Line-of-sight / non-line-of-sight identification auxiliary positioning method suitable for 6G indoor communication

Abstract

The invention discloses a line-of-sight / non-line-of-sight identification auxiliary positioning method suitable for 6G indoor communication, which belongs to the field of wireless communication, and comprises the following steps of: inputting channel fingerprint characteristics of a target positioning point into a pre-constructed line-of-sight / non-line-of-sight classification model according to an optimal channel fingerprint characteristic combination rule; and outputting the line-of-sight / non-line-of-sight label of the target positioning point, selecting a pre-constructed positioning model and a corresponding optimal channel fingerprint feature combination rule according to the output label type, recombining and arranging the channel fingerprint features of the target positioning point according to the optimal channel fingerprint feature combination rule, inputting the channel fingerprint features of the target positioning point into the positioning model, and positioning the target positioning point according to the channel fingerprint feature combination rule. And adaptive K-value weighted prediction is executed, and the position coordinates of the target positioning point are estimated. According to the method, the problem of low positioning precision caused by a large amount of non-line-of-sight transmission and multipath fading in an indoor environment is solved, the precision of indoor positioning and the robustness of an algorithm are remarkably improved, and the method also has potential applicability under the condition of insufficient training data.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a line-of-sight / non-line-of-sight identification auxiliary positioning method suitable for 6G indoor communication. Background Art

[0002] The International Telecommunication Union has released IMT-2030, designating the 26GHz, 40GHz, 60GHz and 70GHz bands as key communication bands for the sixth generation (6G), all of which belong to the millimeter wave (mmWave) band, indicating that mmWave technology has great prospects in 6G. Compared with sub-6GHz, the mmWave band can provide wider communication bandwidth and higher transmission rate. In addition, multiple input multiple output (MIMO) technology can provide additional angle domain information in addition to delay power domain information. Due to the shorter wavelength of mmWave, the antenna size of the transceiver is often much smaller, making it easier to integrate with MIMO technology. Therefore, the combination of mmWave and MIMO technology can bring ultra-high-resolution channel state information (CSI), including arrival angle, departure angle, received signal strength (RSS) and arrival time, which is expected to meet the centimeter-level indoor positioning accuracy requirements of 6G.

[0003] Existing indoor positioning methods can be divided into geometric-based positioning methods (GPMs) and fingerprint-based positioning methods (FPMs). GPMs use geometric principles based on channel characteristics to estimate the target position, which is usually susceptible to interference from non-line-of-sight (NLoS) transmissions and has strict requirements on transmitter and receiver hardware, resulting in low positioning accuracy and high cost. In contrast, FPMs do not require dedicated equipment and can mitigate the interference of NLoS transmissions to a certain extent. FPMs usually include two stages: offline and online. In the offline stage, FPMs establish a channel fingerprint database and train a positioning model. Initial FPMs usually collect RSS as a channel fingerprint feature, but indoor environments usually contain more NLoS propagation paths, resulting in severe RSS fluctuations, which affects positioning accuracy and stability. In contrast, CSI provides higher granularity and stability and can more accurately reflect multipath information. In the online stage, FPMs estimate the actual location of the terminal through the model trained in the offline stage.

[0004] The estimation algorithms used in FPMs can be divided into three main categories: deterministic algorithms, probabilistic algorithms, and machine learning algorithms. Deterministic algorithms are represented by the weighted K-nearest neighbor (WKNN) algorithm, which can provide accurate predictions without offline training. However, WKNN cannot reflect the implicit relationship between fingerprints and locations and has poor stability. The representative of probabilistic algorithms is the Naive Bayes (NB) estimation, whose positioning accuracy depends largely on whether the assumed probability distribution is consistent with the true data distribution. In most cases, machine learning algorithms are usually better than the two algorithms mentioned above in terms of prediction accuracy. At the same time, with the rise of artificial intelligence (AI) in the field of communications, more and more researchers are using artificial neural networks (ANN) for indoor positioning, such as convolutional neural networks (CNN), residual networks, bidirectional long-short-term memory (BiLSTM), etc. However, the accuracy of ANN depends on a large number of samples, which is challenging for indoor positioning. In addition, ANN has a complex structure, many parameters, and is difficult to train.

[0005] As an integrated machine learning algorithm based on decision trees, random forest (RF) has shown excellent performance in AI, classification prediction and other fields and has been widely studied. RF can process high-dimensional data without data dimension reduction or normalization, and has a fast training speed, which is very suitable for solving multi-classification problems in indoor positioning. However, RF outputs the label with the highest number of votes, making its accuracy highly dependent on a specific label. In addition, different training sets show different sensitivities to different features, so that using the same feature set in different scenarios will affect the stability of positioning accuracy. In addition, although FPMs can partially alleviate the interference of NLoS propagation, it still cannot completely eliminate the impact of NLoS propagation on positioning accuracy. Summary of the invention

[0006] The present invention provides a LoS / NLoS identification-assisted positioning method (IAPM) suitable for 6G indoor communication, which solves the problem of low positioning accuracy caused by a large number of NLoS transmissions and multipath fading in indoor environments, and realizes the ultra-high accuracy and lower latency required by the 6G vision.

[0007] The embodiment of the present invention provides a line-of-sight / non-line-of-sight identification auxiliary positioning method applicable to 6G indoor communication, comprising the following steps:

[0008] Collecting channel state information of the target positioning point, and extracting channel fingerprint features of the target positioning point according to the channel state information;

[0009] Inputting the channel fingerprint feature of the target positioning point into a pre-built line-of-sight / non-line-of-sight classification model according to the optimal channel fingerprint feature combination rule, and outputting the line-of-sight / non-line-of-sight label of the target positioning point;

[0010] According to the line-of-sight / non-line-of-sight label of the target positioning point, a pre-constructed positioning model and a corresponding optimal channel fingerprint feature combination rule are selected, the channel fingerprint features of the target positioning point are recombined and arranged according to the optimal channel fingerprint feature combination rule, input into the positioning model, and adaptive K value weighted prediction is performed to estimate the position coordinates of the target positioning point.

[0011] Optionally, in one embodiment of the present invention, the channel state information of the target positioning point is collected using wireless channel simulation software or wireless channel measurement equipment based on ray tracing, wherein the wireless channel simulation software based on ray tracing collects the channel state information of the target positioning point, including:

[0012] In the wireless channel simulation software based on ray tracing, an indoor scene is constructed and simulation parameters are set. The channel simulation results and each target positioning point can be marked with line-of-sight / non-line-of-sight labels. Each ray in the simulation result corresponds to a set of channel state information, including: received signal strength, azimuth arrival angle, elevation arrival angle and arrival time;

[0013] The channel state information of the target positioning point is collected by using a wireless channel measurement device, including: after calibrating the wireless channel measurement device, performing channel measurement on the target positioning point and recording the coordinates of the target positioning point and the line-of-sight / non-line-of-sight label; and performing channel estimation on the channel measurement result by using a spatial alternating generalized expectation algorithm to obtain the channel state information of each subcarrier.

[0014] Optionally, in one embodiment of the present invention, extracting a channel fingerprint feature of the target positioning point according to the channel state information includes:

[0015] Reconstruct the delay / angular power spectral density based on the collected channel state information;

[0016] Extracting channel fingerprint features based on the normalized maximum power path, the channel fingerprint features including: received signal strength, azimuth arrival angle, elevation arrival angle and arrival time;

[0017] Channel characteristics are extracted based on the reconstructed delay / angular power spectral density, including received power kurtosis, received power skewness, rise time, RMS delay spread, RMS angular spread and Ricean K factor.

