UWB signal-based object recognition method and apparatus

Abstract

The present disclosure relates to a UWB signal-based object recognition method. The UWB signal-based object recognition method comprises the steps of: receiving a first UWB signal associated with a first object; generating a first sampling signal by performing sampling on the first UWB signal at a first sampling timing on the basis of a sampling window; converting the generated first sampling signal into a first digital signal using an ADC; and providing the converted first digital signal to a trained artificial intelligence model so as to recognize the first object corresponding to the first digital signal.

Description

Method and device for object recognition based on UWB signals

[0001] The present disclosure relates to a method and device for object recognition based on UWB signals, and more particularly, to a method and device for object recognition based on UWB signals, which are two-dimensional data of time and amplitude.

[0002] Recently, artificial intelligence technologies for object recognition and classification have been widely used in various industries. Typically, object recognition models use visual images acquired from vision sensors such as cameras to recognize and classify objects. However, this type of image-based object recognition suffers from limitations in the visual range that can be captured. To overcome this, technologies for object recognition using radar signals such as FMCW are being developed. However, using frequency-domain FMCW signals has limitations in recognizing stationary objects, and the use of ultra-high-speed ADCs increases system complexity.

[0003] The present invention is derived from research conducted as part of the ICT Convergence Industry Innovation Technology Development Project of the Ministry of Science and ICT (Project Unique Number: 1711193657, Project Number: 2017-0-00418-007, Project Management Agency: Information and Communications Technology Planning and Evaluation Institute, Research Project Title: Research on Time-Domain Artificial Intelligence Radar SoC (System On a Chip) Design Using Ultra-High-Speed ​​Sampling Technique, Project Performing Agency: Yonsei University Industry-Academic Cooperation Foundation, Research Period: 2023.01.01 - 2023.12.31).

[0004] Meanwhile, the Korean government, which provided the task, has no property interest in any aspect of the present invention.

[0005] The present disclosure provides a method for object recognition based on a UWB signal, a computer program stored in a computer-readable medium, a computer-readable medium storing the computer program, and a device (system) for solving the above-described problems.

[0006] In various embodiments of the present disclosure, a computing device can sample the entire detection range by periodically moving the sampling window, thereby effectively recognizing multiple objects existing within the detection range without specifying a separate sampling start timing.

[0007] In various embodiments of the present disclosure, a computing device can perform continuous sampling by determining a delay of a sampling window based on a DTC, thereby effectively performing sampling for a plurality of objects without setting a separate time path.

[0008] In various embodiments of the present disclosure, a high-speed UWB signal can be converted into a low-speed sampling signal, and when a low-speed sampling signal is used, effective signal conversion can be performed using only a low-speed ADC without having to install a high-speed ADC that is difficult to implement.

[0009] In various embodiments of the present disclosure, when the reference delay and the sampling time of the sampling window are set to be the same, continuous sampling can be performed by moving the sampling window without a temporal gap. Furthermore, since the sampling window is moved by applying a periodic delay rather than relying on the timing of the received UWB signal to generate the sampling window, the complexity of the radar system can be reduced.

[0010] Embodiments of the present disclosure will be described below with reference to the accompanying drawings, wherein like reference numerals represent similar elements, but are not limited thereto.

[0011] FIG. 1 is a diagram illustrating an example of a computing device recognizing objects using radar signals according to one embodiment of the present disclosure.

[0012] FIG. 2 is a functional block diagram showing the internal configuration of a computing device according to one embodiment of the present disclosure.

[0013] FIG. 3 is an exemplary graph showing the pulse period of a system clock and the dead time of a UWB signal according to one embodiment of the present disclosure.

[0014] FIG. 4 is a block diagram illustrating an example of a DTC generating a delay according to one embodiment of the present disclosure.

[0015] FIG. 5 is an exemplary diagram showing an artificial neural network according to one embodiment of the present disclosure.

[0016] FIG. 6 is a flowchart illustrating an example of an object recognition method based on a UWB signal according to one embodiment of the present disclosure.

[0017] FIG. 7 is a block diagram showing a hardware configuration of a computing device according to one embodiment of the present disclosure.

