Method and apparatus for measuring three-dimensional position using a radar sensor

Abstract

A method for three-dimensional (3D) position measurement using a radio detection and ranging (radar) sensor, comprising: transmitting a transmission signal with a carrier frequency varying over time through the radar sensor; obtaining, by the radar sensor, a reflected signal obtained by reflection of the transmission signal by an object; obtaining a beat frequency signal indicating a frequency difference between the transmission signal and the reflected signal; and estimating 3D position information of the object based on a set of sample data of different frequency bands extracted from the beat frequency signal.

Description

[0001] Cross - reference to related applications

[0002] This application claims the benefit of Korean Patent Application No. 10 - 2019 - 0148329, filed on November 19, 2019, with the Korean Intellectual Property Office, the entire disclosure of which is incorporated herein by reference for all purposes. Technical field

[0003] The following description relates to three - dimensional (3D) position measurement using a radio detection and ranging (radar) sensor. Background art

[0004] Recently released vehicles are generally equipped with active safety systems to protect drivers and reduce the risk of accidents. The active safety system may need sensors configured to identify the external environment. Among such sensors, radio detection and ranging (radar) sensors are widely used. Radar sensors can be used because they are more robust against the effects of weather or other external environmental conditions compared to other sensors configured to identify the external environment.

[0005] Signals of frequency - modulated continuous wave (FMCW) are often used in radar sensors because such signals are easy to generate and have relatively high performance in detecting vehicle information. A radar sensor using such FMCW signals can transmit a chirp signal whose frequency is linearly modulated, and the corresponding radar system can analyze the signal reflected from a target and estimate the distance to the target and the speed of the target. Summary of the invention

[0006] This summary of the invention is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to help determine the scope of the claimed subject matter.

[0007] In one general aspect, a method for three - dimensional (3D) position measurement using a radio detection and ranging (radar) sensor includes: transmitting a transmission signal whose carrier frequency varies over time through the radar sensor; obtaining a reflected signal obtained by reflecting the transmission signal from an object through the radar sensor; obtaining a beat signal indicating a frequency difference between the transmission signal and the reflected signal; and estimating 3D position information of the object based on a set of sample data of different frequency bands extracted from the beat signal.

[0008] The 3D position information of the estimation object may include: extracting a plurality of sample data groups from a beat frequency signal corresponding to a chirp signal, the plurality of sample data groups including sample data groups of different frequency bands; and estimating a distance and an elevation angle related to the position of the object based on the sample data groups of different frequency bands.

[0009] Estimating the distance and the elevation angle may include: estimating the distance and the elevation angle related to the position of the object by applying a time-frequency analysis method to the sample data groups of different frequency bands.

[0010] Estimating the distance and the elevation angle may include: simultaneously calculating the distance and the elevation angle by a time-frequency analysis method.

[0011] The time-frequency analysis method may be a short-time Fourier transform (STFT) or a wavelet transform (WT).

[0012] The 3D position information of the estimation object may include: using the change in the center direction of the transmission signal for each frequency band among the frequency bands of the transmission antennas included in the radar sensor to estimate the elevation angle related to the position of the object.

[0013] The transmission antennas may be arranged horizontally in the radar sensor.

[0014] The 3D position information may include a distance, an azimuth angle, and an elevation angle related to the position of the object.

[0015] The 3D position information of the estimation object may include: estimating the azimuth angle related to the position of the object based on the beat frequency signals respectively corresponding to the receiving antennas of the radar sensor.

[0016] Estimating the azimuth angle may include: estimating the azimuth angle by digital beamforming, where the digital beamforming estimates the direction of the received reflected signal based on the differences occurring between the beat frequency signals.

[0017] The carrier frequency may be modulated based on a frequency modulation model using linear frequency modulation.

[0018] In another general aspect, a non-transitory computer-readable storage medium stores instructions that, when executed by a processor, cause the processor to execute the above method.

[0019] In another general aspect, a device for performing three-dimensional (3D) position measurement includes: a radio detection and ranging (radar) sensor configured to transmit a transmission signal whose carrier frequency changes over time and obtain a reflected signal obtained by reflecting the transmission signal by an object; and a processor configured to obtain a beat frequency signal indicating a frequency difference between the transmission signal and the reflected signal and estimate the 3D position information of the object based on sample data groups of different frequency bands extracted from the beat frequency signal.

[0020] The processor may also be configured to: extract a plurality of sample data groups from a beat frequency signal corresponding to a chirp signal, the plurality of sample data groups including sample data groups of different frequency bands; and estimate a distance and an elevation angle related to the position of an object based on the sample data groups of different frequency bands.

[0021] The processor may also be configured to: estimate a distance and an elevation angle related to the position of an object by applying a time-frequency analysis method to the sample data groups of different frequency bands.

[0022] The time-frequency analysis method may be a short-time Fourier transform (STFT) or a wavelet transform (WT).

[0023] The processor may also be configured to: estimate an elevation angle related to the position of an object by using a change in a center direction of a transmission signal for each of the frequency bands included in the transmission antennas included in the radar sensor.

[0024] The processor may also be configured to: estimate an azimuth angle related to the position of an object based on beat frequency signals respectively corresponding to the receiving antennas of the radar sensor.

[0025] The processor may also be configured to: estimate the azimuth angle by digital beamforming, where the digital beamforming estimates a direction of a received reflected signal based on a difference occurring between the beat frequency signals.

[0026] In another general aspect, a device for performing three-dimensional (3D) position measurement includes a radio detection and ranging (radar) sensor and a processor. The radar sensor is configured to: transmit a transmission signal by using a plurality of transmission antennas corresponding to respective frequency bands of a transmission signal whose carrier frequency changes over time; and obtain a reflected signal obtained by reflecting the transmission signal by an object. The processor is configured to: acquire beat frequency signals respectively corresponding to the receiving antennas of the radar sensor and indicating a frequency difference between the transmission signal and the reflected signal; and estimate 3D position information of the object based on sample data groups of different frequency bands extracted from the beat frequency signals.

[0027] The 3D position information may include a distance and an elevation angle of the position of the object relative to the position of the device.

[0028] The processor may also be configured to: estimate the 3D position information by estimating an elevation angle related to the position of the object based on a change in a center direction of the transmission signal for each of the respective frequency bands of the transmission signal.

[0029] The processor may also be configured to: estimate the 3D position information by estimating a distance and an elevation angle related to the position of the object by applying a time-frequency analysis method to the sample data groups of different frequency bands.

