Electronic device for positioning and operation method therefor

Abstract

One embodiment of the present disclosure relates to an electronic device for positioning and an operation method therefor. The operation method for an electronic device according to one embodiment of the present disclosure may comprise the operations of: receiving a plurality of ultra-wideband (UWB) signals; classifying the plurality of UWB signals into a line of sight (LOS) signal, a non-line of sight (NLOS) signal, or an unclassified signal, by using a classification model that is based on a support vector machine (SVM) algorithm trained for signal classification; and determining a position of the electronic device on the basis of at least one UWB signal classified as the LOS signal.

Description

Electronic device for position measurement and method of operation thereof

[0001] Various embodiments of the present disclosure relate to an electronic device for positioning and a method of operating the same.

[0002] Technological advancements have led to the emergence of a variety of electronic devices, including portable terminals (e.g., smartphones), portable multimedia devices, and wearable devices. New technologies are also being applied to existing electronic devices, such as cameras and home appliances. Consequently, electronic devices are now able to utilize these new technologies to provide new forms of services.

[0003] Positioning technology is one of the technologies that enables these new forms of services. Depending on the purpose and the electronic device performing the positioning, a wide variety of positioning technologies exist. Electronic devices can use positioning technology to measure the location of people or objects and provide useful location-based services (LBS) in various fields, such as living, disaster relief, safety, and logistics.

[0004] Recently, these location measurement technologies have been combined with artificial intelligence technology to enable faster and more accurate location measurement.

[0005] One embodiment of the present disclosure provides an electronic device for positioning and a method of operating the same.

[0006] The technical problems to be achieved in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present invention belongs from the description below.

[0007] An operating method of an electronic device according to one embodiment of the present disclosure may include an operation of receiving a plurality of ultra-wideband (UWB) signals, an operation of classifying the plurality of UWB signals into line of sight (LOS) signals, non-line of sight (NLOS) signals, or unclassified signals using a classification model based on a support vector machine (SVM) algorithm trained for signal classification based on information related to the signals, and an operation of determining a position of the electronic device based on at least one UWB signal classified as an LOS signal.

[0008] An electronic device according to one embodiment of the present disclosure includes: a communication circuit; a memory storing instructions; and at least one processor connected to the communication circuit and the memory and executing the instructions, wherein the at least one processor is configured to receive a plurality of ultra-wideband (UWB) signals by executing the instructions, classify the plurality of UWB signals into line of sight (LOS) signals, non-line of sight (NLOS) signals, or unclassified signals using a classification model based on a support vector machine (SVM) algorithm trained for signal classification, and determine a position of the electronic device based on at least one UWB signal classified as an LOS signal.

[0009] According to one embodiment of the present disclosure, an electronic device for positioning capable of providing high accuracy quickly and a method of operating the same can be provided.

[0010] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly understood by a person having ordinary skill in the art to which the present disclosure belongs from the description below.

[0011] FIG. 1 is a block diagram of an electronic device within a network environment according to one embodiment.

[0012] FIG. 2 is a block diagram briefly illustrating the configuration of an electronic device according to one embodiment.

[0013] FIG. 3 is a diagram illustrating a method for performing position measurement using multiple UWB anchors according to one embodiment.

[0014] FIG. 4 illustrates a method for a UWB device according to one embodiment to perform UWB ranging in the DL-TDoA manner.

[0015] FIG. 5 illustrates an example of a ranging block structure for a downlink TDoA scheme according to one embodiment.

[0016] FIG. 6 is a diagram illustrating an example of a position measurement environment of an electronic device according to one embodiment of the present disclosure.

[0017] FIG. 7 is a diagram for explaining a method of classifying a UWB signal into a line of sight (LOS) signal and a non-line of sight (NLOS) signal using a classification model based on a trained support vector machine (SVM) algorithm for signal classification according to one embodiment of the present disclosure.

[0018] FIG. 8 is a diagram illustrating a method for classifying a UWB signal into an LOS signal or an NLOS signal using a classification model based on a trained SVM algorithm for signal classification according to another embodiment of the present disclosure.

[0019] FIG. 9 is a diagram illustrating a method for classifying a UWB signal into an LOS signal or an NLOS signal using a trained convolutional neural network (CNN)-based classification model for signal classification according to an embodiment of the present disclosure.

[0020] FIG. 10 is a diagram illustrating a method for an electronic device according to one embodiment of the present disclosure to perform signal classification.

[0021] FIG. 11 is a flowchart illustrating an operation method of an electronic device for position measurement according to one embodiment of the present disclosure.

[0022] FIG. 12 is a flowchart illustrating an operation method of an electronic device for position measurement according to another embodiment of the present disclosure.

[0023] FIG. 13 is a flowchart illustrating a method of operating an electronic device based on the number of unclassified signals according to one embodiment.

[0024] FIG. 14 is a flowchart illustrating an operation method of an electronic device based on stride according to one embodiment.

[0025] An operating method of an electronic device according to one embodiment of the present disclosure may include an operation of receiving a plurality of ultra-wideband (UWB) signals, an operation of classifying the plurality of UWB signals into line of sight (LOS) signals, non-line of sight (NLOS) signals, or unclassified signals using a classification model based on a support vector machine (SVM) algorithm trained for signal classification based on information related to the signals, and an operation of determining a position of the electronic device based on at least one UWB signal classified as an LOS signal.

[0026] In one embodiment, the method further includes an operation of obtaining information related to a signal, including a power difference value representing a difference between total Rx power and first path power for each UWB signal, based on the plurality of UWB signals, and the operation of classifying the plurality of UWB signals as LOS signals, NLOS signals, or unclassified signals may include an operation of classifying the plurality of UWB signals based on the information related to the signals.

[0027] In one embodiment, the operation of determining the location of the electronic device based on at least one UWB signal classified as the LOS signal may include the operation of classifying the unclassified signal as the LOS signal or the NLOS signal using a classification model based on a convolutional neural network (CNN) trained for signal classification based on channel impulse response (CIR) data corresponding to the unclassified signal; and the operation of determining the location of the electronic device based on at least one UWB signal classified as the LOS signal.

[0028] In one embodiment, the method further includes an operation of determining whether to classify the unclassified signal as the LOS signal or the NLOS signal based on at least one of the number of unclassified signals, the time for performing positioning of the electronic device, and the positioning period of the electronic device, and when determining to classify the unclassified signal as the LOS signal or the NLOS signal, an operation of classifying the unclassified signal as the LOS signal or the NLOS signal using a classification model based on a CNN trained for the signal classification may be performed.

[0029] In one embodiment, when it is determined not to classify the unclassified signal as the LOS signal or the NLOS signal, the position of the electronic device can be determined based only on at least one UWB signal classified as the LOS signal using a classification model based on a support vector machine (SVM) algorithm trained for signal classification.

[0030] In one embodiment, the positioning cycle of the electronic device may be determined based on a distance between the electronic device and an external electronic device.

[0031] In one embodiment, the information related to the signal includes a mean excess delay value, and the operation of obtaining the information related to the signal may include an operation of obtaining information related to a signal including the mean excess delay value for each UWB signal based on the plurality of UWB signals.

[0032] In one embodiment, the operation of classifying each of the UWB signals as an LOS signal, an NLOS signal, or an unclassified signal may include: an operation of determining a hyperplane for classifying each of the UWB signals as an LOS signal and an NLOS signal using a classification model based on an SVM algorithm trained for the signal classification; an operation of determining a gray zone having a plane having a predetermined distance on each side of the hyperplane as a boundary; an operation of determining a UWB signal included in one side of the hyperplane as an LOS signal, a UWB signal included in the other side of the hyperplane as an NLOS signal, and a operation of determining a UWB signal included in the gray zone as an unclassified signal.

[0033] In one embodiment, each of the UWB signals may include a UWB message for Downlink Time Difference of Arrival (DL-TDoA) positioning.

[0034] An electronic device according to one embodiment of the present disclosure includes: a communication circuit; a memory storing instructions; and at least one processor connected to the communication circuit and the memory and executing the instructions, wherein the at least one processor is configured to receive a plurality of ultra-wideband (UWB) signals by executing the instructions, classify the plurality of UWB signals into line of sight (LOS) signals, non-line of sight (NLOS) signals, or unclassified signals using a classification model based on a support vector machine (SVM) algorithm trained for signal classification, and determine a position of the electronic device based on at least one UWB signal classified as an LOS signal.

[0035] In one embodiment, the at least one processor may be configured to obtain signal-related information including a power difference value representing a difference between total Rx power and first path power for each UWB signal, based on the plurality of UWB signals, by executing the instructions.

[0036] In one embodiment, the at least one processor may be configured to, by executing the instructions, classify the unclassified signal into the LOS signal or the NLOS signal using a classification model based on a convolutional neural network (CNN) trained for signal classification based on channel impulse response (CIR) data corresponding to the unclassified signal, and determine the location of the electronic device based on at least one UWB signal classified as the LOS signal.

