Radar data processing device and local travel resolution adjustment method

Abstract

A radar data processing device and method are provided. The method generates a radar image map; predicts a region of interest (ROI) based on the generated radar image map; senses radar data using a radar sensor; identifies the sensed radar data based on steering information; adjusts the steering information based on the predicted ROI; and determines direction of arrival (DoA) information corresponding to the sensed radar data based on the adjusted steering information. The radar data processing device can locally adjust at least one of range resolution, angular resolution, or Doppler velocity resolution based on the ROI predicted from the radar image map, thereby generating accurate radar data processing results for a primary region.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority from Korean Patent Application No. 10-2019-0076965 filed on June 27, 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 following description relates to techniques for processing radar data and local range resolution adjustment methods. Background Art

[0004] An advanced driver assistance system (ADAS) is a driver assistance system that uses sensors provided inside or outside a vehicle to enhance driver safety and convenience and help the driver avoid or prevent dangerous situations.

[0005] Sensors used in ADAS include cameras, infrared sensors, ultrasonic sensors, light detection and ranging (LIDAR), and radio detection and ranging (RADAR). Compared to optical sensors, radar can reliably measure objects around the vehicle without being affected by the surrounding environment, such as weather. Summary of the Invention

[0006] This Summary is provided to introduce a selection of concepts in a simplified form 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 be used as an aid in determining the scope of the claimed subject matter.

[0007] In one general aspect, a radar data processing method includes generating a radar image map; predicting a region of interest (ROI) based on the generated radar image map; adjusting steering information based on the predicted ROI; sensing radar data using a radar sensor; and determining direction of arrival (DoA) information corresponding to the sensed radar data based on the adjusted steering information. Adjusting the steering information may include assigning a preset number of candidate steering vectors included in the steering information to each of one or more target trips based on the ROI.

[0008] Assigning candidate steering vectors to each of the one or more target trips based on the ROI may include densely arranging the candidate steering vectors in the ROI by adjusting a distribution of candidate steering vectors for each of the one or more target trips in the steering information.

[0009] Assigning candidate turning vectors to each of the one or more target trips based on the ROI may include: increasing the number of candidate turning vectors associated with the ROI in the turning information; and decreasing the number of candidate turning vectors associated with the remaining areas other than the ROI in the turning information.

[0010] Adjusting the steering information may include: selecting a plurality of target trips for calculating DoA information within a maximum sensing range of the radar sensor based on the ROI; and assigning a preset number of candidate steering vectors in the steering information to each of the selected target trips based on the ROI.

[0011] Selecting the plurality of target trips based on the ROI may include densely arranging the candidate steering vectors in the ROI by adjusting a distribution of the plurality of target trips.

[0012] Selecting multiple target trips based on the ROI may include: increasing the number of target trips for which DoA information is to be calculated for an area corresponding to the ROI in the turning information; and decreasing the number of target trips for which DoA information is to be calculated for an area corresponding to the remaining area other than the ROI in the turning information.

[0013] Determining the DoA information may include: retrieving a target steering vector that matches the sensed radar data from among candidate steering vectors for each target range within the maximum sensing range of the radar sensor from the steering information; and determining a steering angle mapped to the retrieved target steering vector as the DoA information corresponding to the radar data.

[0014] Adjusting the turning information may include assigning, in the turning information, a candidate turning vector to a run where the new potential object was detected at a basic angular resolution when a new potential object is detected in a run where no object was detected in a previous frame.

[0015] Determining the DoA information may further include: skipping determining the DoA information for a target trip for which no object is detected in the current frame, among the target trips for which DoA calculation is to be performed in the adjusted steering information.

[0016] The radar data processing method may further include calculating Doppler velocity information based on the DoA information.

[0017] The calculation of the Doppler velocity information may include adjusting a local resolution of the Doppler velocity information based on the predicted ROI.

[0018] Predicting the ROI may include: calculating DoA information corresponding to the previous frame based on radar data collected from the previous frame; generating coordinate information of nearby objects corresponding to the previous frame based on the DoA information corresponding to the previous frame and self-positioning of the radar data processing device; and predicting the ROI of the current frame based on a radar image map after the previous frame generated from the coordinate information corresponding to the previous frame.

[0019] The method may include generating a map indicating at least one of the following as a radar image map of the current frame based on DoA information of the current frame: object occupancy probability and radar signal reception strength of nearby points around the radar data processing device.

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

[0021] The method may include changing at least one of a speed, an acceleration, and a steering operation of a vehicle in which the radar data processing device is installed based on the radar image map.

[0022] Adjusting the steering information may include selecting a target course with a basic resolution from the steering information when the object is not detected in the radar image map of the previous frame, and placing the candidate steering vector in the selected target course.

[0023] The radar sensor may include a field of view (FOV) that includes directions different than the longitudinal direction of the vehicle.

[0024] In another general aspect, a radar data processing method includes generating a radar image map; predicting a region of interest (ROI) based on the generated radar image map; adjusting a local range resolution of the radar data based on the predicted ROI; and detecting a range from a target point reflecting the radar data based on the adjusted local range resolution.

[0025] Adjusting the local range resolution may include: reducing the range resolution for the ROI within the maximum sensing range of the radar sensor; and increasing the range resolution for the remaining areas except the ROI.

[0026] Detecting the range may include detecting the range from the target point in units of reduced range resolution when the radar data is reflected from the target point corresponding to the ROI; and detecting the range from the target point in units of increased range resolution when the radar data is reflected from the target point corresponding to the remaining area.

[0027] Adjusting the local travel resolution may include consistently maintaining the overall travel resolution during travel detection.

[0028] In another general aspect, a radar data processing device includes a radar sensor configured to sense radar data; and a processor configured to: generate a radar image map; predict a region of interest (ROI) based on the generated radar image map; adjust steering information used to identify the sensed radar data based on the predicted ROI; and determine direction of arrival (DoA) information corresponding to the radar data based on the adjusted steering information.

[0029] Other features and aspects will become apparent from the following detailed description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a diagram illustrating an example of recognizing a surrounding environment through a radar data processing method according to one or more embodiments.

[0031] Figure 2 is a diagram illustrating an example of a radar data processing method according to one or more embodiments.

[0032] Figure 3 is a diagram illustrating an example of a radar sensor according to one or more embodiments.

[0033] Figure 4 is a flow chart illustrating an example of a method of processing direction of arrival (DOA) information in accordance with one or more embodiments.

[0034] Figure 5 is a diagram illustrating an example of resolution when processing DOA information according to one or more embodiments.

[0035] Figure 6 is a flow chart illustrating an example of a radar data processing method according to one or more embodiments.

[0036] Figure 7 is a diagram illustrating an example of a radar data processing method according to one or more embodiments.

[0037] Figure 8 is a diagram illustrating an example of predicting a region of interest (ROI) according to one or more embodiments.

[0038] Figure 9 is a diagram illustrating an example of allocating a steering vector in steering information to each of trips according to one or more embodiments.

[0039] Figure 10 is a diagram illustrating an example of locally adjusting a range resolution of a range in steering information according to one or more embodiments.

[0040] Figure 11is a diagram illustrating an example of calculating Doppler velocity according to one or more embodiments.

