Apparatus, system and method for radar antenna calibration

Abstract

For example, a radar device may include a mismatch calibrator configured to determine antenna mismatch calibration information to calibrate antenna mismatch of a radar antenna array including a plurality of receive (Rx) antennas, and a processor that processes radar Rx data and generates radar information based on the radar Rx data and the antenna mismatch calibration information, where the radar Rx data is based on Rx radar signals received by the plurality of Rx antennas.

Description

[Technical field]

[0001] [Cross reference] This application claims the benefit of and priority to U.S. Provisional Patent Application No. 62 / 993,227, filed March 23, 2020, entitled “APPARATUS, SYSTEM AND METHOD OF RADAR ANTENNA CALIBRATION,” the entire disclosure of which is incorporated herein by reference.

[0002] TECHNICAL FIELD The aspects described herein relate generally to radar antenna calibration. [Background technology]

[0003] Multiple-input multiple-output (MIMO) radar is a technology that can reduce the number of physical array apertures and antenna elements by transmitting orthogonal signals from a multi-element transmit (Tx) array and processing the received signals through a multi-element receive (Rx) array.

[0004] Since antennas are manufactured with some enhanced phase and gain as their base state, if the antenna elements are not properly calibrated, the antenna array may malfunction. [Brief description of the drawings]

[0005] For simplicity and clarity of the figures, the elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. In addition, reference numerals may be repeated among the figures to indicate corresponding or similar elements. The figures are listed below.

[0006] [Figure 1] FIG. 1 is a schematic block diagram of a vehicle implementing a radar in accordance with some demonstrative aspects.

[0007] [Diagram 2]FIG. 1 is a schematic block diagram of a robot implementing radar in accordance with some demonstrative aspects.

[0008] [Diagram 3] 1 is a schematic block diagram of a radar device in accordance with some demonstrative aspects.

[0009] [Figure 4] FIG. 1 is a schematic block diagram of a frequency modulated continuous wave (FMCW) radar device in accordance with some demonstrative embodiments.

[0010] [Diagram 5] FIG. 1 is a schematic diagram of an extraction scheme that may be implemented to extract range and speed (Doppler) estimates from digital received radar data values ​​in accordance with some demonstrative aspects.

[0011] [Figure 6] 1 is a schematic diagram of an angle determination scheme that may be implemented to determine Angle of Arrival (AoA) information based on incoming wireless signals received by a receive antenna array, in accordance with some demonstrative aspects.

[0012] [Figure 7] FIG. 1 is a schematic diagram of a multiple-input multiple-output (MIMO) radar antenna scheme that may be implemented based on a combination of transmit (Tx) and receive (Rx) antennas in accordance with some demonstrative aspects.

[0013] [Figure 8] FIG. 1 is a schematic block diagram of a radar front end and a radar processor in accordance with some demonstrative aspects.

[0014] [Figure 9] 1 is a schematic diagram of a graph illustrating two angle-of-arrival (AoA) images and corresponding histograms for the two AoA images, in accordance with some demonstrative embodiments;

[0015] [Figure 10]1 is a schematic diagram of a graph illustrating a weighting function in accordance with some demonstrative aspects.

[0016] [Figure 11] FIG. 13 is a schematic diagram of a graph showing a comparison of three simulated AoA images, in accordance with some demonstrative embodiments.

[0017] [Figure 12] 11 is a schematic diagram of a heat map illustrating simulated side lobe levels as a function of weighting function parameters in accordance with some empirical aspects;

[0018] [Figure 13] 11 is a schematic diagram of a simulated heat map of simulated side lobe levels as a function of weighting function parameters in accordance with some empirical aspects; FIG.

[0019] [Figure 14] 1 is a schematic diagram of simulated multiple-input multiple-output (MIMO) radar performance results in accordance with some demonstrative aspects.

[0020] [Figure 15] 1 is a schematic diagram of a graph showing simulated AoA images for multiple range / target scenarios in accordance with some demonstrative embodiments.

[0021] [Figure 16] 1 is a schematic diagram of a graph illustrating simulated performance grades in accordance with certain demonstrative embodiments;

[0022] [Figure 17] 1 is a simplified flowchart of a method of radar antenna calibration in accordance with some demonstrative aspects.

[0023] [Figure 18] 1 is a schematic diagram of a product in accordance with some demonstrative embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0024] In the following detailed description, numerous specific details are set forth to provide a thorough understanding of some aspects. However, one skilled in the art will appreciate that some aspects may be practiced without these specific details. In other instances, well-known methods, procedures, components, units, and / or circuits have not been described in detail so as not to obscure the description.

[0025] For example, descriptions herein utilizing terms such as "processing," "calculating," "calculating," "determining," "establishing," "analyzing," "checking," or the like may refer to operations and / or processes of a computer, computing platform, computing system, or other electronic computing device that manipulates data represented as physical (e.g., electronic) quantities in the computer's registers and / or memory and / or transforms such data into other data that is similarly represented as physical quantities in the computer's registers and / or memory or other information storage medium that may store instructions for performing operations and / or processes.

[0026] As used herein, the terms "plurality" and "a plurality" include, for example, "multiple" or "two or more." For example, "a plurality of items" includes two or more items.

[0027] The words "exemplary" and "demonstrative" are used herein to mean "serving as an example, instance, demonstration, or illustration." Any aspect or design described herein as "exemplary" or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs.

[0028] References to "one embodiment," "an embodiment," "exemplary embodiment," "various embodiments," etc. indicate that the embodiment so described may include a particular feature, structure, or characteristic, but not all embodiments necessarily include the particular feature, structure, or characteristic. Moreover, repeated use of the phrase "in one embodiment" may refer to the same embodiment, but does not necessarily do so.

[0029] As used herein, unless otherwise specified, the use of ordinal adjectives such as "first," "second," "third," etc. to describe a common object merely indicates that different instances of a similar object are being referred to, and is not intended to imply that the objects so described must be in a given order, temporally, spatially, in ranking, or in any other manner.

[0030] The phrases "at least one" and "one or more" may be understood to include a quantity greater than or equal to one, e.g., one, two, three, four, .... The phrase "at least one of" in reference to a group of elements may be used herein to mean at least one element from the group of elements. For example, the phrase "at least one of" in reference to a group of elements may be used herein to mean one of the listed elements, a plurality of the listed elements, a plurality of individual listed elements, or a plurality of individual listed elements.

[0031] The term "data" as used herein may be understood to include information in any suitable analog or digital format, for example, provided as a file, a portion of a file, a set of files, a signal or stream, a portion of a signal or stream, and a set of signals or streams, and the like. Additionally, the term "data" may be used to mean a reference to information in the form of a pointer, for example. However, the term "data" is not limited to the foregoing examples and may take various forms and / or represent any information as understood in the art.

[0032] The term "processor" or "controller" may be understood to include any kind of technical entity that allows for the handling of any suitable type of data and / or information. The data and / or information may be handled according to one or more specific functions executed by the processor or controller. Furthermore, a processor or controller may be understood as any kind of circuit, for example, any kind of analog or digital circuit. Thus, a processor or controller may be or include an analog circuit, a digital circuit, a mixed signal circuit, a logic circuit, a processor, a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a field programmable gate array (FPGA), an integrated circuit, an application specific integrated circuit (ASIC), and the like, or any combination thereof. Any other kind of implementation of the respective functions described in more detail below may be understood as a processor, controller, or logic circuit. It will be understood that any two (or more) processors, controllers, or logic circuits detailed herein may be realized as a single entity having equivalent functionality or the like, and conversely, any single processor, controller, or logic circuit detailed herein may be realized as two (or more) separate entities having equivalent functionality or the like.

[0033] The term "memory" is understood as a computer-readable medium (e.g., a non-transitory computer-readable medium) in which data or information may be stored for retrieval. Thus, references to "memory" may be understood to refer to volatile or non-volatile memory, including, among others, random access memory (RAM), read-only memory (ROM), flash memory, solid-state storage devices, magnetic tape, hard disk drives, optical drives, or any combination thereof. As used herein, the term memory also encompasses, among others, registers, shift registers, processor registers, data buffers. The term "software" may be used to refer to any type of executable instructions and / or logic, including firmware.

[0034] A "vehicle" may be understood to include any type of driven object. By way of example, a vehicle may be a driven object having a combustion engine, an electric engine, a reaction engine, a motorized object, a hybrid-driven object, or a combination thereof. A vehicle may be or include, among others, a car, a bus, a minibus, a van, a truck, a mobile home, a vehicle trailer, a motorcycle, a bicycle, a tricycle, a locomotive, a freight train, a motion robot, a personal mobility, a boat, a ship, a submarine, a submarine, a drone, an aircraft, a rocket.