[0018] The present disclosure can be implemented in various ways, including a method, a device (system), a computer program stored on a computer-readable medium, or a computer-readable medium having a computer program stored thereon.

[0019] According to one embodiment of the present disclosure, a method for object recognition based on a UWB signal, performed by at least one processor, includes the steps of receiving a first UWB signal associated with a first object, performing sampling on the first UWB signal at a first sampling timing based on a sampling window to generate a first sampling signal, converting the generated first sampling signal into a first digital signal using an ADC, and providing the converted first digital signal to a learned artificial intelligence model to recognize a first object corresponding to the first digital signal.

[0020] According to one embodiment of the present disclosure, a sampling window includes a plurality of samplers. The step of performing sampling on a first UWB signal to generate a first sampling signal includes the step of performing sampling on the first UWB signal by determining whether each capacitor associated with the plurality of samplers stores a charge.

[0021] According to one embodiment of the present disclosure, the step of performing sampling on the first UWB signal to generate the first sampling signal further includes the step of sequentially transmitting charges stored in each capacitor during a dead time of the first UWB signal to generate the first sampling signal.

[0022] According to one embodiment of the present disclosure, the artificial intelligence model includes a model combining CNN and LSTM.

[0023] According to one embodiment of the present disclosure, the method further includes the steps of receiving a second UWB signal associated with a second object, determining a delay for moving a sampling window, performing sampling on the second UWB signal at a second sampling timing subsequent to a first sampling timing by moving the sampling window based on the determined delay to generate a second sampling signal, converting the generated second sampling signal into a second digital signal using an ADC, and providing the converted second digital signal to an artificial intelligence model to recognize a second object corresponding to the second digital signal.

[0024] According to one embodiment of the present disclosure, the step of determining a delay for moving the sampling window includes the step of determining a delay for moving the sampling window based on a plurality of unit delays included in the DTC.

[0025] According to one embodiment of the present disclosure, the step of determining a delay for moving a sampling window based on a plurality of unit delays included in a DTC includes the step of calculating a binary code corresponding to a period of a system clock and the step of determining a delay for moving the sampling window by operating at least some of the plurality of unit delays based on the calculated binary code.

[0026] According to one embodiment of the present disclosure, the step of generating a binary code corresponding to a system clock includes the step of generating a binary code for further operating one unit delay included in the DTC when one cycle of the system clock increases.

[0027] According to one embodiment of the present disclosure, the delay time of each unit delay included in the plurality of unit delays is determined to be the same as the sampling time of the sampling window.

[0028] A computer program stored in a computer-readable recording medium is provided for executing a method according to one embodiment of the present disclosure on a computer.

[0029] A computing device according to one embodiment of the present disclosure includes a communication module, a memory, and at least one processor connected to the memory and configured to execute at least one computer-readable program contained in the memory. The at least one program includes instructions for receiving a first UWB signal associated with a first object, sampling the first UWB signal at a first sampling timing based on a sampling window to generate a first sampling signal, converting the generated first sampling signal into a first digital signal using an ADC, and providing the converted first digital signal to a trained artificial intelligence model to recognize a first object corresponding to the first digital signal.

[0030] Hereinafter, specific details for implementing the present disclosure will be described in detail with reference to the attached drawings. However, in the following description, specific descriptions of widely known functions or configurations will be omitted if they may unnecessarily obscure the gist of the present disclosure.

[0031] In the attached drawings, identical or corresponding components are assigned the same reference numerals. Furthermore, in the description of the embodiments below, duplicate descriptions of identical or corresponding components may be omitted. However, even if a description of a component is omitted, it is not intended that such component is not included in any embodiment.

[0032] The advantages and features of the disclosed embodiments, and the methods for achieving them, will become clearer with reference to the embodiments described below, along with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various different forms. These embodiments are provided solely to ensure the completeness of the present invention and to fully inform those skilled in the art of the scope of the invention.