[0030] Other features and aspects will become apparent from the following detailed description, the drawings, and the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1a and Figure 1b is a schematic diagram showing an example of using a radio detection and ranging (radar) sensor to measure a three-dimensional (3D) position.

[0032] Figure 2 is a schematic diagram showing an example of changing the center direction of a transmission signal in the vertical direction according to a frequency band.

[0033] Figure 3 is a schematic diagram showing an example of the configuration of a 3D position measurement device.

[0034] Figure 4 is a schematic diagram showing an example of a 3D position measurement device.

[0035] Figure 5 is a schematic diagram showing an example of the antenna arrangement of a radar sensor.

[0036] Figure 6 is a schematic diagram showing an example of the receiving antenna array of a radar sensor.

[0037] Figure 7 is a schematic diagram showing an example of estimating distance information and elevation angle information based on a reflected signal.

[0038] Figure 8 and Figure 9 is a flowchart showing an example of a 3D position measurement method using a radar sensor.

[0039] Figure 10 is a schematic diagram showing an example of a computing device.

[0040] Throughout the drawings and the detailed description, like reference numerals refer to like elements. The drawings may not be drawn to scale, and the relative dimensions, proportions, and depictions of elements in the drawings may be enlarged for clarity, illustration, and convenience. DETAILED DESCRIPTION

[0041] The following detailed description is provided to assist the reader in obtaining a comprehensive understanding of the methods, apparatuses, and / or systems described herein. However, after understanding the disclosure of this application, various changes, modifications, and equivalents of the methods, apparatuses, and / or systems described herein will be apparent. For example, the order of operations described herein is merely illustrative and is not limited to those set forth herein, but may be significantly changed after understanding the disclosure of this application, except for operations that must be performed in a certain order. Additionally, descriptions of known features may be omitted for greater clarity and conciseness.

[0042] The features described herein may be implemented in different forms and are not to be construed as limited to the examples described herein. Instead, the examples described herein are provided only to illustrate some of the many possible ways of implementing the methods, apparatuses, and / or systems described herein, which will be apparent after understanding the disclosure of this application.

[0043] Note that herein, the use of the term "may" with respect to an example or embodiment (e.g., what an example or embodiment may include or implement) means that there is at least one example or embodiment in which such a feature is included or implemented, and all examples and embodiments are not limited thereto.

[0044] Throughout the specification, when an element such as a layer, region, or substrate is described as being "on," "connected to," or "coupled to" another element, it may be directly "on," "connected to," or "coupled to" the other element, or there may be one or more other elements intervening therebetween. Conversely, when an element is described as being "directly on," "directly connected to," or "directly coupled to" another element, there may be no other elements intervening therebetween. As used herein, the term "and / or" includes any one and any combination of any two or more of the associated listed items.

[0045] Although terms such as "first," "second," and "third" may be used herein to describe various members, components, regions, layers, or portions, these members, components, regions, layers, or portions should not be limited by these terms. Instead, these terms are only used to distinguish one member, component, region, layer, or portion from another. Thus, a first member, component, region, layer, or portion referred to in an example may also be referred to as a second member, component, region, layer, or portion without departing from the teachings of the examples described herein.

[0046] The terms used herein are for describing various examples only and are not intended to limit the present disclosure. Unless the context clearly indicates otherwise, the articles "a," "an," and "the" are also intended to include the plural forms. The terms "comprising," "including," and "having" mean the presence of the stated features, numbers, operations, components, elements, and / or combinations thereof, but do not preclude the presence or addition of one or more other features, numbers, operations, components, elements, and / or combinations thereof.

[0047] After understanding the disclosure of the present application, it will be obvious to combine the features of the examples described herein in various ways. In addition, although the examples described herein have various configurations, other configurations can become obvious after understanding the disclosure of the present application.

[0048] Figure 1a and Figure 1b is a schematic diagram showing an example of using a radio detection and ranging (radar) sensor to measure a three-dimensional (3D) position.

[0049] Referring to Figure 1a , a device 100 for measuring a 3D position (hereinafter referred to as a "3D position measurement device") can use a radar sensor 110 to measure the 3D positions of one or more objects (e.g., object 120 and object 130) adjacent to the 3D position measurement device 100. According to an example, the 3D position measurement device 100 can be applied to an advanced driver assistance system (ADAS), which provides safety and convenience for a driver through various sensors located inside or outside a vehicle, an identification system for face recognition or gesture recognition, a monitoring / security system, etc. Hereinafter, an example of estimating the 3D positions of objects existing around the 3D position measurement device 100 will be mainly described, where the 3D position measurement device 100 is provided in a vehicle. However, the scope of the present disclosure is not limited to the examples described herein. The 3D position measurement device 100 can also be used in various other applications that utilize the 3D position information of an object.

[0050] The radar sensor 110 can be disposed inside or outside the 3D position measurement device 100, and can radiate a transmission signal for estimating the positions of objects (e.g., objects 120 and 130) through one or more transmission antennas. In an example, the radar sensor 110 can be a frequency modulated continuous wave (FMCW) radar sensor that has a plurality of transmission antennas and is configured to radiate an FMCW transmission signal with a carrier frequency varying over time through the transmission antennas. The transmission signal radiated from the radar sensor 110 can be reflected by one or more objects (e.g., objects 120 and 130), and one or more reflected signals can be obtained based on the transmission signal reflected by the one or more objects. The reflected signals can be obtained through one or more reception antennas included in the radar sensor 110. In an example, the radar sensor 110 can have a plurality of reception antennas.

[0051] Then, the 3D position measurement device 100 can estimate the 3D positions of one or more objects (e.g., objects 120 and 130) by analyzing the reflected signals obtained via the reception antennas. In an example, the 3D position measurement device 100 can estimate the distance from the 3D position measurement device 100 to an object present in the vicinity, and the azimuth angle corresponding to the position of the object in the horizontal direction or horizontal angle. Additionally, as Figure 1b shown, the 3D position measurement device 100 can estimate the elevation angle corresponding to the position of the object 130 in the vertical direction or vertical angle. As described above, the 3D position measurement device 100 can estimate 3D position information including the distance, azimuth angle, and elevation angle related to the positions of objects (e.g., objects 120 and 130). According to an example, in addition to the reflected signals obtained using the radar sensor 110, the 3D position measurement device 100 can also estimate the 3D positions of objects based on data obtained by other sensors (e.g., an image sensor, etc.). The reflected signals described herein can also be referred to as received signals.

