CN111770527A - Visible and invisible channel identification method and device based on two-dimensional features - Google Patents

Visible and invisible channel identification method and device based on two-dimensional features Download PDF

Info

Publication number
CN111770527A
CN111770527A CN202010586663.1A CN202010586663A CN111770527A CN 111770527 A CN111770527 A CN 111770527A CN 202010586663 A CN202010586663 A CN 202010586663A CN 111770527 A CN111770527 A CN 111770527A
Authority
CN
China
Prior art keywords
channel
threshold
threshold value
visual
impulse response
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
Application number
CN202010586663.1A
Other languages
Chinese (zh)
Other versions
CN111770527B (en
Inventor
王威
刘鑫一
谢景丽
侯俊
庞继龙
徐志麟
杨芬
宋吉婷
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Changan University
Original Assignee
Changan University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Changan University filed Critical Changan University
Priority to CN202010586663.1A priority Critical patent/CN111770527B/en
Publication of CN111770527A publication Critical patent/CN111770527A/en
Application granted granted Critical
Publication of CN111770527B publication Critical patent/CN111770527B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • 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
    • 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
    • H04B17/327—Received signal code power [RSCP]
    • 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
    • H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
    • 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
    • H04B17/364—Delay profiles
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • 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
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a method and a device for identifying visible and non-visible channels based on two-dimensional characteristics, which are used for acquiring channel information of a data packet and processing and synthesizing channel state information; performing Fourier inversion on each channel state information data, and calculating the power of channel impact response; setting a first threshold, filtering noise of the channel impact response obtained in S2 to obtain the position of the maximum value of the filtered channel impact response, finding out the amplitude of the channel impact response of a point corresponding to the maximum value, and calculating the kurtosis; using a single characteristic of RMS delay expansion to find the most suitable threshold value, and using the most suitable threshold value as a second threshold value; calculating the RMS delay spread of the channel impulse response based on the threshold two; according to the method, the line coefficient of a threshold value is calculated according to the kurtosis and the RMS delay spread, whether the data packet is received in a line-of-sight environment or a non-line-of-sight environment is judged according to the threshold value line, the line-of-sight/non-line-of-sight identification is carried out by adopting two characteristics, and compared with a single characteristic, double judgment can accurately improve the identification accuracy.