[0033] The terms used in this specification will be briefly explained, followed by a detailed description of the disclosed embodiments. The terms used in this specification have been selected from widely used, current terms, taking into account the functions of the present invention. However, these terms may vary depending on the intentions of engineers working in the relevant fields, precedents, the emergence of new technologies, etc. Furthermore, in certain cases, terms may be arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant description of the invention. Therefore, the terms used in this specification should not be defined simply based on their names, but rather based on their meanings and the overall content of the present disclosure.

[0034] In this specification, singular expressions include plural expressions unless the context clearly indicates otherwise. Furthermore, plural expressions include singular expressions unless the context clearly indicates otherwise. When a part of the specification is said to include a component, this does not exclude other components, but rather implies that other components may be included, unless otherwise specifically stated.

[0035] In this disclosure, terms such as “comprise,” “comprising,” and the like may indicate the presence of features, steps, operations, elements, and / or components, but such terms do not exclude the addition of one or more other features, steps, operations, elements, components, and / or combinations thereof.

[0036] In this disclosure, when a particular component is referred to as being "coupled," "combined," "connected," or "reacting" with any other component, the particular component may be directly coupled, combined, and / or connected to, or reacting with, the other component, but is not limited thereto. For example, one or more intermediate components may exist between the particular component and the other component. Furthermore, "and / or" in the present disclosure may include each of one or more of the listed items, or a combination of at least some of one or more of the listed items.

[0037] In this disclosure, terms such as "first," "second," etc. are used to distinguish specific components from other components, and the components described by these terms are not limited. For example, the "first" component may be an element of the same or similar form as the "second" component.

[0038] In the present disclosure, "UWB (ultra-wideband) signal" refers to an ultra-wideband signal, and may refer to two-dimensional data in the time domain composed of time and amplitude. Here, the UWB signal is not limited to referring to a single piece of data, and may include multiple pieces of data. In addition, "UWB device" may refer to a radar device that generates or receives a UWB signal.

[0039]

[0040] FIG. 1 is a diagram illustrating an example of a computing device (100) recognizing objects using a radar signal according to one embodiment of the present disclosure. According to one embodiment, the computing device (100) is a device for recognizing objects using a radar signal such as a UWB signal, and may include a transmitter / receiver of a radar signal (e.g., a UWB device), an analog-to-digital converter (ADC) for converting an analog signal into a digital signal, an artificial intelligence model trained to perform object recognition by receiving a digital signal converted by the ADC, and the like.

[0041] According to one embodiment, the computing device (100) can recognize a first object (110) and / or a second object (120) using a UWB signal. Here, the UWB signal may refer to an impulse signal that is emitted from the computing device (100), hits the first object (110) and / or the second object (120), and then returns. Such a UWB signal may be expressed as two-dimensional data in the time domain rather than a frequency-based signal such as a frequency-modulated continuous wave (FMCW). In this way, when a UWB signal in the time domain is used, the computing device (100) can effectively recognize not only a dynamic object but also a static object.

[0042] According to one embodiment, the computing device (100) can recognize a plurality of objects, i.e., a first object (110) and / or a second object (120), by performing multiple samplings on a UWB signal. For example, the computing device (100) can receive a first UWB signal associated with the first object (110) and a second UWB signal associated with the second object (120). In this case, the computing device (100) can sequentially move a sampling window to generate a first digital signal corresponding to the first object (110) and a second digital signal corresponding to the second object (120). The computing device (100) can provide the first digital signal and the second digital signal generated in this way to an artificial intelligence model for object recognition to recognize the first object (110) and the second object (120).

[0043] In FIG. 1, the computing device (100) is illustrated as recognizing two objects, but this is not limited thereto, and the computing device (100) can recognize all of the multiple objects existing within the detection range. With this configuration, the computing device (100) can sample the entire detection range by periodically moving the sampling window, and thus, can effectively recognize multiple objects existing within the detection range without specifying a separate sampling start timing.

[0044]

[0045] FIG. 2 is a functional block diagram illustrating an internal configuration of a computing device (100) according to one embodiment of the present disclosure. According to one embodiment, the computing device (100) may include a signal receiving unit (202), a sampling unit (204), an object recognition unit (206), etc., as any device for recognizing dynamic and / or static objects based on UWB signals in the time domain. For example, the computing device (100) may recognize multiple objects by periodically moving a sampling window.

