Indoor positioning method, apparatus, and storage medium

Abstract

The application provides an indoor positioning method and device and a storage medium, relates to the field of terminal positioning, and can solve the problem of poor accuracy of determining the position information of a to-be-measured point. The method comprises the following steps: acquiring a CSI-RS, the CSI-RS being used for evaluating the channel parameters of the CSI-RS; performing channel estimation processing on the CSI-RS to obtain an estimation result of the channel parameters, the estimation result comprising real-time AoA data and real-time ToF data of at least one to-be-measured point path; and determining the position information of the at least one to-be-measured point path based on the estimation result of the channel parameters. The application embodiment is applied to a terminal positioning process.

Description

Technical Field

[0001] This application relates to the field of terminal positioning technology, and in particular to an indoor positioning method, device and storage medium. Background Technology

[0002] Currently, when performing indoor positioning, the terminal can collect Wireless Fidelity (WIFI) information and location context information to form a mapping relationship and establish a mapping database. Then, during the positioning stage, the location information of the point to be measured is obtained by matching the database.

[0003] However, in the above method, if the WIFI signal at the location of the terminal is weak, the network-side device cannot form an accurate mapping relationship based on the WIFI information and the location context information. As a result, the database matching cannot obtain accurate location information of the test point during the positioning stage, thus the accuracy of determining the location information of the test point is poor. Summary of the Invention

[0004] This application provides an indoor positioning method, device, and storage medium, which can solve the problem of poor accuracy in determining the location information of the point to be measured.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application provides an indoor positioning method, which includes: acquiring a Channel State Information-Reference Signal (CSI-RS), the CSI-RS being used to evaluate the channel parameters of the CSI-RS; performing channel estimation processing on the CSI-RS to obtain an estimation result of the channel parameters, the estimation result being real-time Angle-of-Arrival (AoA) data and real-time Time-of-Flight (ToF) data of at least one path to be measured; and determining the location information of at least one path to be measured based on the estimation result of the channel parameters.

[0007] Based on the above technical solution, the indoor positioning method provided in this application allows the indoor positioning device to acquire CSI-RS, thereby performing channel estimation on the CSI-RS to obtain channel parameter estimation results. These estimation results include real-time AoA data and real-time ToF data for at least one path to be measured. Furthermore, the indoor positioning device can determine the location information of at least one path to be measured based on the estimated CSI-RS channel parameters. In this solution, since AoA and ToF parameters have more feature information (e.g., direction information) than Received Signal Strength Indicator (RSSI) parameters, the indoor positioning device can perform indoor positioning more accurately using AoA and ToF parameters, thus improving the accuracy of indoor positioning.

[0008] In a first possible implementation of the first aspect, channel estimation processing is performed on CSI-RS to obtain channel parameter estimation results, including: modeling the CSI-RS channel state of each path to be tested in at least one path to be tested to obtain target data, where the target data is the sum of the path components of each path to be tested; performing discrete Fourier sampling processing on the target data to obtain the signal transmission model of each path to be tested; and performing eigenvalue processing on each path to be tested based on the signal transmission model of each path to be tested to obtain AoA data and ToF data of each path to be tested.

[0009] In the second possible implementation of the first aspect, based on the signal transmission model of each path to be tested, eigenvalue processing is performed on each path to be tested to obtain AoA data and ToF data for each path to be tested. This includes: performing eigenvalue decomposition processing on the signal transmission model to obtain the spatiotemporal two-dimensional spectral function corresponding to the signal transmission model; and performing search processing on the spatiotemporal two-dimensional spectral function to obtain AoA data and ToF data for each path to be tested.

[0010] In the third possible implementation of the first aspect, the location information of at least one test point path is determined based on channel parameters, including: acquiring a ToF offline fingerprint database and an AoA offline fingerprint database; for each test point path in the at least one test point path, comparing the similarity between the ToF data and each ToF data in the ToF offline fingerprint database to obtain M first distances, where the M first distances are the distances between each ToF data in the ToF offline fingerprint database and the ToF data satisfying a preset threshold, and M is an integer greater than or equal to 2; mapping the M first distances to the AoA offline fingerprint database to obtain a target AoA offline database, where the data precision in the target AoA offline database is greater than the data precision in the AoA offline database; comparing the similarity between the AoA data and each AoA data in the target AoA offline fingerprint database to obtain N second data, where the N second data are the data where the distance between each AoA data in the target AoA offline fingerprint database and the AoA data satisfying a preset threshold, where N is less than M, and N is an integer greater than 0; and averaging the N second data to obtain the location information of the test point.