[0037] In one embodiment, the at least one processor may be configured to determine whether to classify the unclassified signal as the LOS signal or the NLOS signal based on at least one of the number of the unclassified signals, the time for performing positioning of the electronic device, and the positioning period of the electronic device by executing the instructions, and, when determining to classify the unclassified signal as the LOS signal or the NLOS signal, classify the unclassified signal as the LOS signal or the NLOS signal using a classification model based on a CNN trained for the signal classification.

[0038] In one embodiment, the at least one processor may be configured to determine the position of the electronic device based only on at least one UWB signal classified as the LOS signal using a classification model based on a support vector machine (SVM) algorithm trained for signal classification, when the at least one processor determines not to classify the unclassified signal as the LOS signal or the NLOS signal by executing the instructions.

[0039] In one embodiment, the positioning cycle of the electronic device may be determined based on a distance between the electronic device and an external electronic device.

[0040] In one embodiment, the information associated with the signal includes a mean excess delay value, and the at least one processor may be configured to obtain, by executing the instructions, information associated with the signal including the mean excess delay value for each UWB signal based on the plurality of UWB signals.

[0041] In one embodiment, the at least one processor may be configured to determine a hyperplane for classifying each UWB signal into an LOS signal and an NLOS signal by using a classification model based on an SVM algorithm trained for signal classification by executing the instructions, determine a gray zone having a plane having a predetermined distance on each side of the hyperplane as a boundary, determine a UWB signal included in one side of the hyperplane as an LOS signal, determine a UWB signal included in the other side of the hyperplane as an NLOS signal, and determine a UWB signal included in the gray zone as an unclassified signal.

[0042] In one embodiment, each of the UWB signals may include a UWB message for Downlink Time Difference of Arrival (DL-TDoA) positioning.

[0043] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0044] In describing the embodiments, descriptions of technical details that are well known in the technical field to which the present disclosure pertains and are not directly related to the present disclosure will be omitted. This is to convey the gist of the present disclosure more clearly without obscuring it by omitting unnecessary explanations.

[0045] For the same reason, some components in the attached drawings are exaggerated, omitted, or schematically depicted. Furthermore, the dimensions of each component do not entirely reflect its actual size. Identical or corresponding components in each drawing are assigned the same reference numbers.

[0046] The advantages and features of the present disclosure, and methods for achieving them, will become clearer with reference to the embodiments described below in detail together with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms. These embodiments are provided only to ensure that the disclosure of the present disclosure is 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. Like reference numerals designate like elements throughout the specification. In addition, when describing the present disclosure, if a specific description of a related function or configuration is determined to unnecessarily obscure the gist of the present disclosure, the detailed description thereof will be omitted. In addition, the terms described below are terms defined in consideration of the functions of the present disclosure, and may vary depending on the intention or custom of the user or operator. Therefore, the definitions should be made based on the contents throughout the specification.

[0047] At this time, it will be understood that each block of the processing flowchart drawings and combinations of the flowchart drawings can be performed by computer program instructions. These computer program instructions can be installed in a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, so that the instructions executed by the processor of the computer or other programmable data processing equipment create a means for performing the functions described in the flowchart block(s). These computer program instructions can also be stored in a computer-available or computer-readable memory that can direct a computer or other programmable data processing equipment to implement the functions in a specific manner, so that the instructions stored in the computer-available or computer-readable memory can also produce a manufactured item that includes an instruction means for performing the functions described in the flowchart block(s). Since the computer program instructions can also be installed on a computer or other programmable data processing equipment, a series of operations are performed on the computer or other programmable data processing equipment to create a computer-executable process, and the instructions that cause the computer or other programmable data processing equipment to perform the operations for performing the functions described in the flowchart block(s) can also provide operations for performing the functions described in the flowchart block(s).

[0048] Additionally, each block may represent a module, segment, or portion of code that contains one or more executable instructions for performing a specific logical function(s). It should also be noted that in some alternative implementation examples, the functions described in the blocks may occur out of order. For example, two blocks depicted in succession may actually be executed substantially concurrently, or the blocks may sometimes be executed in reverse order, depending on their respective functions.

[0049] Here, the term '~ part' used in this embodiment means software or hardware components such as FPGA (Field Programmable Gate Array) or ASIC (Application Specific Integrated Circuit), and the '~ part' performs certain roles. However, the '~ part' is not limited to software or hardware. The '~ part' may be configured to be on an addressable storage medium or may be configured to play one or more processors. Therefore, as an example, the '~ part' includes components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, 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'. Additionally, the components and '~parts' may be implemented to activate one or more CPUs within a device or secure multimedia card. In addition, in an embodiment, the '~parts' may include one or more processors.

[0050] The term 'terminal' or 'device' used herein may refer to a mobile station (MS), user equipment (UE), user terminal (UT), wireless terminal, access terminal (AT), terminal, subscriber unit (SS), 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 a cellular telephone, a smart phone having a wireless communication function, a personal digital assistant (PDA) having a wireless communication function, a wireless modem, a portable computer having a wireless communication function, a photographing device such as a digital camera having a wireless communication function, a gaming device having a wireless communication function, a music storage and playback home appliance having a wireless communication function, an internet home appliance capable of wireless internet access and browsing, as well as portable units or terminals integrating combinations of such functions. In addition, the terminal may include, but is not limited to, an M2M (Machine to Machine) terminal, an MTC (Machine Type Communication) terminal / device. In this specification, a terminal may also be referred to as an electronic device or simply a device.

[0051] FIG. 1 is a block diagram of an electronic device within a network environment according to one embodiment.

[0052] FIG. 1 is a block diagram of an electronic device (101) within a network environment (100), according to one embodiment.

[0053] Referring to FIG. 1, in a network environment (100), an electronic device (101) may communicate with an electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with at least one of an electronic device (104) or a server (108) via a second network (199) (e.g., a long-range wireless communication network). In one embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include a processor (120), a memory (130), an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a connection terminal (178), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)).

[0054] The processor (120) may, for example, execute software (e.g., a program (140)) to control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) and perform various data processing or calculations. According to one embodiment, as at least a part of the data processing or calculation, the processor (120) may store a command or data received from another component (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the command or data stored in the volatile memory (132), and store the resulting data in a non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or a secondary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor)) that may operate independently or together therewith. For example, if the electronic device (101) includes a main processor (121) and a secondary processor (123), the secondary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a specified function. The secondary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0055] The auxiliary processor (123) may control at least a portion of functions or states associated with at least one component (e.g., a display module (160), a sensor module (176), or a communication module (190)) of the electronic device (101), for example, on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). In one embodiment, the auxiliary processor (123) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, in the electronic device (101) itself where the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (108)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. For example, the learning algorithm can include a support vector machine (SVM) algorithm. The artificial intelligence model can include multiple artificial neural network layers.The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0056] The memory (130) can store various data used by at least one component (e.g., processor (120) or sensor module (176)) of the electronic device (101). The data can include, for example, software (e.g., program (140)) and input data or output data for commands related thereto. The memory (130) can include volatile memory (132) or non-volatile memory (134).

[0057] The program (140) may be stored as software in the memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0058] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0059] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. In one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0060] The display module (160) can visually provide information to an external party (e.g., a user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0061] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), output sound through the sound output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0062] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0063] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). In one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0064] The connection terminal (178) may include a connector through which the electronic device (101) may be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0065] The haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. According to one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0066] The camera module (180) can capture still images and videos. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0067] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

[0068] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0069] The communication module (190) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (194) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (104) via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0070] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) can support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC realization.

[0071] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). In one embodiment, the antenna module (197) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). In one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (198) or the second network (199), may be selected from the plurality of antennas, for example, by the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device via the at least one selected antenna. In some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197). In one embodiment, the antenna module (197) may form a mmWave antenna module. In one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high-frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high-frequency band.

[0072] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0073] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0074] Electronic devices according to various embodiments disclosed in the present disclosure may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to embodiments of the present disclosure are not limited to the aforementioned devices.

[0075] The various embodiments of the present disclosure and the terminology used therein are not intended to limit the technical features described in the present disclosure to specific embodiments, but should be understood to include various modifications, equivalents, or substitutes of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise. In the present disclosure, each of the phrases "A or B," "at least one of A and B," "at least one of A or B," "A, B, or C," "at least one of A, B, and C," and "at least one of A, B, or C" can include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0076] The term "module" used in various embodiments of the present disclosure may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0077] Various embodiments of the present disclosure may be implemented as software (e.g., a program (#40)) including one or more commands stored in a storage medium (e.g., an internal memory (#36) or an external memory (#38)) readable by a machine (e.g., an electronic device (#01)). For example, a processor (e.g., a processor (#20)) of the machine (e.g., an electronic device (#01)) may call at least one command among the one or more commands stored from the storage medium and execute it. This enables the machine to operate to perform at least one function according to the at least one command called. The one or more commands may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0078] According to one embodiment, the method according to various embodiments disclosed in the present disclosure may be provided as a computer program product. The computer program product may be traded between sellers and buyers as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)) or may be provided through an application store (e.g., Play Store). TM ) or directly between two user devices (e.g., smart phones), online distribution (e.g., downloading or uploading). In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily created in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0079] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0080] FIG. 2 is a block diagram briefly illustrating the configuration of an electronic device according to one embodiment.

