Method and apparatus for position measurement in wireless LAN system

Abstract

The present disclosure relates to a method and apparatus for position measurement in a wireless local area network (LAN) system. A method performed by an electronic apparatus, according to an embodiment of the present disclosure, comprises the steps of: acquiring, via an inertial measurement unit (IMU) sensor, relative position information of the electronic device; acquiring, via a wireless fidelity (Wi-Fi) module, fine time measurement (FTM) distance information between an access point (AP) and the electronic device; analyzing, via the Wi-Fi module, channel state information (CSI) between the AP and the electronic device; and estimating position coordinates of the electronic device through application of an extended Kalman filter on the basis of the relative position information, the FTM distance information, and an analysis result of the CSI.

Description

Method and device for location measurement in a wireless LAN system

[0001] The present disclosure relates to a wireless LAN network system. More specifically, the present disclosure relates to a method and apparatus for measuring location in a wireless LAN system.

[0002] A Wireless Local Area Network (WLAN), also known as Wi-Fi, is a network that enables internet access via mobile devices or laptops within a certain distance from an access point (AP). WLAN technology continues to evolve in line with the rise of the internet and the expansion of the smartphone market, and is being utilized to provide high-speed data services throughout the city, including in schools, airports, hotels, and offices.

[0003] The WiFi Alliance defines WiFi as a Wireless Local Area Network (WLAN) product based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard. IEEE 802.11a and b, published in 1997 and 1999 respectively, are standards utilizing unlicensed bands at 2.4 GHz or 5 GHz; IEEE 802.11b provides a transmission speed of 11 Mbps, while IEEE 802.11a provides a transmission speed of 54 Mbps. IEEE 802.11g provides a transmission speed of 54 Mbps by applying Orthogonal Frequency-Division Multiplexing (OFDM) at 2.4 GHz. IEEE 802.11n applies multiple input multiple output OFDM (MIMO-OFDM) to provide a transmission speed of 300 Mbps using four spatial streams. IEEE 802.11n supports a channel bandwidth of up to 40 MHz, in which case it provides a transmission speed of 600 Mbps.

[0004] Subsequently, the IEEE 802.11ac standard was introduced, which uses a maximum bandwidth of 160 MHz and supports eight spatial streams to support speeds up to 1 Gbit / s, and the IEEE 802.11ax standard was introduced, which provides multi-user MIMO (MU-MIMO) in the uplink and downlink and supports spatial frequency reuse and dynamic fragmentation. Since then, research is underway on 802.11be, which aims to achieve theoretical speeds of 46 Gbps by supporting up to 320 ultra-wide channels, multi-link operation, and 4kQAM.

[0005] As mobile communication technology advances, the need for indoor positioning is increasing; however, GPS (Global Positioning System), a representative technology for outdoor location tracking services, is difficult to use as an indoor positioning means due to its satellite-based nature. Accordingly, there is a demand for methods to efficiently and accurately measure location even indoors using various communication technologies, such as Wi-Fi.

[0006] According to one embodiment of the present disclosure, a method performed by an electronic device is provided. The method comprises: acquiring relative position information of the electronic device through an inertial measurement unit (IMU) sensor; acquiring fine time measurement (FTM) distance information between an access point (AP) and the electronic device through a wireless fidelity (Wi-Fi) module; analyzing channel state information (CSI) between the AP and the electronic device through the Wi-Fi module; and estimating the position coordinates of the electronic device by applying an extended Kalman filter based on the relative position information, the FTM distance information, and the analysis results of the CSI.

[0007] According to one embodiment of the present disclosure, an electronic device is provided. The electronic device includes an inertial measurement unit (IMU) sensor; a wireless fidelity (Wi-Fi) module; and a control unit. The control unit is configured to obtain relative position information of the electronic device through the IMU sensor, obtain fine time measurement (FTM) distance information between an access point (AP) and the electronic device through the Wi-Fi module, analyze channel state information (CSI) between the AP and the electronic device through the Wi-Fi module, and estimate the position coordinates of the electronic device by applying an extended Kalman filter based on the relative position information, the FTM distance information, and the analysis results of the CSI.

[0008] According to various embodiments of the present disclosure, accurate and highly reliable location measurement can be provided in diverse and complex indoor environments without building additional infrastructure. In addition, the accumulation of errors can be prevented at a low computational cost through map matching techniques.

[0009] Figure 1 is a diagram illustrating an example of a wireless communication network.

[0010] Figure 2 is a diagram illustrating an example of the structure of an electronic device that performs WLAN connection.

[0011] Figure 3 is a diagram illustrating an example of a link setup process for a typical wireless LAN.

[0012] Figure 4 is a diagram illustrating the procedure of the Wi-Fi FTM protocol.

[0013] Figure 5 is a diagram illustrating a Wi-Fi FTM-based location measurement method.

[0014] Figure 6 is a diagram illustrating various scenarios in which RPs are deployed in a Wi-Fi FTM-based location measurement method.

[0015] FIG. 7 is a diagram illustrating the schematic flow of a position measurement method proposed in the present disclosure.

[0016] FIG. 8 is a diagram illustrating a more specific flow of the position measurement method proposed in the present disclosure.

[0017] FIG. 9 is a diagram illustrating a method for deriving input parameters of an extended Kalman filter for positioning according to one embodiment of the present disclosure.

[0018] FIG. 10 is a diagram illustrating the process of an extended Kalman filter for positioning according to one embodiment of the present disclosure.

[0019] FIG. 11 is a diagram illustrating a preprocessing process of map matching according to one embodiment of the present disclosure.

[0020] FIG. 12 is a drawing illustrating a map matching method according to one embodiment of the present disclosure.

[0021] FIG. 13 is a diagram illustrating a method for determining the validity of estimated position coordinates in a map matching process according to one embodiment of the present disclosure.

[0022] FIG. 14 is a drawing illustrating the operation of an electronic device according to one embodiment of the present disclosure.

[0023] FIG. 15 is a drawing illustrating the structure of an electronic device according to one embodiment of the present disclosure.

[0024] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0025] In describing the embodiments, technical details that are well known in the art to which this disclosure belongs and are not directly related to this disclosure are omitted. This is intended to convey the essence of this disclosure more clearly without obscuring it by omitting unnecessary explanations.

[0026] For the same reason, some components in the attached drawings have been exaggerated, omitted, or schematically depicted. Additionally, the size of each component does not entirely reflect its actual dimensions. Identical or corresponding components in each drawing have been assigned the same reference number.

[0027] The advantages and features of the present disclosure and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below but may be implemented in various different forms. The embodiments of the present disclosure are provided merely to make the present disclosure complete and to fully inform those skilled in the art of the scope of the disclosure, and the present disclosure is defined only by the scope of the claims. Throughout the specification, like reference numerals refer to like components.

[0028] At this point, it will be understood that each block of the process flow diagrams and combinations of the flow diagrams can be executed by computer program instructions. Since these computer program instructions can be loaded into the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, the instructions executed through the processor of the computer or other programmable data processing equipment create means to perform the functions described in the flow diagram block(s). Since these computer program instructions can also be stored in computer-available or computer-readable memory that can be directed toward the computer or other programmable data processing equipment to implement the function in a specific way, the instructions stored in computer-available or computer-readable memory can also produce a manufactured item containing means of instruction to perform the function described in the flow diagram block(s).

[0029] Since computer program instructions can be loaded onto a computer or other programmable data processing equipment, instructions that execute a computer or other programmable data processing equipment by performing a series of operation steps on the computer or other programmable data processing equipment to create a process executed by the computer may also provide steps for executing the functions described in the flowchart block(s).

[0030] Additionally, each block may represent a module, segment, or part of code containing one or more executable instructions for executing a specific logical function(s). It should also be noted that in some alternative execution examples, the functions mentioned in the blocks may occur out of order. For instance, two blocks described in succession may actually be executed substantially simultaneously, or the blocks may be executed in reverse order depending on the corresponding function.

