Positioning method, apparatus, storage medium, and chip system

Abstract

Embodiments of the present application provide a positioning method and device, a storage medium and a chip system, and relate to the field of positioning. In the method, the LMF can filter M first parameter sets of M base stations, that is, select N first parameter sets from the M first parameter sets, and then obtain the position information of the first device based on the N first parameter sets. In this way, positioning can be performed based on various types of positioning parameters, and by filtering the first parameter sets used for positioning, the error of the positioning parameters can be reduced, the quality and stability of the positioning parameters can be improved, and thus the positioning accuracy and robustness can be improved.

Description

Technical Field

[0001] This application relates to the field of positioning technology, and in particular to a positioning method, device, storage medium and chip system. Background Technology

[0002] With the continuous development of communication technology, the number of terminals requiring positioning is also increasing, placing higher demands on positioning accuracy.

[0003] Currently, positioning can be performed based on a single type of positioning parameter. For example, positioning can be achieved using one of the following positioning parameters: time of arrival (TOA), angle of arrival (AOA), and received signal strength (RSS).

[0004] However, due to the influence of multipath effects, attenuation, interference and other factors, the positioning parameters obtained based on the estimation have errors, and the errors of the positioning parameters cannot be effectively compensated during the positioning process, thus limiting the positioning accuracy. Summary of the Invention

[0005] This application provides a positioning method, apparatus, storage medium, and chip system, which are applied in the field of positioning technology to improve positioning accuracy.

[0006] In a first aspect, embodiments of this application propose a positioning method applied to a third device, which may be, for example, a location management function (LMF). Exemplarily, the method includes: the third device receiving M first parameter groups from M second devices, each first parameter group including at least two of the following: RSS, TDOA, or AOA, the first parameter group being determined by the second device based on a reference signal sent by the first device; determining N first parameter groups from the M first parameter groups, where M is greater than N, and M and N are positive integers; and determining the location information of the first device based on the N first parameter groups.

[0007] Based on the above scheme, the third device can filter the M sets of first parameters received from M base stations. For example, it can select N sets of first parameters from the M sets and then obtain the location information of the first device based on these N sets of first parameters. In this way, not only can positioning be performed based on various types of positioning parameters, but by filtering the acquired M sets of first parameters, the error of the positioning parameters can be reduced, and the quality and stability of the positioning parameters can be improved, thereby improving the positioning accuracy and robustness.

[0008] In one possible implementation, determining N sets of first parameters from the M sets of first parameters includes: receiving M sets of first information from the M second devices, the first information being used to determine the measurement quality of the first parameter set corresponding to the second device; and determining N sets of first parameters from the M sets of first information.

[0009] The measurement quality of the first parameter set acquired by the corresponding second device can be determined using this first information. The third device can then select N first parameter sets from the M first information sets from the M second devices, and obtain the location information of the first device based on these N first parameter sets. In other words, the third device can determine whether the first parameter set corresponding to the second device can be selected to determine the location information of the first device based on the first information reported by the second device.

[0010] Thus, by filtering the M sets of first parameters obtained through the first information, the error of the positioning parameters can be reduced, the quality and stability of the positioning parameters can be improved, thereby improving the positioning accuracy and positioning robustness.

[0011] In one possible implementation, the first information includes a second parameter group corresponding to the second device, the second parameter group including measurement quality parameters corresponding to the parameters in the first parameter group; the method further includes: determining M first indicators based on the M second parameter groups corresponding to the M second devices.

[0012] In other words, the second device reports its acquired second parameter set to the third device. The parameters included in this second parameter set can be used to evaluate the measurement quality of the parameters in the first parameter set. That is, the third device can use this second parameter set to measure the measurement quality of the parameters in the corresponding second device's first parameter set, and thus determine whether positioning services for the first device can be provided based on the first parameter set.

[0013] Thus, based on this second set of parameters, the first set of parameters with low measurement quality can be eliminated, which can reduce the error of the positioning parameters, reduce redundant calculations, improve the quality and stability of the positioning parameters, and thus improve the positioning accuracy and positioning robustness.

[0014] In one possible implementation, the first information includes a first indicator, which is determined based on a second parameter set corresponding to the second device, the second parameter set including measurement quality parameters corresponding to the parameters in the first parameter set.

[0015] In other words, the M second devices can determine their own first indicators based on the second parameter sets they acquire. Each of the M second devices can report its own first indicator to the third device. The third device, upon receiving the M first indicators, can then eliminate first parameter sets with low measurement quality based on these M indicators. This not only reduces transmission overhead but also reduces positioning parameter errors, improves the quality and stability of positioning parameters, thereby enhancing positioning accuracy and robustness.

[0016] In one possible implementation, the second parameter group includes one or more of the following parameters: the first parameter The second parameter is used to characterize the measurement quality corresponding to the time measurement quality. The resolution used to characterize the quality of time measurements; the third parameter The fourth parameter is used to characterize the azimuth quality corresponding to the quality of the angle measurement. The resolution used to characterize the quality of angle measurements; the fifth parameter. The sixth parameter is used to characterize the phase quality corresponding to the phase measurement quality. , is used to characterize the resolution of phase measurement quality.

[0017] By using one or more of the following parameters included in the second parameter value, the measurement quality of each parameter included in the first parameter group can be determined. This helps the second or third device to filter the M first parameter groups based on the M second parameter groups corresponding to the M second devices, thereby eliminating the first parameter groups of second devices with low measurement quality, reducing positioning parameter errors, and improving positioning accuracy and robustness.

[0018] In one possible implementation, the N first parameter groups are the N first parameter groups among the M first parameter groups where the first indicator is the largest; or, the N first parameter groups are the first parameter groups among the M first parameter groups where the first indicator is greater than or equal to the second threshold value.

[0019] One possible scenario is that the third device can select the N first indicators with the largest values ​​from the M first indicators. These N first indicators correspond to N second devices. The third device can locate the first device based on the first parameter group of these N second devices, thereby determining the location information of the first device.

[0020] Another possibility is that the third device can select N first indicators that are greater than or equal to the second threshold value based on the relationship between the second threshold value and the M first indicators. These N first indicators correspond to N second devices. The third device can locate the first device based on the first parameter group of these N second devices, thereby determining the location information of the first device.

[0021] In this way, some parameter sets of second equipment with low measurement quality can be eliminated, reducing the error of positioning parameters, thereby improving positioning accuracy and positioning robustness.

[0022] In one possible implementation, the M second devices of the first... The first indicator of the second equipment With the first The second parameter group corresponding to the second device includes one or more parameters that are positively correlated.

[0023] When one or more parameters in the second parameter set increase, the first index of the second device also increases, indicating better measurement quality of the first parameter set of the second device. Conversely, when one or more parameters in the second parameter set decrease, the first index of the second device decreases, indicating worse measurement quality of the first parameter set of the second device.

[0024] In this way, the second or third device can determine the first index by using one or more parameters included in the second parameter set, and then measure the accuracy or reliability of the first parameter set of the corresponding second device when used for positioning based on the magnitude of the first index.

[0025] In one possible implementation, the M second devices are the first... The first indicator of the second equipment With the first The second parameter set corresponding to each second device satisfies:

[0026] ;in, , , , , , Indicates the weighting coefficient. This represents the bias value.

[0027] Understandable. , , , , , as well as Some or all of the weighting factors can be pre-configured, predefined by the protocol, or dynamically indicated, etc., and this application embodiment does not limit this. For example, one or more of the above weighting factors can be dynamically adjusted in different application scenarios.

[0028] It should also be understood that the relationship between the first indicator and the second parameter group described above is only an example. For example, other mathematical relationships may also be satisfied between the first indicator and the second parameter group, etc. This application does not limit this.

[0029] Thus, the second or third device can determine the first indicator of the second device based on the second parameter set by using the relationship between the first indicator and the second parameter set.

[0030] In one possible implementation, the second parameter set also includes the signal-to-noise ratio. ,Should and They are positively correlated.

[0031] When the signal-to-noise ratio (SNR) included in the second parameter set increases, the first specification of the second device also increases, indicating better measurement quality of the first parameter set of the second device. Conversely, when the SNR included in the second parameter set decreases, the first specification of the second device decreases, indicating worse measurement quality of the first parameter set of the second device.

[0032] In this way, the second or third device can determine the first index by the signal-to-noise ratio included in the second parameter set, and then measure the accuracy or reliability of the first parameter set of the corresponding second device when used for positioning based on the magnitude of the first index.

[0033] In one possible implementation, satisfy:

[0034] ;in, This represents the weighting coefficient.

[0035] Understandable. It can be pre-configured, predefined by the protocol, or dynamically indicated, etc. The embodiments of this application do not limit this.

[0036] Thus, the second or third device can determine the first indicator of the second device based on the second parameter set by using the relationship between the first indicator and the second parameter set.

[0037] In one possible implementation, determining the location information of the first device based on the N first parameter sets includes: determining the location information of the first device based on the N first parameter sets using a squeeze and excitation net-convolutional neural network (SE-CNN).

[0038] The third device, for example, can be pre-configured with SE-CNN. It can input the acquired N sets of first parameters into the SE-CNN. The different parameters included in these N sets can be divided into different channels according to parameter category. That is, different channels can include N localization parameters of the same type. By assigning different weights to the parameters of different channels through SE-CNN, the channels with higher importance can be strengthened. This improves localization accuracy.

[0039] In one possible implementation, the SE-CNN includes a squeeze and excitation net (SENet) in which at least two parameters in the first parameter set correspond to different weights.

[0040] On the one hand, by squeezing the incentive network during training and learning, the weights (or weights) of different parameters can be continuously optimized and dynamically adjusted. For example, in far-field applications, the influence of the AOA parameter can be strengthened, and correspondingly, a larger weight can be assigned to the channel corresponding to the AOA parameter; in near-field applications, the influence of the RSS parameter can be strengthened, and correspondingly, a larger weight can be assigned to the channel corresponding to the RSS parameter. In this way, it can adapt to different environments or application scenarios and improve positioning accuracy.