[0081] Referring to FIG. 2, an electronic device (200) according to an embodiment of the present disclosure may include a communication circuit (210), a memory (220), and a processor (230). However, the configuration of the electronic device (200) is not limited thereto, and may include only some of the components described above in FIG. 2, or may further include at least one or more other components (e.g., an antenna module or a display module) in addition to the components described above. For example, the electronic device (200) of FIG. 2 may correspond to the electronic device (101) of FIG. 1. Accordingly, the communication circuit (210) may correspond to the communication module (190) or the wireless communication module (192) of FIG. 1. Additionally, the memory (220) and the processor (230) may correspond to the memory (130) and the processor (120) of FIG. 1, and if the electronic device (200) further includes other components, the other components may also correspond to the components of FIG. 1.

[0082] The communication circuit (210) can support short-range wireless communication between the electronic device (200) and an external electronic device. For example, the communication circuit (210) can transmit and receive signals and / or data using a frequency band supported by wireless communication according to a prescribed wireless communication protocol with the external electronic device. In one embodiment, the communication circuit (210) can communicate with the external electronic device via a short-range wireless communication network such as ultra-wideband (UWB), Bluetooth, low-power Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA). However, the short-range wireless communication network is not limited to the examples described above, and various types of short-range wireless communication networks can be used.

[0083] In one embodiment, the communication circuit (210) may include an UWB module. UWB is a wireless communication technology that uses a bandwidth of 500 MHz or more according to the IEEE 802.15.4 standard, and can be used to measure accurate distances and angles due to its characteristic of using a wide frequency band. UWB may also refer to the band itself to which UWB communication is applied. The IEEE 802.15.4z standard has enhanced security and increased usability by introducing a scrambled timestamp sequence (STS) function that prevents external attackers from accessing or manipulating UWB communication at the physical layer. Using UWB technology, safe and accurate distance measurement between devices is possible, and thereby, the relative position of the electronic device (200) can be determined or the absolute position of the electronic device (200) can be determined based on the distance from fixed devices.

[0084] The memory (220) stores instructions for the operation of the electronic device (200). At least one instruction may correspond to the program (140) of FIG. 1. The instructions may be executed by the processor (230), and by executing the instructions by the processor (230), the electronic device (200) may perform operations according to an embodiment of the present disclosure.

[0085] The processor (230) can control at least one other component of the electronic device (200) and perform various data processing or calculations. For example, the processor (230) can execute instructions for controlling the communication circuit (210). By executing the instructions for controlling the communication circuit (210), the processor (230) can perform short-range wireless communication between external electronic devices. The processor (230) can be composed of at least one processor.

[0086] In one embodiment, the processor (230) may perform position measurement by executing instructions. More specifically, the processor (230) may be configured to receive a plurality of ultra-wideband (UWB) signals by executing instructions, obtain signal-related information including a power difference value representing a difference between total Rx power and first path power for each UWB signal based on the plurality of UWB signals, classify each of the UWB signals into a line of sight (LOS) signal, a non-line of sight (NLOS) signal, or an unclassified signal based on the signal-related information using a classification model based on a support vector machine (SVM) algorithm trained for signal classification, and determine a position of the electronic device based on at least one UWB signal classified as an LOS signal. At this time, the information related to the signal includes a mean excess delay value, and the processor (230) may be configured to acquire information related to the signal including the mean excess delay value for each UWB signal based on a plurality of UWB signals. In addition, each UWB signal may include a UWB message for DL-TDoA (Downlink Time Difference of Arrival) positioning.

[0087] In one embodiment, the processor (230) may be configured to classify the unclassified signal into the LOS signal or the NLOS signal using a classification model based on a convolutional neural network (CNN) trained for signal classification based on channel impulse response (CIR) data corresponding to the unclassified signal, and to determine the location of the electronic device based on at least one UWB signal classified as the LOS signal.

[0088] In one embodiment, the processor (230) may be configured to determine whether to classify the unclassified signal as the LOS signal or the NLOS signal based on at least one of the number of unclassified signals, the time for performing positioning of the electronic device, and the positioning period of the electronic device, and, when determining to classify the unclassified signal as the LOS signal or the NLOS signal, classify the unclassified signal as the LOS signal or the NLOS signal using a CNN-based classification model trained for the signal classification. In this case, the positioning period of the electronic device (200) may be determined based on the distance between the electronic device (200) and the external electronic device.

[0089] In one embodiment, the processor (230) may be configured to determine the position of the electronic device based only on at least one UWB signal classified as the LOS signal using a classification model based on a support vector machine (SVM) algorithm trained for signal classification, when determining not to classify the unclassified signal as the LOS signal or the NLOS signal.

[0090] In one embodiment, the processor (230) may be configured to determine a hyperplane for classifying each UWB signal into an LOS signal and an NLOS signal using a classification model based on an SVM algorithm trained for the signal classification, determine a gray zone having a plane having a predetermined distance on each side of the hyperplane as a boundary, determine a UWB signal included in one side of the hyperplane as an LOS signal, determine a UWB signal included in the other side of the hyperplane as an NLOS signal, and determine a UWB signal included in the gray zone as an unclassified signal.

[0091] Specific actions will be explained in detail below.

[0092] According to one embodiment, the operations described as being performed by the electronic device (200) in the present disclosure may be understood to be performed by the processor (230).

[0093] FIG. 3 is a diagram illustrating a method for performing position measurement using multiple UWB anchors according to one embodiment.

[0094] The electronic device (200) can communicate with a plurality of UWB anchors (311, 312, 313, 314) using a communication circuit (210). The electronic device (200) can obtain the distance and / or direction to the plurality of UWB anchors (311, 312, 313, 314) using a signal received from at least one antenna. At this time, the electronic device (200) may first perform a connection with the plurality of UWB anchors (311, 312, 313, 314) and then perform a measurement operation for the distance and / or direction.

[0095] A plurality of UWB anchors (311, 312, 313, 314) include UWB modules and support UWB communication, thereby enabling communication with the electronic device (200). In one embodiment, the plurality of UWB anchors (311, 312, 313, 314) may include components for communicating with the electronic device (200), for example, a processor and an antenna module. However, this is only an example of the components of the plurality of UWB anchors (311, 312, 313, 314), and the configuration of the target electronic device (300) is not limited thereto, and may include only some of the above-described components, or may further include at least one or more other components in addition to the above-described components. In addition, although four UWB anchors are described as an example in FIG. 3, the present invention is not limited thereto, and a greater or lesser number of UWB anchors may exist.

[0096] In one embodiment, the electronic device (200) can measure the distance to a UWB anchor (311, 312, 313, 314). For example, the electronic device (200) can measure the distance from the UWB anchor (311, 312, 313, 314) based on the strength of a signal received from the UWB anchor (311, 312, 313, 314). Alternatively, the electronic device (200) can also measure the distance from the target electronic device (300) based on the transmission time of packets exchanged with the UWB anchor (311, 312, 313, 314).

[0097] In one embodiment, the electronic device (200) may measure the distance to the target electronic device (300) using a method such as single side two way ranging (SS-TWR) or double side two way ranging (DS-TWR), or may measure the distance to the UWB anchors (311, 312, 313, 314) using a method such as uplink time difference of arrival (UL-TDoA) or downlink time difference of arrival (DL-TDoA).

[0098] Referring to FIG. 3, a plurality of UWB anchors (311, 312, 313, 314) are illustrated that exchange DTMs (Downlink TDoA Messages) with each other to provide a positioning service to the electronic device (200). In general, the electronic device (200) can obtain two-dimensional coordinates of the electronic device (200) by measuring distances to three locations, and when measuring distances to four or more locations, can obtain three-dimensional coordinates of the electronic device (200). When performing position measurement using a plurality of UWB anchors as shown in FIG. 3, the electronic device (200) can obtain two-dimensional coordinates of the electronic device (200) by measuring distances to three UWB anchors, and can obtain three-dimensional coordinates of the electronic device (200) by measuring distances to four UWB anchors. For example, the electronic device (200) can determine the absolute position (e.g., absolute coordinates) or relative position (e.g., relative coordinates) of the electronic device (200) through trilateration using multiple UWB anchors. However, the present invention is not limited thereto, and the electronic device (200) can determine the position of the electronic device (200) in various ways using multiple UWB anchors.