[0046] According to one embodiment, the signal receiving unit (202) can receive a UWB signal that measures an arbitrary object. For example, when an arbitrary UWB device (e.g., a UWB device included in the computing device (100)) emits a pulse signal to an object and then measures a UWB signal that returns, the signal receiving unit (202) can receive the UWB signal measured in this way. In this case, the UWB signal that hits the object and returns can have a waveform of different shapes depending on the shape and position of the object. The signal receiving unit (202) can receive a first UWB signal associated with a first object and / or a second UWB signal associated with a second object.

[0047] According to one embodiment, the sampling unit (204) may move the sampling window to generate a first sampling signal corresponding to the first UWB signal and / or a second sampling signal corresponding to the second UWB signal. Here, the sampling window is for sensing a UWB signal corresponding to a time interval of a specific size, and may include a plurality of samplers (e.g., 40 samplers), and each of the plurality of samplers may be associated with a capacitor for storing a charge.

[0048] In order to perform sampling, when receiving a first UWB signal and / or a second UWB signal, the sampling unit (204) can determine whether or not to store charges in each capacitor associated with a plurality of samplers to perform sampling for the first UWB signal and / or the second UWB signal. Then, the sampling unit (204) can sequentially transmit the charges stored in each capacitor during a dead time of the first UWB signal and / or the second UWB signal to generate the first sampling signal and / or the second sampling signal.

[0049] According to one embodiment, the sampling unit (204) can perform continuous sampling on the first UWB signal and the second UWB signal by determining a delay for moving the sampling window. For example, the sampling unit (204) can perform sampling on the first UWB signal at a first sampling timing. Then, the sampling unit (204) can perform sampling on the second UWB signal at a second sampling timing subsequent to the first sampling timing by moving the sampling window based on the determined delay, thereby generating a second sampling signal. That is, the sampling unit (204) can repeatedly perform sampling by moving the sampling window according to the determined delay.

[0050] According to one embodiment, the sampling unit (204) may determine a delay for moving the sampling window based on a plurality of unit delays included in a digital to time converter (DTC). For example, whenever one unit delay included in the DTC is turned on, a delay may occur for a time corresponding to one unit delay. To determine the delay time, the sampling unit (204) may generate a binary code corresponding to a cycle of a system clock, and determine a delay for moving the sampling window by operating at least some of the unit delays among the plurality of unit delays based on the generated binary code.

[0051] After moving the sampling window according to the determined delay and sampling a plurality of UWB signals, the object recognition unit (206) can convert the generated first sampling signal and / or the second sampling signal into a first digital signal and / or a second digital signal using an analog to digital converter (ADC). Then, the object recognition unit (206) can provide the generated first digital signal and / or the second digital signal to a learned artificial intelligence model to recognize a first object corresponding to the first digital signal and / or a second object corresponding to the second digital signal. Here, the artificial intelligence model can include a model combining a convolution neural network (CNN) and a long short-term memory (LSTM).

[0052] In Fig. 2, each functional configuration included in the computing device (100) is separately described, but this is only to aid understanding of the invention, and a single computing device may perform two or more functions. By this configuration, the computing device (100) determines the delay of the sampling window based on the DTC and performs continuous sampling, thereby effectively performing sampling for multiple objects without setting a separate time path.

[0053]

[0054] FIG. 3 is an exemplary graph (300) illustrating a pulse period (310) of a system clock and a dead time (320) of a UWB signal according to one embodiment of the present disclosure. According to one embodiment, a high-speed UWB signal may be converted into a low-speed sampling signal. As illustrated, a dead time (320) during which no signal is transmitted may occur between a first UWB signal and a second UWB signal. That is, after one UWB signal is transmitted during one pulse period (310), a dead time (320) may occur during the remaining time.