[0035] A "ground vehicle" may be understood to include any type of vehicle configured to traverse the ground, such as, for example, on-road, on-track, on one or more rails, off-road, or the like.

[0036] An "autonomous vehicle" may refer to a vehicle that can implement at least one navigation change without driver input. A navigation change may refer to or include one or more changes in steering, braking, acceleration / deceleration, or any other movement-related behavior of the vehicle. A vehicle may be described as autonomous if it is not fully autonomous, e.g., fully operable by a driver, but also fully operable without driver input. An autonomous vehicle may include a vehicle that can operate under driver control during certain periods and without driver control during other periods. Additionally or alternatively, an autonomous vehicle may include a vehicle that controls only some aspects of vehicle navigation, such as steering to keep the vehicle course between lane restrictions, or some steering maneuvers, e.g., under certain conditions but not all conditions, but leaves other aspects of vehicle navigation, e.g., braking under certain conditions, to the driver. Additionally or alternatively, an autonomous vehicle may include a vehicle that shares control of one or more aspects of vehicle navigation under certain circumstances, e.g., hands-on, such as responsive to driver input, and / or a vehicle that controls one or more aspects of vehicle navigation under certain circumstances, e.g., hands-off, such as independent of driver input. Additionally or alternatively, an autonomous vehicle may include a vehicle that controls one or more aspects of vehicle navigation under certain circumstances, e.g., under certain environmental conditions, such as spatial domain, roadway conditions, or the like. In some aspects, an autonomous vehicle may handle some or all aspects of braking, speed control, steering, and / or any other additional operation of the vehicle. An autonomous vehicle may include a vehicle that can operate without a driver.The degree of autonomy of a vehicle may be represented or determined by the vehicle's Society of Automotive Engineers (SAE) level, such as those defined by the SAE in, for example, SAE J30162018: Taxonomy and definitions for terms related to driving automation systems for on road motor vehicles, or by other relevant professional organizations. The SAE levels may have values ​​ranging from a minimum level, such as Level 0 (illustratively, substantially no driving automation), to a maximum level, such as Level 5 (illustratively, full driving automation).

[0037] The phrase "vehicle operational data" may be understood to describe any type of characteristic associated with the operation of a vehicle. By way of example, "vehicle operational data" may represent a condition of a vehicle, such as the type of tires on the vehicle, the type of vehicle, and / or the age of the vehicle. More generally, "vehicle operational data" may represent or include static characteristics or static vehicle operational data (illustratively, characteristics or data that do not change over time). As another example, in addition or alternatively, "vehicle operational data" may represent or include characteristics that change during the operation of the vehicle, such as environmental conditions, such as weather conditions or road conditions during the operation of the vehicle, fuel oil levels, liquid levels, operating parameters of a drive source of the vehicle, or the like. More generally, "vehicle operational data" may represent or include changing characteristics or changing vehicle operational data (illustratively, characteristics or data that change over time).

[0038] Some aspects may be used in conjunction with various devices and systems, such as radar sensors, radar devices, radar systems, vehicles, vehicle systems, autonomous vehicle systems, vehicular communication systems, vehicular devices, airborne platforms, waterborne transportation platforms, road infrastructure, sports capture infrastructure, urban surveillance infrastructure, static infrastructure platforms, indoor platforms, moving platforms, robotic platforms, industrial platforms, sensor devices, user equipment (UE), mobile devices (MD), radio stations (STA), sensor devices, non-vehicular devices, mobile or portable devices, and the like.

[0039] Some embodiments may be used in conjunction with radio frequency (RF) systems, radar systems, vehicle radar systems, autonomous systems, robotic systems, detection systems, or the like.

[0040] Some demonstrative embodiments may be used in conjunction with RF frequencies in frequency bands having a start frequency greater than 10 gigahertz (GHz), e.g., frequency bands having a start frequency between 10 GHz and 120 GHz. For example, some demonstrative embodiments may be used in conjunction with RF frequencies having a start frequency greater than 30 GHz, e.g., greater than 45 GHz, e.g., greater than 60 GHz. For example, some demonstrative embodiments may be used in conjunction with automotive radar frequency bands, e.g., frequency bands between 76 GHz and 81 GHz. However, other embodiments may be implemented utilizing any other suitable frequency bands, e.g., frequency bands greater than 140 GHz, frequency bands at 300 GHz, sub-terahertz (THz) bands, THz bands, infrared (IR) bands, and / or any other frequency bands.

[0041] As used herein, the term "circuitry" may refer to, be a part of, or include an application specific integrated circuit (ASIC), an integrated circuit, an electronic circuit, a processor (shared, dedicated, or group) that executes one or more software or firmware programs, and / or a memory (shared, dedicated, or group), a combinatorial logic circuit, and / or other suitable hardware components that provide the described functionality. In some aspects, a circuit may be implemented in, or functionality associated with, a circuit may be implemented by, one or more software or firmware modules. In some aspects, a circuit may include logic that is at least partially operable in hardware.

[0042] The term "logic" may refer to computing logic that is embedded in a circuit of a computing device and / or that is stored in a memory of a computing device, for example. For example, the logic may be accessible to a processor of the computing device to execute the computing logic to perform computing functions and / or operations. In one example, the logic may be embedded in various types of memory and / or firmware, for example, various chips and / or silicon blocks of a processor. The logic may be included in and / or implemented as part of various circuits, for example, radio circuits, receiver circuits, control circuits, transmitter circuits, transceiver circuits, processor circuits, and / or the like. In one example, the logic may be embedded in volatile and / or non-volatile memory, including random access memory, read-only memory, programmable memory, magnetic memory, flash memory, persistent memory, and / or the like. The logic may be executed by one or more processors, for example, using memory, for example, registers, buffers, stacks, and the like, coupled to the one or more processors as necessary to execute the logic.

[0043] The term "communicate" as used herein with respect to a signal includes transmitting a signal and / or receiving a signal. For example, a device capable of communicating a signal may include a transmitter that transmits a signal and / or a receiver that receives a signal. The verb communicate may be used to refer to the action of transmitting or receiving. In one example, the phrase "communicating a signal" may refer to the action of transmitting a signal at a transmitter and may not necessarily include the action of receiving a signal at a receiver. In another example, the phrase "communicating a signal" may refer to the action of receiving a signal at a receiver and may not necessarily include the action of transmitting a signal at a transmitter.

[0044] The term "antenna" as used herein may include any suitable configuration, structure, and / or arrangement of one or more antenna elements, components, units, assemblies, and / or arrays. In some aspects, an antenna may implement transmit and receive functions using separate transmit and receive antenna elements. In some aspects, an antenna may implement transmit and receive functions using common and / or integrated transmit / receive elements. An antenna may include, for example, a phased array antenna, a single element antenna, a set of switched beam antennas, and / or the like. In one example, an antenna may be implemented as a separate element or an integrated element, for example, as an on-module antenna, an on-chip antenna, or according to any other antenna architecture.

[0045] Some demonstrative aspects are described herein with respect to RF radar signals. However, other aspects may be implemented with respect to or in conjunction with any other radar, radio, IR, acoustic, optical, wireless communication signals, communication schemes, networks, standards, and / or protocols. For example, some demonstrative aspects may be implemented with respect to systems that utilize optical and / or acoustic signals, such as Light Detection and Ranging (LiDAR) systems, and / or sonar systems.

[0046] Reference is now made to FIG. 1, which generally illustrates a block diagram of a radar-implemented vehicle 100 in accordance with some demonstrative aspects.

[0047] In some demonstrative embodiments, vehicle 100 may include a car, truck, motorcycle, bus, train, air vehicle, water vehicle, cart, golf cart, electric cart, road agent, or any other vehicle.

[0048] In some demonstrative aspects, vehicle 100 may include radar device 101, e.g., as described below. For example, radar device 101 may include a radar detection device, a radar sensing device, a radar sensor, or the like, e.g., as described below.

[0049] In some demonstrative aspects, the radar device 101 may be implemented as part of a vehicle system, for example a system implemented and / or mounted on the vehicle 100.

[0050] In one example, the radar device 101 may be implemented as part of an autonomous vehicle system, a self-driving system, a driver assistance system and / or a driver support system, and / or the like.

[0051] For example, a radar device 101 may be installed in a vehicle 101 to detect nearby objects, e.g., for autonomous driving.

[0052] In some demonstrative aspects, radar device 101 may be configured to detect targets in the vicinity of vehicle 100, e.g., in the far vicinity and / or in the close vicinity, using, for example, RF and analog chains, capacitor structures, large spiral transformers, and / or any other electronic or electrical elements, for example, as described below. In one example, radar device 101 may be mounted on vehicle 100, e.g., placed directly on vehicle 100, or attached to vehicle 100.

