Method and apparatus for identifying line-of-sight channel of indoor scene based on polarization and k-factor

Abstract

The application provides a method and device for identifying a line-of-sight channel of an indoor scene based on polarization and K factors. The method provided by the application comprises the following steps: collecting channel data under different polarization modes in an indoor environment; analyzing the channel data, and calculating time delay characteristics and K factors of the channel; based on the time delay characteristics and the K factors, analyzing time similarity and space similarity between channels, and dividing a whole measurement period and a transmitting end antenna array into multiple regions; based on the time delay characteristics and the K factors of all channels, calculating a first average value of the overall time delay characteristics and the overall K factors, and calculating second average values of the time delay characteristics and the K factors of all channels in each region after the division; comparing the second average values of each region with the first average value, and determining a preliminary region state in combination with time delay characteristic similarity of multiple channel data; determining a dynamic threshold based on a cumulative distribution function of the K factors, optimizing the preliminary region state based on the dynamic threshold, and obtaining a line-of-sight classification result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of line-of-sight channel identification, and in particular to an indoor scene line-of-sight channel identification method and device based on polarization and K factors. BACKGROUND

[0002] With the rapid development of 6G communication technology, the fine management of signal propagation characteristics in indoor scenes, as an important application environment for wireless communication, has become increasingly prominent. Indoor line-of-sight (LOS) channels, due to their small propagation loss, high signal strength, and stable path, ensure positioning accuracy and communication quality. The accurate identification of LOS and non-line-of-sight (NLOS) channels is a key to improving the performance of indoor wireless communication systems and user experience, and it also provides an important basis for data analysis in indoor near-field environments. Therefore, it is of great significance to carry out research on indoor line-of-sight channel identification.

[0003] Currently, the identification of LOS and NLOS in the academic field mainly focuses on channel characteristic parameters and intelligent algorithms. Traditional methods are mostly based on K factors, time of arrival (TOA), phase difference, etc. For example, a detector is designed by deriving the probability density function of the K factor, or the variance of the phase difference is used for discrimination. With the development of machine learning, algorithms such as convolutional neural networks (CNN) are widely used to improve identification accuracy by extracting channel impulse response (CIR) features and combining time-frequency domain analysis. The identification accuracy of some solutions in specific scenarios can reach more than 98%. However, existing identification methods have obvious limitations. On the one hand, traditional methods based on K factors and other parameters rely on fixed thresholds, but in 6G near-field communication and large-scale MIMO systems, the K factor threshold of indoor dynamic scenes will change due to very short communication distances. It is difficult to reflect the real characteristics of the channel by identifying LOS and NLOS with a fixed threshold specified by a certain standard. On the other hand, existing methods lack systematic and dynamic comprehensive consideration, making it difficult to adapt to complex and changing indoor environments.

[0004] Therefore, there is an urgent need for a method to solve the adaptability problem of existing methods in dynamic scenarios and achieve accurate classification of line-of-sight, non-line-of-sight, and obstructed line-of-sight channels. SUMMARY

[0005] Therefore, the present application provides an indoor scene line-of-sight channel identification method and device based on polarization and K factors to solve the adaptability problem of existing methods in dynamic scenarios and achieve accurate classification of line-of-sight, non-line-of-sight, and obstructed line-of-sight channels.

[0006] Specifically, the present application is implemented through the following technical solutions:

[0007] The first aspect of the present application provides an indoor scene line-of-sight channel identification method based on polarization and K factors, which comprises:

[0008] collecting channel data under different polarization modes in a laboratory indoor environment;

[0009] analyzing the channel data to calculate the delay characteristics and K factors of the channels;

[0010] based on the delay characteristics and K factors, analyzing the time similarity and spatial similarity between channels, and dividing the entire measurement period and the transmitting end antenna array into multiple regions based on the time similarity and spatial similarity;

[0011] based on the calculated delay characteristics and K factors of all channels, calculating the first average values of the overall delay characteristics and K factors, and calculating the second average values of the delay characteristics and K factors of all channels in each region for each divided region;

[0012] comparing the second average values of each region with the corresponding first average values, and determining the preliminary region state in combination with the delay characteristic similarity of multiple channel data;

[0013] determining a dynamic threshold based on the cumulative distribution function of the K factors, optimizing the preliminary region state based on the dynamic threshold, and obtaining the line-of-sight classification result.

[0014] The second aspect of the present application provides an indoor scene line-of-sight channel identification device based on polarization and K factors, which comprises an acquisition module, a calculation module, a division module, a determination module and an optimization module;

[0015] The acquisition module is used to collect channel data under different polarization modes in a laboratory indoor environment.

[0016] The calculation module is used to analyze the channel data to calculate the delay characteristics and K factors of the channels.

[0017] The division module is used to analyze the time similarity and spatial similarity between channels based on the delay characteristics and K factors, and divide the entire measurement period and the transmitting end antenna array into multiple regions based on the time similarity and spatial similarity.

[0018] The calculation module is also used to calculate the first average values of the overall delay characteristics and K factors based on the calculated delay characteristics and K factors of all channels, and calculate the second average values of the delay characteristics and K factors of all channels in each region for each divided region.

[0019] The determination module is used to compare the second average values of each region with the corresponding first average values, and determine the preliminary region state in combination with the delay characteristic similarity of multiple channel data.

[0020] The optimization module is configured to determine a dynamic threshold based on a cumulative distribution function of the K-factor, and optimize the preliminary region state based on the dynamic threshold to obtain a line-of-sight classification result.

[0021] The method and device for identifying a line-of-sight channel of an indoor scene based on polarization and a K-factor provided in the present application realize line-of-sight identification through an overall process of "multi-dimensional feature extraction, region division, preliminary determination, and dynamic optimization". First, channel data in different polarization modes is collected, and polarization characteristic differences (e.g., different polarizations have different performances in obstacle reflection) are used to enrich feature dimensions. Then, time delay characteristics and a K-factor are calculated, regions with similar propagation characteristics are divided based on time and space similarities, and a preliminary state determination is completed based on differences in average values of time delay characteristics and K-factors and time delay characteristic similarities between regions and the whole. This process fuses multiple features and polarization information, and reduces single-dimensional bias. Then, a dynamic threshold is determined based on a cumulative distribution function of the K-factor for secondary adjustment. The dynamic threshold can adapt to actual distribution laws of the K-factor in different scenes, and avoids limitations of a fixed threshold. The preliminary determination reduces basic misjudgments through cross verification of multiple features and supplement of polarization characteristics, and lays a reliable foundation for secondary optimization. For example, consistency of multiple polarization data can exclude accidental errors in a single polarization. The secondary optimization of the dynamic threshold can correct misjudgments of boundary regions in the preliminary determination, adapt to changes in indoor environments (e.g., changes in K-factor distribution caused by obstacle movement), finally improve accuracy of line-of-sight identification and adaptability to complex indoor scenes, form a closed loop of "comprehensive feature capture and accurate deviation correction", and make the classification result more consistent with the actual physical environment. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 A flowchart of the method for identifying a line-of-sight channel of an indoor scene based on polarization and a K-factor provided in Embodiment One of the present application;