[0041] On the other hand, SE-CNN can assign different weights to channels corresponding to different parameters, with low computational complexity, making it suitable for deployment on third-party devices (e.g., LMF) and relatively simple to implement.

[0042] Secondly, embodiments of this application propose a positioning method applied to a second device, such as a base station. Exemplarily, the method includes: the second device determining first information, the first information used to determine the measurement quality of a first parameter set corresponding to the second device, the first parameter set including at least two of the following: RSS, TDOA, or AOA, the first parameter set being determined by the second device based on a reference signal transmitted by the first device; and transmitting the first information.

[0043] Based on the above scheme, the second device can report first information to the third device. This first information allows the determination of the measurement quality of the first parameter set corresponding to the second device. The third device, based on the M first information pieces received from the M second devices, can determine which second parameter sets from the M first parameter sets corresponding to the M second devices can participate in the positioning of the first device, and which can not. In this way, positioning can be performed based on multiple types of positioning parameters. Furthermore, by filtering the acquired M first parameter sets, positioning parameter errors can be reduced, improving the quality and stability of the positioning parameters, thereby enhancing positioning accuracy and robustness.

[0044] In one possible implementation, the first information includes a second parameter group corresponding to the second device, the second parameter group including the measurement quality parameters corresponding to the parameters in the first parameter group.

[0045] In one possible implementation, the first information includes a first indicator, and the method further includes: determining the first indicator based on a second parameter set corresponding to the second device, wherein the second parameter set includes measurement quality parameters corresponding to the parameters in the first parameter set.

[0046] In other words, the M second devices can determine their own first indicators based on the second set of parameters they acquire. Then, each of the M second devices can report its own first indicator to the third device. This reduces transmission overhead.

[0047] In one possible implementation, the second parameter group includes one or more of the following parameters: the first parameter The second parameter is used to characterize the measurement quality corresponding to the time measurement quality. The resolution used to characterize the quality of time measurements; the third parameter The fourth parameter is used to characterize the azimuth quality corresponding to the quality of the angle measurement. The resolution used to characterize the quality of angle measurements; the fifth parameter. The sixth parameter is used to characterize the phase quality corresponding to the phase measurement quality. , is used to characterize the resolution of phase measurement quality.

[0048] In one possible implementation, the first indicator of the second device is positively correlated with one or more parameters included in the second parameter group corresponding to the second device.

[0049] In one possible implementation, the second parameter set also includes the signal-to-noise ratio (SNR), which is positively correlated with the first metric.

[0050] Regarding the second aspect, please refer to the detailed content of the first aspect for further explanation; it will not be repeated here.

[0051] Thirdly, embodiments of this application provide a positioning device applied to a third device, the device comprising: a transceiver module and a processing module. The transceiver module receives M first parameter sets from M second devices, each first parameter set including at least two of the following: Received Signal Strength (RSS), Time Difference of Arrival (TDOA), or Angle of Arrival (AOA), the first parameter sets being determined by the second devices based on reference signals transmitted by the first device; the processing module determines N first parameter sets from the M first parameter sets, where M is greater than N, and M and N are positive integers; the processing module determines the location information of the first device based on the N first parameter sets.

[0052] Thirdly, embodiments of this application provide a positioning device applied to a second device, the device comprising: a transceiver module and a processing module. The processing module is used to determine first information, which is used to determine the measurement quality of a first parameter set corresponding to the second device. The first parameter set includes at least two of the following: RSS, TDOA, or AOA. The first parameter set is determined by the second device based on a reference signal sent by the first device. The transceiver module is used to transmit the first information.

[0053] The third and fourth aspects are the implementation on the device side, which correspond to the first and second aspects. The explanations, supplements, and descriptions of the beneficial effects of the first and second aspects also apply to the third and fourth aspects, and will not be repeated here.

[0054] Fifthly, a positioning device is provided, including a processor. The processor is coupled to a memory and can be used to execute instructions or data in the memory to implement the method in any possible implementation of the first aspect described above. Optionally, the positioning device further includes a memory. Optionally, the positioning device further includes a communication interface, and the processor is coupled to the communication interface.

[0055] In one implementation, the communication interface can be a transceiver, or an input / output interface.

[0056] In another implementation, the positioning device is a chip configured in the second device. When the positioning device is a chip configured in the second device, the communication interface can be an input / output interface.

[0057] Sixthly, a positioning device is provided, including a processor. The processor is coupled to a memory and can be used to execute instructions or data in the memory to implement the method in any possible implementation of the second aspect described above. Optionally, the positioning device further includes a memory. Optionally, the positioning device further includes a communication interface, and the processor is coupled to the communication interface.

[0058] In one implementation, the communication interface can be a transceiver, or an input / output interface.

[0059] In another implementation, the positioning device is a chip configured in a third device. When the positioning device is a chip configured in a third device, the communication interface can be an input / output interface.

[0060] In a seventh aspect, a processor is provided, comprising: an input circuit, an output circuit, and a processing circuit. The processing circuit is configured to receive signals through the input circuit and transmit signals through the output circuit, causing the processor to execute a method in any possible implementation of any aspect.

[0061] In specific implementation, the processor can be one or more chips, the input circuit can be input pins, the output circuit can be output pins, and the processing circuit can be transistors, gate circuits, flip-flops, and various logic circuits. The input signal received by the input circuit can be received and input by, for example, but not limited to, a receiver, and the signal output by the output circuit can be, for example, but not limited to, output to and transmitted by a transmitter. Furthermore, the input circuit and the output circuit can be the same circuit, which is used as both the input circuit and the output circuit at different times. This application does not limit the specific implementation of the processor and various circuits.

[0062] Eighthly, a positioning device is provided, including a processor and a memory. The processor is used to read instructions stored in the memory and to receive signals via a receiver and transmit signals via a transmitter to execute the method in any possible implementation of any of the above aspects.

[0063] Optionally, the processor may be one or more, and the memory may be one or more.

[0064] Ninthly, a computer program product is provided, comprising: a computer program (also referred to as code or instructions) that, when run, causes a computer to perform a method in any possible implementation of any of the preceding aspects.

[0065] In a tenth aspect, a computer-readable storage medium is provided that stores a computer program (also referred to as code or instructions) that, when run on a computer, causes the computer to perform the methods in any possible implementation of any of the preceding aspects.

[0066] Eleventhly, embodiments of this application provide a chip system including one or more processors for calling and executing instructions stored in memory, causing the methods in any of the above aspects or possible implementations to be executed. The chip system may be composed of chips or may include chips and other discrete devices.

[0067] The chip system may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0068] In a twelfth aspect, a communication system is provided, including the aforementioned third device and second device. Optionally, the communication system may further include other devices that communicate with the second device and / or the third device. Attached Figure Description

[0069] Figure 1 This is a schematic diagram of the system architecture provided in the embodiments of this application;

[0070] Figure 2 This is a flowchart illustrating a positioning method provided in an embodiment of this application;

[0071] Figure 3 This is the node filtering system model provided in the embodiments of this application;

[0072] Figure 4 This is a schematic diagram of node filtering provided in an embodiment of this application;

[0073] Figure 5 This is a schematic diagram of the positioning network structure provided in an embodiment of this application;

[0074] Figure 6 This is a schematic diagram of the extrusion excitation network provided in an embodiment of this application;

[0075] Figure 7 This is another flowchart illustrating a positioning method provided in an embodiment of this application;

[0076] Figure 8 This is a schematic diagram of the structure of a positioning device provided in an embodiment of this application;

[0077] Figure 9 This is another schematic block diagram of the positioning device 900 provided in the embodiments of this application. Detailed Implementation

[0078] In the embodiments of this application, terms such as "first" and "second" are used to distinguish identical or similar items with substantially the same function and purpose. For example, "first device" and "second device" are used only to distinguish different devices and do not limit their order of execution. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply that they are different.

[0079] It should be noted that, in the embodiments of this application, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplarily" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0080] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, a--c, bc, or abc, where a, b, and c can be single or multiple.

[0081] The technical solutions provided in this application can be used in any communication system, such as a third-generation partnership project (3GPP) communication system, for example, radio frequency identification (RFID) systems, long-term evolution (LTE) systems, fifth-generation (5G) mobile communication systems, new radio (NR) communication systems, vehicle-to-everything (V2X) systems, and systems that integrate LTE and 5G networks. They can also be applied to non-terrestrial network (NTN) systems, device-to-device (D2D) communication systems, machine-to-machine (M2M) communication systems, Internet of Things (IoT) systems, ambient IoT (A-IoT) systems, universal mobile telecommunications systems (UMTS) systems, code division multiple access (CDMA) systems, and other next-generation communication systems. Alternatively, they can be non-3GPP communication systems, such as wireless local area networks (WLANs). Networks, WLANs, etc., are not restricted.

[0082] The electronic devices in this application embodiment may include handheld devices with positioning functions, vehicle-mounted devices, etc. For example, some electronic devices include: mobile phones, tablets, PDAs, laptops, mobile internet devices (MIDs), virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, cellular phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), handheld devices with wireless communication capabilities, computing devices or other processing devices connected to a wireless modem, in-vehicle devices, terminal devices in 5G networks, or terminal devices in future evolved public land mobile networks (PLMNs), etc., and the embodiments of this application are not limited to these.

[0083] By way of example and not limitation, in this embodiment, the electronic device can also be a wearable device. Wearable devices, also known as wearable smart devices, are a general term for devices that utilize wearable technology to intelligently design and develop everyday wearables, such as glasses, gloves, watches, clothing, and shoes. Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not merely hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are feature-rich, large in size, and can achieve complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those that focus on a specific type of application function and require the use of other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0084] Furthermore, in this embodiment of the application, the electronic device can also be a terminal device in the Internet of Things (IoT) system. IoT is an important part of the future development of information technology. Its main technical feature is to connect objects to the network through communication technology, thereby realizing an intelligent network of human-machine interconnection and object-to-object interconnection.