[0053] In some demonstrative aspects, the vehicle 100 may include a single radar device 101. In other aspects, the vehicle 100 may include multiple radar devices 101, for example, at multiple locations around the vehicle 100.

[0054] In some demonstrative aspects, the radar device 101 may be implemented as a component within a suite of sensors used in driver-assisted and / or autonomous vehicles, for example, due to the radar's ability to operate in nearly all weather conditions.

[0055] In some demonstrative aspects, the radar device 101 may be configured to support use in autonomous vehicles, for example, as described below.

[0056] In one example, the radar device 101 may determine the class, position, orientation, speed, intent, perceptual understanding of the environment, and / or any other information corresponding to objects within the environment.

[0057] In another example, the radar device 101 may be configured to determine one or more parameters and / or information for one or more operations and / or tasks, such as route planning and / or any other task.

[0058] In some demonstrative aspects, the radar device 101 may be configured to map a scene by measuring echoes (reflectivities) of targets and differentiating them, for example, primarily by range, velocity, azimuth, and / or elevation, as described below.

[0059] In some demonstrative aspects, the radar device 101 may be configured to detect and / or sense one or more objects located in the vicinity of the vehicle 100, e.g., in the far vicinity and / or in the close vicinity, and provide one or more parameters, attributes, and / or information regarding the objects.

[0060] In some demonstrative aspects, the objects may include other vehicles, pedestrians, traffic signs, traffic lights, roads, road elements (e.g., pavement intersections, edge lines), hazards (e.g., tires, boxes, cracks in the road surface), and / or the like.

[0061] In some demonstrative embodiments, the one or more parameters, attributes, and / or information about the object may include the range of the object from vehicle 100, the angle of the object relative to vehicle 100, the position of the object relative to vehicle 100, the relative speed of the object relative to vehicle 100, and / or the like.

[0062] In some demonstrative aspects, the radar device 101 may include a multiple-input multiple-output (MIMO) radar device 101, for example, as described below. In one example, the MIMO radar device may be configured to utilize “spatial filtering” processing, e.g., beamforming, and / or any other mechanism, on one or both of the transmit (Tx) and / or receive (Rx) signals.

[0063] Some illustrative aspects are described below with respect to a radar device implemented as a MIMO radar, e.g., radar device 101. However, in other aspects, radar device 101 may be implemented as any other type of radar utilizing multiple antenna elements, e.g., a single-input multiple-output (SIMO) radar or a multiple-input single-output (MISO) radar.

[0064] Some demonstrative aspects may be implemented with respect to a radar device implemented as a MIMO radar, e.g., radar device 101, as described below. However, in other aspects, radar device 101 may be implemented as any other type of radar, e.g., an electronic beam steering radar, a synthetic aperture radar (SAR), an adaptive radar and / or a cognitive radar that alters transmissions according to environmental and / or ego states, a reflect array radar, or the like.

[0065] In some demonstrative aspects, the radar device 101 may include an antenna array 102, a radar front end 103 configured to communicate radar signals via the antenna array 102, and a radar processor 104 configured to generate radar information based on the radar signals, for example as described below.

[0066] In some demonstrative aspects, the radar processor 104 may be configured to process radar information of the radar device 101 and / or control one or more operations of the radar device 101, for example, as described below.

[0067] In some demonstrative aspects, the radar processor 104 may include or be implemented in part or in whole by circuitry and / or logic, e.g., memory circuitry and / or logic, one or more processors including circuitry and / or logic. Additionally or alternatively, one or more functions of the radar processor 104 may be implemented by a machine and / or logic that may be executed by one or more processors, e.g., as described below.

[0068] In one example, the radar processor 104 may include, for example, at least one memory coupled to the one or more processors, which may be configured to, for example, at least temporarily store, for example, at least a portion of the information processed by the one or more processors and / or circuits, and / or may be configured to store logic utilized by those processors and / or circuits.

[0069] In other aspects, the radar processor 104 may be implemented by one or more additional or alternative elements of the vehicle 100.

[0070] In some demonstrative aspects, the radar front end 103 may include, for example, one or more (radar) transmitters and one or more (radar) receivers, for example, as described below.

[0071] In some demonstrative aspects, the antenna array 102 may include multiple antennas that transmit radar signals. For example, the antenna array 102 may include multiple transmit antennas in the form of a transmit antenna array and multiple receive antennas in the form of a receive antenna array. In another example, the antenna array 102 may include one or more antennas that are used as both transmit and receive antennas. In the latter case, for example, the radar front end 103 may include a duplexer, e.g., circuitry that separates the transmitted signal from the received signal.

[0072] In some demonstrative aspects, as shown in FIG. 1, the radar front end 103 and antenna array 102 may be controlled by, for example, a radar processor 104 to transmit a wireless transmission signal 105.

[0073] In some demonstrative aspects, as shown in FIG. 1, a wireless transmission signal 105 may be reflected by an object 106 resulting in an echo 107 .

[0074] In some demonstrative aspects, the radar device 101 may receive echoes 107, for example via the antenna array 102 and the radar front end 103, and the radar processor 104 may generate radar information, for example by calculating information regarding the position, radial velocity (Doppler), and / or direction of the object 106, for example with respect to the vehicle 100.

[0075] In some demonstrative aspects, the radar processor 104 may be configured to provide radar information to a vehicle controller 108 of the vehicle 100, for example, for autonomous operation of the vehicle 100.

[0076] In some demonstrative aspects, at least a portion of the functionality of radar processor 104 may be implemented as part of vehicle controller 108. In other aspects, the functionality of radar processor 104 may be implemented as part of any other element of radar device 101 and / or vehicle 100. In other aspects, radar processor 104 may be implemented as a separate part of or as part of any other element of radar device 101 and / or vehicle 100.

[0077] In some demonstrative aspects, the vehicle controller 108 may be configured to control one or more functions, operational modes, components, devices, systems, and / or elements of the vehicle 100.

[0078] In some demonstrative aspects, vehicle controller 108 may be configured to control one or more vehicle systems of vehicle 100, for example, as described below.

[0079] In some demonstrative embodiments, the vehicle systems may include, for example, a steering system, a braking system, a driving system, and / or any other system of the vehicle 100.

[0080] In some demonstrative aspects, the vehicle controller 108 may be configured to control the radar device 101 and / or process one or more parameters, attributes, and / or information from the radar device 101.

[0081] In some demonstrative aspects, vehicle controller 108 may be configured to control vehicle systems of vehicle 100 based on radar information from radar device 101 and / or one or more other sensors of vehicle 100, such as, for example, a light detection and ranging (LiDAR) sensor, a camera sensor, and / or the like.

[0082] In one example, the vehicle controller 108 may control the steering system, braking system, and / or any other vehicle systems of the vehicle 100, for example, based on information from the radar device 101, for example, based on one or more objects detected by the radar device 101.

[0083] In other aspects, the vehicle controller 108 may be configured to control any other additional or alternative functions of the vehicle 100.

[0084] Some illustrative aspects are described herein with respect to radar device 101 implemented in a vehicle, e.g., vehicle 100. In other aspects, the radar device, e.g., radar device 101, may be implemented as part of any other element of a transportation system or network, e.g., as part of road infrastructure and / or as any other element of a transportation network or system. Other aspects may be implemented with respect to any other system, environment, and / or apparatus that may be implemented in any other object, environment, location, or place. For example, radar device 101 may be part of a non-vehicle device that may be implemented in, for example, an indoor location, a fixed infrastructure outdoors, or any other location.

[0085] In some demonstrative aspects, the radar device 101 may be configured to support security uses. In one example, the radar device 101 may be configured to determine the nature of the movement, e.g., human intrusion, animal intrusion, environmental movement, and the like, to identify the threat level of the detected event, and / or any other additional or alternative movements.

[0086] For example, some demonstrative aspects may be implemented with any other additional or alternative devices and / or systems, such as with a robot, for example, as described below.

[0087] In other aspects, the radar device 101 may be configured to support any other use and / or application.

[0088] Reference is now made to FIG. 2, which generally illustrates a block diagram of a robot 200 implementing a radar, in accordance with some demonstrative embodiments.

[0089] In some demonstrative embodiments, the robot 200 may include a robotic arm 201. The robot 200 may be implemented, for example, in a factory for handling an object 213, which may be, for example, a part to be installed in a product being manufactured. The robotic arm 201 may include multiple moving members, for example, moving members 202, 203, 204, and a support 205. Moving the moving members 202, 203, and / or 204 of the robotic arm 201, for example, via actuation of associated motors, may enable physical interaction with the environment to enable the performance of a task, for example, the handling of the object 213.