[0018] Optionally, in one embodiment of the present invention, performing adaptive K value weighted prediction to estimate the position coordinates of the target positioning point includes:

[0019] After the channel fingerprint features of the target positioning point are rearranged according to the optimal channel fingerprint feature combination, the selected positioning model is input to obtain the scores score_x and score_y of the horizontal and vertical coordinates of the offline reference point in the channel fingerprint database;

[0020] The K-means clustering algorithm is used to divide the scores score_x and score_y into high-level and low-level categories. The number of coordinates contained in the high-level category is the adaptive K value K. x , K y ;

[0021] The K with the highest score x The horizontal axis x k and its score score_x k , K y The ordinate y k and its score score_y k , respectively with the horizontal axis score score_x k , vertical coordinate score score_y k The horizontal axis x k , vertical coordinate y k The weight of the target positioning point is obtained by calculating the final predicted coordinates (x T ,y T ):

[0022]

[0023] Where k is K x , K y The index of .

[0024] Optionally, in one embodiment of the present invention, the method further includes: constructing a line-of-sight / non-line-of-sight recognition model, specifically including the following steps:

[0025] A reference grid is established in the positioning area, with the grid lines perpendicular to the coordinate axes of the Cartesian coordinate system. The intersection of the grids is taken as the offline reference point, the line-of-sight / non-line-of-sight label of the offline reference point is marked, and the channel status information of the offline reference point is collected;

[0026] Extracting channel fingerprint features of offline reference points based on the channel fingerprint structure, building a channel fingerprint database based on the channel fingerprint features, offline reference point coordinates and line-of-sight / non-line-of-sight labels, and dividing the channel fingerprint database into a training set and a validation set;

[0027] The channel fingerprint features and line-of-sight / non-line-of-sight labels of the training set are input into the random forest model for training. After executing the feature selection module, the line-of-sight / non-line-of-sight classification model M is output. I And the corresponding optimal channel fingerprint feature combination rule f I ;

[0028] The horizontal coordinate x and the corresponding channel fingerprint feature, the vertical coordinate y and the corresponding channel fingerprint feature of the offline reference point marked as the line-of-sight label in the training set are respectively input into the random forest model for training. After executing the feature selection module, the corresponding positioning model M is output. IP _Lx, M IP _Ly and the corresponding optimal channel fingerprint feature combination rule f IP _Lx, f IP _Ly;

[0029] The horizontal coordinate x and the corresponding channel fingerprint feature, the vertical coordinate y and the corresponding channel fingerprint feature of the offline reference point marked as non-line-of-sight label in the training set are respectively input into the random forest model for training. After executing the feature selection module, the positioning model M is output. IP _Nx, M IP _Ny and the corresponding optimal channel fingerprint feature combination rule f IP _Nx, f IP _Ny.

[0030] Optionally, in one embodiment of the present invention, marking the line-of-sight / non-line-of-sight label of the offline reference point includes:

[0031] If there is line-of-sight transmission between the offline reference point and the transmitter, the line-of-sight / non-line-of-sight label of the offline reference point is marked as 1. If there is non-line-of-sight transmission between the offline reference point and the transmitter, the line-of-sight / non-line-of-sight label of the offline reference point is marked as 0.

[0032] Optionally, in one embodiment of the present invention, the channel state information of the offline reference point is collected by using wireless channel simulation software or wireless channel measurement equipment based on ray tracing, wherein the wireless channel simulation software based on ray tracing collects the channel state information of the offline reference point, including:

[0033] In the wireless channel simulation software based on ray tracing, an indoor scene is constructed and simulation parameters are set. The channel simulation results and each offline reference point can be marked with line-of-sight / non-line-of-sight labels. Each ray in the simulation result corresponds to a set of channel state information, including: received signal strength, azimuth arrival angle, elevation arrival angle and arrival time.

[0034] Collecting the channel state information of the offline reference point by using the wireless channel measurement equipment includes: after calibrating the wireless channel measurement equipment, performing channel measurement on the offline reference points one by one and recording the coordinates of the offline reference points and the line-of-sight / non-line-of-sight labels; and performing channel estimation on the channel measurement results by using the spatial alternating generalized expectation algorithm to obtain the channel state information of each subcarrier.

[0035] Optionally, in one embodiment of the present invention, the channel fingerprint features and line-of-sight / non-line-of-sight labels of the training set are input into the random forest model for training, and after executing the feature selection module, the line-of-sight / non-line-of-sight classification model M is output. I And the corresponding optimal channel fingerprint feature combination rule f I ,include:

[0036] After the channel fingerprint features and line-of-sight / non-line-of-sight labels of the training set are input into the random forest model for training, the random forest model gives the importance score of the channel fingerprint features;

[0037] According to the importance scores of the channel fingerprint features, the channel fingerprint features of the training set are sorted in descending order of scores, and the sorting rule f is recorded;

[0038] For the channel fingerprint features of the sorted training set, select the first n channel fingerprint features each time and record the sorting rule f of the channel fingerprint features n , input the first n channel fingerprint features selected together with the line-of-sight / non-line-of-sight labels into the random forest model for training and generate n training models, and input the channel fingerprint features of the validation set according to f n After reordering, input the nth training model respectively to estimate the line-of-sight / non-line-of-sight labels of the validation set under the n training models;

[0039] Calculate the consistency ratio of the line-of-sight / non-line-of-sight labels of the n estimated validation sets and the line-of-sight / non-line-of-sight labels of the real validation sets, and get the accuracy of the n training models. Take the training model with the highest accuracy as the line-of-sight / non-line-of-sight classification model M. I , Line-of-sight / non-line-of-sight classification model M I The corresponding channel fingerprint feature sorting rule is denoted as f I .

[0040] Optionally, in one embodiment of the present invention, the horizontal coordinate x and the corresponding channel fingerprint feature, the vertical coordinate y and the corresponding channel fingerprint feature of the offline reference point marked as the line-of-sight label in the training set are respectively input into the random forest model for training, and after executing the feature selection module, the corresponding positioning model M is output. IP _Lx, M IP _Ly and the corresponding optimal channel fingerprint feature combination rule f IP _Lx, f IP _Ly, including:

[0041] After the channel fingerprint features marked as line-of-sight labels in the training set and the corresponding horizontal (vertical) coordinates x(y) are input into the random forest model for training, the training model gives the importance score of the channel fingerprint features;

[0042] According to the importance scores of the channel fingerprint features, the channel fingerprint features of the training set are sorted in descending order of scores, and the sorting rule f is recorded;

[0043] For the sorted training set channel fingerprint features, select the first n channel fingerprint features each time and record the sorting rule f of the channel fingerprint features n , the first n channel fingerprint features selected together with the corresponding horizontal (vertical) coordinates x(y) are input into the random forest model for training and n training models M are generated x (M y ), the channel fingerprint features marked as line-of-sight labels in the verification set are pressed by f n After reordering, input the nth training model M respectively x (M y ), estimate the horizontal (vertical) coordinate x(y) of the validation set under n training models;

[0044] Combine the estimated horizontal (vertical) coordinates x(y) of the validation set into complete coordinates, calculate the absolute error between the estimated complete coordinates and the true validation set coordinates, calculate the average absolute error of the n training models, and take the training model with the lowest average absolute error as the positioning model M IP _Lx, M IP _Ly, the channel fingerprint feature sorting rule corresponding to the positioning model is recorded as f IP _Lx, f IP _Ly.