[0031] In this embodiment, the term "part" as used refers to a software or hardware component such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC), and the "part" performs certain roles. However, the meaning of "part" is not limited to software or hardware. The "part" may be configured to reside in an addressable storage medium or may be configured to run one or more processors. Accordingly, according to some embodiments, the "part" includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and "parts" may be combined into a smaller number of components and "parts" or further separated into additional components and "parts." In addition, the components and 'parts' may be implemented to utilize one or more CPUs within the device or secure multimedia card. Also, according to some embodiments, the 'parts' may include one or more processors.

[0032] Exemplary embodiments are described below in relation to wireless LAN systems solely for the sake of simplicity. It should be understood that the exemplary embodiments are equally applicable to systems using signals of one or more wired standards or protocols (e.g., Ethernet and / or HomePlug, PLC standards), as well as other wireless networks (e.g., cellular networks, pico networks, femto networks, satellite networks). As used herein, the terms WLAN and Wi-Fi® may include communications controlled by the IEEE 802.11 family of standards, BLUETOOTH®, HiperLAN (a set of wireless standards comparable to IEEE 802.11 standards, mainly used in Europe), and other technologies having a relatively short wireless propagation range. Accordingly, the terms WLAN and Wi-Fi may be used interchangeably herein. Additionally, although the following describes an infrastructure WLAN system including one or more APs and multiple wireless stations (STAs), exemplary embodiments are equally applicable to other WLAN systems including, for example, multiple WLANs, peer-to-peer (or independent basic service set) systems, Wi-Fi Direct systems and / or hotspots.

[0033] Additionally, while this specification describes the exchange of data frames between wireless devices, exemplary embodiments may be applied to the exchange of any data unit, packet, and / or frame between wireless devices. Accordingly, the term "frame" may include any frame, packet, or data unit such as, for example, protocol data units (PDUs), media access control (MAC) protocol data units (MPDUs), and physical layer convergence procedure (PLCP) protocol data units (PPDUs). The term A-MPDU may mean aggregated MPDUs. In the following, a wireless LAN, or WLAN network, may be a network implementing at least one of the IEEE 802.11 wireless communication protocol standard family, such as as defined by the IEEE 802.11-2016 standard or its amendments (including, but not limited to, 802.11ah, 802.11ad, 802.11ay, 802.11ax, 802.11az, 802.11ba, and 802.11be).

[0034] In the following description, many specific details, such as examples of specific components, circuits, and processes, are presented to provide a thorough understanding of the present disclosure. As used herein, the term “connected” means being directly connected or being connected through one or more intervening components or circuits. The term “connected AP” means an access point to which a given wireless station is currently associated and / or connected (e.g., there exists a communication channel or link established between the access point and the given wireless station). Additionally, in the following description and for illustrative purposes, specific nomenclature is presented to provide a thorough understanding of exemplary embodiments. However, it will be apparent to those skilled in the art that these specific details may not be necessary to carry out the exemplary embodiments. In other cases, to avoid obscuring the present disclosure, well-known circuits and devices are illustrated in block diagram form.

[0035] The operating principles of the present disclosure will be described in detail below with reference to the attached drawings. In describing the present disclosure below, specific descriptions of related known functions or configurations will be omitted if it is determined that such detailed descriptions would unnecessarily obscure the essence of the present disclosure. Furthermore, the terms described below are defined in consideration of their functions in the present disclosure, and these may vary depending on the intentions or practices of the user or operator. Therefore, their definitions should be based on the content throughout this specification.

[0036] Figure 1 is a diagram illustrating an example of a wireless communication network.

[0037] Referring to FIG. 1, the wireless communication network (100) may be an example of a wireless LAN, such as a Wi-Fi network. The wireless communication network (100) may include a number of wireless communication devices, such as an AP (102) and a number of STAs (stations, 104). Although only one AP (102) is shown, the wireless communication network (100) may also include a number of APs (102).

[0038] A STA is a logical entity that includes a physical layer interface for a MAC and a wireless medium, and includes APs and non-AP STAs (Non-AP stations). Among the STAs, a portable terminal operated by a user is a Non-AP STA, and when simply referred to as STA, it may also refer to a Non-AP STA. Hereinafter, STA may refer to a non-AP STA. Each of the STAs (104) may be referred to as a terminal or a device. The terms 'terminal' or 'device' used in this specification may be referred to as a mobile station (MS), user equipment (UE), user terminal (UT), wireless terminal, access terminal (AT), terminal, subscriber unit, subscriber station (SS), wireless device, wireless communication device, wireless transmit / receive unit (WTRU), mobile node, mobile, or other terms. Various embodiments of the terminal may include cellular telephones, smartphones with wireless communication capabilities, personal handheld terminals (PDAs) with wireless communication capabilities, wireless modems, portable computers with wireless communication capabilities, imaging devices such as digital cameras with wireless communication capabilities, gaming devices with wireless communication capabilities, music storage and playback appliances with wireless communication capabilities, internet appliances capable of wireless internet access and browsing, as well as portable units or terminals integrating combinations of such functions. Additionally, the terminal may include machine-to-machine (M2M) terminals and machine-type communication (MTC) terminals / devices, but is not limited thereto. In this specification, the terminal may be referred to as an electronic device or simply a device.

[0039] An AP (102) is an entity that provides access to a distribution system (DS) via a wireless medium to an Associated Station (STA) connected to it. An AP may also be called a central controller, a base station (BS), a Node-B, a base transceiver system (BTS), or a site controller.

[0040] An exemplary coverage area (106) of an AP (102) capable of representing the basic service area (BSA) of a wireless communication network (100) is illustrated. The AP (102) periodically broadcasts beacon frames (beacon frames may be interchangeable with beacons) containing a basic service set identifier (BSSID) to enable any STA (104) within the wireless range of the AP (102) to be associated with or re-associated with the AP (102) to establish or maintain individual communication links (108) (or may be referred to as Wi-Fi links) with the AP (102). The AP (102) can provide access to external networks for various STAs (104) within the WLAN through individual communication links (108).

[0041] A single AP (102) and an associated set of STAs (104) may be referred to as a basic service set (BSS) managed by the individual AP (102). The BSS may be identified to users by a service set identifier (SSID), as well as to other devices by a BSSID, which may be the MAC address of the AP (102).

[0042] BSS can be classified into infrastructure BSS and independent BSS (IBSS). The BSS shown in Fig. 1 is an IBSS, and it is also possible to establish an infrastructure BSS (not shown). An infrastructure BSS includes one or more STAs and APs, and in principle, communication between non-AP STAs in an infrastructure BSS is carried out via an AP, but if a direct link is established between non-AP STAs, direct communication between non-AP STAs is also possible.

[0043] Multiple infrastructure BSSs can be interconnected via DS. Multiple BSSs connected via DS are called an extended service set (ESS). STAs included in an ESS can communicate with each other, and within the same ESS, STAs can move from one BSS to another while communicating seamlessly.

[0044] A DS is a mechanism that connects multiple APs; it does not necessarily have to be a network, and there are no restrictions on its form as long as it can provide a specified distribution service. For example, a DS can be a wireless network such as a mesh network, or it can be a physical structure that connects APs to each other.

[0045] Additionally, AP (102) and STA (104) may be referred to as AP-MLD (access point multi-link device) and STA-MDL, respectively. This may mean that AP and STA can support multi-link operation.

[0046] Below, an example of a hierarchical structure according to the 802.11 standard is described.

[0047] The 802.11 standard document develops MAC and PHY protocols corresponding to Wi-Fi wireless access technology. The Data Link Layer (DLL) includes the MAC sublayer, which is responsible for media access control. It receives packets from the upper layer, 802.1X Port Filtering, via the MAC_SAP interface, constructs them into IEEE 802.11 MAC frames, and transmits them to the physical layer. The physical layer includes the PLCP (Physical Layer Convergence Procedure) sublayer and the PDM (Physical Medium Dependent) sublayer. The PLCP sublayer is responsible for converting the IEEE 802.11 MAC frames constructed by the MAC sublayer into PLCP frames. The PLCP frames are then transmitted to the target terminal through the PMD sublayer.