[0090] In some demonstrative embodiments, the robot arm 201 may include multiple joint elements, such as joint elements 207, 208, 209, which may connect, for example, members 202, 203, and / or 204 to one another and to the support 205. For example, the joint elements 207, 208, 209 may have one or more joints, each of which may provide rotatable motion, such as rotational motion, and / or translational motion, such as displacement, to an associated member and / or may provide motion of the members relative to one another. Movement of members 202, 203, 204 may be initiated by suitable actuators.

[0091] In some demonstrative embodiments, the member furthest from the support 205, e.g., member 204, may be referred to as the end effector 204 and may include one or more tools, e.g., a claw for gripping an object, a welding tool, or the like. Other members closer to the support 205, e.g., members 202, 203, may be utilized to change the position of the end effector 204, e.g., in three-dimensional space. For example, the robotic arm 201 may be configured to function similarly to a human arm, e.g., possibly with a tool at its end.

[0092] In some demonstrative embodiments, the robot 200 may include a (robot) controller 206 configured to implement interaction with the environment, e.g., by controlling actuators of the robot arm according to a control program, e.g., to control the robot arm 201 according to a task to be performed.

[0093] In some demonstrative embodiments, an actuator may include a component adapted to affect a mechanism or process in response to being actuated. The actuator may respond to a command provided by the controller 206 by performing a mechanical movement (so-called actuation). This means that the actuator, typically a motor (or electromechanical converter), may be configured to convert electrical energy into mechanical energy upon actuation (i.e., actuation).

[0094] In some demonstrative embodiments, the controller 206 may be in communication with a radar processor 210 of the robot 200 .

[0095] In some demonstrative aspects, the radar front end 211 and the radar antenna array 212 may be coupled to the radar processor 210. In one example, the radar front end 211 and / or the radar antenna array 212 may be included as part of the robotic arm 201, for example.

[0096] In some demonstrative aspects, radar front end 211, radar antenna array 212, and radar processor 210 may be operable as and / or configured to form a radar device. For example, as described above, antenna array 212 may be configured to perform one or more functions of antenna array 102 (FIG. 1), radar front end 211 may be configured to perform one or more functions of radar front end 103 (FIG. 1), and / or radar processor 210 may be configured to perform one or more functions of radar processor 104 (FIG. 1).

[0097] In some demonstrative aspects, for example, the radar front end 211 and the antenna array 212 may be controlled by, for example, a radar processor 210 to transmit a wireless transmission signal 214.

[0098] In some demonstrative aspects, as shown in FIG. 2, a wireless transmission signal 214 may be reflected by an object 213 resulting in an echo 215 .

[0099] In some demonstrative aspects, echoes 215 may be received, for example, via antenna array 212 and radar front end 211, and radar processor 210 may generate radar information, for example, by calculating information regarding the position, speed (Doppler), and / or direction of object 213, for example relative to robot arm 201.

[0100] In some demonstrative aspects, the radar processor 210 may be configured to provide the radar information to a robot controller 206 of the robot arm 201 to, for example, control the robot arm 201. For example, the robot controller 206 may be configured to control the robot arm 201 based on the radar information to, for example, grab the object 213 and / or perform any other action.

[0101] Referring to FIG. 3, this diagram illustrates a schematic of a radar device 300 in accordance with some demonstrative aspects.

[0102] In some demonstrative aspects, the radar apparatus 300 may be implemented as part of a device or system 301, for example as described below.

[0103] For example, the radar apparatus 300 may be implemented as part of and / or configured to perform one or more operations and / or functions of a device or system described above with reference to Figures 1 and / or 2. In other aspects, the radar apparatus 300 may be implemented as part of any other device or system 301.

[0104] In some demonstrative aspects, the radar device 300 may include an antenna array that may include one or more transmit antennas 302 and one or more receive antennas 303. In other aspects, any other antenna arrangement may be implemented.

[0105] In some demonstrative aspects, the radar device 300 may include a radar front end 304 and a radar processor 309.

[0106] In some demonstrative aspects, as shown in FIG. 3, for example as described below, one or more transmit antennas 302 may be coupled to a transmitter (or transmitter array) 305 of a radar front-end 304 and / or one or more receive antennas 303 may be coupled to a receiver (or receiver array) 306 of the radar front-end 304.

[0107] In some demonstrative aspects, the transmitter 305 may include one or more elements, such as an oscillator, a power amplifier, and / or one or more other elements, configured to generate a wireless transmit signal that is transmitted by one or more transmit antennas 302, e.g., as described below.

[0108] In some demonstrative aspects, for example, the radar processor 309 may provide digital radar transmission data values ​​to the radar front end 304. For example, the radar front end 304 may include a digital-to-analog converter (DAC) 307 that converts the digital radar transmission data values ​​to an analog transmission signal. The transmitter 305 may convert the analog transmission signal to a wireless transmission signal to be transmitted by the transmit antenna 302.

[0109] In some demonstrative aspects, the receiver 306 may include one or more elements, such as one or more mixers, one or more filters, and / or one or more other elements, configured to process and downconvert wireless signals received via one or more receive antennas 303, e.g., as described below.

[0110] In some demonstrative aspects, for example, receiver 306 may convert radio receive signals received via one or more receive antennas 303 to analog receive signals. Radar front-end 304 may include an analog-to-digital (ADC) converter 308 that generates digital radar receive data values ​​based on the analog receive signals. For example, radar front-end 304 may provide the digital radar receive data values ​​to radar processor 309.

[0111] In some demonstrative aspects, radar processor 309 may be configured to process the digital radar reception data values ​​to detect one or more objects, for example, in an environment of device / system 301. This detection may include determining information including, for example, one or more of the range, speed (Doppler), direction, and / or any other information of the one or more objects, for example, relative to system 301.

[0112] In some demonstrative aspects, the radar processor 309 may be configured to provide the determined radar information to a system controller 310 of the device / system 301. For example, the system controller 310 may include a vehicle controller, e.g., if the device / system 301 includes a vehicle device / system, a robot controller, e.g., if the device / system 301 includes a robotic device / system, or any other type of controller in the case of any other type of device / system 301.

[0113] In some demonstrative aspects, the system controller 310 may be configured to control one or more controlled system components 311 of the system 301, such as motors, brakes, steering, and the like, e.g., with one or more corresponding actuators.

[0114] In some demonstrative aspects, radar device 300 may include storage 312 or memory 313 to store, for example, information processed by radar 300, such as digital radar reception data values ​​processed by radar processor 309, radar information generated by radar processor 309, and / or any other data processed by radar processor 309.

[0115] In some demonstrative aspects, the device / system 301 may include an application processor 314 and / or a communications processor 315, for example, to at least partially implement one or more functions of the system controller 310 and / or to perform communications between the system controller 310, the radar device 300, the controlled system components 311, and / or one or more additional elements of the device / system 301, for example.

[0116] In some demonstrative aspects, radar device 300 may be configured to generate and transmit radio transmission signals in a manner that may support range, speed, and / or direction determination, for example, as described below.

[0117] For example, a radar radio transmission signal may be configured to include multiple pulses, e.g., a pulsed transmission may include the transmission of a short, high-power burst combined with a period during which the radar device waits for an echo.

[0118] For example, continuous wave (CW) may be used instead as the radio transmission signal to better support highly dynamic situations, e.g., in an automotive scenario, however, while a constant frequency continuous wave may support speed determination, it may not allow range determination, e.g., due to the lack of time marks that may allow distance calculation.

[0119] In some demonstrative aspects, the wireless transmission signal 105 (FIG. 1) may be transmitted according to a technology that can support range, velocity, and / or direction determination, such as, for example, Frequency Modulated Continuous Wave (FMCW) radar, Phase Modulated Continuous Wave (PMCW) radar, Orthogonal Frequency Division Multiplexing (OFDM) radar, and / or any other type of radar technology, as described below.

[0120] Referring now to FIG. 4, a schematic diagram of an FMCW radar system in accordance with some demonstrative embodiments is shown.

[0121] In some demonstrative aspects, FMCW radar device 400 may include a radar front end 401 and a radar processor 402. For example, radar front end 304 ( FIG. 3 ) may include one or more elements of and / or perform one or more operations and / or functions of radar front end 401, and / or radar processor 309 ( FIG. 3 ) may include one or more elements of and / or perform one or more operations and / or functions of radar processor 402.

[0122] In some demonstrative aspects, the FMCW radar device 400 may be configured to communicate a radio signal in accordance with FMCW radar techniques, for example, rather than transmitting a radio transmission signal at a constant frequency.

[0123] In some demonstrative aspects, the radio front end 401 may be configured to ramp up and reset the frequency of the transmit signal, for example, periodically, according to, for example, a sawtooth waveform 403. In other aspects, a triangular waveform or any other suitable waveform may be used.