[0045] Optionally, in one embodiment of the present invention, the horizontal coordinate x and the corresponding channel fingerprint feature, the vertical coordinate y and the corresponding channel fingerprint feature of the offline reference point marked as a non-line-of-sight label in the training set are respectively input into the random forest model for training, and after executing the feature selection module, the positioning model M is output. IP _Nx, M IP _Ny and the corresponding optimal channel fingerprint feature combination rule fIP _Nx, f IP _Ny, including:

[0046] After the channel fingerprint features and the corresponding horizontal (vertical) coordinates x(y) marked as non-line-of-sight labels in the training set are input into the random forest model for training, the training model gives the importance score of the channel fingerprint features;

[0047] According to the importance scores of the channel fingerprint features, the channel fingerprint features of the training set are sorted in descending order of scores, and the sorting rule f is recorded;

[0048] For the sorted training set channel fingerprint features, select the first n channel fingerprint features each time and record the sorting rule f of the channel fingerprint features n , the first n channel fingerprint features selected together with the corresponding horizontal (vertical) coordinates x(y) are input into the random forest model for training and n training models M are generated x (M y ), the channel fingerprint features marked as non-line-of-sight labels in the verification set are pressed according to f n After reordering, input the nth training model M respectively x (M y ), estimate the horizontal (vertical) coordinate x(y) of the validation set under n training models;

[0049] Combine the estimated horizontal coordinate x and vertical coordinate y of the validation set into the complete coordinate, and calculate the absolute error between the estimated complete coordinate and the true validation set coordinate. Then calculate the average absolute error of the n training models, and take the training model with the lowest average absolute error as the positioning model M IP _Nx, M IP _Ny, the channel fingerprint feature sorting rule corresponding to the positioning model is recorded as f IP _Nx, f IP _Ny.

[0050] The line-of-sight / non-line-of-sight identification assisted positioning method for 6G indoor communication proposed in an embodiment of the present invention includes a line-of-sight / non-line-of-sight identification module, a feature selection module and an optimized weighted random forest (IWRF) algorithm. Among them, the RF-based IWRF algorithm adopts weighted prediction and adaptive K value to avoid the dependence of the prediction result on a specific label; the feature selection module adaptively adjusts the fingerprint feature sensitivity during the training process to ensure the optimal selection of the fingerprint feature and its accuracy; the line-of-sight / non-line-of-sight identification module adopts the IWRF algorithm to separate line-of-sight and non-line-of-sight data, which greatly reduces the interference of indoor non-line-of-sight transmission on positioning accuracy, and reduces the complexity of the algorithm. In addition, the present invention also proposes a novel channel fingerprint structure based on channel state information, which includes reconstructing the channel impulse response and extracting channel fingerprint features based on channel state information based on the normalized maximum power (Maximum received power, MRP) path and delay / angle delay power density spectrum. The algorithm proposed in the present invention has high accuracy and robustness, and is superior to the existing algorithm. In addition, the proposed algorithm shows robustness to environmental changes, indicating potential applicability even without a large amount of training data. Therefore, the method can meet the indoor positioning accuracy of the 6G system to the centimeter level.

[0051] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0053] Figure 1 A flowchart of a line-of-sight / non-line-of-sight identification auxiliary positioning method applicable to 6G indoor communication provided according to an embodiment of the present invention;

[0054] Figure 2 A schematic diagram of online and offline application scenarios of a line-of-sight / non-line-of-sight identification auxiliary positioning method applicable to 6G indoor communication according to an embodiment of the present invention;

[0055] Figure 3 A simulation scene graph constructed in Wireless Insite according to an embodiment of the present invention;

[0056] Figure 4 A schematic diagram of a channel measurement scenario according to an embodiment of the present invention;

[0057] Figure 5A two-dimensional distribution diagram of a channel measurement scenario according to an embodiment of the present invention;

[0058] Figure 6 : is a spatial distribution diagram of the received power when the transmitting end is located at (0,0) according to an embodiment of the present invention, (a) is a spatial distribution diagram of the total received power, and (b) is a spatial distribution diagram of the MRP path received power;

[0059] Figure 7 A Cumulative Distribution Function (CDF) curve of the mean absolute error of the positioning between the total received power and the MRP path received power using the weighted K nearest neighbor algorithm according to an embodiment of the present invention;

[0060] Figure 8 A schematic diagram showing a comparison of delay power spectrum density between ray tracing simulation and channel measurement according to an embodiment of the present invention;

[0061] Fig. 9 is a CDF curve of positioning errors of various algorithms under ray tracing simulation data according to an embodiment of the present invention;

[0062] Fig.10 1 is a CDF curve of positioning errors of various algorithms under actual measurement data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0064] Figure 1 The present invention provides a flowchart of a line-of-sight / non-line-of-sight identification auxiliary positioning method suitable for 6G indoor communication according to an embodiment of the present invention.

[0065] like Figure 1 As shown, the line-of-sight / non-line-of-sight identification auxiliary positioning method applicable to 6G indoor communication includes the following steps:

[0066] Step S101: collecting channel state information of a target positioning point, and extracting a channel fingerprint feature of the target positioning point according to the channel state information.

[0067] In an embodiment of the present invention, the channel state information of the target positioning point may be collected by using wireless channel simulation software or wireless channel measurement equipment based on ray tracing.

[0068] Specifically, the wireless channel simulation software based on ray tracing collects channel state information of the target positioning point, including:

[0069] In the wireless channel simulation software based on ray tracing, an indoor scene is constructed and simulation parameters such as the transmitter position, target positioning position, electromagnetic wave frequency, bandwidth, transmission power, propagation mechanism, etc. are set; in the wireless channel simulation software based on ray tracing, the channel simulation results are output and each target positioning point can be marked with a line-of-sight / non-line-of-sight label. In the simulation results, each ray corresponds to a set of channel state information, including: received signal strength, azimuth arrival angle, elevation arrival angle, and arrival time.

[0070] The channel state information of the target positioning point is collected by using a wireless channel measurement device, including: after calibrating the wireless channel measurement device, channel measurement is performed on the target positioning point and the coordinates of the target positioning point and the line-of-sight / non-line-of-sight label are recorded; the channel measurement result is estimated by using a spatial alternating generalized expectation algorithm to obtain the channel state information of each subcarrier, including: received signal strength, azimuth arrival angle, elevation arrival angle and arrival time.

[0071] Specifically, the channel fingerprint features of the target positioning point are extracted according to the channel state information, including:

[0072] Reconstruct the delay / angular power spectral density based on the collected channel state information;

[0073] Channel fingerprint features are extracted based on the normalized maximum power path. The channel fingerprint features include: received signal strength, azimuth arrival angle, elevation arrival angle and arrival time;

[0074] Channel characteristics are extracted based on the reconstructed delay / angular power spectral density, including received power kurtosis, received power skewness, rise time, RMS delay spread, RMS angular spread and Ricean K factor.

[0075] Step S102: input the channel fingerprint features of the target positioning point into a pre-built line-of-sight / non-line-of-sight classification model according to the optimal channel fingerprint feature combination rule, and output the line-of-sight / non-line-of-sight label of the target positioning point.

[0076] Step S103, according to the line-of-sight / non-line-of-sight label of the target positioning point, select a pre-built positioning model and a corresponding optimal channel fingerprint feature combination rule, re-arrange the channel fingerprint features of the target positioning point according to the optimal channel fingerprint feature combination rule, input the positioning model, and perform adaptive K value weighted prediction to estimate the position coordinates of the target positioning point.

[0077] Optionally, in one embodiment of the present invention, performing adaptive K value weighted prediction to estimate the position coordinates of the target positioning point includes:

[0078] After the channel fingerprint features of the target positioning point are rearranged according to the optimal channel fingerprint feature combination and input into the selected positioning model, the scores score_x and score_y of the horizontal and vertical coordinates of the offline reference point in the channel fingerprint database are obtained;

[0079] The K-means clustering algorithm is used to divide the scores score_x and score_y into high-level and low-level categories. The number of coordinates contained in the high-level category is the adaptive K value K. x , K y ;

[0080] The K with the highest score x The horizontal axis x k and its score score_x k , K y The ordinate y k and its score score_y k , respectively with the horizontal axis score score_x k , vertical coordinate score score_y k The horizontal axis x k , vertical coordinate y k The weight of the final predicted target positioning point coordinates (x T ,y T ):

[0081]

[0082] Where k is K x , K y The index of .