[0023] Figure 2 A structural schematic diagram of the device for identifying a line-of-sight channel of an indoor scene based on polarization and a K-factor provided in Embodiment Two of the present application. DETAILED DESCRIPTION

[0024] The exemplary embodiments will be described in detail herein below with reference to the drawings. When the following description refers to the drawings, identical numbers on different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0025] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in this application, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "and / or," as used herein, refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0026] It is to be understood that, although the terms first, second, third, etc. can be used herein to describe various information, these terms are not intended to denote a particular order or hierarchy. These terms are used merely for the purpose of distinguishing between two or more information. For example, without departing from the scope of the present application, a first information can be termed a second information, and similarly, a second information can be termed a first information. The word "if" as used herein means "when" or "upon" or "in response to the determination" depending on the context.

[0027] The specific embodiments are given below to detail the technical scheme of the present application.

[0028] Figure 1 The flow chart of the indoor scene line-of-sight channel identification method based on polarization and K factor provided by Embodiment One of the present application is shown in FIG. 1. Please refer to Figure 1 The method provided by the present embodiment can include:

[0029] S101, collect channel data under different polarization modes in a laboratory indoor environment.

[0030] Specifically, different polarization modes refer to the vibration direction of the electric field strength of a single electromagnetic wave. Common polarization modes include vertical polarization and horizontal polarization. Co-polarization and cross-polarization are polarization matching relationships between the transmitting end and the receiving end. Co-polarization usually refers to the polarization direction of the transmitting end and the receiving end being consistent (e.g., both vertical polarization); cross-polarization refers to the polarization direction of the transmitting end and the receiving end being perpendicular (e.g., the transmitting end vertical polarization, the receiving end horizontal polarization).

[0031] Further, channel data refers to signal characteristic parameters recorded when an electromagnetic wave propagates through a channel (i.e., a propagation path) in the propagation process. The channel data in the present application mainly includes K factor, time delay characteristics, etc. Specific channel data will be described in the following embodiments.

[0032] In a specific implementation, the collecting channel data under different polarization modes in the laboratory indoor environment comprises: setting a fixed antenna array at the transmitting end, deploying the array elements in a vertical polarization mode; configuring two antennas with different polarization modes at the receiving end; the antennas comprise a vertical polarization antenna and a horizontal polarization antenna; connecting a channel measuring instrument to the transmitting end and the receiving end, the transmitting end continuously transmits signals through the antenna array, the antennas of the receiving end are fixed on a movable device and move at a constant speed along a preset route; during the movement of the receiving end, the vertical polarization antenna and the horizontal polarization antenna synchronously receive the signals transmitted by the transmitting end, and the channel measuring instrument collects and records the channel data under the two polarization modes in real time.

[0033] Specifically, a fixed antenna array is set at the transmitting end, and the array elements are deployed in a vertical polarization mode. Two antennas with different polarization modes, i.e., a vertical polarization antenna and a horizontal polarization antenna, are configured at the receiving end. The transmitting end and the receiving end are connected with a channel measuring instrument. The transmitting end continuously transmits signals through the antenna array, and the two antennas of the receiving end are fixed on a movable device, which moves at a constant speed along a preset route. During the movement of the receiving end, the vertical polarization antenna and the horizontal polarization antenna synchronously receive the signals transmitted by the transmitting end, and the channel measuring instrument collects and records the channel data under the two polarization modes in real time.

[0034] For example, in an embodiment, the measurement is performed in the marine communication laboratory of Zhejiang Ocean University in an indoor laboratory environment, the carrier frequency band is 3.4 GHz, the transmitting end adopts a 4x32-element super-large antenna array which is fixed and static, and all the array elements are arranged in a vertical polarization mode. The receiving end adopts two antennas arranged in different polarization modes, wherein RX1 is arranged in a vertical polarization mode and RX2 is arranged in a horizontal polarization mode. The receiving antennas are fixed on a cart and move at a constant speed along a predetermined route. There is a column between the transmitting end and the receiving end, which will form an obstruction in the propagation path. A high-precision channel measuring instrument is used to collect experimental data in an indoor laboratory environment for the application of a 3.4 GHz super-large array. Table 1 is a laboratory indoor environment configuration table provided by the present application.

[0035] Table 1

[0036] Carrier frequency 3.4 GHz Signal interval 80 μs Signal bandwidth 100 MHz Time delay resolution 10 ns On-top transmit power 14.7 dBm TX antenna Triangular periodic log antenna TX antenna number 128 TX antenna gain 6.5 dBi RX antenna number 2 RX antenna gain 5.6 dBi TX antenna polarization Vertical polarization RX antenna polarization Horizontal and vertical polarization TX antenna height [0.6, 1.35] m RX antenna height 1.25 m Course distance 6 m Average speed 0.6 m / s File size 0.261 GB

[0037] S102, analyzing the channel data and calculating the delay characteristics and K factor of the channel.

[0038] The delay characteristics include the average delay and the root mean square delay.

[0039] Specifically, the delay characteristic is a parameter for describing the time difference of multipath signal propagation, and in the present application, the delay characteristic includes the average delay and the root mean square delay. The average delay refers to the average time for all multipath signals to arrive at the receiving end, reflecting the overall propagation delay level of the multipath signals. The root mean square delay refers to the dispersion degree of the multipath signal delay relative to the average delay, embodying the dispersion degree of the multipath signals (the greater the value, the more dispersed the multipath propagation). The K factor (Rician K-factor) is a parameter for measuring the relative strength of the line-of-sight component and the multipath component, defined as the ratio of the line-of-sight (LOS) signal power to the total power of all non-line-of-sight (NLOS) multipath signals. The greater the K factor, the higher the proportion of the line-of-sight component, and the smaller the channel interference caused by multipath; on the contrary, the higher the proportion of the multipath component.

[0040] In a specific implementation, the analysis of the channel data and the calculation of the delay characteristic of the channel include: determining each channel path from the channel data, obtaining the power delay profile and the path delay corresponding to each channel path; taking the power delay profile of each channel path as a weight, performing weighted summation on the path delay of the corresponding channel path, and then dividing by the sum of the power delay profiles of all channel paths to obtain the average delay of the channel. The power delay profile is obtained by averaging the energy of the channel impulse response function over a time interval.