[0124] In some demonstrative aspects, for example, the radar processor 402 may be configured to provide the waveform 403 to the front end 401 in digital form, for example, as a series of digital values.

[0125] In some demonstrative aspects, the radar front end 401 may include a DAC 404 that converts the waveform 403 into an analog form and provides it to a voltage controlled oscillator 405. For example, the oscillator 405 may be configured to generate an output signal that may be frequency modulated according to the waveform 403.

[0126] In some demonstrative aspects, the oscillator 405 may be configured to generate an output signal including a radio transmit signal, which may be provided to and transmitted by one or more transmit antennas 406.

[0127] In some demonstrative aspects, the radio transmission signal generated by the oscillator 405 may have the form of a series of chirps 407, which may be the result of modulation of a sinusoid by a sawtooth waveform 403.

[0128] In one example, the chirp 407 may correspond to a sinusoid of an oscillator signal that is frequency modulated by the "teeth" of the sawtooth waveform 403, for example, from a minimum frequency to a maximum frequency.

[0129] In some demonstrative aspects, the FMCW radar device 400 may include one or more receive antennas 408 that receive a radio receive signal. The radio receive signal may be based on, for example, echoes of a radio transmit signal in addition to any noise, interference, or the like.

[0130] In some demonstrative aspects, the radar front end 401 may include a mixer 409 that mixes a radio transmit signal with a radio receive signal into a mixed signal.

[0131] In some demonstrative aspects, the radar front end 401 may include a filter, such as a low pass filter (LPF) 410, that may be configured to filter the mixed signal from the mixer 409 to provide a filtered signal. For example, the radar front end 401 may include an ADC 411 that converts the filtered signal into digital received data values ​​that may be provided to the radar processor 402. In another example, the filter 410 may be a digital filter, and the ADC 411 may be disposed between the mixer 409 and the filter 410.

[0132] In some demonstrative aspects, the radar processor 402 may be configured to process the digital received data values ​​to provide radar information including, for example, range, speed (velocity / Doppler), and / or direction (AoA) information of one or more objects.

[0133] In some demonstrative aspects, the radar processor 402 may be configured to perform a first Fast Fourier Transform (FFT) (also referred to as a “range FFT”) to extract a delay response from the digital received data values ​​that can be used to extract range information, and / or a second FFT (also referred to as a “Doppler FFT”) to extract a Doppler shift response from the digital received data values ​​that can be used to extract velocity information.

[0134] In other aspects, any other additional or alternative methods may be utilized to extract range information. In one example, in a digital radar implementation, correlation with the transmitted signal may be used, for example according to a matched filter implementation.

[0135] 5, which illustrates generally an extraction scheme that may be implemented to extract range and speed (Doppler) estimates from digital received radar data values, according to some demonstrative aspects. For example, radar processor 104 (FIG. 1), radar processor 210 (FIG. 2), radar processor 309 (FIG. 3), and / or radar processor 402 (FIG. 4) may be configured to extract range and / or speed (Doppler) estimates from digital received radar data values ​​according to one or more aspects of the extraction scheme of FIG.

[0136] 5, wireless receive signals, including, for example, echoes of wireless transmit signals, may be received by a receive antenna array 501. The wireless receive signals may be processed by a wireless radar front end 502 to generate digital receive data values, for example, as described above. The wireless radar front end 502 may provide the digital receive data values ​​to a radar processor 503, which may process the digital receive data values ​​to provide radar information, for example, as described above.

[0137] In some demonstrative aspects, the digital received data values ​​may be represented in the form of a data cube 504. For example, the data cube 504 may include digitized samples of wireless received signals, where the digitized samples are based on wireless signals transmitted from a transmit antenna and received by M receive antennas. In some demonstrative aspects, for example, for a MIMO implementation, there may be multiple transmit antennas and the number of samples may be multiplied accordingly.

[0138] In some demonstrative embodiments, a layer of datacube 504, eg, a horizontal layer of datacube 504, may include samples for one antenna, eg, each of M antennas.

[0139] In some demonstrative aspects, the data cube 504 may include K chirp samples. For example, as shown in FIG. 5, the chirp samples may be arranged in the so-called "slow time" direction.

[0140] In some demonstrative aspects, the data cube 504 may include, for a chirp, e.g., L samples (e.g., L=512) or any other number of samples per chirp. For example, as shown in FIG. 5, the samples per chirp may be arranged in the so-called "fast time" direction of the data cube 504.

[0141] In some demonstrative aspects, the radar processor 503 may be configured to process a number of samples, e.g., L samples, collected per chirp and per antenna with a first FFT. The first FFT may be performed, for example, per chirp and per antenna, such that the result of processing the data cube 504 with the first FFT may again have three dimensions, e.g., the size of the data cube 504, including values ​​for L range bins instead of values ​​for L sampling times.

[0142] In some demonstrative aspects, the radar processor 503 may be configured to process the results of the processing of the data cube 504 with a first FFT, for example, by processing the results according to a second FFT along the chirp, for each antenna and for each range bin, for example.

[0143] For example, a first FFT may be in the "fast time" direction and a second FFT may be in the "slow time" direction.

[0144] In some demonstrative aspects, the results of the second FFT, when aggregated across antennas, for example, may provide a range / Doppler (R / D) map 505. The R / D map may have FFT peaks 506, including peaks of FFT output values ​​(in terms of absolute value) for particular range / speed combinations, such as, for example, range / Doppler bins. For example, a range / Doppler bin may correspond to a range bin and a Doppler bin. For example, the radar processor 503 may consider a peak to potentially correspond to an object at a range and speed corresponding to, for example, the range bin and speed bin of the peak.

[0145] In some demonstrative aspects, the extraction scheme of FIG. 5 may be implemented for an FMCW radar, such as FMCW radar 400 (FIG. 4), as described above. In other aspects, the extraction scheme of FIG. 5 may be implemented for any other radar type. In one example, the radar processor 503 may be configured to determine a range / Doppler map 505 from digital received data values ​​of a PMCW radar, OFDM radar, or any other radar technology. For example, in an adaptive or cognitive radar, the pulses, waveforms, and / or modulation within a frame may change over time, for example, according to the environment.

[0146] 3, in some demonstrative aspects, the receive antenna arrangement 303 may be implemented using a receive antenna array having multiple receive antennas (or receive antenna elements). For example, the radar processor 309 may be configured to determine an angle of arrival of a received wireless signal, e.g., echo 105 (FIG. 1) and / or echo 215 (FIG. 2). For example, the radar processor 309 may be configured to determine a direction of a detected object, e.g., with respect to the device / system 301, based on, e.g., the angle of arrival of the received wireless signal, e.g., as described below.

[0147] Referring to FIG. 6, this diagram illustrates generally an angle determination scheme that may be implemented to determine angle-of-arrival (AoA) information based on incoming wireless signals received by a receive antenna array 600, in accordance with some demonstrative aspects.

[0148] 6 shows an angle determination scheme based on signals received at a receive antenna array. In some demonstrative aspects, for example in a virtual MIMO array, the angle determination may be based on signals transmitted by an array of Tx antennas.

[0149] 6 shows a one-dimensional angle determination scheme. Other multi-dimensional angle determination schemes may be implemented, for example two-dimensional or three-dimensional schemes.

[0150] In some demonstrative aspects, as shown in FIG. 6, a receive antenna array 600 may include M antennas (numbered 1 through M from left to right).

[0151] Assume that the echo comes from an object located in the upper left direction, as shown by the arrow in Figure 6. Correspondingly, the direction of the echo, e.g., the incoming radio signal, may be toward the lower right. According to this example, the further to the left the receiving antenna is located, the earlier it will receive a particular phase of the incoming radio signal.

[0152] For example, the phase difference, denoted Δφ, between two antennas of the receive antenna array 601 may be determined, for example, as follows:

number

[0153] In some demonstrative aspects, the radar processor 309 (FIG. 3) may be configured to utilize this relationship between the phase and angle of the incoming radio signal to determine the angle of arrival of the echo, for example, by performing an FFT, such as, for example, a third FFT across the antenna (an “angle FFT”).

[0154] In some demonstrative aspects, multiple transmit antennas, e.g., in the form of an antenna array having multiple transmit antennas, may be used to, e.g., increase spatial resolution, e.g., to provide higher resolution radar information. For example, a MIMO radar device may utilize a virtual MIMO radar antenna, which may be formed as a convolution of multiple transmit antennas convolved with multiple receive antennas.

[0155] Referring to FIG. 7, this diagram illustrates a schematic diagram of a MIMO radar antenna scheme that may be implemented based on a combination of transmit (Tx) and receive (Rx) antennas in accordance with some demonstrative embodiments.