[0099] In Fig. 3, anchor #1 (311) can act as an initiator anchor, and the remaining anchors, i.e., anchor #2 (312), anchor #3 (313), and anchor #4 (314), can act as responder anchors. The electronic device (200) can receive DTMs exchanged between UWB anchors (311, 312, 313, 314) and, based on the received DTMs, calculate TDoA with multiple UWB anchors. For example, the UWB tag (420) can calculate, based on the received DTMs, TDoA1, which is a TDoA between anchor #1 (311), which is an initiator anchor, and anchor #2 (312), which is a responder anchor, TDoA2, which is a TDoA between anchor #1 (311), which is an initiator anchor, and anchor #3 (313), which is a responder anchor, and TDoA3, which is a TDoA between anchor #1 (311), which is an initiator anchor, and anchor #4 (314), which is a responder anchor. The electronic device (200) can determine (or estimate) its own location using the calculated TDoAs.

[0100] According to one embodiment, when using UWB communication, there is an advantage in that the delay time is short and the error is very small, so the distance can be measured precisely.

[0101] FIG. 4 illustrates a method for a UWB device according to one embodiment to perform UWB ranging in the DL-TDoA manner.

[0102] DL-TDoA may be referred to as Downlink Time Difference of Arrival, reverse TDoA, etc., and the basic operation may be that the electronic device (200) overhears (or receives) the message of the UWB anchor when multiple UWB anchors broadcast messages or exchange messages with each other. DL-TDoA, like Uplink TDoA, may be classified as a type of one way ranging. The electronic device (200) performing the DL-TDoA operation may overhear the messages transmitted by two UWB anchors and calculate the TDoA proportional to the difference in distance between each UWB anchor and the electronic device (200). The electronic device (200) may use the TDoA with multiple UWB anchors to calculate the relative distance to the UWB anchor and use it for positioning. The operation of a UWB anchor for DL-TDoA may be similar to, for example, DS-TWR as defined in IEEE 802.15.4z, and may further include other useful timing information to enable the electronic device (200) to calculate TDoA. DL-TDoA may be referred to as DL-TDoA localization.

[0103] Referring to FIG. 4, anchor #1 (401) can operate as an initiator anchor, and the remaining anchors, i.e., anchor #2 (402), ..., anchor #n (n), can operate as responder anchors. For example, as illustrated in FIG. 3, one initiator anchor (311) and three responder anchors (312, 313, 314) can operate as UWB anchors.

[0104] In operation 401, the initiator anchor, anchor #1 (401), may initiate a DL-TDoA round by transmitting or broadcasting a Poll DTM. Here, the Poll DTM may include scheduling information (e.g., a ranging slot index) for each responder anchor to transmit a Response DTM in an assigned ranging slot. The Poll DTM may further include transmission time information (a transmission timestamp) indicating a time at which the Poll DTM is transmitted. The Poll DTM may further include a round index of the current ranging round in which the Poll DTM is transmitted and a block index of the current ranging block. The Poll DTM may further include location information of the UWB anchor transmitting the Poll DTM.

[0105] In one embodiment, the Responder anchors that received the Poll DTM, i.e., anchor #2 (402), ..., anchor #n (n), can check the transmission time of the Response DTM and / or the slot (ranging slot index) for transmitting the Response DTM by referring to the scheduling information included in the Poll DTM.

[0106] In operations 420, ..., 430, the Responder anchors that received the Poll DTM, i.e., anchor #2 (402), ..., anchor #n (n), can transmit or broadcast the Response DTM with reference to the scheduling information included in the Poll DTM. More specifically, anchor #2 (402), ..., anchor #n (n), can transmit or broadcast the Response DTM at the transmission time point of the configured Response DTM and / or in the slot (ranging slot index) for transmitting the Response DTM with reference to the scheduling information included in the Poll DTM. The Response DTM may include response time information indicating the time between the time of receiving the Poll DTM and the time of transmitting the Response DTM. In addition, the Response DTM may also include a transmission time (transmission timestamp) indicating the time of transmitting the Response DTM. A Response DTM may further include a round index of the current ranging round in which the Response DTM is transmitted and a block index of the current ranging block. Each Response DTM may further include location information of the UWB anchor transmitting the Response DTM.

[0107] In operation 440, the Initiator anchor, i.e., anchor #1 (401), which has received the Response DTM, may additionally transmit or broadcast the Final DTM. Here, the Final DTM may include a response time indicating the time between the time each Response DTM is received and the time the Final DTM is transmitted. That is, the Final DTM may include a list of response times, and the list may include response times indicating the time between the time each Response DTM is received and the time the Final DTM is transmitted. The Final DTM may further include a transmission time (transmission timestamp) indicating the time the Final DTM is transmitted. The Final DTM may further include a round index of the current ranging round in which the Final DTM is transmitted and a block index of the current ranging block.

[0108] In operation 450, the electronic device (200) can receive (or overhear) the Poll DTM and Final DTM transmitted or broadcasted by the Initiator anchor, i.e., Anchor #1 (401), and the Response DTM transmitted or broadcasted by each of the responder anchors, i.e., Anchor #2 (402), ..., Anchor #n (n). The electronic device (200) can obtain information included in each DTM message and reception time information (reception timestamp) indicating the time at which each DTM message was received, and can calculate TDoA values ​​using the obtained information. Thereafter, the electronic device (200) can determine (or estimate) the location of the electronic device (200) using the calculated TDoA values. For example, the electronic device (200) can determine the location of the electronic device (200) by calculating distances to a plurality of anchors using TDoA. Through this process, the electronic device (200) can perform location measurement without exposing its own location.

[0109] In one embodiment, each DTM may be included in a MAC frame and transmitted via a UWB signal (or PHY packet).

[0110] FIG. 5 illustrates an example of a ranging block structure for a downlink TDoA scheme according to one embodiment.

[0111] The ranging block structure illustrated in FIG. 5 can be used, for example, to perform UWB ranging as described in FIG. 4. Referring to FIG. 5, the ranging block may include multiple ranging rounds. In one embodiment, a ranging round may include multiple ranging slots. A ranging round may include multiple ranging slots allocated for each ranging message transmitted by multiple anchors. For example, as illustrated in FIG. 3, when there is one Initiator anchor and three Responder anchors, the ranging slot may include a first ranging slot allocated for transmitting / receiving a Poll message of the Initiator anchor, a second ranging slot allocated for transmitting / receiving a response message of the first Responder anchor, a third ranging slot allocated for transmitting / receiving a response message of the second Responder anchor, a fourth ranging slot allocated for transmitting / receiving a response message of the third Responder anchor, and a fifth ranging slot allocated for transmitting / receiving a final message of the Initiator anchor. In addition, a ranging slot index may be assigned to each ranging slot. For example, the first ranging slot may be assigned a ranging slot index of 0, the second ranging slot may be assigned a ranging slot index of 1, the third ranging slot may be assigned a ranging slot index of 2, the fourth ranging slot may be assigned a ranging slot index of 3, and the fifth ranging slot may be assigned a ranging slot index of 4. However, this is merely an example, and the present invention is not limited thereto, and ranging slot indices may be assigned in various ways.

[0112] Through a ranging block structure such as that illustrated in FIG. 5, multiple anchors can exchange ranging messages in a single cycle through their own ranging rounds within a single ranging block, and the electronic device (200) can receive (or overhear) these ranging messages to calculate its own location. This operation can be repeated for each ranging block. Through this, the location of the electronic device (200) can be updated at the interval of the ranging round or the interval of the ranging block.

[0113] When performing position measurement using short-range wireless communication such as UWB, in a non-line of sight (NLOS) environment where obstacles exist in the signal path, signal reflection or signal strength attenuation and absorption may cause a decrease in distance measurement performance and positioning performance.

[0114] One embodiment of the present disclosure provides a method for quickly classifying UWB signals received (or overheard) by an electronic device (200) into LOS (line of sight) signals and NLOS (non-line of sight) signals, and performing position measurement of the electronic device (200) using the LOS signals. According to one embodiment, the electronic device (200) can perform position measurement more quickly and with higher accuracy.

[0115] A method for measuring the position of an electronic device (200) according to one embodiment of the present disclosure can be applied to various environments. For example, one embodiment of the present disclosure can be applied to a tagless gate, a digital car key, or a point of service (POS).

[0116] FIG. 6 is a diagram illustrating an example of a position measurement environment of an electronic device according to one embodiment of the present disclosure.

[0117] In FIG. 6, it is assumed that an electronic device (200) approaches or is located in a gate access area where a gate and six UWB anchors (611, 612, 613, 614, 615, 616) are located. However, this is merely an example, and the embodiments of the present disclosure are not limited thereto, and may be applied to other situations, and a different number of UWB anchors may be used to determine the location of the electronic device (200).

