Method and device for processing radar signals

Abstract

A radio detection and ranging (radar) signal processing device obtains radar data by compensating for variations in the carrier frequency of a sensed radar signal and outputs a radar image based on the obtained radar data. The radar signal processing method includes obtaining a difference frequency signal based on a radar transmit signal and a radar reflection signal, where the radar transmit signal is generated based on a frequency modulation model and the radar reflection signal is obtained by reflecting the radar transmit signal from an object; and generating radar data by compensating the difference frequency signal for variations in the carrier frequency of the frequency modulation model.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority from Korean Patent Application No. 10-2019-0175440 filed on December 26, 2019, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference in its entirety for all purposes. Technical Field

[0003] The present disclosure relates to techniques for processing radio detection and ranging (radar) signals. Background Art

[0004] Advanced Driver Assistance Systems (ADAS) enhance driver safety and convenience by enabling sensors installed inside or outside the vehicle. ADAS can assist the driver by detecting objects and warning them of dangerous road conditions.

[0005] Sensors used for ADAS may include cameras, infrared sensors, ultrasonic sensors, light detection and ranging (lidar) sensors, and radio detection and ranging (radar) sensors. Compared to optical sensors, radar sensors can reliably measure objects near the vehicle regardless of environmental conditions, such as weather. Summary of the Invention

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

[0007] In one general aspect, a radio detection and ranging (radar) signal processing method is provided, comprising: obtaining a difference frequency signal based on a radar transmit signal and a radar reflection signal, wherein the radar transmit signal is generated based on a frequency modulation model and the radar reflection signal is obtained by reflecting the radar transmit signal from an object; and generating radar data by compensating the difference frequency signal for carrier frequency variations of the frequency modulation model.

[0008] Obtaining the difference frequency signal may include: obtaining sampling data by sampling the difference frequency signal at sampling points, and generating radar data may include: compensating for an error caused by a carrier frequency corresponding to a corresponding sampling point for a sampling value corresponding to each sampling point in the sampling data.

[0009] Generating the radar data may include applying a phase normalization model configured to normalize a carrier frequency of the beat frequency signal to a reference frequency to the beat frequency signal.

[0010] The phase normalization model may include combining a result of converting a difference frequency signal in a time domain into data in another domain based on a carrier frequency according to a frequency modulation model and a result of inversely converting the data in another domain to the time domain based on a reference frequency.

[0011] Another domain may include an angle domain.

[0012] The phase normalization model may include: phase normalization matrices corresponding to sampling points of the difference frequency signal respectively.

[0013] The phase normalization model may include: a first matrix operation that converts a value in the time domain corresponding to each sampling point of the difference frequency signal into angle information using a carrier frequency corresponding to a corresponding sampling point of the frequency modulation model; and a second matrix operation that inversely converts the angle information into the time domain using a reference frequency.

[0014] A radar signal processing method may include: radiating a radar transmission signal including a chirp signal, a carrier frequency of the chirp signal being modulated based on a frequency modulation model; and sensing a radar reflection signal.

[0015] Sensing the radar reflection signal may include individually sensing the radar reflection signal by a receiving sub-antenna in the radar sensor.

[0016] Obtaining the beat frequency signal may include calculating the beat frequency signal corresponding to a frequency difference between the radar transmission signal and the radar reflection signal.

[0017] The frequency modulation model may be a model having a pattern in which the carrier frequency changes linearly or a model having a pattern in which the carrier frequency changes nonlinearly.

[0018] The radar signal processing method may include: calculating at least one of angle of arrival (AoA) information or distance information based on radar data.

[0019] The radar signal processing method may include generating a radar image map of the environment based on at least one of the AoA information or the distance information.

[0020] The radar signal processing method may include visualizing the radar image map via a display.

[0021] The radar signal processing method may include: changing at least one of the speed, acceleration, or steering of the vehicle based on at least one of the AoA information or the distance information.

[0022] In another general aspect, a radio detection and ranging (radar) signal processing device is provided, comprising: a radar sensor configured to radiate a radar transmit signal generated based on a frequency modulation model, and sense a radar reflection signal when the radar transmit signal is reflected by an object; and a processor configured to obtain a difference frequency signal based on the radar transmit signal and the radar reflection signal, generate radar data by compensating the difference frequency signal for carrier frequency variations of the frequency modulation model, and calculate at least one of angle of arrival (AoA) information or spacing information based on the radar data.

[0023] The difference frequency signal may include the frequency difference between the radar transmit signal and the radar reflection signal.

[0024] The processor may be configured to obtain a difference frequency signal at a preset sampling point based on the radar transmission signal and the radar reflection signal.

[0025] The difference frequency signal may be generated based on a signal waveform of the radar transmission signal and a signal waveform of the radar reflection signal.

[0026] In another general aspect, a signal processing method is provided, comprising: generating and radiating a transmission signal having a frequency varying within a frequency band; obtaining a reflected signal when the transmission signal is reflected from an object; obtaining a difference frequency signal at a sampling point based on the transmission signal and the reflected signal; compensating the difference frequency signal for a value corresponding to the sampling point and to which the frequency variation of the transmission signal is applied; and calculating an angle of arrival (AoA) of the object using the compensated difference frequency signal.

[0027] The frequency band may be greater than or equal to 2 gigahertz (GHz).

[0028] At least three antennas can obtain reflected signals.

[0029] The antennas may be equidistant from each other.

[0030] The frequency of the transmitted signal can be varied linearly within the frequency band.

[0031] The resolution of AoA can be less than or equal to 1 degree (°).

[0032] Compensating the difference frequency signal for a value corresponding to a sampling point and to which a frequency variation of a transmission signal is applied includes applying a phase normalization matrix to which a frequency variation at each sampling point is applied to sampling data of the difference frequency signal corresponding to the corresponding sampling point.

[0033] The phase normalization matrix corresponding to each sampling point can be a matrix based on a combination of a first matrix operation and a second matrix operation, wherein the first matrix operation converts a value in the time domain corresponding to the sampling point into a value in another domain using the frequency at the sampling point in the sampled data, and the second matrix operation inversely converts the value in the other domain into the time domain using the reference frequency.

[0034] Another domain may include an angle domain.

[0035] Other features and aspects will become apparent from the following detailed description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 An example of recognizing the surrounding environment through a radio detection and ranging (radar) signal processing method is shown.

[0037] Figure 2 An example of a radar signal processing device is shown.

[0038] Figure 3 An example of a radar sensor is shown.

[0039] Figure 4 An example of a receiving antenna array of a radar sensor is shown.

[0040] Figure 5 An example of error caused by carrier frequency variation is shown.

[0041] Figure 6 An example of a method of updating a radar image is shown.

[0042] Figure 7 An example of a radar signal processing method is shown.

[0043] Figure 8 An example of a phase change based on a carrier frequency change at each sampling point in a radar signal processing method is shown.

[0044] Figure 9 An example of phase normalization of a radar signal is shown.

[0045] Figure 10 An example of applying the phase normalization model to the difference frequency signal is shown.

[0046] Figure 11 An example of improving angular resolution and pitch resolution by phase normalization is shown.

[0047] Figure 12 Another example of a radar signal processing device is shown.

[0048] Throughout the drawings and detailed description, unless otherwise described or provided, the same reference numerals should be understood to refer to the same elements, features, and structures. The drawings may not be drawn to scale, and the relative sizes, proportions, and depictions of elements in the drawings may be exaggerated for clarity, illustration, and convenience. DETAILED DESCRIPTION

[0049] The following detailed description is provided to help the reader obtain a comprehensive understanding of the methods, devices and / or systems described herein. However, after understanding the disclosure of the present application, various changes, modifications and equivalents of the methods, devices and / or systems described herein will be apparent. For example, the order of operations described herein is merely an example and is not limited to those order of operations set forth herein, but can be significantly changed after understanding the disclosure of the present application, except for operations that must be performed in a certain order. In addition, for greater clarity and brevity, the description of known features may be omitted after understanding the disclosure of the present application.