[0052] Hereinafter, the manner in which the 3D position measurement device 100 uses the radar sensor 110 to measure the 3D positions of objects will be described in more detail.

[0053] Figure 2 is a schematic diagram showing an example of changing the center direction of the transmission signal in the vertical direction according to a frequency band.

[0054] Reference Figure 2, the radar sensor 200 used in the 3D position measurement device can transmit an FMCW transmission signal whose frequency varies with time. The central direction of the transmission signal of the radar sensor 200 can vary in the vertical direction based on the frequency band. The vertical direction can correspond to the direction perpendicular to the horizontal plane. For example, as the carrier frequency of the transmission signal to be transmitted from the radar sensor 200 increases, the central direction of the transmission signal can gradually rise in the vertical direction. This can be attributed to the transmission characteristics based on the arrangement and structure of the transmission antenna of the radar sensor 200. In Figure 2 's example, when the carrier frequency gradually increases from f1 to f4, the central direction of the transmission signal with the carrier frequency f1 can face the bottom, while the central direction of the transmission signal with the carrier frequency f4 can face the top. The 3D position measurement device can use the characteristics of the above-mentioned radar sensor 200 to estimate the elevation angle associated with the position of the object in the vertical direction.

[0055] Figure 3 is a schematic diagram showing an example of the configuration of the 3D position measurement device.

[0056] Reference Figure 3 , the 3D position measurement device 300 includes, for example, a radar sensor 310 and a processor 320.

[0057] The radar sensor 310 can sense radar data. For example, the radar sensor 310 can transmit a transmission signal whose carrier frequency varies with time through one or more transmission antennas, and can obtain a reflected signal obtained by the reflection of the transmission signal by an object through one or more reception antennas. The transmission signal can be an FMCW signal generated based on a frequency modulation model, and the FMCW signal can be radiated outward to be transmitted outside the radar sensor 310 through the transmission antenna. The radiated FMCW signal can be reflected from the object, and the reflected signal obtained by the reflection of the FMCW signal by the object can be received by the reception antenna and form radar data. In the example, the radar sensor 310 can include transmission antennas corresponding to a plurality of transmission channels (indicated by Tx (see Figure 4 )) and reception antennas corresponding to a plurality of reception channels (indicated by Rx (see Figure 4 )) respectively.

[0058] The processor 320 may control the operation and functions of the 3D position measurement device 300. The processor 320 may set the detection area and detection resolution of the 3D position measurement device 300, and adjust the characteristics of the transmitted signal based on the set detection area and the set detection resolution. For example, the processor 320 may adjust the characteristics of the carrier frequency of the transmitted signal (e.g., frequency range, slope, frequency change time, etc.), and / or adjust the characteristics of the intensity of the transmitted signal. The detection area may be set based on the detection range and detection angle. The detection resolution may be set based on the detection unit of the object position (e.g., 1 meter (m) and 50 centimeters (cm)).

[0059] The processor 320 may estimate the 3D position information and / or velocity of an object by analyzing the radar data sensed via the radar sensor 310. The 3D position information may be information specifying the 3D position of the object, and may include, for example, the distance, azimuth angle, and elevation angle related to the position of the object. Hereinafter, examples of measuring the distance, azimuth angle, and elevation angle as the 3D position information will be described in more detail. However, the 3D position information of the object may also be represented by a rectangular coordinate system or a polar coordinate system, and there is no limitation on the method of representing such information.

[0060] In an example, the processor 320 may obtain a beat frequency signal indicating the frequency difference between the transmitted signal and the reflected signal based on the transmitted signal and the reflected signal. The beat frequency signal may also be referred to as an intermediate frequency signal.

[0061] The processor 320 may extract a set of sample data of different frequency bands from the beat frequency signal, and estimate the 3D position information of the object based on the extracted set of sample data. In an example, the processor 320 may extract a set of sample data by sampling the beat frequency signal corresponding to the chirp signal at a plurality of sampling points. The chirp signal may refer to a signal whose frequency changes over time. The processor 320 may sample the beat frequency signal at the sampling points, and obtain a set of sample data by performing an analog-to-digital conversion that converts the sampled analog signal values into corresponding digital signals.

[0062] The processor 320 may estimate the distance and elevation angle related to the position of the object based on the set of sample data of different frequency bands included in the obtained set of sample data. As referred to above Figure 2As described, the processor 320 may estimate the elevation angle related to the position of the object using the varying characteristics of the center direction of the transmitted signal for each frequency band. In an example, the processor 320 may estimate the distance and elevation angle related to the position of the object by applying a time-frequency analysis method to a set of sample data for different frequency bands. The time-frequency analysis method may be a short-time Fourier transform (STFT) or a wavelet transform (WT). According to an example, the processor 320 may use a high-resolution range profile (HRRP) to determine the distance related to the position of the object. Here, the terms "distance" and "spacing" may be used interchangeably. In the following, reference will be made to Figures 4 to 7 Examples of estimating the distance and / or elevation angle related to the position of the object based on the beat frequency signal will be described in more detail.

[0063] The processor 320 may estimate the azimuth angle related to the position of the object based on the beat frequency signals respectively corresponding to the receiving antennas of the radar sensor 310. In an example, the processor 320 may use, for example, digital beamforming (DBF), estimation of signal parameters via rotational invariance techniques (ESPRIT), multiple signal classification (MUSIC), minimum variance distortionless response (MVDR), or the Bartlett method to estimate the azimuth angle related to the position of the object. For example, desirably, the processor 320 may estimate the azimuth angle corresponding to the position of the object in the horizontal direction by DBF. Examples of estimating such an azimuth angle will be described in more detail below.

[0064] In an example, a set of sample data of the beat frequency signal may be represented by Equation 1.

[0065] [Equation 1]

[0066] Y = [Y(1), Y(2),..., Y(i),..., Y(N - 1), Y(N)]

[0067] In Equation 1, i is a time index, a natural number greater than or equal to 1 and less than or equal to N. N is the number of sets of sample data sampled from the beat frequency signal, a natural number greater than or equal to 1. Y is the data obtained by converting the analog value of the beat frequency signal into a digital value. For example, when the receiving antenna array included in the radar sensor 310 includes M receiving antennas, the sample data Y(i) at the i-th sampling point corresponding to the time index i may be represented by Equation 2.

