * |
| 王颖颖等: "室内WiFi定位技术的多参数优化研究", 《计算机工程》 * |
| 田增山等: "基于Wi-Fi多维参数特征的无源目标跟踪技术", 《电子学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112924928B (en) * | 2021-01-26 | 2023-05-02 | 西安交通大学 | Indoor Wi-Fi multi-person detection method based on path separation |
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