[0085] The electronic devices in the embodiments of this application may also be referred to as: terminal equipment, user equipment (UE), mobile station (MS), mobile terminal (MT), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication equipment, user agent, or user device, etc.

[0086] In this embodiment, the electronic device or various network devices include a hardware layer, an operating system layer running on top of the hardware layer, and an application layer running on top of the operating system layer. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and memory (also called main memory). The operating system can be any one or more computer operating systems that implement business processing through processes, such as Linux, Unix, Android, iOS, or Windows. The application layer includes applications such as browsers, address books, word processing software, and instant messaging software.

[0087] In the embodiments of this application, the radio access network (RAN) device can be any device with wireless transceiver capabilities. The RAN device can provide wireless communication services and location services, enabling terminal devices to access the wireless network. The RAN can also be referred to as an access network device or a network device. The RAN device can also be called a RAN node or an access network device.

[0088] In one possible scenario, a RAN node can be a base station, an evolved NodeB (eNodeB), an access point (AP), a transmission reception point (TRP), a next-generation NodeB (gNB), a next-generation base station in a 6th-generation (6G) mobile communication system, or a base station in a future mobile communication system. A RAN node can be a macro base station, a micro base station, an indoor station, a relay node, a donor node, or a radio controller in a cloud radio access network (CRAN) scenario. Optionally, a RAN node can also be a server.

[0089] In another possible scenario, multiple RAN nodes collaborate to assist the terminal in achieving wireless access, with different RAN nodes each implementing a portion of the base station's functions. For example, RAN nodes can be central units (CUs), distributed units (DUs), CU-control plane (CPs), CU-user plane (UPs), or radio units (RUs), etc. CUs and DUs can be set up separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio frequency equipment or radio frequency units, such as remote radio units (RRUs), active antenna units (AAUs), or remote radio heads (RRHs).

[0090] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an open access network (open RAN, O-RAN, or ORAN) system, CU can also be called an open CU (O-CU), DU can also be called an O-DU, CU-CP can also be called an O-CU-CP, CU-UP can also be called an O-CU-UP, and RU can also be called an O-RU. For ease of description, this application uses CU, CU-CP, CU-UP, DU, and RU as examples. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through a software module, a hardware module, or a combination of software and hardware modules.

[0091] The core network functions of mobile communication networks (such as 5G, 6G, etc.) may include core network elements such as LMF. These will not be elaborated upon here.

[0092] LMF can be responsible for location calculation and resolution, positioning process management and coordination, and so on.

[0093] The network elements communicate with each other through interfaces. For example, the signaling plane interface between the terminal equipment and the LMF is the N1 interface. Since the terminal equipment cannot directly interact with the core network equipment, it needs to pass through the access stratum (AS) to transmit non-access stratum (NAS) information, etc., which will not be elaborated on in detail. This application does not limit this.

[0094] The above description of network elements in the core network and the interfaces between them is merely illustrative and should not constitute any limitation on this application. Core network elements can be independent devices or integrated into the same device to implement different functions. This application does not limit the specific form of the aforementioned network elements.

[0095] It is understood that the network elements used in future communication systems may be any of the aforementioned network elements, or network elements with the same or similar functions under other names; this application does not limit this.

[0096] In this embodiment, access network devices and core network elements can be collectively referred to as network devices. The apparatus used to implement the functions of a network device can be a network device itself; it can also be an apparatus capable of supporting the network device in implementing those functions, such as a chip system, hardware circuit, software module, or a hardware circuit plus a software module. This apparatus can be installed in the network device or used in conjunction with the network device. In this embodiment, the example of a network device being used to implement the functions of a network device is used only and does not constitute a limitation on the solutions of this embodiment.

[0097] The network device in this application can be a hardware device, a software function running on dedicated hardware, or a software function running on general-purpose hardware. It can also be a virtualized device, for example, implemented through general-purpose hardware and instantiated virtualization functions, or dedicated hardware and instantiated virtualization functions. Among them, the general-purpose hardware can be a server, such as a cloud server.

[0098] Taking 5G base stations as an example, as a key infrastructure of digital communication systems, the rapid growth in 5G base station throughput has led to increasingly strained positioning resources. Simultaneously, with the increase in the number of users, the corresponding positioning demand has surged, thus placing higher demands on the real-time performance and accuracy of 5G base station positioning. To address the positioning enhancement issue, the 3rd generation partnership project (3GPP) is currently focusing on the following three types of positioning methods:

[0099] 1. Terminal-based positioning: The terminal uses artificial intelligence (AI) or machine learning (ML) to directly or assisted in positioning.

[0100] 2. Terminal-assisted location management function (LMF) positioning: The terminal provides auxiliary information, and the LMF is responsible for estimating the terminal's location. It can be directly located using AI or ML, or the terminal can participate in AI or ML-assisted positioning.

[0101] 3. Base station assisted positioning: The base station can provide auxiliary information to the LMF, and the LMF can use AI or ML models to locate directly, or the base station can participate in AI or ML assisted positioning.

[0102] Figure 1 This is a schematic diagram of the system architecture provided in the embodiments of this application. Regarding the third type of gNB-assisted positioning method described above, the proposed positioning method addresses the terminal positioning problem of the terminal and the base station uplink propagation system.

[0103] For example, a terminal can send a location request message to the LMF (Location Provider Function), which includes the terminal's own identity information and location request signaling. Correspondingly, the LMF can receive this location request message. Here, M base stations can provide services to the cell to which the terminal currently belongs. These M base stations include the base station covering the cell where the terminal is currently located (denoted as the primary base station), and these M base stations can be referred to as the location base stations. Furthermore, the LMF can interact with these M base stations. Further, the LMF can send sounding reference signal (SRS) configuration information to the primary base station, which then sends the SRS configuration information to the terminal. After receiving the SRS configuration information, the terminal generates an SRS report and sends it to the M location base stations. Upon receiving the uplink SRS reported by the terminal, the M location base stations can calculate location parameters, which may include, for example, the time difference of arrival (TDOA), received signal strength (RSS), and angle of arrival (AOA). Positioning parameters are transmitted to the LMF via the new radio positioning protocol A (NRPPa). The LMF can then further obtain positioning results based on the positioning parameters using relevant calculation methods.

[0104] It can be seen that during the uplink and downlink communication between the terminal and the base station, there may be environmental factors such as obstacles, which may cause multipath effects, attenuation, interference and other problems. This may cause errors in the estimated positioning parameters, thus limiting the accuracy of traditional positioning algorithms that rely on a single type of positioning parameter.

[0105] Furthermore, in traditional algorithms for positioning, the use of positioning parameters is relatively limited and cannot effectively integrate multiple types of parameter information. As a result, the inherent parameter errors and information losses cannot be effectively compensated during the positioning calculation process, thus limiting the positioning accuracy.

[0106] To address the aforementioned technical problems, this application provides a positioning method. In a multi-base station positioning scenario, the LMF can filter the M first parameter groups received from M base stations. For example, it can select N first parameter groups with higher measurement quality from the M first parameter groups, and then obtain the location information of the first device based on these N first parameter groups. In this way, positioning can be performed based on various types of positioning parameters, and the filtering of the first parameter groups can reduce positioning parameter errors, improve the quality and stability of positioning parameters, thereby improving positioning accuracy and robustness.

[0107] The methods provided in the embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0108] In the embodiments described below, the interaction between a terminal device, an LMF (Logical Function Module), and base stations (e.g., base station #a, base station #b, and base station c) is used as an example. It should be understood that the terminal device can be replaced by components configured in the terminal device (such as chips, chip systems, processors, etc.), or by logic modules or software capable of implementing all or part of the functions of the terminal device; the base station can also be replaced by components configured in the base station (such as chips, chip systems, processors, etc.), or by logic modules or software capable of implementing all or part of the functions of the base station; the LMF can also be replaced by components configured in the LMF (such as chips, chip systems, processors, etc.), or by logic modules or software capable of implementing all or part of the functions of the LMF.

[0109] For example, the terminal device may be a first device, the base station may be a second device, the LMF may be a third device, and so on. This application embodiment does not limit this.

[0110] It is understood that terminal devices, base stations, or LMFs can also be referred to as nodes, such as communication nodes, etc., and this application does not limit them in this respect.

[0111] Figure 2 This is a flowchart illustrating a positioning method provided in an embodiment of this application. Figure 2 As shown, the positioning method 200 may include S201 to S207.

[0112] S201, the terminal device sends a location request message. Correspondingly, the LMF receives the location request message.

[0113] For example, the location request message may include the identity information of the terminal device and location request signaling to request the location information of the terminal device.

[0114] S202, LMF sends SRS configuration information. Correspondingly, the terminal device receives the SRS configuration information.

[0115] After receiving a location request message from a terminal device, the LMF can send SRS configuration information to multiple base stations. These multiple base stations may, for example, include base station #a, base station #b, and base station #c. It should be understood that base station #a, base station #b, and base station #c are merely examples and should not be construed as limiting the scope of this application's embodiments.

[0116] Furthermore, at least one of the multiple base stations can send SRS configuration information to the terminal device. For example, the base station serving the terminal device may send the SRS configuration information.

[0117] S202 may include S202a and S202b. In S202a, the LMF sends SRS configuration information. Accordingly, multiple base stations receive the SRS configuration information. In S202b, at least one of the multiple base stations sends the SRS configuration information. Accordingly, the terminal device receives the SRS configuration information.

[0118] S203, the terminal device sends an SRS. Correspondingly, multiple base stations receive this SRS.

[0119] Once the terminal device receives the SRS configuration information, it can generate an SRS and then send it. Correspondingly, multiple base stations can receive this SRS.

[0120] Some or all of these multiple base stations may participate in the location service for terminal devices, etc., but this application embodiment does not limit this.

[0121] S204, M base stations among the multiple base stations determine a first parameter group based on the received SRS, the first parameter group including at least two of the following: RSS, TDOA or AOA.