[0068] [Equation 2]

[0069] Y(i) = [s1(i), s2(i),... s m (i),... s M-1 (i), s M (i)] T

[0070] In Equation 2, s m (i) is a sampled value of the i-th sample data obtained by sampling the intensity of the reflected signal received by the m-th receiving antenna among the M receiving antennas. M is a natural number greater than or equal to 2, and m is a natural number greater than or equal to 1 and less than or equal to M. Then, a normalization model can be applied to the sample data Y(i). The normalization model can be represented by Equation 3.

[0071] [Equation 3]

[0072] A pNorm ={A pNorm，1 ,..., A pNorm，i ,..., A pNorm，N}

[0073] In Equation 3, A pNorm,i is the i-th normalization matrix that applies to the value of the i-th sample data in the sample data group. The i-th normalization matrix can be represented by Equation 4.

[0074] [Equation 4]

[0075]

[0076] In Equation 4, A fi is the first matrix operation that converts the time-domain value corresponding to the i-th sample data into angle information using the carrier frequency corresponding to the i-th sample data of the frequency modulation model. A f0 -1 is the inverse matrix of A f0 , and A f0 is the second matrix operation that inversely converts the angle information into the time-domain value using the reference frequency f0. The first matrix operation A fi in Equation 4 can also be represented by Equation 5 and Equation 6.

[0077] [Equation 5]

[0078]

[0079] [Equation 6]

[0080]

[0081] In Equation 5, the first matrix operation A fi can be represented by a set of vectors α fi (θ k ), where K is a natural number greater than or equal to 1, and k is a natural number greater than or equal to 1 and less than or equal to K. In Equation 6, d is the distance between the receiving antennas of the antenna array included in the radar sensor 310. j is an imaginary unit. λ fi is the wavelength corresponding to the carrier frequency of the i-th sample data. θk is the k-th angle in A fi and α fi (θ k ) is the vector corresponding to the angle θ in the carrier frequency of the frequency modulation model corresponding to the i-th time index. A k can be a K×M matrix composed of K rows and M columns. fi Here, the matrix A of formula 5

[0082] The calculation result of the matrix product between fi A and Y(i) of formula 2 fi Y(i) can be a K×1 dimensional vector. In the calculation result of the matrix product A fi Y(i), the element in the k-th row can be the value corresponding to the probability that Y(i) is the k-th angle θ k and can indicate the angle information. Therefore, based on the result of applying the first matrix operation A fi to the i-th sample data Y(i), the azimuth angle information at the i-th time index can be estimated.

[0083] According to the example, the processor 320 can change the detection area or detection resolution based on the result of measuring the 3D position of the object as described above, and adjust the characteristics of the transmitted signal based on the changed result. For example, when it is detected that there is no object nearby, the processor 320 can increase the intensity of the transmitted signal to expand the detection area. In addition, for more precise or closer detection, the processor 320 can adjust the carrier frequency change characteristics of the transmitted signal.

[0084] Figure 4 is a schematic diagram showing an example of a 3D position measurement device.

[0085] Referring Figure 4 , the 3D position measurement device includes, for example, a radar sensor 410 and a signal processor 450.

[0086] The radar sensor 410 can generate an FMCW signal based on the frequency modulation control signal transmitted from the signal processor 450, and can transmit the generated FMCW signal as a transmitted signal through the transmit antenna 430. In addition, the radar sensor 410 can obtain the received signal obtained by the FMCW signal being reflected by the object and incident on the radar sensor 410 through the receive antenna 435. The transmit antenna 430 can include a plurality of transmit antennas, and the receive antenna 435 can include a plurality of receive antennas.

[0087] The radar sensor 410 can include an FMCW signal generator 415, a power amplifier 420, a transmit antenna 430, a receive antenna 435, a low noise amplifier 440, and a mixer 445.

[0088] The FMCW signal generator 415 can generate an FMCW signal 402 with a carrier frequency that changes over time based on the setting information of the transmission signal. The setting information of the transmission signal may include setting information associated with the setting of the detection area or detection resolution. In an example, the FMCW signal generator 415 may include a voltage-controlled oscillator (VCO) circuit for generating various oscillation frequencies and a phase-locked loop (PLL) circuit for improving the output frequency stability of the VCO circuit.

[0089] The FMCW signal generator 415 can generate the FMCW signal 402 by modulating the carrier frequency based on the frequency modulation model 401 defined by the processor 455. The FMCW signal 402 may also be referred to as a chirp signal. The frequency modulation model 401 may be a model indicating the change in the carrier frequency of the transmission signal to be sent by the radar sensor 410. In the frequency modulation model 401, the vertical axis represents the magnitude of the carrier frequency and the horizontal axis represents time. For example, the frequency modulation model 401 may have a mode in which the carrier frequency changes linearly or non-linearly over time.

[0090] In Figure 4 the example, the frequency modulation model 401 may have a mode in which the carrier frequency changes linearly over time. The FMCW signal generator 415 can generate an FMCW signal 402 having the following mode: such that the carrier frequency changes based on the frequency modulation model 401. For example, as shown in the figure, the FMCW signal 402 may correspond to a waveform in which the carrier frequency gradually increases in some time intervals and gradually decreases in other time intervals. In the graph showing the FMCW signal 402, the vertical axis represents the magnitude of the FMCW signal 402, and the horizontal axis represents time.

[0091] The FMCW signal 402 generated by the FMCW signal generator 415 can be transmitted to the power amplifier 420. The power amplifier 420 can amplify the received FMCW signal 402 and transmit the amplified FMCW signal to the transmitting antenna 430. The transmitting antenna 430 can radiate the amplified FMCW signal as a transmission signal.

[0092] The receiving antenna 435 can receive the reflected signal obtained by reflecting the radiated transmission signal by an object and then returning it as a received signal. The low-noise amplifier 440 can only amplify and output the received signal component excluding noise from the received signal. The mixer 445 can demodulate the previous signal, such as the original chirp signal, which is the signal before frequency modulation, from the received signal amplified by the low-noise amplifier 440. Then, the mixer 445 can transmit the demodulated signal to the signal processor 450.

[0093] The signal processor 450 can process the received signal transmitted from the radar sensor 410 and estimate the 3D position information of the object. The signal processor 450 can include, for example, a processor 455, a low-pass filter (LPF) 460, and an analog-to-digital converter (ADC) 465.