[0083] In an embodiment of the present invention, before collecting the channel state information of the target positioning point, a line-of-sight / non-line-of-sight recognition model is constructed, which specifically includes the following steps:

[0084] A reference grid is established in the positioning area, with the grid lines perpendicular to the coordinate axes of the Cartesian coordinate system. The intersection of the grids is taken as the offline reference point, the line-of-sight / non-line-of-sight label of the offline reference point is marked, and the channel status information of the offline reference point is collected;

[0085] The channel fingerprint features of the offline reference points are extracted based on the channel fingerprint structure, and a channel fingerprint database is constructed based on the channel fingerprint features, the offline reference point coordinates and the line-of-sight / non-line-of-sight labels, and the channel fingerprint database is divided into a training set and a verification set;

[0086] The channel fingerprint features and line-of-sight / non-line-of-sight labels of the training set are input into the random forest model for training. After executing the feature selection module, the line-of-sight / non-line-of-sight classification model M is output. I And the corresponding optimal channel fingerprint feature combination rule f I;

[0087] The horizontal coordinate x and the corresponding channel fingerprint feature, the vertical coordinate y and the corresponding channel fingerprint feature of the offline reference point marked as the line-of-sight label in the training set are respectively input into the random forest model for training. After executing the feature selection module, the corresponding positioning model M is output. IP _Lx, M IP _Ly and the corresponding optimal channel fingerprint feature combination rule f IP _Lx, f IP _Ly;

[0088] The horizontal coordinate x and the corresponding channel fingerprint feature, the vertical coordinate y and the corresponding channel fingerprint feature of the offline reference point marked as non-line-of-sight label in the training set are respectively input into the random forest model for training. After executing the feature selection module, the positioning model M is output. IP _Nx, M IP _Ny and the corresponding optimal channel fingerprint feature combination rule f IP _Nx, f IP _Ny.

[0089] Optionally, in one embodiment of the present invention, marking the line-of-sight / non-line-of-sight label of the offline reference point includes:

[0090] If there is line-of-sight transmission between the offline reference point and the transmitter, the line-of-sight / non-line-of-sight label of the offline reference point is marked as 1. If there is non-line-of-sight transmission between the offline reference point and the transmitter, the line-of-sight / non-line-of-sight label of the offline reference point is marked as 0.

[0091] Optionally, in one embodiment of the present invention, the channel state information of the offline reference point is collected by using wireless channel simulation software or wireless channel measurement equipment based on ray tracing, wherein the wireless channel simulation software based on ray tracing collects the channel state information of the offline reference point, including:

[0092] In the wireless channel simulation software based on ray tracing, an indoor scene is constructed and simulation parameters are set. The channel simulation results and each offline reference point can be marked with line-of-sight / non-line-of-sight labels. Each ray in the simulation result corresponds to a set of channel state information, including: received signal strength, azimuth arrival angle, elevation arrival angle and arrival time.

[0093] Collecting the channel state information of the offline reference point by using the wireless channel measurement equipment includes: after calibrating the wireless channel measurement equipment, performing channel measurement on the offline reference points one by one and recording the coordinates of the offline reference points and the line-of-sight / non-line-of-sight labels; and performing channel estimation on the channel measurement results by using the spatial alternating generalized expectation algorithm to obtain the channel state information of each subcarrier.

[0094] Optionally, in one embodiment of the present invention, the channel fingerprint features and line-of-sight / non-line-of-sight labels of the training set are input into the random forest model for training, and after executing the feature selection module, the line-of-sight / non-line-of-sight classification model M is output. I And the corresponding optimal channel fingerprint feature combination rule f I ,include:

[0095] After the channel fingerprint features and line-of-sight / non-line-of-sight labels of the training set are input into the random forest model for training, the random forest model gives the importance score of the channel fingerprint features;

[0096] According to the importance scores of the channel fingerprint features, the channel fingerprint features of the training set are sorted in descending order of scores, and the sorting rule f is recorded;

[0097] For the channel fingerprint features of the sorted training set, select the first n channel fingerprint features each time and record the sorting rule f of the channel fingerprint features n , input the first n channel fingerprint features selected together with the line-of-sight / non-line-of-sight labels into the random forest model for training and generate n training models, and input the channel fingerprint features of the validation set according to f n After reordering, input the nth training model respectively to estimate the line-of-sight / non-line-of-sight labels of the validation set under the n training models;

[0098] Calculate the consistency ratio of the line-of-sight / non-line-of-sight labels of the n estimated validation sets and the line-of-sight / non-line-of-sight labels of the real validation sets, and get the accuracy of the n training models. Take the training model with the highest accuracy as the line-of-sight / non-line-of-sight classification model M. I , Line-of-sight / non-line-of-sight classification model M I The corresponding channel fingerprint feature sorting rule is denoted as f I .

[0099] Optionally, in one embodiment of the present invention, the horizontal coordinate x and the corresponding channel fingerprint feature, the vertical coordinate y and the corresponding channel fingerprint feature of the offline reference point marked as the line-of-sight label in the training set are respectively input into the random forest model for training, and after executing the feature selection module, the corresponding positioning model M is output. IP _Lx, M IP _Ly and the corresponding optimal channel fingerprint feature combination rule f IP _Lx, f IP _Ly, including:

[0100] After the channel fingerprint features marked as line-of-sight labels in the training set and the corresponding horizontal (vertical) coordinates x(y) are input into the random forest model for training, the training model gives the importance score of the channel fingerprint features;

[0101] According to the importance scores of the channel fingerprint features, the channel fingerprint features of the training set are sorted in descending order of scores, and the sorting rule f is recorded;

[0102] For the sorted training set channel fingerprint features, select the first n channel fingerprint features each time and record the sorting rule f of the channel fingerprint features n , the first n channel fingerprint features selected together with the corresponding horizontal (vertical) coordinates x(y) are input into the random forest model for training and n training models M are generated x (M y ), the channel fingerprint features marked as line-of-sight labels in the verification set are pressed by f n After reordering, input the nth training model M respectively x (M y ), estimate the horizontal (vertical) coordinate x(y) of the validation set under n training models;

[0103] Combine the estimated horizontal (vertical) coordinates x(y) of the validation set into complete coordinates, calculate the absolute error between the estimated complete coordinates and the true validation set coordinates, calculate the average absolute error of the n training models, and take the training model with the lowest average absolute error as the positioning model M IP _Lx, M IP _Ly, the channel fingerprint feature sorting rule corresponding to the positioning model is recorded as f IP _Lx, f IP _Ly.

[0104] Optionally, in one embodiment of the present invention, the horizontal coordinate x and the corresponding channel fingerprint feature, the vertical coordinate y and the corresponding channel fingerprint feature of the offline reference point marked as a non-line-of-sight label in the training set are respectively input into the random forest model for training, and after executing the feature selection module, the positioning model M is output. IP _Nx, M IP _Ny and the corresponding optimal channel fingerprint feature combination rule f IP _Nx, f IP _Ny, including:

[0105] After the channel fingerprint features and the corresponding horizontal (vertical) coordinates x(y) marked as non-line-of-sight labels in the training set are input into the random forest model for training, the training model gives the importance score of the channel fingerprint features;

[0106] According to the importance scores of the channel fingerprint features, the channel fingerprint features of the training set are sorted in descending order of scores, and the sorting rule f is recorded;

[0107] For the sorted training set channel fingerprint features, select the first n channel fingerprint features each time and record the sorting rule f of the channel fingerprint features n, the first n channel fingerprint features selected together with the corresponding horizontal (vertical) coordinates x(y) are input into the random forest model for training and n training models M are generated x (M y ), the channel fingerprint features marked as non-line-of-sight labels in the verification set are pressed according to f n After reordering, input the nth training model M respectively x (M y ), estimate the horizontal (vertical) coordinate x(y) of the validation set under n training models;

[0108] Combine the estimated horizontal coordinate x and vertical coordinate y of the validation set into the complete coordinate, and calculate the absolute error between the estimated complete coordinate and the true validation set coordinate. Then calculate the average absolute error of the n training models, and take the training model with the lowest average absolute error as the positioning model M IP _Nx, M IP _Ny, the channel fingerprint feature sorting rule corresponding to the positioning model is recorded as f IP _Nx, f IP _Ny.