[0041] Figure 12 is a diagram illustrating an example of adjusting local run resolution based on a result of predicting an ROI according to one or more embodiments.

[0042] Figure 13 is a diagram illustrating another example of a radar data processing apparatus according to one or more embodiments.

[0043] 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

[0044] 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 may 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.

[0045] 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.

[0046] Various modifications can be made to the following examples. In this article, the examples are not interpreted as being limited to the present disclosure, but should be understood to include all changes, equivalents and replacements within the scope of the idea and technology of the present disclosure.

[0047] Throughout the 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 in between. Conversely, when an element is described as being “directly connected to” or “directly coupled to” another element, there may not be other intervening elements. 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.

[0048] 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.

[0049] 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.

[0050] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those commonly understood by those skilled in the art to which the present disclosure pertains based on their understanding of the disclosure of the present application. Terms such as those defined in commonly used dictionaries should be interpreted as having the same meaning as in the context of the relevant technology and / or the present application, and should not be interpreted as ideal or overly formal meanings unless explicitly defined as such herein. In this document, the 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 a feature is included or implemented, and all examples are not limited thereto.

[0051] Furthermore, in the description of the exemplary embodiments, when it is considered that a detailed description of a structure or function known therefrom after understanding the disclosure of the present application would lead to an obscure interpretation of the exemplary embodiments, such description will be omitted.

[0052] 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.

[0053] An advanced driver assistance system (ADAS) is an advanced driver assistance system that uses sensors provided inside or outside a vehicle to assist the driver in driving in order to improve the driver's safety and convenience, thereby helping the driver to avoid or prevent dangerous situations. The radar system market is growing rapidly due to the impact of increasingly stringent regulations related to safe driving by government authorities in developed countries and the efforts to commercialize autonomous vehicles manufactured by automobile manufacturers and information technology (IT) companies. Sensors suitable for ADAS may include, for example, cameras, millimeter wave radars, infrared sensors, ultrasonic sensors, light detection and ranging (LIDAR), and similar sensors. These types of sensors may differ from one another based on the range to be detected and the functions to be applied, and sensor fusion technology for combining sensors to compensate for the shortcomings of the sensors has recently been required. Hereinafter, a technology using a radar sensor among sensors will be described.

[0054] Figure 1 is a diagram illustrating an example of recognizing a surrounding environment through a radar data processing method.

[0055] The radar data processing device 110 can detect an object in front of the radar data processing device 110 using a sensor 111. The sensor 111, which can be configured to detect an object, can be, for example, an image sensor or a radar sensor, and can detect the range of the object in front of the radar data processing device 110. The term "range" used herein may indicate a distance. For example, a range from A to B may indicate a distance from A to B, and a range between A and B may indicate a distance between A and B. Therefore, the terms "range" and "distance" may be used interchangeably herein.

[0056] 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 where such features are included or implemented, and all examples and embodiments are not limited thereto.

[0057] Figure 1 An example of a sensor that is a radio detection and ranging (RADAR) is shown. Figure 1In the example of FIG, the radar data processing device 110 analyzes the radar signal received from the radar sensor 111 and detects the course of the object 180 existing in front of the radar data processing device 110. The radar sensor 111 may be provided inside or outside the radar data processing device 110. In addition, in addition to the radar signal received from the radar sensor 111, the radar data processing device 110 may also detect the course of the object 180 existing in front of the radar data processing device 110 based on data collected by other sensors (e.g., an image sensor, etc.). The resolution when processing radar data can be categorized into hardware-related resolution and software-related resolution. Hereinafter, regarding how to improve the performance of the resolution, the software-related resolution will be mainly described.

[0058] In an example, the radar data processing device 110 may be provided in a vehicle. The vehicle performs operations such as adaptive cruise control (ACC), autonomous emergency braking (AEB), and blind spot detection (BSD) based on the approach to an object detected by the radar data processing device 110 .

[0059] In addition to detecting travel, radar data processing device 110 may also generate a map 130 of the surrounding environment. Map 130 may indicate the locations of nearby objects around radar data processing device 110, and such nearby objects may include dynamic objects such as vehicles and people, or stationary or background objects such as guardrails and traffic lights.

[0060] To generate the map 130, a single scan may be used. Through the single scan, the radar data processing device 110 obtains a single scan image 120 from the sensor 111 and generates the map 130 from the obtained single scan image 120. The single scan image 120 is generated based on the radar signal sensed by the single radar sensor 111 and indicates a relatively high resolution. The single scan image 120 may be a radar scan image and may include a course indicated by the radar signal received by the radar sensor 111 at an elevation angle. For example, in Figure 1 In the example of FIG. 1 , 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 Examples are shown, and a single scanned image may be represented by other formats based on various examples.

[0061] As used herein, the steering angle refers to an angle relative to a direction (e.g., the direction of travel from a radar data processing device to a target point). For example, the steering angle is the angle between the direction of travel of a radar data processing device (e.g., a vehicle) and the target point relative to the radar data processing device. The steering angle is primarily described herein based on horizontal angles, but is not limited thereto. The steering angle can also be applied to elevation angles.

[0062] Radar data processing device 110 can obtain precise information about the target's shape using a multi-radar map. A multi-radar map is generated by combining multiple radar scan images. For example, radar data processing device 110 generates map 130 by spatiotemporally combining radar scan images acquired as radar sensor 111 moves. Map 130 can be a radar image map.

[0063] Herein, the radar data may include raw radar data sensed by the radar sensor 111 .

[0064] To generate the map 130, direction of arrival (DoA) information can be used. DoA information can indicate the direction from which a radar signal reflected from a target point is received. The radar data processing device 110 can use the DoA information to identify the direction of the target point relative to the radar sensor 111. Therefore, this DoA information can be used to generate radar scan data and a map of the surrounding area. In order to obtain DoA information with fine resolution through the radar data processing device 110, the radar data processing device 110 may need to receive a larger number of radar signals associated with angles and / or distances or travels, and process the phases. However, when the radar sensor 111 receives a larger number of signals and processes the phases, the amount of calculations or operations may increase, and the calculation time may increase accordingly. Below, a method for obtaining DoA information with desired resolution with relatively low operational load will be described.

[0065] Figure 2 is a diagram illustrating an example of a radar data processing method.

[0066] refer to Figure 2 , the radar data processing device 200 includes a radar sensor 210 and a processor 220 .

[0067] In an example, the radar sensor 210 may sense radar data. For example, the radar sensor 210 may transmit a radar signal to the outside and receive a signal reflected from a target point by the transmitted radar signal. The radar sensor 210 may include an antenna corresponding to a receiving channel (Rx channel), and the signal received through the Rx channel may have a different phase depending on the direction in which the signal is received. Figure 3 The radar sensor 210 is described in detail.

