CN111770527B - Visual and non-visual channel identification method and device based on two-dimensional characteristics - Google Patents

Visual and non-visual channel identification method and device based on two-dimensional characteristics Download PDF

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CN111770527B
CN111770527B CN202010586663.1A CN202010586663A CN111770527B CN 111770527 B CN111770527 B CN 111770527B CN 202010586663 A CN202010586663 A CN 202010586663A CN 111770527 B CN111770527 B CN 111770527B
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CN111770527A (en
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王威
刘鑫一
谢景丽
侯俊
庞继龙
徐志麟
杨芬
宋吉婷
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B17/30—Monitoring; Testing of propagation channels
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
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Abstract

The invention discloses a visual and non-visual channel identification method and device based on two-dimensional characteristics, which are used for acquiring data packet channel information and processing synthesized channel state information; performing Fourier inverse transformation on each channel state information data, and calculating the power of channel impulse response; setting a threshold I, filtering noise of the channel impulse response obtained in the step S2, obtaining the position of a maximum value of the channel impulse response after filtering, finding out the amplitude value of the channel impulse response of a point corresponding to the maximum value, and calculating kurtosis; finding the most suitable threshold value by using a single feature of RMS delay spread as a second threshold value; calculating an RMS delay spread of the channel impulse response based on the threshold two; according to kurtosis and RMS delay spread, a threshold line coefficient is calculated, whether the data packet belongs to the line-of-sight environment or is received in the non-line-of-sight environment is judged according to the threshold line.

Description

Visual and non-visual channel identification method and device based on two-dimensional characteristics
Technical Field
The invention belongs to the technical field of wireless communication, relates to the field of location service application, and in particular relates to a visual and non-visual channel identification method and device based on two-dimensional characteristics.
Background
With the rapid development of data services and multimedia services, smart city construction is expanding and in depth, and people are focusing on the application of location based services (Location based Services, LBS) and the demand for high-precision navigation positioning is increasing. The positioning is usually performed outdoors by adopting GPS Beidou navigation and the like based on satellite signals, however, after the satellite signals enter indoors, such as underground garages, mines, warehouses and other complex indoor environments, the positioning signals are emitted, refracted, projected and diffracted to form multipath and Non-line-of-sight (NLOS) transmission phenomena, so that the positioning errors are larger, and the positioning requirements of the indoor environments cannot be met.
Indoor positioning technology is therefore becoming an important research and development effort for researchers. The indoor environment is interfered by a large number of objects (e.g., furniture in a room, movement of a human body) and the like, so that the indoor wireless signal transmission environment is extremely complex, the signals are generally divided into two types, a Line-of-sight (LOS) path and an NLOS path, the NLOS path propagation has the effects of increasing delay on the signals, reducing signal intensity attenuation, changing arrival angles, changing phases, and relatively rapid changes in a short time, and the NLOS path has the effects of reducing the indoor positioning accuracy to a certain extent, so that the indoor LOS/NLOS path is identified and errors caused by the NLOS path are avoided or reduced in order to improve the indoor positioning accuracy.
Various indoor positioning technologies, such as Ultra Wide Band (UWB) indoor positioning technology, radio frequency identification (Radio Frequency Identification, RFID) indoor positioning technology, ultrasonic Wave (Ultrasonic Wave) indoor positioning technology, bluetooth (Bluetooth) indoor positioning technology, zigBee indoor positioning technology, have been studied. Although many of these indoor positioning techniques can be applied in a self-adaptive indoor environment, they all require pre-deployment of positioning network and hardware devices, and have a significant cost overhead. With the rapid rise of the WiFi network and the rapid popularization of the intelligent mobile terminal, the indoor positioning by utilizing WiFi is greatly focused, and WiFi signals widely exist in indoor spaces and comprise various scenes such as families, markets, transportation hubs and the like, so that the indoor positioning system is an ideal positioning source.
With the development of WiFi technology, IEEE 802.11n series communication protocols and wireless lan protocols following the same apply multiple-input-multiple-output (MIMO) and orthogonal frequency division multiplexing (orthogonal frequency division multiplexing, OFDM) technologies, so that channel characteristics between WiFi transceivers can be estimated at a physical layer and stored in the form of channel state information (channel state information, CSI). As a corresponding quantitative representation of the channel frequency, CSI may reflect properties such as scattering, environmental attenuation, power attenuation, etc. in a physical environment. CSI is an essential description of the propagation of wireless signals in space, providing finer channel frequency response information, including richer features, than conventional signal reception strength values (received signal strength indicator, RSSI). CSI information is more accurately located than RSSI.