[0118] Referring to FIG. 6, the electronic device (200) can receive UWB signals from each of six UWB anchors (611, 612, 613, 614, 615, 616). For example, the electronic device (200) can receive a UWB signal including a Poll DTM and / or a UWB signal including a Final DTM from the Initiator anchor. In addition, the electronic device (200) can receive a UWB signal including a Response DTM from each responder anchor.

[0119] In one embodiment, the electronic device (200) can obtain timestamp information (e.g., transmission timestamp and / or reception timestamp) from UWB signals received from six UWB anchors (611, 612, 613, 614, 615, 616), respectively. Based on the obtained timestamp information, the electronic device (200) can measure the distance to each UWB anchor (611, 612, 613, 614, 615, 616) using DL-TDoA. In the case of 3D DL-TDoA positioning, since UWB signals received from at least four UWB anchors are required for DL-TDoA positioning, the electronic device (200) can obtain timestamp information from the four UWB signals to determine (or estimate) its own position.

[0120] FIG. 7 is a diagram for explaining a method of classifying UWB signals into line of sight (LOS) signals and non-line of sight (NLOS) signals using a classification model based on a trained support vector machine (SVM) algorithm for signal classification according to one embodiment of the present disclosure.

[0121] The support vector machine (SVM) algorithm is a field of machine learning. It performs supervised learning for pattern recognition and data analysis, and can be used for classification, regression analysis, and outlier detection. The SVM algorithm can learn using a set of training examples, or data sets, each labeled as belonging to one of two categories. The SVM algorithm can be used to create a non-probabilistic binary linear classifier model that learns from the data set and determines which category new data belongs to. The generated classification model can determine (or select) boundaries in a mapping space and map input data to one or the other of the boundaries. In other words, the SVM algorithm determines which side of the boundary data belongs to, making it suitable for complex classification and for small to medium-sized data sets. The SVM algorithm can be used for nonlinear classification as well as linear classification. Nonlinear classification requires mapping the given data into a high-dimensional feature space. At this point, the kernel trick can be used. The support vector machine (SVM) algorithm has the advantage of being fast and computationally efficient.

[0122] In one embodiment, the electronic device (200) can receive a UWB signal and obtain channel information such as CIR data, First path amplitude, Preamble accumulation count (N), and CIR power (C).

[0123] CIR (channel impulse response) data is data related to the impulse response of the UWB channel through which the UWB signal is transmitted, and can be obtained using the correlation value between the impulse function and the received UWB signal.

[0124] The first path amplitude may include the amplitude of a multipath component signal detected along the first path. In one embodiment, the amplitudes of the signals received first, second, and third among the multipath component signals may be referred to as first path amplitude 1, 2, and 3 (F1, F2, F3).

[0125] Preamble accumulation count (N) refers to the number of accumulated preamble symbols.

[0126] CIR power (C) includes the CIR power. CIR power (C) may include the amplitude of the CIR.

[0127] The electronic device (200) can generate total Rx power and first path power based on the acquired channel information.

[0128] The total Rx power can be calculated as follows:

[0129]

[0130] Additionally, the first path power can be calculated as follows.

[0131]

[0132] In Fig. 7, the power difference value representing the difference between the total Rx power and the first path power can be used as an input value of a classification model based on a support vector machine (SVM) algorithm trained for signal classification. In this case, since one value is used as an input, the result value can also be displayed in one dimension, i.e., as a line.

[0133] Referring to FIG. 7, a hyperplane (710), which is a boundary for classifying LOS signals and NLOS signals, is illustrated. In one embodiment, a UWB signal included on the left side of the hyperplane (710) may be classified as an LOS signal, and a UWB signal included on the right side of the hyperplane (710) may be classified as an NLOS signal.

[0134] In one embodiment, an area near a hyperplane (710) where the probability of being an LOS signal or an NLOS signal is around 50% may be set as a gray zone. More specifically, the gray zone may be an area bordered by planes each having a predetermined distance on both sides of the hyperplane. In one embodiment, a UWB signal included in the gray zone is a signal that cannot be clearly classified as an LOS signal or an NLOS signal even using an SVM algorithm, and may be determined as an unclassified signal. Such an unclassified signal may be abandoned or a process of additionally classifying it as an LOS signal or an NLOS signal with high precision may be performed.

[0135] FIG. 8 is a diagram illustrating a method for classifying a UWB signal into an LOS signal or an NLOS signal using a classification model based on a trained SVM algorithm for signal classification according to another embodiment of the present disclosure.

[0136] As described above, the electronic device (200) can receive a UWB signal, obtain channel information such as CIR data, first path amplitude, preamble accumulation count (N), and CIR power (C), and generate total Rx power and first path power based on the obtained channel information. Furthermore, the electronic device (200) can obtain a mean excess delay value based on the channel information.

[0137] Here, mean excess delay means the average delay time of all paths.

[0138] In Fig. 8, the power difference value and the mean excess delay value, which represent the difference between the total Rx power and the first path power, can be used as input values ​​for a classification model based on a support vector machine (SVM) algorithm trained for signal classification. In this case, since two values ​​are used as inputs, the result value can also be expressed in two dimensions, i.e., as a plane. Unlike Fig. 7, Fig. 8 uses two values ​​as inputs to obtain a two-dimensional result value, which can produce more stable results.

[0139] Referring to FIG. 8, a hyperplane (810), which is a boundary for classifying LOS signals and NLOS signals, is illustrated. In one embodiment, a UWB signal included on the left side of the hyperplane (810) may be classified as an LOS signal, and a UWB signal included on the right side of the hyperplane (810) may be classified as an NLOS signal.

[0140] In one embodiment, an area near a hyperplane (810) where the probability of being an LOS signal or an NLOS signal is around 50% may be set as a gray zone. More specifically, the gray zone may be an area bordered by planes each having a predetermined distance on both sides of the hyperplane. In one embodiment, a UWB signal included in the gray zone is a signal that cannot be clearly classified as an LOS signal or an NLOS signal even using an SVM algorithm, and may be determined as an unclassified signal. Such an unclassified signal may be abandoned or a process of additionally classifying it as an LOS signal or an NLOS signal with high precision may be performed.

[0141] FIG. 9 is a diagram illustrating a method for classifying a UWB signal into an LOS signal or an NLOS signal using a trained convolutional neural network (CNN)-based classification model for signal classification according to an embodiment of the present disclosure.

[0142] The impulse response (CIR) of a UWB channel can be obtained by receiving a UWB signal. For example, when the electronic device (200) receives a UWB signal for DL-TDoA, the electronic device (200) can obtain CIR data based on the received signal. More specifically, the electronic device (200) can obtain CIR data by using the correlation value between the impulse function and the received UWB signal. For example, the electronic device (200) can obtain CIR data corresponding to each UWB signal including a DTM message received by the electronic device (200) during a DL-TDoA process.

[0143] The electronic device (200) can obtain multiple CIR values ​​based on the received UWB signal, and a peak of amplitude can occur after a specific CIR index.

[0144] In one embodiment, an effective CIR (eCIR) may refer to a CIR having a significant value among CIRs. That is, an effective CIR may be a CIR acquired or measured by an actual signal, not noise. The electronic device (200) may process CIR values ​​before a peak occurs as noise and process n CIR values ​​from the peak as eCIR. Alternatively, the electronic device (200) may determine n CIRs as eCIRs starting from a CIR index obtained by subtracting a margin value from a CIR index at which a peak occurs. For example, when a peak occurs at CIR index 750, the margin value is 5 (margin = 5), and the n value is set to 200 (n = 200), the electronic device may identify CIRs from CIR index 745 to CIR index 945 as eCIRs.

[0145] According to one embodiment, when eCIRs are classified from the acquired CIRs and only the eCIRs are used as input data for a CNN model for classifying LOS or NLOS signals, the accuracy of classification can be improved by removing inaccurate noise. In addition, when eCIRs are classified from the acquired CIRs and used, the processing efficiency of the electronic device (200) can be improved by optimizing the input matrix.

[0146] Referring to FIG. 9, n (e.g., 200) eCIR values ​​can be used to classify LOS signals or NLOS signals.

[0147] Meanwhile, the CIR values ​​(eCIR values) of the LOS signal and the NLOS signal may have different characteristics.

[0148] For example, for a LOS signal, it may have at least one of the following characteristics:

[0149] - The peak value of CIR is higher than the peak value of the NLOS signal.

[0150] - After the peak value, the CIR value quickly drops to the noise level.

[0151] - Fewer than one or two peaks

[0152] For example, for an NLOS signal, it may have at least one of the following characteristics:

[0153] - The peak value of CIR is lower than the peak value of the LOS signal.

[0154] - It takes a long time for the CIR value to drop to the noise level after the peak value.

[0155] - Multiple peak values ​​appear due to multipath signals.

[0156] Due to the difference in the characteristics of the CIR values ​​of the LOS signal and the NLOS signal, it is possible to classify whether the UWB signal is an LOS signal or an NLOS signal using the measured CIR data.