[0041] Specifically, from the collected channel data, all existing channel paths are identified and determined. For each determined channel path, its corresponding power delay profile and path delay are extracted. The energy of the channel impulse response function is averaged over a specific time interval to obtain the power delay profile of each channel path, and the corresponding path delay is further matched based on the determined power delay profile. The power delay profile of each channel path is taken as a weight, and the path delay of the channel path is weighted and calculated, and the weighted results of all channel paths are added to obtain a weighted sum. The sum of the power delay profiles of all channel paths is calculated. The weighted sum is divided by the sum of the power delay profiles, and the calculation result is the average delay of the channel. The specific implementation process of extracting the power delay profile and the path delay of the channel path can refer to the description in the related art, which will not be described here.

[0042] For example, in an embodiment, the calculation process of the average delay can be represented as:

[0043] ;

[0044] wherein the average delay is denoted as , the number of channel paths is denoted as , the power delay profile is denoted as , the sum of the power delay profiles of all channel paths is denoted as , and the average delay is calculated by dividing the weighted sum by the sum of the power delay profiles. ; the path delay.

[0045] Optionally, the analyzing the channel data and calculating the delay characteristic of the channel comprises: calculating a difference between a path delay of each channel path and the average delay, and squaring each difference; performing weighted summation on the squared differences with power delay spectrum of each channel path as a weight; dividing the summation result by a sum of power delay spectrums of all channel paths, and taking a square root of the obtained result to obtain a root mean square delay of the channel.

[0046] Specifically, based on the determined channel paths, a path delay of each channel path is extracted, and the calculated average delay is called. For each channel path, a difference between the path delay and the average delay is calculated. Each calculated difference is squared to obtain a squared difference result corresponding to each channel path. The power delay spectrum of each channel path is taken as a weight, and the weight is multiplied by the squared difference result of the corresponding path to obtain a weighted square value of each channel path. The weighted square values of all channel paths are summed to obtain a weighted square summation. A sum of power delay spectrums of all channel paths is calculated. The obtained weighted square summation is divided by the sum of power delay spectrums to obtain a quotient, and a square root of the quotient is calculated. The calculation result is the root mean square delay of the channel.

[0047] For example, in an embodiment, the calculation process of the root mean square delay can be represented as:

[0048] ;

[0049] wherein the root mean square delay is denoted as RMSdelay, the number of channel paths is denoted as N, the power delay spectrum is denoted as PSD, the sum of power delay spectrums of all channel paths is denoted as PSDsum, the path delay is denoted as Pathdelay, and the average delay is denoted as Ave.

[0050] Optionally, the analyzing the channel data and calculating the K-factor of the channel comprises: performing cluster analysis on the delay and energy information in the channel data to determine an energy of an earliest arrival path and an energy of a scattering path; the energy of the scattering path is a difference between the sum of power delay spectrums of all channel paths and the energy of the earliest arrival path; and taking a logarithm of a ratio of the energy of the earliest arrival path to the energy of the scattering path to obtain the K-factor in decibels.

[0051] ​​​​​​​Specifically, time delay information and energy information of each channel path contained in the channel data are extracted. The extracted time delay information and energy information are subjected to cluster analysis, and different paths are distinguished through the cluster results. From the cluster analysis results, the earliest arriving path is identified and determined, and the energy corresponding to the path is extracted as the energy of the earliest arriving path. The sum of the power delay spectrum of all channel paths is calculated, and the energy of the scattering path is obtained by subtracting the energy of the earliest arriving path from the sum of the power delay spectrum of all channel paths. The ratio between the energy of the earliest arriving path and the energy of the scattering path is calculated, and the obtained ratio is subjected to logarithmic operation, and the operation result is the K factor in decibels. The specific implementation process of the cluster analysis can be referred to the description in the related art, and will not be described here.

[0052] For example, in an embodiment, the calculation process of the K factor can be represented as:

[0053] ;

[0054] wherein the K factor is K; the energy of the earliest arriving path is E; and the sum of the power delay spectrum of all channel paths is S.

[0055] The method provided in the embodiment calculates the time delay characteristics (average time delay and root mean square time delay) and the K factor of the channel, and provides reliable basis for the line-of-sight identification through multi-dimensional quantification of the channel propagation characteristics. Specifically, the time delay characteristics are used to quantify the time distribution of the multipath propagation by calculating the time delay, the average time delay and the root mean square time delay of each path. In the line-of-sight scenario, the multipath is less and concentrated, the average time delay is short, and the root mean square time delay is small. In the non-line-of-sight scenario, the multipath is dispersed, the average time delay is longer, and the root mean square time delay is larger. Therefore, the channel state can be distinguished from the time dimension. The K factor is used to calculate the ratio of the energy of the earliest arriving path (usually the line-of-sight path) and the scattering path, and the logarithm of the ratio directly reflects the energy proportion of the line-of-sight component and the multipath component. In the line-of-sight scenario, the K factor is larger, and in the non-line-of-sight scenario, the K factor is smaller, thereby providing a judgment basis from the energy dimension. The two are complementary to each other. The time distribution characteristics of the time delay characteristics can compensate for the deviation of the K factor caused by accidental strong multipath interference, and the energy proportion information of the K factor can correct the abnormality of the time delay characteristics caused by special environment. Through multi-dimensional joint analysis, the misjudgment of a single indicator can be effectively avoided, and finally the precise identification of the line-of-sight, non-line-of-sight and obstructed line-of-sight state is realized, and the accuracy and reliability of the classification result are improved.

[0056] S103, based on the time delay characteristics and the K factor, analyzing the time similarity and the spatial similarity between channels, and dividing the entire measurement period and the transmitting end antenna array into multiple regions based on the time similarity and the spatial similarity.

[0057] ​​​Specifically, the time similarity refers to the correlation degree between the characteristics (such as the delay characteristic, the K factor) of the same channel at different time points, that is, the similarity or consistency of the channel characteristics changing with time, if the channel characteristics at different time points are small, the time similarity is high, and vice versa. The spatial similarity refers to the correlation degree between the channel characteristics corresponding to different spatial positions (here specifically different antennas in the transmitting end antenna array), that is, the similarity of the channels experienced by different antennas in terms of delay characteristics, K factor, etc. If the channel characteristics of different antennas are close, the spatial similarity is high, and vice versa.

[0058] Further, the regions are divided based on the time similarity and the spatial similarity. Considering that the time similarity reflects the stability of the channel in the time dimension, the channel characteristics in the same time-related region change regularly and consistently, which can be regarded as having similar time evolution characteristics; the spatial similarity reflects the distribution law of the channel in the spatial dimension, the channel characteristics experienced by the antennas in the same space-related region are similar, which can be regarded as being in a similar propagation environment. By combining the time and spatial similarity to divide the regions, the entire measurement period and the transmitting end antenna array can be divided into multiple sub-regions with relatively consistent channel characteristics. The channel characteristics in each sub-region are more stable and regular, which facilitates subsequent accurate channel analysis, modeling or line-of-sight identification for different regions, reduces the analysis error caused by too large channel characteristic difference, and improves the effectiveness and accuracy of the overall processing.