[0068] Processor 220 can predict a region of interest (ROI) based on a previous radar image map generated from previous radar data. The ROI can be an area corresponding to the distance and angle from the object when the object or background is predicted to be present. For example, the ROI can be indicated by an angular range and a distance range. For example, if an object is predicted to be 30 meters (m) at 30° to the right relative to the direction of travel of the radar data processing device 200, the ROI can be set to an angular range of 28° to 32° and a distance range of 29m to 31m. However, the ROI is not limited to the examples described above and can be changed based on various examples. For example, the ROI can be predicted based on the self-motion information of the device (e.g., the vehicle on which the radar data processing device 200 is installed) based on the self-positioning in the previous frame and the movement to the position in the current frame. There are no special restrictions when generating the radar image map, so processor 220 can predict the ROI for nearby stationary objects or nearby dynamic objects.

[0069] Radar data sensed by the radar sensor 210 can be identified by steering information, and the processor 220 can adjust the steering information based on the predicted ROI. The steering information can be used to identify the radar data, and the steering information can include a steering vector, angular resolution, range resolution, Doppler velocity resolution, and the arrangement of steering vectors based on each resolution. The term "resolution" used in this article can indicate the ability of the device to identify small differences, for example, the minimum scale unit operating range / full operating range. The resolution can indicate the minimum unit of discrimination. The smaller the resolution of the device, the more accurate the result that the device can output. An area with a small resolution value can indicate a smaller unit of discrimination, thereby improving the resolution. Conversely, an area with a large resolution value can indicate a larger unit of discrimination, thereby reducing and decreasing the resolution.

[0070] The steering vector included in the steering information may be referred to as a "candidate steering vector." When radar data is received at a predetermined angle, the steering vector may include phase information calculated to be included in the radar data. Herein, while a vector including phase information of the sensed radar data is referred to as a radar vector, a steering vector among the candidate steering vectors included in the steering information that is determined to match the radar vector is referred to as a "target steering vector." The steering vector set may be represented by the following equation 1, and the steering vector may be represented by the following equation 2.

[0071] Equation 1

[0072] A=[α(θ1),...,α(θ K )]

[0073] Equation 2

[0074]

[0075] In Equation 1, the steering vector set A may include K steering vectors, where K represents an integer greater than or equal to 1. In Equation 2, d represents the distance between antennas of the antenna array included in the radar sensor. j represents an imaginary unit, and λ represents a wavelength. Furthermore, θi represents the i-th steering angle in the steering vector set, where i represents an integer greater than or equal to 1. α(θi) represents the steering vector corresponding to the steering angle θi.

[0076] As the number K of steering vectors in Equation 1 increases, the amount of time used to retrieve a steering vector that matches the sensed radar signal when determining DoA may increase. In an example, in order to minimize such an increase in processing time for determining DoA, the radar data processing device 200 may maintain the number K of steering vectors and locally adjust the distribution of the steering angle θi for each distance and each angle. The radar data processing device 200 can effectively obtain the desired resolution by using the same amount of calculation or operation for such local adjustment of the resolution. Adjusting the steering information may indicate locally adjusting at least one of the angular resolution, the range resolution, or the Doppler velocity resolution based on the ROI. Hereinafter, reference will be made to each of the following: Figure 9 、 Figure 10 and Figure 11 To describe adjusting angular resolution, adjusting stroke resolution, and adjusting Doppler velocity resolution.

[0077] For example, when the radar sensor 210 includes multiple Rx channels, the phase information of the radar data may indicate a phase difference between a reference phase and the phase of a signal received through each Rx channel. The reference phase may be an arbitrary phase or may be set to the phase of one of the Rx channels. For example, the processor 220 may generate a radar vector having a dimension corresponding to the number of Rx channels of the radar sensor 210 from the radar data. In this example, where the radar sensor includes four Rx channels, the processor 220 may generate a four-dimensional radar vector including a phase value corresponding to each Rx channel. The phase value corresponding to each Rx channel may be a numerical value indicating a phase difference.

[0078] In another example, when radar sensor 210 includes one transmit channel (Tx channel) and four receive channels, a radar signal transmitted through the Tx channel is reflected from a target point, and then the radar signal reflected from the target point is received at different angles through the four receive channels of radar sensor 210. Radar sensor 210 generates a radar vector including phase values for each of the four receive channels. Processor 220 can identify a target steering vector having a phase value most similar to the phase information of the radar vector from among multiple candidate steering vectors, and can determine the reception direction indicated by the identified target steering vector as the DoA information.

[0079] As described above, the processor 220 may determine the direction in which the sensed target point exists relative to the radar data processing device 200 based on the turning information.

[0080] Figure 3 is a diagram illustrating an example of a radar sensor according to one or more embodiments.

[0081] refer to Figure 3 , the radar sensor 310 transmits a signal through the antenna 313 and receives a signal through the antenna 313. The radar sensor 310 can be, for example, a millimeter wave radar, and can measure the distance to the object by analyzing the time of flight (ToF) and analyzing the change in the signal waveform, wherein the time of flight is the amount of time it takes for the emitted radio wave to return after hitting the object. Compared with optical sensors such as cameras, millimeter wave radars can monitor the front or detect objects in front without considering changes in the external environment (for example, the presence of fog and rain). In addition, compared with LIDAR, millimeter wave radars can have desired performance at a cost, and therefore can be a sensor that can compensate for camera defects. For example, the radar sensor 310 is implemented as a frequency modulated continuous wave (FMCW) radar. FMCW radars can be robust to external noise.

[0082] The chirp transmitter 311 of the radar sensor 310 may generate a frequency modulated (FM) signal 302 whose frequency varies with time. For example, the chirp transmitter 311 may generate the FM signal 302 by performing frequency modulation on the chirp signal 301. The chirp signal 301 may indicate a signal whose amplitude increases or decreases linearly with time. The chirp transmitter 311 may generate the FM signal 302 having a frequency corresponding to the amplitude of the chirp signal 301. For example, Figure 3 As shown, the frequency of the waveform of FM signal 302 gradually increases in the interval where the amplitude of chirp signal 301 increases, and the frequency of the waveform of FM signal 302 gradually decreases in the interval where the amplitude of chirp signal 301 decreases. Chirp transmitter 311 transmits FM signal 302 to duplexer 312.

[0083] The duplexer 312 of the radar sensor 310 can determine the transmission path of the signal through the antenna 313 (by Figure 3 Tx in the ) and the receive path (indicated by Figure 3 For example, when the radar sensor 310 is transmitting the FM signal 302, the duplexer 312 may form a signal path from the chirp transmitter 311 to the antenna 313, and transmit the FM signal 302 to the antenna 313 through the formed signal path, thereby transmitting the FM signal 302 to the external source.

[0084] When radar sensor 310 is receiving a signal reflected from an object, duplexer 312 may form a signal path from antenna 313 to spectrum analyzer 316. Antenna 313 may receive a reflected signal returned from an external object or obstacle after the transmitted signal reaches the external object or obstacle and then is reflected, and radar sensor 310 may transmit the reflected signal to spectrum analyzer 316 through the signal path formed from antenna 313 to spectrum analyzer 316.

[0085] The mixer 314 may demodulate a linear signal, such as an original chirp signal, from the received signal before frequency modulation. The amplifier 315 may amplify the amplitude of the demodulated linear signal.