[0055] According to one embodiment, a high-speed UWB signal can be converted into a low-speed sampling signal during a dead time (320). For example, a plurality of samplers including a sampling window can perform sampling on an input high-speed UWB signal and store charges in at least some of the plurality of capacitors to correspond to the UWB signal. Then, the charges stored in at least some of the capacitors can be sequentially transmitted based on a slow clock during the dead time (320). By this process, a high-speed UWB signal can be converted into a low-speed sampling signal, and when a low-speed sampling signal is used, effective signal conversion can be performed using only a low-speed ADC without having to install a high-speed ADC that is difficult to implement.

[0056]

[0057] FIG. 4 is a block diagram illustrating an example of a DTC (400) generating a delay according to one embodiment of the present disclosure. As described above, the DTC (400) may include multiple unit delays. In FIG. 4, the DTC (400) is illustrated as including a first unit delay (402), a second unit delay (404), and a third unit delay (406), but is not limited thereto.

[0058] According to one embodiment, the DTC (400) can output a delay corresponding to binary data obtained from a binary decoder (410). For example, the binary decoder (410) can receive an n-bit binary code and output binary data for operating each unit delay. In this case, whether or not each unit delay included in the DTC (400) operates can be determined based on the output binary data.

[0059] In one embodiment, when only the first unit delay (402) is activated, a time equal to the reference delay may be determined as the final delay. In another example, when the first unit delay (402) and the second unit delay (404) are activated, a time equal to twice the reference delay may be determined as the final delay. In yet another example, when the first unit delay (402), the second unit delay (404), and the third unit delay (406) are activated, a time equal to three times the reference delay may be determined as the final delay.

[0060] At this time, the reference delay, which is the delay time of the unit delay, can be determined to be the same as the sampling time of the sampling window. In this way, when the reference delay and the sampling time of the sampling window are set to be the same, continuous sampling can be performed by moving the sampling window without a temporal gap. In addition, since the sampling window is moved by applying a periodic delay rather than being generated depending on the timing of the received UWB signal, the complexity of the radar system can be reduced.

[0061]

[0062] FIG. 5 is an exemplary diagram illustrating an artificial neural network (500) according to one embodiment of the present disclosure. The artificial neural network (500) is an example of the artificial intelligence model described above, and in machine learning technology and cognitive science, is a statistical learning algorithm implemented based on the structure of a biological neural network or a structure that executes the algorithm.

[0063] According to one embodiment, the artificial neural network (500) may represent a machine learning model having problem-solving capabilities by learning that nodes, which are artificial neurons that form a network by combining synapses like in a biological neural network, repeatedly adjust the weights of synapses so that the error between the correct output corresponding to a specific input and the inferred output is reduced. For example, the artificial neural network (500) may include any language model used in artificial intelligence learning methods such as machine learning and deep learning.

[0064] According to one embodiment, the artificial neural network (500) can be implemented as a multilayer perceptron (MLP) composed of multiple layers of nodes and connections therebetween. The artificial neural network (500) according to the present embodiment can be implemented using one of the structures of various artificial intelligence models including MLP. As illustrated in FIG. 5, the artificial neural network (500) is composed of an input layer (520) that receives an input signal or data (510) from the outside, an output layer (540) that outputs an output signal or data (550) corresponding to the input data, and n hidden layers (530_1 to 530_n) located between the input layer (520) and the output layer (540) that receive signals from the input layer (520), extract characteristics, and transmit them to the output layer (540) (where, n is a positive integer). Here, the output layer (540) receives signals from the hidden layers (530_1 to 530_n) and outputs them to the outside.

[0065] The learning method of the artificial neural network (500) includes a supervised learning method that learns to optimize problem solving through input of a teacher signal (correct answer), and an unsupervised learning method that does not require a teacher signal. According to one embodiment, when the artificial neural network (500) receives a digital signal generated by converting a UWB signal, it can be trained to output the shape, position, etc. of an object corresponding to the input digital signal.

[0066]

[0067] FIG. 6 is a flowchart illustrating an example of a method (600) for object recognition based on a UWB signal according to an embodiment of the present disclosure. The method (600) for object recognition based on a UWB signal may be performed by a processor (e.g., at least one processor of a computing device). The method (600) for object recognition based on a UWB signal may be initiated when the processor receives a first UWB signal associated with a first object (S610).