[0011] In the fourth possible implementation of the first aspect, obtaining the ToF offline fingerprint database and the AoA offline database includes: obtaining historical AOA data and historical ToF data; performing cluster averaging on the historical AOA data and historical ToF data to obtain the AoA offline fingerprint database and the ToF offline fingerprint database.

[0012] Secondly, this application provides an indoor positioning device, comprising: an acquisition unit, a processing unit, and a determination unit. The acquisition unit is used to acquire a Channel State Information Reference Signal (CSI-RS), which is used to evaluate the channel parameters of the CSI-RS. The processing unit is used to perform channel estimation processing on the CSI-RS to obtain an estimation result of the channel parameters, the estimation result including real-time Angle of Arrival (AoA) data and real-time Time of Flight (ToF) data of at least one path to be measured. The determination unit is used to determine the location information of at least one path to be measured based on the estimation result of the channel parameters.

[0013] In a first possible implementation of the second aspect, the processing unit is specifically configured to model the CSI-RS channel state of each path under test in at least one path under test, obtain target data, the target data being the sum of the path components of each path under test; and perform discrete Fourier sampling processing on the target data to obtain the signal transmission model of each path under test; and perform eigenvalue processing on each path under test based on the signal transmission model of each path under test to obtain AoA data and ToF data of each path under test.

[0014] In the second possible implementation of the second aspect, the processing unit is specifically used to perform eigenvalue decomposition on the signal transmission model to obtain the spatiotemporal two-dimensional spectral function corresponding to the signal transmission model; and to perform search processing on the spatiotemporal two-dimensional spectral function to obtain the AoA data and ToF data of each path to be measured.

[0015] In the third possible implementation of the second aspect, the acquisition unit is specifically used to acquire the ToF offline fingerprint database and the AoA offline fingerprint database; the processing unit is specifically used to, for each path of the test point in at least one test point path, compare the similarity between the ToF data and each ToF data in the ToF offline fingerprint database to obtain M first distances, where the M first distances are the distances between each ToF data in the ToF offline fingerprint database and the ToF data satisfying a preset threshold, and M is an integer greater than or equal to 2; map the M first distances to the AoA offline fingerprint database to obtain the target AoA offline database, where the data precision in the target AoA offline database is greater than the data precision in the AoA offline database; compare the similarity between the AoA data and each AoA data in the target AoA offline fingerprint database to obtain N second data, where the N second data are the data where the distance between each AoA data in the target AoA offline fingerprint database and the AoA data satisfying a preset threshold, where N is less than M, and N is an integer greater than 0; and perform average processing on the N second data to obtain the location information of the test point.

[0016] In the fourth possible implementation of the second aspect, the acquisition unit is specifically used to acquire historical AOA data and historical ToF data; and to perform cluster averaging on the historical AOA data and historical ToF data to obtain the AoA offline fingerprint database and the ToF offline fingerprint database.

[0017] Thirdly, this application provides an indoor positioning device, which includes: a processor and a communication interface; the communication interface and the processor are coupled, and the processor is used to run computer programs or instructions to implement the indoor positioning method as described in the first aspect and any possible implementation of the first aspect.

[0018] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a terminal, cause the terminal to perform the indoor positioning method as described in the first aspect and any possible implementation thereof.

[0019] Fifthly, embodiments of this application provide a computer program product containing instructions that, when run on an indoor positioning device, cause the indoor positioning device to perform the indoor positioning method as described in the first aspect and any possible implementation thereof.

[0020] In a sixth aspect, embodiments of this application provide a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run computer programs or instructions to implement the indoor positioning method as described in the first aspect and any possible implementation thereof.