[0086] The spectrum analyzer 316 compares the transmitted chirp signal 301 with the signal 308 that returns after being reflected from the object. The spectrum analyzer 316 detects the frequency difference between the transmitted chirp signal 301 and the reflected signal 308. Figure 3 As shown in graph 309, the frequency difference between transmitted chirp signal 301 and reflected signal 308 is constant during the interval in which the amplitude of transmitted chirp signal 301 increases linearly along the time axis and may 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 transmitted chirp signal 301 and reflected signal 308. Spectrum analyzer 316 may transmit information obtained through such analysis to a processor of the radar data processing device.

[0087] For example, spectrum analyzer 316 may calculate the distance between radar sensor 310 and the object, which is represented by Equation 3.

[0088] Equation 3

[0089]

[0090] In Equation 3, R represents the distance between the radar sensor 310 and the object, c represents the speed of light, and T represents the length of time that the transmitted chirp signal 301 is in the rising interval. b represents the frequency difference between the transmitted chirp signal 301 and the reflected signal 308 at a time point in the rising interval, and is also called the "beat frequency". B represents the modulation bandwidth. Beat frequency f b It can be derived as expressed in the following equation 4.

[0091] Equation 4

[0092]

[0093] In Equation 4, f b Indicates the beat frequency. drepresents the time difference between the time point when the chirp signal 301 is transmitted and the time point when the reflected signal 308 is received, for example, the delay time.

[0094] In an example, multiple radar sensors may be installed in various parts of a vehicle, and a radar data processing device configured to process radar data based on information sensed by the radar sensors may calculate the distance or range from a target point, direction, and relative speed in all directions of the vehicle. In an example, the radar data processing device may be installed in the vehicle. In an example, the radar processing device may be installed in a mobile device installed in the vehicle. The vehicle or mobile device may then provide various driving functions based on information obtained using the information collected by the radar sensors, such as ACC, BSD, lane change assist (LCA), etc.

[0095] In this example, each radar sensor can perform frequency modulation on a chirp signal, transmit an FM signal to an external source, and receive a signal reflected from a target point. The processor of the radar data processing device can determine the distance or range from each radar sensor to the target point based on the frequency difference between the transmitted chirp signal and the received signal.

[0096] Figure 4 is a flow chart illustrating an example of a method of processing DoA information according to one or more embodiments. Figure 4 The operations in the drawings may be performed in the order and manner shown, but the order of some operations may be changed or some operations may be omitted without departing from the spirit and scope of the illustrative examples described. Figure 4 Many of the operations shown in FIG. 1 can be performed in parallel or simultaneously. Figure 4 One or more blocks and combinations of blocks may be implemented by a computer based on dedicated hardware that performs the specified functions, or a combination of dedicated hardware and computer instructions. Figure 4 In addition to the description, Figures 1 to 3 The description also applies to Figure 4 , and is incorporated herein by reference. Therefore, the above description may not be repeated here.

[0097] In an example, the radar data processing device may process the DoA information by applying a multiple signal classification (MUSIC) algorithm to the radar data.

[0098] refer to Figure 4 In operation 410, the radar data processing device calculates a sample covariance matrix. For example, the radar data processing device may calculate the sample covariance matrix based on a result of sampling a radar signal received by a separate Rx channel of the radar sensor.

[0099] In operation 420 , the radar data processing apparatus performs eigendecomposition. For example, the radar data processing apparatus may obtain eigenvalues and eigenvectors by performing eigendecomposition on the sample covariance matrix.

[0100] In operation 430 , the radar data processing apparatus calculates a noise covariance matrix. For example, the radar data processing apparatus divides the sample covariance matrix into a signal component and a noise component.

[0101] In operation 440 , the radar data processing apparatus calculates a spatial spectrum. The radar data processing apparatus forms a spatial spectrum using a noise covariance matrix and obtains DoA information by finding a peak.

[0102] In this example, the resolution of the surrounding image and the algorithm processing time for obtaining DoA information are inversely proportional to each other. In this example, as the resolution value decreases and, therefore, as the resolution is increased, the majority of the time used to calculate DoA information may be occupied by the operation 440 of calculating the spatial spectrum. In the operation of processing the radar image image, the operation 440 of calculating DoA information and calculating the spatial spectrum may require approximately 90% of the total time used for the processing, so it may be desirable to minimize or prevent an increase in the amount of DoA calculations.

[0103] However, the MUSIC algorithm is provided as an example only, and thus other methods or algorithms may be applied to radar data. Other methods or algorithms may include, for example, classic digital beamforming (CDBF), Bartlett's method, minimum variance distortionless response (MVDR), and the like.

[0104] Figure 5 is a diagram illustrating an example of resolution when processing DOA information according to one or more embodiments.

[0105] Figure 5 The results of sensing an object 510 based on sets of turning information with different resolutions are shown. Each space in the grid pattern corresponds to a candidate turning vector included in the turning information. For example, when the turning information includes a greater number of candidate turning vectors, the radar data processing device can more accurately identify the direction from which the radar signal was received, thereby obtaining a sensing result with improved resolution.

[0106] For example, the left portion shows target point 521 sensed based on steering information with fine resolution. The middle portion shows target point 522 sensed based on steering information with intermediate resolution. The right portion shows target point 523 sensed based on steering information with weak resolution. As shown in the figure, when the resolution of the steering information decreases, a larger number of candidate steering vectors can be included densely, thereby obtaining a more accurate image. However, the computational complexity also increases. Conversely, when the resolution of the steering information increases, a smaller number of candidate steering vectors can be included sparsely, thereby obtaining a less accurate image. However, the computational complexity can be reduced.

[0107] In an example, the radar data processing device can perform a method with reduced computational complexity while detecting the object 510 with fine resolution for the important area. Figures 6 to 13 A method is described in which a radar data processing apparatus obtains an image with improved resolution or distinction with low computational complexity based on steering information in which candidate steering vectors are focused on an ROI where an object is predicted to exist.

[0108] Figure 6 is a flow chart illustrating an example of a radar data processing method according to one or more embodiments. Figure 6 The operations in the drawings may be performed in the order and manner shown, but the order of some operations may be changed or some operations may be omitted without departing from the spirit and scope of the illustrative examples described. Figure 6 Many of the operations shown in FIG. 1 can be performed in parallel or simultaneously. Figure 6 One or more blocks and combinations of blocks may be implemented by a computer based on dedicated hardware that performs the specified functions, or a combination of dedicated hardware and computer instructions. Figure 6 In addition to the description, Figures 1 to 5 The description also applies to Figure 6 , and is incorporated herein by reference. Therefore, the above description may not be repeated here.

[0109] refer to Figure 6 In operation 610, the radar data processing device predicts the ROI based on the previously generated radar image map. Figure 8 Describe the prediction of the angle ROI in detail.

[0110] In operation 620, the radar data processing apparatus adjusts steering information used to identify radar data sensed by the radar sensor based on the predicted ROI. Figures 9 to 11 Describes the adjustment of steering information in detail.

[0111] In operation 630 , the radar data processing apparatus determines DoA information corresponding to the radar data based on the adjusted steering information.