[0094] The LPF 460 can filter the low-frequency band signals in the received signal transmitted from the radar sensor 410 to reduce the noise of the high-frequency components included in the received signal. The ADC 465 can convert the received signal, which is an analog signal obtained through such low-pass filtering, into a digital signal. The processor 455 can output a frequency modulation control signal for generating the FMCW signal 402 to the FMCW signal generator 415, and estimate the 3D position information of the object based on the received signal through signal processing operations.

[0095] In the illustrated example, the processor 455 can compare the frequency 408 of the reflected signal (or, denoted by Rx) with the frequency 407 of the transmitted signal (or, denoted by Tx). The processor 455 can detect the difference between the frequency 408 of the reflected signal and the frequency 407 of the transmitted signal, and generate a beat frequency signal indicating the difference between the frequency 408 and the frequency 407. Such a frequency difference between the reflected signal and the transmitted signal can indicate the difference f during the time interval in the frequency modulation model 401 in the graph 409 where the carrier frequency increases with time. beat The frequency difference f beat can be proportional to the distance between the radar sensor 410 and the object. Therefore, the distance between the radar sensor 410 and the object can be derived based on the frequency difference between the reflected signal and the transmitted signal.

[0096] In the example, the distance information related to the position of the object can be calculated as represented by Equation 7.

[0097] [Equation 7]

[0098]

[0099] In Equation 7, R is the distance or spacing between the radar sensor 410 and the object, and c is the speed of light. T chirp is the time length corresponding to the rising interval during which the carrier frequency increases in the transmitted signal. B is the modulation frequency bandwidth. The beat frequency f beat is the frequency difference between the transmitted signal and the reflected signal at a certain time point during the rising interval. The beat frequency f beat can be derived as represented by Equation 8.

[0100] [Equation 8]

[0101]

[0102] In Equation 8, f beat is the beat frequency, and t d is the reciprocating delay time related to the position of the object, corresponding to the time difference or delay time between the time point when the radiation (or, transmission) transmits the signal and the time point when the reflected signal is received. Additionally, B is the modulation frequency bandwidth.

[0103] Figure 5 is a schematic diagram showing an example of the antenna arrangement of the radar sensor.

[0104] Reference Figure 5 , the antenna array 500 of the radar sensor may include transmit antennas 510 and 520 and receive antennas 530 and 540. The transmit antennas 510 and 520 form a plurality of transmit channels, and the receive antennas 530 and 540 form a plurality of receive channels. In the example, the antenna array 500 may be arranged in the form of series-fed patches, and the transmit antennas 510 and 520 and the receive antennas 530 and 540 may be arranged horizontally in the radar sensor, or arranged in the width direction in the radar sensor. Based on the characteristics of this arrangement of the transmit antennas 510 and 520, there may be a characteristic indicating a change in the central direction of the transmit signal based on the frequency band change, which was described above with reference to Figure 2 . However, there may not be such a change in the horizontal direction as the change in the central direction of the transmit signal based on the frequency band. In another example, the radar sensor may include an antenna with frequency scanning characteristics.

[0105] Figure 6 is a schematic diagram showing an example of the receive antenna array of the radar sensor.

[0106] The 3D position measurement device can estimate the azimuth angle related to the position of the object by analyzing the reflected signals obtained via the receive antennas that respectively form a plurality of receive channels. Referring to Figure 6 , the reflected signals received through the receive channels respectively may have a phase difference from the reference phase. The reference phase may be any phase or the phase of one of the receive channels.

[0107] The 3D position measurement device can generate a radar vector whose dimension corresponds to the number of receive channels based on the reflected signals received through the receive channels. For example, when the radar sensor has four receive channels, the 3D position measurement device can generate a four-dimensional radar vector including the phase values corresponding to each of the receive channels. The phase value corresponding to each of the receive channels may be a numerical value indicating the aforementioned phase difference.

[0108] For another example, when the radar sensor includes one transmitting channel and four receiving channels, the transmitted signal radiated through the transmitting channel can be reflected from the object position, and the reflected signals can be received through these four receiving channels respectively. For example, as shown in the figure, the receiving antenna array 610 includes a first receiving antenna 611, a second receiving antenna 612, a third receiving antenna 613, and a fourth receiving antenna 614. In Figure 6 the example, the phase of the signal to be received by the first receiving antenna 611 is set as the reference phase. In this example, when the receiving antenna array 610 receives the reflected signal 608 reflected from the position of the same object, the additional distance Δ of the distance from the object position to the second receiving antenna 612 compared with the distance from the object position to the first receiving antenna 611 can be expressed by Equation 9.

[0109] [Equation 9]

[0110] Δ = d × sin(θ)

[0111] In Equation 9, θ is the angle of arrival (AOA) of the reflected signal 608 received from the object position, and d is the distance between the receiving antennas. c is the speed of light in air, taken as a constant. Here, c = fλ, so the phase change W in the second receiving antenna 612 caused by the additional distance Δ can be expressed by Equation 10.

[0112] [Equation 10]

[0113]

[0114] The phase change W can correspond to the phase difference between the waveform of the reflected signal received by the first receiving antenna 611 and the waveform of the reflected signal received by the second receiving antenna 612. In Equation 10 above, the wavelength λ can be inversely proportional to the frequency f of the reflected signal 608. For example, when the change in the carrier frequency of the frequency modulation model is small, in the frequency modulation model, the frequency f can be considered as a single initial frequency (e.g., f0). Therefore, when determining the phase change W based on the received reflected signal 608, the 3D position measurement device can determine the AOA θ and estimate the azimuth angle related to the position of the object based on the determined AOA θ.

[0115] Figure 7 is a schematic diagram showing an example of estimating distance information and elevation angle information based on the reflected signal.

[0116] Refer to Figure 7 , in the first graph 710, the solid line represents the rough waveform of the transmitted signal 712 to be transmitted by the transmitting antenna, and the dashed line represents the rough waveform of the reflected signal 714 to be received by the receiving antenna.

[0117] In Figure 7In the example, for ease of description, a transmission signal 712 having a mode in which the carrier frequency linearly increases will be described. For example, the carrier frequency may linearly increase from an initial frequency f c to a final frequency f c +BW by the frequency bandwidth BW.

[0118] In the example, the distance to the object can be estimated based on the time difference between the transmission signal 712 and the reflected signal 714.