[0021] Specifically, the chip provided in this application embodiment also includes a memory for storing computer programs or instructions. Attached Figure Description

[0022] Figure 1 One of the flowcharts for an indoor positioning method provided in this application embodiment;

[0023] Figure 2 A second flowchart illustrating an indoor positioning method provided in this application embodiment;

[0024] Figure 3 A flowchart of an indoor positioning method provided in this application embodiment;

[0025] Figure 4 A schematic diagram illustrating an example of a space-time two-dimensional spectral function provided in this application embodiment;

[0026] Figure 5 A flowchart of an indoor positioning method provided in this application embodiment;

[0027] Figure 6 This is a schematic diagram of the structure of an indoor positioning device provided in an embodiment of this application;

[0028] Figure 7 This is a schematic diagram of another indoor positioning device provided in an embodiment of this application;

[0029] Figure 8 This is a schematic diagram of the structure of a chip provided in an embodiment of this application. Detailed Implementation

[0030] The indoor positioning method, device, and storage medium provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0031] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0032] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0033] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0034] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0035] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0036] Currently, with the intelligent upgrading of various industries, the demand for location information in the interconnected society is increasing daily, especially for indoor location information. Indoor positioning is of great value for location-based services (LBS) in commercial buildings, safety rescue, and other fields. The 3GPP 23.273 standard defines the relevant architecture for 5G positioning. 5G positioning consists of user equipment (UE), 5G radio access network, and 5G core network. In existing technologies, a mapping relationship is formed by receiving and reporting WIFI information and location context information collected by the mobile terminal and establishing a mapping database. Then, the location information of the test point is obtained by matching the database during the positioning stage. Alternatively, the 5G base station obtains the 5G base station location information from satellites, calculates the difference with the stored true value to obtain calibration information, and broadcasts it to users in the area through the 5G base station. Then, the user terminal obtains the terminal's location information through satellites and corrects the terminal's location information according to the received broadcast calibration information to obtain the positioning result. Alternatively, indoor positioning is achieved using multipath energy fingerprinting, specifically in the offline signal acquisition stage and the online stage. In the offline signal acquisition phase, multipath parameters for wireless signal estimation are obtained and RSSI received signal strength is calculated. Simultaneously, a fingerprint database of location and RSSI is established. In the online phase, the measured RSSI values ​​are matched against the fingerprint database to obtain the top 10% of similar fingerprint points. Finally, these fingerprint points are averaged to obtain the user's location information. However, in the above method, if the RSSI received signal strength is weak, the database matching during the positioning phase may not yield accurate location information for the test point, resulting in poor accuracy in determining the test point's location.

[0037] To address the problem of poor accuracy in determining the location information of a test point, which is unsolvable in existing technologies, this application provides an indoor positioning method. The indoor positioning device can acquire a CSI-RS (Common Signal-Reference System), and then perform channel estimation on the CSI-RS to obtain estimated channel parameters. These estimated parameters include real-time AoA (Aspect-of-Area) data and real-time ToF (Time-of-Flight) data for at least one path to the test point. Based on the estimated channel parameters, the indoor positioning device can then determine the location information of the test point. In this solution, since AoA and ToF parameters contain more feature information (such as direction information) than RSSI parameters, the indoor positioning device can perform indoor positioning more accurately using AoA and ToF parameters, thus improving the accuracy of indoor positioning.

[0038] like Figure 1 The diagram shows a flowchart of an indoor positioning method provided in an embodiment of this application. The method includes the following steps S101 to S103:

[0039] S101, Indoor positioning device acquires CSI-RS.

[0040] In this embodiment of the application, the CSI-RS described above is used to evaluate the channel parameters of the CSI-RS.

[0041] In this embodiment of the application, the indoor positioning device can receive CSI-RS parameters configured by the base station based on signaling to measure and evaluate the CSI-RS signal, and then use the channel parameters to determine the location information of the indoor positioning device.

[0042] In this embodiment of the application, the indoor positioning device can obtain CSI-RS through signaling sent by the base station.