[0059] In specific implementation, based on the delay characteristic and the K factor, the time similarity and the spatial similarity between channels are analyzed, and based on the time similarity and the spatial similarity, the entire measurement period and the transmitting end antenna array are divided into multiple regions, including: extracting the delay characteristic and K factor data at different time points, calculating the correlation coefficient of the data at adjacent time points, and analyzing the time similarity of the channel; extracting the delay characteristic and K factor data corresponding to different antenna elements in the transmitting end antenna array, calculating the correlation coefficient of the data between the array elements, and analyzing the spatial similarity of the channel; according to the strength change of the time similarity, when the time similarity is lower than a preset time threshold, segmenting, and dividing the entire measurement period into several time sub-segments; according to the distribution characteristics of the spatial similarity, when the spatial similarity is lower than a preset spatial threshold, blocking, and dividing the transmitting end antenna array into several spatial sub-arrays; combining the time sub-segments and the spatial sub-arrays to form multiple time-space regions.

[0060] Specifically, the delay characteristics (average delay, root mean square delay) and K-factor data corresponding to different time instants are extracted from the calculated channel data. The correlation coefficients between the delay characteristics and K-factor data of adjacent two time instants are calculated for the extracted data of different time instants, and the time similarity of the channel is analyzed through the coefficients. The delay characteristics and K-factor data corresponding to different antenna elements are extracted from the transmitting antenna array. The correlation coefficients between the delay characteristics and K-factor data of different antenna elements in the transmitting antenna array are calculated, and the spatial similarity of the channel is analyzed through the coefficients. According to the strength change of the time similarity obtained by analysis, a preset time threshold is set, and when the time similarity of adjacent time instants is lower than the preset time threshold, the entire measurement period is segmented and divided into several time subsegments. According to the distribution characteristics of the spatial similarity obtained by analysis, a preset spatial threshold is set, and when the spatial similarity between different antenna elements in the transmitting antenna array is lower than the preset spatial threshold, the antenna array is segmented and divided into several spatial subarrays. Each time subsegment and each spatial subarray are combined one by one to form multiple time-space regions containing time and space information.

[0061] In S104, the first average values of the overall delay characteristics and K-factor are calculated based on the calculated delay characteristics and K-factor of all channels, and the second average values of the delay characteristics and K-factor of all channels in each region are calculated for each divided region.

[0062] Specifically, the delay characteristics (including the average delay and root mean square delay of each channel) and K-factor data of all channels are collected. The first average value of the overall average delay is obtained by adding the average delays of all channels and dividing the sum by the total number of channels, the first average value of the overall root mean square delay is obtained by adding the root mean square delays of all channels and dividing the sum by the total number of channels, and the first average value of the overall K-factor is obtained by adding the K-factors of all channels and dividing the sum by the total number of channels. For each divided region, the delay characteristics (average delay, root mean square delay) and K-factor data of all channels in the region are extracted. The second average value of the average delay in the region is obtained by adding the average delays of all channels in the region and dividing the sum by the number of channels in the region, the second average value of the root mean square delay in the region is obtained by adding the root mean square delays of all channels in the region and dividing the sum by the number of channels in the region, and the second average value of the K-factor in the region is obtained by adding the K-factors of all channels in the region and dividing the sum by the number of channels in the region.

[0063] For example, the application is described by taking co-polarization antennas and cross-polarization antennas as examples. The acquired data includes: co-polarization K-factor second average values, cross-polarization K-factor second average values of each region, and overall co-polarization K-factor first average value and overall cross-polarization K-factor first average value; co-polarization time delay characteristic second average values, cross-polarization time delay characteristic second average values of each region, and overall co-polarization time delay characteristic first average value and overall cross-polarization time delay characteristic first average value.

[0064] In S105, the second average value of each region is compared with the corresponding first average value, and the time delay characteristic similarity of the multiple channel data is used to determine a preliminary region state.

[0065] Specifically, the preliminary region state refers to a preliminary attribute judgment made for each divided region based on the comparison result of the second average value (average value of the time delay characteristic and K-factor in the region) of each region and the overall first average value (average value of all channel time delay characteristics and K-factor), and the time delay characteristic similarity of the region and the overall time delay characteristic. This state reflects the overall characteristics of the channel of the region in terms of multipath propagation, such as whether it belongs to a line-of-sight region, a non-line-of-sight region, an obstructed line-of-sight region, etc.

[0066] In a specific implementation, the co-polarization K-factor second average value of each region is compared with the overall co-polarization K-factor first average value to obtain a first branch and a second branch; based on the cross-polarization K-factor second average value of each region in the first branch and the second branch, the overall cross-polarization K-factor is compared respectively to obtain a third branch and a fourth branch corresponding to the first branch, and a fifth branch and a sixth branch corresponding to the second branch; wherein the cross-polarization K-factor second average value of each region in the third branch and the sixth branch is not greater than the overall cross-polarization K-factor first average value, the third branch is a line-of-sight region, and the sixth branch is a non-line-of-sight region; for the fourth branch and the fifth branch, the comparison signs of the region co-polarization time delay characteristic and the overall co-polarization time delay characteristic, and the comparison signs of the region cross-polarization time delay characteristic and the overall cross-polarization time delay characteristic are calculated respectively; if the comparison signs of the time delay characteristics under the two polarizations are consistent, it is determined that the time delay characteristics are similar, and for the regions with similar time delay characteristics under the fourth branch and the fifth branch, it is determined as an obstructed line-of-sight region; for the regions with different time delay characteristics under the fourth branch, it is determined as a line-of-sight region; for the regions with different time delay characteristics under the fifth branch, it is determined as a non-line-of-sight region.

[0067] Specifically, the second average value of the co-polarization K factor of each region is compared with the first average value of the overall co-polarization K factor one by one, and two branches are obtained: if the second average value of the co-polarization K factor of the region is greater than the first average value of the overall co-polarization K factor, it is classified into the first branch; if the second average value of the co-polarization K factor of the region is less than or equal to the first average value of the overall co-polarization K factor, it is classified into the second branch. For each region in the first branch, the second average value of the cross-polarization K factor is compared with the first average value of the overall cross-polarization K factor: if the second average value of the cross-polarization K factor of the region is less than or equal to the first average value of the overall cross-polarization K factor, it is classified into the third branch; if the second average value of the cross-polarization K factor of the region is greater than the first average value of the overall cross-polarization K factor, it is classified into the fourth branch. For each region in the second branch, the second average value of the cross-polarization K factor is compared with the first average value of the overall cross-polarization K factor: if the second average value of the cross-polarization K factor of the region is greater than the first average value of the overall cross-polarization K factor, it is classified into the fifth branch; if the second average value of the cross-polarization K factor of the region is less than or equal to the first average value of the overall cross-polarization K factor, it is classified into the sixth branch. All regions corresponding to the third branch are directly determined as line-of-sight regions, and all regions corresponding to the sixth branch are directly determined as non-line-of-sight regions. For each region in the fourth branch and the fifth branch, the comparison symbol of the second average value of the region co-polarization delay characteristic and the first average value of the overall co-polarization delay characteristic (if the region value is greater than the overall value, the symbol is marked as “+”; if the region value is less than or equal to the overall value, the symbol is marked as “-”) and the comparison symbol of the second average value of the region cross-polarization delay characteristic and the first average value of the overall cross-polarization delay characteristic (the symbol rule is the same as above) are calculated. If the co-polarization delay characteristic comparison symbol and the cross-polarization delay characteristic comparison symbol of a certain region are consistent, it is determined that the delay characteristic of the region is similar: the regions with similar delay characteristics in the fourth branch are determined as occluded line-of-sight regions, and the regions with similar delay characteristics in the fifth branch are determined as occluded line-of-sight regions. If the co-polarization delay characteristic comparison symbol and the cross-polarization delay characteristic comparison symbol of a certain region are inconsistent, it is determined that the delay characteristic of the region is not similar: the regions with dissimilar delay characteristics in the fourth branch are determined as line-of-sight regions, and the regions with dissimilar delay characteristics in the fifth branch are determined as non-line-of-sight regions.