[0050] The features described herein may be implemented in different forms and should not be construed as limited to the examples described herein. Rather, the examples described herein are provided merely to illustrate some of the many possible ways to implement the methods, devices, and / or systems described herein, which will become apparent upon understanding the disclosure of this application.

[0051] Throughout this specification, when a component is described as being “connected to” or “coupled to” another component, it may be directly “connected to” or “coupled to” the other component, or one or more other components may be present between them. Conversely, when an element is described as being “directly connected to” or “directly coupled to” another element, there may not be other elements present between them. Similarly, similar expressions such as “between” and “directly between” and “adjacent to” and “immediately adjacent to” should be understood in the same manner. As used herein, the term “and / or” includes any one and any combination of any two or more of the associated listed items.

[0052] Although terms such as "first," "second," and "third" may be used herein to describe various components, assemblies, regions, layers, or portions, these components, components, regions, layers, or portions should not be limited by these terms. Instead, these terms are merely used to distinguish one component, component, region, layer, or portion from another component, component, region, layer, or portion. Thus, a first component, component, region, layer, or portion mentioned in the examples described herein may also be referred to as a second component, component, region, layer, or portion without departing from the teachings of the examples.

[0053] The terms used herein are only used to describe various examples 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 plural forms. The terms "include", "comprising" and "having" indicate the presence of the recited 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.

[0054] In this document, use of the term "may" with respect to an example or embodiment (e.g., with respect to what an example or embodiment may include or implement) means that there is at least one example or embodiment in which such features are included or implemented, and all examples are not limited thereto.

[0055] Additionally, in the description of the exemplary embodiments, when a detailed description of a configuration or function known based on an understanding of the disclosure of the present application is considered redundant, such description will be omitted.

[0056] Hereinafter, examples will be described in detail with reference to the accompanying drawings, and like reference numerals in the drawings refer to like elements throughout.

[0057] Advanced driver assistance systems (ADAS) can be used to enhance driver safety and convenience by implementing sensors located inside or outside the vehicle. Sensors suitable for ADAS may include, for example, cameras, millimeter wave (mm wave) radio detection and ranging (radar) sensors, infrared sensors, ultrasonic sensors, and light detection and ranging (lidar) sensors. Different types of sensors may have different sensing ranges and functions. Below, a technique using radar signals, which are a type of radio frequency (RF) signal, will be described.

[0058] Figure 1 An example of recognizing the surrounding environment by using a radar signal processing method is shown.

[0059] exist Figure 1 In the example of , the radar signal processing device 110 can detect the distance to the object 180 by analyzing the radar signal received by the radar sensor 111. The term "distance" used herein may mean distance, for example, the distance from A to B may mean the distance from A to B, and the distance between A and B may mean the distance between A and B. Therefore, the terms "distance" and "distance" may be used interchangeably. The radar sensor 111 may be provided inside or outside the radar signal processing device 110. The radar signal processing device 110 detects the distance to the object 180 based on data collected by other sensors (e.g., an image sensor) and the radar signal received from the radar sensor 111.

[0060] The term "resolution" used herein may indicate the ability of a device (e.g., a sensor and a radar) to recognize minute differences (e.g., the ability to distinguish between two objects that are spaced apart from each other). Resolution may represent the minimum unit for recognizing a difference, and may be represented, for example, by "resolution = (minimum scale unit for recognition) / (total working range)". Therefore, the smaller the resolution value of a device, the more accurate the result that the device can output. The resolution value may also be referred to as a resolution unit. For example, when the resolution value of a device is small, the device can recognize small units, thereby outputting results with improved resolution and accuracy. Conversely, when the resolution value of a device is large, the device cannot recognize small units, thereby outputting results with reduced resolution and accuracy.

[0061] For example, the radar signal processing device 110 may be provided in a vehicle. For example, the vehicle may perform functions such as adaptive cruise control (ACC), autonomous emergency braking (AEB), blind spot detection (BSD), accident avoidance, and lane detection based on the distance detected by the radar signal processing device 110.

[0062] In addition to detecting ranges, radar signal processing device 110 may generate a map 130 of the nearby environment. Map 130 may indicate the locations of targets present around radar signal processing device 110, and these nearby targets may include dynamic objects (e.g., vehicles and humans) or stationary or background objects (e.g., guardrails and traffic lights), as examples only.

[0063] The image 130 may be generated using a single scan technique. By using a single scan, the radar signal processing device 110 may obtain a single scan image 120 from the sensor and generate the image 130 based on the obtained single scan image 120. The single scan image 120 may be generated based on the radar signal sensed by the single radar sensor 111 and may represent the spacing or distance indicated by the elevation angle of the received radar signal with a higher resolution. For example, in Figure 1 In the example of FIG, the horizontal axis of the single scan image 120 indicates the steering angle of the radar sensor 111, and the vertical axis of the single scan image 120 indicates the distance from the radar sensor 111 to the target. The format of the single scan image is not limited to Figure 1 format shown, and may be expressed in another format based on various examples.

[0064] The steering angle may indicate an angle corresponding to the direction from radar signal processing device 110 toward the target point. For example, the steering angle may be the angle between the driving direction or travel direction of radar signal processing device 110 (e.g., a vehicle) and the target point based on radar signal processing device 110. The steering angle is primarily described herein based on a horizontal angle, but is not limited thereto. The steering angle may also be applied to an elevation angle.

[0065] Radar signal processing device 110 can obtain information about the shape of a target using a multi-radar map. The multi-radar map is generated by combining multiple radar scan images. For example, radar signal processing device 110 can generate map 130 by spatiotemporally combining multiple radar scan images obtained while radar sensor 111 is moving. Map 130 can be a type of radar image map used for parking guidance, for example.

[0066] To generate map 130, angle of arrival (AoA) information can be used. AoA information can indicate the direction from which a radar signal reflected from a target point is received. Radar signal processing device 110 can use the AoA information based on radar sensor 111 to identify the direction in which the target point exists. Therefore, AoA information can be used to generate radar scan data and a map of the surrounding environment.

[0067] The radar data may include raw data sensed by the radar sensor 111. Hereinafter, a method of removing a phase error that may be caused in the raw data due to a carrier frequency variation will be described.

[0068] Figure 2 An example of a radar signal processing device is shown.

[0069] refer to Figure 2 , the radar signal processing device 200 includes a radar sensor 210 and a processor 220 .

[0070] The radar sensor 210 can sense radar data. The radar sensor 210 can radiate the generated radar signal to the outside and receive a reflected signal, which is a signal returned to the radar sensor 210 after the radiated radar signal is reflected from a target point (e.g., an object). The radar signal radiated to the outside may be referred to as a radar transmit signal in this article, and the reflected signal may be referred to as a radar reflected signal in this article. The radar transmit signal may include a chirp signal, the carrier frequency of which is modulated based on a frequency modulation model. The radar transmit signal may have a frequency that varies within a preset frequency band. For example, the frequency of the radar transmit signal may vary linearly in the frequency band. The radar sensor 210 may include antennas corresponding to multiple receiving channels (represented as Rx in the drawings). The signals respectively received through the receiving channels may have different phases based on the receiving direction in which each signal is received. Reference will be made to Figure 3 Radar sensor 210 is described in more detail.

[0071] The processor 220 may generate a beat frequency signal based on the radar transmission signal and the radar reflection signal. The beat frequency signal may indicate a signal having a frequency corresponding to the frequency difference between the radar transmission signal and the radar reflection signal. The processor 220 may obtain the beat frequency signal at a plurality of preset sampling points based on the radar transmission signal and the radar reflection signal. The processor 220 may generate radar data by compensating the beat frequency signal for the carrier frequency variation of the frequency modulation model. For example, the processor 220 may compensate the beat frequency signal for a value to which the frequency variation of the radar transmission signal corresponding to the sampling point is applied. Figures 5 to 11 This carrier frequency compensation for the beat frequency is described in detail. Processor 220 can calculate AoA information and range information based on the generated radar data. AoA information can be information indicating the direction from which a radar signal reflected from a target point is received. Range information can be information indicating the range or distance to the target point (which reflected the radar signal).