[0068] The processor may perform sampling on the first UWB signal at a first sampling timing based on a sampling window to generate a first sampling signal (S620). Here, the sampling window may include a plurality of samplers. The processor may perform sampling on the first UWB signal by determining whether charge is stored in each capacitor associated with the plurality of samplers. In this case, the processor may sequentially transmit the charge stored in each capacitor during the dead time of the first UWB signal to generate the first sampling signal.

[0069] The processor may convert a first sampling signal generated using an ADC into a first digital signal (S630). Then, the processor may provide the converted first digital signal to a trained artificial intelligence model to recognize a first object corresponding to the first digital signal (S640). Here, the artificial intelligence model may include a model combining CNN and LSTM. For example, a CNN-based model may be used for static objects, and an LSTM-based model may be used for dynamic objects.

[0070] When the processor receives a second UWB signal associated with a second object, the processor may determine a delay for moving the sampling window. For example, the processor may determine a delay for moving the sampling window based on a plurality of unit delays included in the DTC. In this case, the processor may calculate a binary code corresponding to a cycle of the system clock, and determine a delay for moving the sampling window by operating at least some of the unit delays among the plurality of unit delays based on the calculated binary code. For example, when one cycle of the system clock increases, the processor may calculate a binary code for further operating one unit delay included in the DTC.

[0071] The processor can generate a second sampling signal by sampling the second UWB signal at a second sampling timing subsequent to the first sampling timing by moving the sampling window based on the determined delay. Then, the processor can convert the generated second sampling signal into a second digital signal using an ADC and provide the converted second digital signal to an artificial intelligence model to recognize a second object corresponding to the second digital signal.

[0072]

[0073] FIG. 7 is a block diagram illustrating a hardware configuration of a computing device (100) according to one embodiment of the present disclosure. The computing device (100) may include a memory (710), a processor (720), a communication module (730), and an input / output interface (740). As illustrated in FIG. 7, the computing device (100) may be configured to communicate information and / or data via a network using the communication module (730).

[0074] The memory (710) may include any non-transitory computer-readable recording medium. According to one embodiment, the memory (710) may include a non-volatile mass storage device such as a random access memory (RAM), a read only memory (ROM), a disk drive, a solid state drive (SSD), a flash memory, etc. As another example, a non-volatile mass storage device such as a ROM, an SSD, a flash memory, a disk drive, etc. may be included in the computing device (100) as a separate permanent storage device distinct from the memory. In addition, an operating system and at least one program code may be stored in the memory (710).

[0075] These software components may be loaded from a computer-readable recording medium separate from the memory (710). This separate computer-readable recording medium may include a recording medium directly connectable to the computing device (100), for example, a computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD / CD-ROM drive, a memory card, etc. As another example, the software components may be loaded into the memory (710) via a communication module (730) other than a computer-readable recording medium. For example, at least one program may be loaded into the memory (710) based on a computer program that is installed by files provided by developers or a file distribution system that distributes installation files of applications via the communication module (730).

[0076] The processor (720) may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. The instructions may be provided to another user terminal (not shown) or another external system via the memory (710) or the communication module (730).

[0077] The communication module (730) may provide a configuration or function for a user terminal (not shown) and a computing device (100) to communicate with each other via a network, and may provide a configuration or function for the computing device (100) to communicate with an external system (e.g., a separate cloud system, etc.). For example, control signals, commands, data, etc. provided under the control of the processor (720) of the computing device (100) may be transmitted to the user terminal and / or the external system via the communication module (730) and the network via the communication module of the user terminal and / or the external system.

[0078] In addition, the input / output interface (740) of the computing device (100) may be a means for interfacing with a device (not shown) for input or output that is connected to the computing device (100) or that the computing device (100) may include. In FIG. 7, the input / output interface (740) is illustrated as an element configured separately from the processor (720), but is not limited thereto, and the input / output interface (740) may be configured to be included in the processor (720). The computing device (100) may include more components than those illustrated in FIG. 7. However, there is no need to explicitly illustrate most of the conventional components.

[0079] The processor (720) of the computing device (100) may be configured to manage, process and / or store information and / or data received from multiple user terminals and / or multiple external systems.