[0043] Optionally, in the embodiments of this application, the signaling can be any of the following: Radio Resource Control (RRC) signaling, Physical Downlink Control Channel (PDCCH) or Physical Downlink Shared Channel (PDSCH).

[0044] For example, for any 5G base station, assuming the base station consists of M receiving antennas, and the receiving antennas are a uniform array with a distance d between two adjacent antennas, then the CSI-RS signal transmitted by the base station can be expressed by the following formula:

[0045]

[0046] S102. The indoor positioning device performs channel estimation processing on the CSI-RS to obtain the estimation results of the channel parameters.

[0047] In this embodiment of the application, the above estimation results include real-time AoA data and real-time ToF data of at least one path to be measured.

[0048] In this embodiment, the indoor positioning device can actively initiate a Sounding Reference Signal (SRS) to the network-side device, which then performs signal measurement and location calculation. Furthermore, the network-side device can send a CSI-NR signal, allowing the indoor positioning device to estimate the ToF and AoA parameters of the CSI-NR signal.

[0049] In this embodiment, the indoor positioning device can collect CSI signals from the 5G communication system to obtain CSI data. Then, based on the CSI data, the number of incident paths is estimated. A space-time two-dimensional spectrum algorithm can then be used to perform spectral estimation on the CSI signals, thereby obtaining the AoA and ToF information for each path of the downlink channel.

[0050] Optionally, in the embodiments of this application, combined with Figure 1 ,like Figure 2 As shown, step S102 can be specifically implemented through the following steps S102a to S102c:

[0051] S102a, The indoor positioning device models the CSI-RS channel state of each path to be tested in at least one path to be tested, and obtains the target data.

[0052] In this embodiment of the application, the target data is the sum of the path components of each path to be tested in at least one path to be tested.

[0053] In this embodiment of the application, the indoor positioning device can obtain the target data by modeling the CSI-RS channel state of each path of at least one path of test points using the following formula 2.

[0054] For example, the indoor positioning device can model the CSI-RS channel state of each path to obtain the sum of the K path components received by the m-th antenna, which can be achieved through the following formula:

[0055]

[0056] S102b: The indoor positioning device performs discrete Fourier sampling processing on the target data to obtain the signal transmission model of each path to be measured.

[0057] In this embodiment, the indoor positioning device can obtain the signal transmission model of each path to be measured using the Discrete Fourier Transform algorithm, which can be specifically achieved using the following formula three:

[0058] X = HΓ + N

[0059] H=[h(θ1,τ1)…h(θ K ,τ K )] T

[0060]

[0061] S102c: The indoor positioning device performs feature value processing on each path to be measured based on the signal transmission model of each path to be measured, and obtains the AoA data and ToF data of each path to be measured.

[0062] In this embodiment of the application, the signal transmission model described above can be a covariance matrix.

[0063] In this embodiment, the indoor positioning device can obtain target data by modeling the channel state of each path to be tested in at least one path to be tested, and perform discrete Fourier processing on the target data to obtain the signal transmission model of each path to be tested. Then, based on the signal transmission model, the AoA data and ToF data of each path to be tested can be obtained. By obtaining the AoA data and ToF data, the accuracy of indoor positioning can be improved.

[0064] Optionally, in the embodiments of this application, combined with Figure 2 ,like Figure 3 As shown, step S102c can be specifically implemented through the following steps S201 and S202:

[0065] S201. The indoor positioning device performs eigenvalue decomposition on the signal transmission model to obtain the spatiotemporal two-dimensional spectral function corresponding to the signal transmission model.

[0066] In this embodiment, the indoor positioning device can perform eigenvalue decomposition on the covariance matrix, estimate the channel parameters based on the orthogonality between the signal subspaces of different eigenvectors, and obtain the spatiotemporal two-dimensional spectral function, which can be specifically achieved through the following formula:

[0067]

[0068] S202. The indoor positioning device performs a search process on the spatiotemporal two-dimensional spectral function to obtain the AoA data and ToF data for each path of the point to be measured.

[0069] like Figure 4 As shown, the indoor positioning device can use a two-dimensional spatiotemporal spectral function to search for AoA and ToF data of the direct path based on the shortest transmission time. Figure 4 One spectral peak point corresponds to one transmission path.