Description

Visible and invisible channel identification method and device based on two-dimensional features
Technical Field
The invention belongs to the technical field of wireless communication, relates to the field of position service application, and particularly relates to a visible and invisible channel identification method and device based on two-dimensional characteristics.
Background
With the rapid development of data Services and multimedia Services, the smart city is being expanded and built deeply, people begin to pay attention to the application of Location Based Services (LBS), and the demand for high-precision navigation and positioning is increasing. Positioning is usually performed outdoors by using GPS (global positioning system) Beidou navigation and the like depending on satellite signals, but after the satellite signals enter indoors, for example, complicated indoor environments such as underground garages, mines, warehouses and the like, the positioning signals are transmitted, refracted, projected and diffracted to form multipath and Non-line-of-sight (NLOS) transmission phenomena, so that the positioning error is large, and the requirement of the indoor environment on positioning cannot be met.
Indoor positioning technology is therefore the focus of research and development for researchers. Compared with an outdoor environment, an indoor environment is interfered by blocking of a large number of objects (such as indoor furniture and human body movement), so that an indoor wireless signal transmission environment is extremely complex, signals are generally divided into two types, namely a Line-of-sight (LOS) path and an NLOS path, delay increase of signal propagation of the NLOS path is generated, signal strength attenuation, change of arrival angle, phase change, and relatively sharp change in a short time, and the like, and generally, the indoor positioning accuracy is reduced to a certain extent due to the influence of the NLOS path, so that the indoor LOS/NLOS path needs to be identified to improve the indoor positioning accuracy, and errors caused by the NLOS path are avoided or reduced.
Various indoor positioning technologies, such as an Ultra Wide Band (UWB) indoor positioning technology, a Radio Frequency Identification (RFID) indoor positioning technology, an Ultrasonic Wave (Ultrasonic Wave) indoor positioning technology, a Bluetooth (Bluetooth) indoor positioning technology, and a ZigBee indoor positioning technology, have been studied. Although most of the indoor positioning technologies can be applied to an adaptive indoor environment, positioning networks and hardware devices need to be deployed in advance, and cost is very high. Along with the rapid rise of the WiFi network and the rapid popularization of the intelligent mobile terminal, the WiFi signal widely exists in indoor spaces including various scenes such as families, markets and transportation hubs and is an ideal positioning source due to great attention obtained by indoor positioning by utilizing WiFi.
With the development of WiFi technology, the IEEE 802.11n series communication protocol and the wireless lan protocol behind it apply multiple-input multiple-output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) technologies, so that the channel characteristics between WiFi transceivers can be estimated at the physical layer and stored in the form of Channel State Information (CSI). As a quantitative characterization of channel frequency response, CSI may reflect scattering, environmental attenuation, power attenuation, and other properties in the physical environment. Compared with the traditional signal received strength indicator (RSSI), the CSI is a description of the nature of the propagation of the wireless signal in the space, and provides finer channel frequency response information, including richer characteristic quantities. CSI information is more accurately located than RSSI.
In the identification method, "a wireless network adaptive authorization intervention method" applied by Nanjing post and telecommunications university, publication number: CN 106792552), wherein there are deficiencies: only one threshold is set singly, and the accuracy of the identification of points near the threshold is not considered, so that the signals near the threshold have larger misjudgment degrees.
Disclosure of Invention
The invention aims to provide a visual and non-visual channel identification method and device based on two-dimensional characteristics, which solve the problem of identifying sight distance/non-sight distance paths in a complex indoor environment, improve positioning accuracy and achieve the aim of high-accuracy position service quality.
In order to achieve the purpose, the invention adopts the technical scheme that: a visual and non-visual channel identification method based on two-dimensional features comprises the following steps:
s1, acquiring data packet channel information and processing the synthesized channel state information;
s2, carrying out Fourier inversion on each channel state information data obtained in the S1 to obtain corresponding channel impact response; and calculating the power of the channel impulse response;
s3, setting a first threshold, filtering noise of the channel impulse response obtained in S2, comparing the power of the channel impulse response obtained in S2 with the first threshold, taking out the power value larger than the first threshold, and discarding the power value smaller than the first threshold;
s4, obtaining the position of the maximum value of the channel impulse response after filtering, and finding out the amplitude of the channel impulse response of the point corresponding to the maximum value;