[0112] The steering information may include a set of candidate steering vectors that are preset and stored along with locally adjusted resolution information, and each candidate steering vector may be mapped one-to-one to a feature value. For example, when the pre-stored candidate steering vectors include phase information and the feature value mapped to each candidate steering vector is a steering angle, the radar data processing device determines a target steering vector corresponding to the radar vector of the received radar data from among the pre-stored candidate steering vectors. The radar data processing device outputs the steering angle mapped to the determined target steering vector.

[0113] Determining the target turning vector may include, for example, determining the target turning vector as the target turning vector from among pre-stored candidate turning vectors that has the smallest difference from the radar vector (e.g., the turning vector having the smallest Euclidean distance from the radar vector). Alternatively, determining the target turning vector may include determining the target turning vector as the target turning vector from among the candidate turning vectors that has parameters that are most similar to specific parameters among the various parameters of the radar vector. Determining the target turning vector is not limited to that described above, and thus the target turning vector may be determined using various methods.

[0114] In an example, the radar data processing device may determine a steering angle mapped to the determined target steering vector as DoA information corresponding to the radar data.

[0115] As the number of candidate steering vectors for the ROI in the steering information increases, the steering angle indicated by each candidate steering vector can be subdivided, so the radar data processing device can determine DoA information with more improved angular resolution and range resolution for the ROI.

[0116] In an example, the radar data processing device can be referenced by the above Figure 6The described radar data processing method prevents performance degradation that can be caused by errors in self-estimated velocity and inaccuracies in Doppler velocity estimation. For example, while the self-estimated velocity and Doppler velocity of nearby objects may include errors, the radar data processing device can minimize the impact of these errors by predicting the ROI based on the radar image map and adjusting steering information, thereby relatively accurately updating the radar image map of the nearby environment. Furthermore, the radar data processing device can generate an accurate radar image map even during high-speed movement with low data transmission bandwidth between the radar sensor and the processor during the data acquisition period when data is obtained from the radar sensor. This is because only the local resolution is adjusted, while the overall resolution remains the same. Therefore, processing time for processing radar data does not increase, but resolution can be locally increased for ROIs where, for example, a primary object is present.

[0117] Figure 7 is a diagram illustrating an example of a radar data processing method according to one or more embodiments. Figure 7 The operations in the drawings may be performed in the order and manner shown, but the order of some operations may be changed or some operations may be omitted without departing from the spirit and scope of the illustrative examples described. Figure 7 Many of the operations shown in FIG. 1 can be performed in parallel or simultaneously. Figure 7 One or more blocks and combinations of blocks may be implemented by a computer based on dedicated hardware that performs the specified functions, or a combination of dedicated hardware and computer instructions. Figure 7 In addition to the description, Figures 1 to 6 The description also applies to Figure 7 , and is incorporated herein by reference. Therefore, the above description may not be repeated here.

[0118] refer to Figure 7 In operation 710, the radar data processing device detects a distance from a target point. For example, the radar data processing device processes a radar signal reflected from a target point and identifies a distance from the target point that reflected the radar signal.

[0119] In operation 720, the radar data processing device determines DoA information. For example, the radar data processing device identifies radar data for each target point based on the steering information adjusted for the current frame in operation 770. The radar data processing device identifies a target steering vector that matches the radar data from the steering information, including candidate steering vectors focused on the ROI. The radar data processing device determines the steering angle corresponding to the identified target steering vector in each target run as the DoA information for the radar data. For example, the radar data processing device estimates the DoA information using algorithms such as the MUSIC algorithm, the Bartlett algorithm, the MVDR algorithm, the Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), and similar algorithms.

[0120] In operation 730, the radar data processing device detects potential objects. For example, the radar data processing device selects target points corresponding to potential objects from among the target points for which DoA information has been estimated, and uses the selected target points to update the radar image map. Target points corresponding to potential objects may be points potentially predicted to be objects. For example, the radar data processing device selects target points within the radar sensor's field of view (FOV). The radar data processing device excludes target points outside the FOV from the radar image map update performed in operation 750. For another example, when two target points have similar DoA information, the radar data processing device selects one and excludes the other. This is because when two target points have identical or very similar DoA information, they may be essentially the same point. Therefore, using the same target points to generate a map may not improve resolution, but may increase operational or computational load. For example, the radar data processing device may use constant false alarm rate (CFAR) detection to detect potential objects.

[0121] In operation 740, the radar data processing device transforms the coordinates of the target point. In an example, the radar data processing device generates coordinate information for nearby objects based on the DoA information and the radar data processing device's self-positioning. For example, the target point detected as a potential object in operation 730 may have relative coordinates defined relative to the radar sensor's axis of travel and the DoA axis. The radar data processing device transforms the relative coordinates of the target point identified by the radar data into absolute coordinates.

[0122] In operation 750, the radar data processing device updates the radar image map. For example, the radar data processing device generates a radar image map based on the coordinate information of nearby objects obtained in operation 740. The radar image map may be a map indicating target points detected around the radar data processing device (e.g., indicating the absolute coordinates of the target points). The radar image map includes multiple spaces, each of which indicates object occupancy probability or radar signal reception strength.

[0123] The object occupancy probability may indicate the probability of an object occupying the absolute coordinates indicated by each space. The radar signal reception strength indicates the strength of the signal reflected and received from the absolute coordinates indicated by each space. A radar image graph indicating the object occupancy probability may be referred to as an occupancy grid graph (OGM), and a radar image graph indicating the radar signal reception strength may be referred to as an intensity grid graph (IGM). However, the types of radar image graphs are not limited to the aforementioned examples.

[0124] In an example, the radar data processing device may generate a map indicating at least one of object occupancy probability or radar signal reception strength at points near the device based on the DoA information of the current frame as a radar image map for the current frame. For example, the radar data processing device may generate a radar scan image of a radar sensor based on the DoA information. The radar data processing device generates a radar image map of the environment or conditions near the radar data processing device based on the radar scan images generated by each of the multiple radar sensors.

[0125] Furthermore, in operation 760, the radar data processing device predicts the ROI of the current frame based on information from the previous frame. For example, the radar data processing device may set the ROI in the current frame so that the ROI includes the area where the object was detected in the previous frame. This is because if the object existed in the previous frame, it is likely to exist in the current frame.

[0126] For example, the radar data processing device may calculate the DoA information corresponding to the previous frame from the radar data collected for the previous frame in operation 720. In operation 740 for the previous frame, the radar data processing device may generate coordinate information of nearby objects corresponding to the previous frame based on the DoA information corresponding to the previous frame and the self-positioning of the radar data processing device. The radar data processing device calculates the coordinate information of nearby objects corresponding to the previous frame in operation 740, and updates the radar image map up to the previous frame based on the coordinate information of nearby objects corresponding to the previous frame in operation 750. For the current frame, the radar data processing device predicts the ROI of the current frame based on the radar image map up to the previous frame in operation 760.

[0127] In operation 770, the radar data processing device adjusts the steering information. In an example, the radar data processing device performs an ROI focusing operation. For example, the radar data processing device locally reduces the resolution of radar data processing by focusing on the predicted ROI. Examples of adjusting the steering information based on the ROI may include: adjusting the angular resolution (reducing the reference angle to the reference angle). Figure 9 Describe), adjust the stroke resolution (refer to Figure 10Describe), and adjust the Doppler velocity resolution (refer to Figure 11 for description).