[0070] In this embodiment, the indoor positioning device can obtain the spatiotemporal two-dimensional spectral function corresponding to the signal transmission model by performing eigenvalue decomposition on the transmission model. Then, it can perform search processing on the spatiotemporal two-dimensional spectral function to obtain the AoA data and ToF data of each path to be measured. By obtaining the AoA data and ToF data, the accuracy of indoor positioning can be improved.

[0071] S103. The indoor positioning device determines the location information of at least one path to the point to be measured based on the estimation results of the channel parameters.

[0072] In this embodiment of the application, after obtaining the ToF parameters and AoA parameters, the indoor positioning device can establish an offline ToF fingerprint database and an offline AoA fingerprint database in the offline stage, and obtain the location information of at least one path of the test point by matching the database in the online stage.

[0073] Optionally, in the embodiments of this application, combined with Figure 1 ,like Figure 5 As shown, step S103 can be specifically implemented through the following steps S103a to S103e:

[0074] S103a, The indoor positioning device acquires the ToF offline fingerprint database and the AoA offline fingerprint database.

[0075] In this embodiment of the application, the indoor positioning device can determine the location information of at least one path of the point to be measured by acquiring the ToF offline fingerprint database and the AoA offline fingerprint database.

[0076] Optionally, in this embodiment of the application, step S103a can be implemented by the following steps S301 and S302:

[0077] S301. The indoor positioning device acquires historical AOA data and historical ToF data.

[0078] In this embodiment of the application, the indoor positioning device can perform multiple evaluations of CSI-RS to obtain historical AoA data and historical ToF data.

[0079] In this embodiment of the application, the aforementioned historical AOA data and historical ToF data can be one or more.

[0080] It should be noted that the specific implementation method can be found in the above embodiments, and will not be repeated here to avoid repetition.

[0081] S302. The indoor positioning device performs clustering and averaging on historical AOA data and historical ToF data to obtain the AoA offline fingerprint database and the ToF offline fingerprint database.

[0082] In this embodiment of the application, during the offline phase, the indoor positioning device can perform multiple data collections. By clustering and averaging the data collected multiple times, the estimation error caused by occasional outliers is reduced, thereby making the AoA data and ToF data obtained by the indoor positioning device more accurate.

[0083] S103b. For each test point path in at least one test point path, the indoor positioning device compares the similarity of the ToF data with each ToF data in the ToF offline fingerprint database to obtain M first distances.

[0084] In this embodiment of the application, the above-mentioned M first distances are the distances between each ToF data in the ToF offline fingerprint database and the ToF data whose similarity satisfies a preset threshold, where M is an integer greater than or equal to 2.

[0085] In this embodiment of the application, the indoor positioning device can compare the similarity of the ToF data with each ToF data in the ToF offline fingerprint database using Euclidean distance, thereby obtaining M first distances.

[0086] S103c: The indoor positioning device maps M first distances to the AoA offline fingerprint database to obtain the target AoA offline database.

[0087] In this embodiment of the application, the data precision in the target AoA offline database is greater than the data precision in the AoA offline database.

[0088] In this embodiment of the application, the amount of data in the target AoA offline database is less than the amount of data in the AoA offline database.

[0089] In this embodiment of the application, the indoor positioning device can map M first distances to the AoA offline fingerprint database, thereby determining L AoA data in the AoA offline fingerprint database based on the M first distances, and encapsulating the M AoA data into a target AoA offline database, where L is an integer greater than or equal to M.

[0090] S103d, the indoor positioning device compares the similarity of the AoA data with each AoA data in the target AoA offline fingerprint database to obtain N second data.

[0091] In this embodiment of the application, the above-mentioned N second data are data in which the distance between each AoA data in the target AoA offline fingerprint database and the AoA data meets a preset threshold, where N is less than M and N is an integer greater than 0.

[0092] In this embodiment of the application, the indoor positioning device can compare the similarity of the AoA data with each AoA data in the target AoA offline fingerprint database using Euclidean distance to obtain N second data.

[0093] S103e: The indoor positioning device averages N second data points to obtain the location information of the point to be measured.