[0122] For example, the M base stations can be referred to as positioning devices (e.g., positioning base stations) for implementing positioning requests from terminal devices. Each of the M base stations can calculate a first parameter set based on the received uplink SRS. This first parameter set can include at least two of the following: RSS, TDOA, or AOA. This first parameter set is the positioning parameter, and the location information of the terminal device can be derived based on it. Different base stations can correspond to different first parameter sets.

[0123] Optionally, the RSS in the first parameter group can be replaced, for example, with the amplitude of the received signal, etc., and this application embodiment does not limit this.

[0124] After determining the corresponding first parameter set for the M base stations, since there are a large number of M base stations, base stations with higher positioning quality can be selected from them. For example, N base stations can be determined from the M base stations, and then the location information of the terminal device can be determined based on the first parameter set corresponding to the N base stations. Based on this, there are two possible scenarios:

[0125] One possible scenario is that the M base stations each filter themselves based on a first threshold value to determine which base stations can use the first parameter group obtained from the received SRS to determine the location information of the terminal device, denoted as scenario one.

[0126] One possible scenario is that the M base stations report their corresponding first parameter sets to the LMF. The LMF then filters these M base stations to determine which base stations can use the first parameter sets obtained from the received SRS to determine the location information of the terminal device. This is referred to as scenario two.

[0127] In other words, the M base stations or LMFs perform a filtering process on the acquired M sets of first parameters, thereby determining the location information of the terminal device based on at least one set of parameters obtained after filtering. This makes the acquired location information of the terminal device more accurate.

[0128] The following section will describe the two possible scenarios.

[0129] For scenario one: The first threshold value can be predefined or preconfigured for the M base stations. By comparing the first indicator of each base station with the first threshold value, it can be determined which devices in the M base stations have the first parameter set that can be used to determine the location information of the terminal device.

[0130] In this case, the method includes: S205a, the M base stations determine N first parameter groups from the M first parameter groups;

[0131] S206a, N base stations respectively transmit their corresponding N first parameter groups. Correspondingly, the LMF receives these N first parameter groups.

[0132] Among them, the N base stations are the base stations corresponding to the N first parameter groups determined from the M first parameter groups respectively.

[0133] A base station's first indicator can be used to characterize its positioning quality. This first indicator can be confirmed based on a corresponding second parameter set. This second parameter set includes one or more of the following parameters: [First parameter] The second parameter is used to characterize the measurement quality corresponding to the time measurement quality. The resolution used to characterize the quality of time measurements; the third parameter The fourth parameter is used to characterize the azimuth quality corresponding to the quality of the angle measurement. The resolution used to characterize the quality of angle measurements; the fifth parameter. The sixth parameter is used to characterize the phase quality corresponding to the phase measurement quality. , is used to characterize the resolution of phase measurement quality.

[0134] Table 1 shows the parameters in the second parameter group determined based on timing measurement quality, angle measurement quality, and phase measurement quality.

[0135] Table 1

[0136]

[0137] The timing measurement quality can also be referred to as time-based measurement quality or time measurement quality, etc., and this application does not limit it in the embodiments.

[0138] It can be seen that, regarding the quality of timing measurements, the higher the measurement quality, the higher the corresponding first parameter. The higher the resolution, the higher the corresponding second parameter. The higher the quality of angle measurement, the higher the corresponding third parameter. The higher the resolution, the higher the corresponding fourth parameter. The higher the quality, the better. Regarding phase measurement quality, higher measurement quality corresponds to the fifth parameter. The higher the resolution, the higher the corresponding sixth parameter. The higher.

[0139] As an example, the parameter values ​​differ for different measurement qualities or resolutions.

[0140] Taking time-series measurement quality as an example, the measurement quality value is n1, corresponding to the first parameter #1; the measurement quality value is n2, corresponding to the first parameter #2, and so on, which will not be elaborated further. Similarly, when the resolution is 1 second (s), it corresponds to the second parameter #1; when the resolution is 1 picosecond (ps), it corresponds to the second parameter #2, and so on, which will not be elaborated further.

[0141] Another example is to classify different levels based on different measurement quality or resolution, with different parameter values ​​corresponding to different levels.

[0142] Taking time series measurement quality as an example, the measurement quality value range is: At that time, it is divided into level #1, and level #1 corresponds to the first parameter #1; the range of measured quality values ​​is as follows: At that time, it is divided into level #2, level #2 corresponds to the first parameter #2, and so on, which will not be elaborated further.

[0143] The base station can determine its first indicator based on the parameters in the second parameter group.

[0144] Optionally, the M base stations of the first The first indicator for each base station It is positively correlated with the second parameter group, which includes one or more parameters. It is a positive integer less than or equal to M.

[0145] In other words, when the first parameter Second parameter Third parameter The fourth parameter Fifth parameter The sixth parameter When it increases, the first indicator It also increases accordingly.

[0146] Optionally, the M base stations of the first The first indicator for each base station The second parameter group corresponding to this base station includes one or more parameters that can satisfy:

[0147] ;in, , , , , , Indicates the weighting coefficient. This represents the bias value.

[0148] It's understandable that, depending on the specific application scenario or communication requirements, [the following can be done / adjusted / adjusted]: , , , , , Adjust the weighted coefficients accordingly.

[0149] For example, in applications with fewer obstacles, the weight of angle measurement quality is higher, so the weighting coefficient of the third parameter corresponding to the angle measurement quality can be adjusted. Increase, and / or, the weighting coefficient of the fourth parameter corresponding to the angle measurement quality. Increase the size.

[0150] For example, in application scenarios with many obstacles, the weight of phase measurement quality is relatively high. Therefore, the weighting coefficient of the fifth parameter corresponding to phase measurement quality can be increased. Increase, and / or, the weighting coefficient of the sixth parameter corresponding to the phase measurement quality. Increase it. Correspondingly, the weighting coefficient of the third parameter corresponding to the angle measurement quality can also be increased. Decrease, and / or, the weighting factor of the fourth parameter corresponding to the angular measurement quality. Turn it down.

[0151] It is understood that the above application scenarios with many or few obstacles are merely examples and should not constitute any limitation on the embodiments of this application.

[0152] Optionally, the second parameter set also includes signal-to-noise ratio. , No. The first indicator for each base station It is positively correlated with the signal-to-noise ratio. That is, as the signal-to-noise ratio increases, the first indicator... It also increases accordingly.

[0153] For example, the first The first indicator for each base station The second parameter group corresponding to this base station includes one or more parameters that can satisfy: .in, This represents the weighting coefficient.

[0154] The first indicator for each base station can be obtained using the formula described above. Furthermore, each base station can compare its own first indicator with a first threshold value to determine whether it needs to report its acquired first parameter set to the LMF.

[0155] For example, when the first The first indicator for each base station When the value is greater than or equal to the first threshold, it can be determined that the first parameter set of the base station can be used to determine the location information of the terminal device. That is, the first... Each base station can report its first set of acquired parameters to the LMF. In other words, the first set of parameters... The first parameter group corresponding to each base station can be used to determine the location information of the terminal device.

[0156] Conversely, when the first The first indicator for each base station When the value is less than the first threshold, it can be determined that the first parameter set of the base station can be disregarded for determining the location information of the terminal device. That is, the first... Each base station does not need to report its first set of parameters to the LMF.

[0157] Based on this, the M base stations have completed their own screening, thereby determining whether the first set of parameters they acquire can be used to determine the location information of the terminal device.

[0158] For scenario two: Each of the M base stations can send its corresponding first parameter set to the LMF, and the LMF receives the M first parameter sets. The LMF then determines which of the M base stations' first parameters can be used to determine the location information of the terminal device.

[0159] In this case, the method includes: S205b, M base stations respectively transmit a first parameter group. Correspondingly, the LMF receives the M first parameter groups;

[0160] S206b, LMF determines N first parameter groups from the M first parameter groups.

[0161] For example, the base station can transmit the first parameter set to the LMF via NRPPa.

[0162] Example 1: The M base stations can send their respective second parameter groups to the LMF. After receiving the M second parameter groups corresponding to the M base stations, the LMF can determine the first index of the base station corresponding to each second parameter group based on each second parameter group, thereby obtaining the M first indexes of the M base stations.

[0163] For details regarding the second parameter group and the first indicator for determining the base station based on the second parameter group, please refer to the detailed explanation in Case 1 above, which will not be repeated here.

[0164] One possible approach is that the LMF can select the largest N (M greater than or equal to N, where N is a positive integer) first indicators from the M first indicators, with each of these N first indicators corresponding to one of the N base stations. Based on this, the LMF completes the selection of the M base stations and can then determine the location information of the terminal device based on the N first parameter groups corresponding to these N base stations.

[0165] It is understood that, due to the geometric distribution and interference from obstacles in the scene, the number of base stations required to determine the location information of the terminal device is N. The value of N can be predefined or preconfigured by the protocol, etc., and this embodiment does not limit this.

[0166] Figure 3 This is the node screening system model provided in the embodiments of this application. The M base stations (i.e., base station nodes) can be denoted as base station node #1, base station node #2, ..., base station node #M, respectively. The M base station nodes can report their corresponding first parameter groups to the LMF, and the LMF can calculate the first index of each base station node, thereby obtaining the M first indices of the M base station nodes.

[0167] LMF can sort the M primary indicators. For example, the M primary indicators can be sorted in descending order. LMF can retain the first parameter groups of the base station nodes corresponding to the top N (top-N) primary indicators, and thus determine the location information of the terminal device based on the N first parameter groups.

[0168] It is understood that the LMF sorting the M first indicators in descending order is only an example. For example, the M first indicators can also be sorted in ascending order. The LMF can retain the first parameter group of the base station node corresponding to the sorted N first indicators, so that the location information of the terminal device can be determined based on the N first parameter groups, etc. This application embodiment does not limit this.

[0169] Another possibility is that the LMF can compare the M first indicators with the second threshold value to determine the first set of parameters that can be used to determine the location information of the terminal device.