In the identification method, the 'a wireless network self-adaptive authorization intervention method' applied by Nanjing mail university, publication number: CN 106792552), among which the disadvantages are: only one threshold is set singly, and the accuracy of the point identification around the threshold is not considered, so that the signal around the threshold is subjected to a larger degree of decision error.
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 resolving a sight distance/non-sight distance path in a complex indoor environment, improve positioning precision and achieve the aim of high-precision position service quality.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a visual and non-visual channel identification method based on two-dimensional characteristics comprises the following steps:
s1, acquiring data packet channel information and processing synthesized channel state information;
s2, carrying out Fourier inverse transformation on each channel state information data obtained in the S1 to obtain a corresponding channel impact response; and calculating the power of the channel impulse response;
s3, setting a first threshold value, filtering noise of the channel impulse response obtained in the step S2, comparing power of the channel impulse response obtained in the step S2 with the first threshold value, taking out power values larger than the first threshold value, and discarding power values smaller than the first threshold value;
s4, obtaining the position of the maximum value of the channel impulse response after filtering, and finding out the amplitude value of the channel impulse response of the point corresponding to the maximum value;
s5, calculating kurtosis by using the amplitude obtained in the step S4;
s6, finding out the most suitable threshold value by using a single feature of RMS delay expansion, and taking the most suitable threshold value as a second threshold value;
s7, calculating the RMS delay spread of the channel impulse response based on the threshold value II;
and S8, calculating coefficients a and b of a threshold line y=ax+b according to the kurtosis obtained in the S5 and the RMS delay spread obtained in the 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 expressed as:
Figure BDA0002554911320000031
wherein H (f) k ) Representing the carrier center frequency f k CSI information of (f) k ) I and & lt H (f) k ) Respectively represent H (f) k ) Amplitude and phase information of (a) is provided.
Performing Fourier inverse change on the CSI information obtained in the step S1 to obtain channel impulse response;
Figure BDA0002554911320000032
wherein a is i ,θ i ,τ i The amplitude, phase and channel delay on the ith path are respectively represented, N refers to the number of all paths, and δ (τ) is a Dirac delta function.
The specific case of setting the threshold value one in S3 is as follows:
s31, acquiring an absolute value of a noise part of the channel impulse response;
s32, calculating a variance of 4 times the absolute value of the S31;
s33, multiplying the variance obtained in S32 by log10 multiplied by 20 times and adding 6, and setting the value as a threshold value I.
In S5, kurtosis is calculated using the following formula:
Figure BDA0002554911320000041
where E {. Cndot. } represents the desired delay in sampling, μ |h| Sum sigma |h| Mean and standard deviation of |h (τ) | of CIR amplitude are shown, respectively.
The threshold value II is set in S6 as follows:
1) Taking out the maximum power of each CIR;
2) The maximum power is reduced by 21 and is set to the threshold value two.
The specific implementation process of S6 comprises the following steps:
subtracting the training threshold from the maximum value of the power obtained in S3; the threshold value of training is a plurality of values which do not exceed the maximum value of the power and are not lower than the minimum value of the power obtained in the step S3;
filtering the channel impulse response of S2 with a trained threshold value, calculating the power of the channel impulse response, comparing the trained threshold value with the maximum value of the channel impulse response, and taking out all points larger than the trained threshold value as h (tau l )：
Figure BDA0002554911320000042
Wherein τ l = (L-1) Δτ= (L-i)/B, l=1, x.
In S7, using the channel delay, the threshold for training and the channel impulse response obtained in S2, several RMS delay spread characteristics are calculated, wherein the RMS delay spread characteristics are calculated as follows
Figure BDA0002554911320000043
And finding out the threshold value with the highest accuracy rate for identifying the visual distance and the non-visual distance as a threshold value II.
S8, a specific judgment formula is as follows:
Figure BDA0002554911320000044
and S8, calculating a when the threshold line coefficient is calculated, and then calculating b based on the a.
The invention relates to a visual range and non-visual range 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 processor 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, and the processor can realize the visual and non-visual channel identification method based on the two-dimensional characteristics when executing part or all of the computer executable program.
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 compared with a single characteristic, the accuracy of identification is increased, the single characteristic identifies the signal through a threshold value, the signal around the threshold value is easy to judge and mistakes, the two-dimensional characteristic judgment just solves the problem, the signal around the threshold value I can be judged through a threshold value II, the signal around the threshold value II can be judged through a threshold value I, and the double judgment can accurately improve the accuracy of identification.