[0048] Various management frames that manage Wi-Fi wireless access are not transmitted at the upper layers of 802.1X. Instead, these management frames are transmitted as requests and responses between Station Management Entities (SMEs) located within each terminal. An SME is a layer-independent entity that may exist within a separate management plane or appear to be off-the-side. For example, if an AP wants to configure a BSS, it instructs the transmission of beacons via the MLME_SAP interface, specifically the MLME-START.request and MLME-START.confirm primitives. If an STA wants to establish an association with the corresponding AP, it instructs the transmission of association Request / Response frames via the MLME-ASSOCIATE.request, MLME-ASSOCIATE.response, MLME-ASSOCIATE.confirm, and MLME-ASSOCIATE.indication primitives. Meanwhile, if you wish to set operational parameter values ​​related to the physical layer, the SME can set various physical layer parameter values ​​through the PLCP_SAP interface.

[0049] Figure 2 is a diagram illustrating an example of the structure of an electronic device that performs WLAN connection.

[0050] Referring to FIG. 2, the electronic device (200) may be connected to an AP (210), and the electronic device (200) may include a processor (230) and a communication module (220). The electronic device (200) may be the STA (104) of FIG. 1, in which case the electronic device (200) may be connected to the AP (210) as illustrated. Alternatively, the electronic device (200) may be the AP (102) of FIG. 1, in which case the electronic device may be connected to the STA (104) and / or another AP as illustrated in FIG. 1.

[0051] The communication module (220) can receive a communication signal from the outside or transmit a communication signal to the outside based on a Wi-Fi communication method (e.g., IEEE Std 802.11™). For example, the communication module (220) can operate based on Wi-Fi communication methods such as IEEE 802.11ac, 802.11ax, 802.11be, or 802.11bn, and in particular, IEEE 802.11be or 802.11bn supports a wider bandwidth, higher data throughput, and shorter latency compared to IEEE 802.11ax, thereby improving performance.

[0052] The communication module (220) may include a transceiver (224) for transmitting and receiving data with an external device and a communication processor (222) (e.g., a communication processor (not shown), or a short-range wireless communication module (e.g., a Wi-Fi chipset)). Depending on various embodiments, the communication module (220) may further include memory.

[0053] According to various embodiments, the transceiver (224) can convert a baseband transmission signal into a wireless signal or convert a received wireless signal into a baseband reception signal.

[0054] According to various embodiments, the communication module (220) may further include, in addition to the transceiver (224) and the communication processor (222), components for OFDM or OFDMA (orthogonal frequency division multiple access), such as a modulator, a digital-analog converter, a frequency converter, an A / D converter, an amplifier, and / or a demodulator.

[0055] According to various embodiments not shown, the electronic device (200) may be electrically connected to a communication module of the AP (210) and may include at least one antenna module that supports a communication protocol and / or frequency band supported by the communication module of the AP (210).

[0056] A communication processor (222) can control a transceiver (224) to form a communication connection with an AP (210). For example, the communication connection may include a Wi-Fi network. For example, a communication processor (222) can control a transceiver (224) to form a wireless connection with an AP (210) using a WLAN standard in the 2.4 GHz, 5 GHz, or 6 GHz band such as IEEE 802.11ac, 802.11ax, 802.11be, or 802.11bn. Alternatively, a communication processor (222) can control a transceiver (224) to form a wireless connection with an AP (210) using a WLAN standard in the 60 GHz band such as IEEE 802.11ad or 802.11ay. In addition, the method of communicating between the electronic device (200) and the AP (210) using a WLAN standard can be referred to as a communication method based on STA mode.

[0057] According to various embodiments, the processor (230) may include an application processor. The processor (230) may perform a specified operation of the electronic device (200) or control other hardware (e.g., a communication module (220)) to perform a specified operation.

[0058] According to various embodiments, the AP (210) may support the operation of transmitting packets to an external network and / or the operation of receiving packets from an external network based on a connection between a plurality of electronic devices (e.g., electronic device (200)) and an external network (e.g., the Internet, an external LAN, or a cellular network).

[0059] For example, the AP (210) may be a wireless router. The AP (210) may be a dedicated wireless router or a general-purpose device that supports mobile hotspot functions, and there are no limitations on its implementation. For example, the AP (210) may include the same components (e.g., a processor and / or a communication module) as the electronic device (200). Additionally, the AP (210) may transmit and receive data with an external device, such as a server. For example, the AP (210) may transmit at least some of the data received from the server to the electronic device (200).

[0060] If the electronic device (200) of FIG. 2 corresponds to the AP (102), the electronic device (200) may include a separate communication module for connection with an external network, although not shown. This communication module may be controlled by a processor (230) or by a separate processor. The separate communication module may include a transceiver and a processor, and may also include memory. Additionally, the electronic device (200) may include a separate antenna module or a wired connection device for connection with an external network.

[0061] Figure 3 is a diagram illustrating an example of a link setup process for a typical wireless LAN.

[0062] In order for an STA to set up a link and transmit and receive data on a network, it must first discover the network, perform authentication, establish an association, and go through authentication procedures for security. The link setup process can also be referred to as the session initiation process or the session setup process. Additionally, the discovery, authentication, association, and security setup processes of the link setup process can be collectively referred to as the association process.

[0063] Referring to FIG. 3, the STA (300) can perform a network discovery operation. The network discovery operation may include a scanning operation of the STA (300). That is, in order for the STA (300) to access a network, it must find a network that it can join. Before joining a wireless network, the STA (300) must identify a compatible network, and the process of identifying networks existing in a specific area is called scanning.

[0064] Scanning methods include active scanning and passive scanning. In active scanning, the STA (300) performing the scanning moves between channels and sends a probe request frame (322) to search for nearby APs and waits for a response. The responder sends a probe response frame (324) as a response to the probe request frame to the STA that sent the probe request frame. Here, the responder may be the AP or STA that last sent a beacon frame from the BSS of the channel being scanned. In FIG. 3, an example of a BSS that becomes the responder is shown where the AP (310) sends a beacon frame (320). In an IBSS, the responder is not constant because the STAs within the IBSS take turns sending beacon frames. For example, if an STA transmits a probe request frame on channel 1 and receives a probe response frame on channel 1, the STA can store the BSS-related information included in the received probe response frame and move to the next channel to perform scanning in the same way.

[0065] Scanning operations may be performed using a passive scanning method. In passive scanning, the STA performing the scanning detects beacon frames while switching between channels. A beacon frame is one of the management frames in IEEE 802.11, which announces the presence of a wireless network and is periodically transmitted to allow the scanning STA to find the wireless network and join it. Figure 3 illustrates an example of a BSS in which an AP (310) periodically transmits beacon frames (320) to an STA (300), and in an IBSS, STAs within the IBSS take turns transmitting beacon frames. When the scanning STA receives a beacon frame, it stores information about the BSS included in the beacon frame and records the beacon frame information in each channel while moving to another channel. When comparing active scanning and passive scanning, active scanning has the advantage of having less delay and power consumption than passive scanning.

[0066] After the STA (300) discovers the network, an authentication process may be performed. This authentication process may be referred to as the first authentication process to clearly distinguish it from the security setup operation (350) described later. The authentication process includes the STA (300) sending an authentication request frame (330) to the AP (310), and in response, the AP (310) sending an authentication response frame (332) to the STA (300). The authentication frame used in the authentication request / response corresponds to a management frame.

[0067] The authentication frame may include information regarding the authentication algorithm number, authentication transaction sequence number, status code, challenge text, Robust Security Network (RSN), Finite Cyclic Group, etc. These are some examples of information that may be included in the authentication request / response frame, and they may be replaced with other information or additional information may be included.

[0068] AP (310) can determine whether to allow authentication for the STA based on the information included in the received authentication request frame. AP (310) can provide the result of the authentication processing to the STA (300) through an authentication response frame.

[0069] After the STA is successfully authenticated, an association process can be performed. The association process includes the STA (300) sending an association request frame (340) to the AP (310), and in response, the AP (310) sending an association response frame (342) to the STA (300).