[0170] For example, when the first The first indicator for each base station When the value is greater than or equal to the second threshold, it can be determined that the first parameter set of the base station can be used to determine the location information of the terminal device. Conversely, when the value is less than or equal to the second threshold, it can be determined that the first parameter set of the base station can be used to determine the location information of the terminal device. The first indicator for each base station If the value is less than the second threshold, it can be determined that the first parameter set of the base station can be used to determine the location information of the terminal device.

[0171] It is understood that the second threshold value may be predefined or preconfigured by the protocol, etc., and this application embodiment does not limit it in this way.

[0172] In Example 2, the M base stations can each send their respective first indicators to the LMF. That is, the M base stations can determine their respective first indicators based on their corresponding second parameter sets. Furthermore, the M base stations can each report their respective first indicators to the LMF. Correspondingly, the LMF can receive the M first indicators from the M base stations.

[0173] For details regarding the second parameter group and the first indicator for determining the base station based on the second parameter group, please refer to the detailed explanation in Case 1 above, which will not be repeated here.

[0174] Similar to Example 1 in Scenario 2 above, the LMF can select the largest N first indicators among the M first indicators. These N first indicators correspond to N base stations. The LMF can then determine the location information of the terminal device based on the N first parameter groups corresponding to these N base stations.

[0175] Alternatively, the LMF can select first indicators that are greater than or equal to the second threshold value from the M first indicators. For example, L first indicators can be selected from the M first indicators that are greater than or equal to the second threshold value. These L first indicators correspond to L base stations, and the LMF can then determine the location information of the terminal device based on the L first parameter groups corresponding to these L base stations.

[0176] Optionally, LMF can also perform anomaly detection on the received M first indicators.

[0177] It is understandable that during the process of receiving SRS transmitted by the terminal, the M base stations may experience malfunctions, or due to multipath effects, attenuation, interference from obstacles, or environmental interference, the M base stations may suffer certain losses (or anomalies) in the process of receiving signals, which may in turn cause anomalies in the first index determined based on the second parameter set. Therefore, LMF can perform anomaly detection on the determined M first indices.

[0178] For example, the first of the M base stations If the first indicator of a base station is significantly greater than the threshold value of the first indicator, it indicates that the first base station... An anomaly occurred during signal reception at one of the base stations. In this case, it is possible to choose a location method that does not rely on the first set of parameters obtained from that base station.

[0179] Optionally, the M base stations can perform anomaly detection on the received SRS.

[0180] It is understandable that during the process of receiving SRS transmitted by the terminal, the M base stations may experience malfunctions, or the SRS received by the M base stations may be subject to certain losses (or anomalies) due to multipath effects, attenuation, interference from obstacles, or environmental interference. This allows the quality of positioning based on the first set of parameters obtained by the base station to be determined. Therefore, the base station can perform anomaly detection based on the received SRS.

[0181] For example, a base station determines whether the SRS has timed out by measuring the reception time. If the reception time exceeds a certain threshold, it indicates that the SRS has timed out, thus confirming an anomaly in the base station's reception process. Consequently, the positioning quality based on the first set of parameters acquired by the base station is poor, and positioning can be performed using a method other than the first set of parameters acquired by the base station.

[0182] For example, the base station determines whether there are any abnormalities in the transmission of the SRS by determining the path through which it receives the signal. For instance, by determining the ratio of the length of the direct path to the length of the reflected path, the base station can determine the quality of the positioning achieved by the first set of parameters acquired by the base station.

[0183] When the ratio is large (or the length of the direct path is greater than the length of the reflected path), it indicates that the SRS encounters fewer obstacles during transmission. This means the positioning quality using the first parameter set acquired by the base station is good, and positioning can be based on the first parameter set acquired by the base station. Conversely, when the ratio is small (or the length of the direct path is less than the length of the reflected path), it indicates that the SRS encounters more obstacles during transmission. This means the positioning quality using the first parameter set acquired by the base station is poor, and it can be determined that the base station's received signal is abnormal, thus positioning can be chosen not to be based on the first parameter set acquired by the base station.

[0184] Alternatively, the first set of parameters obtained from base stations that are determined to be abnormal can be retained for positioning. For example, the first set of parameters that are abnormal can be input into the AI ​​or ML model mentioned below for training, thereby increasing the diversity of data training and retaining the influence of various abnormal situations or interferences on positioning.

[0185] Figure 4 This is a schematic diagram of node filtering provided in an embodiment of this application. The terminal device sends an SRS (Service Response System). The M base stations receiving the SRS can obtain a first parameter set based on the SRS, and each of the M base stations can report the first parameter set to the LMF (Local Level Function). The M base stations can also obtain a second parameter set based on timing measurement quality, angle measurement quality, phase measurement quality, etc., and each of the M base stations can report its corresponding second parameter set to the LMF, or they can report their respective first indicators to the LMF.

[0186] LMF can filter the M base stations based on the received M sets of second parameters or M sets of first indicators. For example, LMF can select base stations with high positioning quality from the M base stations, and then determine the location information of the terminal device based on the first parameter sets corresponding to these selected base stations.

[0187] S207, LMF determines the location information of the terminal device based on the N first parameter groups.

[0188] For example, LMF can locate the terminal device based on AI or ML models (or algorithms) using the N first parameter groups, thereby obtaining the location information of the terminal device.

[0189] It is understandable that the number N of the N first parameter groups can be a fixed value or not.

[0190] For example, the aforementioned N first parameter groups corresponding to N base stations selected from M base stations, where N is a fixed value. That is, these multiple first parameter groups are N sets of first parameter groups.

[0191] For example, M base stations determine whether their corresponding first parameter sets can be used to determine the location information of a terminal device based on a first threshold value; or, the LMF determines which of the M first parameter sets corresponding to the M base stations can be used to determine the location information of the terminal device based on a second threshold value. In both cases, the number of determined first parameter sets is not fixed. That is, the number of multiple first parameter sets determined based on the threshold value is not fixed.

[0192] For a given number N of the multiple first parameter groups, LMF can predefine or preconfigure an AI or ML model whose input dimension is N. 1 1 C. Where N represents the number of the first parameter group (or the number of base stations selected), N is a fixed value, and C represents the types of parameters included in the first parameter group, "1 "1" indicates spatial dimension.

[0193] When the number N of the multiple first parameter groups is not a fixed value, LMF can predefine or preconfigure multiple AI or ML models as candidate models, with different AI or ML models having an input dimension of N. 1 1 C. The value of N in different candidate models may be the same or different, etc., and this application does not limit this. The multiple candidate models may include a first candidate model, and the value of N in the input dimension of the first candidate model is consistent with the number of the finally determined first parameter group.

[0194] LMF can select an AI or ML model from multiple AI or ML models whose number of input channels matches the number of the first parameter group based on the final determined number of the first parameter group, and then locate the terminal device based on the AI ​​or ML model.

[0195] As mentioned above Figure 3 As shown, the LMF can input N sets of first parameters into the input layer of the neural network, where the input dimension of the input layer of the neural network element is N. 1 1 C.

[0196] It is understood that the above method for determining N first parameter groups from M first parameter groups using LMF is merely an example, but the scope of protection of this application is not limited thereto, and should not constitute any limitation on the embodiments of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application, or other feature engineering, models, algorithms, and solutions that can implement the method of determining N first parameter groups from M first parameter groups, should be covered within the scope of protection of this application.

[0197] Figure 5 This is a schematic diagram of the localization network structure provided in an embodiment of this application. This localization network structure can be an example of the aforementioned AI or ML model. See also... Figure 5 This localization network structure can consist of an input layer, a global pooling layer, a fully connected layer (FC), a convolutional block, and an output layer.

[0198] At the input end, the "N" of the input layer 1 1 "C" represents a 4D tensor, N represents the batch size, C represents the number of channels, and "1" represents the number of channels. "1" represents the spatial dimension. The global pooling layer can quickly compress the spatial dimension, and then the dimension can be mapped through the fully connected layer of the C node. The nonlinearity is increased by activating the rectified linear unit (ReLU) function, and then the probability value between 0 and 1 is output through the sigmoid function. Figure 5 "in The "" indicates that the Sigmoid output is multiplied "element-wise" with the results of subsequent modules, thus enabling feature weighting.

[0199] A convolutional block can consist of multiple convolutional layers and activation functions. For example, the activation function could be the ReLU function. Initial feature extraction can be performed using multiple convolutional layers. Figure 5 "in The "+" indicates that the input before convolution is added to the result after convolution "element by element", which avoids gradient vanishing, speeds up training, and preserves the original feature information.

[0200] Convolutional block A includes convolutional layer 1, convolutional layer 2, convolutional layer 3, and convolutional layer 4. The kernel size corresponding to convolutional layer 1 is (5... 1 1), The number of output channels is 8; the kernel size corresponding to convolutional layer 2 is (3 3 3), the number of output channels is 32; the kernel size corresponding to convolutional layer 3 is (3 3 3), the number of output channels is 16; the kernel size corresponding to convolutional layer 4 is (3 3 3) The number of output channels is 32.

[0201] Convolutional block B includes convolutional layers 5, 6, and 7. The kernel size corresponding to convolutional layer 5 is (3 3 3), the number of output channels is 32; the kernel size corresponding to convolutional layer 6 is (3 3 3), the number of output channels is 16; the kernel size corresponding to convolutional layer 7 is (1 1 1) The number of output channels is 32.

[0202] Convolutional block C includes convolutional layer 8, convolutional layer 10, and convolutional layer 10. The kernel size corresponding to convolutional layer 8 is (3 3 3), the number of output channels is 32; the kernel size corresponding to convolutional layer 9 is (3 3 3), the number of output channels is 16; the kernel size corresponding to convolutional layer 10 is (1 1 1) The number of output channels is 32.

[0203] At the output, the final features are extracted through the last convolutional layer 11, and then activated by the ReLU function. The kernel size of convolutional layer 11 is (3... 3 3) The output channel count is 32. A 2-node fully connected layer then transforms the convolutional features into a 2D vector. Finally, the output layer outputs the regression result based on the regression problem.