[0072] In the following, reference will be made to Figure 3 Radar signals are combined to describe radar transmission signals and radar reflection signals.

[0073] Figure 3 An example of a radar sensor is shown.

[0074] refer to Figure 3 , the radar sensor 310 can radiate signals through the antenna 313 and receive signals through the antenna 313. Although for the sake of convenience in describing Figure 3 While antenna 313 is shown as a single antenna, the number of antennas is not limited to the example shown. For example, the transmit antenna and receive antenna may be implemented by different devices, and the transmit antenna may include one or more transmit sub-antennas, while the receive antenna may include one or more receive sub-antennas. For example, the receive antenna may include three or more antennas, such as receive sub-antennas, to receive radar reflection signals. In this example, the receive sub-antennas may be spaced evenly apart.

[0075] The radar sensor 310 can be, for example, a millimeter wave radar, and can estimate the distance to the target by analyzing the time of flight (ToF), where the time of flight is the amount of time it takes for the radiated radio wave to return after hitting the object. For example, compared to light sensors such as cameras, millimeter wave radars can detect the front without considering changes in external environmental elements (e.g., fog and rain, etc.). In addition, compared to lidar sensors, millimeter wave radars can be more efficient in terms of performance-cost ratio, and millimeter wave radars can make up for the shortcomings of cameras. The radar sensor 310 can be implemented as, for example, a frequency modulated continuous wave (FMCW) radar, but is not limited thereto. FMCW radars can be robust to external noise.

[0076] Chirp transmitter 311 of radar sensor 310 can generate a frequency modulated (FM) signal 302 whose frequency varies over time. For example, chirp transmitter 311 can generate FM signal 302 by performing frequency modulation based on frequency modulation model 301. FM signal 302 can also be referred to as a chirp signal. Frequency modulation model 301 can be a model that indicates the variation in the carrier frequency of a radar transmission signal within a given transmission time. In frequency modulation model 301, the vertical axis can indicate the carrier frequency, and the horizontal axis can indicate time. For example, frequency modulation model 301 can be a model with a pattern in which the carrier frequency varies linearly. For another example, frequency modulation model 301 can be a model with a pattern in which the carrier frequency varies nonlinearly.

[0077] Figure 3 The frequency modulation pattern 301 shown may indicate a signal whose frequency increases or decreases linearly over time. The chirp transmitter 311 may generate an FM signal 302 having a carrier frequency that conforms to the frequency modulation pattern 301. For example, Figure 3 As shown, the FM signal 302 may have a waveform in which the carrier frequency gradually increases in one interval and a waveform in which the carrier frequency gradually decreases in the remaining interval. The chirp transmitter 311 may transmit the FM signal 302 to the duplexer 312.

[0078] Duplexer 312 of radar sensor 310 can determine the transmission and reception paths of the signal passing through antenna 313. For example, when radar sensor 310 radiates FM signal 302, duplexer 312 forms a signal path from chirp transmitter 311 to antenna 313, transmits FM signal 302 to antenna 313 via this formed signal path, and then radiates the FM signal externally. When radar sensor 310 receives a signal reflected from an object, duplexer 312 can form a signal path from antenna 313 to spectrum analyzer 316. Antenna 313 can receive a reflected signal, which is a signal that reflects off the object after the radiated signal transmitted from antenna 313 reaches the object and then returns to antenna 313. Radar sensor 310 can transmit the reflected signal to spectrum analyzer 316 via the signal path formed from antenna 313 to spectrum analyzer 316. The signal radiated by antenna 313 may be referred to herein as a radar transmission signal, while the signal received by antenna 313 may be referred to herein as a radar reflection signal.

[0079] The mixer 314 demodulates a linear signal, for example, an original chirp signal, from the received signal before frequency modulation. The amplifier 315 may amplify the amplitude of the demodulated linear signal.

[0080] Spectrum analyzer 316 may compare frequency 308 of the radar reflection signal returned from the object after being reflected by the object with frequency 307 of the radar transmission signal. Frequency 307 of the radar transmission signal may vary based on the carrier frequency variation indicated by frequency modulation model 301. Spectrum analyzer 316 may detect the difference between frequency 308 of the radar reflection signal and frequency 307 of the radar transmission signal. Figure 3 As shown in graph 309 , this frequency difference between the radar transmission signal and the radar reflection signal can be constant during the interval in which the carrier frequency increases linearly along the time axis in frequency modulation model 301 and can be proportional to the distance between radar sensor 310 and the object. Therefore, the distance between radar sensor 310 and the object can be derived from the frequency difference between the radar transmission signal and the radar reflection signal. Spectrum analyzer 316 can transmit the analyzed information to a processor in the radar data processing device.

[0081] For example, spectrum analyzer 316 may calculate the distance between radar sensor 310 and the target point as shown in Equation 1 below.

[0082] Equation 1

[0083]

[0084] In Equation 1, R represents the distance between the radar sensor 310 and the target point, and c represents the speed of light. chirp Indicates the duration of the rising interval of the carrier frequency of the frequency modulation model 301. IF The frequency difference between the radar transmission signal and the radar reflection signal at a point in the rising interval is also called the difference frequency or intermediate frequency. B represents the modulation bandwidth. The difference frequency f can be obtained as shown in the following equation 2 IF .

[0085] Equation 2

[0086]

[0087] In Equation 2, f IF Indicates the difference frequency. d The time difference (e.g., delay time) between the time point when the radar transmission signal is radiated and the time point when the radar reflection signal is received can indicate the round-trip delay time with respect to the target point.

[0088] In an example, multiple radar sensors may be installed in various parts of a vehicle, and a radar signal processing device may calculate the distance to a target point, the direction, and the relative speed of the vehicle in various directions based on the information sensed by the radar sensors. The radar signal processing device may be provided in the vehicle, and this calculated information may be used to provide various functions such as ACC, BSD, and lane change assist (LCA) that are beneficial to vehicle driving.

[0089] Each of the radar sensors may radiate a radar transmission signal including a chirp signal to the outside, and receive a radar reflection signal reflected from a target point, wherein the frequency of the chirp signal is modulated based on a frequency modulation model. The processor of the radar signal processing device may determine a distance from each of the radar sensors to the target point based on a frequency difference between the radiated radar transmission signal and the received radar reflection signal.

[0090] In addition, when the radar sensor 310 includes multiple receiving channels, the processor of the radar signal processing device can obtain the AoA of the radar reflection signal reflected from the target point. Figure 4 An example structure of a radar sensor including a plurality of receiving channels is described.

[0091] Figure 4 An example of a receiving antenna array of a radar sensor is shown.

[0092] In an example, when a radar sensor includes multiple receive channels, the phase information of the radar data may indicate the phase difference between a reference phase and the phase of a signal received by each receive channel. The reference phase may be an arbitrary phase or may be set to the phase of a receive channel among the receive channels. For example, the radar signal processing device may set the reference phase for one receive sub-antenna to the phase of an adjacent receive sub-antenna.

[0093] Furthermore, the processor of the radar signal processing device can generate a radar vector having a dimension corresponding to the number of receiving channels of the radar sensor based on the radar data. For example, if the radar sensor includes four receiving channels, the processor can generate a four-dimensional radar vector including a phase value corresponding to each receiving channel. The phase value corresponding to each receiving channel can be represented as a numerical value indicating the aforementioned phase difference.

[0094] For example, when the radar sensor includes one transmitting channel (denoted as Tx in the drawing) and four receiving channels, the radar signal radiated through the transmitting channel can be reflected from the target point and then received through the four receiving channels. Figure 4In the example of FIG, when the receiving antenna array 410 of the radar sensor includes a first receiving sub-antenna 411, a second receiving sub-antenna 412, a third receiving sub-antenna 413, and a fourth receiving sub-antenna 414, the phase of the signal received by the first receiving sub-antenna 411 can be set as the reference phase. In this example, when the radar reflection signal 408 reflected from the same target point is received by the receiving antenna array 410, the additional distance Δ between the distance or spacing from the target point to the first receiving sub-antenna 411 and the distance or spacing from the target point to the second receiving sub-antenna 412 can be expressed by the following equation 3.