[0070] For example, the associated request frame may include information regarding various capabilities, beacon listen interval, SSID, supported rates, supported channels, RSN (robust security network), mobility domain, supported operating classes, traffic indication map broadcast request, interworking service capabilities, etc.

[0071] For example, an association response frame may include information related to various capabilities, status code, association ID (AID), support rate, enhanced distributed channel access (EDCA) parameter set, received channel power indicator (RCPI), received signal to noise indicator (RSNI), mobility domain, timeout interval (association comeback time), overlapping BSS scan parameters, TIM broadcast response, QoS map, etc.

[0072] This is a partial example of the information that may be included in the associated request / response frame, and it may be replaced with other information or additional information may be included.

[0073] Although not yet described, a security setup process can be performed after the STA is successfully associated with the network. The security setup process may be described as an authentication process through RSNA (robust security network association) requests / responses, and the authentication process (330) may be called the first authentication process, and the security setup process may also be called the authentication process.

[0074] The security setup process may include, for example, a private key setup process through a 4-way handshake via an EAPOL (extensible authentication protocol over LAN) frame, or may be performed according to a security method not defined in the IEEE 802.11 standard.

[0075] Meanwhile, as mobile communication technology advances, the need for indoor positioning is increasing. However, GPS (global positioning system), a representative technology for outdoor location tracking services, is difficult to use as an indoor positioning means because it is based on satellite communication. Accordingly, there is a demand for methods to efficiently and accurately measure location even indoors using various communication technologies such as Wi-Fi. For example, the Wi-Fi FTM (fine timing measurement) protocol is a technology that measures the distance between Wi-Fi-enabled devices. Since being adopted as an IEEE 802.11mc standard in 2016, it has been applied as a method for indoor positioning in various products up to the present.

[0076] Figure 4 is a diagram illustrating the procedure of the Wi-Fi FTM protocol.

[0077] Referring to FIG. 4, a Wi-Fi FTM protocol procedure between an electronic device (400) and an AP (410) is illustrated. In this figure, the electronic device (400) may be a Wi-Fi-enabled IoT device such as a STA, a user terminal, or a smartphone, and may be referred to as the initiator of the FTM procedure. The AP (410) may be a base station or router that supports Wi-Fi functions and may be referred to as the responder of the FTM procedure.

[0078] In step S420, the electronic device (400) can transmit an FTM request frame to the AP (410). The AP (410) can accept or reject the FTM request received from the electronic device (400).

[0079] If AP (410) accepts the FTM request received from the electronic device (400), in step S430, AP (410) can send an acknowledgment (ACK) frame to the electronic device (400) to initiate an FTM-ACK frame exchange.

[0080] In step S440, the AP (410) can transmit a first FTM frame ('Ping') to the electronic device (400) at time T1 and wait for an ACK response from the electronic device (400). The electronic device (400) can receive the first FTM frame at time T2.

[0081] In step S450, the electronic device (400) may transmit an ACK frame ('Pong') to the AP (410) at time T3. The AP (410) may receive the ACK frame at time T4. The ACK frame may contain information about time stamps T2 and T3.

[0082] In step S460, the AP (410) may transmit a second FTM frame to the electronic device (400). The second FTM frame may include information regarding time stamps T1 and T4.

[0083] According to the above procedure, the round-trip time (RTT) between the electronic device (400) and the AP (410) can be calculated using the following mathematical formula 1.

[0084] [Mathematical Formula 1]

[0085]

[0086] Meanwhile, according to various embodiments of the present disclosure, a pair of FTM frames and ACK frames can be defined as a single burst, and more accurate measurements can be performed by repeating multiple bursts. Since errors may occur in a single FTM measurement due to signal noise or other environmental factors, more reliable RTT and distance can be obtained by performing multiple FTM bursts and calculating the average value.

[0087] At this time, each FTM burst provides an independent RTT value, and based on this, the average RTT can be calculated. For example, assuming that n FTM bursts are used, and if the set of timestamps corresponding to each burst is defined as {T1(1), T2(1), T3(1), T4(1)}, {T1(2), T2(2), T3(2), T4(2)}, … {T1(n), T2(n), T3(n), T4(n)}, the RTT between the electronic device (400) and the AP (410) can be calculated using the following mathematical formula 2.

[0088] [Mathematical Formula 2]

[0089]

[0090] Based on the RTT calculated by the method described above, the distance d between the electronic device (400) and the AP (410) can be calculated using the following mathematical formula 3.

[0091] [Mathematical Formula 3]

[0092]

[0093] Here, c is the speed of light. am.

[0094] Figure 5 is a diagram illustrating a Wi-Fi FTM-based location measurement method.

[0095] Referring to FIG. 5, a Wi-Fi FTM-based location measurement system can estimate the location of an electronic device (500) through multi-rateration using at least three reference points (RPs) (510, 520, 530) arranged in a manner surrounding the user. The RPs may be APs or Wi-Fi routers. Alternatively, the RPs may be other devices that support Wi-Fi and may be placed in fixed locations and each may be assumed to know its own location information.

[0096] For example, the electronic device (500) can measure the distance to each RP (510, 520, 530) using the Wi-Fi FTM protocol described above. Assuming each circle has the distance as its radius, the location of the electronic device (500) can be estimated by finding the point where the circles intersect according to triangulation.

[0097] Alternatively, the difference (residual) between the distance from the initial position estimated through the above triangulation method to each RP (510, 520, 530) and the actual measured distance can be calculated. Subsequently, the position is repeatedly updated in a direction that reduces the error using the Gauss-Newton Method, and by repeating this process and minimizing the residual, a two-dimensional coordinate representing the final position of the electronic device (500) can be calculated.

[0098] Figure 6 is a diagram illustrating various scenarios in which RPs are deployed in a Wi-Fi FTM-based location measurement method.

[0099] Referring to FIG. 6, an ideal arrangement (600) of RPs for a Wi-Fi FTM-based location measurement system is illustrated. For example, if each RP is positioned at a corner of an indoor space and arranged symmetrically with respect to one another, and there are no obstacles within the space, the accuracy of the Wi-Fi FTM-based location estimation value can be expected to be high.

[0100] On the other hand, in a scenario (610) where there are many obstacles within the indoor space, the accuracy of the FTM may be reduced because the line-of-sight (LoS) condition between the electronic device or user and the RP is unlikely to be satisfied. Additionally, in a scenario (620) where the RPs are not arranged in a way that surrounds the electronic device or user, for example, in a situation where three RPs are arranged in a line, the accuracy of the position estimation value through the aforementioned multi-lateration may be reduced because the point where the circles corresponding to each RP intersect each other is unclear.

[0101] In other words, existing Wi-Fi FTM-based positioning systems alone may find it difficult to maintain a high level of accuracy and precision in complex existing indoor environments, such as those where the reference points are asymmetrically or arbitrarily placed, or where various obstacles like walls, furniture, and other equipment are present. Therefore, a more efficient and accurate positioning method is required.

[0102] FIG. 7 is a diagram illustrating the schematic flow of a position measurement method proposed in the present disclosure.

[0103] Referring to FIG. 7, an electronic device (700) according to one embodiment of the present disclosure may include an IMU (inertial measurement unit) sensor (710) and a Wi-Fi module (720). The electronic device (700) may be an IoT device that supports Wi-Fi, such as a STA or a user terminal or a smartphone.

[0104] As previously explained, since existing Wi-Fi FTM-based positioning systems rely on various indoor conditions, a positioning method utilizing an IMU sensor that detects movement independently without relying on external signals or specific environments can be considered. The IMU may consist of an accelerometer that measures the linear acceleration of the device by measuring gravity and accelerated motion, and a gyroscope that measures angular velocity by measuring the rotational motion of the device; it may also include a magnetometer that provides directionality by measuring the Earth's magnetic field.