[0068] The method provided by the embodiment first divides the first branch and the second branch by comparing the co-polarization K factor with the overall co-polarization K factor, then obtains the third to sixth branches by comparing the regional cross-polarization K factor with the overall cross-polarization K factor based on the two branches respectively, directly determines that the third branch is a line-of-sight region and the sixth branch is a non-line-of-sight region, and further compares the regional co-polarization and the overall co-polarization delay characteristics and the regional cross-polarization and the overall cross-polarization delay characteristics of the fourth branch and the fifth branch, determines the line-of-sight, line-of-sight or non-line-of-sight region according to whether the signs are consistent, and determines the line-of-sight, line-of-sight or non-line-of-sight region. The hierarchical comparison of the dual-polarization K factor is the core means, the co-polarization K factor (a key parameter reflecting the direct path strength) is used to preliminarily screen the approximate range of the channel state, and then the cross-polarization K factor (reflecting polarization deflection and scattering) is used to further narrow the determination boundary, reducing the one-sidedness of the single polarization parameter judgment, and for the branches that cannot be directly determined after the comparison of the dual-polarization K factor, the sign consistency verification of the delay characteristics is introduced, which combines the representation ability of the K factor to the direct / scattering path strength and uses the reflection of the delay characteristics to the consistency of multipath propagation, realizes the complementary verification of multi-dimensional parameters, effectively avoids the misjudgment caused by the non-stationarity of a single parameter (such as the traditional fixed K factor threshold) in the channel characteristic space; and the hierarchical comparison logic makes the determination process progressive and the boundary clear, directly locks the line-of-sight (third branch) and non-line-of-sight (sixth branch) regions with clear characteristics, and then focuses on the fourth and fifth branches with fuzzy characteristics for fine verification, which improves the determination efficiency and ensures that the determination result is highly matched with the actual physical occlusion (such as the LOS / OLOS / NLOS switching caused by obstacles such as columns and conference tables) in the laboratory indoor scene, and finally guarantees the accuracy of the regional state determination.

[0069] In S106, a dynamic threshold is determined based on the cumulative distribution function of the K factor, the preliminary regional state is optimized based on the dynamic threshold, and a line-of-sight classification result is obtained.

[0070] Specifically, the cumulative distribution function (CDF) is a function describing the probability of a random variable (K factor) taking a value less than or equal to a certain value. For the K factor, its cumulative distribution function can reflect the probability distribution of the K factor value less than or equal to a certain threshold in the whole or in a region, and can intuitively present the statistical distribution characteristics (such as the numerical concentration range and the distribution form) of the K factor. The dynamic threshold is a judgment standard calculated based on the cumulative distribution function of the K factor and adjusted according to the data distribution characteristics. It is not a fixed value, but is dynamically determined according to the statistical law of the actual K factor (such as the distribution interval and the probability critical point), and can adapt to the distribution difference of the K factor in different scenarios. The line-of-sight classification result is the final state judgment of each region after optimization by the dynamic threshold, which is the correction and confirmation of the preliminary regional state (line-of-sight region, non-line-of-sight region, and occluded line-of-sight region), and finally determines the specific category of each region belonging to line-of-sight, non-line-of-sight, or occluded line-of-sight, providing an accurate classification conclusion for channel line-of-sight identification.

[0071] Further, the application considers that the preliminary regional state is determined by average value comparison and time delay characteristic similarity, and there may be misjudgments (such as atypical edge region characteristics) caused by statistical deviation (such as local data fluctuation) or fixed judgment logic; the dynamic threshold is generated based on the overall cumulative distribution function of the K factor, and can reflect the global statistical law of the data. By using the dynamic threshold to optimize the preliminary result, the errors caused by local feature deviation in the preliminary judgment can be corrected, the regional state division is more in line with the statistical characteristics of the overall channel environment, and the robustness and accuracy of the classification are improved.

[0072] In specific implementation, the determination of the dynamic threshold based on the cumulative distribution function of the K factor, the optimization of the preliminary regional state based on the dynamic threshold, and the obtaining of the line-of-sight classification result include: calculating the K factor distribution of the line-of-sight region, the occluded line-of-sight region, and the non-line-of-sight region in the preliminary regional state, and constructing the cumulative distribution function of the overall K factor; determining the first dynamic threshold for distinguishing the line-of-sight region from the occluded line-of-sight region, and the second dynamic threshold for distinguishing the occluded line-of-sight region from the non-line-of-sight region according to the K factor probability density characteristics of the three regions in the cumulative distribution function; comparing the second average value of the K factor of the preliminary judgment as the line-of-sight region with the first dynamic threshold, and if the threshold condition is met, the line-of-sight state is retained, otherwise the occluded line-of-sight state is corrected; comparing the second average value of the K factor of the preliminary judgment as the non-line-of-sight region with the second dynamic threshold, and if the threshold condition is met, the non-line-of-sight state is retained, otherwise the occluded line-of-sight state is corrected; comparing the second average value of the K factor of the preliminary judgment as the occluded line-of-sight region with the first and second dynamic thresholds, and if it is between the two thresholds, the occluded line-of-sight state is maintained, otherwise the corresponding state is corrected according to the threshold boundary; integrating and verifying the results to obtain the final line-of-sight classification result.