[0095] Equation 3

[0096] Δ=d·sin(θ)

[0097] In Equation 3, θ represents the AoA of the received radar reflection signal 408 from the target point. d represents the distance or spacing between the receiving sub-antennas. c is the speed of light in air, which is taken as a constant. c = fλ. Therefore, the phase change W of the second receiving sub-antenna 412 caused by the additional distance Δ can be calculated as shown in Equation 4 below.

[0098] Equation 4

[0099]

[0100] Phase change W may correspond to the phase difference between the signal waveform received by first receiving sub-antenna 411 and the signal waveform received by second receiving sub-antenna 412. In Equation 4, λ is inversely proportional to f, where f represents the frequency of radar reflection signal 408. When the carrier frequency variation of the frequency modulation model is small, frequency f in Equation 4 can be considered as a single initial frequency in the frequency modulation model, for example, f0. Therefore, once phase change W is determined based on the received signal, the radar signal processing device can determine AoAθ.

[0101] However, when the carrier frequency of the frequency modulation model (e.g., 77 GHz) varies in a wide bandwidth greater than or equal to 2 GHz (e.g., 4 GHz, 5 GHz, or 7 GHz), such carrier frequency variations cannot be considered insignificant, and errors in AoA and spacing estimation may occur. Figure 5 Describe the errors that may result from this carrier frequency variation.

[0102] Figure 5 An example of errors caused by carrier frequency variation is shown in accordance with one or more embodiments.

[0103] exist Figure 5In the example of FIG, for ease of description, frequency modulation model 501 is described as having a pattern in which the carrier frequency increases linearly within a given transmission time. For example, the sampling interval in frequency modulation model 501 may be an interval in which the frequency increases linearly from an initial frequency f0 to a final frequency f0+BW (where the bandwidth BW is increased at the initial frequency f0). In this example, bandwidth BW may be a sampling bandwidth corresponding to the sampling interval and may be less than the modulation bandwidth B described above with reference to Equation 1. Radar reflection signal 502 received by the radar sensor may indicate a frequency corresponding to the carrier frequency. Radar reflection signal 502 may be sensed individually by multiple receiving sub-antennas. The difference frequency signal y(t) of radar reflection signal 502 sensed by the receiving sub-antennas of the radar sensor may be modeled as shown in Equation 5 below.

[0104] Equation 5

[0105]

[0106] In Equation 5, α represents the path loss attenuation, f c represents the carrier frequency of the radar reflection signal 502, t d represents the round trip delay associated with the target point separated by a spacing or distance R. B represents the swept bandwidth of the transmitted chirp. The form and swept bandwidth of the transmitted chirp can be defined by the frequency modulation model 501. c represents the chirp duration and may correspond to T described above with reference to Equation 1 chirp The round trip delay t in Equation 5 d It can be expressed by the following equation 6. t represents the time point when the radar sensor receives the radar reflection signal 502.

[0107] Equation 6

[0108]

[0109] In Equation 6, R 0 represents the distance to the target point. c represents a constant indicating the speed of light in air. d represents the distance between the receiving sub-antennas. θ represents the AoA from the target point. The round-trip delay t d t d,0 represents the component based on the distance from the target point, and the round-trip delay t d t d,θ The distance-based component and the AoA-based component in Equation 5 can be expressed by Equations 7 to 10.

[0110] Equation 7

[0111]

[0112] Equation 8

[0113]

[0114] Equation 9

[0115]

[0116] Equation 10

[0117]

[0118] In Equation 8, and The value of the item may be small and not be considered.

[0119] The radar signal processing device can detect the component φ in Equation 9 by performing frequency analysis (e.g., Fourier transform) on the difference frequency signal sensed by each receiving sub-antenna. t (t d,0 ). The radar signal processing device can be based on the component φ t (t d,0 ) to estimate the distance to the target point. Furthermore, the component φ0 in Equation 8 may be a constant component. Therefore, the radar signal processing device may determine that the phase change W between the difference frequency signals received by the receiving sub-antennas is the component φ0 and estimate the AoA based on this component φ0.

[0120] The radar reflection signals 502 corresponding to a chirp may be reflected from the same target point, and therefore, the round-trip delays may be the same. Due to the same round-trip delays, the radar signal processing device can calculate the same AoA at the initial time point, the middle time point, and the end time point of the radar reflection signal 502. However, since the component φ in Equation 10 t (t d,θ ), so even in one chirp, the phase change W will change in time, and thus an error in the AoA based on the change in Equation 4 may occur. Figure 5 In the example of , the radar signal processing device can sense the first difference frequency signal 511, the second difference frequency signal 512, and the third difference frequency signal 513 through the first receiving sub-antenna, the second receiving sub-antenna, and the third receiving sub-antenna, respectively. In this example, since the component φ in Equation 10 t (t d,θ ), so in one chirp, the phase change 581 at the initial point, the phase change 582 at the middle point, and the phase change 583 at the end point may be different. This is because the component φ t (t d,θ ) varies based on time t.

[0121] Radar signal processing equipment can generate radar data The phase in the radar data is normalized by a phase normalization operation 550 that normalizes the phase of the carrier frequency. Figure 5 As shown in the phase-normalized radar data In the embodiment, the first difference frequency signal 521, the second difference frequency signal 522, and the third difference frequency signal 523 can have the same phase changes 591, 592, and 593 in the chirp period of the radar reflection signal. Therefore, the radar signal processing device can prevent errors in AoA estimation through phase normalization. The following will describe a method for compensating for errors that may occur due to changes in carrier frequency through phase normalization operation 550. Through phase normalization operation 550, the component φ in equation 10 can be removed. t (t d,θ ).

[0122] Combine Figure 5 The above equation is provided as an example to describe the error in AoA estimation that may occur due to the variation of carrier frequency. That is, by not applying the carrier frequency that varies in the transmission band, but by calculating AoA by assuming it to be constant, an error due to φ may occur. t (t d,θ In an example, the above equation may not be used to calculate the actual AoA, but may only be used to describe that one of the reasons for such AoA error may be that the change of carrier frequency is not applied.

[0123] Figure 6 An example of a method of updating a radar image is shown.

[0124] refer to Figure 6 , in operation 610, the radar signal processing device compensates for the error due to the carrier frequency variation for the beat frequency signal obtained from the radar signal. As described above, the radar reflection signal sensed by the radar sensor may indicate a signal obtained when the radar transmission signal is reflected from the target point, and therefore, the carrier frequency of the radar reflection signal may also change according to the frequency modulation model. The radar signal processing device may generate radar data based on compensating for the carrier frequency variation of the frequency modulation model for the beat frequency signal corresponding to the frequency difference between the radar transmission signal and the radar reflection signal. The radar data may be data indicating that the error due to the carrier frequency variation in the beat frequency signal has been compensated, and may also be referred to as normalized data. Hereinafter, reference will be made to Figures 7 to 10 Describe in detail the compensation for carrier frequency variations.

[0125] In operation 620, the radar signal processing device detects the distance to the target point. For example, the radar signal processing device may identify the distance to the target point that reflected the radar transmission signal by processing the radar data generated in operation 610. In this example, the radar signal processing device may identify the distance to the target point by performing a Fourier transform on the radar data. However, the method for identifying the distance to the target point is not limited to Fourier transform. For example, the radar signal processing device may use a high-resolution range profile (HRRP) to identify the distance to the target point from the radar data.