s5, calculating the kurtosis by using the amplitude obtained in the S4;
s6, finding the most suitable threshold value from the single characteristics of the RMS delay spread to be used as a second threshold value;
s7, calculating RMS delay spread of channel impulse response based on the second threshold value;
and S8, calculating coefficients a and b of a threshold line y, ax + b according to the kurtosis obtained in S5 and the RMS delay spread obtained in S7, and judging whether the data packet belongs to the line-of-sight environment or is received in the non-line-of-sight environment according to the threshold line.
In S1, the amplitude and phase of each subcarrier in the channel transfer function are represented as:
Figure BDA0002554911320000031
wherein, H (f)k) Representing carrier centre frequency fkCSI information of, | H (f)k) I and ∠ H (f)k) Each represents H (f)k) Amplitude and phase information of.
Fourier inversion is carried out on the CSI information obtained in the S1, and channel impact response is obtained;
Figure BDA0002554911320000032
wherein, ai,θi,τiRespectively representing the amplitude, phase and channel delay of the ith path, wherein N refers to the number of all paths, and (tau) is a Dirac delta function.
S3 sets the threshold value one as follows:
s31, acquiring the absolute value of the noise part of the channel impulse response;
s32, calculating the variance of the absolute value of S31 times;
at S33, the variance obtained at S32 is multiplied by 20 times log10 and added to 6 to obtain a threshold value of one.
In S5, the kurtosis is calculated using the following formula:
Figure BDA0002554911320000041
where E {. cndot } represents the sample expected delay, μ|h|And σ|h|Mean and standard deviation of | h (τ) | representing CIR amplitude, respectively.
The second threshold is specifically set in S6 as follows:
1) taking out the maximum power of each CIR;
2) the maximum power is reduced by 21 to set the threshold value of two.
The specific implementation process of S6 includes:
subtracting the training threshold from the maximum of the power obtained in S3; the training threshold is a number of values that do not exceed the maximum power value and are not lower than the minimum power value obtained in S3;
training for channel impulse response of S2Filtering the signal by the training threshold value, calculating the power of the channel impulse response, comparing the training threshold value with the maximum value of the channel impulse response, and taking out all points which are greater than the training threshold value and recording as h (tau)l)：
Figure BDA0002554911320000042
Wherein, taul(L-1) Δ τ ═ (L-i)/B, L ═ 1, x., L ═ 3201, B is the bandwidth;
in S7, a plurality of RMS delay spread characteristics are calculated by using the channel delay, the threshold value used for training and the channel impulse response obtained in S2, and the RMS delay spread characteristics are calculated as
Figure BDA0002554911320000043
And finding out a threshold value with the highest accuracy for identifying the sight distance and the non-sight distance as a second threshold value.
In S8, the specific determination formula is:
Figure BDA0002554911320000044
in the calculation of the threshold line coefficient in S8, a is calculated first, and then b is calculated based on a.
A visual distance and non-visual distance channel identification device comprises an information receiving device, one or more processors and a memory, wherein the information receiving device and the memory are connected with the processors through an I/O interface, the memory is used for storing computer executable programs, the processors read part or all of the computer executable programs from the memory and execute the computer executable programs, the processor can realize the visual distance and non-visual distance channel identification method based on two-dimensional characteristics when executing part or all of the computer executable programs, and the memory is also used for storing information data acquired by the information receiving device.
Compared with the prior art, the invention has at least the following beneficial effects: the invention adopts two characteristics to identify the sight distance/non-sight distance, and increases the identification accuracy by comparing with a single characteristic, the single characteristic identifies the signal through a threshold value, the signal appearing around the threshold value is easy to judge wrongly, and the two-dimensional characteristic judgment just solves the problem, the signal appearing around the threshold value I can be judged through a threshold value II, and on the contrary, the signal appearing around the threshold value II can be judged through the threshold value I, and the double judgment can accurately improve the identification accuracy.
Drawings
FIG. 1 is a probability density function of kurtosis and RMS delay spread in a LOS scenario computed using the present invention after preprocessing the training set signals.
Fig. 2 is a probability density function of kurtosis and RMS delay spread in NLOS scenarios computed after preprocessing the training set signal using the present invention.
FIG. 3 is a graph of kurtosis and RMS delay spreads for different scenarios calculated after preprocessing the training set signals using the present invention.
Wherein the x-axis represents RMS delay spread and the y-axis represents kurtosis; red represents LOS scenes and blue represents NLOS scenes.
FIG. 4 is a graph of kurtosis and RMS delay spreads calculated for different scenarios after preprocessing of the test set signals using the present invention.
Fig. 5 is a flow chart of an alternative embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the drawings.