[0073] Specifically, the K-factor data of three types of regions in the preliminary region state is extracted: the second average value of K-factor of all regions in the line-of-sight region is extracted from the determined line-of-sight region to form a line-of-sight region K-factor data set; the second average value of K-factor of all regions in the occluded line-of-sight region is extracted from the occluded line-of-sight region to form an occluded line-of-sight region K-factor data set; the second average value of K-factor of all regions in the non-line-of-sight region is extracted from the non-line-of-sight region to form a non-line-of-sight region K-factor data set. The probability density curve (or frequency distribution histogram) of the overall K-factor cumulative distribution function is drawn to observe the distribution interval of the line-of-sight region K-factor data (usually concentrated in a higher value range), the distribution interval of the occluded line-of-sight region K-factor data (usually between the line-of-sight and non-line-of-sight), and the distribution interval of the non-line-of-sight region K-factor data (usually concentrated in a lower value range). The intersection point or probability density mutation point of the distribution of the three types of data is identified: the lower limit position of the line-of-sight region data distribution (i.e. the vicinity of the minimum value of the line-of-sight region K-factor data), and the upper limit position of the non-line-of-sight region data distribution (i.e. the vicinity of the maximum value of the non-line-of-sight region K-factor data), while observing the overlapping boundary of the occluded line-of-sight region data and the previous two types of data. Based on the K-factor distribution intersection of the line-of-sight region and the occluded line-of-sight region, the critical value of the overlapping interval of the two types of data distribution is selected as the first dynamic threshold. For example, if the line-of-sight region K-factor data is mainly distributed in [K1_min, K1_max], the occluded line-of-sight region data is mainly distributed in [K2_min, K2_max], and the overlapping interval is [K_overlap_low, K_overlap_high], the upper limit value (K_overlap_high) of the overlapping interval is determined as the first dynamic threshold, ensuring that most data in the line-of-sight region is higher than the threshold, and most data in the occluded line-of-sight region is lower than the threshold. Similarly, based on the K-factor distribution intersection of the occluded line-of-sight region and the non-line-of-sight region, the critical value of the overlapping interval of the two types of data distribution is determined as the second dynamic threshold. For example, if the occluded line-of-sight region K-factor data distribution interval is [K2_min, K2_max], the non-line-of-sight region data is mainly distributed in [K3_min, K3_max], and the overlapping interval is [K_overlap_low', K_overlap_high'], the lower limit value (K_overlap_low') of the overlapping interval is determined as the second dynamic threshold, ensuring that most data in the non-line-of-sight region is lower than the threshold, and most data in the occluded line-of-sight region is higher than the threshold.

[0074] For each region preliminarily determined as a line-of-sight region, the second average value of the K factor thereof is extracted, and the value is compared with the first dynamic threshold value: if the average value is higher than the first dynamic threshold value, meeting the threshold condition of the line-of-sight region, the line-of-sight state thereof is retained; if the average value is lower than or equal to the first dynamic threshold value, the state thereof is corrected to the occluded line-of-sight state. For each region preliminarily determined as a non-line-of-sight region, the second average value of the K factor thereof is extracted, and the value is compared with the second dynamic threshold value: if the average value is lower than the second dynamic threshold value, meeting the threshold condition of the non-line-of-sight region, the non-line-of-sight state thereof is retained; if the average value is higher than or equal to the second dynamic threshold value, the state thereof is corrected to the occluded line-of-sight state. For each region preliminarily determined as an occluded line-of-sight region, the second average value of the K factor thereof is extracted, and the value is compared with the first dynamic threshold value and the second dynamic threshold value: if the average value is between the first dynamic threshold value and the second dynamic threshold value (i.e. lower than the first dynamic threshold value and higher than the second dynamic threshold value), the occluded line-of-sight state thereof is maintained; if the average value is higher than or equal to the first dynamic threshold value, the state thereof is corrected to the line-of-sight state; if the average value is lower than or equal to the second dynamic threshold value, the state thereof is corrected to the non-line-of-sight state. The states of all regions after verification and correction are summarized to form the final line-of-sight classification result.

[0075] The method provided by the embodiment determines the first dynamic threshold value and the second dynamic threshold value based on the K factor distribution characteristics (probability density curve, distribution interval, overlapping boundary, etc.) of the three types of preliminary regions, rather than using fixed values, so that the actual statistical law of the K factor in different scenarios can be adapted, for example, the boundary between the line-of-sight and the occluded line-of-sight changes with the scattering intensity in the environment, and the dynamic threshold value can capture such changes. Meanwhile, the correction process verifies the preliminary state by comparing the second average value of the K factor of each region with the dynamic threshold value (the line-of-sight region needs to be higher than the first threshold value, the non-line-of-sight region needs to be lower than the second threshold value, and the occluded line-of-sight region needs to be between the two), forming a closed-loop logic of “data distribution → threshold adaptation → boundary verification”. This way first avoids the problem of insufficient adaptability of the fixed threshold value in complex environments (for example, a single threshold value cannot cover the K factor difference in different propagation scenarios), so that the threshold value is more consistent with the actual data characteristics. Second, through the correction of the preliminary state by the dynamic threshold value, the misjudgment caused by local statistical deviation (for example, some regions are preliminarily determined as line-of-sight but the actual K factor is at the boundary between the line-of-sight and the occluded line-of-sight) can be corrected, ensuring that the division of the three types of regions (line-of-sight, non-line-of-sight, and occluded line-of-sight) is more consistent with the actual K factor distribution law. Finally, the dynamic adjustment mechanism combined with the data statistical characteristics greatly improves the accuracy and robustness of the line-of-sight classification result, providing a more reliable classification basis for channel line-of-sight identification, so that it can better adapt to changes in different propagation environments.

[0076] Optionally, after obtaining the line-of-sight classification result, the method further comprises: comparing the line-of-sight classification result with the positions and occlusion ranges of obstacles in the laboratory indoor physical environment, and counting the error between the actual proportion of each type of state and the line-of-sight classification result; when the error exceeds a preset threshold, readjusting the relevance determination criterion or the dynamic threshold of the K factor of the region division, recalculating the line-of-sight classification result, and repeating the above steps until the adaptation error of the line-of-sight classification result and the physical environment is within a preset range.