[0156] In some demonstrative aspects, as shown in Figure 7, a radar MIMO arrangement may include a transmit antenna array 701 and a receive antenna array 702. For example, one or more transmit antennas 302 (Figure 3) may be implemented to include the transmit antenna array 701 and / or one or more receive antennas 303 (Figure 3) may be implemented to include the receive antenna array 702.

[0157] In some demonstrative aspects, an antenna array including multiple antennas for both transmitting radio transmission signals and receiving echoes of the radio transmission signals may be used to provide multiple virtual channels, as shown by the dashed lines in Figure 7. For example, a virtual channel may be formed as a convolution of the transmit and receive antennas, e.g., as a Kronecker product, representing, for example, a virtual steering vector of a MIMO radar.

[0158] In some demonstrative aspects, a transmit antenna, eg, each transmit antenna, may be configured to transmit an individual wireless transmit signal, eg, having a phase associated with the respective transmit antenna.

[0159] For example, an array of N transmit antennas and M receive antennas may be implemented to provide a virtual MIMO array of size N x M. For example, the virtual MIMO array may be formed according to a Kronecker product operation applied to the Tx steering vector and the Rx steering vector.

[0160] 8 is a schematic block diagram of radar front end 804 and radar processor 834, according to some demonstrative embodiments. For example, radar front end 103 (FIG. 1), radar front end 211 (FIG. 1), radar front end 304 (FIG. 3), radar front end 401 (FIG. 4), and / or radar front end 502 (FIG. 5) may include one or more elements of radar front end 804 and / or perform one or more operations and / or functions of radar front end 804.

[0161] In some demonstrative aspects, the radar front end 804 may be implemented as part of a MIMO radar that utilizes a MIMO radar antenna 881 including multiple Tx antennas 814 configured to transmit multiple Tx RF signals (also referred to as “Tx radar signals”), e.g., as described below, and multiple Rx antennas 816 configured to receive multiple Rx RF signals (also referred to as “Rx radar signals”), e.g., based on the Tx radar signals.

[0162] In some demonstrative aspects, the MIMO antenna array 881, antenna 814, and / or antenna 816 may include or be part of any type of antenna suitable for transmitting and / or receiving radar signals. For example, the MIMO antenna array 881, antenna 814, and / or antenna 816 may be implemented as part of any suitable configuration, structure, and / or arrangement of one or more antenna elements, components, units, assemblies, and / or arrays. For example, the MIMO antenna array 881, antenna 814, and / or antenna 816 may be implemented as part of a phased array antenna, a multiple element antenna, a set of switched beam antennas, and / or the like. In some aspects, the MIMO antenna array 881, antenna 814, and / or antenna 816 may be implemented to support transmit and receive functions using separate transmit and receive antenna elements. In some aspects, the MIMO antenna array 881, antenna 814, and / or antenna 816 may be implemented to support transmit and receive functions using common and / or integrated transmit / receive elements.

[0163] In some demonstrative embodiments, the MIMO radar antenna 881 may include a rectangular MIMO antenna array and / or a curved array, for example, shaped to fit the vehicle design. In other embodiments, any other form, shape, and / or configuration of the MIMO radar antenna 881 may be implemented.

[0164] In some demonstrative aspects, the radar front end 804 may include one or more radios configured to generate and transmit Tx RF signals via a Tx antenna 814 and / or process Rx RF signals received via an Rx antenna 816, e.g., as described below.

[0165] In some demonstrative aspects, the radar front end 804 may include at least one transmitter (Tx) 883 including circuitry and / or logic configured to generate and / or transmit a Tx radar signal via the Tx antenna 814.

[0166] In some demonstrative aspects, the radar front end 804 may include at least one receiver (Rx) 885 that includes circuitry and / or logic to receive and / or process an Rx radar signal received via the Rx antenna 816, e.g., based on the Tx radar signal.

[0167] In some demonstrative aspects, the transmitter 883 and / or the receiver 885 may include circuitry, logic, radio frequency (RF) elements, RF circuitry, and / or RF logic, baseband elements, baseband circuitry, and / or baseband logic, modulation elements, modulation circuitry, and / or modulation logic, demodulation elements, demodulation circuitry, and / or demodulation logic, amplifiers, analog-to-digital converters and / or digital-to-analog converters, filters, and / or the like.

[0168] In some demonstrative aspects, the transmitter 883 may include multiple Tx chains 810 configured, e.g., to each generate and transmit a Tx RF signal via a Tx antenna 814, and / or the receiver 885 may include multiple Rx chains 812 configured, e.g., to each receive and process an Rx RF signal received via an Rx antenna 816.

[0169] In some demonstrative aspects, radar processor 834 may be configured to generate radar information 813 based on, for example, radar signals transmitted by MIMO radar antenna 881, for example, as described below. For example, radar processor 104 (FIG. 1), radar processor 210 (FIG. 1), radar processor 309 (FIG. 3), radar processor 402 (FIG. 4), and / or radar processor 503 (FIG. 5) may include one or more elements of radar processor 834 and / or perform one or more operations and / or functions of radar processor 834.

[0170] In some demonstrative aspects, the radar processor 834 may be configured to generate radar information 813, for example, based on radar Rx data 811 received from multiple Rx chains 812. For example, the radar Rx data 811 may be based on an Rx RF signal received via an Rx antenna 816.

[0171] In some demonstrative aspects, the radar processor 834 may include an input 832 that receives radar Rx data 811 from multiple Rx chains 812.

[0172] In some demonstrative aspects, the radar processor 834 may include at least one processor 836, which may be configured, for example, to process the radar Rx data 811 and / or to execute one or more operations, methods, and / or algorithms.

[0173] In some demonstrative aspects, radar processor 834 may include at least one memory 838, e.g., coupled to processor 836. For example, memory 838 may be configured to store data processed by radar processor 834. For example, memory 838 may store, e.g., at least temporarily, at least a portion of the information processed by processor 836 and / or logic utilized by processor 836.

[0174] In some demonstrative aspects, memory 838 may be configured to store at least a portion of the radar data, e.g., a portion of the radar Rx data or all of the radar Rx data, for processing by processor 836, e.g., as described below.

[0175] In some demonstrative aspects, memory 838 may be configured to store processed data, which may be generated by processor 836, for example, in a process for generating radar information 813, as described below.

[0176] In some demonstrative aspects, the memory 838 may be configured to store range and / or Doppler information, which may be generated by the processor 836 based on, for example, the radar Rx data, as described below. In one example, the range and / or Doppler information may be determined based on a cross-correlation (XCORR) operation, which may be applied to the radar Rx data. Any other additional or alternative operations, algorithms, and / or procedures may be utilized to generate the range and / or Doppler information.

[0177] In some demonstrative aspects, the memory 838 may be configured to store AoA information, which may be generated by the processor 836 based on, for example, radar Rx data, range information, and / or Doppler information, for example, as described below. In one example, the AoA information may be determined based on an AoA estimation algorithm. Any other additional or alternative operations, algorithms, and / or procedures may be utilized to generate the AoA information.

[0178] In some demonstrative aspects, the radar processor 834 may be configured to generate radar information 813 including one or more of range information, Doppler information, and / or AoA information, for example, as described below.

[0179] In some demonstrative aspects, radar information 813 may include point cloud 1 (PC1) information, including raw point cloud estimates of, for example, range, radial velocity, azimuth angle, and / or elevation angle.

[0180] In some demonstrative aspects, radar information 813 may include point cloud 2 (PC2) information, which may be generated, for example, based on PC1 information. For example, PC2 information may include clustering information, tracking information, such as tracking probability and / or density functions, bounding box information, classification information, orientation information, and the like.

[0181] In some demonstrative aspects, the radar processor 834 may be configured to generate radar information 813 in the form of four-dimensional (4D) image information, e.g., a cube, which may represent 4D information corresponding to one or more detected targets.

[0182] In some demonstrative aspects, for example, the 4D image information may include a range value, e.g., based on range information, a velocity value, e.g., based on Doppler information, an azimuth angle value, e.g., based on azimuth angle AoA information, an elevation angle value, e.g., based on elevation angle AoA information, and / or any other value.

[0183] In some demonstrative aspects, the radar processor 834 may be configured to generate radar information 813 in any other form and / or to include any other additional or alternative information.

[0184] In some demonstrative aspects, the radar processor 834 may be configured to process signals transmitted via the MIMO radar antenna 881 as signals of a virtual MIMO array formed by the convolution of multiple Rx antennas 816 and multiple Tx antennas 814.

[0185] In some demonstrative aspects, the radar front end 804 and / or the radar processor 834 may be configured to utilize MIMO techniques, e.g., to support a reduced physical array aperture, e.g., array size, and / or to utilize a reduced number of antenna elements. For example, the radar front end 804 and / or the radar processor 834 may be configured to transmit orthogonal signals via a Tx array that includes a plurality of N elements, e.g., the Tx antenna 814, and process received signals via an Rx array that includes a plurality of M elements, e.g., the Rx antenna 816.