Referring to fig. 5, a method for identifying visual and non-visual channels based on two-dimensional features includes the following steps:
s1, acquiring data packet channel information and processing the synthesized channel state information;
s2, carrying out Fourier inversion on each channel state information data obtained in the S1 to obtain corresponding channel impact response; and calculating the power of the channel impulse response;
s3, setting a first threshold, filtering noise of the channel impulse response obtained in S2, comparing the power of the channel impulse response obtained in S2 with the first threshold, taking out the power value larger than the first threshold, and discarding the power value smaller than the first threshold;
s4, obtaining the position of the maximum value of the channel impulse response after filtering, and finding out the amplitude of the channel impulse response of the point corresponding to the maximum value;
s5, calculating the kurtosis by using the amplitude obtained in the S4;
s6, finding the most suitable threshold value from the single characteristics of the RMS delay spread to be used as a second threshold value; the specific implementation process comprises the following steps:
subtracting the training threshold from the maximum of the power obtained in S3; the training threshold is a number of values that do not exceed the maximum power value and are not lower than the minimum power value obtained in S3;
filtering the channel impulse response of S2 with the training threshold value, calculating the power of the channel impulse response, comparing the training threshold value with the maximum value of the channel impulse response, and taking out all points greater than the training threshold value as h (tau)l)：
Figure BDA0002554911320000061
Wherein, taul(L-1) Δ τ ═ (L-i)/B, L ═ 1, x., L ═ 3201, B is the bandwidth;
calculating a plurality of RMS delay spread characteristics by using the channel delay, the threshold value used for training and the channel impulse response obtained by S2, finding out the threshold value with the highest accuracy for identifying the line-of-sight and the non-line-of-sight,
Figure BDA0002554911320000071
s7, calculating RMS delay spread of channel impulse response based on the second threshold value;
and S8, calculating coefficients a and b of a threshold line y, ax + b according to the kurtosis obtained in S5 and the RMS delay spread obtained in S8, and judging whether the data packet belongs to the line-of-sight environment or is received in the non-line-of-sight environment according to the threshold line.
The amplitude and phase of each subcarrier in the channel transfer function in S1 are represented as:
Figure BDA0002554911320000072
wherein, H (f)k) Representing carrier centre frequency fkCSI information of, | H (f)k) I and ∠ H (f)k) Each represents H (f)k) Amplitude and phase information of.
Fourier inversion is carried out on the CSI information obtained in the S1, and channel impact response is obtained;
Figure BDA0002554911320000073
wherein, ai,θi,τiRespectively representing the amplitude, phase and channel delay of the ith path, wherein N refers to the number of all paths, and (tau) is a Dirac delta function.
S3 sets the threshold value one as follows:
s31, acquiring the absolute value of the noise part of the channel impulse response;
s32, calculating the variance of the absolute value of S31 times;
at S33, the variance obtained at S32 is multiplied by 20 times log10 and added to 6 to obtain a threshold value of one.
In S5, the kurtosis is calculated using the following formula:
Figure BDA0002554911320000074
where E {. cndot } represents the sample expected delay, μ|h|And σ|h|Mean and standard deviation of | h (τ) | representing CIR amplitude, respectively.
The second threshold is specifically set in S6 as follows:
1) taking out the maximum power of each CIR; 2) the maximum power is reduced by 21 to set the threshold value of two.
In S8, the specific determination formula is:
Figure BDA0002554911320000081
one can carry out
(1) Obtaining a data packet
A2.4 GHz-5.4 GHz channel measurement platform is built in an 8 x 4m conference room for channel measurement, firstly, 5 anchor nodes are arranged in the conference room to serve as transmitting antenna positions, and target nodes are arranged in the conference room at intervals of 0.05m to serve as receiving antenna positions. Selecting 3 anchor nodes as a line of sight (LOS) scene, using the remaining 2 anchor nodes as a non line of sight (NLOS) scene, collecting CSI information by using a notebook and a wireless router supporting the IEEE 802.11n standard, storing received data packets, wherein each CSI information represents the frequency, the amplitude and the phase of each subcarrier and represents signal state information of a transmission signal from a transmitting end to a receiving end,
Figure BDA0002554911320000082
wherein, H (f)k) Representing carrier centre frequency fkCSI information of, | H (f)k) I and ∠ H (f)k) Each represents H (f)k) Amplitude and phase information of;
(2) and (2) carrying out IFT change on the data of each CSI information acquired in the step (1) to obtain a Channel Impulse Response (CIR) corresponding to the IFT change:
Figure BDA0002554911320000083
wherein, ai,θi,τiRespectively representing the amplitude, phase and time delay of the ith path, wherein N refers to the number of all paths, and (tau) is a Dirac delta function.
(3) Calculating the power of channel impulse response;
(4) acquiring the absolute value of the noise part of the CIR, calculating the variance of 4 times of the CIR, and adding 6 to the log10 of which the value is multiplied by 20 times to set the value as a first threshold value;