[0094] In this embodiment of the application, the indoor positioning device can obtain the location information of the point to be measured by averaging N second data points using the following formula:

[0095]

[0096] This application provides an indoor positioning device that can acquire CSI-RS (Common Signal-Reference System) and perform channel estimation on the CSI-RS to obtain channel parameter estimation results. These estimation results include real-time AoA (Aspect-of-Atmosphere) data and real-time ToF (Time-of-Flight) data for at least one path to the target point. Based on the channel parameter estimation results, the indoor positioning device can determine the location information of the target point. In this solution, since AoA and ToF parameters have more feature information (e.g., direction information) than RSSI parameters, the indoor positioning device can perform indoor positioning more accurately using AoA and ToF parameters, thus improving the accuracy of indoor positioning.

[0097] This application embodiment can divide the indoor positioning device into functional modules or functional units according to the above method example. For example, each function can be divided into a separate functional module or functional unit, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or in software functional modules or functional units. The module or unit division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0098] like Figure 6 The diagram shown is a structural schematic of an indoor positioning device provided in an embodiment of this application. The device includes: an acquisition unit 201, a processing unit 202, and a determination unit 203.

[0099] The acquisition unit 201 is used to acquire the Channel State Information Reference Signal (CSI-RS), which is used to evaluate the channel parameters of the CSI-RS. The processing unit 202 is used to perform channel estimation processing on the CSI-RS to obtain the estimation results of the channel parameters, including real-time Angle of Arrival (AoA) data and real-time Time of Flight (ToF) data for at least one path to be measured. The determination unit 203 is used to determine the location information of at least one path to be measured based on the channel parameter estimation results.

[0100] In one possible implementation, the processing unit 202 is specifically used to model the CSI-RS channel state of each path under test in at least one path under test to obtain target data, which is the sum of the path components of each path under test; and to perform discrete Fourier sampling processing on the target data to obtain the signal transmission model of each path under test; and to perform eigenvalue processing on each path under test according to the signal transmission model of each path under test to obtain the AoA data and ToF data of each path under test.

[0101] In one possible implementation, the processing unit 202 is specifically used to perform eigenvalue decomposition on the signal transmission model to obtain the spatiotemporal two-dimensional spectral function corresponding to the signal transmission model; and to perform search processing on the spatiotemporal two-dimensional spectral function to obtain the AoA data and ToF data of each path to be measured.

[0102] In one possible implementation, the acquisition unit 201 is specifically used to acquire the ToF offline fingerprint database and the AoA offline fingerprint database; the processing unit 202 is specifically used to, for each path of the test point in at least one test point path, compare the similarity between the ToF data and each ToF data in the ToF offline fingerprint database to obtain M first distances, where the similarity between each ToF data in the ToF offline fingerprint database and the ToF data satisfies a preset threshold, and M is an integer greater than or equal to 2; map the M first distances to the AoA offline fingerprint database to obtain the target AoA offline database, where the data precision in the target AoA offline database is greater than the data precision in the AoA offline database; compare the similarity between the AoA data and each AoA data in the target AoA offline fingerprint database to obtain N second data, where the distance between each AoA data in the target AoA offline fingerprint database and the AoA data satisfies a preset threshold, where N is less than M, and N is an integer greater than 0; and perform average processing on the N second data to obtain the location information of the test point.

[0103] In one possible implementation, the acquisition unit 201 is specifically used to acquire historical AOA data and historical ToF data; and to perform cluster averaging on the historical AOA data and historical ToF data to obtain the AoA offline fingerprint database and the ToF offline fingerprint database.

[0104] This application provides an indoor positioning device. Since AoA and ToF parameters have more feature information (such as direction information) than RSSI parameters, the indoor positioning device can perform indoor positioning more accurately through AoA and ToF parameters, thus improving the accuracy of indoor positioning.

[0105] When implemented in hardware, the acquisition unit 201 in this embodiment can be integrated onto the communication interface, and the processing unit 202 and the determination unit 203 can be integrated onto the processor. Specific implementation methods are as follows: Figure 7 As shown.