[0126] In operation 630, the radar signal processing device determines AoA information. For example, the radar signal processing device may identify radar data for each target point based on the steering information. The steering information may include multiple candidate steering vectors. For example, when radar data is received at a specific angle, the steering vector may include phase information calculated for the radar data. The radar signal processing device may identify a target steering vector that matches the phase information of the radar data from the steering information including the candidate steering vectors. The radar signal processing device may determine the steering angle corresponding to the identified target steering vector at each target spacing as the AoA information of the radar data. The radar signal processing device may estimate the AoA information using techniques such as the Multiple Signal Classification (MUSIC) algorithm, the Bartlett algorithm, the Minimum Variance Distortionless Response (MVDR) algorithm, digital beamforming (DBF), and the Estimation of Signal Parameters via Rotationally Invariant Technique (ESPRIT).

[0127] In addition, the radar signal processing device can detect potential objects. For example, the radar signal processing device can select a target point corresponding to the potential object from the target points whose AoA information is estimated in operation 630, and use the selected target point to update the radar image map. The target point corresponding to the potential object can be a target point where the object is expected to exist. For example, the radar signal processing device selects a target point within the field of view (FOV) of the radar sensor. The radar signal processing device can exclude target points that deviate from the FOV according to operation 650 of updating the radar image map. To detect potential objects, the radar signal processing device can use, for example, a constant false alarm rate (CFAR) detection method and a joint probability data association (JPDA) method.

[0128] In operation 640, the radar signal processing device transforms the coordinates of the target point. In an example, the radar signal processing device may generate coordinate information near the object based on the AoA information and self-positioning. For example, a target point detected as a potential object may have relative coordinates defined by a range axis and an AoA axis relative to the radar sensor. The radar signal processing device may transform the relative coordinates of the target point identified through the radar data into absolute coordinates.

[0129] In operation 650, the radar signal processing device updates the radar image map. For example, the radar signal processing device may generate a radar image map based on the coordinate information of nearby objects obtained in operation 640. The radar image map may be a map indicating target points detected in the surrounding environment and indicating, for example, the absolute coordinates of the target points. The radar image map may include: multiple spaces, each indicating object occupancy probability or radar signal reception strength. The object occupancy probability may be the probability of an object occupying the absolute coordinates indicated by each space. The radar signal reception strength may be the strength of the signal reflected and received from the absolute coordinates indicated by each space. In the radar image map, the map indicating object occupancy probability may be referred to as an occupancy grid map (OGM), and the map indicating radar signal reception strength may be referred to as an intensity grid map (IGM). However, the types of radar image maps are not limited to the aforementioned example maps.

[0130] In an example, the radar signal processing device can generate a map indicating object occupancy probabilities and / or radar signal reception strengths at points near the radar signal processing device based on the AoA information as a radar image map for the current frame. For example, the radar signal processing device can generate a radar scan image of a radar sensor based on the AoA information. In this example, the radar signal processing device can generate a radar image map associated with the situation or environment surrounding the radar signal processing device based on radar scan images generated by multiple radar sensors. The radar signal processing device can use radar data that has been compensated for errors caused by carrier frequency variations to generate a radar image map with improved resolution.

[0131] However, the order of operations is not limited to the reference Figure 6 The operations described above are sequential, and at least one of the operations may be performed in temporal series or in parallel with another operation. Furthermore, while the radar signal processing device is described above as calculating both AoA information and distance information, the radar signal processing device may calculate either one. The radar signal processing device may generate a radar image of the surrounding environment based on the AoA information and / or the distance information.

[0132] Figure 7 An example of a radar signal processing method is shown. Figure 8 An example of a phase change based on a carrier frequency change at each sampling point in a radar signal processing method is shown.

[0133] refer to Figure 7 and Figure 8In operation 710, the radar signal processing device obtains a beat frequency signal based on a radar transmission signal 870 generated based on a frequency modulation model and a radar reflection signal 880 obtained when the radar transmission signal 870 is reflected from an object. For example, the radar signal processing device may calculate a beat frequency signal corresponding to the difference between the frequency 807 of the radar transmission signal 870 and the frequency 808 of the radar reflection signal 880. The radar signal processing device may not directly measure the beat frequency f IF Instead, the radar signal processing device measures the signal waveform 881 of the radar reflection signal 880. In addition, the radar signal processing device can generate a difference frequency signal based on the given signal waveform 871 of the radar transmission signal 870 and the signal waveform 881 of the radar reflection signal 880. The radar signal processing device can calculate the difference frequency signal obtained by mixing 850 (for example, multiplying the radar transmission signal 870 and the radar reflection signal 880). Figure 8 In the example, y m (t) represents a difference frequency signal calculated based on the radar transmission signal 870 and the radar reflection signal 880 received by the m-th receiving sub-antenna among the M receiving sub-antennas. In this example, M represents an integer greater than or equal to 2, and m represents an integer greater than or equal to 1 and less than or equal to M. For better understanding, Figure 8 The radar transmission signal 870, the radar reflection signal 880, and the difference frequency signal are shown as having lower frequencies than actual frequencies, but their frequencies are not limited to the frequencies shown.

[0134] The radar signal processing device can obtain sampling data by sampling the difference frequency signal at multiple sampling points. The sampling data may include sampling values ​​obtained from preset sampling points. For example, referring to Figure 8 , s m (i) represents a sampled value obtained by sampling the strength of the signal received at the i-th sampling point of the m-th receiving sub-antenna among the M receiving sub-antennas. In this example, i represents a time index, and N represents the number of samples of the difference frequency signal. N is an integer greater than or equal to 1, and i is an integer greater than or equal to 1 and less than or equal to N.

[0135] Due to the carrier frequency variation in the radar transmission signal 870 and the radar reflection signal 880, the phase variation at each sampling point may be different. Figure 8 In the example of Equation 10, the first frequency f1, the second frequency f2, the third frequency f3, ..., and the Nth frequency f N The corresponding component φ t (t d,θ ) can be applied to the first sampling value s obtained by sampling at the first sampling point, respectively. m(1) The second sampling value s obtained by sampling at the second sampling point m (2) The third sampling value s obtained by sampling at the third sampling point m (3), ..., and the Nth sampling value s obtained by sampling at the Nth sampling point m (N). Due to this frequency change at each sampling point, the frequency and phase of the difference frequency signal can vary instead of being fixed, such as Figure 8 For ease of description, Figure 8 In the example shown, a time delay is explicitly shown between radar transmit signal 870 and radar reflection signal 880. However, radar signals can be transmitted or propagated at the speed of light, so the time delay can be very small, and therefore the frequency difference can also be very small. Therefore, it can be assumed that at a given sampling point, the carrier frequency of radar transmit signal 870 and the carrier frequency of radar reflection signal 880 are substantially the same.

[0136] In operation 720, the radar signal processing device generates radar data by compensating the difference frequency signal for the carrier frequency variation of the frequency modulation model. The radar signal processing device can compensate for the error caused by the carrier frequency corresponding to each sampling point in the sampled data. Here, the carrier frequency corresponding to the corresponding sampling point can be the frequencies f1, f2, f3, ..., and f2 corresponding to the sampling point of the frequency 808 of the radar reflection signal 880. N However, as described above, the carrier frequency 807 of the radar transmission signal 870 and the carrier frequency 808 of the radar reflection signal 880 may be substantially the same. Therefore, the value of the frequency 807 of the radar transmission signal 870, rather than the frequency 808 of the radar reflection signal 880, may be used as the carrier frequency corresponding to the corresponding sampling point to perform such error compensation. Figures 9 and 10 Compensation for errors that may be caused by carrier frequency is described in detail.

[0137] While this document primarily describes a radar signal processing method, examples are not limited thereto. The phase normalization operation described above can generally be applied to a reflected signal received when a signal processing device configured to perform the signal processing method transmits a transmitted signal having a carrier frequency that varies according to a frequency modulation model, and the transmitted signal is reflected from a target point. For example, the transmitted signal and the reflected signal can be signals having a carrier-frequency-based wave and propagating using this wave, and include, for example, radar signals, ultrasonic signals, electromagnetic signals, and optical signals.

[0138] Figure 9 An example of phase normalization of a radar signal is shown.