(5) comparing the power of the CIR with a first threshold value, and filtering out points smaller than the first threshold value;
(6) taking out the position of the filtered maximum value, and finding out the amplitude of the CIR of the point corresponding to the maximum value;
(7) calculating kurtosis for the extracted amplitudes
Figure BDA0002554911320000091
Where E {. cndot } represents the sample expected delay, μ|h|And σ|h|Mean and standard deviation of | h (τ) | representing CIR amplitude, respectively.
(8) Taking out the maximum power of each CIR, subtracting 21 from the maximum power, and setting the maximum power as a second threshold;
(9) calculating the RMS delay spread of the CIR by the threshold two set in the step (8);
(10) and (3) calculating coefficients a and b of a threshold line y which is ax + b according to the kurtosis obtained in the step (7) and the RMS delay spread obtained in the step (9), and judging whether the data packet belongs to a line calculated by the kurtosis and the RMS delay spread and received in a line-of-sight environment or a line in a non-line-of-sight environment according to the threshold line to judge whether the data packet belongs to an LOS environment or an NLOS environment.
Fig. 1 is a probability density function of kurtosis and RMS delay spread in LOS scenarios computed after preprocessing a training set signal using the present invention, and fig. 2 is a probability density function of kurtosis and RMS delay spread in NLOS scenarios computed after preprocessing a training set signal using the present invention.
According to the two-dimensional scatter diagram shown in fig. 3, red is a cross-shaped point and blue is a star-shaped point; and setting a threshold line as y as ax + b, establishing a cycle range of a, then establishing a cycle range of b corresponding to each a, finding all the threshold lines corresponding to b of different a, testing and identifying the accuracy of the visual distance/non-visual distance based on the threshold lines, taking out a pair of a and b with the highest accuracy, and testing the test set, wherein the ax + b is 0.06x + 12.3.
Under an LOS/NLOS path, the kurtosis of a signal is different from the distribution probability of RMS delay spread, when the kurtosis of measurement data and the value of the RMS delay spread obey the probability density under an LOS environment, the LOS path is considered at the moment, and on the contrary, if the measurement data belongs to the probability density distribution under the NLOS path, the LOS path belongs to the NLOS path.
Because the kurtosis and RMS delay spread of the signal are smaller than those of the LOS environment in the NLOS environment in the training process, the LOS data and the NLOS data can be classified by finding a proper distinguishing line:
Figure BDA0002554911320000101
as shown in fig. 4, where the x-axis represents RMS delay spread and the y-axis represents kurtosis; red (cross-shaped dots) represents LOS scenes and blue (star-shaped dots) represents NLOS scenes. The straight line represents ax + b to be 0.06x +12.3, and through experimental simulation training, 405 data packets are trained, wherein 264 data packets are trained in an LOS environment, 141 data packets are trained in an NLOS environment, and the accuracy is 0.9160. The test set is 199 data packets, wherein 100 data packets are in an LOS environment, 99 data packets are in an NLOS environment, and the accuracy is 0.8492.
The invention also provides a visible distance and non-visible distance channel identification device, which comprises an information receiving device, one or more processors and a memory, wherein the information receiving device and the memory are connected with the processors through an I/O interface, the memory is used for storing computer executable programs, the processors read part or all of the computer executable programs from the memory and execute the computer executable programs, the processor can realize the visible and non-visible channel identification method based on kurtosis and RMS delay spread when executing part or all of the computer executable programs, and the memory is also used for storing information data acquired by the information receiving device.
The information receiving device adopts a vector network analyzer, and the vector network analyzer is connected with the processor through an I/O interface.
The visual distance and non-visual distance channel identification device also comprises an output device, wherein the output device is a printer or a display, and is connected with the output end of the processor; the output device can also be a touch screen, and the touch screen is connected with the processor through an I/O interface.
Further, when the device executes step 1, the device may acquire the data packet channel information through the information receiving device, and process the data packet channel information into the composite channel state information in real time by the processor; and may store the state information to memory.
The visible distance and non-visible distance channel identification device can adopt a notebook computer, a tablet computer, a desktop computer, a mobile phone or a workstation.
Alternatively, the processor according to the present invention may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or a Programmable Logic Device (PLD).
The memory of the invention can be an internal storage unit of a notebook computer, a tablet computer, a desktop computer, a mobile phone or a workstation, such as a memory and a hard disk; an external memory unit, such as a removable hard disk or a flash memory card, may also be used.