[0109] Combination Figure 2 As shown, the line-of-sight / non-line-of-sight identification auxiliary positioning method suitable for 6G indoor communication of the present invention is described in detail through a specific embodiment.

[0110] In a specific embodiment, the line-of-sight / non-line-of-sight identification auxiliary positioning method applicable to 6G indoor communication specifically includes the following steps:

[0111] Step S1, establish a reference grid in the positioning area at intervals of 0.5m×0.5m, with the grid lines perpendicular to the coordinate axes of the Cartesian coordinate system, take the intersection of the grid as the offline reference point, mark the line-of-sight / non-line-of-sight label of the offline reference point, and collect the channel state information of the offline reference point. In this embodiment, the area of ​​the positioning area is 46m×17m. There are 12 pillars in the environment. There are a large number of non-line-of-sight transmissions, but line-of-sight transmission is still dominant. In addition, the height of the offline reference point is set to 1.5m, and the height of the transmitter is set to 2.8m. Both the transmitter and the receiver use omnidirectional antennas. The transmitter is a multi-input multi-output array antenna, and the receiver is a single antenna. In this example, the channel state information of the offline reference point is collected using both the commercial ray tracing simulation software Wireless InSite and the wireless channel measurement equipment. The following sub-steps are specifically included in Wireless InSite:

[0112] Step S101, set the height of the offline reference point to 1.5m, the height of the transmitter to 2.8m, both the transmitter and the receiver use omnidirectional antennas, the transmitter is a 4×4 array antenna, the receiver is a 2×2 array antenna, the transmitter position coordinates are (13.59m, 14.11m), the offline reference point is located at the intersection of the 0.5m×0.5m grid, the electromagnetic wave frequency is 60GHz, the bandwidth is 3GHz, the transmission power is 23dBm, the maximum allowable reflection order is 6th order, the diffraction order is 1st order, and the constructed scene model is as follows Figure 3 shown.

[0113] Step S102: In Wireless InSite, output the channel simulation results and each reference point can be marked with a line-of-sight / non-line-of-sight label. Each ray in the simulation result corresponds to a set of channel state information, including: received signal strength, azimuth arrival angle, elevation arrival angle and arrival time.

[0114] Collecting the channel state information of the offline reference point using the wireless channel measurement device includes the following sub-steps:

[0115] Step S103: Indoor channel measurement scenario Figure 4 As shown, the scene is Figure 3 The Wireless InSite modeling scenario shown in the figure is consistent. The transmitting and receiving antennas are set to 4×8 dual-polarization cylindrical MIMO and 4×4 dual-polarization planar MIMO, with a bandwidth of 700MHz. After calibrating the wireless channel measurement equipment, the channel is measured one by one at the offline reference points and the coordinates of the offline reference points and the line-of-sight / non-line-of-sight labels are recorded. A total of 130 offline reference points are collected. Among them, there are 90 reference points in the aisle, representing the line-of-sight environment, and 30 reference points on the table, representing the non-line-of-sight environment, such as Figure 5 As shown; 80% of the points in the allocated reference points are used to construct the offline database.

[0116] Step S104: Use the spatial alternating generalized expectation algorithm to perform channel estimation on the channel measurement results to obtain channel state information of each subcarrier, including: received signal strength, azimuth arrival angle, elevation arrival angle and arrival time.

[0117] Step S2: extract channel fingerprint features based on the novel channel fingerprint structure, and build a channel fingerprint database based on the channel fingerprint features, offline reference point coordinates and line-of-sight / non-line-of-sight labels, and randomly select part of the data from the channel fingerprint database as a training set and part of the data as a verification set. The novel channel fingerprint structure includes the following sub-steps:

[0118] Step S201, reconstructing the delay / angle power spectrum density based on the channel state information in step S102 or step S104; after obtaining multipath parameters such as angle, delay, phase, power, etc., reconstructing the average delay power spectrum of multiple antenna pairs as the delay power spectrum of the MIMO system through the following formula, thereby reducing noise interference:

[0119]

[0120] Among them, Nsc is a constant, indicating the number of subcarriers, here it is 1024, N T 、N R are the number of array elements of the transmitting and receiving antenna arrays, respectively, and h pq (τ) is H pq (f) is the inverse Fourier transform, H pq (f) is the channel transfer function between the transmitting antenna element p and the receiving antenna element q, and the calculation formula is:

[0121]

[0122] Where L is the number of multipaths, α l is the multipath amplitude, is the phase, τ l is the multipath delay, and f is the frequency within the bandwidth.

[0123] The angular power spectral density can be obtained by the angle and received power of L multipaths:

[0124]

[0125] Where pl represents the power of the lth path, δ(·) represents the Dirac-delta function, and θ is the azimuth arrival angle Φ AAoA 、Azimuth departure angle Φ AAoD , pitch arrival angle θ EAoA and the pitch departure angle θ EAoD ,θ l Φ represents the lth path AAoA , Φ AAoD ,θ EAoA or θ EAoD .

[0126] Step S202: extract channel fingerprint features based on the MRP path, including: received signal strength, azimuth arrival angle, elevation arrival angle, and arrival time; the MRP path represents the propagation path with the highest normalized power during signal transmission, and contributes the most to the power received at the receiving end. In 6G indoor millimeter wave communication scenarios, the MRP paths at most receiving ends are usually line-of-sight paths. Even if the MRP paths are non-line-of-sight paths, their power loss is still acceptable due to the characteristics of millimeter waves, and usually only undergoes low-order reflections or diffraction. Therefore, the received power intensity of the MRP path can greatly reduce the interference caused by non-line-of-sight propagation, such as Figure 6 In addition, when the deterministic weighted K nearest neighbor algorithm is used for positioning, it can be clearly seen from the cumulative distribution function of the positioning mean absolute error that the received power intensity of the MRP path significantly improves the relative positioning accuracy, as shown in Figure 7 In addition, from Figure 8 It can be seen that the delay power spectral density reconstructed from the ray tracing simulation data is basically consistent with the delay power spectral density of the channel measurement data at the MRP path.

[0127] Step S203, extract channel features based on the reconstructed delay / angular power spectral density, including: received power kurtosis, received power skewness, rise time, root mean square delay spread, root mean square angle spread and Rice K factor. Extracting only the channel features of the MRP path will lead to the loss of valuable information, especially in non-line-of-sight environments. To solve this problem, a channel fingerprint feature based on delay / angular power spectral density is introduced to compensate for the information loss generated by the channel fingerprint feature based on the MRP path. In addition, these multipath characteristics can also better achieve line-of-sight / non-line-of-sight recognition.

[0128] 1) Received power peakness:

[0129]

[0130] Where E(·) represents the expectation, μ h and σ h yes The mean and standard deviation of can be expressed as:

[0131]

[0132] 2) Received power skewness

[0133]

[0134] 3) Rise time

[0135]

[0136] 4) RMS delay spread

[0137]

[0138] Among them, μ τ It can be calculated by the following formula:

[0139]

[0140] 5) RMS angle expansion

[0141]

[0142] in, It can be φ AAoA ,φ AAoD ,θ EAoA or θ EAoD , μ θ It can be calculated by the following formula:

[0143]

[0144] 6) Rice K factor

[0145]

[0146] Among them, G a , G v Can be calculated by the following formula:

[0147]

[0148] Therefore, in the embodiment of the present invention, the offline channel fingerprint database is constructed with a dimension of N RP ×18 matrix, where N RP is the number of offline reference points. The first 15 columns are channel fingerprint features, columns 16 and 17 are the geographic coordinates of the offline reference points, and the last column is a binary label representing line-of-sight / non-line-of-sight, where the line-of-sight label is assigned to 1 and the non-line-of-sight label is assigned to 0. Channel fingerprint features include Δτ,σ DS ,σ AS , in, σ DS Including Φ AAoA , Φ AAoD ,θ EAoA , and θ EAoD .