[0105] The electronic device (700) can obtain relative position information (vector or matrix representing position change) of the electronic device (700) based on acceleration and / or angular velocity measurements through the IMU sensor (710). Additionally, the electronic device (700) can measure the distance to an indoor RP (or AP) according to the FTM procedure described in FIG. 4 through the Wi-Fi module (720).

[0106] The electronic device (700) can derive the estimated position coordinates of the electronic device (700) by using relative position information obtained using an IMU sensor (710) and FTM-based distance values ​​measured through a Wi-Fi module (720) as inputs to an extended Kalman filter (EKF) (730).

[0107] Meanwhile, in order to prevent the accumulation of measurement errors of the IMU sensor (710) and to improve the accuracy of the position estimation value according to the extended Kalman filter (730), map matching (740) may be additionally performed to adjust the position estimation value, and then the final position coordinates of the electronic device (700) may be determined.

[0108] FIG. 8 is a diagram illustrating a more specific flow of the position measurement method proposed in the present disclosure.

[0109] Referring to FIG. 8, an electronic device (800) according to one embodiment of the present disclosure may include an IMU sensor (810) and a Wi-Fi module (820). The electronic device (800) may be an IoT device that supports Wi-Fi, such as a STA or a user terminal or a smartphone.

[0110] In one example, the electronic device (800) can measure acceleration and / or angular velocity through an IMU sensor (810) and apply this to a deep learning model (830) (e.g., ResNet18) to obtain relative position information of the electronic device (800) (vector or matrix representing position change).

[0111] In one example, the electronic device (800) can measure the distance to an indoor RP (or AP) according to the FTM procedure described in FIG. 4 through a Wi-Fi module (820). The electronic device (800) can perform a process (840) of acquiring channel state information (CSI) between the RP (or AP) and analyzing it through the Wi-Fi module (820). Based on the results of the CSI analysis, the electronic device (800) can adjust the measurement noise covariance matrix R to reflect the degree of error or noise that may occur during the measurement process of the extended Kalman filter.

[0112] In one example, the electronic device (800) can estimate the position coordinates of the electronic device (800) by using relative position information obtained using an IMU sensor (810), an FTM-based distance value measured through a Wi-Fi module (820), and an adjusted measurement noise covariance matrix R as inputs to an extended Kalman filter (860). A specific method for deriving the input parameters of the extended Kalman filter (860) will be described later in FIG. 9.

[0113] In one example, the electronic device (800) can estimate the position coordinates of the electronic device (800) by performing state prediction, state covariance prediction, Kalman gain update, state estimation, and state covariance update, which are processes of the extended Kalman filter (860). A specific method for applying the extended Kalman filter (860) will be described later in FIG. 10.

[0114] In one example, the electronic device (800) may additionally perform map matching (870) to adjust the estimated position coordinates and determine the final position coordinates of the electronic device (800) in order to prevent the accumulation of measurement errors of the IMU sensor (810) and to improve the accuracy of the position estimation value according to the extended Kalman filter (860). The specific method of the map matching (870) will be described later in FIGS. 11 to 13.

[0115] FIG. 9 is a diagram illustrating a method for deriving input parameters of an extended Kalman filter for positioning according to one embodiment of the present disclosure.

[0116] Referring to FIG. 9, an electronic device (900) according to one embodiment of the present disclosure may include an IMU sensor (910) and a Wi-Fi module (920). The electronic device (900) may be an IoT device that supports Wi-Fi, such as a STA or a user terminal or a smartphone.

[0117] In one example, the electronic device (900) can measure acceleration and / or angular velocity through an IMU sensor (910). Optionally, the measured values ​​can be applied to a deep learning model (930) to obtain relative position information of the electronic device (900) (a vector or matrix representing a change in position).

[0118] The IMU sensor (910) can provide acceleration and angular velocity information, but since a pedestrian may move with sudden changes in movement or various patterns while holding the electronic device (900), complex calculations may be required to determine relative position information. Therefore, it may be desirable to apply the acceleration and angular velocity information measured by the IMU sensor (910) to a deep learning model (930) to derive the relative position information of the electronic device (900).

[0119] In one example, a type of ResNet (Residual Neural Network) can be considered as the deep learning model (930) above. ResNet is a deep learning model that can generally be used in tasks such as image recognition and classification, and can resolve the vanishing gradient problem in general neural networks, where learning performance decreases as the layers become deeper, through residual connections, which are a structure in which input values ​​are directly added to the output.

[0120] For example, the electronic device (900) may use ResNet18, a type of ResNet consisting of 18 layers, as a deep learning model (930). More specifically, the electronic device (900) may use acceleration and angular velocity measurements obtained from an IMU sensor (910) to normalize into multidimensional time-series data (e.g., acceleration along the x, y, and z axes, angular velocity along the x, y, and z axes) and use this as input to the deep learning model (930). The deep learning model (930) can extract important patterns (e.g., sudden changes in movement, specific velocity patterns, etc.) within this data and thereby predict the position change of the electronic device (900), i.e., relative position information.

[0121] In one example, the electronic device (900) can measure the distance to an indoor RP (or AP) according to the FTM procedure described in FIG. 4 through a Wi-Fi module (920). Additionally, the electronic device (900) can identify whether the channel between the RP (or AP) is a LoS environment or an NLoS (non-LoS) environment by performing a process (940) of analyzing the CSI between the RP (or AP) obtained through the Wi-Fi module (920).

[0122] More specifically, the RP (or AP) may transmit a "training sequence" or "pilot signal" consisting of a predetermined pattern, and the electronic device (900) may estimate the channel state using this signal. The "training sequence" or "pilot signal" may be included in a physical layer frame defined in the IEEE 802.11 standard. The electronic device (900) may estimate the CSI using complex channel characteristics (e.g., amplitude and phase information) from multiple frequency subcarriers. Alternatively, the electronic device (900) may obtain the CSI while performing the FTM protocol procedure described in FIG. 4 with the RP (or AP).

[0123] The electronic device (900) can perform a process (940) of analyzing CSI to derive the time-of-flight (ToF) and reception strength of the signal corresponding to each path among the multipath consisting of n paths between RP (or AP).

[0124] For example, the signal between the electronic device (900) and the RP (or AP) may be transmitted and received through signal paths that are reflected off walls or obstacles, in addition to the direct signal path. If an obstacle is present at the location of the direct signal path, the signal strength received through the direct path may be weaker than the signal received through reflection due to signal attenuation caused by the obstacle. In this case, an error may occur in which the result of the FTM algorithm, which calculates d through Equation 3 by measuring the RTT based on the signal strength received, is calculated to be greater than the actual distance between the electronic device (900) and the RP (or AP).

[0125] The electronic device (900) of the present disclosure can determine whether the environment between the electronic device (900) and the RP (or AP) is closer to the LoS or NLoS environment through the following process in order to determine the reliability of the distance measurement result through FTM, taking into account the error that may be induced according to the above-described cause. In addition, based on this determination result, the reliability weight for the measurement value can be determined by adjusting the element values ​​of matrix R used in the extended Kalman filter described later.

[0126] More specifically, the electronic device (900) samples n paths in a time series and each path ToF τ corresponding to k and century P k {τ, a pair of k , P k It is possible to determine}. In general, since ToF is proportional to the path distance and the signal reception strength is inversely proportional to the square of the distance, the larger the ToF value, the lower the signal reception strength tends to be measured. Therefore, if the path with the smallest ToF value is assumed to be the direct path, the proportion of the signal strength of the direct path among the signal strengths of the entire multipath can be expressed as Equation 4 below.

[0127] [Mathematical Formula 4]

[0128]

[0129] The electronic device (900) can identify whether the channel between RPs (or APs) is in a LoS environment or an NLoS environment by comparing the proportion of the signal strength of the direct path among the signal strengths of the entire multipath with a predetermined threshold value (e.g., 0.5). For example, if the proportion of the signal strength of the direct path among the signal strengths of the entire multipath is greater than or equal to the threshold value, the electronic device (900) can determine that the channel between RPs (or APs) is in a LoS environment. Alternatively, if the proportion of the signal strength of the direct path among the signal strengths of the entire multipath is less than or equal to the threshold value, the electronic device (900) can determine that the channel between RPs (or APs) is in an NLoS environment. In another embodiment, the electronic device (900) may use the proportion of the signal strength of the direct path among the signal strengths of the entire multipath described above as a parameter for determining the channel environment itself, without comparing it with a specific threshold value.