[0139] In an example, the radar signal processing device may apply a phase normalization model 950 to the difference frequency signal. The phase normalization model 950 may be a model that normalizes the carrier frequency of the difference frequency signal to a reference frequency. For example, the phase normalization model 950 may be a model configured to normalize the sampled data sampled at each time index in the difference frequency signal from the carrier frequency corresponding to the corresponding time index to the reference frequency. The time index may be an index that discretely indicates the time t as an analog value. The time index may be predetermined by the radar signal processing device. The carrier frequency corresponding to each time index may also be predetermined. For example, the determined carrier frequency may be Figure 8 The frequency corresponding to each time index of the frequency 807 of the radar transmission signal 870. The phase normalization model 950 may include: a plurality of phase normalization matrices 951 corresponding to a plurality of sampling points of the difference frequency signal. Each of the phase normalization matrices 951 may be a matrix that normalizes the carrier frequency that changes according to the time index in the frequency modulation model 901 to a reference frequency. Return to reference Figure 8 When a single difference frequency signal is sampled at N sampling points, a phase normalization matrix may be configured for each of the N sampling points, and a total of N phase normalization matrices may be configured. Among the N phase normalization matrices, the i-th phase normalization matrix corresponding to the i-th sampling point may include an element for compensating for an error of the i-th frequency occurring in a sample value at the i-th sampling point.

[0140] For example, reference Figure 8 , M sampling values ​​can be obtained from M sub-antennas at each sampling point, so a total of M×N sampling values ​​can be obtained at N sampling points. For example, the frequency 807 of the radar transmission signal 870 and the frequency 808 of the radar reflection signal 880 can be obtained according to Figure 9 In this example, the M sampling values ​​at the first sampling point (eg, s1(1), s2(1), ..., and s M (1)), the first phase normalization matrix for the M sampling values ​​at the second sampling point (e.g., s1(2), s2(2), ..., and s M (2)), the second phase normalization matrix for the M sampling values ​​at the third sampling point (e.g., s1(3), s2(3), ..., and s M (3)), and the M sampling values ​​at the Nth sampling point (e.g., s1(N), s2(N), ..., and s M (N)) is an N-th phase normalized matrix. In this example, N represents an integer greater than or equal to 1. Figure 10 The phase normalization matrix is ​​described in more detail.

[0141] In an example, the radar signal processing device may apply a phase normalization matrix to which a frequency change at each sampling point is applied to the sampling data corresponding to each sampling point in the difference frequency signal. Figure 9 The AoA estimation result 991 of the difference frequency signal y(t) whose phase has not been normalized may have different AoAs for time indexes (e.g., a time index corresponding to f0, a time index corresponding to f0+BW / 2, and a time index corresponding to f0+BW). Referring to the result obtained by applying the phase normalization model 950 to the difference frequency signal y(t), The AoA estimation result 992 obtained may have the same AoA for all time indexes. Therefore, the radar signal processing device may calculate the AoA of the object using the compensated difference frequency signal.

[0142] Figure 10 An example of applying the phase normalization model to the difference frequency signal is shown.

[0143] refer to Figure 10 Phase normalization model 1000 may include operations that combine a first operation 1010 and a second operation 1020, wherein the first operation 1010 converts the difference frequency signal in the time domain into data in another domain based on the carrier frequency according to the frequency modulation model; and the second operation 1020 inversely converts the data in the other domain back to the time domain based on the reference frequency. Through the first operation 1010, each sampled data in the difference frequency signal Y can be converted into data in another domain. The data in the other domain can indicate information corresponding to the same round-trip delay, such as AoA information. Through the second operation 1020, the data in the other domain can be converted into values ​​in the time domain corresponding to the same reference frequency. For example, the other domain can be the angle domain.

[0144] For example, the difference frequency signal Y can be expressed by the following equation 11.

[0145] Equation 11

[0146] Y=[Y(1),Y(2),...,Y(i),...,Y(N-1),Y(N)]

[0147] In Equation 11, i represents a time index, and N represents the number of samples of the beat frequency signal. N is an integer greater than or equal to 1, and i is an integer greater than or equal to 1 and less than or equal to N. Y represents the data obtained by converting the beat frequency signal from an analog value to a digital value. For example, when the radar sensor's receive antenna array includes M receive channel antennas, the sampled data Y(i) at the i-th sampling point corresponding to time index i can be expressed as follows in Equation 12.

[0148] Equation 12

[0149] Y(i)=[s1(i), S2(i), ...s m (i)..., s M-1 i), s M (i)] T

[0150] In Equation 12, s m (i) represents a value obtained by sampling the strength of a signal received by the m-th receiving sub-antenna among the M receiving sub-antennas at the i-th sampling point. M is an integer greater than or equal to 2, and m is an integer greater than or equal to 1 and less than or equal to M.

[0151] The phase normalization model 1000 can be expressed by the following Equation 13.

[0152] Equation 13

[0153] A pNorm ={A pNorm，1 ,...,A pNorm，i ,...,A pNorm，N}

[0154] In Equation 13, A pNorm,i The i-th phase normalization matrix is ​​represented by the following equation 14, which is applied to the sample value at the i-th sampling point of the sampled data (eg, the i-th sampling vector described above with reference to equation 12).

[0155] Equation 14

[0156]

[0157] In Equation 14, A fi A represents a first matrix operation that converts the time domain value corresponding to the i-th sampling point of the sampled data into another domain value (for example, angle information) using the carrier frequency corresponding to the i-th sampling point of the frequency modulation model. f0 -1 Indicates A f0 The inverse matrix of the phase normalization matrix A corresponding to each sampling point is converted into a second matrix operation using the reference frequency f0 to convert another domain value (for example, angle information) into a time domain value. pNorm,i It can be a matrix that combines the first matrix operation and the second matrix operation described above with reference to Equation 14. The first matrix operation A of Equation 14 fi It can be expressed by the following equations 15 and 16.

[0158] Equation 15

[0159]

[0160] Equation 16

[0161]

[0162] In Equation 15, the first matrix operation A fi Can be turned into a vector set Represents. K is an integer greater than or equal to 1, and k is an integer greater than or equal to 1 and less than or equal to K. For example, when the FOV of the radar sensor is 180° and K steering vectors are linearly arranged in the FOV, the AoA resolution may be 0.35°. In this example, K may be 512. However, the example is not limited to the foregoing example. For another example, when K is 180, the AoA resolution may be 1°, and when K is 360, the AoA resolution may be 0.5°. K can be determined based on the desired AoA resolution (e.g., less than or equal to 1°). In Equation 16, d represents the distance or spacing between the receiving sub-antennas of the antenna array included in the radar sensor, and j represents an imaginary unit. represents the wavelength corresponding to the carrier frequency at the i-th sampling point, θ k Indicates A fi The kth steering angle in . represents the steering angle θ with respect to the carrier frequency corresponding to the i-th time index of the frequency modulation model k The corresponding steering vector.

[0163] A fi It can be a K×M matrix, consisting of K rows and M columns. The steering matrix A in Equation 15 fi The result of the matrix multiplication A with Y(i) in Equation 12 fi Y(i) can be calculated as a K×1 dimensional vector. The matrix multiplication result A fi The k-th row element in Y(i) can be the AoA of Y(i) corresponding to the k-th steering angle θ k The value corresponding to the probability of , and can indicate angle information. Therefore, the result of applying the first matrix operation to the i-th sample data Y(i) can indicate the angle information corresponding to the i-th time index.

[0164] For example, when the second matrix operation A f0 -1 When applied to angle information, the frequencies of the time domain values ​​of the sampled data can be unified to the same reference frequency f0. The reference frequency can be determined as any frequency, for example, the carrier frequency f of the i-th time index c Therefore, the radar signal processing device can normalize the i-th phase by combining the first matrix operation and the second matrix operation. pNorm,i Applied to the i-th sampling value to calculate or obtain the phase-normalized radar data of the i-th time index This is shown in Equation 17 below.