Claims (10)

1. A visual and non-visual channel identification method based on two-dimensional features is characterized by comprising the following steps:
s1, acquiring data packet channel information and processing the synthesized channel state information;
s2, carrying out Fourier inversion on each channel state information data obtained in the S1 to obtain corresponding channel impact response; and calculating the power of the channel impulse response;
s3, setting a first threshold, filtering noise of the channel impulse response obtained in S2, comparing the power of the channel impulse response obtained in S2 with the first threshold, taking out the power value larger than the first threshold, and discarding the power value smaller than the first threshold;
s4, obtaining the position of the maximum value of the channel impulse response after filtering, and finding out the amplitude of the channel impulse response of the point corresponding to the maximum value;
s5, calculating the kurtosis by using the amplitude obtained in the S4;
s6, finding the most suitable threshold value from the single characteristics of the RMS delay spread to be used as a second threshold value;
s7, calculating RMS delay spread of channel impulse response based on the second threshold value;
and S8, calculating coefficients a and b of a threshold line y, ax + b according to the kurtosis obtained in S5 and the RMS delay spread obtained in S7, and judging whether the data packet belongs to the line-of-sight environment or is received in the non-line-of-sight environment according to the threshold line.
2. The method for visual and non-visual channel recognition based on two-dimensional features of claim 1, wherein in S1, the amplitude and phase of each subcarrier in the channel transfer function are expressed as:
Figure FDA0002554911310000011
wherein, H (f)k) Representing carrier centre frequency fkCSI information of, | H (f)k) I and ∠ H (f)k) Each represents H (f)k) Amplitude and phase information of.
3. The method for identifying visual and non-visual channels based on two-dimensional features according to claim 1, wherein the CSI information obtained at S1 is subjected to fourier inversion to obtain channel impulse response;
Figure FDA0002554911310000012
wherein, ai,θi,τiRespectively representing the amplitude, phase and channel delay of the ith path, wherein N refers to the number of all paths, and (tau) is a Dirac delta function.
4. The method for identifying visual and non-visual channels based on two-dimensional features according to claim 1, wherein the threshold value of S3 is set as follows:
s31, acquiring the absolute value of the noise part of the channel impulse response;
s32, calculating the variance of the absolute value of S31 times;
at S33, the variance obtained at S32 is multiplied by 20 times log10 and added to 6 to obtain a threshold value of one.
5. The method of claim 1, wherein the kurtosis is calculated in S5 according to the following formula:
Figure FDA0002554911310000021
where E {. cndot } represents the sample expected delay, μ|h|And σ|h|Mean and standard deviation of | h (τ) | representing CIR amplitude, respectively.
6. The method for identifying visual and non-visual channels based on two-dimensional features according to claim 1, wherein the threshold two is set in S6 as follows:
1) taking out the maximum power of each CIR;
2) the maximum power is reduced by 21 to set the threshold value of two.
7. The method for identifying visual and non-visual channels based on two-dimensional features according to claim 1, wherein the specific implementation process of S6 includes:
subtracting the training threshold from the maximum of the power obtained in S3; the training threshold is a number of values that do not exceed the maximum power value and are not lower than the minimum power value obtained in S3;
filtering the channel impulse response of S2 with the training threshold value, calculating the power of the channel impulse response, comparing the training threshold value with the maximum value of the channel impulse response, and taking out all points greater than the training threshold value as h (tau)l)：
Figure FDA0002554911310000031
Wherein, taul(L-1) Δ τ ═ (L-i)/B, L ═ 1, x., L ═ 3201, B is the bandwidth;
in S7, a plurality of RMS delay spread characteristics are calculated by using the channel delay, the threshold value used for training and the channel impulse response obtained in S2, and the RMS delay spread characteristics are calculated as
Figure FDA0002554911310000032
And finding out a threshold value with the highest accuracy for identifying the sight distance and the non-sight distance as a second threshold value.
8. The method for identifying visual and non-visual channels based on two-dimensional features according to claim 1, wherein in S8, the specific determination formula is:
Figure FDA0002554911310000033
9. the method for identifying visual and non-visual channels based on two-dimensional features according to claim 1, wherein the threshold line coefficients in S8 are calculated by calculating a first and then calculating b based on a.
10. A device for identifying visible and non-visible channels, which comprises an information receiving device, one or more processors and a memory, wherein the information receiving device and the memory are connected with the processors through an I/O interface, the memory is used for storing a computer executable program, the processor reads part or all of the computer executable program from the memory and executes the computer executable program, the processor can realize the method for identifying visible and non-visible channels based on two-dimensional characteristics according to any one of claims 1 to 9 when executing part or all of the computer executable program, and the memory is also used for storing information data acquired by the information receiving device.
CN202010586663.1A 2020-06-24 2020-06-24 Visual and non-visual channel identification method and device based on two-dimensional characteristics Active CN111770527B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010586663.1A CN111770527B (en) 2020-06-24 2020-06-24 Visual and non-visual channel identification method and device based on two-dimensional characteristics

