CN107241696B - Multipath effect distinguishing method and distance estimation method based on channel state information - Google Patents
Multipath effect distinguishing method and distance estimation method based on channel state information Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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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
- G01S5/0205—Details
- G01S5/0215—Interference
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/318—Received signal strength
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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
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
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Abstract
The invention provides a multipath effect distinguishing method based on channel state information. The method comprises the following steps: extracting the characteristics of the multipath effect from the collected channel state information data of the point to be tested; and taking the characteristics as input, and judging the multipath effect condition of the point to be tested by utilizing a multipath effect strong and weak classification model, wherein the multipath effect classification model is obtained by training the characteristics extracted from historical channel state information data based on a classification algorithm. The method of the invention can judge the condition of the multipath effect based on the time domain and frequency domain characteristics in the channel state information data, and can be further used for distance estimation or positioning of the terminal.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a multipath effect distinguishing method based on Channel State Information (CSI).
Background
With the rapid development of intelligent terminal devices and mobile internet technologies and the widespread use of a large number of sensors in mobile intelligent devices, location-based services are increasingly widely used. For example, in a shopping mall, personalized service and commodity recommendation work is performed by analyzing the moving position track of a customer; in prison, factory or coal mine safety monitoring, mobile personnel can be monitored and managed by acquiring the moving track of the mobile terminal.
In the prior art, the mobile motion trajectory information of the outdoor user can utilize a Global Positioning System (GPS) or a Beidou positioning system, but for the indoor user, because a building blocks satellite signals, positioning systems such as the GPS can not perform accurate positioning, and methods such as infrared positioning, ultrasonic positioning, radio frequency identification positioning, Bluetooth positioning and indoor positioning based on wifi signal strength are generally adopted.
However, the indoor positioning method based on wifi signal strength, which is widely used at present, can only reflect the signal receiving strength of the whole channel, and cannot well eliminate the interference of multipath effect, which becomes an important factor affecting the indoor positioning accuracy.
Therefore, there is a need for improvement of the prior art to provide an accurate multipath effect discrimination method and to further locate the movement trajectory of the end user based on the discriminated multipath effect.
Disclosure of Invention
The present invention is directed to overcoming the above-mentioned drawbacks of the prior art and providing a multipath effect discriminating method and a distance estimating method based on channel state information.
According to a first aspect of the present invention, there is provided a method of multipath discrimination based on channel state information. The method comprises the following steps:
step 1: extracting the characteristics of the multipath effect from the collected channel state information data of the point to be tested;
step 2: and taking the characteristics as input, and judging the multipath effect condition of the point to be tested by utilizing a multipath effect strong and weak classification model, wherein the multipath effect classification model is obtained by training the characteristics extracted from historical channel state information data based on a classification algorithm.
In the multipath effect discrimination method of the present invention, the characteristics include a ratio of time domain line-of-sight path signal strength to non-line-of-sight path signal strength in the channel state information data, a frequency domain variance of the channel state information data, and a frequency domain skewness of the channel state information data.
In the multi-path effect distinguishing method, the ratio of the time domain line-of-sight path signal intensity to the non-line-of-sight path signal intensity is the ratio of the amplitude of the first wavelet to the sum of the amplitudes of other 29 wavelet wavelets in 30 time domains obtained after Fourier transform of channel state information data.
In the multipath effect discrimination method, the classification algorithm comprises a decision tree, a Bayesian algorithm, a neural network algorithm and a support vector machine.
According to a second aspect of the present invention, there is provided a distance estimation method based on channel state information. The method comprises the following steps:
step 51: obtaining the multipath effect condition of the point to be tested according to the multipath effect distinguishing method;
step 52: screening out points which are less interfered by the multipath effect based on the multipath effect condition;
step 53: and estimating the distance of each test point according to the corresponding received signal strength indication screened out to be less interfered by the multipath effect.
In the distance estimation method of the present invention, step 52 includes: and for each point to be tested, calculating the percentage of the data judged to be weak in multipath effect in the total data of the point, and if the percentage exceeds a preset threshold value, determining that the point is a point which is less interfered by multipath effect.
In the distance estimation method of the present invention, the threshold is 60%.