[0130] The electronic device (900) can use (1) relative position information, (2) FTM-based distance measurement values, and (3) information about the channel environment (LoS / NLoS) obtained by the method described above as input parameters for an extended Kalman filter for positioning.

[0131] FIG. 10 is a diagram illustrating the process of an extended Kalman filter for positioning according to one embodiment of the present disclosure.

[0132] The Extended Kalman Filter (EKF) is an extension that relaxes the linearity assumption of the conventional Kalman filter to enable its use in more general systems, and in this disclosure, it can be used to estimate the nonlinear state of an electronic device.

[0133] In step S1010, the electronic device can perform a state prediction procedure to predict the current state based on the previous state and new input data.

[0134] The state prediction procedure may include a process of acquiring relative position information based on an IMU sensor as new input data. For the current time point t, the relative position information based on the IMU sensor, that is, the amount of change in the (x,y) coordinates of the electronic device predicted through the IMU sensor and a deep learning model. It can be expressed as shown in the following mathematical formula 5.

[0135] [Mathematical Formula 5]

[0136]

[0137] Previous state for state prediction ((x,y) coordinates of the electronic device at previous time point t-1) It can be expressed as shown in the following mathematical formula 6.

[0138] [Mathematical Formula 6]

[0139]

[0140] Predicted state (predicted (x,y) coordinates of the electronic device at the current time t) It can be expressed as shown in the following mathematical formula 7.

[0141] [Mathematical Formula 7]

[0142]

[0143] Here, the state transition matrix F can be expressed as shown in Equation 8 below.

[0144] [Mathematical Formula 8]

[0145]

[0146] In step S1020, the electronic device may perform a state covariance prediction procedure to predict the uncertainty of the state. In the state covariance prediction procedure, when predicting how the current state will change, the reliability of the prediction (i.e., how much error may exist) can be calculated through the state covariance matrix.

[0147] Predicted state covariance matrix P at current time t t - It can be expressed as shown in the following mathematical formula 9.

[0148] [Mathematical Formula 9]

[0149]

[0150] Here, P t-1 is the state covariance matrix at the previous time t-1, and Q is the system noise covariance matrix representing the uncertainty of the system itself or the error caused by external noise, which can be expressed as shown in Equation 10 below.

[0151] [Mathematical Formula 10]

[0152]

[0153] Here, the parameter , is a constant and can be a value determined in advance before the system starts.

[0154] In step S1030, the electronic device may perform a Kalman gain update procedure to calculate a Kalman gain that determines how much to trust the measured value and the predicted value. The Kalman gain is a weight that determines which of the two values ​​is more trusted when combining the measured value and the predicted value; a larger Kalman gain indicates that the measured value is more trusted, while a smaller Kalman gain indicates that the predicted value is more trusted.

[0155] Kalman profit K t It can be expressed as shown in the following mathematical formula 11.

[0156] [Mathematical Formula 11]

[0157]

[0158] Here, H t is the observation matrix of the Kalman filter, and R represents the level of measurement noise for the FTM.

[0159] Matrix H t It can be expressed as shown in the following mathematical formula 12.

[0160] [Mathematical Formula 12]

[0161]

[0162] Here, represents the coordinates of each RP (or AP) among n RPs (or APs).

[0163] Matrix R can be expressed as shown in Equation 13 below.

[0164] [Mathematical Formula 13]

[0165]

[0166] Here, the parameter is the FTM measure variable for each RP (or AP) among n RPs (or APs). It indicates the noise level of each element.

[0167] The above Kalman gain update procedure may include a process of adjusting matrix R. More specifically, if the electronic device can determine the channel state between RPs (or APs) according to the method described above in FIG. 9. If it is determined that the channel state satisfies the LoS condition, the electronic device By adjusting the value to a lower level It can provide higher reliability for. On the other hand, if the channel state is determined to be an NLoS environment, the electronic device Adjust the value high The reliability of can be assigned a low value.

[0168] Here, Adjusting the value lower and / or higher may mean subtracting or adding by a predetermined offset. In another embodiment, the electronic device omits an explicit LoS / NLoS determination and corresponds to the weight of the direct path signal strength among the signal strengths of the entire multipath. The value can also be adjusted linearly or non-linearly.

[0169] In step S1040, the electronic device may perform a state estimation procedure to estimate the current state based on the predicted state and actual measurements.

[0170] The state estimation procedure may include comparing the predicted state with the actual measurement and using Kalman gain to assign weights based on the degree of reliability of the predicted state and the actual measurement.

[0171] Estimated state (estimated (x,y) coordinates of the electronic device at the current time t) It can be expressed as shown in the following mathematical formula 14.

[0172] [Mathematical Formula 14]

[0173]

[0174] Here, represents the distance between the electronic device and the RP (or AP) calculated through the predicted state, and can be expressed as shown in Equation 15 below.

[0175] [Mathematical Formula 15]

[0176]

[0177] The above estimated state (estimated (x,y) coordinates of the electronic device at the current time t) The final position coordinates can be determined through the map matching process described later in FIGS. 11 to 13, or corrected to be determined as final position coordinates, and then used in the state prediction procedure of the extended Kalman filter at the next time point.

[0178] In step S1050, the electronic device may perform a state covariance update procedure to update the state uncertainty. The state covariance matrix P updated at the current time point t. t It can be expressed as shown in the following mathematical formula 16.

[0179] [Mathematical Formula 16]

[0180]

[0181] Updated state covariance matrix P t It re-evaluates the uncertainty regarding the estimated state, and the Kalman gain K t and observation matrix H t It can be used to correct the uncertainty of the predicted state and reduce the error. Updated state covariance matrix P t It can be used in the state covariance prediction procedure of the extended Kalman filter at the next time point.

[0182] The procedures of the extended Kalman filter described above may be performed sequentially or each procedure may be performed independently, and some procedures may be omitted as necessary. Subsequently, to prevent the accumulation of measurement errors from the IMU sensor and to improve the accuracy of the position estimation value based on the extended Kalman filter, map matching may be additionally performed to adjust the position estimation value.

[0183] FIG. 11 is a diagram illustrating a preprocessing process of map matching according to one embodiment of the present disclosure.

[0184] Referring to Fig. 11, the pre-processing steps required before performing map matching of the position estimation values ​​are illustrated.

[0185] In step S1100, a map image of an indoor space where the electronic device or user may be present can be obtained. The map image may have various image formats such as JPEG / JPG (joint photographic experts group), PNG (portable network graphics), GIF (graphics interchange format), TIFF (tagged image file format), and BMP (bitmap). The map image may be an indoor floor plan or a 2D floor plan representing the internal structure of a building obtained from user input or an external server. Alternatively, the map image may be generated by scanning the indoor environment through a sensor such as a camera or LIDAR of the electronic device.

[0186] In step S1110, the electronic device can determine the correspondence between the pixels of the map image and the horizontal plane coordinates. Through this, the electronic device can define the ratio between the actual distance and the pixels on the image. For example, if the length of one side of the map image is 200 pixels and the length of the corresponding part in the actual indoor space is 20m, it can be determined that a ratio of 1 meter per 10 pixels is applied to the map image.

[0187] In step S1120, the electronic device may display lines on the map image that pedestrians cannot move along. For example, the user may input to display boundaries such as walls or obstacles, or the electronic device may utilize internal AI technology or deep learning algorithms to automatically recognize areas / boundaries that pedestrians cannot pass through and display them on the map image.

[0188] Through the process described above, the electronic device can obtain (preprocessed) map information for performing map matching of position estimation values. Alternatively, the electronic device may omit the process described above and obtain map information that has already been preprocessed from user input or an external server.

[0189] FIG. 12 is a drawing illustrating a map matching method according to one embodiment of the present disclosure.