[0119] The 3D position measurement device can calculate a beat frequency corresponding to the frequency difference between the transmission signal 712 and the reflected signal 714, and obtain a beat frequency signal indicating the change of the beat frequency over time. An example of the beat frequency signal is shown in the second graph 720. The beat frequency can correspond to the round-trip time or distance until the transmission signal 712 is reflected by the object as the reflected signal 714 and the reflected signal 714 is received. The beat frequency can be used to estimate distance information associated with the distance to the object. In the example, the 3D position measurement device can obtain a beat frequency signal 722 corresponding to the chirp signal.

[0120] The 3D position measurement device can sample the beat frequency signal 722 corresponding to the chirp signal (corresponding to one period). Then, the 3D position measurement device can obtain a set of sample data by converting the signal values obtained by sampling the beat frequency signal 722 into digital values. Here, the measurable beat frequency can be determined based on the sampling frequency for sampling the beat frequency signal 722, and this can be associated with a detection area that is the measurable distance.

[0121] In the example, all sets of sample data sampled from the beat frequency signal 722 include a first set of sample data 732, a second set of sample data 734, a third set of sample data 736, and a fourth set of sample data 738 corresponding to different frequency bands, respectively. The first set of sample data 732 is a set of sample data 742 corresponding to the first frequency band, and the second set of sample data 734 is a set of sample data 744 corresponding to the second frequency band. Additionally, the third set of sample data 736 is a set of sample data 746 corresponding to the third frequency band, and the fourth set of sample data 738 is a set of sample data 748 corresponding to the fourth frequency band. The first to fourth frequency bands may have ranges corresponding to the four frequency bands into which the frequency band from the initial frequency f c to the final frequency f c +BW is divided as described above.

[0122] In this example, distance information related to the position of the object can be derived from the results of applying the time-frequency analysis method to each of the sample data groups 732, 734, 736, and 738. Additionally, elevation angle information related to the position of the object can be derived from the results of separating each of the sample data groups 732, 734, 736, and 738 into different frequency bands and applying the time-frequency analysis method to the sample data included in each of the sample data groups 732, 734, 736, and 738. To estimate the elevation angle information, all or some of the sample data groups obtained by sampling the chirp signal can be used.

[0123] As described above, the transmitted signal to be transmitted from the transmitting antenna can be different in the central direction for each frequency band in the vertical direction in which the transmitted signal travels. The 3D position measurement device can use this characteristic of the transmitted signal to separate the sample data groups 732, 734, 736, and 738 of different frequency bands, and perform a time-frequency analysis method such as Fourier transform or WF to estimate the elevation angle information in the vertical direction. In the example, the 3D position measurement device can simultaneously derive distance information and elevation angle information related to the position of the object by the time-frequency analysis method. The elevation angle information in the vertical direction can be estimated in the time domain, and the distance information can be estimated in the frequency domain.

[0124] Through this processing method as described above, the 3D position measurement device can effectively estimate the position of the object in the elevation angle direction without using antennas with different performances or without applying a complex antenna arrangement. Therefore, the 3D position of the object can be measured only through relatively less complex control and a simple antenna arrangement, and the cost for measuring the 3D position can be reduced.

[0125] In the example, the 3D position measurement device can estimate the azimuth angle related to the position of the object by DBF. For example, the 3D position measurement device can calculate the differences that occur between the reflected signals received by the receiving antennas respectively, and estimate the direction of the received reflected signal based on the calculated differences. The 3D position measurement device can estimate the azimuth angle of the object based on the estimated direction.

[0126] Figure 8 and Figure 9 are flowcharts showing examples of 3D position measurement methods using a radar sensor.

[0127] Reference Figure 8, in operation 810, the 3D position measurement device transmits a transmission signal with a carrier frequency varying over time through a radar sensor. For example, the 3D position measurement device can generate an FMCW signal by modulating the carrier frequency based on a frequency modulation model using a linear frequency modulation method, and can radiate the generated FMCW signal through one or more transmission antennas of the radar sensor. In operation 820, the 3D position measurement device obtains a reflected signal obtained by reflecting the transmission signal by an object through the radar sensor. For example, the 3D position measurement device can receive the reflected signal through one or more receiving antennas of the radar sensor.

[0128] In operation 830, the 3D position measurement device obtains a beat frequency signal indicating the frequency difference between the transmission signal and the reflected signal. For example, the 3D position measurement device can calculate the beat frequency signal for each receiving channel corresponding to a receiving antenna respectively. In operation 840, the 3D position measurement device estimates the 3D position information of the object based on the beat frequency signal. For example, the 3D position measurement device can estimate the 3D position information of the object based on a set of sample data of different frequency bands extracted from the beat frequency signal. Hereinafter, reference will be made to Figure 9 Operation 840 will be described in more detail.

[0129] Reference Figure 9 , in operation 910, the 3D position measurement device extracts a set of sample data from the beat frequency signal. For example, the 3D position measurement device can extract a set of sample data by sampling the beat frequency signal at sampling points and converting the signal values of the sampled beat frequency signal into corresponding digital signal values. The 3D position measurement device can extract a set of sample data from the beat frequency signal corresponding to the chirp signal.

[0130] In operation 920, the 3D position measurement device estimates the distance and elevation angle related to the position of the object based on the sets of sample data of different frequency bands included in the extracted set of sample data. For example, the 3D position measurement device can utilize the change in the central direction of the transmission signal of each frequency band for the transmission antennas included in the radar sensor to estimate the elevation angle related to the position of the object. For example, the 3D position measurement device can estimate the distance and elevation angle related to the position of the object by applying a time-frequency analysis method to the sets of sample data of different frequency bands. For example, the time-frequency analysis method can include STFT and WT. The 3D position measurement device can calculate the distance and elevation angle simultaneously by the time-frequency analysis method. To describe operation 920 of estimating the distance and / or elevation angle in more detail, reference can be made to the description made above with reference to Figure 4 and Figure 7 for description.

[0131] In operation 930, the 3D position measurement device estimates the azimuth angle related to the position of the object based on the beat frequency signals respectively corresponding to the receiving antennas of the radar sensor. For example, the 3D position measurement device can use the DBF method, MUSIC method, ESPRIT, MVDR method, or Bartlett method to estimate the azimuth angle related to the position of the object. For example, the 3D position measurement device can estimate the azimuth angle by the DBF method, where the DBF method estimates the direction of the received reflected signal based on the differences occurring between the beat frequency signals. To describe operation 930 of estimating the azimuth angle in more detail, reference can be made to the content described above with reference to Figure 3 and Figure 6 the content that has been described.