[0106] Figure 7A schematic diagram of another possible structure of the indoor positioning device involved in the above embodiments is shown. The indoor positioning device includes a processor 302 and a communication interface 303. The processor 302 is used to control and manage the operation of the indoor positioning device, for example, executing the steps performed by the processing unit 202 and the determining unit 203, and / or performing other processes of the technology described herein. The communication interface 303 is used to support communication between the indoor positioning device and other network entities, for example, executing the steps performed by the acquiring unit 201. The indoor positioning device may also include a memory 301 and a bus 304. The memory 301 is used to store the program code and data of the indoor positioning device.

[0107] The memory 301 may be a memory in an indoor positioning device, and the memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk or solid-state drive; the memory may also include a combination of the above types of memory.

[0108] The processor 302 described above can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0109] Bus 304 can be an Extended Industry Standard Architecture (EISA) bus, etc. Bus 304 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0110] Figure 8 This is a schematic diagram of the structure of chip 170 provided in an embodiment of this application. Chip 170 includes one or more (including two) processors 1710 and communication interfaces 1730.

[0111] Optionally, the chip 170 also includes a memory 1740, which may include read-only memory and random access memory, and provides operation instructions and data to the processor 1710. A portion of the memory 1740 may also include non-volatile random access memory (NVRAM).

[0112] In some implementations, memory 1740 stores elements such as execution modules or data structures, or subsets thereof, or extended sets thereof.

[0113] In this embodiment of the application, the corresponding operation is executed by calling the operation instructions stored in the memory 1740 (the operation instructions can be stored in the operating system).

[0114] The processor 1710 described above can implement or execute various exemplary logic blocks, units, and circuits described in conjunction with the disclosure of this application. The processor can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, units, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0115] The memory 1740 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk or solid-state drive; the memory may also include combinations of the above types of memory.

[0116] The Bus 1720 can be an Extended Industry Standard Architecture (EISA) bus, etc. The Bus 1720 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The symbol is represented by only one line, but this does not mean that there is only one bus or one type of bus.

[0117] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0118] This application provides a computer program product containing instructions that, when run on a computer, cause the computer to execute the indoor positioning method described in the above method embodiments.

[0119] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the indoor positioning method in the method flow shown in the above method embodiments.

[0120] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires; portable computer disks; hard disks; random access memory (RAM); read-only memory (ROM); erasable programmable read-only memory (EPROM); registers; hard disks; optical fibers; portable compact disc read-only memory (CD-ROM); optical storage devices; magnetic storage devices; or any suitable combination thereof; or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). In the embodiments of this application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0121] Embodiments of the present invention provide a computer program product containing instructions that, when executed on a computer, cause the computer to perform the indoor positioning method as described in the above method embodiments.

[0122] Since the indoor positioning device, computer-readable storage medium, and computer program product in the embodiments of the present invention can be applied to the above methods, the technical effects they can achieve can also be referred to the above method embodiments. The embodiments of the present invention will not be repeated here.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0124] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0126] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An indoor positioning method, characterized in that, The method includes: Acquire a Channel State Information Reference Signal (CSI-RS), wherein the CSI-RS is used to evaluate the channel parameters of the CSI-RS; Channel estimation processing is performed on the CSI-RS to obtain the estimation results of the channel parameters. The estimation results include real-time angle of arrival (AoA) data and real-time time of flight (ToF) data of at least one path of the point to be measured. Obtain the ToF offline fingerprint database and the AoA offline fingerprint database; For each test point path in the at least one test point path, the similarity of the ToF data is compared with each ToF data in the ToF offline fingerprint database to obtain M first distances. The M first distances are the distances between each ToF data in the ToF offline fingerprint database and the ToF data where the similarity satisfies a preset threshold, and M is an integer greater than or equal to 2. The M first distances are mapped to the AoA offline fingerprint database to obtain the target AoA offline database, wherein the data precision in the target AoA offline database is greater than that in the AoA offline database. The similarity of the AoA data is compared with that of each AoA data in the target AoA offline database to obtain N second data. The N second data are data in which the distance between each AoA data in the target AoA offline database and the AoA data satisfies the preset threshold, where N is less than M and N is an integer greater than 0. The location information of the point to be measured is obtained by averaging the N second data points.