In the distance estimation method of the present invention, step 53 includes estimating the distance of each test point according to the following formula:
wherein RSSI (d) is the RSSI value after propagation distance d, RSSI (d)0) Is at a distance reference point d0Is the RSSI value of (1), n is the path loss exponent, XσIs a random variable that reflects the energy decay caused by occlusion.
Compared with the prior art, the invention has the advantages that: the method comprises the steps of extracting the characteristics reflecting the multipath effect from Channel State Information (CSI) data, and judging the condition of the terminal at each point interfered by the multipath effect by using a classification model; the distance of the terminal is calculated by using the points which are interfered by the multipath effect to improve the positioning precision of the terminal.
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The invention is illustrated and described only by way of example and not by way of limitation in the scope of the invention as set forth in the following drawings, in which:
fig. 1 shows a flow diagram of a CSI-based multipath discrimination method according to one embodiment of the present invention.
Fig. 2 is a graph showing the magnitude of each wavelet in a wifi system.
FIG. 3 shows a schematic diagram of a decision tree according to one embodiment of the invention.
Fig. 4 shows a flowchart of a CSI-based distance estimation method according to an embodiment of the present invention.
Fig. 5 shows an error comparison graph of the distance estimation method of the present invention and the existing average distance estimation method.
Detailed Description
In order to make the objects, technical solutions, design methods, and advantages of the present invention more apparent, the present invention will be further described in detail by specific embodiments with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Fig. 1 shows a flow diagram of a CSI-based multipath discrimination method according to one embodiment of the present invention. The method specifically comprises the following steps:
1) s110, collecting CSI data
CSI is Channel State Information (Channel State Information) for measuring Channel conditions, which can describe a wireless Channel State more finely and reliably distinguish multipath components.
The CSI data of various typical scenes can be collected by utilizing equipment such as a notebook router, for example, scenes with small multipath effect interference (such as outdoor football fields) and scenes with large multipath effect interference (such as indoor work station areas), halls, corridors and the like can be collected. Specifically, for each scene, it may be set to acquire a point at a certain distance, for example, at intervals of 0.5m or 1m, and acquire CSI values corresponding to each point.
2) S120, extracting characteristics of the collected CSI data
The purpose of this step is to extract features reflecting multipath components from the CSI data.
Firstly, processing acquired data of each point by utilizing inverse Fourier transform, and converting the data into a time domain signal; then, extracting features from the processed data, where the extracted features include a ratio of CSI time domain line-of-sight path signal intensity to non-line-of-sight path signal intensity, for example, for a wifi system, a ratio of an amplitude of a first wavelet to a sum of other 29 wavelet amplitudes in time domain 30 wavelets obtained after performing inverse fourier transform on CSI data (see fig. 2), and a larger ratio indicates a weaker multipath effect; the frequency domain variance of the CSI data, namely the variance of the frequency domain amplitude values of 30 wavelets of the CSI signal, wherein the smaller the frequency domain variance is, the weaker the multipath effect is; the frequency domain skewness of the CSI data, namely the skewness of the frequency domain amplitude values of 30 wavelets of the CSI signal, is smaller, and the multipath effect is weaker. The feature extraction process for the inverse fourier transform process belongs to the prior art, and is not described herein again.
The CSI reflects the frequency characteristics of the subcarrier channel, including both amplitude and phase information. The inverse Fourier transform is carried out on the wavelet of the CSI, so that the signal can be converted from a frequency domain to a time domain, and the signal intensity of the signal reaching a receiving end along different paths is clearly observed. By the method, the multipath effect condition can be well reflected from the time domain and frequency domain signals, and the extracted time domain and frequency domain characteristics can accurately represent the strength of the multipath effect interference on the receiving end.
4) S130, generating training samples according to the extracted features
Feature vectors are formed by features extracted from the CSI data, and the environment type identifier is added to generate training samples, which are shown in table 1.
TABLE 1
In this step, only a small amount of data is depicted in an exemplary manner for clarity, and those skilled in the art will appreciate that in practical applications, data may be collected as training samples multiple times for different locations in each scene, thereby improving the accuracy of classification.
5) S140, training a classification model by using the training samples
And training a classification model by using a classification algorithm according to a training set consisting of all training samples to obtain a discrimination model of the multipath effect strength of each data acquisition point.
The adopted classification algorithm comprises a decision tree, a Bayesian algorithm, a neural network algorithm, a Support Vector Machine (SVM), and the like.