[0204] One possible implementation is that the AI ​​or ML model could be, for example, a squeeze and excitation net-convolutional neural network (SE-CNN). That is, a squeeze and excitation net (SENet) is introduced into a convolutional neural network structure. It can be understood that the squeeze and excitation net is a type of application of channel attention mechanisms.

[0205] In the SE-CNN-based localization method, multiple sets of first parameter groups, including RSS, TDOA, AOA, and other localization parameters, can be input into the SE-CNN for localization. An SENet module is introduced into the convolutional neural network structure, where each parameter corresponds to a channel. The global spatial information of each channel is aggregated and compressed through a squeeze operation, and the dependencies between channels are modeled through an excitation mechanism. Weight coefficients are then generated to recalibrate each channel. Essentially, this structure learns and calibrates the importance of feature channels in the convolutional network, thereby enhancing the expressive power of key channels and suppressing redundant information.

[0206] Figure 6 This is a schematic diagram of the extrusion excitation network provided in an embodiment of this application. See also... Figure 6 During the SE-CNN training phase, the principle of adjusting the channel weights of the squeeze-excitation network is as follows:

[0207] 1. Data channel division.

[0208] In the data input layer, "N" 1 1 "C" represents a 4D tensor, N represents the batch size (i.e., the number of parameters in the first parameter group), and C represents the number of channels. One channel can correspond to one parameter in the first parameter group. "1" indicates spatial dimension.

[0209] For example, the parameters included in the first parameter group can be divided into multiple data channels according to the type of parameters. Taking N first parameter groups as an example, these N first parameter groups are also the dataset, and each first parameter group includes three parameters: RSS, TDOA, and AOA. According to the parameter categories included in the first parameter group, these N first parameter groups can be divided into three channels, such as RSS, TDOA, and AOA channels. That is, C is three. In this way, each channel includes N parameters of the same category.

[0210] For example, among the N first parameter groups, the first first parameter group includes RSS#1, TDOA#1, and AOA#1; the second first parameter group includes RSS#2, TDOA#2, and AOA#2; the third first parameter group includes RSS#3, TDOA#3, and AOA#3; and so on, without further details.

[0211] Divide the N first parameter groups into three channels. Channel #1 is the RSS channel, channel #2 is the AOA channel, and channel #3 is the AOA channel. Channel #1 can include N RSSs, denoted as RSS#1, RSS#2, ..., RSS#N. Similarly, channel #2 can include N AOAs, denoted as AOA#1, AOA#2, ..., AOA#N. Similarly, channel #3 can include N AOAs, denoted as AOA#1, AOA#2, ..., AOA#N.

[0212] 2. Perform global compression on the data of each channel.

[0213] The squeeze-excitation network can effectively compress the data of each channel through a global average pooling layer, thereby preserving the global statistical characteristics of each channel.

[0214] One possible approach is to compress the N parameters into a single parameter by averaging the N parameters for each channel. Based on this, each channel can retain an average value to represent the global statistical characteristics of that channel.

[0215] For example, the average value of N RSSs in channel #1 (RSS#1, RSS#2, ..., RSS#N) is calculated, and the final average value is the global statistical characteristic of that channel.

[0216] It is understood that the above-mentioned averaging of data for each channel to achieve compression is merely an example, and the embodiments of this application do not limit this.

[0217] 3. Data channel weight generation.

[0218] For example, after training (or learning) with a fully connected layer #1, ReLU, a fully connected layer #2, and a sigmoid structure, different channels can correspond to different weights (or weights) in different scenarios. That is, different parameters can be assigned different weights, and the range of weight values ​​can be, for example, [missing information]. .

[0219] Specifically, the number of channels C can be compressed using a fully connected layer #1, thus performing a squeeze operation, which is equivalent to dimensionality reduction. Then, ReLU activation is applied, introducing non-linearity. Further, a fully connected layer #2 can restore the compressed number of channels to the original number, for example, to C channels, which is equivalent to dimensionality increase. Finally, a Sigmoid function can be used to output the channel number. The probability value is the weight of each channel.

[0220] It's understandable that the weight of each channel can be used to characterize the importance of each channel, or in other words, the importance of the positioning parameters corresponding to each channel. The larger the weight, the higher the importance of the positioning parameters of that channel, or the more critical they are to the positioning.

[0221] For example, in different scenarios where the terminal device is far from or near the serving base station, the three parameters included in the first parameter group have different effects on the final determination of the terminal device's location information, so different weights can be assigned to different channels.

[0222] For example, under far-field conditions, the AOA parameter in the first parameter group is more important (or more advantageous) for determining the location information of the terminal device, so in this case, a larger weight can be assigned to the AOA channel; under near-field conditions, the RSS parameter in the first parameter group is more important (or more advantageous) for determining the location information of the terminal device, so in this case, a larger weight can be assigned to the RSS channel, and so on. The embodiments of this application do not limit this.

[0223] 4. Channel recalibration.

[0224] This process assigns importance to each of the C channels using data from those channels, essentially weighting the data along the channel dimension. For example, after channel recalibration, the data from the C channels, weighted according to their respective channel weights, can be fed into subsequent networks for fitting. Figure 6 "in The expression indicates that the output of the Sigmoid function (i.e., the weights of different channels) is multiplied element-wise with the data of the C channels, thereby achieving weighted data for different channels.

[0225] In this way, parameter channels that are advantageous (or more important) in determining the location information of the terminal device can be assigned greater weights, while parameter channels that are disadvantageous (or less important) in determining the location information of the terminal device can be assigned smaller weights, thereby highlighting the role of advantageous parameters in specific scenarios.

[0226] This is understandable; furthermore, residual structures can be combined to improve the training stability and localization accuracy of SE-CNN.

[0227] Based on this, LMF can use the SE-CNN localization method to input multiple localization parameters, including RSS, TDOA, AOA, etc., from the first parameter group into SE-CNN for localization, thereby determining the location information of the terminal device.

[0228] Optionally, the LMF can send specific location information of the terminal device. Correspondingly, the terminal device or a fourth device can receive this location information. The fourth device may be, for example, other third-party devices, etc., and this embodiment does not limit this.

[0229] Based on the above technical solution, LMF selects N sets of first parameters from the M sets of first parameters received from M base stations, and then uses these N sets of first parameters to locate the first device, thereby obtaining the location information of the first device. This allows for positioning based on multiple types of positioning parameters, improving positioning accuracy. Furthermore, in complex channel environments and multi-base station positioning scenarios, locating the first device using the selected N sets of first parameters with high measurement quality reduces positioning parameter errors, improves the quality and stability of positioning parameters, and thus enhances positioning accuracy and robustness.

[0230] Furthermore, compared to traditional geometric localization algorithms and general backpropagation (BP) neural network localization algorithms, which process multiple parameters with equal input, the squeeze excitation network in the SE-CNN localization algorithm can divide different localization parameters into different channels according to parameter categories. This allows for in-depth exploration of the reliability differences between different localization parameters and supports assigning different weights to different channels based on the importance of different localization parameters in different application scenarios. It has the advantage of parameter feature enhancement, enabling weighted training along the parameter channel dimension to strengthen the dominance of different features in localization decisions. Thus, on the one hand, the squeeze excitation network has a lightweight structure, making it easy to implement as an optimization module in the localization process; on the other hand, it can dynamically adjust the weights of different localization parameters, enabling deeper training in different scenarios and improving localization accuracy and robustness.

[0231] Figure 7 This is another flowchart illustrating a positioning method provided in an embodiment of this application. Figure 7 The method 700 shown is based on the aforementioned Figure 2 Based on method 200, the method provided in this application embodiment will be described in detail using the interaction between a third device and a second device as an example. It should be understood that the second device can be replaced by a component configured in the second device (such as a chip, chip system, processor, etc.), or a logic module or software capable of implementing all or part of the functions of the second device; the third device can also be replaced by a component configured in the third device (such as a chip, chip system, processor, etc.), or a logic module or software capable of implementing all or part of the functions of the third device, and this application embodiment does not limit this.

[0232] For example, the second device may be a base station, the third device may be an LMF, etc., and the embodiments of this application are not limited thereto.

[0233] like Figure 7 As shown, the positioning method 700 may include S701 to S703.

[0234] S701, M second devices transmit a first parameter group, which includes at least two of the following: RSS, TDOA, or AOA, and the first parameter group is determined by the second devices based on a reference signal transmitted by the first device. Accordingly, a third device receives the M first parameter groups.

[0235] Understandable. Figure 7 The second device shown can represent M second devices, each of which can send its acquired first parameter set to the third device. Correspondingly, the third device can receive the first parameter set from the M second devices.

[0236] The first parameter set can be determined by the second device based on the reference signal sent by the first device. For example, the first device can send an SRS, and M second devices can determine their own corresponding first parameter set based on the SRS they receive.

[0237] For details regarding the first parameter group, please refer to the detailed content of S204 in Method 200, which will not be repeated here.

[0238] S702, the third device determines N first parameter groups from the M first parameter groups, where M is greater than N, and M and N are positive integers.

[0239] After receiving M sets of first parameters, the third device can determine N first parameters from these M sets by filtering.

[0240] One possible implementation of S702 is:

[0241] Step 1: M second devices send M pieces of first information, which are used to determine the measurement quality of the first parameter set corresponding to each second device. Correspondingly, the third device receives the M pieces of first information from the M second devices.

[0242] Step 2: Based on the M pieces of first information, the third device determines N sets of first parameters from the M sets of first parameters.

[0243] The following sections will provide detailed explanations of steps one and two.

[0244] In step one, the measurement quality of the first parameter group corresponding to the second device can be determined through the first information of the second device. Thus, this first information allows for the selection of which first parameter groups from the M first parameter groups of the M second devices can be used for locating the first device, and which first parameter groups can be excluded from this purpose.

[0245] The M pieces of first information received by the third device have the following two possible scenarios:

[0246] One possibility is that the first information includes a second parameter group corresponding to the second device, and the second parameter group includes the measurement quality parameters corresponding to the parameters in the first parameter group.