[0165] Equation 17

[0166]

[0167] The phase normalization model 1000 may include a phase normalization matrix (e.g., N phase normalization matrices in the case of N sampling points), in which the first matrix operation and the second matrix operation are combined by matrix multiplication for each sampling point of the difference frequency signal. The radar signal processing device may generate radar data The phase of the radar data is normalized by applying the first to Nth phase normalization matrices to the sampled data at the sampling points respectively.

[0168] Although for ease of description, Figure 10 1010 and the second operation 1020 are shown in a continuous order, but the example is not limited thereto. For example, as shown in the aforementioned equation, the phase normalization model 1000 combining the first operation 1010 and the second operation 1020 by matrix multiplication can be immediately applied to the difference frequency data Y.

[0169] As described above, when the sampling points and steering angles are determined by the radar signal processing device, the phase normalization matrix can be pre-calculated and stored. Furthermore, the carrier frequencies corresponding to the sampling points can also be obtained from the frequency modulation model. Therefore, the phase normalization matrix represented by equations 15 and 16 above can be pre-calculated and stored and immediately applied to the obtained set of difference frequency data.

[0170] Figure 11 An example of improving angular resolution and pitch resolution by phase normalization is shown.

[0171] The radar signal processing device may determine the angle indicated by one of the plurality of steering vectors 1191 as AoA and determine that the target point is within one of the plurality of identifiable spacings 1192. Thus, the angular resolution can correspond to the angular difference between the steering vectors 1191, and the pitch resolution may correspond to the spacing difference between the discernible spacings 1192. In the example, when the angular resolution When the spacing resolution is reduced, the radar signal processing device can distinguish two target points located at a smaller angular interval. When reduced, the radar signal processing device can identify two target points located at a smaller spacing interval.

[0172] The first turning information 1110 may represent the angular resolution in radar signal processing in a general narrowband. and spacing resolution In narrow band, angular resolution and spacing resolution The third steering information 1130 having a larger bandwidth than that in the case of the first steering information 1110 may indicate a spacing resolution. However, in the third turn information 1130, it may appear as described above. Figure 5 Describes the angle error caused by carrier frequency variation Therefore, the angular resolution The angular resolution of the third turning information 1130 is improved by applying multiple input multiple output (MIMO). When the fourth steering information 1140 is received, the angle error caused by the carrier frequency change May be greater than the improved angular resolution By applying MIMO, it is possible to estimate high-resolution AoA. However, the number of transmitting antennas and receiving antennas may increase, and therefore, the hardware size may increase. The angle error of the fourth steering information 1140 is removed by dividing the frequency band. This will increase the spacing resolution This results in degradation like the second steering information 1120 .

[0173] As mentioned above Figures 1 to 9 As mentioned above, the radar signal processing device can compensate the error caused by the carrier frequency change for the difference frequency signal. By compensating, the spacing resolution of the turning information 1150 can be improved. and angular resolution Therefore, the radar signal processing device can estimate the elevation angle and height of an aerial obstacle (e.g., the top of a tunnel and a traffic light) that is located within the target interval and at a specific height above the ground with greater accuracy, and prevent a collision with the obstacle by notifying the vehicle of the estimation result or controlling the vehicle based on the estimation result.

[0174] Figure 12 Another example of a radar signal processing device is shown.

[0175] The computing device 1200 may be a device configured to process radar signals using the above radar signal processing method. In an example, the computing device 1200 may correspond to the above reference Figure 2The radar signal processing device 200 described above. The computing device 1200 may be, for example, an image processing device, a smartphone, a wearable device, a tablet computer, a netbook, a laptop computer, a desktop computer, a personal digital assistant (PDA), a head-mounted display (HMD), a digital camera, a global positioning system (GPS) navigation, a personal navigation device, a portable navigation device (PND), a television (TV), an intelligent vacuum cleaner, a smart home device, a walking assistance device, a robot, an autonomous vehicle, and a driving assistance device provided in a vehicle.

[0176] refer to Figure 12 , the computing device 1200 includes a processor 1210, a storage device 1220, a camera 1230, an input device 1240, an output device 1250, and a network interface 1260. The processor 1210, the storage device 1220, the camera 1230, the input device 1240, the output device 1250, and the network interface 1260 can communicate with each other through a communication bus 1270.

[0177] The processor 1210 can execute functions and instructions in the computing device 1200. For example, the processor 1210 can process instructions stored in the storage device 1220. The processor 1210 can execute the above reference Figures 1 to 11 One or more or all of the described operations.

[0178] The storage device 1220 can store information or data required by the processor 1210 for processing. For example, a pre-calculated phase normalization matrix can be stored in the storage device 1220. The storage device 1220 may include a non-transitory computer-readable storage medium or device, such as a random access memory (RAM), a dynamic RAM (DRAM), a static RAM (SRAM), a magnetic hard disk, an optical disk, a flash memory, an electrically erasable programmable read-only memory (EPROM), a floppy disk, and other types of computer-readable storage media known in the relevant art. The storage device 1220 can store instructions to be executed by the processor 1210 and can store relevant information while the computing device 1200 is executing software or applications.

[0179] The camera 1230 may capture an image including a plurality of image frames. For example, the camera 1230 may generate a frame image.

[0180] The input device 1240 may receive input from the user through tactile input, video input, audio input, or touch input. The input device 1240 may include, for example, a keyboard, a mouse, a touch screen, a microphone, and other devices that can detect input from the user and send the detected input.

[0181] Output device 1250 can provide output from computing device 1200 to a user via visual, auditory, or tactile channels. Output device 1250 can include, for example, a display, a touchscreen, a speaker, a vibration generator, and other devices capable of providing output to a user. Network device 1260 can communicate with external devices via a wired or wireless network. In an example, output device 1250 can provide the user with the results of processing radar signals using at least one of visual, auditory, or tactile information. For example, when computing device 1200 is installed in a vehicle, computing device 1200 can visualize a radar image on a display. In another example, computing device 1200 can change at least one of the speed or velocity, acceleration, or steering of the vehicle in which computing device 1200 is installed based on AoA information, spacing information, and / or the radar image. However, examples are not limited to the foregoing, and computing device 1200 can perform functions such as ACC, BSD, LCA, automatic emergency braking (AEB), and self-positioning.

[0182] In this article Figure 2 、 Figure 3 and Figure 12The radar signal processing device 200, spectrum analyzer 316, chirp transmitter 311 and other devices and equipment, units, modules and components described are implemented by hardware components. Examples of hardware components that can be used to perform the operations described in this application include controllers, sensors, generators, drivers, memories, comparators, arithmetic logic units, adders, subtractors, multipliers, dividers, integrators, and any other electronic components configured to perform the operations described in this application, where appropriate. 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). A 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, a 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 a processor or a computer can execute instructions or software, for example, 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 instructions or software. For the sake of brevity, the singular term "processor" or "computer" can be used in the description of the examples described in this application, but multiple processors or computers can be used in other examples, or the processor or computer can 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 one or more different processing configurations, examples of which include a single processor, independent processors, parallel processors, single instruction multiple data (SISD) multiprocessing, single instruction multiple data (SIMD) multiprocessing, multiple instruction single data (MISD) multiprocessing, multiple instruction multiple data (MIMD) multiprocessing, a controller and arithmetic logic unit (ALU), a DSP, a microcomputer, an FPGA, a programmable logic unit (PLU), a central processing unit (CPU), a graphics processing unit (GPU), a neural processing unit (NPU), or any other device capable of responding to and executing instructions in a defined manner.

[0183] Perform the operations described in this application Figures 1 to 11 The method shown in is performed by computing hardware, for example, by one or more processors or computers, wherein the computing hardware is implemented as described above as execution instructions or software to perform the operations performed by these methods described in the application. For example, a single operation or two or more operations can be performed by a single processor, or two or more processors, or a processor and a controller. One or more operations can be performed by one or more processors or a processor and a controller, and one or more other operations can be performed by one or more other processors or another processor and another controller. One or more processors or a processor and a controller can perform a single operation or two or more operations.