[0149] Step S3: Input the channel fingerprint features and line-of-sight / non-line-of-sight labels of the training set into RF for training. After executing the feature selection module, output the line-of-sight / non-line-of-sight classification model M. I And the corresponding optimal channel fingerprint feature combination rule f I; The feature selection module includes the following sub-steps:

[0150] Step S301: After the channel fingerprint features and line-of-sight / non-line-of-sight labels of the training set are input into RF for training, the training model gives the importance score of the channel fingerprint features;

[0151] Step S302: sort the channel fingerprint features of the training set in descending order according to the importance scores of the channel fingerprint features, and record the sorting rule f;

[0152] Step S303: For the sorted training set channel fingerprint features, select the first n channel fingerprint features each time and record the sorting rule f of the channel fingerprint features. n , input the first n channel fingerprint features selected together with the line-of-sight / non-line-of-sight labels into RF for training and generate n training models, and convert the channel fingerprint features of the validation set into f n After reordering, input the nth training model respectively to estimate the line-of-sight / non-line-of-sight labels of the validation set under the n training models;

[0153] Step S304: Calculate the consistency ratio between the line-of-sight / non-line-of-sight labels of the n estimated validation sets and the line-of-sight / non-line-of-sight labels of the real validation sets, that is, calculate the accuracy of the n training models, and take the training model with the highest accuracy as M. I The corresponding channel fingerprint feature sorting rule of this model is denoted as f I .

[0154] Step S4: The horizontal coordinate x and the corresponding channel fingerprint feature, the vertical coordinate y and the corresponding channel fingerprint feature of the offline reference point marked with the line-of-sight label in the training set are input into RF for training respectively. After executing the feature selection module, the positioning model M is output. IP _Lx, M IP _Ly and the corresponding optimal channel fingerprint feature combination rule f IP _Lx, f IP The feature selection module includes the following sub-steps:

[0155] Step S401: After the channel fingerprint features marked as line-of-sight labels in the training set and the corresponding horizontal (vertical) coordinates x(y) are input into RF for training, the training model gives the channel fingerprint feature importance score;

[0156] Step S402: sort the channel fingerprint features of the training set in descending order according to the importance scores of the channel fingerprint features, and record the sorting rule f;

[0157] Step S403: For the sorted training set channel fingerprint features, select the first n channel fingerprint features each time and record the sorting rule f of the channel fingerprint features. n, the first n channel fingerprint features selected together with the corresponding horizontal (vertical) coordinates x(y) are input into RF for training and n training models M are generated x (M y ), the channel fingerprint features marked as line-of-sight labels in the verification set are pressed by f n After reordering, input the nth training model M respectively x (M y ), estimate the horizontal (vertical) coordinate x(y) of the validation set under n training models;

[0158] Step S404: Combine the estimated horizontal (vertical) coordinates x(y) of the validation set into a complete coordinate, and calculate the relative error between the coordinate and the actual validation set coordinate. Then calculate the average relative error of the n training models, and take the training model with the lowest average relative error as M. IP _Lx, M IP _Ly, the channel fingerprint feature sorting rule corresponding to this model is denoted as f IP _Lx, f IP _Ly.

[0159] Step S5: The horizontal coordinate x and the corresponding channel fingerprint feature, the vertical coordinate y and the corresponding channel fingerprint feature of the offline reference point marked with the non-line-of-sight label in the training set are input into RF for training respectively. After executing the feature selection module, the positioning model M is output. IP _Nx, M IP _Ny and the corresponding optimal channel fingerprint feature combination rule f IP _Nx, f IP _Ny; the feature selection module comprises the following sub-steps:

[0160] Step S501: After the channel fingerprint features and the corresponding horizontal (vertical) coordinates x(y) marked as non-line-of-sight labels in the training set are input into RF for training, the training model gives the channel fingerprint feature importance score;

[0161] Step S502: sort the channel fingerprint features of the training set in descending order according to the importance scores of the channel fingerprint features, and record the sorting rule f;

[0162] Step S503: For the sorted training set channel fingerprint features, select the first n channel fingerprint features each time and record the sorting rule f of the channel fingerprint features. n , the first n channel fingerprint features selected together with the corresponding horizontal (vertical) coordinates x(y) are input into RF for training and n training models M are generated x (M y ), the channel fingerprint features marked as non-line-of-sight labels in the verification set are pressed according to f n After reordering, input the nth training model M respectivelyx (M y ), estimate the horizontal (vertical) coordinate x(y) of the validation set under n training models;

[0163] Step S504: Combine the estimated horizontal coordinate x and vertical coordinate y of the validation set into a complete coordinate, and calculate the relative error between the coordinate and the actual validation set coordinate. Then calculate the average relative error of the n training models, and take the training model with the lowest average relative error as M. IP _Nx, M IP _Ny, the channel fingerprint feature sorting rule corresponding to this model is denoted as f IP _Nx, f IP _Ny.

[0164] Step S6, collecting the channel state information of the target positioning point, and extracting the channel fingerprint features of the target positioning point based on the novel channel fingerprint structure; the channel state information of the offline reference point can be collected by using wireless channel simulation software or wireless channel measurement equipment based on ray tracing. In this example, the channel state information of the offline reference point is collected by using both the commercial ray tracing simulation software Wireless InSite and the wireless channel measurement equipment. In WirelessInSite, the following sub-steps are specifically included:

[0165] Step S601, set the height of the offline reference point to 1.5m, the height of the transmitter to 2.8m, both the transmitter and the receiver use omnidirectional antennas, the transmitter is a 4×4 array antenna, the receiver is a 2×2 array antenna, the transmitter position coordinates are (13.59m, 14.11m), 200 target positioning points are randomly arranged in the positioning scene, the electromagnetic wave frequency is 60GHz, the bandwidth is 3GHz, the transmission power is 23dBm, the maximum allowable reflection order is 6th order, and the diffraction order is 1st order;

[0166] Step S602: In Wireless InSite, output the channel simulation results and each target positioning point can be marked with a line-of-sight / non-line-of-sight label. Each ray in the simulation result corresponds to a set of channel state information, including: received signal strength, azimuth arrival angle, elevation arrival angle and arrival time;

[0167] Using wireless channel measurement equipment to collect channel state information of the target positioning point includes the following sub-steps:

[0168] Step S603: Set the transmitting and receiving antennas to 4×8 dual-polarization cylindrical MIMO and 4×4 dual-polarization planar MIMO, with a bandwidth of 700 MHz. After calibrating the wireless channel measurement equipment, perform channel measurement on the offline reference points one by one and record the coordinates of the offline reference points and the line-of-sight / non-line-of-sight tags. Collect the channel state information of 130 offline reference points in total, of which 90 reference points are in the aisle, representing the line-of-sight environment, and 30 reference points are on the table, representing the non-line-of-sight environment; allocate 20% of the reference points as target positioning points;

[0169] Step S604: Use the spatial alternating generalized expectation algorithm to perform channel estimation on the channel measurement results to obtain channel state information of each subcarrier, including: received signal strength, azimuth arrival angle, elevation arrival angle and arrival time;

[0170] The novel channel fingerprint structure comprises the following sub-steps:

[0171] Step S605, reconstructing the delay / angular power spectrum density based on the channel state information in S602 or step S604;

[0172] Step S606: extract channel fingerprint features based on the MRP path, including: received signal strength, azimuth arrival angle, elevation arrival angle and arrival time;

[0173] Step S607, extracting channel characteristics based on the reconstructed delay / angular power spectral density, including: received power kurtosis, received power skewness, rise time, root mean square delay spread, root mean square angle spread and Ricean K factor.