[0132] Figure 10 FIG. is a schematic diagram showing an example of the computing device 1000.

[0133] Reference Figure 10 , the computing device 1000 can be a device that can perform the function of measuring the 3D position of the object as described above. In an example, the computing device 1000 can perform the operations and functions of the 3D position measurement device 300 described above with reference to Figure 3 . The computing device 1000 can be used for automatic or autonomous driving, driver assistance, face recognition, gesture recognition, monitoring / security systems, etc. For example, the computing device 1000 can operate as described above by being disposed in an image processing device, a smart phone, a wearable device, a tablet computer, a netbook, a laptop computer, a desktop computer, a head-mounted display (HMD), an autonomous vehicle, or a smart vehicle.

[0134] Reference Figure 10 , the computing device 1000 includes, for example, a processor 1010, a memory 1020, a sensor 1030, an input device 1040, an output device 1050, and a network device 1060. The processor 1010, the memory 1020, the sensor 1030, the input device 1040, the output device 1050, and the network device 1060 can communicate with each other through a communication bus 1070.

[0135] The processor 1010 can execute the functions and instructions in the computing device 1000. For example, the processor 1010 can process the instructions stored in the memory 1020. The processor 1010 can execute one or more or all of the operations described above with reference to Figures 1a to 9 .

[0136] The memory 1020 may store information or data to be processed by the processor 1010. The memory 1020 may store instructions to be executed by the processor 1010. The memory 1020 may include a non-transitory computer-readable storage medium, such as random access memory (RAM), dynamic RAM (DRAM), static RAM (SRAM), magnetic hard disk, optical disk, flash memory, electrically erasable programmable read-only memory (EPROM), floppy disk, or other types of computer-readable storage media well-known in the relevant technical field.

[0137] The sensor 1030 may include one or more sensors, such as a radar sensor, an image sensor, etc.

[0138] The input device 1040 may receive input from a user through tactile input, video input, audio input, or touch input. The input device 1040 may include, for example, a keyboard, a mouse, a touch screen, a microphone, and / or other devices that can detect input from a user and send the detected input.

[0139] The output device 1050 may provide the output of the computing device 1000 to the user through visual, auditory, or tactile channels. The output device 1050 may include, for example, a liquid crystal display (LCD), a light-emitting diode (LED) display, a touch screen, a speaker, a vibration generator, and / or other devices that can provide output to the user. In an example, the output device 1050 may provide the result of the processor 1010 estimating the 3D position information of an object using any combination of visual information, auditory information, or tactile information.

[0140] The network device 1060 may communicate with external devices through a wired or wireless network. For example, the network device 1060 may communicate with external devices through a wired communication method or a wireless communication method including, for example, Bluetooth, WiFi, third-generation (3G) communication, long-term evolution (LTE) communication, etc.

[0141] Performing the operations described in this application Figures 1a to 10The processors 320, 455, and 1010, the FMCW signal generator 415, the memory 1020, the communication bus 1070, the processor, the memory, and other components and devices are implemented by hardware components configured to perform the operations performed by the hardware components described in this application. Examples of hardware components that can be used to perform the operations described in this application, where appropriate, include controllers, sensors, generators, drivers, memories, comparators, arithmetic logic units, adders, subtracters, multipliers, dividers, integrators, and any other electronic components configured to perform the operations described in this application. In other examples, one or more hardware components for performing the operations described in this application are implemented by computing hardware (e.g., by one or more processors or computers). The processor or computer can be implemented by one or more processing elements, e.g., logic gate arrays, controllers, and arithmetic logic units, digital signal processors, microcomputers, programmable logic controllers, field programmable gate arrays, programmable logic arrays, microprocessors, or any other device or combination of devices configured to respond and execute instructions in a defined manner to achieve the desired result. In one example, the processor or computer includes or is connected to one or more memories storing instructions or software executed by the processor or computer. The hardware components implemented by the processor or computer can execute instructions or software, e.g., an operating system (OS) and one or more software applications running on the OS, to perform the operations described in this application. The hardware components can also access, manipulate, process, create, and store data in response to the execution of the instructions or software. For the sake of brevity, the singular terms "processor" or "computer" may be used in the description of the examples described in this application, but in other examples, multiple processors or computers may be used, or the processor or computer may include multiple processing elements, or multiple types of processing elements, or both. For example, a single hardware component or two or more hardware components can be implemented by a single processor, or two or more processors, or a processor and a controller. One or more hardware components can be implemented by one or more processors, or a processor and a controller, and one or more other hardware components can be implemented by one or more other processors, or another processor and another controller. One or more processors or a processor and a controller can implement a single hardware component, or two or more hardware components. The hardware components can have any one or more of different processing configurations, examples of which include single processor, independent processor, parallel processor, single instruction single data (SISD) multiprocessing, single instruction multiple data (SIMD) multiprocessing, multiple instruction single data (MISD) multiprocessing, and multiple instruction multiple data (MIMD) multiprocessing.

[0142] performing the operations described in this application Figures 1a to 10The methods shown are performed by computing hardware, such as by one or more processors or computers, where the computing hardware is implemented as described above to execute instructions or software to perform the operations performed by these methods in this application. For example, a single operation or two or more operations may be performed by a single processor, or two or more processors, or a processor and a controller. One or more operations may be performed by one or more processors, or a processor and a controller, and one or more other operations may be performed by one or more other processors, or another processor and another controller. One or more processors or a processor and a controller may perform a single operation or two or more operations.

[0143] Instructions or software for controlling computing hardware (e.g., one or more processors or computers) to implement the hardware components and perform the methods described above may be written as a computer program, code segment, instruction, or any combination thereof, for individually or jointly instructing or configuring one or more processors or computers to operate as a machine or special-purpose computer to perform the operations performed by the hardware components and the methods described above. In one example, the instructions or software include machine code directly executable by one or more processors or computers, such as machine code generated by a compiler. In another example, the instructions or software include higher-level code executable by one or more processors or computers using an interpreter. The instructions or software may be written in any programming language based on the block diagrams and flowcharts shown in the figures and the corresponding descriptions in the specification, which disclose algorithms for performing the operations performed by the hardware components and the methods described above.