[0184] The instructions or software for controlling a processor or computer to implement the hardware components and perform the methods as described above are written as computer programs, code segments, instructions, or any combination thereof, for individually or collectively instructing or configuring a processor or computer to operate as a machine or special-purpose computer to perform the operations performed by the hardware components and the methods described above. In an example, the instructions or software include at least one of the following: an applet, a dynamic link library (DLL), middleware, firmware, a device driver, an application program storing a radio detection and ranging (radar) signal processing method. In one example, the instructions or software include machine code directly executed by the processor or computer, such as machine code generated by a compiler. In another example, the instructions or software include high-level code executed by the processor or computer using an interpreter. A person skilled in the art can easily write instructions or software based on the block diagrams and flow charts shown in the accompanying drawings and the corresponding descriptions in the specification, wherein an algorithm for performing the operations performed by the hardware components and the methods described above is disclosed.

[0185] The instructions or software that controls the processor or computer-implemented hardware components and performs the methods described above, as well as any associated data, data files, and data structures, are 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 programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), dynamic random-access memory (DRAM), static random-access memory (SRAM), flash memory, non-volatile 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-R LTH, BD-RE, Blu-ray or optical disk storage device, hard disk drive (HDD), solid state drive (SSD), flash memory, card type memory (such as, multimedia card or micro card (for example, Secure Digital (SD) or Extreme Digital (XD))), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, and any other device configured as follows: to store instructions or software and any associated data, data files and data structures in a non-transitory manner, and to provide instructions or software and any associated data, data files and data structures to a processor or computer so that the processor or computer can execute the instructions. In one example, the instructions or software and any associated data, data files and data structures are distributed on a networked computer system so that 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.

[0186] Although this 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 should be considered merely as descriptive and not for purposes of limitation. The description of features or aspects in each example is considered applicable to similar features or aspects in other examples. Suitable results may be achieved if the described techniques are performed in a different order and / or if components in the described systems, architectures, devices, or circuits are combined in different ways and / or replaced or supplemented by other components or their equivalents.

[0187] Therefore, the scope of the 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 are to be construed as being included in the disclosure.

Claims

1. A radio detection and ranging "radar" signal processing method comprising: obtaining a difference frequency signal based on a chirp signal of a radar transmission signal generated based on a frequency modulation model and a radar reflection signal obtained by reflecting the radar transmission signal from an object; as well as Radar data is generated by normalizing a phase change between the beat frequency signal and another beat frequency signal based on a carrier frequency change of the chirp signal so that the phase change has the same value at each of a plurality of sampling points of the chirp signal.

2. The radar signal processing method according to claim 1, wherein generating the radar data comprises: A phase normalization model configured to normalize a carrier frequency of the difference frequency signal to a reference frequency is applied to the difference frequency signal.

3. The radar signal processing method according to claim 2, wherein the phase normalization model comprises: A result of converting the difference frequency signal in the time domain into data in another domain based on the carrier frequency according to the frequency modulation model and a result of inversely converting the data in another domain to the time domain based on the reference frequency are combined. The radar signal processing method according to claim 3 , wherein the another domain comprises an angle domain.

5. The radar signal processing method according to claim 2, wherein the phase normalization model comprises: Phase normalization matrices corresponding to the sampling points of the difference frequency signal respectively.

6. The radar signal processing method according to claim 2, wherein the phase normalization model comprises: A first matrix operation is performed to convert a value in the time domain corresponding to each sampling point of the difference frequency signal into angle information using a carrier frequency corresponding to a corresponding sampling point of the frequency modulation model; as well as A second matrix operation inversely converts the angle information into the time domain using the reference frequency.

7. The radar signal processing method according to claim 1, further comprising: radiating the radar transmit signal including a chirp signal, wherein a carrier frequency of the chirp signal is modulated based on the frequency modulation pattern; as well as The radar reflection signal is sensed.

8. The radar signal processing method according to claim 7, wherein sensing the radar reflection signal comprises: The radar reflection signals are individually sensed by receiving sub-antennas in the radar sensor.

9. The radar signal processing method according to claim 1, wherein obtaining the difference frequency signal comprises: The beat frequency signal corresponding to the frequency difference between the radar transmission signal and the radar reflection signal is calculated. 10 . The radar signal processing method according to claim 1 , wherein the frequency modulation model is a model having a pattern in which a carrier frequency changes linearly or a model having a pattern in which a carrier frequency changes nonlinearly.

11. The radar signal processing method according to claim 1 , further comprising: At least one of angle of arrival (AoA) information or distance information is calculated based on the radar data.

12. The radar signal processing method according to claim 11, further comprising: A radar image map of an environment is generated based on the at least one of the AoA information or the distance information.

13. The radar signal processing method according to claim 12, further comprising: The radar image map is visualized via a display.

14. The radar signal processing method according to claim 11, further comprising: Based on the at least one of the AoA information or the distance information, at least one of a speed, an acceleration, or a steering of the vehicle is changed. 15 . A non-transitory computer-readable storage medium storing instructions, which, when executed by a processor, cause the processor to perform the radar signal processing method according to claim 1 .

16. A radio detection and ranging "radar" signal processing device comprising: a radar sensor configured to: radiate a radar transmission signal generated based on a frequency modulation model, and sense a radar reflection signal when the radar transmission signal is reflected by an object; as well as The processor is configured to: Obtaining a difference frequency signal based on a chirp signal of the radar transmission signal and the radar reflection signal; generating radar data by normalizing a phase change between the beat frequency signal and another beat frequency signal based on a carrier frequency change of the chirp signal so that the phase change has the same value at each of a plurality of sampling points of the chirp signal; as well as At least one of angle of arrival (AoA) information or spacing information is calculated based on the radar data.

17. The radar signal processing device according to claim 16, wherein: The difference frequency signal corresponds to a frequency difference between the radar transmission signal and the radar reflection signal. 18 . The radar signal processing device according to claim 17 , wherein the processor is further configured to obtain the difference frequency signal at a preset sampling point based on the radar transmission signal and the radar reflection signal.

19. The radar signal processing device according to claim 16, wherein: The difference frequency signal is generated based on a signal waveform of the radar transmission signal and a signal waveform of the radar reflection signal.

20. A signal processing method, comprising: Generate and radiate a chirp signal of a transmission signal having a frequency varying within a frequency band; obtaining a reflected signal when the transmitted signal is reflected from an object; Obtaining a difference frequency signal at a sampling point based on the transmitted signal and the reflected signal; Normalizing a phase change between the difference frequency signal and another difference frequency signal based on a carrier frequency change of the chirp signal so that the phase change has the same value at each of a plurality of sampling points of the chirp signal; as well as The normalized difference frequency signal is used to calculate the angle of arrival AoA of the object. The signal processing method according to claim 20 , wherein the frequency band is greater than or equal to 2 GHz. The signal processing method according to claim 20 , wherein at least three antennas obtain the reflected signal. The signal processing method according to claim 22 , wherein the at least three antennas are equidistant from each other. The signal processing method according to claim 20 , wherein the frequency of the transmission signal changes linearly in the frequency band. The signal processing method according to claim 20 , wherein a resolution of the AoA is less than or equal to 1 degree.

26. The signal processing method according to claim 20, wherein: Normalizing a phase change between the difference frequency signal and the other difference frequency signal includes: A phase normalization matrix to which frequency variation at each sampling point is applied is applied to sampling data of the difference frequency signal corresponding to the corresponding sampling point.

27. The signal processing method according to claim 26, wherein a phase normalization matrix corresponding to each sampling point is a matrix based on a combination of a first matrix operation and a second matrix operation, wherein the first matrix operation converts a value in the time domain corresponding to the sampling point into a value in another domain using a frequency at the sampling point in the sampled data, and the second matrix operation inversely converts the value in the other domain into the time domain using a reference frequency. The signal processing method of claim 27 , wherein the another domain comprises an angular domain.

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