Taking a decision tree as an example, which is a tree structure, each non-leaf node represents a feature, for example, a ratio of CSI time domain line-of-sight path signal strength to non-line-of-sight path signal strength, frequency domain variance, frequency domain skewness, etc. in this embodiment, each branch represents an output of the feature over a certain value range, and each leaf node stores a category, for example, the category is classified into two categories, i.e., strong multipath effect or weak multipath effect. The process of using the decision tree to make a decision is to start from the root node, test the corresponding characteristic attributes in the items to be classified, select an output branch according to the value of the characteristic attributes until the leaf node is reached, and take the category stored by the leaf node as a decision result.
Fig. 3 shows a schematic diagram of generating a decision tree based on the training samples in table 1, for example, a threshold value of a ratio of CSI time domain line-of-sight path signal strength to non-line-of-sight path signal strength may be defined as 5, a threshold value of frequency domain variance may be defined as 1, and a threshold value of frequency domain skewness may be defined as 0.5, and the classification determination of multipath effects on the training samples in table 1 is performed, for example, samples 10, 11, 13, 15, and 18 are determined as weak multipath effects, while other samples are determined as strong multipath effects.
Fig. 4 shows a flow diagram of a method for multipath discrimination and distance estimation using an obtained classification model according to an embodiment of the present invention.
1) S410, collecting test data and extracting CSI data characteristics
The step includes collecting the CSI data to be tested to extract the CSI data features, and the specific process is the same as steps S110 and S120. Further, Received Signal Strength Indications (RSSI) are collected simultaneously for each collection point.
2) S420, the extracted features are used as input, and the trained classification model is used for distinguishing the multipath effect condition
In the step, for each point to be tested, the characteristics extracted from the collected CSI data are input into a trained classification model, and the strength judgment result of the multipath effect of each point can be obtained.
3) S430, screening out the points with small interference by the multipath effect
Under the condition that one acquisition point corresponds to a plurality of acquisition data, the percentage of the data of each point which is judged to be weak in multipath effect in the total data of the point is calculated according to the judgment result of the multipath effect. For example, if the percentage exceeds a certain threshold (e.g., 60%), the point is considered to be a point that is less interfered by multipath effects.
4) S440, estimating distance according to RSSI of the screened points
And substituting the RSSI data of the screened points into a path loss propagation model for distance estimation.
For example, one Path Loss model suitable for complex indoor environments is the Log-normal Distance Path Loss model, LDPL (Log-normal Distance Path Loss), expressed as:
where PL (d) is the path loss after propagation distance d, in dB;
is at a distance reference point d0The path loss at (a) can be obtained by actual measurement, for example, a reference path loss at a distance of 1 meter; n is a path loss index, indicating the rate at which path loss increases with distance, which depends on the surrounding environment and building type; xσIs a random variable that reflects the energy decay caused by occlusion.
The corresponding relation between the logarithmic distance loss model and the RSSI is as follows:
wherein RSSI (d) is the RSSI value after propagation distance d, RSSI (d)0) Is at the reference point d0The RSSI value of (c).
The selected RSSI value corresponding to the point with small multipath effect interference is substituted into the formula (2) to obtain the distance estimation of the point, and the motion trail of the mobile terminal can be measured in the mode, so that the positioning accuracy of the terminal, particularly the indoor terminal, is provided.
In order to further verify the effect of the present invention, fig. 5 shows the error comparison between the distance estimation method of the present invention and the existing average distance estimation method, wherein three typical indoor scenes, namely corridor, station and hall, are respectively shown, and the vertical axis is the average distance estimation error (meter), so that it can be seen that the error of the estimation of weak multipath effect points screened by the present invention is significantly smaller than the average distance estimation error not screened.
In summary, the invention can accurately select the points with less interference by the multipath effect through the multi-path effect strong and weak discrimination model based on the CSI, and provides the auxiliary positioning points for the track constraint for the indoor positioning.
The method can be applied to wireless systems such as a wifi system, a WCDMA system or LTE and the like, and is used for judging the degree of multipath effect in the moving process of the terminal and positioning the position of the terminal.