[0128] Figure 8 is a diagram illustrating an example of prediction of ROI according to one or more embodiments.

[0129] In the example, the radar data processing device generates or updates the radar image map 810 up to the previous frame, as described above with reference to Figure 7 The radar image 810 is Figure 8 810 is shown as a grid pattern including multiple spaces indicating object occupancy probability or signal reception strength. However, radar image graph 810 is not limited to the example graph shown and can be generated as a point cloud. The radar data processing device detects objects from radar image graph 810. For example, as shown in the figure, the radar data processing device detects first object 811 and second object 812. The radar data processing device predicts a region of interest (ROI) including the area occupied by the detected objects. For example, as shown in the figure, the radar data processing device detects first ROI 821 and second ROI 822.

[0130] For example, the radar sensor 891 is provided with a FOV having a direction different from the longitudinal direction of the vehicle 890. Figure 8 The FOV is shown to cover approximately 90° from the vehicle 890. 0 However, this is only an example and the FOV can be larger than Figure 8 Furthermore, in order to maximize the amount of information obtained by the radar sensor 891, the radar sensor 891 may be arranged at an angle different from the direction of travel 895 of the radar data processing device (e.g., the vehicle 890). Figure 8 In the example of , radar sensor 891 is arranged to observe an oblique line relative to a direction of travel 895 of vehicle 890 , and radar image map 810 is generated to cover the FOV of radar sensor 891 .

[0131] The radar data processing device divides the maximum sensing range of the radar sensor 891 into a plurality of ranges 830 and calculates DoA information for each range 830. The range 830 may be divided at intervals of 2 meters (m), which is a unit of range resolution, and may include distances of 10m, 12m, 14m, 16m, 18m, and 20m from the radar sensor 891. However, this is only an example, and the target range may be divided at intervals other than 2m. Figure 9 The calculation of the DoA information for each trip 830 is described in detail.

[0132] Figure 9is a diagram illustrating an example of allocating a steering vector in steering information to each of trips according to one or more embodiments.

[0133] In an example, the radar data processing device assigns a preset number of candidate turning vectors included in the turning information to each of the one or more target trips based on the ROI. Figure 9 As shown, the radar data processing device selects target trips for which DoA calculation is to be performed at the same trip resolution (e.g., 2 meters) and allocates the same number of candidate steering vectors to each of the selected target trips. Within each target trip, the candidate steering vectors may be arranged based on the same angular resolution or may be arranged based on an angular resolution that is locally adjusted based on the ROI.

[0134] For example, the radar data processing device densely arranges candidate turning vectors in the ROI by adjusting the distribution of candidate turning vectors for each target trip in the turning information. The radar data processing device increases the number of candidate turning vectors for the ROI in the turning information. Furthermore, the radar data processing device decreases the number of candidate turning vectors for the remaining regions excluding the ROI in the turning information.

[0135] exist Figure 9 In the example shown in FIG, target run 930 is 12 m, 14 m, 16 m, and 18 m. Based on the radar image map up to the previous frame, objects exist in run 932 of 14 m and run 933 of 16 m. Therefore, the radar data processing device predicts an ROI so that the ROI includes run 932 (R = 14 m) and run 933 (R = 16 m). The radar data processing device densely allocates candidate steering vectors to the angular runs corresponding to the ROI in each target run (e.g., angular runs 942 and 943), and allocates a smaller number of candidate steering vectors to the remaining region or regions.

[0136] For example, in Figure 9 In the example shown in FIG. 1 , the local angular resolution of candidate steering vectors arranged in a first angular run 942 corresponding to a first ROI in a first target run (R=14 m) and a second angular run 943 corresponding to a second ROI in a second target run (R=16 m) can be finer than the local angular resolution of candidate steering vectors arranged in the remaining angular runs. Furthermore, the number of candidate steering vectors to be assigned to the target runs can be the same, and the resolution of only the local region can be adjusted without changing the overall resolution.

[0137] exist Figure 9 In the example of FIG. 1 , the candidate steering vector is based on the above-described reference equation 2 and Figure 9 The arrows in the figure indicate the direction of the vector corresponding to the steering angle.

[0138] In addition, when a new potential object is detected in a course where no object was detected in a previous frame, the radar data processing device assigns a candidate turning vector to the course where the new potential object was detected with a basic angular resolution in the turning information. Figure 9 In the example shown in FIG, no object was detected in run 931 of 12 meters and run 934 of 18 meters in the previous frame, and runs 931 and 934 may be the remaining area outside the ROI. Therefore, the radar data processing device uses basic angular resolution to evenly distribute candidate turning vectors 941 and 944 to these target runs 931 and 934.

[0139] When the steering information is adjusted, the radar data processing device retrieves a target steering vector from the steering information that matches the sensed radar data among candidate steering vectors for each target range within the maximum sensing range of the radar sensor. The radar data processing device determines the steering angle mapped to the retrieved target steering vector as the DoA information corresponding to the radar data.

[0140] However, the radar data processing device may not necessarily determine DoA information for all target trips. For example, the radar data processing device may skip determining DoA information for target trips for which no object is detected in the current frame, among all target trips for which DoA calculation is performed in the adjusted steering information.

[0141] Furthermore, when no object is detected in the radar image of the previous frame, the radar data processing device may select a course with basic resolution from the steering information and arrange the candidate turning vector within the selected course. Therefore, when no object is detected in the radar image of the previous frame, the radar data processing device may include candidate turning vectors with basic course resolution and basic angular resolution in the steering information.

[0142] Figure 10 is a diagram illustrating an example of locally adjusting a range resolution of a range in steering information according to one or more embodiments.

[0143] In this example, the radar data processing device selects multiple target trips for which DoA calculations are to be performed within the maximum sensing range of the radar sensor based on ROI 1020. For example, the radar data processing device adjusts the distribution of the target trips and densely arranges candidate turning vectors 1040 in ROI 1020. The radar data processing device increases the number of target trips for which DoA calculations are to be performed for the range or area corresponding to ROI 1020 in the turning information, and decreases the number of target trips for which DoA calculations are to be performed for the range or area corresponding to the remaining area in the turning information other than ROI 1020.

[0144] For example, Figure 10 As shown in FIG. 1 , when the radar data processing device selects a target run 1030 for which DoA is to be calculated, the radar data processing device densely selects runs 1031 corresponding to ROI 1020. As a result, the number of runs corresponding to the remaining region or regions decreases, while the number of runs 1031 corresponding to ROI 1020 increases. As described above, ROI 1020 is predicted so that it includes the region where object 1010 was detected from the previous radar image map.

[0145] Then, the radar data processing device allocates a preset number of candidate turning vectors 1040 in the turning information to each selected target trip based on the ROI 1020 .

[0146] Figure 11 is a diagram illustrating an example of calculating Doppler velocity according to one or more embodiments.