[0077] Specifically, the basic data of the laboratory indoor physical environment is collected, including the specific position coordinates, size parameters of obstacles (such as walls, furniture, equipment, etc.), and the occlusion ranges formed by these obstacles (clearly indicating which regions are completely occluded, partially occluded, or unoccluded). According to the physical environment data, the actual line-of-sight state of each region in the laboratory is determined: unoccluded regions are marked as actual line-of-sight regions, completely occluded regions are marked as actual non-line-of-sight regions, and partially occluded regions are marked as actual occluded line-of-sight regions, and the proportions of the three types of actual states in the overall environment (actual line-of-sight proportion, actual non-line-of-sight proportion, and actual occluded line-of-sight proportion) are counted. The obtained line-of-sight classification result is extracted, and the number proportions of line-of-sight regions, non-line-of-sight regions, and occluded line-of-sight regions (classification line-of-sight proportion, classification non-line-of-sight proportion, and classification occluded line-of-sight proportion) are counted. The error between the actual proportion and the classification proportion is calculated: the error of the line-of-sight state, the error of the non-line-of-sight state, and the error of the occluded line-of-sight state are calculated respectively, and the comprehensive error (average error or weighted error of the three) is calculated according to the demand. The calculated error is compared with the preset error threshold to determine whether it exceeds the threshold range. If the error exceeds the preset threshold, the adjustment link is entered: the relevance determination criterion of region division is adjusted: the preset threshold of time similarity is increased or decreased (the division granularity of time sub-section is changed), and the preset threshold of spatial similarity is increased or decreased (the division granularity of spatial sub-array is changed). Or adjust the dynamic threshold of the K factor: reanalyze the cumulative distribution function of the K factor, and move the positions of the first dynamic threshold (the boundary between line-of-sight and occluded line-of-sight) and the second dynamic threshold (the boundary between occluded line-of-sight and non-line-of-sight). Based on the adjusted relevance determination criterion or dynamic threshold, the steps of region division, preliminary region state determination, and dynamic threshold optimization are re-executed to generate a new line-of-sight classification result. Repeat the above steps, compare the new classification result with the actual state of the physical environment again, count the error, and determine whether it is within the preset range, until the adaptation error of the line-of-sight classification result and the laboratory indoor physical environment meets the preset requirement.

[0078] The method provided in the embodiment, in the first aspect, realizes accurate recognition through a closed loop of "multi-dimensional feature extraction, region division, preliminary judgment and dynamic optimization". First, channel data in different polarization modes is collected, feature dimensions are enriched by using the characteristic difference between vertical and horizontal polarization in obstacle reflection and scattering, time delay characteristics and K factors are calculated, regions with similar propagation characteristics are divided based on time and space similarity, preliminary judgment is completed based on the average value difference between regions and the whole and the time delay characteristic similarity, and finally, the dynamic threshold determined by the cumulative distribution function of the K factor is adjusted again. The polarization characteristics supplement reduce the deviation of a single polarization, the preliminary judgment realizes rapid screening of regions with clear characteristics, the dynamic threshold adapts to environmental changes, and the combination of the three improves the recognition accuracy and adaptability to complex indoor scenes, so that the result is more in line with the actual physical environment.

[0079] In the second aspect, the cumulative distribution function is constructed according to the K factor distribution of the preliminary region, the first (to distinguish between line-of-sight and obstructed line-of-sight) and second (to distinguish between obstructed line-of-sight and non-line-of-sight) dynamic thresholds are determined according to the probability density characteristics of the three types of regions, and the preliminary state is corrected through threshold comparison. The dynamic threshold avoids the problem of insufficient adaptability of the fixed threshold when the environment changes, can be in real time in line with the real distribution law of the K factor, corrects the misjudgment caused by local statistical deviation, makes the division of the three types of regions more in line with the actual energy distribution characteristics, and greatly improves the robustness and accuracy of the classification result.

[0080] Corresponding to the foregoing embodiment of the indoor scene line-of-sight channel recognition method based on polarization and K factors, the present application also provides an embodiment of an indoor scene line-of-sight channel recognition device based on polarization and K factors.

[0081] Figure 2 The structure schematic diagram of the indoor scene line-of-sight channel recognition device based on polarization and K factors provided in the second embodiment of the present application is shown in FIG. 2. Please refer to Figure 2 The device provided in the embodiment includes an acquisition module 210, a calculation module 220, a division module 230, a determination module 240 and an optimization module 250.

[0082] The acquisition module 210 is configured to collect channel data in different polarization modes in a laboratory indoor environment.

[0083] The calculation module 220 is configured to analyze the channel data and calculate the time delay characteristics and K factors of the channel.

[0084] The division module 230 is configured to analyze the time similarity and space similarity between channels based on the time delay characteristics and K factors, and divide the entire measurement period and the transmitting end antenna array into multiple regions based on the time similarity and space similarity.

[0085] The computing module 220 is further configured to calculate the overall delay characteristic and the first average of the K factor based on the calculated delay characteristics of all channels and the K factor, and calculate the second average of the delay characteristic and the K factor of all channels in each region for each divided region.

[0086] The determining module 240 is configured to compare the second average of each region with the corresponding first average, and determine the preliminary region state in combination with the delay characteristic similarity of the plurality of channels.

[0087] The optimization module 250 is configured to determine a dynamic threshold based on the cumulative distribution function of the K factor, and optimize the preliminary region state based on the dynamic threshold to obtain the line-of-sight classification result.

[0088] The apparatus of the embodiment can be used to execute the steps of the method embodiment, and the specific implementation principle and implementation process are similar, and thus will not be described here. Figure 1 The functions and roles of the units in the apparatus are achieved in the implementation process of the corresponding steps in the above method, and thus will not be described here.

[0089] The functions and roles of the units in the apparatus are achieved in the implementation process of the corresponding steps in the above method, and thus will not be described here.

[0090] For the apparatus embodiment, since it basically corresponds to the method embodiment, the related parts can be referred to the part of the method embodiment. The apparatus embodiment described above is only illustrative, and the units described as separate components can be or can not be physically separated, and the components displayed as units can be or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. According to actual needs, part or all of the modules can be selected to achieve the purpose of the scheme of the present application. Those skilled in the art can understand and implement it without creative labor.

[0091] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for line-of-sight channel recognition in indoor scenes based on polarization and K-factor, characterized in that, The method includes: Channel data under different polarization modes were collected in a laboratory environment. The channel data is analyzed to calculate the channel's delay characteristics and K-factor; Based on the aforementioned delay characteristics and K-factor, the temporal and spatial similarities between channels are analyzed. Based on the temporal and spatial similarities, the entire measurement period and the transmitting antenna array are divided into multiple regions. Based on the calculated delay characteristics and K-factor of all channels, the first average value of the overall delay characteristics and K-factor is calculated respectively. For each region after division, the second average value of the delay characteristics and K-factor of all channels in each region is calculated. The second average value of each region is compared with the corresponding first average value, and the preliminary region status is determined by combining the similarity of the time delay characteristics of multiple channel data. A dynamic threshold is determined based on the cumulative distribution function of the K factor, and the preliminary region state is optimized based on the dynamic threshold to obtain the line-of-sight classification result.