[0157] In one embodiment, a classification model based on a convolutional neural network (CNN) trained for signal classification can classify a received UWB signal as an LOS signal or an NLOS signal based on the CIR data described above. More specifically, the CNN-based classification model can be generated by using normalized CIR data or non-normalized CIR data as input data for learning, calculating the probability that a UWB signal is an LOS signal and the probability that the UWB signal is an NLOS signal based on parameters used for signal classification, comparing the resulting value with the probability that the UWB signal is an actual LOS signal and the probability that the UWB signal is an NLOS signal, calculating an error, and updating the parameters in a way that reduces the calculated error. The updated parameters can be used for processing the next input data, and by repeating the above process for all input data sets for learning, the accuracy of the CNN-based classification model can be improved. The CNN-based classification model requires a large amount of input data and thus has a lower processing speed than the SVM-based classification model, but has the advantage of high classification accuracy.

[0158] FIG. 10 is a diagram illustrating a method for an electronic device according to one embodiment of the present disclosure to perform signal classification.

[0159] Referring to FIG. 10, the electronic device (200) can receive at least one UWB signal for DL-TDoA. In one embodiment, the electronic device (200) can receive the UWB signal using the communication circuit (210). The electronic device (200) stores the received UWB signal in a buffer.

[0160] Thereafter, the electronic device (200) can perform channel diagnostics based on the received UWB signal and obtain information related to the signal. More specifically, the electronic device (200) can obtain channel information and obtain information related to the signal based on the obtained channel information. In one embodiment, the channel information may include CIR data, First path amplitude, Preamble accumulation count (N), CIR power (C), etc. In addition, the information related to the signal may include total Rx power generated based on the obtained channel information, first path power, and a power difference value indicating a difference between the total Rx power and the first path power. Furthermore, the information related to the signal may further include a mean excess delay value.

[0161] The electronic device (200) can classify the received UWB signal into an LOS signal, an NLOS signal, or an unclassified signal using a classification model based on an SVM algorithm trained for signal classification based on a power difference value, or a power difference value and an average excess delay value.

[0162] If the received UWB signal is an unclassified signal included in the gray area, the electronic device (200) can obtain CIR data corresponding to the unclassified signal from the UWB signal stored in the buffer.

[0163] Thereafter, the electronic device (200) can classify the unclassified signal as an LOS signal or an NLOS signal using a classification model based on a CNN trained for signal classification based on CIR data corresponding to the unclassified signal.

[0164] According to one embodiment, the electronic device (200) can provide a classification method having high speed and high accuracy by first classifying a UWB signal using a classification model based on an SVM algorithm having a high processing speed, and then classifying a UWB signal included in an area with low accuracy using a classification model based on a CNN.

[0165] FIG. 11 is a flowchart illustrating an operation method of an electronic device for position measurement according to one embodiment of the present disclosure.

[0166] Referring to FIG. 11, in operation 1110, the electronic device (200) may receive a plurality of ultra-wideband (UWB) signals. In one embodiment, each UWB signal may include a UWB message for Downlink Time Difference of Arrival (DL-TDoA) positioning. The UWB message may be a message including a payload information element (IE) transmitted by a UWB device (e.g., an enhanced ranging device (ERDEV)). The UWB message may include, for example, a ranging initiation message (RIM), a ranging response message (RRM), a ranging final message (RFM), a control message (CM), a measurement report message (MRM), a ranging result report message (RRRM), a control update message (CUM), and a one-way ranging (OWR) message. Additionally, a plurality of messages may be merged into one message.

[0167] In operation 1120, the electronic device (200) can obtain information related to a signal including a power difference value representing the difference between the total Rx power and the first path power for each UWB signal based on a plurality of UWB signals.

[0168] In one embodiment, the electronic device (200) can obtain channel information such as CIR data, First path amplitude, Preamble accumulation count (N), and CIR power (C) based on the received UWB signal, and can obtain information related to the signal based on the obtained channel information.

[0169] CIR (channel impulse response) data is data related to the impulse response of the UWB channel through which the UWB signal is transmitted, and can be obtained using the correlation value between the impulse function and the received UWB signal.

[0170] The first path amplitude may include the amplitude of a multipath component signal detected along the first path. In one embodiment, the amplitudes of the signals received first, second, and third among the multipath component signals may be referred to as first path amplitude 1, 2, and 3 (F1, F2, F3).

[0171] Preamble accumulation count (N) refers to the number of accumulated preamble symbols.

[0172] CIR power (C) includes the CIR power. CIR power (C) may include the amplitude of the CIR.

[0173] The electronic device (200) can generate total Rx power and first path power based on the acquired channel information.

[0174] The total Rx power can be calculated as follows:

[0175]

[0176] Additionally, the first path power can be calculated as follows.

[0177]

[0178] Thereafter, the electronic device (200) can obtain a power difference value representing the difference between the total Rx power and the first path power. At this time, the information related to the signal can include the power difference value.

[0179] In one embodiment, the electronic device (200) may obtain a mean excess delay value based on channel information. The mean excess delay refers to the average delay time of all paths. In this case, the signal-related information may further include the mean excess delay value.

[0180] In operation 1130, the electronic device (200) can classify each UWB signal into a line of sight (LOS) signal, a non-line of sight (NLOS) signal, or an unclassified signal using a classification model based on a support vector machine (SVM) algorithm trained for signal classification based on information related to the signal.

[0181] In one embodiment, the electronic device (200) may determine a hyperplane for classifying each UWB signal into an LOS signal and an NLOS signal using a classification model based on an SVM algorithm trained for signal classification. Thereafter, the electronic device (200) may determine a gray zone whose boundaries are planes having a predetermined distance on each side of the hyperplane. At this time, the electronic device (200) may determine the gray zone according to a preset standard. For example, the electronic device (200) may set the gray zone so that a predetermined ratio of UWB signals among the entire UWB signals are included. Furthermore, the electronic device (200) may determine a UWB signal included on one side of the hyperplane as an LOS signal, determine a UWB signal included on the other side of the hyperplane as an NLOS signal, and determine a UWB signal included in the gray zone as an unclassified signal.

[0182] According to one embodiment, the electronic device (200) can quickly classify a UWB signal into an LOS signal, an NLOS signal, or an unclassified signal by using a classification model based on a trained support vector machine (SVM) algorithm for signal classification that enables fast classification with a small amount of computation.

[0183] In operation 1140, the electronic device (200) can determine the position of the electronic device (200) based on at least one UWB signal classified as a LOS signal. The electronic device (200) can determine the position of the electronic device (200) by measuring the distance to an anchor that transmits or broadcasts at least one UWB signal classified as a LOS signal. In general, one initiator UWB anchor and two responder UWB anchors are required to obtain two-dimensional coordinates of the electronic device (200), and one initiator UWB anchor and three responder UWB anchors are required to obtain two-dimensional coordinates. However, this is only when time synchronization is achieved between the initiator UWB anchor and the responder UWB anchors, and otherwise, a larger number of responder UWB anchors may be required.

[0184] According to one embodiment, the electronic device (200) can perform position measurement more quickly and with higher accuracy by determining the position of the electronic device (200) using only the LOS signal.

[0185] According to one embodiment, the electronic device (200) can quickly classify LOS signals and NLOS signals by using a classification model based on a support vector machine (SVM) algorithm trained for signal classification that enables fast classification with a small amount of computation. The electronic device (200) can perform position measurement more quickly and with higher accuracy by determining the position of the electronic device (200) using only the LOS signal, excluding the NLOS signal that may be reflected or cause signal interference such as signal intensity attenuation and absorption.

[0186] In the above-described embodiment, the electronic device (200) is described as determining a gray area and determining a UWB signal included in the gray area as an unclassified signal. However, the electronic device (200) may also classify the UWB signal into an LOS signal and an NLOS signal using a hyperplane without setting a gray area. In this case, the electronic device (200) may determine the location of the electronic device (200) using only the UWB signal classified as an LOS signal using the hyperplane.

[0187] In one embodiment, the electronic device (200) may classify the unclassified signal as a LOS signal or a NLOS signal using a classification model based on a convolutional neural network (CNN) trained for signal classification based on channel impulse response (CIR) data corresponding to the unclassified signal. Thereafter, the electronic device (200) may determine the location of the electronic device based on at least one UWB signal classified as a LOS signal.

[0188] According to one embodiment, the electronic device (200) can provide a classification method having high speed and high accuracy by first classifying a UWB signal using a classification model based on an SVM algorithm having a high processing speed, and then classifying a UWB signal included in an area with low accuracy using a classification model based on a CNN.

[0189] FIG. 12 is a flowchart illustrating an operation method of an electronic device for position measurement according to another embodiment of the present disclosure.

[0190] Below, the overlapping parts with Fig. 11 are briefly explained.