[0247] For details regarding the second parameter group and the first indicator, please refer to the detailed content of Case 1 in Method 200, which will not be repeated here.

[0248] As shown in Table 1 of the aforementioned method 200, the parameters included in the second parameter group can be used to determine the accuracy or precision of the parameters in the first parameter group, etc., and this application embodiment does not limit this.

[0249] For example, the parameters in the first parameter group include TDOA, and the second parameter group may include measurement quality parameters corresponding to the TDOA parameter. These measurement quality parameters may include, for example, the first parameter and the second parameter in the second parameter group. The magnitude of the first parameter in the second parameter group corresponds to the magnitude of the time-series measurement quality, which can be used to determine the accuracy of the TDOA parameter estimation. The magnitude of the second parameter in the second parameter group corresponds to the magnitude of the resolution of the time-series measurement quality, which can be used to determine the fineness of the TDOA parameter estimation.

[0250] Optionally, the method further includes: the third device determining M first indicators based on the M second parameter groups corresponding to the M second devices.

[0251] That is, the third device can calculate and obtain the first index of each of the M second devices based on the M second parameters received from the M second devices.

[0252] For details regarding the third device determining the first indicator based on the second parameter set, please refer to section S204 of method 200; further details will not be repeated here.

[0253] One possibility is that the first information includes a first indicator, which is determined based on a second parameter set corresponding to the second device, and the second parameter set includes measurement quality parameters corresponding to the parameters in the first parameter set.

[0254] That is, the M second devices can report their respective first indicators to the third device. The first indicators can be used by the third device to filter out a portion of the first parameter groups from the M first parameter groups, and then locate the first device based on the filtered portion of the first parameter groups.

[0255] Optionally, the method further includes: the M second devices determining M first indicators based on the M second parameter groups corresponding to the M second devices.

[0256] For details regarding the second device determining the first indicator based on the second parameter set, please refer to section S204 of method 200; further details will not be provided here.

[0257] Prior to step one, the method further includes: a second device determining the first information.

[0258] For example, the first information includes a first indicator, which the second device can determine based on a corresponding second parameter set. That is, it determines the first information.

[0259] In step two, the third device can determine N first parameter groups from the M first parameter groups based on the M first information.

[0260] One possibility is that the N first parameter groups are the N first parameter groups with the largest first index among the M first parameter groups.

[0261] For example, the M first pieces of information are M first indicators. The third device can arrange the M first indicators in descending order and select the first parameter group corresponding to the second device corresponding to the top N first indicators. The N first indicators of the second device are the N largest first indicators among the M first indicators. Here, N can be a fixed value.

[0262] It is understandable that when the M first indicators are arranged in descending order, for example, the Nth first indicator has the same value as at least one first indicator following the Nth first indicator. In this case, the third device can select any one of the Nth first indicator and the at least one first indicator, denoted as the Nth first indicator. The first parameter group of the second device corresponding to the Nth first indicator can be used as one of the N first parameter groups selected from the M first parameter groups.

[0263] One possibility is that the N first parameter groups are the first parameter groups among the M first parameter groups whose first index is greater than or equal to the second threshold value.

[0264] For example, the third device can select N first indicators from the M first indicators of the M second devices, where the first indicators are greater than or equal to the second threshold value. The first parameter groups of the N second devices corresponding to the N first indicators can be used as the N first parameter groups filtered from the M first parameter groups.

[0265] That is, the second threshold value can be used to select N first parameter groups with higher measurement quality from M first parameter groups.

[0266] S703, the third device determines the location information of the first device based on the N first parameter groups.

[0267] For details regarding S703, please refer to the detailed content of S207 in the aforementioned method 200, which will not be repeated here.

[0268] It is understandable that the relevant instructions in Method 700 can be found in the detailed content of Method 200, and will not be repeated here.

[0269] Based on the above technical solution, the third device selects N sets of first parameters from the M sets of first parameters received from M base stations, and then uses these N sets of first parameters to locate the first device, thereby obtaining the location information of the first device. In this way, not only can positioning be performed based on various types of positioning parameters, but also by selecting N sets of first parameters with high measurement quality to locate the first device. Furthermore, it reduces positioning parameter errors, improves the quality and stability of positioning parameters, and thus enhances positioning accuracy and robustness.

[0270] Figure 8 This is a schematic diagram of a positioning device provided in an embodiment of this application. It is understood that this positioning device can correspondingly implement the operations or steps of the second or third device in the foregoing method embodiments. The positioning device can be a second device or a component configurable on a second device, such as a chip or chip module; the positioning device can also be a third device or a component configurable on a third device, such as a chip or chip module. Figure 8 As shown, the positioning device may include a transceiver module 810 and a processing module 820. Optionally, the transceiver module 810 may be an integrated transmitting and receiving module, or it may be separate.

[0271] In one possible design, the positioning device 800 may correspond to the third device in the above method embodiments, or a component (such as a circuit, chip, or chip system) configured in the third device. The positioning device 800 may be used to perform the steps or processes performed by the third device in any of the above method embodiments.

[0272] For example, the transceiver module 810 is used to receive M first parameter sets from M second devices, each first parameter set including at least two of the following: received signal strength RSS, time difference of arrival TDOA, or angle of arrival AOA, which is determined by the second device based on a reference signal transmitted by the first device; the processing module 820 is used to determine N first parameter sets from the M first parameter sets, where M is greater than N and M and N are positive integers; the processing module 820 is used to determine the location information of the first device based on the N first parameter sets.

[0273] Optionally, the transceiver module 810 is further configured to receive M first information from the M second devices, the first information being used to determine the measurement quality of the first parameter group corresponding to the second device; the processing module 820 is further configured to determine N first parameter groups from the M first parameter groups based on the M first information.

[0274] Optionally, the first information includes a second parameter group corresponding to the second device, and the second parameter group includes measurement quality parameters corresponding to the parameters in the first parameter group; the processing module 820 is further configured to determine M first indicators based on the M second parameter groups corresponding to the M second devices.

[0275] Optionally, the first information includes a first indicator, which is determined based on a second parameter group corresponding to the second device, and the second parameter group includes measurement quality parameters corresponding to the parameters in the first parameter group.

[0276] Optionally, the second parameter group includes one or more of the following parameters: first parameter The second parameter is used to characterize the measurement quality corresponding to the time measurement quality. The resolution used to characterize the quality of time measurements; the third parameter The fourth parameter is used to characterize the azimuth quality corresponding to the quality of the angle measurement. The resolution used to characterize the quality of angle measurements; the fifth parameter. The sixth parameter is used to characterize the phase quality corresponding to the phase measurement quality. , is used to characterize the resolution of phase measurement quality.

[0277] Optionally, the N first parameter groups are the N first parameter groups with the largest first index among the M first parameter groups; or, the N first parameter groups are the first parameter groups among the M first parameter groups where the first index is greater than or equal to the second threshold value.

[0278] Optionally, the M second devices of the first The first indicator of the second equipment With the first The second parameter group corresponding to the second device includes one or more parameters that are positively correlated.

[0279] Optionally, the M second devices The first indicator of the second equipment With the first The second parameter set corresponding to each second device satisfies: ;in, , , , , , Indicates the weighting coefficient. This represents the bias value.

[0280] Optionally, the second parameter set also includes signal-to-noise ratio. , and They are positively correlated.

[0281] Optionally, satisfy: ;in, This represents the weighting coefficient.

[0282] Optionally, the processing module 820 is also used to determine the location information of the first device based on the N first parameter groups using SE-CNN.

[0283] Optionally, the SE-CNN includes a squeeze excitation network in which at least two parameters in the first parameter group correspond to different weights.

[0284] The above are merely examples; for detailed steps or procedures, please refer to the descriptions in the foregoing embodiments.

[0285] In one possible design, the positioning device 800 may correspond to the second device in the above method embodiments, or to a component (such as a circuit, chip, or chip system) configured in the second device. The positioning device 800 may be used to perform the steps or processes performed by the second device in any of the above method embodiments.

[0286] For example, the processing module 820 is used to determine first information, which is used to determine the measurement quality of a first parameter set corresponding to the second device. The first parameter set includes at least two of the following: received signal strength RSS, time difference of arrival TDOA, or angle of arrival AOA. The first parameter set is determined by the second device based on a reference signal sent by the first device. The transceiver module 810 is used to send the first information.

[0287] Optionally, the first information includes a second parameter group corresponding to the second device, and the second parameter group includes the measurement quality parameters corresponding to the parameters in the first parameter group.

[0288] Optionally, the first information includes a first indicator, and the transceiver module 810 is further configured to determine the first indicator based on the second parameter group corresponding to the second device, wherein the second parameter group includes the measurement quality parameters corresponding to the parameters in the first parameter group.

[0289] Optionally, the second parameter group includes one or more of the following parameters: first parameter The second parameter is used to characterize the measurement quality corresponding to the time measurement quality. The resolution used to characterize the quality of time measurements; the third parameter The fourth parameter is used to characterize the azimuth quality corresponding to the quality of the angle measurement. The resolution used to characterize the quality of angle measurements; the fifth parameter. The sixth parameter is used to characterize the phase quality corresponding to the phase measurement quality. , is used to characterize the resolution of phase measurement quality.

[0290] Optionally, the first indicator of the second device is positively correlated with one or more parameters included in the second parameter group corresponding to the second device.

[0291] Optionally, the second parameter set also includes the signal-to-noise ratio, which is positively correlated with the first metric.

[0292] The above are merely examples; for detailed steps or procedures, please refer to the descriptions in the foregoing embodiments.