[0186] In some demonstrative aspects, using MIMO technology to transmit orthogonal signals from a Tx array having N elements and processing the received signals in an Rx array having M elements may be equivalent to a radar using transmission from one antenna and reception by N*M antennas, for example, under the far-field approximation. For example, the radar front end 804 and / or radar processor 834 may be configured to use the MIMO antenna array 881 as a virtual array with an equivalent array size N*M, which may define the location of the virtual element as a convolution of the locations of physical elements, such as antennas 814 and / or 816.

[0187] In some demonstrative aspects, there may be a need to provide a technical solution to efficiently and / or accurately calibrate antenna arrays. For example, antennas may be manufactured with some enhanced phase and gain as their base state, and accordingly, the antenna array may malfunction if, for example, antenna elements of the array are not calibrated.

[0188] In some demonstrative aspects, calibrating an antenna array by placing targets at known locations and with expected gain and phase on each antenna may result in one or more technical inefficiencies, shortcomings, and / or problems in one or more use cases and / or scenarios. For example, a single target may not be sufficient to calibrate an antenna because the antenna pattern is non-isotropic. Accordingly, multiple antennas at various locations may be required to calibrate the entire antenna array. Such a solution may be costly in terms of time and cost.

[0189] Some demonstrative aspects may be configured to provide a technical solution that supports calibration of the antenna array at multiple instances, such as one or more defined instances, in a dynamic manner, and / or in real time, e.g., after installation and / or during operation of the radar front end 804. In one example, the radar processor 834 may be configured to support calibration of the MIMO antenna array 881, e.g., at or after installation of one or more antenna elements of the MIMO antenna array 881, and / or at one or more later points in time, e.g., after treatment of one or more antenna elements in the antenna array 881 and / or after treatment to the surroundings of the MIMO antenna array 881. In one example, if the MIMO antenna array 881 is placed behind a car bumper, calibration may be performed based on installation, treatment, and / or changes to the car bumper, e.g., after an accident, since the car bumper may introduce some misalignment into the calibration of the MIMO antenna array 881.

[0190] In some demonstrative aspects, the radar processor 834 may be configured to perform mismatch calibration to calibrate mismatch of the MIMO antenna array 881, e.g., to improve performance of the antenna array 881, e.g., as described below.

[0191] In some demonstrative aspects, the radar processor 834 may be configured to perform mismatch calibration, for example, based on a cost function, as described below.

[0192] In some empirical aspects, the cost function may be configured to represent, for example, the sharpness of an angle-of-arrival (AoA) image, along with, for example, the level of a side lobe in the AoA image, which may represent, for example, the amount of energy arriving from an incorrect angle, as described below.

[0193] In some demonstrative aspects, the cost function may be configured to take the mismatch repair as its domain and output a score value, e.g., a single real-valued score for each AoA image, e.g., as described below.

[0194] In some demonstrative aspects, the cost function may be optimized, for example, based on one or more criteria, for example, as described below.

[0195] In some demonstrative aspects, the radar processor 834 may be configured to perform mismatch calibration, for example, to provide a technical solution that may even avoid factory calibration and / or the use of special hardware.

[0196] In some demonstrative aspects, the radar processor 834 may be configured to perform mismatch calibration of the MIMO antenna array 881 according to a calibration technique that may be performed within a relatively short period of time, for example, within a few minutes.

[0197] In some demonstrative aspects, the radar processor 834 may be configured to perform mismatch calibration of the MIMO antenna array 881 at one or more times, for example as described below.

[0198] In some demonstrative aspects, the radar processor 834 may be configured to automatically and / or autonomously perform mismatch calibration of the MIMO antenna array 881 according to a timing scheme, for example, once a day, once a week, once a month, and / or at any other time interval.

[0199] In some demonstrative aspects, the radar processor 834 may be configured to perform mismatch calibration of the MIMO antenna array 881 based on one or more events and / or criteria.

[0200] In one example, radar processor 834 may be configured to perform mismatch calibration of MIMO antenna array 881 based on installation and / or disposition of one or more elements of MIMO antenna array 881, e.g., after installation of antenna array 881 on vehicle 100 ( FIG. 1 ), and / or based on disposition, replacement, and / or adjustment of one or more elements of MIMO antenna array 881, for example.

[0201] In one example, the radar processor 834 may be configured to perform mismatch calibration of the MIMO antenna array 881, for example, based on the installation and / or treatment of one or more other elements, which may have an impact on the antenna array 881, such as a new calibration of the antenna array, e.g., the Tx antenna 814 and the Rx antenna 816, after a treatment to the vehicle 100 (FIG. 1), e.g., replacement of a bumper of the vehicle 100 (FIG. 1), and / or any other treatment, maintenance, or service.

[0202] In some demonstrative aspects, configuring the radar processor 834 to perform mismatch calibration of the MIMO antenna array 881, e.g., as described herein, may provide a technical solution that can mitigate, reduce, and / or even avoid errors in radar information that may be due to changes in one or more elements of the vehicle 100 ( FIG. 1 ) and / or the radar front end 804, including, for example, aging, temperature changes, vehicle accidents, radar movement and / or installation, and the like.

[0203] In some demonstrative aspects, the radar processor 834 may include a mismatch calibrator 833 configured to determine antenna mismatch calibration information 851 to calibrate antenna mismatch of, for example, a radar antenna array 881 including multiple Rx antennas 816, as described below.

[0204] In some demonstrative aspects, the processor 836 may be configured to determine the radar information 813, for example, based on the radar Rx data 811 and the antenna mismatch calibration information 851. For example, the radar Rx data 811 may be based on a radar signal received at an Rx antenna 816, for example, as described below.

[0205] In some demonstrative aspects, for example, radar information 813 may include AoA information, for example, as described below. In other aspects, radar information 813 may include any other additional or alternative information.

[0206] In some demonstrative aspects, mismatch calibrator 833 and / or processor 836 may be implemented as part of radar processor 834. In other aspects, mismatch calibrator 833 and / or processor 836 may be implemented as separate and / or dedicated elements, e.g., processors, of a radar device, such as radar device 101 (FIG. 1).

[0207] In some demonstrative aspects, the mismatch calibrator 833 may be configured to determine antenna mismatch calibration information 851 to calibrate at least one of gain mismatch and / or phase mismatch of the radar antenna array 881, for example, as described below.

[0208] In some demonstrative aspects, the mismatch calibrator 833 may be configured to determine antenna mismatch calibration information 851 that includes, for example, a vector of multiple complex numbers, where a complex number among the multiple complex numbers corresponds to a Rx antenna among the Rx antennas 816, for example, as described below.

[0209] In some demonstrative aspects, the complex number of multiple complex numbers may represent, for example, a gain mismatch and a phase mismatch corresponding to the Rx antenna of multiple Rx antennas, as described below.

[0210] In some demonstrative aspects, the mismatch calibrator 833 may be configured to dynamically calibrate the antenna mismatch of the radar antenna array 881, e.g., by updating antenna mismatch calibration information in real time, e.g., as described below.

[0211] In some demonstrative aspects, the mismatch calibrator 833 may be configured to dynamically calibrate the antenna mismatch of the radar antenna array 881, e.g., by repeatedly determining antenna mismatch calibration information 851, e.g., based on calibration update criteria, e.g., as described below.

[0212] In some demonstrative aspects, the mismatch calibrator 833 may be configured to determine the antenna mismatch calibration information 851 based on, for example, a score function, eg, as described below.

[0213] In some demonstrative aspects, the score function may be configured to provide a score value corresponding to a hypothetical antenna mismatch, e.g., based on the hypothetical antenna mismatch and hypothetical radar information provided by the radar Rx data 811, e.g., as described below.

[0214] In some demonstrative aspects, the mismatch calibrator 833 may be configured to determine an identified antenna mismatch that minimizes a score function, e.g., as described below, and to determine antenna mismatch calibration information 851 based on the identified antenna mismatch.

[0215] In some demonstrative aspects, the mismatch calibrator 833 may be configured to determine the antenna mismatch calibration information, for example, by minimizing the numerical gradient of a score function, for example, as described below.

[0216] In some demonstrative aspects, the mismatch calibrator 833 may be configured to perform multiple calculation iterations. For example, one calculation iteration may include randomly selecting an initial hypothetical antenna mismatch, determining a minimum of a numerical slope of a scoring function, e.g., based on the initial hypothetical antenna mismatch, and updating the antenna mismatch calibration information, e.g., based on the minimum of the numerical slope of the scoring function, e.g., as described below.