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FIG. 1 is a probability density function of kurtosis and RMS delay spread in a LOS scene calculated after preprocessing a training set signal using the present invention.
FIG. 2 is a probability density function of kurtosis and RMS delay spread in an NLOS scene calculated after preprocessing a training set signal using the present invention.
FIG. 3 shows kurtosis and RMS delay spread for different scenarios calculated after preprocessing a training set signal using the present invention.
Wherein the x-axis represents RMS delay spread and the y-axis represents kurtosis; red represents LOS scene and blue represents NLOS scene.
FIG. 4 shows kurtosis and RMS delay spread for different scenarios calculated after preprocessing a test set signal using the present invention.
Figure 5 is a flow chart of an alternative embodiment of the present invention.
Detailed Description
The invention will be described in detail with reference to the drawings accompanying the detailed description.
Referring to fig. 5, a visual and non-visual channel recognition method based on two-dimensional features includes the steps of:
s1, acquiring data packet channel information and processing synthesized channel state information;
s2, carrying out Fourier inverse transformation on each channel state information data obtained in the S1 to obtain a corresponding channel impact response; and calculating the power of the channel impulse response;
s3, setting a first threshold value, filtering noise of the channel impulse response obtained in the step S2, comparing power of the channel impulse response obtained in the step S2 with the first threshold value, taking out power values larger than the first threshold value, and discarding power values smaller than the first threshold value;
s4, obtaining the position of the maximum value of the channel impulse response after filtering, and finding out the amplitude value of the channel impulse response of the point corresponding to the maximum value;
s5, calculating kurtosis by using the amplitude obtained in the step S4;
s6, finding out the most suitable threshold value by using a single feature of RMS delay expansion, and taking the most suitable threshold value as a second threshold value; the specific implementation process comprises the following steps:
subtracting the training threshold from the maximum value of the power obtained in S3; the threshold value of training is a plurality of values which do not exceed the maximum value of the power and are not lower than the minimum value of the power obtained in the step S3;
filtering the channel impulse response of S2 with a trained threshold value, calculating the power of the channel impulse response, comparing the trained threshold value with the maximum value of the channel impulse response, and taking out all points larger than the trained threshold value as h (tau l )：
Figure BDA0002554911320000061
Wherein τ l = (L-1) Δτ= (L-i)/B, l=1, x.
Calculating a plurality of RMS delay spread characteristics by using the channel time delay, the threshold value for training and the channel impulse response obtained in the step 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 the RMS delay spread of the channel impulse response based on the threshold value II;
and S8, calculating coefficients a and b of a threshold line y=ax+b according to the kurtosis obtained in the S5 and the RMS delay spread obtained in the 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 expressed as:
Figure BDA0002554911320000072
wherein H (f) k ) Representing the carrier center frequency f k CSI information of (f) k ) I and & lt H (f) k ) Respectively represent H (f) k ) Amplitude and phase information of (a) is provided.
Performing Fourier inverse change on the CSI information obtained in the step S1 to obtain channel impulse response;
Figure BDA0002554911320000073
/>
wherein a is i ,θ i ,τ i The amplitude, phase and channel delay on the ith path are respectively represented, N refers to the number of all paths, and δ (τ) is a Dirac delta function.
The specific case of setting the threshold value one in S3 is as follows:
s31, acquiring an absolute value of a noise part of the channel impulse response;
s32, calculating a variance of 4 times the absolute value of the S31;
s33, multiplying the variance obtained in S32 by log10 multiplied by 20 times and adding 6, and setting the value as a threshold value I.
In S5, kurtosis is calculated using the following formula:
Figure BDA0002554911320000074
where E {. Cndot. } represents the desired delay in sampling, μ |h| Sum sigma |h| Mean and standard deviation of |h (τ) | of CIR amplitude are shown, respectively.
The threshold value II is set in S6 as follows:
1) Taking out the maximum power of each CIR; 2) The maximum power is reduced by 21 and is set to the threshold value two.