[0190] Referring to FIG. 12, a process of performing map matching based on the map information described in FIG. 11 is illustrated in order to prevent the accumulation of measurement errors of the IMU sensor described in FIG. 10 and to improve the accuracy of the position estimation value according to the extended Kalman filter.

[0191] In step S1200, the electronic device is in its previous state (the (x,y) coordinates of the electronic device at the previous time point t-1) vector Estimated location coordinates based on whether it intersects the line depicted in the map information It is possible to determine whether it is valid.

[0192] If the estimated location coordinates are If valid, in step S1210a, the electronic device has the estimated position coordinates It can be determined as the final position coordinates of the electronic device.

[0193] On the other hand, if the estimated location coordinates If it is invalid, in step S1210, the electronic device's previous position coordinates It can scatter particles around. Here, scattering particles means the previous position coordinates Location coordinates with the same standard deviation as the system noise centered on This may mean assigning them randomly. Here, the number of particles m may be a predetermined value or an arbitrary value, and may be modified considering the channel environment between the electronic device and the RP (or AP) and / or the processing load of the electronic device.

[0194] In step S1220, the electronic device can verify the validity of the estimated position coordinates corresponding to each particle. More specifically, the electronic device can verify each coordinate vector It can check whether it intersects the line depicted in the map information. Similar to the above-described step S1200, the electronic device can check each coordinate Estimated location coordinates corresponding to You can check whether it is valid.

[0195] In step S1230, the electronic device has the estimated position coordinates corresponding to each particle It can delete invalid ones. In other words, the electronic device uses the estimated position coordinates corresponding to each particle. If it is invalid, it can be excluded from the candidate set for deriving the final location coordinates.

[0196] In step S1240, the electronic device has the estimated position coordinates corresponding to each particle The final position coordinates can be determined based only on valid ones. In other words, the electronic device uses the estimated position coordinates corresponding to each particle. If it is determined to be valid, it may be included in the candidate group for deriving the final position coordinates. The electronic device may determine the final position coordinates by averaging the position coordinates of the candidate group. Alternatively, the electronic device may determine any one of the position coordinates of the candidate group as the final position coordinate.

[0197] FIG. 13 is a diagram illustrating a method for determining the validity of estimated position coordinates in a map matching process according to one embodiment of the present disclosure.

[0198] Referring to FIG. 13, the process of determining the validity of the estimated position coordinates corresponding to each particle, which is performed in step S1220 of FIG. 12 described above, can be similarly applied in step S1200 of FIG. 12 described above.

[0199] As described, the coordinates of the first particle (1301) vector Coordinates of the first path (1310a) and the second particle (1302) to which the applied vector A second path (1320a) to which the above is applied can be assumed. Among these, the first path (1310a) passes through the line (1330a) shown in the map information, so the estimated location coordinates is invalid, and since the second path (1320a) does not pass through the line (1330a) shown in the map information, the estimated location coordinates It can be judged to be valid.

[0200] According to one embodiment of the present disclosure, an electronic device may use a pixel-based image processing method to determine the validity of estimated location coordinates. More specifically, a first path (1310a), a second path (1320a), and a line (1330a) shown in the map information may each correspond to a pixel area (1310b, 1320b, 1330b). In this case, in the pixel area (1330b), the grayscale value of each pixel may all appear as '0'. Here, since the pixel area (1310b) corresponding to the first path includes pixels with a grayscale value of '0', the electronic device [can] determine the estimated location coordinates It can be determined that it is invalid. On the other hand, since the pixel area (1320b) corresponding to the second path does not contain pixels with a grayscale value of '0', the electronic device estimates the position coordinates It can be determined that it is valid.

[0201] In this drawing, for ease of understanding, only cases where the grayscale values ​​of the pixels are '0' or '255' are shown, but the validity of the corresponding estimated location coordinates can be determined based on whether the grayscale values ​​of the pixels included in each path exceed a specific threshold. In addition, in addition to grayscale values, RGB (Red, Green, Blue) values, RGBA (Red, Green, Blue, Alpha) values, or HSV (Hue, Saturation, Value) values ​​representing image color information may also be used.

[0202] FIG. 14 is a drawing illustrating the operation of an electronic device according to one embodiment of the present disclosure.

[0203] Referring to FIG. 14, the operation of an electronic device according to the embodiments of FIG. 1 to FIG. 13 proposed in the present disclosure is illustrated.

[0204] In step S1410, the electronic device can obtain relative position information of the electronic device through the IMU sensor.

[0205] In step S1420, the electronic device can obtain FTM distance information between the AP and the electronic device through the Wi-Fi module.

[0206] In step S1430, the electronic device can analyze the CSI between the AP and the electronic device through the Wi-Fi module.

[0207] In step S1440, the electronic device can estimate the position coordinates of the electronic device by applying an extended Kalman filter based on the relative position information, the FTM distance information, and the CSI analysis results.

[0208] Optionally, the relative position information can be obtained by applying the acceleration and angular velocity values ​​of the electronic device measured by the IMU sensor to a deep learning model.

[0209] Optionally, analyzing the CSI may include the electronic device calculating the proportion of the signal strength of the direct path among the signal strengths of the entire multipath between the AP and the electronic device, and, based on the calculation result, identifying whether the channel between the AP and the electronic device is a LoS environment or an NLoS environment.

[0210] Optionally, the electronic device may adjust a parameter representing the noise level of the FTM distance information based on the identification result, and update the Kalman gain of the extended Kalman filter based on the adjusted parameter.

[0211] Optionally, the estimated position coordinates may be based on the updated Kalman gain.

[0212] Optionally, if the channel between the AP and the electronic device is determined to be a LoS environment, the parameter may be reduced.

[0213] Optionally, if the channel between the AP and the electronic device is determined to be an NLoS environment, the parameter may be increased.

[0214] Optionally, the electronic device may acquire map information about the space in which the electronic device exists, identify whether the estimated location coordinates are valid based on the map information, and determine the final location coordinates of the electronic device based on the identification result.

[0215] Optionally, determining the final position coordinates may include determining the final position coordinates as the estimated position coordinates if the electronic device has valid estimated position coordinates.

[0216] Optionally, determining the final position coordinates may include, when the electronic device is not valid, identifying a plurality of position coordinates randomly assigned around the previous position coordinates of the electronic device, identifying whether a plurality of estimated position coordinates corresponding to each of the plurality of position coordinates are valid, and determining the final position coordinates as the average value of at least one valid position coordinate among the plurality of estimated position coordinates.

[0217] FIG. 15 is a drawing illustrating the structure of an electronic device according to one embodiment of the present disclosure.

[0218] Referring to FIG. 15, the electronic device (1500) may include a control unit (1510), an IMU sensor (1520), and a Wi-Fi module (1530). The control unit (1510), the IMU sensor (1520), and the Wi-Fi module (1530) of the electronic device (1500) may operate according to at least one or a combination thereof of the methods corresponding to the above-described embodiments.

[0219] The control unit (1510) may be defined as a circuit or application-specific integrated circuit or at least one processor. The control unit (1510) may control the overall operation of the electronic device (1500) according to the embodiments proposed in this disclosure. For example, the control unit (1510) may control the signal flow between each block to perform operations according to the flowchart described above. Specifically, the control unit (1510) may control the operation proposed in this disclosure to measure the indoor location of the electronic device (1500) according to the embodiments described above.

[0220] The IMU sensor (1520) may be composed of an accelerometer that measures the linear acceleration of the device by measuring gravity and acceleration motion, and a gyroscope that measures the rotational motion of the device to measure angular velocity, and may also include a magnetometer that measures the Earth's magnetic field to provide directionality.

[0221] The Wi-Fi module (1530) may include a communication processor and a transceiver. For example, the communication processor may form a communication connection with an AP. For example, the communication connection may include a Wi-Fi network. For example, the communication processor may control the transceiver to form a wireless connection with the AP using a WLAN standard in the 2.4 GHz, 5 GHz, or 6 GHz band such as IEEE 802.11ac, 802.11ax, 802.11be, or 802.11bn. Alternatively, the communication processor may control the transceiver to form a wireless connection with the AP using a WLAN standard in the 60 GHz band such as IEEE 802.11ad or 802.11ay.