[0191] Referring to FIG. 12, in operation 1210, the electronic device (200) may receive a plurality of ultra-wideband (UWB) signals. In one embodiment, each UWB signal may include a UWB message for DL-TDoA (Downlink Time Difference of Arrival) positioning.

[0192] In operation 1220, the electronic device (200) can obtain information related to a signal including a power difference value representing the difference between the total Rx power and the first path power for each UWB signal based on a plurality of UWB signals.

[0193] In one embodiment, the electronic device (200) may obtain channel information such as CIR data, First path amplitude, Preamble accumulation count (N), and CIR power (C) based on the received UWB signal, and may obtain signal-related information based on the obtained channel information. In addition, the signal-related information may include a total reception power generated based on the obtained channel information, a first path power, and a power difference value indicating a difference between the total reception power and the first path power. Furthermore, the signal-related information may further include a mean excess delay value.

[0194] In operation 1230, the electronic device (200) can classify each UWB signal into an LOS signal, an NLOS signal, or an unclassified signal using a classification model based on an SVM algorithm trained for signal classification based on information related to the signal.

[0195] In one embodiment, the electronic device (200) may determine a hyperplane for classifying each UWB signal into an LOS signal and an NLOS signal using a classification model based on an SVM algorithm trained for signal classification. Thereafter, the electronic device (200) may determine a gray zone whose boundaries are planes having a predetermined distance on each side of the hyperplane. At this time, the electronic device (200) may determine the gray zone according to a preset standard. For example, the electronic device (200) may set the gray zone so that a predetermined ratio of UWB signals among the entire UWB signals are included. Furthermore, the electronic device (200) may determine a UWB signal included on one side of the hyperplane as an LOS signal, determine a UWB signal included on the other side of the hyperplane as an NLOS signal, and determine a UWB signal included in the gray zone as an unclassified signal.

[0196] According to one embodiment, the electronic device (200) can quickly classify a UWB signal into an LOS signal, an NLOS signal, or an unclassified signal by using a classification model based on a trained support vector machine (SVM) algorithm for signal classification that enables fast classification with a small amount of computation.

[0197] In operation 1240, the electronic device (200) may determine whether to classify the unclassified signal as an LOS signal or an NLOS signal based on at least one of the number of unclassified signals, the time for performing positioning of the electronic device (200), and the positioning period of the electronic device (200). Here, the number of unclassified signals refers to the number of signals classified as unclassified signals when the electronic device (200) classifies each UWB signal using a classification model based on an SVM algorithm trained for signal classification. The time for performing positioning of the electronic device (200) refers to the time required for the electronic device (200) to select (or choose) an anchor transmitting an LOS signal and to actually measure a location. The positioning period refers to a period for performing positioning of the electronic device (200), and may include a period of a ranging round or a period of a ranging block.

[0198] In other words, the electronic device (200) can perform secondary classification operations on unclassified signals only when there is room in the system. This will be described in more detail with reference to FIGS. 13 and 14.

[0199] FIG. 13 is a flowchart illustrating a method of operating an electronic device based on the number of unclassified signals according to one embodiment.

[0200] In Fig. 13, it is assumed that the electronic device (200) performs positioning using 6 anchors. Here, the positioning cycle of the electronic device (200) is assumed to be 200 ms. In addition, it can be assumed that the process of receiving a DL-TDoA signal from 1 anchor takes 6 ms, the process of performing classification using a classification model based on the SVM algorithm takes 1 ms, the process of selecting anchors and performing positioning takes 30 ms, the process of requesting and receiving CIR data takes 60 ms, and the process of performing classification using a classification model based on a CNN (convolutional neural network) takes 2 ms. In addition, it can be assumed that the process of starting the classification operation of the electronic device (200) takes 2 ms.

[0201] Referring to FIG. 13, when there is no UWB signal included in the gray zone, the electronic device starts a classification operation (2 ms), receives DL-TDoA signals from 6 anchors (6*6 ms = 36 ms), performs classification using a classification model based on the SVM algorithm (1 ms), and selects an anchor transmitting a LOS signal to perform positioning (30 ms), which takes a total of 69 ms (= 2 ms + 36 ms + 1 ms + 30 ms).

[0202] When there is one UWB signal included in the gray area, the electronic device (200) can perform classification using a CNN-based classification model for one unclassified signal. At this time, the electronic device starts a classification operation (2 ms), receives DL-TDoA signals from 6 anchors (6*6 ms = 36 ms), performs classification using a classification model based on an SVM algorithm (1 ms), requests and receives CIR data for the unclassified signal (60 ms), performs classification using a CNN-based classification model for the unclassified signal (2 ms), and selects an anchor transmitting a LOS signal to perform positioning (30 ms), which takes a total of 131 ms (= 2 ms + 36 ms + 1 ms + 60 ms + 2 ms + 30 ms). Therefore, if there is one UWB signal included in the gray area, even if the electronic device (200) performs classification using a CNN-based classification model for one unclassified signal, the entire process can be performed within 200 ms, which is the positioning cycle of the electronic device (200).

[0203] When there are two UWB signals included in the gray area, the electronic device (200) can perform classification using a CNN-based classification model for the two unclassified signals. At this time, the electronic device starts the classification operation (2 ms), receives DL-TDoA signals from 6 anchors (6*6 ms = 36 ms), performs classification using a classification model based on the SVM algorithm (1 ms), requests and receives CIR data for the two unclassified signals (2*60 ms = 120 ms), performs classification using a CNN-based classification model for the two unclassified signals (2*2 ms = 4 ms), and selects an anchor transmitting a LOS signal to perform positioning (30 ms), and a total of 193 ms (= 2 ms + 36 ms + 1 ms + 120 ms + 4 ms + 30 ms) is required for the process. Therefore, when there are two UWB signals included in the gray area, even if the electronic device (200) performs classification using a CNN-based classification model for the two unclassified signals, the entire process can be performed within 200 ms, which is the positioning cycle of the electronic device (200).

[0204] When there are three UWB signals included in the gray area, the electronic device (200) can perform classification using a CNN-based classification model for the three unclassified signals. At this time, the electronic device starts the classification operation (2 ms), receives DL-TDoA signals from six anchors (6*6 ms = 36 ms), performs classification using a classification model based on an SVM algorithm (1 ms), requests and receives CIR data for three unclassified signals (3*60 ms = 180 ms), performs classification using a CNN-based classification model for the three unclassified signals (3*2 ms = 6 ms), and selects an anchor transmitting a LOS signal to perform positioning (30 ms), and the process takes a total of 255 ms (= 2 ms + 36 ms + 1 ms + 180 ms + 6 ms + 30 ms). When there are three UWB signals included in the gray area, if the electronic device (200) performs classification using a CNN-based classification model for the three unclassified signals, the entire process can be performed within 200 ms, which is the positioning cycle of the electronic device (200). Therefore, in this case, instead of performing classification using a CNN-based classification model for the unclassified signals, the position of the electronic device can be determined based on only at least one UWB signal classified as a LOS signal using a machine learning model based on an SVM algorithm in operation 1230.

[0205] FIG. 14 is a flowchart illustrating an operation method of an electronic device based on stride according to one embodiment.

[0206] In the present disclosure, a stride may be a unit representing a distance between an electronic device (200) and an external electronic device (e.g., GATE). Referring to FIG. 14, a distance of 0 to 1 m from a gate may be set to a stride value of 0, a distance of 1 to 3 m may be set to a stride value of 1, and a distance of 3 to 5 m may be set to a stride value of 2. However, this is merely an example for explaining an operating method of an electronic device, and the stride may be set in various ways without being limited thereto.

[0207] In one embodiment, when the distance between the electronic device (200) and the external electronic device, GATE, is far, the positioning cycle of the electronic device (200) is set long to save power of the electronic device (200), and when the distance between the electronic device (200) and the external electronic device, GATE, is close, the positioning cycle of the electronic device (200) is set short to allow the electronic device (200) to perform positioning frequently, thereby increasing positioning accuracy. At this time, the positioning cycle can be set using a stride. For example, assuming that the positioning cycle of the electronic device (200) is 200 ms as in FIG. 13, when the stride value is 0, the positioning cycle of the electronic device (200) can be maintained at 200 ms, when the stride value is 1, the positioning cycle of the electronic device (200) can be doubled and set to 400 ms, and when the stride value is 2, the positioning cycle of the electronic device (200) can be tripled and set to 400 ms.

[0208] According to one embodiment, when the distance between the electronic device (200) and an external electronic device (e.g., GATE) is far, the positioning cycle is set long to save power of the electronic device (200), and when the distance between the electronic device (200) and the external electronic device (e.g., GATE) is close, the positioning cycle of the electronic device (200) is set short to allow the electronic device (200) to perform positioning frequently, thereby increasing positioning accuracy.

[0209] Even if there are three UWB signals included in the gray area in Fig. 13, if the stride value is set to 2 or 3, classification can be performed using a CNN-based classification model for unclassified signals as the positioning cycle of the electronic device (200) increases, thereby increasing classification accuracy.