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010586663.1A CN111770527B (en) 2020-06-24 2020-06-24 Visual and non-visual channel identification method and device based on two-dimensional characteristics

Publications (2)

Publication Number Publication Date
CN111770527A true CN111770527A (en) 2020-10-13
CN111770527B CN111770527B (en) 2023-06-02

Family

ID=72722090

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010586663.1A Active CN111770527B (en) 2020-06-24 2020-06-24 Visual and non-visual channel identification method and device based on two-dimensional characteristics

Country Status (1)

Country Link
CN (1) CN111770527B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114978377A (en) * 2021-02-20 2022-08-30 大唐移动通信设备有限公司 Channel scene identification method and device and receiving device
CN115114571A (en) * 2022-06-26 2022-09-27 长安大学 Non-line-of-sight identification method based on kernel principal component analysis and reinforcement unsupervised learning

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101893707A (en) * 2010-05-27 2010-11-24 华为技术有限公司 Method and device for identifying non-line-of-sight propagation and base station
CN104467990A (en) * 2014-11-26 2015-03-25 清华大学 Method and device for recognizing line-of-sight propagation path of wireless signals
CN106792808A (en) * 2016-12-08 2017-05-31 南京邮电大学 Los path recognition methods under a kind of indoor environment based on channel condition information
CN109151707A (en) * 2018-01-22 2019-01-04 中南大学 A kind of sighting distance/obstructed path recognition methods under moving condition
CN109934031A (en) * 2019-03-20 2019-06-25 中南大学 The method and system of differentiation LOS/NLOS based on RFID system
US20200132826A1 (en) * 2018-10-26 2020-04-30 Metawave Corporation Method and apparatus for non-line-of-sight detection and coded radar signals