2. The method according to claim 1, characterized in that, The dynamic threshold is determined based on the cumulative distribution function of the K factor, and the preliminary region state is optimized based on the dynamic threshold to obtain the line-of-sight classification result, including: Calculate the K-factor distribution of the line-of-sight region, the occluded line-of-sight region, and the non-line-of-sight region in the preliminary regional state, and construct the cumulative distribution function of the overall K-factor; Based on the K-factor probability density characteristics of the three regions in the cumulative distribution function, a first dynamic threshold for distinguishing between the viewing distance region and the occluded viewing distance region, and a second dynamic threshold for distinguishing between the occluded viewing distance region and the non-viewing distance region are determined. The second average value of the K factor initially determined to be the viewing distance region is compared with the first dynamic threshold. If the threshold condition is met, the viewing distance state is retained; otherwise, it is corrected to the occluded viewing distance state. The second average value of the K factor initially determined to be a non-line-of-sight region is compared with the second dynamic threshold. If the threshold condition is met, the non-line-of-sight state is retained; otherwise, it is corrected to an obstructed line-of-sight state. The second average value of the K factor, which is initially determined to be an area with obstructed viewing distance, is compared with the first and second dynamic thresholds. If it is between the two thresholds, the obstructed viewing distance state is maintained; otherwise, it is corrected to the corresponding state according to the threshold boundary. The verification results are integrated to obtain the final line-of-sight classification result.

3. The method according to claim 1, characterized in that, The step of comparing the second average value of each region with the corresponding first average value, and determining the preliminary region state by combining the similarity of the time delay characteristics of multiple channel data, includes: The second average value of the copolarization K-factor in each region is compared with the first average value of the overall copolarization K-factor to obtain the first branch and the second branch; Based on the second average value of the cross-polarization K-factor of each region in the first branch and the second branch, it is compared with the overall cross-polarization K-factor to obtain the third branch and the fourth branch under the first branch, and the fifth branch and the sixth branch under the second branch; wherein, the second average value of the cross-polarization K-factor of each region in the third branch and the sixth branch is not greater than the first average value of the overall cross-polarization K-factor, the third branch is the line-of-sight region, and the sixth branch is the non-line-of-sight region. For the fourth and fifth branches, the comparison signs of the regional co-polarization delay characteristics and the global co-polarization delay characteristics are calculated respectively, as well as the comparison signs of the regional cross-polarization delay characteristics and the global cross-polarization delay characteristics. If the signs of the time delay characteristics under the two polarizations are consistent, the time delay characteristics are determined to be similar. For regions with similar time delay characteristics under the fourth and fifth branches, they are determined to be occlusion range regions; for regions with dissimilar time delay characteristics under the fourth branch, they are determined to be range regions; and for regions with dissimilar time delay characteristics under the fifth branch, they are determined to be non-range regions.

4. The method according to claim 1, characterized in that, Based on the aforementioned delay characteristics and K-factor, the temporal and spatial similarities between channels are analyzed. Based on these temporal and spatial similarities, the entire measurement period and the transmitting antenna array are divided into multiple regions, including: Extract the overall time delay characteristics and K-factor data of the transmitting antenna array at different times, calculate the correlation coefficient of data at adjacent times, and analyze the time similarity of the channel; Extract the time delay characteristics and K-factor data of different antenna elements in the transmitting antenna array at the same time, calculate the correlation coefficient between the data of array elements, and analyze the spatial similarity of the channel; Based on the changes in the strength of the time similarity, when the time similarity is lower than a preset time threshold, the entire measurement period is divided into several time segments. Based on the distribution characteristics of the spatial similarity, when the spatial similarity is lower than a preset spatial threshold, the antenna array is divided into several spatial subarrays. By combining time segments with spatial subarrays, multiple spatiotemporal regions are formed.

5. The method according to claim 1, characterized in that, The time delay characteristics include average time delay and root mean square time delay. The analysis of the channel data and the calculation of the channel's time delay characteristics include: Each channel path is determined from the channel data, and the power delay spectrum and path delay corresponding to each channel path are obtained; The power delay spectrum of each channel path is used as a weight to sum the path delay of the corresponding channel path, and then divided by the sum of the power delay spectra of all channel paths to obtain the average delay of the channel; wherein, the power delay spectrum is obtained by averaging the energy of the channel impulse response function over the time interval.

6. The method according to claim 5, characterized in that, The analysis of the channel data and the calculation of the channel delay characteristics include: Calculate the difference between the path delay of each channel path and the average delay, and square each difference. The power delay spectrum of each channel path is used as the weight, and the squared differences are summed in a weighted manner. Divide the summation result by the sum of the power delay spectra of all channel paths, and take the square root of the result to obtain the root mean square delay of the channel.

7. The method according to claim 1, characterized in that, The analysis of the channel data and the calculation of the channel's K-factor include: Cluster analysis is performed on the time delay and energy information in the channel data to determine the energy of the earliest arrival path and the energy of the scattering path; the energy of the scattering path is the difference between the sum of the power delay spectra of all channel paths and the energy of the earliest arrival path. The K-factor, expressed in decibels, is obtained by taking the logarithm of the ratio of the energy of the earliest arriving path to the energy of the scattering path.

8. The method according to claim 1, characterized in that, The acquisition of channel data under different polarization modes in a laboratory indoor environment includes: A fixed antenna array is set up at the transmitting end, and the array elements are deployed in a vertical polarization manner; Two antennas with different polarizations are configured at the receiving end; the antennas include a vertically polarized antenna and a horizontally polarized antenna; A channel measuring instrument is connected to the transmitting end and the receiving end. The transmitting end continuously transmits signals through an antenna array, and the antenna of the receiving end is fixed on a movable device and moves at a constant speed along a preset route. During the movement of the receiving end, the vertically polarized antenna and the horizontally polarized antenna synchronously receive the signal emitted by the transmitting end, and the channel measurement instrument collects and records the channel data under the two polarization modes in real time.

9. The method according to claim 1, characterized in that, After obtaining the distance classification result, the method further includes: By comparing the line-of-sight classification results with the location and occlusion range of obstacles in the laboratory's indoor physical environment, the actual proportion of each state and the error between the line-of-sight classification results are statistically analyzed. When the error exceeds a preset threshold, the correlation judgment criteria for region division or the dynamic threshold of the K factor is readjusted, and the line-of-sight classification result is recalculated until the line-of-sight classification result and the physical environment adaptation error are within the preset range.

10. An indoor scene line-of-sight channel recognition device based on polarization and K-factor, characterized in that, The device includes an acquisition module, a calculation module, a partitioning module, a determination module, and an optimization module; The acquisition module is used to acquire channel data under different polarization modes in a laboratory indoor environment. The calculation module is used to analyze the channel data and calculate the channel's delay characteristics and K-factor. The partitioning module is used to analyze the temporal and spatial similarity between channels based on the time delay characteristics and the K factor, and to divide the entire measurement period and the transmitting antenna array into multiple regions based on the temporal and spatial similarity. The calculation module is also used to calculate the first average value of the overall delay characteristics and K factor based on the calculated delay characteristics and K factor of all channels, and to calculate the second average value of the delay characteristics and K factor of all channels in each region after division. The determining module is used to compare the second average value of each region with the corresponding first average value, and combine the similarity of the delay characteristics of multiple channel data to determine the preliminary region status. The optimization module is used to determine a dynamic threshold based on the cumulative distribution function of the K factor, and to optimize the preliminary region state based on the dynamic threshold to obtain the line-of-sight classification result.

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