[0222] Methods according to the claims or embodiments described in the specification of the present disclosure may be implemented in the form of hardware, software, or a combination of hardware and software.

[0223] When implemented in software, a computer-readable storage medium may be provided for storing one or more programs (software modules). One or more programs stored in the computer-readable storage medium are configured for execution by one or more processors within an electronic device. One or more programs include instructions that cause the electronic device to execute methods according to the claims or embodiments described in the specification of this disclosure.

[0224] Such programs (software modules, software) may be stored in random access memory, non-volatile memory including flash memory, ROM (Read Only Memory), Electrically Erasable Programmable Read Only Memory (EEPROM), magnetic disc storage devices, Compact Disc-ROM (CD-ROM), Digital Versatile Discs (DVDs), or other forms of optical storage devices, magnetic cassettes. Alternatively, they may be stored in memory composed of some or all of these. Additionally, each constituent memory may include multiple units.

[0225] Additionally, the program may be stored on an attachable storage device accessible via a communication network such as the Internet, Intranet, Local Area Network (LAN), Wide LAN (WLAN), or Storage Area Network (SAN), or a combination thereof. Such a storage device may be connected to a device performing an embodiment of the present disclosure through an external port. Additionally, a separate storage device on a communication network may be connected to a device performing an embodiment of the present disclosure.

[0226] In the specific embodiments of the present disclosure described above, the components included in one embodiment are expressed in a singular or plural form according to the specific embodiment presented. However, the singular or plural expression is selected to suit the situation presented for convenience of explanation, and the present disclosure is not limited to singular or plural components; even if a component is expressed in the plural form, it may be composed in the singular form, or even if a component is expressed in the singular form, it may be composed in the plural form.

[0227] The flowcharts described above illustrate exemplary methods that may be implemented in accordance with the principles of the present disclosure, and various modifications may be made to the methods illustrated in the flowcharts of this specification. For example, although they are illustrated as a series of steps, the various steps in each figure may overlap, occur in parallel, occur in a different order, or occur multiple times. In other examples, steps may be omitted or replaced with other steps. The values ​​described above are merely examples, and it is fully possible to apply other values.

[0228] Meanwhile, the embodiments of the present disclosure disclosed in this specification and drawings are merely specific examples provided to facilitate the explanation of the technical content of the present disclosure and to aid in understanding the present disclosure, and are not intended to limit the scope of the present disclosure. That is, it is obvious to those skilled in the art that other variations based on the technical concept of the present disclosure are possible. Furthermore, each of the above embodiments may be combined and operated together as needed.

[0229] Furthermore, the order of description in the drawings illustrating the method of the present invention does not necessarily correspond to the order of execution, and the order of execution may be changed or executed in parallel. Alternatively, the drawings illustrating the method of the present invention may omit some components and include only some components to the extent that the essence of the present invention is not compromised.

Claims

1. In a method performed by an electronic device, A step of obtaining relative position information of the electronic device through an IMU (inertial measurement unit) sensor; A step of obtaining FTM (fine time measurement) distance information between an AP (access point) and the electronic device through a Wi-Fi (Wireless Fidelity) module; A step of analyzing CSI (channel state information) between the AP and the electronic device through the above Wi-Fi module; and A method comprising the step of estimating the position coordinates of the electronic device by applying an extended Kalman filter based on the relative position information, the FTM distance information, and the analysis result of the CSI.

2. In Paragraph 1, A method characterized in that the above relative position information is obtained by applying the acceleration value and angular velocity value of the electronic device measured by the IMU sensor to a deep learning model.

3. In Paragraph 1, The step of analyzing the above CSI is, A step of calculating the proportion of the signal strength of the direct path among the signal strengths of the entire multipath between the AP and the electronic device; and A method characterized by including a step of identifying whether the channel between the AP and the electronic device is a line of sight (LoS) environment or a non-LoS (NLoS) environment based on the above calculation result.

4. In Paragraph 3, Based on the above identification result, a step of adjusting a parameter representing the noise level of the FTM distance information; and The method further includes the step of updating the Kalman gain of the extended Kalman filter based on the above-mentioned adjusted parameters, and A method characterized in that the above-mentioned estimated position coordinates are based on the above-mentioned updated Kalman gain.

5. In Paragraph 4, If the channel between the AP and the electronic device is determined to be in a LoS environment, the parameter is reduced, and A method characterized in that the parameter is increased when the channel between the AP and the electronic device is determined to be in an NLoS environment.

6. In Paragraph 1, A step of obtaining map information about the space in which the above electronic device exists; A step of identifying whether the estimated location coordinates are valid based on the map information above; and A method further comprising the step of determining the final position coordinates of the electronic device based on the above identification result.

7. In Paragraph 6, The step of determining the final position coordinates above is, A method characterized by including the step of determining the final position coordinates as the estimated position coordinates when the estimated position coordinates are valid.

8. In Paragraph 6, The step of determining the final position coordinates above is, If the above estimated position coordinates are invalid, a step of identifying a plurality of position coordinates randomly assigned around the previous position coordinates of the electronic device; A step of identifying whether a plurality of estimated location coordinates corresponding to each of the above plurality of location coordinates are valid; and A method comprising the step of determining the final position coordinate as the average value of at least one valid position coordinate among the plurality of estimated position coordinates.

9. In electronic devices, IMU (inertial measurement unit) sensor; Wi-Fi (Wireless Fidelity) module; and It includes a control unit, and the control unit, said control unit Through the above IMU sensor, relative position information of the electronic device is obtained, and Through the above Wi-Fi module, FTM (fine time measurement) distance information between the AP (access point) and the electronic device is obtained, and Through the above Wi-Fi module, the CSI (channel state information) between the AP and the electronic device is analyzed, and An electronic device characterized by being configured to estimate the position coordinates of the electronic device by applying an extended Kalman filter based on the relative position information, the FTM distance information, and the analysis result of the CSI.

10. In Paragraph 9, The electronic device is characterized in that the above relative position information is obtained by applying the acceleration value and angular velocity value of the electronic device measured by the IMU sensor to a deep learning model.

11. In Paragraph 9, The above control unit, in the process of analyzing the above CSI, Calculate the proportion of the signal strength of the direct path among the total multipath signal strength between the AP and the electronic device, and An electronic device characterized by being configured to identify whether the channel between the AP and the electronic device is a LoS (line of sight) environment or an NLoS (non-LoS) environment based on the above calculation result.

12. In Paragraph 9, The above control unit is, Based on the above identification result, a parameter representing the noise level of the above FTM distance information is adjusted, and Based on the above-mentioned adjusted parameters, it is configured to update the Kalman gain of the extended Kalman filter, and The above estimated position coordinates are based on the above updated Kalman gain, and If the channel between the AP and the electronic device is determined to be in a LoS environment, the parameter is reduced, and An electronic device characterized in that the parameter increases when the channel between the AP and the electronic device is determined to be in an NLoS environment.

13. In Paragraph 9, The above control unit is, Obtain map information about the space where the above electronic device exists, and Based on the map information above, identify whether the estimated location coordinates are valid, and An electronic device characterized by being configured to determine the final position coordinates of the electronic device based on the above identification result.

14. In Paragraph 13, The above control unit, in the process of determining the final position coordinates, An electronic device characterized by being configured such that, if the above-mentioned estimated position coordinates are valid, the above-mentioned final position coordinates are determined by the above-mentioned estimated position coordinates.

15. In Paragraph 13, The above control unit, in the process of determining the final position coordinates, If the above estimated location coordinates are invalid, multiple location coordinates randomly assigned around the previous location coordinates of the electronic device are identified, and Identify whether multiple estimated location coordinates corresponding to each of the above multiple location coordinates are valid, and An electronic device characterized by determining the final position coordinate as the average value of at least one valid position coordinate among the plurality of estimated position coordinates.

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