[0144] Instructions or software for controlling computing hardware (e.g., one or more processors or computers) to implement the hardware components and perform the methods described above, as well as any associated data, data files, and data structures, may be recorded, stored, or fixed in or on one or more non-transitory computer-readable storage media. Examples of non-transitory computer-readable storage media include read-only memory (ROM), random access memory (RAM), flash memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-RLTH, BD-RE, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, and any other device configured to store instructions or software and any associated data, data files, and data structures in a non-transitory manner and provide the instructions or software and any associated data, data files, and data structures to one or more processors or computers so that the one or more processors or computers can execute the instructions. In one example, the instructions or software and any associated data, data files, and data structures are distributed across a networked computer system such that the one or more processors or computers store, access, and execute the instructions and software and any associated data, data files, and data structures in a distributed manner.

[0145] Although the present disclosure includes specific examples, it will be apparent after understanding the disclosure of this application that various changes in form and detail may be made to these examples without departing from the spirit and scope of the claims and their equivalents. The examples described herein are considered to be illustrative only and not for purposes of limitation. The description of a feature or aspect in each example should be considered applicable to similar features or aspects in other examples. Appropriate results may be achieved if the described techniques are performed in a different order and / or if the components in the described system, architecture, device, or circuit are combined in a different manner and / or replaced or supplemented by other components or their equivalents. Accordingly, the scope of the present disclosure is defined not by the detailed description but by the claims and their equivalents, and all variations within the scope of the claims and their equivalents should be construed as being included in the present disclosure.

Claims

1. A method for position measurement using a radio detection and ranging "radar" sensor, comprising: Transmitting a transmission signal with a carrier frequency varying over time through a plurality of transmission antennas included in the radar sensor by the radar sensor; Obtaining a reflected signal obtained by reflecting the transmission signal by an object through the radar sensor; Obtaining beat frequency signals respectively corresponding to receiving antennas included in the radar sensor and indicating a frequency difference between the transmission signal and the reflected signal; And Estimating position information of the object based on a set of sample data of different frequency bands extracted from the beat frequency signals, wherein estimating the position information of the object includes: Using a change in a center direction of the transmission signal for each frequency band among frequency bands of the transmission antennas to estimate an elevation angle related to the position of the object.

2. The method according to claim 1, wherein Estimating the position information of the object includes: Extracting a plurality of sets of sample data from the beat frequency signals corresponding to chirp signals, the plurality of sets of sample data including the sets of sample data of different frequency bands; and Estimating a distance and an elevation angle related to the position of the object based on the sets of sample data of different frequency bands.

3. The method according to claim 2, wherein Estimating the distance and the elevation angle includes: Estimating the distance and the elevation angle related to the position of the object by applying a time-frequency analysis method to the sets of sample data of different frequency bands.

4. The method according to claim 3, wherein, Estimating the distance and the elevation angle includes: Simultaneously calculating the distance and the elevation angle by the time-frequency analysis method.

5. The method according to claim 3, wherein The time-frequency analysis method is a short-time Fourier transform STFT or a wavelet transform WT.

6. The method according to claim 1, wherein The transmission antennas are arranged horizontally in the radar sensor.

7. The method according to claim 1, wherein The position information includes a distance, an azimuth angle, and an elevation angle related to the position of the object.

8. The method according to claim 7, wherein Estimating the position information of the object includes: Estimating the azimuth angle by digital beamforming, wherein the digital beamforming estimates a direction of receiving the reflected signal based on a difference occurring between the beat frequency signals.

9. The method according to claim 1, wherein Estimating the position information of the object includes: Estimating an azimuth angle related to the position of the object based on beat frequency signals respectively corresponding to receiving antennas of the radar sensor.

10. The method according to claim 1, wherein, The carrier frequency is modulated based on a frequency modulation model using linear frequency modulation.

11. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to execute the method according to claim 1.

12. A device for position measurement, comprising: A radio detection and ranging "radar" sensor configured to transmit a transmission signal with a carrier frequency varying over time through a plurality of transmission antennas included in the radar sensor and obtain a reflected signal obtained by reflecting the transmission signal by an object; And A processor configured to obtain beat frequency signals respectively corresponding to receiving antennas included in the radar sensor and indicating a frequency difference between the transmission signal and the reflected signal and estimate position information of the object based on a set of sample data of different frequency bands extracted from the beat frequency signals, wherein the processor is further configured to: Estimate the elevation angle related to the position of the object by using the change in the center direction of the transmission signal for each of the frequency bands of the transmission antenna.

13. The device according to claim 12, wherein, The processor is further configured to: Extract a plurality of sample data groups from the beat frequency signals corresponding to the chirp signals, the plurality of sample data groups including sample data groups of different frequency bands; and Estimate the distance and elevation angle related to the position of the object based on the sample data groups of different frequency bands.

14. The apparatus according to claim 13, wherein, The processor is further configured to: Estimate the distance and the elevation angle related to the position of the object by applying a time-frequency analysis method to the sample data groups of different frequency bands.

15. The apparatus according to claim 14, wherein The time-frequency analysis method is a short-time Fourier transform STFT or a wavelet transform WT.

16. The apparatus according to claim 12, wherein The processor is further configured to: Estimate the azimuth angle related to the position of the object based on the beat frequency signals respectively corresponding to the receiving antennas included in the radar sensor.

17. The apparatus according to claim 16, wherein, The processor is further configured to: Estimate the azimuth angle by digital beamforming, where the digital beamforming estimates the direction of receiving the reflected signal based on the differences occurring between the beat frequency signals.

18. A device for performing position measurement, comprising: A radio detection and ranging "radar" sensor configured to: Transmit the transmission signal by using a plurality of transmission antennas corresponding to respective frequency bands of a transmission signal whose carrier frequency varies with time; and Obtain a reflected signal obtained by reflecting the transmission signal by an object; And A processor configured to: Obtain beat frequency signals respectively corresponding to the receiving antennas included in the radar sensor and indicating the frequency difference between the transmission signal and the reflected signal; and Estimate the position information of the object based on the sample data groups of different frequency bands extracted from the beat frequency signals, wherein the processor is further configured to: estimate the elevation angle related to the position of the object based on the change in the center direction of the transmission signal for each of the respective frequency bands of the transmission signal.

19. The apparatus according to claim 18, wherein, The position information includes the distance and elevation angle of the position of the object relative to the position of the device.

20. The apparatus according to claim 18, wherein, The processor is further configured to: estimate the position information by estimating the distance and elevation angle related to the position of the object by applying a time-frequency analysis method to the sample data groups of different frequency bands.

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