[0080]

[0081] The above-described methods and / or various embodiments may be realized by digital electronic circuits, computer hardware, firmware, software, and / or a combination thereof. Various embodiments of the present disclosure may be implemented as a computer program that is executed by a data processing device, for example, one or more programmable processors and / or one or more computing devices, or stored on a computer-readable recording medium and / or a computer-readable recording medium. The above-described computer program may be written in any form of programming language, including a compiled language or an interpreted language, and may be distributed in any form, such as a standalone program, a module, a subroutine, etc. The computer program may be distributed through a single computing device, multiple computing devices connected through the same network, and / or multiple computing devices distributed to be connected through multiple different networks.

[0082] The methods and / or various embodiments described above may be performed by one or more processors configured to execute one or more computer programs that process, store, and / or manage any function, function, etc. by operating on the basis of input data or generating output data. For example, the methods and / or various embodiments of the present disclosure may be performed by special purpose logic circuits such as Field Programmable Gate Arrays (FPGAs) or Application Specific Integrated Circuits (ASICs), and an apparatus and / or system for performing the methods and / or embodiments of the present disclosure may be implemented as special purpose logic circuits such as FPGAs or ASICs.

[0083] The one or more processors executing the computer program may include a general-purpose or special-purpose microprocessor and / or one or more processors of any type of digital computing device. The processor may receive instructions and / or data from each of read-only memory and random-access memory, or may receive instructions and / or data from the read-only memory and the random-access memory. In the present invention, components of a computing device performing the methods and / or embodiments may include one or more processors for executing instructions, and one or more memory devices for storing instructions and / or data.

[0084] According to one embodiment, the computing device can transmit and receive data to and from one or more mass storage devices for storing data. For example, the computing device can receive and / or transfer data from a magnetic disc or an optical disc. A computer-readable storage medium suitable for storing instructions and / or data associated with a computer program may include, but is not limited to, any form of non-volatile memory, including semiconductor memory devices such as Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable PROM (EEPROM), flash memory devices, and the like. For example, the computer-readable storage medium may include a magnetic disk such as an internal hard disk or a removable disk, a magneto-optical disk, a CD-ROM, and a DVD-ROM disk.

[0085] To provide interaction with a user, a computing device may include, but is not limited to, a display device (e.g., a cathode ray tube (CRT), a liquid crystal display (LCD), etc.) for providing or displaying information to the user, and a pointing device (e.g., a keyboard, a mouse, a trackball, etc.) for allowing the user to provide input and / or commands to the computing device. That is, the computing device may further include any other types of devices for providing interaction with the user. For example, the computing device may provide any form of sensory feedback to the user, including visual feedback, auditory feedback, and / or tactile feedback, for interaction with the user. In this regard, the user may provide input to the computing device through various gestures, such as visual, vocal, or motion.

[0086] In the present invention, various embodiments may be implemented in a computing system that includes backend components (e.g., a data server), middleware components (e.g., an application server), and / or front-end components. In this case, the components may be interconnected via any form or medium of digital data communication, such as a communications network. For example, the communications network may include a Local Area Network (LAN), a Wide Area Network (WAN), etc.

[0087] A computing device based on the exemplary embodiments described herein may be implemented using hardware and / or software configured to interact with a user, including a user device, a user interface (UI) device, a user terminal, or a client device. For example, the computing device may include a portable computing device, such as a laptop computer. Additionally or alternatively, the computing device may include, but is not limited to, Personal Digital Assistants (PDAs), tablet PCs, game consoles, wearable devices, Internet of Things (IoT) devices, virtual reality (VR) devices, augmented reality (AR) devices, and the like. The computing device may further include other types of devices configured to interact with a user. Furthermore, the computing device may include a portable communication device (e.g., a mobile phone, a smart phone, a cordless cellular phone, etc.) suitable for wireless communication over a network, such as a mobile communication network. The computing device may be configured to communicate wirelessly with a network server using wireless communication technologies and / or protocols, such as Radio Frequency (RF), Microwave Frequency (MWF), and / or Infrared Ray Frequency (IRF).