[0174] Step S7: The channel fingerprint features of the target positioning point are combined according to the optimal channel fingerprint feature combination rule f I After reorganization and permutation, input line-of-sight / non-line-of-sight classification model M I , estimate the line-of-sight / non-line-of-sight label of the target positioning point;

[0175] Step S8, according to the estimated line-of-sight / non-line-of-sight label of the target positioning point, select the corresponding positioning model and the corresponding optimal channel fingerprint feature combination rule, re-arrange the channel fingerprint features of the target positioning point according to the optimal channel fingerprint feature combination, input the positioning model, and perform adaptive K value weighted prediction, and finally estimate the position coordinates of the target positioning point. The specific process of selecting the corresponding positioning model and the corresponding optimal channel fingerprint feature combination according to the estimated line-of-sight / non-line-of-sight label of the target positioning point is as follows: if the estimated line-of-sight / non-line-of-sight label of the target positioning point is 1, then select the training model M IP _Lx, M IP _Ly, and the corresponding channel fingerprint feature sorting rule f IP _Lx, f IP_Ly, if the estimated line-of-sight / non-line-of-sight label of the target positioning point is 0, then select the training model M IP _Nx, M IP _Ny, and the corresponding channel fingerprint feature sorting rule f IP _Nx, f IP _Ny.

[0176] The specific steps of adaptive K value weighted prediction are as follows:

[0177] Step S801: after the channel fingerprint features of the target positioning point are rearranged according to the optimal channel fingerprint feature combination and input into the selected positioning model, the scores score_x and score_y of the horizontal and vertical coordinates of the offline reference point in the channel fingerprint database can be obtained;

[0178] Step S802: Use K-means clustering algorithm to accurately divide the scores score_x and score_y into two categories, one with a higher score and one with a lower score. The number of coordinates in the category with a higher score is the adaptive K value K. x , K y ;

[0179] Step S803: The K with the highest score x The horizontal axis x k and its score score_x k , K y The ordinate y k and its score score_y k , respectively with the horizontal axis score score_x k , vertical coordinate score score_y k The horizontal axis x k , vertical coordinate y k The weight of the final predicted target positioning point coordinates (x T ,y T ):

[0180]

[0181] Where k is K x , K y The index of .

[0182] like Fig. 9As shown, the ray tracing simulation data verifies the superior performance of the proposed line-of-sight / non-line-of-sight recognition assisted positioning method suitable for 6G indoor communication, and has the fastest error convergence speed. The error of the proposed method is within 1m with a probability of 95%. Compared with BiLSTM, the performance of the proposed method is significantly enhanced, and the average positioning error is improved by about 45%. Through weighted prediction and adaptive K value, the positioning performance of weighted random forest (WRF) is 30% higher than that of RF. With the help of feature selection and line-of-sight / non-line-of-sight recognition modules, the positioning performance of IWRF is 17% higher than that of WRF, which proves the effectiveness of feature selection and line-of-sight / non-line-of-sight recognition. Feature selection reduces redundant information while preventing mutual interference between channel fingerprint features, ultimately improving positioning accuracy. Furthermore, in a measurement environment with more interference factors, the performance of the proposed method still remains optimal, proving the robustness of the method proposed in the present invention, such as Fig.10 As shown. Among the compared algorithms, the proposed method shows the fastest error convergence and the highest degree of error concentration, which shows that the method has potential application value for practical indoor positioning. Therefore, the line-of-sight / non-line-of-sight identification auxiliary positioning method suitable for 6G indoor communication proposed in the present invention has high accuracy, strong stability, higher versatility, and can meet the centimeter-level positioning accuracy required by the 6G system.

[0183] The line-of-sight / non-line-of-sight recognition assisted positioning method suitable for 6G indoor communication of the embodiment of the present invention extracts channel fingerprint features for offline reference points; inputs the channel fingerprint feature set of offline reference points into the optimized weighted random forest algorithm to perform line-of-sight / non-line-of-sight recognition and positioning training respectively, and outputs the recognition and positioning models respectively; extracts channel fingerprint features for target positioning points; inputs the channel fingerprint features of the target positioning points into the line-of-sight / non-line-of-sight recognition model, and inputs the channel fingerprint features of the target positioning points into the corresponding positioning model according to the predicted line-of-sight / non-line-of-sight labels, and outputs the predicted coordinates. The present invention can solve the problem of low positioning accuracy caused by multipath fading and non-line-of-sight propagation in indoor environments, greatly improves the accuracy and robustness of positioning, and is superior to existing algorithms. In simulations and actual measurements of different scenarios, this method has shown optimal performance, and has potential applicability even without a large amount of training data.

[0184] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0185] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0186] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

Claims

1. A line-of-sight / non-line-of-sight identification auxiliary positioning method suitable for 6G indoor communication, characterized in that: The following steps are involved: Collecting channel state information of the target positioning point, and extracting channel fingerprint features of the target positioning point according to the channel state information; Inputting the channel fingerprint feature of the target positioning point into a pre-built line-of-sight / non-line-of-sight classification model according to the optimal channel fingerprint feature combination rule, and outputting the line-of-sight / non-line-of-sight label of the target positioning point; According to the line-of-sight / non-line-of-sight label of the target positioning point, a pre-constructed positioning model and a corresponding optimal channel fingerprint feature combination rule are selected, the channel fingerprint features of the target positioning point are recombined and arranged according to the optimal channel fingerprint feature combination rule, input into the positioning model, and adaptive K value weighted prediction is performed to estimate the position coordinates of the target positioning point.

2. The method according to claim 1, characterized in that The channel state information of the target positioning point is collected by using wireless channel simulation software or wireless channel measurement equipment based on ray tracing, wherein the wireless channel simulation software based on ray tracing collects the channel state information of the target positioning point, including: In the wireless channel simulation software based on ray tracing, an indoor scene is constructed and simulation parameters are set. The channel simulation results and each target positioning point can be marked with line-of-sight / non-line-of-sight labels. Each ray in the simulation result corresponds to a set of channel state information, including: received signal strength, azimuth arrival angle, elevation arrival angle and arrival time; The channel state information of the target positioning point is collected by using a wireless channel measurement device, including: after calibrating the wireless channel measurement device, performing channel measurement on the target positioning point and recording the coordinates of the target positioning point and the line-of-sight / non-line-of-sight label; and performing channel estimation on the channel measurement result by using a spatial alternating generalized expectation algorithm to obtain the channel state information of each subcarrier.

3. The method according to claim 2, characterized in that Extracting a channel fingerprint feature of the target positioning point according to the channel state information includes: Reconstruct the delay / angular power spectral density based on the collected channel state information; Extracting channel fingerprint features based on the normalized maximum power path, the channel fingerprint features including: received signal strength, azimuth arrival angle, elevation arrival angle and arrival time; Channel characteristics are extracted based on the reconstructed delay / angular power spectral density, including received power kurtosis, received power skewness, rise time, RMS delay spread, RMS angular spread and Ricean K factor.

4. The method according to claim 1, characterized in that: Performing adaptive K value weighted prediction to estimate the position coordinates of the target positioning point includes: After the channel fingerprint features of the target positioning point are rearranged according to the optimal channel fingerprint feature combination, the selected positioning model is input to obtain the scores score_x and score_y of the horizontal and vertical coordinates of the offline reference point in the channel fingerprint database; The K-means clustering algorithm is used to divide the scores score_x and score_y into high-level and low-level categories. The number of coordinates contained in the high-level category is the adaptive K value K. x , K y ; The K with the highest score x The horizontal axis x k and its score score_x k , K y The vertical coordinate y k and its score score_y k , respectively with the horizontal axis score score_x k , vertical coordinate score score_y k The horizontal axis x k , vertical coordinate y k The weight of the target positioning point is obtained by calculating the final predicted coordinates (x T ,y T ): Where k is K x , K y The index of .