[0147] Reference below Figure 11 The operations 1110, 1120, 1140, 1150, 1160 and 1170 described may be similar to those described above with reference to Figure 7 Operations 710, 720, 740, 750, 760 and 770 are described. However, Figure 7 In the example of , the operation 730 of detecting an object is performed before the operation 720 of determining DoA information, but the example is not limited thereto. Figure 11 In the example of FIG1 , operation 1130 of detecting an object may be performed after operation 1120 of determining DoA information. The data format of the object detection result reflected in the DoA information determination operation and the data format of the object detection result reflected in the coordinate transformation may vary depending on whether the object detection operation is performed before or after the DoA information determination operation. Furthermore, in operation 1145, the radar data processing device performs self-localization and determines the absolute coordinates of the radar data processing device's current position. In operation 1140, the radar data processing device transforms the coordinates of the potential object based on the results of the self-localization performed in operation 1145.

[0148] In this example, the radar image map updated in operation 1150 can be used to calculate Doppler velocity. For example, in operation 1180, the radar data processing device calculates Doppler velocity information based on the DoA information. In this example, the radar data processing device adjusts the local resolution of the Doppler velocity information based on the ROI predicted from the radar image map. For example, Doppler map 1190 is a graph indicating Doppler information (e.g., Doppler velocity) of a target point sensed by a radar sensor. In Doppler map 1190, the horizontal axis indicates the Doppler value, and the vertical axis indicates the distance from the target point. The Doppler value is the Doppler velocity and indicates the relative velocity of the target point relative to the radar sensor, e.g., the difference between the velocity of the target point and the velocity of the radar sensor. Doppler map 1190 can be generated based on the frequency difference between the signal transmitted by the radar sensor and the reflected signal. However, the format of the Doppler map is not limited to the aforementioned example and can vary based on the design.

[0149] like Figure 11 As shown, the radar data processing device locally adjusts the Doppler velocity resolution so that, on the Doppler axis, the Doppler velocity resolution decreases in Doppler runs 1192 and 1193 corresponding to the ROI 1191 and increases in the remaining runs. Therefore, the radar data processing device determines the Doppler velocity in finer units for the Doppler runs 1192 and 1193 corresponding to the ROI 1191.

[0150] Figure 12 is a diagram illustrating an example of adjusting local run resolution based on a result of predicting an ROI according to one or more embodiments.

[0151] refer to Figure 12 In operation 1260 , the radar data processing device predicts an ROI based on the previously generated radar image map.

[0152] In operation 1270, the radar data processing device adjusts the local range resolution for the radar data sensed by the radar sensor based on the predicted ROI. Figure 10 As described above, the radar data processing device performs ranging processing on the range (or range range) corresponding to the ROI in the first dimension, and performs ranging processing on the range range corresponding to the remaining area in the second dimension lower than the first dimension. For example, the radar data processing device can consistently maintain the overall range resolution in range detection, thereby maintaining processing performance.

[0153] In operation 1210, the radar data processing device detects the range to the target point from which the radar data was reflected based on the adjusted local range resolution. For example, for radar data reflected from a target point corresponding to the ROI, the radar data processing device detects the range to the target point using units of reduced range resolution. Furthermore, for radar data reflected from target points corresponding to the remaining area, the radar data processing device detects the range to the target point using units of increased range resolution. Therefore, based on the local range resolution adjusted in operation 1270, the radar data processing device generates a more accurate range measurement result for the ROI than for the remaining area.

[0154] Subsequently, in operation 1250, the radar data processing device calculates various radar-related information sets (e.g., DoA information and coordinates of the target point) based on the detected distance from the target point, and updates the radar image map based on the calculated radar-related information. Figures 1 to 11 The radar image map is generated or updated as described, but is not limited thereto.

[0155] The above reference Figure 12 The operations described above can be compared with Figures 1 to 11 At least one of the described operations is performed sequentially or in parallel in time.

[0156] Figure 13 is a diagram illustrating another example of a radar data processing apparatus according to one or more embodiments.

[0157] exist Figure 13 In the example, the computing device 1300 may be a radar data processing device configured to process radar data using the above radar data processing method. Figure 2 The radar data processing device 200 is described. The computing device 1300 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), and a head-mounted display (HMD).

[0158] refer to Figure 13 , the computing device 1300 may include a processor 1310, a storage device 1320, a camera 1330, an input device 1340, an output device 1350, and a network interface 1360. The processor 1310, the storage device 1320, the camera 1330, the input device 1340, the output device 1350, and the network interface 1360 may communicate with each other via a communication bus 1370.

[0159] The processor 1310 can execute functions and instructions in the computing device 1300. For example, the processor 1310 processes instructions stored in the storage device 1320. The processor 1310 can execute the above reference Figures 1 to 12 One or more of the methods or operations described.

[0160] The storage device 1320 may store information or data required for the execution of the processor 1310. The storage device 1320 may include a computer-readable storage medium or device. The storage device 1320 may store instructions to be executed by the processor 1310 and related information when the computing device 1300 is running software or applications.

[0161] The camera 1330 may capture an image including a plurality of image frames. For example, the camera 1330 may generate a frame image.

[0162] The input device 1340 can receive input from the user, as non-limiting examples, the input is tactile input, video input, audio input and touch input. As non-limiting examples, the input device 1340 can detect input from a keyboard, mouse, touch screen, microphone and user, and include other devices configured to transmit detected input.

[0163] Output device 1350 can provide output from computing device 1300 to a user via visual, audio, or tactile channels. As non-limiting examples, output device 1350 can include a display, a touch screen, a speaker, a vibration generator, and other devices configured to provide output to a user. Network interface 1360 can communicate with external devices via a wired or wireless network. In an example, output device 1350 can use at least one of visual, auditory, or tactile information to provide the user with the results of processing radar data. For example, when computing device 1300 is set or installed in a vehicle, computing device 1300 can visualize a radar image through a display. Computing device 1300 can adjust at least one of the vehicle's speed, acceleration, or steering operation based on the radar image.

[0164] Radar data processing equipment and Figures 1 to 13The radar sensor 210, processor 220, radar sensor 310, chirp transmitter 311, spectrum analyzer 316, amplifier 315, duplexer 312, antenna 313, mixer 314, processor 1310, storage device 1320, camera 1330, input device 1340, output device 1350, and network interface 1360 that perform the operations described herein are implemented by or represent hardware components. Examples of hardware components that can be used to perform the operations described herein 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 herein, where appropriate. In other examples, one or more hardware components for performing the operations described herein 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., a logic gate array, a controller and an arithmetic logic unit, a digital signal processor, a microcomputer, a programmable logic controller, a field programmable gate array, a programmable logic array, a microprocessor, or any other device or combination of devices configured to respond and execute instructions in a defined manner to achieve a 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, such as 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. A hardware component can have any one or more of different processing configurations, the examples of which include single processors, independent processors, parallel processors, 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.

[0165] Perform the operations described in this application Figures 1 to 13 The methods shown in the are performed by computing hardware, for example, by one or more processors or computers, wherein the computing hardware is implemented as described above to execute instructions or software to perform the operations performed by these methods described in this 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.