S8, a specific judgment formula is as follows:
Figure BDA0002554911320000081
one possible embodiment
(1) Acquiring data packets
Setting up a channel measurement platform of 2.4 GHz-5.4 GHz in a conference room of 8 x 4m for channel measurement, firstly arranging 5 anchor nodes in the conference room to serve as transmitting antenna positions, and arranging target nodes in the conference room at intervals of 0.05m to serve as receiving antenna positions. Selecting 3 anchor nodes as line of sight (LOS) scenes, selecting the rest 2 anchor nodes as non-line of sight (NLOS) scenes, acquiring CSI information by using a notebook and a wireless router supporting IEEE 802.11n standard, storing the received data packet, wherein each CSI information represents the frequency, amplitude, phase and signal state information of each subcarrier from a transmitting end to a receiving end,
Figure BDA0002554911320000082
wherein H (f) k ) Representing the carrier center frequency f k CSI information of (f) k ) I and & lt H (f) k ) Respectively represent H (f) k ) Amplitude and phase information of (a);
(2) Performing IFT change on the data of each piece of CSI information acquired in the step (1) to obtain a corresponding channel impulse response (Channel Impulse Response, CIR):
Figure BDA0002554911320000083
wherein a is i ,θ i ,τ i The amplitude, phase and delay on the ith path are respectively represented, N refers to the number of all paths, and δ (τ) is the Dirac delta function.
(3) Calculating the power of the channel impulse response;
(4) Acquiring the absolute value of a noise part of the CIR, calculating the variance of 4 times of the CIR, multiplying the value by log10 of 20 times and adding 6 to the value, and setting the value as a threshold value I;
(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 value of the CIR of the point corresponding to the maximum value;
(7) Calculating kurtosis for the extracted amplitude
Figure BDA0002554911320000091
Where E {. Cndot. } represents the desired delay in sampling, μ |h| Sum sigma |h| Mean sum of |h (τ) | representing CIR amplitude, respectivelyStandard deviation.
(8) Taking out the maximum power of each CIR, subtracting 21 from the maximum power, and setting the maximum power as a threshold value II;
(9) Calculating the RMS delay spread of the CIR with the threshold two set in step (8);
(10) And (3) calculating coefficients a and b of a threshold line y=ax+b according to kurtosis obtained in the step (7) and RMS delay spread obtained in the step (9), and judging whether the data packet belongs to an LOS environment or an NLOS environment according to the line calculated by kurtosis and RMS delay spread and received in a non-line-of-sight environment or the line calculated by using kurtosis and RMS delay spread according to the threshold line.
Fig. 1 is a probability density function of kurtosis and RMS delay spread in LOS scene calculated after preprocessing the training set signal using the present invention, and fig. 2 is a probability density function of kurtosis and RMS delay spread in NLOS scene calculated after preprocessing the training set signal using the present invention.
According to the two-dimensional scatter diagram shown in fig. 3, red is a cross point, and blue is a star point; and setting the threshold line as y=ax+b, firstly establishing a circulation range of a, then establishing a circulation range of b corresponding to each a, finding all the threshold lines of b corresponding to different a, testing and identifying accuracy of the sight distance/non-sight distance based on the threshold line, and taking out a pair of a and b with highest accuracy, wherein ax+b=0.06x+12.3, and testing the test set.
In the LOS/NLOS path, the distribution probability of the kurtosis and the RMS delay spread of the signal is different, when the kurtosis and the RMS delay spread of the measured data are subjected to the probability density in the LOS environment, we consider the path as the LOS path, otherwise, if the measured data belong to the probability density distribution in the NLOS path, the path belongs to the NLOS path.
Because the kurtosis and RMS delay spread of the signal are smaller in the NLOS environment than in the LOS environment during training, we can classify LOS data from NLOS data by finding the appropriate line of distinction:
Figure BDA0002554911320000101
as shown in fig. 4, where the x-axis represents RMS delay spread and the y-axis represents kurtosis; red (cross points) represents LOS scene and blue (star points) represents NLOS scene. The straight line represents ax+b=0.06x+12.3, and training is performed through experimental simulation, training set 405 data packets, wherein 264 data packets are used in LOS environment, 141 data packets are used in NLOS environment, and accuracy is 0.9160. The test set is 199 data packets, wherein the LOS environment is 100 data packets, the NLOS environment is 99 data packets, and the accuracy is 0.8492.
The invention also provides a visual range and non-visual range 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 both connected with the processor 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, and the processor can realize the kurtosis and RMS delay spread-based visual and non-visual channel identification method when executing part or all of the computer executable program.
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 the output device 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 information receiving device may acquire the data packet channel information, and the processor processes the data packet channel information in real time to synthesize channel state information; and may store the status information to a memory.