It should be noted that, although the steps are described in a specific order, the steps are not necessarily performed in the specific order, and in fact, some of the steps may be performed concurrently or even in a changed order as long as the required functions are achieved.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device. The computer readable storage medium may include, for example, but is not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (9)
1. A distance estimation method for multipath effect discrimination based on channel state information, comprising the steps of:
step 1: extracting characteristics capable of representing the degree of strength of the multipath interference from the collected channel state information data of the point to be tested, wherein the characteristics comprise the ratio of time domain line-of-sight path signal strength to non-line-of-sight path signal strength in the channel state information data, the frequency domain variance of the channel state information data and the frequency domain skewness of the channel state information data;
step 2: the characteristic is used as input, and a multipath effect strong and weak classification model is used for judging the strong and weak degree of the to-be-tested point under the interference of the multipath effect, wherein the multipath effect strong and weak classification model is obtained by training the characteristic extracted from historical channel state information data based on a classification algorithm;
and step 3: and determining points with small interference of the multipath effect based on the strength of the points to be tested, which are interfered by the multipath effect, and estimating the distance of each point to be tested by using the wireless signals of the points with small interference of the multipath effect.
2. The method according to claim 1, wherein the multipath strong and weak classification model is obtained based on decision tree classification algorithm training, and the step 2 comprises: aiming at a point to be tested, judging whether the ratio of the time domain line-of-sight path signal intensity to the non-line-of-sight path signal intensity is less than or equal to the threshold value of the corresponding node of the decision tree, if so, classifying the point to be tested as strong multipath effect, otherwise, continuing the subsequent steps; judging whether the frequency domain variance of the points to be tested is larger than the threshold value of the corresponding node of the decision tree, if so, classifying the points to be tested as strong multipath effect, otherwise, continuing the following steps; and judging whether the frequency domain skewness of the test points is greater than the threshold value of the corresponding nodes of the decision tree, if so, classifying the points to be tested as strong multipath effects, and otherwise, classifying the points to be tested as weak multipath effects.
3. The method of claim 1, wherein the ratio of the time-domain line-of-sight signal strength to the non-line-of-sight signal strength is a ratio of an amplitude of a first wavelet to a sum of amplitudes of 29 other wavelets of the time-domain 30 wavelets obtained by fourier transforming the channel state information data.
4. The method of claim 1, wherein the classification algorithm is one of a decision tree, a bayesian algorithm, a neural network algorithm, or a support vector machine.
5. The method of claim 1, wherein step 3 comprises:
and for each point to be tested, calculating the percentage of the data judged to be weak in multipath effect in the total data of the point, and if the percentage exceeds a preset threshold value, determining that the point is a point which is less interfered by multipath effect.
6. The method of claim 5, wherein the threshold is 60%.
7. The method of claim 1, wherein step 3 comprises estimating the distance of each test point according to the formula:
wherein RSSI (d) is the RSSI value after propagation distance d, RSSI (d)0) Is the RSSI value at the distance reference point, n is the path loss exponent, XσIs a random variable that reflects the energy decay caused by occlusion.
8. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
9. A computer device comprising a memory and a processor, on which memory a computer program is stored which is executable on the processor, characterized in that the steps of the method of any of claims 1 to 7 are implemented when the processor executes the program.
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| CN107797153B (en) * | 2017-10-23 | 2019-07-12 | 上海百芝龙网络科技有限公司 | A kind of door and window open and-shut mode detection method based on WiFi signal |
| CN108882171B (en) * | 2018-06-14 | 2020-11-13 | 西北师范大学 | CSI-based personnel trajectory tracking method |
| CN108828570B (en) * | 2018-06-22 | 2020-07-10 | 北京邮电大学 | Distance measurement method and distance measurement device based on path loss factor dynamic estimation |
| CN109316192B (en) * | 2018-09-13 | 2020-07-17 | 清华大学 | Sitting posture detection method and device based on mobile network channel state information |
| CN110047503B (en) * | 2018-09-25 | 2021-04-16 | 上海无线通信研究中心 | Multipath effect suppression method for sound wave |
| CN109362083B (en) * | 2018-10-31 | 2021-09-07 | 北京交通大学 | High-speed railway wireless channel database construction and data preprocessing method |
| CN114938490B (en) * | 2022-05-16 | 2024-04-09 | 中国联合网络通信集团有限公司 | Terminal positioning method, device and storage medium |
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