2. The method according to claim 1, characterized in that, The process of performing channel estimation on the CSI-RS to obtain the estimation results of the channel parameters includes: Model the CSI-RS channel state of each of the at least one test point path to obtain target data, wherein the target data is the sum of the path components of each test point path; The target data is subjected to discrete Fourier sampling processing to obtain the signal transmission model of each path of the point to be measured; Based on the signal transmission model of each test point path, feature value processing is performed on each test point path to obtain the AoA data and ToF data of each test point path.

3. The method according to claim 2, characterized in that, The step involves performing feature value processing on each path of the test point based on its signal transmission model to obtain the AoA and ToF data for each path of the test point, including: The signal transmission model is subjected to eigenvalue decomposition to obtain the spatiotemporal two-dimensional spectral function corresponding to the signal transmission model; The space-time two-dimensional spectral function is searched to obtain the AoA data and ToF data for each path of the test point.

4. The method according to claim 1, characterized in that, The acquisition of the ToF offline fingerprint database and the AoA offline database includes: Obtain historical AOA data and historical ToF data; The historical AOA data and the historical ToF data are clustered and averaged to obtain the AoA offline fingerprint database and the ToF offline fingerprint database.

5. An indoor positioning device, characterized in that, The device includes: an acquisition unit, a processing unit, and a determination unit; The acquisition unit is used to acquire a channel state information reference signal (CSI-RS), and the CSI-RS is used to evaluate the channel parameters of the CSI-RS. The processing unit is used to perform channel estimation processing on the CSI-RS to obtain the estimation results of the channel parameters. The estimation results include real-time angle of arrival (AoA) data and real-time time of flight (ToF) data of at least one path of the point to be measured. The determining unit is configured to acquire a Time-of-Flight (ToF) offline fingerprint database and an AoA (Aspect-of-Authority) offline fingerprint database; for each test point path in the at least one test point path, compare the similarity of the ToF data with each ToF data in the ToF offline fingerprint database to obtain M first distances, wherein the M first distances are the distances between each ToF data in the ToF offline fingerprint database and the ToF data satisfying a preset threshold, and M is an integer greater than or equal to 2; map the M first distances to the AoA offline fingerprint database to obtain a target AoA offline database, wherein the data precision in the target AoA offline database is greater than the data precision in the AoA offline database; compare the similarity of the AoA data with each AoA data in the target AoA offline database to obtain N second data, wherein the N second data are the data where the distance between each AoA data in the target AoA offline database and the AoA data satisfies the preset threshold, and N is less than M, where N is an integer greater than 0; and average the N second data to obtain the location information of the test point.

6. The apparatus according to claim 5, characterized in that, The processing unit is specifically configured to model the CSI-RS channel state of each of the at least one test point path to obtain target data, wherein the target data is the sum of the path components of each test point path; and to perform discrete Fourier sampling processing on the target data to obtain the signal transmission model of each test point path; and to perform feature value processing on each test point path according to the signal transmission model of each test point path to obtain the AoA data and ToF data of each test point path.

7. The apparatus according to claim 6, characterized in that, The processing unit is specifically used to perform eigenvalue decomposition on the signal transmission model to obtain the spatiotemporal two-dimensional spectral function corresponding to the signal transmission model; and to perform search processing on the spatiotemporal two-dimensional spectral function to obtain the AoA data and ToF data of each path to be measured.

8. The apparatus according to claim 5, characterized in that, The acquisition unit acquires historical AOA data and historical ToF data; and performs cluster averaging on the historical AOA data and historical ToF data to obtain the AoA offline fingerprint database and the ToF offline fingerprint database.

9. An indoor positioning device, characterized in that, include: A processor and a communication interface; the communication interface is coupled to the processor, the processor being used to run computer programs or instructions to implement the indoor positioning method as described in any one of claims 1-4.

10. A computer-readable storage medium storing instructions, characterized in that, When the computer executes the instruction, the computer performs the indoor positioning method as described in any one of claims 1-4.

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