[0166] Instructions or software for controlling computing hardware (e.g., one or more processors or computers) to implement hardware components and perform methods as described above can be written as computer programs, code segments, instructions, or any combination thereof, for individually or collectively instructing or configuring one or more processors or computers to operate as machines or special-purpose computers to perform the operations performed by the above-mentioned hardware components and methods. In one example, the instructions or software include machine code directly executed by one or more processors or computers, such as machine code generated by a compiler. In another example, the instructions or software include more advanced code executed by one or more processors or computers using an interpreter. Instructions or software can be written in any programming language based on the block diagrams and flow charts shown in the accompanying drawings and the corresponding descriptions used herein (which disclose algorithms for performing the operations performed by the hardware components and methods as described above).

[0167] Instructions or software for controlling computing hardware (e.g., one or more processors or computers) to implement 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), 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, 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, any other device configured to store instructions or software and any related data, data files and data structures in a non-transitory manner, and provide instructions or software and any related data, data files and data structures to one or more processors or computers so that 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 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.

[0168] 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.

[0169] 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 radar data processing method, comprising: generating a radar image map by spatiotemporally combining a plurality of radar scan images obtained as the radar sensor moves, the radar image map including a plurality of spaces, each of the plurality of spaces indicating an object occupancy probability or a radar signal reception strength; Predicting a region of interest (ROI) based on the generated radar image; adjusting steering information based on the predicted ROI, the steering information comprising a plurality of candidate steering vectors; sensing radar data using the radar sensor; as well as determining direction of arrival (DoA) information corresponding to the sensed radar data based on the adjusted steering information, the DoA information being a direction from which a radar signal reflected from a target point is received, and Each candidate steering vector includes phase information of radar data received at a predetermined angle.

2. The radar data processing method according to claim 1, wherein: Adjusting the steering information includes: A preset number of candidate turning vectors included in the turning information are assigned to each of the one or more target trips based on the ROI.

3. The radar data processing method according to claim 2, wherein: Allocating the preset number of candidate steering vectors to each of the one or more target trips based on the ROI includes: The candidate turning vectors are densely arranged in the ROI by adjusting the distribution of the candidate turning vectors for each of the one or more target trips in the turning information.

4. The radar data processing method according to claim 2, wherein: Allocating the preset number of candidate steering vectors to each of the one or more target trips based on the ROI includes: increasing the number of candidate turning vectors associated with the ROI in the turning information; and The number of candidate turning vectors associated with remaining regions other than the ROI is reduced in the turning information.

5. The radar data processing method according to claim 1, wherein: Adjusting the steering information includes: selecting a plurality of target ranges for calculating the DoA information within the maximum sensing range of the radar sensor based on the ROI; and A preset number of candidate turning vectors in the turning information are allocated to each of the selected target trips based on the ROI.

6. The radar data processing method according to claim 5, wherein: Selecting the plurality of target trips based on the ROI includes: The candidate turning vectors are densely arranged in the ROI by adjusting the distribution of the plurality of target trips.

7. The radar data processing method according to claim 5, wherein: Selecting the plurality of target trips based on the ROI includes: Increasing the number of target trips for calculating the DoA information for the area corresponding to the ROI in the turning information; and The number of target trips for which the DoA information is to be calculated is reduced for regions corresponding to the remaining regions excluding the ROI in the turning information.

8. The radar data processing method according to claim 1, wherein: Determining the DoA information includes: retrieving, from the steering information, a target steering vector that matches the sensed radar data among candidate steering vectors for each target range within the maximum sensing range of the radar sensor; and A steering angle mapped to the retrieved target steering vector is determined as DoA information corresponding to the radar data.

9. The radar data processing method according to claim 1, wherein: Adjusting the steering information includes: When a new potential object is detected in a range where no object was detected in a previous frame, a candidate turning vector is assigned in the turning information to the range to detect the new potential object at a basic angular resolution.

10. The radar data processing method according to claim 1, wherein: Determining the DoA information further includes: Among the target distances for which the DoA information is to be calculated in the adjusted steering information, determination of the DoA information is skipped for a target distance in which no object is detected in a current frame.

11. The radar data processing method according to claim 1 , further comprising: Doppler velocity information is calculated based on the DoA information.

12. The radar data processing method according to claim 11, wherein: Calculating the Doppler velocity information includes: The local resolution of the Doppler velocity information is adjusted based on the predicted ROI.

13. The radar data processing method according to claim 1, wherein predicting the ROI comprises: calculating DoA information corresponding to a previous frame based on radar data collected from the previous frame; generating coordinate information of nearby objects corresponding to the previous frame based on the DoA information corresponding to the previous frame and the self-positioning of the radar data processing device; as well as The ROI of the current frame is predicted based on a radar image map after the previous frame generated from the coordinate information corresponding to the previous frame.

14. The radar data processing method according to claim 1, further comprising: The radar image map is visualized via a display.

15. The radar data processing method according to claim 1, further comprising: At least one of the speed, acceleration, and steering operation of the vehicle on which the radar data processing device is installed is changed based on the radar image map.

16. The radar data processing method according to claim 1, wherein: Adjusting the steering information includes: When no object is detected in the radar image map of the previous frame, a target course is selected from the steering information with a basic resolution, and the candidate steering vector is arranged in the selected target course.

17. The radar data processing method according to claim 1, wherein: The radar sensor includes a field of view FOV including a direction different from a longitudinal direction of the vehicle.

18. A radar data processing method, comprising: generating a radar image map by spatiotemporally combining a plurality of radar scan images obtained as the radar sensor moves, the radar image map including a plurality of spaces, each of the plurality of spaces indicating an object occupancy probability or a radar signal reception strength; Predicting a region of interest (ROI) based on the generated radar image; Adjusting the local range resolution of radar data based on the predicted ROI; as well as The range from the target point reflecting the radar data is detected based on the adjusted local range resolution.

19. The radar data processing method according to claim 18, wherein: Adjusting the local stroke resolution includes: reducing a range resolution for an ROI within a maximum sensing range of the radar sensor; and Increase the stroke resolution for the remaining areas except the ROI.

20. The radar data processing method according to claim 19, wherein detecting the travel distance comprises: When radar data is reflected from a target point corresponding to the ROI, detecting a range from the target point in units of a reduced range resolution; as well as When radar data is reflected from a target point corresponding to the remaining area, a range from the target point is detected in units of increased range resolution.

21. The radar data processing method according to claim 18, wherein: Adjusting the local stroke resolution includes: Maintains overall stroke resolution consistently during stroke detection. 22 . A non-transitory computer-readable storage medium storing instructions, which, when executed by one or more processors, cause the one or more processors to perform the radar data processing method according to claim 1 .

23. A radar data processing device comprising: a radar sensor configured to sense radar data; as well as The processor is configured to: generating a radar image map by spatiotemporally combining a plurality of radar scan images obtained as the radar sensor moves, the radar image map including a plurality of spaces, each of the plurality of spaces indicating an object occupancy probability or a radar signal reception strength; Predicting a region of interest (ROI) based on the generated radar image; adjusting steering information identified from sensed radar data based on the predicted ROI, the steering information comprising a plurality of candidate steering vectors, and determining direction of arrival (DoA) information corresponding to the radar data based on the adjusted steering information, the DoA information being a direction from which a radar signal reflected from a target point is received, and Each candidate steering vector includes phase information of radar data received at a predetermined angle.

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