[0293] The positioning device provided in this application embodiment can perform the actions of the second or third device in the aforementioned method embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0294] It should be noted that, in the above embodiments, the transmitting module can actually be a transmitter, and the receiving module can actually be a receiver, or the transmitting and receiving modules can be implemented through a transceiver, or through a communication port. The processing module can be implemented in software via a processing element, or in hardware. For example, the processing module can be at least one separately established processing element, or it can be integrated into a chip in the aforementioned device. Alternatively, it can be stored as program code in the memory of the aforementioned device, and its functions can be called and executed by a processing element of the aforementioned device. Furthermore, all or part of these modules can be integrated together, or they can be implemented independently. The processing element mentioned here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0295] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs). As another example, when a module is implemented by a processing element calling program code, that processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together to implement a system-on-a-chip (SOC).

[0296] Figure 9 This is another schematic block diagram of the positioning device 900 provided in the embodiments of this application. The positioning device 900 may be a chip, chip system, or processor, etc., in a terminal device or network device that implements the above-described methods. The positioning device 900 can be used to implement the methods described in the above-described method embodiments; for details, please refer to the descriptions in the above-described method embodiments.

[0297] like Figure 9As shown, the positioning device 900 may include one or more processors 910, which may also be referred to as processing units or processing modules, and can implement certain control functions. The processor 910 may be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, while the central processing unit can be used to control the positioning device 900 (e.g., a base station, baseband chip, user, user chip), execute software programs, and process data from the software programs.

[0298] In an alternative design, the processor 910 may also store instructions and / or data that can be executed by the processor 910 to cause the positioning device 900 to perform the methods described in the above method embodiments.

[0299] In another alternative design, the positioning device 900 may include a communication interface 920 for implementing receiving and transmitting functions. For example, the communication interface 920 may be a transceiver circuit, interface, interface circuit, or transceiver. The transceiver circuit, interface, interface circuit, or transceiver for implementing receiving and transmitting functions may be separate or integrated. The aforementioned transceiver circuit, interface, interface circuit, or transceiver may be used for reading and writing code / data, or it may be used for transmitting or relaying signals.

[0300] Optionally, the positioning device 900 may include one or more memories 930, which may store instructions that can be executed on the processor 910, causing the positioning device 900 to perform the methods described in the above method embodiments. Optionally, the memories 930 may also store data. Optionally, the processor 910 may also store instructions and / or data. The processor 910 and the memories 930 may be provided separately or integrated together.

[0301] It should be understood that, in one possible design, the steps in the method embodiments provided in this application can be implemented by integrated logic circuits in the processor's hardware or by instructions in software form. The steps of the methods disclosed in the embodiments of this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are not provided here.

[0302] In one implementation, the positioning device 900 may correspond to the third device in the above method embodiments and may be used to execute the various steps and / or processes executed by the third device in the above method embodiments. The processor 910 may be used to execute instructions stored in the memory 930, and when the processor 910 executes the instructions stored in the memory, the processor 910 is used to execute the various steps and / or processes of the above method embodiments corresponding to the third device.

[0303] In another implementation, the positioning device 900 may correspond to the second device in the above method embodiments and may be used to execute the various steps and / or processes executed by the second device in the above method embodiments. The processor 910 may be used to execute instructions stored in the memory 930, and when the processor 910 executes the instructions stored in the memory, the processor 910 is used to execute the various steps and / or processes of the above method embodiments corresponding to the second device.

[0304] It should be understood that the aforementioned processing device can be one or more chips. For example, the processing device can be a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a system-on-chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0305] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0306] According to the method provided in the embodiments of this application, this application also provides a chip system, which includes one or more processors for calling and executing instructions stored in memory, thereby causing the method described in the embodiments of this application to be executed. The chip system may be composed of chips or may include chips and other discrete devices.

[0307] The chip system may include input circuits or interfaces for transmitting information or data, and output circuits or interfaces for receiving information or data.

[0308] According to the method provided in the embodiments of this application, this application also provides a communication system, which includes the aforementioned first device, second device and third device.

[0309] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute the various steps or processes executed by the third device or the second device in any of the foregoing method embodiments.

[0310] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code, which, when run on a computer, causes the computer to execute the various steps or processes executed by the third device or the second device in any of the foregoing method embodiments.

[0311] The computer-readable storage medium may be the aforementioned volatile memory or non-volatile memory, or it may include both volatile memory and non-volatile memory.

[0312] In the embodiments of this application, the terms and English abbreviations are exemplary examples given for ease of description and should not be construed as limiting the application in any way. This application does not preclude the possibility of defining other terms that can achieve the same or similar functions in existing or future agreements.

[0313] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated.

[0314] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus 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 coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0315] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0316] In summary, the above are merely preferred embodiments of the technical solutions of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A positioning method, characterized in that, The method includes: Receive M first parameter sets from M second devices, each first parameter set including at least two of the following parameters: received signal strength RSS, time difference of arrival TDOA, or angle of arrival AOA, wherein the first parameter set is determined by the second devices based on the reference signal sent by the first devices; Receive M pieces of first information from the M second devices; wherein, the first information is used to determine the measurement quality of the first parameter group corresponding to the second device, the measurement quality is determined based on the second parameter group, the second parameter group includes measurement quality parameters corresponding to at least two parameters in the first parameter group, the second parameter group includes at least two of the following categories: quality and resolution for testing time measurement of TDOA, quality and resolution for testing angle measurement of AOA, and quality and resolution for testing phase measurement of TDOA and AOA; Based on the M pieces of first information, N sets of first parameters are determined from the M sets of first parameters, where M is greater than N and M and N are positive integers; Based on a preset neural network model and the N sets of first parameters, the location information of the first device is determined.

2. The method according to claim 1, characterized in that, The first information includes the second parameter group corresponding to the second device; the method further includes: Based on the M second parameter groups corresponding to the M second devices, M first indicators are determined.

3. The method according to claim 1, characterized in that, The first information includes a first indicator, which is determined based on a second parameter group corresponding to the second device.

4. The method according to claim 1, characterized in that, The quality and resolution of the time measurement used to test TDOA include: a first parameter. Used to characterize the measurement quality corresponding to the time measurement quality, and the second parameter , used to characterize the resolution of time measurement quality; The quality and resolution of the angle measurement used to test AOA include: the third parameter This is used to characterize the azimuth quality corresponding to the angular measurement quality, and the fourth parameter. , used to characterize the resolution of angle measurement quality; The quality and resolution of the phase measurements used to test TDOA and AOA include: the fifth parameter The phase quality, used to characterize the phase measurement quality, and the sixth parameter. , is used to characterize the resolution of phase measurement quality.

5. The method according to claim 3, characterized in that, The N first parameter groups are the N first parameter groups with the largest first index among the M first parameter groups; or... The N first parameter groups are the first parameter groups among the M first parameter groups in which the first index is greater than or equal to the second threshold value.

6. The method according to claim 5, characterized in that, The M second devices of the first The first indicator of the second equipment With the first The second parameter group corresponding to each second device includes one or more parameters that are positively correlated.

7. The method according to claim 6, characterized in that, The second parameter group also includes signal-to-noise ratio. The and They are positively correlated.

8. The method according to claim 6 or 7, characterized in that, The determination of the location information of the first device based on the preset neural network model and the N first parameter groups includes: The location information of the first device is determined by using a squeeze-excitation network-convolutional neural network SE-CNN based on the N first parameter groups.

9. The method according to claim 8, characterized in that, The SE-CNN includes a squeeze excitation network, in which at least two parameters in the first parameter group correspond to different weights.

10. A positioning method, characterized in that, Applied to a second device, the method includes: First information is determined, which is used to determine the measurement quality of the first parameter set corresponding to the second device. The first parameter set includes at least two of the following parameters: received signal strength RSS, time difference of arrival TDOA, or angle of arrival AOA. The first parameter set is determined by the second device based on the reference signal sent by the first device. Send the first information so that the third device determines N first parameter groups based on M first parameter groups, and determines the location information of the first device based on a preset neural network model and N first parameter groups; M is greater than N, and M and N are positive integers; The measurement quality is determined based on a second parameter set, which includes measurement quality parameters corresponding to at least two parameters in the first parameter set. The second parameter set includes at least two of the following categories: the quality and resolution of time measurement for testing TDOA, the quality and resolution of angle measurement for testing AOA, and the quality and resolution of phase measurement for testing TDOA and AOA.

11. The method according to claim 10, characterized in that, The first information includes the second parameter group corresponding to the second device.

12. The method according to claim 11, characterized in that, The first information includes a first indicator, and the method further includes: The first indicator is determined based on the second parameter group corresponding to the second device.

13. The method according to claim 11 or 12, characterized in that, The quality and resolution of the time measurement used to test TDOA include a first parameter. Used to characterize the measurement quality corresponding to the time measurement quality, and the second parameter , used to characterize the resolution of time measurement quality; The quality and resolution of the angle measurement used to test AOA include: the third parameter This is used to characterize the azimuth quality corresponding to the angular measurement quality, and the fourth parameter. , used to characterize the resolution of angle measurement quality; The quality and resolution of the phase measurements used to test TDOA and AOA include: the fifth parameter The phase quality, used to characterize the phase measurement quality, and the sixth parameter. , is used to characterize the resolution of phase measurement quality.

14. The method according to claim 13, characterized in that, The first indicator of the second device is positively correlated with one or more parameters included in the second parameter group corresponding to the second device.

15. The method according to claim 14, characterized in that, The second parameter group also includes the signal-to-noise ratio (SNR), and the first indicator is positively correlated with the SNR.

16. A positioning device, characterized in that, include: Processor and memory; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the positioning device to perform the method as described in any one of claims 1-9, or causing the positioning device to perform the method as described in any one of claims 10-15.

17. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-9, or when the computer program is executed by a processor, it implements the method as described in any one of claims 10-15.

18. A chip system, characterized in that, It includes at least one processor and a communication interface, the communication interface and the at least one processor being interconnected via a line, the at least one processor being configured to run a computer program or instructions to perform the method as described in any one of claims 1-9, or to perform the method as described in any one of claims 10-15.

19. A computer program product, characterized in that, Includes a computer program that, when run, causes a computer to perform the method as described in any one of claims 1-9, or causes a computer to perform the method as described in any one of claims 10-15.

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