[0088] The various embodiments of the present invention, including specific structural and functional details, are exemplary. Therefore, the embodiments of the present disclosure are not limited to those described above and may be implemented in various other forms. Furthermore, the terminology used herein is intended to describe certain embodiments and is not intended to limit the embodiments. For example, singular terms and the above may be interpreted to include plural forms, unless the context clearly dictates otherwise.

[0089] In the present invention, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by those of ordinary skill in the art to which they pertain. Furthermore, commonly used terms, such as terms defined in dictionaries, should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology.

[0090] While the present invention has been described in connection with certain embodiments herein, various modifications and variations can be made without departing from the scope of the present disclosure, which would be apparent to those skilled in the art. Furthermore, such modifications and variations are intended to fall within the scope of the claims appended to this specification.

Claims

1. An object recognition method based on an ultra-wideband (UWB) signal performed by at least one processor, A step of receiving a first UWB signal associated with a first object; A step of generating a first sampling signal by performing sampling on the first UWB signal at a first sampling timing based on a sampling window; A step of converting the generated first sampling signal into a first digital signal using an ADC (analog to digital converter); and A step of providing the converted first digital signal to a learned artificial intelligence model to recognize the first object corresponding to the first digital signal; An object recognition method based on UWB signals including:

2. In paragraph 1, The above sampling window includes a plurality of samplers, The step of performing sampling on the first UWB signal to generate a first sampling signal comprises: A step of performing sampling for the first UWB signal by determining whether or not to store charge in each capacitor associated with the plurality of samplers; An object recognition method based on UWB signals including:

3. In paragraph 2, The step of performing sampling on the first UWB signal to generate a first sampling signal comprises: A step of generating the first sampling signal by sequentially transmitting the charge stored in each of the capacitors during the dead time of the first UWB signal; An object recognition method based on UWB signals including more.

4. In paragraph 1, The above artificial intelligence model is an object recognition method based on UWB signals, including a model combining a convolution neural network (CNN) and a long short-term memory (LSTM).

5. In paragraph 1, A step of receiving a second UWB signal associated with a second object; A step of determining a delay for moving the above sampling window; A step of generating a second sampling signal by performing sampling on the second UWB signal at a second sampling timing subsequent to the first sampling timing by moving the sampling window based on the determined delay; A step of converting the generated second sampling signal into a second digital signal using the ADC; and A step of providing the converted second digital signal to the artificial intelligence model to recognize the second object corresponding to the second digital signal; An object recognition method based on UWB signals including more.

6. In paragraph 5, The step of determining the delay for moving the above sampling window is: A step of determining a delay for moving the sampling window based on a plurality of unit delays included in a DTC (digital to time converter); An object recognition method based on UWB signals including:

7. In paragraph 6, The step of determining a delay for moving the sampling window based on a plurality of unit delays included in the above DTC is: A step of generating a binary code corresponding to the cycle of the system clock; and A step of determining a delay for moving the sampling window by operating at least some of the unit delays among the plurality of unit delays based on the generated binary code; An object recognition method based on UWB signals including:

8. In paragraph 7, The step of generating a binary code corresponding to the above system clock is: A step of generating a binary code for further operating one unit delay included in the DTC when one cycle of the above system clock increases; An object recognition method based on UWB signals including:

9. In paragraph 6, An object recognition method based on a UWB signal, wherein the delay time of each unit delay included in the above plurality of unit delays is determined to be the same as the sampling time of the sampling window.

10. A computer-readable, non-transitory recording medium having recorded thereon a program for executing the object recognition method based on UWB signals described in Article 1.

11. As a computing device, Communication module; memory; and At least one processor connected to said memory and configured to execute at least one computer-readable program contained in said memory Including, At least one of the above programs, Receive a first UWB signal associated with a first object, A first sampling signal is generated by performing sampling on the first UWB signal at a first sampling timing based on a sampling window, Converting the generated first sampling signal into a first digital signal using an ADC, A computing device including commands for providing the converted first digital signal to a learned artificial intelligence model to recognize the first object corresponding to the first digital signal.

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