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101893707A (en) * 2010-05-27 2010-11-24 华为技术有限公司 Method and device for identifying non-line-of-sight propagation and base station
CN104467990A (en) * 2014-11-26 2015-03-25 清华大学 Method and device for recognizing line-of-sight propagation path of wireless signals
CN106792808A (en) * 2016-12-08 2017-05-31 南京邮电大学 Los path recognition methods under a kind of indoor environment based on channel condition information
CN109151707A (en) * 2018-01-22 2019-01-04 中南大学 A kind of sighting distance/obstructed path recognition methods under moving condition
US20200132826A1 (en) * 2018-10-26 2020-04-30 Metawave Corporation Method and apparatus for non-line-of-sight detection and coded radar signals
CN109934031A (en) * 2019-03-20 2019-06-25 中南大学 The method and system of differentiation LOS/NLOS based on RFID system

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
FEI WANG等: "LOS/NLOS Channel Identification Technology Based on CNN" *
纪元法;杨政权;孙希延;宋建国;卢伟军;: "基于信道特征的超宽带非视距鉴别及定位算法" *
魏思菁;周亭亭;: "基于仿射传播聚类的LOS/NLOS环境识别算法" *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114978377A (en) * 2021-02-20 2022-08-30 大唐移动通信设备有限公司 Channel scene identification method and device and receiving device
CN114978377B (en) * 2021-02-20 2023-09-26 大唐移动通信设备有限公司 Channel scene recognition method, device and receiving device
CN115114571A (en) * 2022-06-26 2022-09-27 长安大学 Non-line-of-sight identification method based on kernel principal component analysis and reinforcement unsupervised learning

Also Published As

Publication number Publication date
CN111770527B (en) 2023-06-02

Similar Documents

Publication Publication Date Title
CN104812061B (en) A kind of indoor ranging and localization method based on MIMO-OFDM channel state information
US11467248B2 (en) Position estimation device and communication device
Wong et al. Using WLAN infrastructure for angle-of-arrival indoor user location
Amar et al. Direct position determination (DPD) of multiple known and unknown radio-frequency signals
Xiong et al. Towards fine-grained radio-based indoor location
CN106909779B (en) MIMO radar Cramér-Rao lower bound calculation method based on distributed treatment
Wei et al. AOD/AOA/TOA-based 3D positioning in NLOS multipath environments
US9763216B2 (en) Radiator localization
Rea et al. Filtering noisy 802.11 time-of-flight ranging measurements from commoditized wifi radios
CN108646213B (en) Direct wave AOA (automatic optical inspection) judgment method in indoor multipath environment
Xiong et al. Synchronicity: Pushing the envelope of fine-grained localization with distributed mimo
WO2015177559A1 (en) Method for localising scattering elements in a 3d environment
CN111770527B (en) Visual and non-visual channel identification method and device based on two-dimensional characteristics
WO2020259055A1 (en) Wireless positioning method, positioning apparatus, and network device
Kong et al. Comparisons of TDOA triangulation solutions for indoor positioning
WO2010034769A1 (en) Method and system for estimating the location of a mobile terminal
Li et al. An iterative 5G positioning and synchronization algorithm in NLOS environments with multi-bounce paths
CN113194427B (en) Identification method, system and device based on soft-decision visual and non-visual channels
CN104105198B (en) signal processing method, device and equipment
Schmitz et al. TDOA fingerprinting for localization in non-line-of-sight and multipath environments
CN111770528A (en) Sight distance and non-sight distance identification method and device based on channel parameter extraction method
Ribeiro et al. Propagation parameter estimation in MIMO systems using mixture of angular distributions model
Vin et al. Multipath component distance-based fingerprinting technique for non-cooperative outdoor localization in NLOS scenarios
He A novel wireless sensor networks multilateration positioning method based on quartic double-well bistable stochastic resonance technique
Dai et al. A cooperative device free wireless sensing design and analysis for target position estimation

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