[0210] Going back to the description of Figure 12,

[0211] If the electronic device (200) decides to reclassify the unclassified signal in operation 1240, the electronic device (200) performs operation 1250 to classify the unclassified signal as an LOS signal or an NLOS signal using an artificial intelligence model based on a CNN trained for signal classification based on CIR data corresponding to the unclassified signal.

[0212] In operation 1260, the electronic device (200) can determine the position of the electronic device (200) based on at least one UWB signal classified as a LOS signal. The electronic device (200) can determine the position of the electronic device (200) by measuring the distance to an anchor that has transmitted or broadcasted at least one UWB signal classified as a LOS signal.

[0213] According to one embodiment, the electronic device (200) can provide a classification method having high speed and high accuracy by first classifying a UWB signal using a classification model based on an SVM algorithm having a high processing speed, and then classifying a UWB signal included in an area with low accuracy using a classification model based on a CNN.

[0214] If the electronic device (200) decides to reclassify the unclassified signal in operation 1240, the electronic device (200) performs operation 1270 to determine the location of the electronic device based only on at least one UWB signal classified as a LOS signal using a machine learning model based on an SVM algorithm.

[0215] According to one embodiment, the electronic device (200) can maintain the overall system performance above a certain level by performing a secondary classification operation on unclassified signals only when the system has room to spare.

[0216] According to one embodiment of the present disclosure, a fast measurement speed and high accuracy can be provided by minimizing the number of measurements.

[0217] [Table 1] is a table showing an algorithm of an electronic device (200) according to one embodiment of the present disclosure. The algorithm of the electronic device (200) shown in [Table 1] is an algorithm for performing position measurement when the minimum LOS signal required for position measurement is secured.

[0218]

[0219] Referring to [Table 1], the electronic device (200) receives UWB signals from k anchors, and classifies each of the received UWB signals into a line of sight (LOS) signal, a non-line of sight (NLOS) signal, or an unclassified signal using a classification model based on a support vector machine (SVM) algorithm.

[0220] In order to obtain the three-dimensional coordinates of the electronic device (200), at least four anchors are required. If there are four or more UWB signals classified as LOS signals, the electronic device (200) performs position measurement using the corresponding UWB signals.

[0221] If there are four or more UWB signals classified as LOS signals and unclassified signals included in the gray area, the unclassified signals are classified as LOS signals or NLOS signals using a classification model based on a convolutional neural network (CNN) trained for signal classification based on the channel impulse response (CIR) data corresponding to the unclassified signals. Thereafter, if there are four or more UWB signals classified as LOS signals, the electronic device (200) performs position measurement using the corresponding UWB signals.

[0222] According to one embodiment, the electronic device (200) can perform position measurement when the minimum LOS signal required for position measurement is secured. In this way, the algorithm illustrated in [Table 1] can be performed in combination with the other embodiments described above.

[0223] Meanwhile, the embodiments of the present disclosure disclosed in this specification and drawings are merely specific examples presented to easily explain the technical content of the present disclosure and aid in understanding of the present disclosure, and are not intended to limit the scope of the present disclosure. In other words, it will be apparent to those skilled in the art to which the present disclosure pertains that other modified examples based on the technical concept of the present disclosure are possible. Furthermore, the above-described embodiments may be combined and operated as needed.

Claims

1. In the method of operating an electronic device, An operation of receiving multiple ultra-wideband (UWB) signals; An operation of classifying the plurality of UWB signals into LOS (line of sight) signals, NLOS (non-line of sight) signals, or unclassified signals using a classification model based on a trained SVM (support vector machine) algorithm for signal classification; and A method comprising an operation of determining a position of the electronic device based on at least one UWB signal classified as the LOS signal.

2. In paragraph 1, Based on the plurality of UWB signals, the method further includes an operation of obtaining information related to the signal, the information including a power difference value representing the difference between the total Rx power and the first path power for each UWB signal. The operation of classifying the above plurality of UWB signals into LOS signals, NLOS signals, or unclassified signals comprises: A method comprising the operation of classifying the plurality of UWB signals based on information associated with the signals.

3. In paragraph 1, An operation of determining a location of the electronic device based on at least one UWB signal classified as the LOS signal is provided. An operation of classifying the unclassified signal into the LOS signal or the NLOS signal using a classification model based on a convolutional neural network (CNN) trained for signal classification based on CIR (channel impulse response) data corresponding to the unclassified signal; and A method comprising the operation of determining a location of the electronic device based on at least one UWB signal classified as the LOS signal.

4. In paragraph 3, Further comprising an operation of determining whether to classify the unclassified signal as the LOS signal or the NLOS signal based on at least one of the number of the unclassified signals, the time for performing positioning of the electronic device, and the positioning period of the electronic device. A method for performing an operation of classifying the unclassified signal into the LOS signal or the NLOS signal using a CNN-based classification model trained for signal classification when determining to classify the unclassified signal into the LOS signal or the NLOS signal.

5. In paragraph 4, A method for determining the position of the electronic device based only on at least one UWB signal classified as the LOS signal, using a classification model based on a support vector machine (SVM) algorithm trained for signal classification, when it is decided not to classify the unclassified signal as the LOS signal or the NLOS signal.

6. In paragraph 4, The positioning cycle of the above electronic device is: A method, wherein the method is determined based on the distance between the electronic device and an external electronic device.

7. In paragraph 2, Information related to the above signal is: Contains the mean excess delay value, The operation of obtaining information related to the above signal is: A method comprising: obtaining information related to a signal including the average excess delay value for each UWB signal based on the plurality of UWB signals.

8. In paragraph 1, The operation of classifying each of the above UWB signals as LOS signals, NLOS signals, or unclassified signals is as follows: An operation of determining a hyperplane for classifying each UWB signal into a LOS signal and a NLOS signal using a classification model based on a trained SVM algorithm for the above signal classification; An operation of determining a gray zone whose boundaries are planes having a predetermined distance on each side of the above hyperplane; A method including an operation of determining a UWB signal included in one side of the hyperplane as an LOS signal, determining a UWB signal included in the other side of the hyperplane as an NLOS signal, and determining a UWB signal included in the gray area as an unclassified signal.

9. In electronic devices, communication circuit; Memory for storing instructions; and At least one processor connected to the above communication circuit and memory and executing the instructions, The at least one processor, by executing the instructions, Receives multiple UWB (ultra-wideband) signals, Based on the information related to the above signals, a classification model based on a trained SVM (support vector machine) algorithm is used to classify the plurality of UWB signals into LOS (line of sight) signals, NLOS (non-line of sight) signals, or unclassified signals. An electronic device configured to determine a position of the electronic device based on at least one UWB signal classified as the LOS signal.

10. In paragraph 9, The at least one processor, by executing the instructions, An electronic device configured to obtain information related to a signal, including a power difference value representing a difference between total Rx power and first path power for each UWB signal, based on the plurality of UWB signals 11. In paragraph 9, The at least one processor, by executing the instructions, classifies the unclassified signal into the LOS signal or the NLOS signal using a classification model based on a convolutional neural network (CNN) trained for signal classification based on CIR (channel impulse response) data corresponding to the unclassified signal. An electronic device configured to determine a location of the electronic device based on at least one UWB signal classified as the LOS signal.

12. In paragraph 11, The at least one processor, by executing the instructions, Based on at least one of the number of the unclassified signals, the time for performing positioning of the electronic device, and the positioning period of the electronic device, it is determined whether to classify the unclassified signal as the LOS signal or the NLOS signal, An electronic device configured to classify the unclassified signal as the LOS signal or the NLOS signal by using a CNN-based classification model trained for signal classification when determining to classify the unclassified signal as the LOS signal or the NLOS signal.

13. In paragraph 12, The at least one processor, by executing the instructions, An electronic device configured to determine the position of the electronic device based only on at least one UWB signal classified as the LOS signal using a classification model based on a support vector machine (SVM) algorithm trained for signal classification, when it is determined not to classify the unclassified signal as the LOS signal or the NLOS signal.

14. In paragraph 9, Information related to the above signal is: Contains the mean excess delay value, The at least one processor, by executing the instructions, An electronic device configured to obtain information related to a signal including the average excess delay value for each UWB signal based on the plurality of UWB signals.

15. In paragraph 9, The at least one processor, by executing the instructions, For the above signal classification, a hyperplane is determined to classify each UWB signal into a LOS signal and a NLOS signal using a classification model based on the trained SVM algorithm. A gray zone is determined by defining a plane having a predetermined distance on each side of the hyperplane as the boundary, An electronic device configured to determine a UWB signal included in one side of the hyperplane as an LOS signal, determine a UWB signal included in the other side of the hyperplane as an NLOS signal, and determine a UWB signal included in the gray area as an unclassified signal.

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