5. The method according to claim 2, characterized in that: The method further includes: constructing a line-of-sight / non-line-of-sight recognition model, specifically including the following steps: A reference grid is established in the positioning area, with the grid lines perpendicular to the coordinate axes of the Cartesian coordinate system. The intersection of the grid is taken as the offline reference point, the line-of-sight / non-line-of-sight label of the offline reference point is marked, and the channel status information of the offline reference point is collected; Extracting channel fingerprint features of offline reference points based on the channel fingerprint structure, building a channel fingerprint database based on the channel fingerprint features, offline reference point coordinates and line-of-sight / non-line-of-sight labels, and dividing the channel fingerprint database into a training set and a validation set; The channel fingerprint features and line-of-sight / non-line-of-sight labels of the training set are input into the random forest model for training. After executing the feature selection module, the line-of-sight / non-line-of-sight classification model M is output. I And the corresponding optimal channel fingerprint feature combination rule f I ; The horizontal coordinate x and the corresponding channel fingerprint feature, the vertical coordinate y and the corresponding channel fingerprint feature of the offline reference point marked as the line-of-sight label in the training set are respectively input into the random forest model for training. After executing the feature selection module, the corresponding positioning model M is output. IP _Lx, M IP _Ly and the corresponding optimal channel fingerprint feature combination rule f IP _Lx, f IP _Ly; The horizontal coordinate x and the corresponding channel fingerprint feature, the vertical coordinate y and the corresponding channel fingerprint feature of the offline reference point marked as non-line-of-sight label in the training set are respectively input into the random forest model for training. After executing the feature selection module, the positioning model M is output. IP _Nx, M IP _Ny and the corresponding optimal channel fingerprint feature combination rule f IP _Nx,f IP _Ny.

6. The method according to claim 5, characterized in that Line-of-sight / non-line-of-sight labels for offline reference points, including: If there is line-of-sight transmission between the offline reference point and the transmitter, the line-of-sight / non-line-of-sight label of the offline reference point is marked as 1. If there is non-line-of-sight transmission between the offline reference point and the transmitter, the line-of-sight / non-line-of-sight label of the offline reference point is marked as 0.

7. The method according to claim 5, characterized in that The channel state information of the offline reference point is collected by using wireless channel simulation software or wireless channel measurement equipment based on ray tracing, wherein the wireless channel simulation software based on ray tracing collects the channel state information of the offline reference point, including: In the wireless channel simulation software based on ray tracing, an indoor scene is constructed and simulation parameters are set. The channel simulation results and each offline reference point can be marked with line-of-sight / non-line-of-sight labels. Each ray in the simulation result corresponds to a set of channel state information, including: received signal strength, azimuth arrival angle, elevation arrival angle and arrival time. Collecting the channel state information of the offline reference point by using the wireless channel measurement equipment includes: after calibrating the wireless channel measurement equipment, performing channel measurement on the offline reference points one by one and recording the coordinates of the offline reference points and the line-of-sight / non-line-of-sight labels; and performing channel estimation on the channel measurement results by using the spatial alternating generalized expectation algorithm to obtain the channel state information of each subcarrier.

8. The method according to claim 5, characterized in that The channel fingerprint features and line-of-sight / non-line-of-sight labels of the training set are input into the random forest model for training. After executing the feature selection module, the line-of-sight / non-line-of-sight classification model M is output. I And the corresponding optimal channel fingerprint feature combination rule f I ,include: After the channel fingerprint features and line-of-sight / non-line-of-sight labels of the training set are input into the random forest model for training, the random forest model gives the importance score of the channel fingerprint features; According to the importance scores of the channel fingerprint features, the channel fingerprint features of the training set are sorted in descending order of scores, and the sorting rule f is recorded; For the channel fingerprint features of the sorted training set, select the first n channel fingerprint features each time and record the sorting rule f of the channel fingerprint features n , input the first n channel fingerprint features selected together with the line-of-sight / non-line-of-sight labels into the random forest model for training and generate n training models, and input the channel fingerprint features of the validation set according to f n After reordering, input the nth training model respectively to estimate the line-of-sight / non-line-of-sight labels of the validation set under the n training models; Calculate the consistency ratio of the line-of-sight / non-line-of-sight labels of the n estimated validation sets and the line-of-sight / non-line-of-sight labels of the real validation sets, and get the accuracy of the n training models. Take the training model with the highest accuracy as the line-of-sight / non-line-of-sight classification model M. I , Line-of-sight / non-line-of-sight classification model M I The corresponding channel fingerprint feature sorting rule is denoted as f I .

9. The method according to claim 5, characterized in that The horizontal coordinate x and the corresponding channel fingerprint feature, the vertical coordinate y and the corresponding channel fingerprint feature of the offline reference point marked as the line-of-sight label in the training set are respectively input into the random forest model for training. After executing the feature selection module, the corresponding positioning model M is output. IP _Lx, M IP _Ly and the corresponding optimal channel fingerprint feature combination rule f IP _Lx, f IP _Ly, including: After the channel fingerprint features marked as line-of-sight labels in the training set and the corresponding horizontal (vertical) coordinates x(y) are input into the random forest model for training, the training model gives the importance score of the channel fingerprint features; According to the importance scores of the channel fingerprint features, the channel fingerprint features of the training set are sorted in descending order of scores, and the sorting rule f is recorded; For the sorted training set channel fingerprint features, select the first n channel fingerprint features each time and record the sorting rule f of the channel fingerprint features n , the first n channel fingerprint features selected together with the corresponding horizontal (vertical) coordinates x(y) are input into the random forest model for training and n training models M are generated x (M y ), the channel fingerprint features marked as line-of-sight labels in the verification set are pressed by f n After reordering, input the nth training model M respectively x (M y ), estimate the horizontal (vertical) coordinate x(y) of the validation set under n training models; Combine the estimated horizontal (vertical) coordinates x(y) of the validation set into complete coordinates, calculate the absolute error between the estimated complete coordinates and the true validation set coordinates, calculate the average absolute error of the n training models, and take the training model with the lowest average absolute error as the positioning model M IP _Lx, M IP _Ly, the channel fingerprint feature sorting rule corresponding to the positioning model is recorded as f IP _Lx, f IP _Ly.

10. The method according to claim 5, characterized in that The horizontal coordinate x and the corresponding channel fingerprint feature, the vertical coordinate y and the corresponding channel fingerprint feature of the offline reference point marked as non-line-of-sight label in the training set are respectively input into the random forest model for training. After executing the feature selection module, the positioning model M is output. IP _Nx, M IP _Ny and the corresponding optimal channel fingerprint feature combination rule f IP _Nx,f IP _Ny, including: After the channel fingerprint features and the corresponding horizontal (vertical) coordinates x(y) marked as non-line-of-sight labels in the training set are input into the random forest model for training, the training model gives the importance score of the channel fingerprint features; According to the importance scores of the channel fingerprint features, the channel fingerprint features of the training set are sorted in descending order of scores, and the sorting rule f is recorded; For the sorted training set channel fingerprint features, select the first n channel fingerprint features each time and record the sorting rule f of the channel fingerprint features n , the first n channel fingerprint features selected together with the corresponding horizontal (vertical) coordinates x(y) are input into the random forest model for training and n training models M are generated x (M y ), the channel fingerprint features marked as non-line-of-sight labels in the verification set are pressed according to f n After reordering, input the nth training model M respectively x (M y ), estimate the horizontal (vertical) coordinate x(y) of the validation set under n training models; Combine the estimated horizontal coordinate x and vertical coordinate y of the validation set into the complete coordinate, and calculate the absolute error between the estimated complete coordinate and the true validation set coordinate. Then calculate the average absolute error of the n training models, and take the training model with the lowest average absolute error as the positioning model M IP _Nx, M IP _Ny, the channel fingerprint feature sorting rule corresponding to the positioning model is recorded as f IP _Nx,f IP _Ny.

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