The visual distance and non-visual distance channel recognition device can adopt a notebook computer, a tablet computer, a desktop computer, a mobile phone or a workstation.
Alternatively, the processor of 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 can be an internal memory 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; external storage units such as removable hard disks or flash memory cards may also be used.

Claims (8)

1. The visual and non-visual channel identification method based on the two-dimensional characteristics is characterized by comprising the following steps of:
s1, acquiring data packet channel information and processing synthesized channel state information;
s2, carrying out Fourier inverse transformation on each channel state information data obtained in the S1 to obtain a corresponding channel impact response; and calculating the power of the channel impulse response;
s3, setting a first threshold value, filtering noise of the channel impulse response obtained in the step S2, comparing power of the channel impulse response obtained in the step S2 with the first threshold value, taking out power values larger than the first threshold value, and discarding power values smaller than the first threshold value;
s4, obtaining the position of the maximum value of the channel impulse response after filtering, and finding out the amplitude value of the channel impulse response of the point corresponding to the maximum value;
s5, calculating kurtosis by using the amplitude obtained in the step S4; kurtosis was calculated using the following:
Figure FDA0004177226620000011
where E {. Cndot. } represents the desired delay in sampling, μ |h| Sum sigma |h| Mean and standard deviation of |h (τ) | representing the CIR amplitude, respectively;
s6, finding out the most suitable threshold value by using a single feature of RMS delay expansion, and taking the most suitable threshold value as a second threshold value; subtracting the training threshold from the maximum value of the power obtained in S3; the threshold value of training is a plurality of values which do not exceed the maximum value of the power and are not lower than the minimum value of the power obtained in the step S3;
threshold for training of channel impulse response to S2Filtering the values, 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 larger than the training threshold value as h (tau l )：
Figure FDA0004177226620000012
Wherein τ l = (L-1) Δτ= (L-i)/B, l=1, x.
S7, calculating the RMS delay spread of the channel impulse response based on the threshold value II; calculating a plurality of RMS delay spread characteristics by using the channel time delay, the threshold value used for training and the channel impulse response obtained in the step S2, wherein the RMS delay spread characteristics are calculated as follows:
Figure FDA0004177226620000021
finding out a threshold value with highest accuracy in identifying the visual distance and the non-visual distance as a threshold value II;
and S8, calculating coefficients a and b of a threshold line y=ax+b according to the kurtosis obtained in the S5 and the RMS delay spread obtained in the 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 identifying visible and invisible channels based on two-dimensional characteristics according to claim 1 wherein in S1, the amplitude and phase of each subcarrier in the channel transfer function are expressed as:
Figure FDA0004177226620000022
wherein H (f) k ) Representing the carrier center frequency f k CSI information of (f) k ) I and & lt H (f) k ) Respectively represent H (f) k ) Amplitude and phase information of (a) is provided.
3. The method for identifying visible and invisible channels based on two-dimensional characteristics according to claim 1, wherein the CSI obtained in S1 is subjected to inverse fourier transform to obtain a channel impulse response;
Figure FDA0004177226620000023
wherein a is i ,θ i ,τ i The amplitude, phase and channel delay on the ith path are respectively represented, N refers to the number of all paths, and δ (τ) is a Dirac delta function.
4. The method for identifying visible and invisible channels based on two-dimensional characteristics according to claim 1, wherein the specific steps of setting the threshold value of S3 are as follows:
s31, acquiring an absolute value of a noise part of the channel impulse response;
s32, calculating a variance of 4 times the absolute value of the S31;
s33, multiplying the variance obtained in S32 by log10 multiplied by 20 times and adding 6, and setting the value as a threshold value I.
5. The method for identifying visible and invisible channels based on two-dimensional characteristics according to claim 1, wherein the threshold value set in S6 is as follows:
1) Taking out the maximum power of each CIR;
2) The maximum power is reduced by 21 and is set to the threshold value two.
6. The method for identifying visible and invisible channels based on two-dimensional characteristics according to claim 1, wherein in S8, a specific judgment formula is:
Figure FDA0004177226620000031
7. the method for identifying visual and non-visual channels based on two-dimensional features according to claim 1, wherein in S8, a is calculated first and b is calculated based on a when the threshold line coefficient is calculated.
8. The visual range and non-visual range channel identification device is characterized by comprising an information receiving device, one or more processors and a memory, wherein the information receiving device and the memory are connected with the processor 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 visual and non-visual channel identification method based on the two-dimensional characteristics according to any one of claims 1 to 7 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.
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