[0217] In some demonstrative aspects, mismatch calibrator 833 may be configured to perform at least 100 iterations.

[0218] In some demonstrative aspects, mismatch calibrator 833 may be configured to perform at least 500 iterations.

[0219] In some demonstrative aspects, mismatch calibrator 833 may be configured to perform at least 1000 iterations.

[0220] In other aspects, any other number of iterations may be performed.

[0221] In some demonstrative aspects, the mismatch calibrator 833 may be configured to determine a score value corresponding to a hypothetical antenna mismatch, e.g., based on a histogram of hypothetical noise values ​​corresponding to the hypothetical antenna mismatch and radar information provided by the radar Rx data 811, e.g., as described below.

[0222] In some demonstrative aspects, the score function may be configured to provide a first score value for a first hypothetical antenna mismatch and a second score value for a second hypothetical antenna mismatch, e.g., as described below.

[0223] In some demonstrative aspects, a first hypothetical antenna mismatch may result in a first histogram that includes the highest histogram value within a first range of hypothetical noise values, for example as described below.

[0224] In some demonstrative aspects, a second hypothetical antenna mismatch may result in a second histogram that includes the highest histogram values ​​within a second range of hypothetical noise values.

[0225] For example, the first range of hypothetical noise values ​​may be higher than the second range of hypothetical noise values, eg, if the first score value is higher than the second score value, eg, as described below.

[0226] In some demonstrative aspects, the mismatch calibrator 833 may be configured to determine a score value corresponding to a hypothetical antenna mismatch, for example, based on a histogram of an AoA image resulting from the hypothetical antenna mismatch and the radar Rx data 811, as described below.

[0227] In some demonstrative aspects, the mismatch calibrator 833 may be configured to determine an AoA image based on a discrete Fourier transform (DFT) applied to hypothetical antenna mismatch and radar Rx data 811, for example, as described below.

[0228] In some demonstrative aspects, the mismatch calibrator 833 may be configured to utilize a score function, e.g., as described below, to provide a score value corresponding to the hypothetical antenna mismatch based on a predefined weighting function applied to the histogram, for example.

[0229] In some empirical implementations, the weighting function may be monotonically increasing over a predefined range of hypothetical noise values, for example as described below.

[0230] In some empirical implementations, the weighting function may be monotonically increasing for hypothetical noise values ​​between -70 dB and -40 dB, for example, as described below.

[0231] In some empirical aspects, the weighting function may be constant for hypothetical noise values ​​above -20 dB, for example, as described below.

[0232] In other embodiments, the weighting function may be monotonically increasing for any other range of hypothetical noise values ​​and / or the weighting function may be constant for any other hypothetical noise values.

[0233] In other aspects, any other cost function and / or weighting function may be implemented, for example based on any other noise levels, noise level ranges, and / or any other weights and / or parameters.

[0234] Referring to FIG. 9, this figure illustrates generally a graph 910 showing an AoA image and a graph 920 showing a histogram corresponding to the AoA image of graph 910, in accordance with some demonstrative aspects.

[0235] In some demonstrative aspects, the AoA image 912 may be based on the Rx radar signals received by the MIMO antenna array without misalignment. For example, the AoA image 912 may include an AoA image within fixed range bins, which may be determined, for example, by applying a DFT to the radar Rx data 811 (FIG. 8), assuming no misalignment of the MIMO antenna array 881 (FIG. 8).

[0236] In some demonstrative aspects, the AoA image 914 may be based on Rx radar signals received by the MIMO antenna array with some misalignment. For example, the AoA image 914 may include an AoA image within fixed range bins, which may be determined, for example, by applying a DFT to the radar Rx data 811 (FIG. 8) prior to the DFT, e.g., assuming a misalignment of the MIMO antenna array 881 (FIG. 1). For example, the AoA image 914 may correspond to a 1 dB gain misalignment and a 20 degree phase misalignment, e.g., a uniformly distributed ±1 dB gain misalignment and ±20 degree phase misalignment.

[0237] In some empirical aspects, as shown in FIG. 9, the mismatch may result in a “corrupted” AoA image 914 that may provide worse results, e.g., in terms of noise level, compared to an “original” AoA image without the mismatch.

[0238] In some demonstrative aspects, as shown in FIG. 9, a histogram 922 may be generated based on AoA image 912 and a histogram 924 may be generated based on AoA image 914.

[0239] In some empirical aspects, as shown in graphs 910 and 920, the “original” AoA image 912 may be characterized by lower side lobes and a lower noise level, for example, compared to the higher side lobes and higher noise level of the “corrupted” AoA image 914.

[0240] For example, as shown in graphs 910 and 920, a “corrupted” AoA image 914, which may be affected by antenna mismatch, may have a noise level primarily around −40 dB, while an “original” AoA image 912, which is not affected by antenna mismatch, may have a noise level primarily below −60 dB.

[0241] Referring again to FIG. 8, in some demonstrative aspects, the mismatch calibrator 833 may be configured to determine the antenna mismatch calibration information 851 based on, for example, a score function denoted f, which may be defined, for example, as follows:

number

[0242] In some demonstrative aspects, the score function f may be configured to provide a score value corresponding to a hypothetical antenna mismatch, e.g., based on the hypothetical antenna mismatch and hypothetical radar information provided by the radar Rx data 811, e.g., as described below.

[0243] In some demonstrative aspects, the score function f may be configured to provide a score value corresponding to a hypothetical antenna mismatch, e.g., based on a histogram, e.g., similar to that of FIG. 9, of hypothetical noise values ​​corresponding to the hypothetical antenna mismatch and radar information provided by the radar Rx data 811, e.g., as described below.

[0244] In some demonstrative aspects, the score function f may be configured to provide a first score value for a first hypothetical antenna mismatch that may result in a first histogram that includes the highest histogram value within a first range of hypothetical noise values.

[0245] In some demonstrative aspects, the score function f may be configured to provide a second hypothetical antenna mismatch with a second score value, e.g., lower than the first score value, that may result in a second histogram that includes the highest histogram values ​​within a second range of hypothetical noise values, e.g., the second range of hypothetical noise values ​​may be lower than the first range of hypothetical noise values, e.g., as described below.

[0246] In some demonstrative embodiments, the score function f may be configured to provide higher score values ​​to histograms that include noise values ​​within a higher range of noise values, such as histogram 924 (FIG. 9).

[0247] In some demonstrative embodiments, the score function f may be configured to provide lower score values ​​to histograms that include noise values ​​within a lower range of noise values, such as histogram 922 (FIG. 9).

[0248] In some empirical embodiments, the score function f may be configured to give higher score values ​​to more "righty" histograms, which contain primarily higher noise levels.

[0249] In some demonstrative aspects, the score function f may be implemented by a weighting function that may be configured to provide a score value corresponding to a hypothetical antenna mismatch, e.g., by applying the weighting function to a histogram, e.g., as described below.

[0250] In some demonstrative aspects, a weighting function may be multiplied with the result of the histogram to obtain, for example, a score value, as described below.

[0251] In some demonstrative aspects, a weighting function may be configured to apply weights to the histogram, e.g., as described below, where the weights may be monotonically increasing, e.g., in one or more ranges, and / or the weights may provide equally high weights to very high noise levels, e.g., by providing similar or identical weights to the 0 dB bin and the -20 dB bin.

[0252] In some empirical implementations, the weighting function may be monotonically increasing over a predefined range of hypothetical noise values, for example as described below.

[0253] In some empirical embodiments, the weighting function may be monotonically increasing for hypothetical noise values ​​between -70 dB and -40 dB.

[0254] In other aspects, the weighting function may be monotonically increasing for any other range of hypothetical noise values.

[0255] In some empirical aspects, the weighting function may be constant for hypothetical noise values ​​above -20 dB, for example, as described below.

[0256] In other aspects, the weighting function may be constant for any other hypothetical noise value.

[0257] In some empirical aspects, the weighting functions may be determined and / or adjusted based on simulation and / or testing results, for example, as described below.

[0258] 10, which diagrammatically illustrates a graph 1000 showing weighting functions, in accordance with some demonstrative aspects. For example, mismatch calibrator 833 (FIG. 8) may be configured to implement the weighting function of FIG. 10 or any other weighting function to determine mismatch calibration information 851 (FIG. 8).

[0259] In some empirical embodiments, the graph 1000 may be monotonically increasing over a predefined range of hypothetical noise values, for example, for hypothetical noise values ​​of -70 dB to -40 dB, as shown in Figure 10. In other embodiments, the weighting function may be defined to be monotonically increasing for any other range of values.

[0260] In some empirical aspects, as shown in FIG. 10, a graph 1000 of the weighting function may be constant for hypothetical noise values, for example, above −20 dB.

[0261] Referring again to FIG. 8, in some demonstrative aspects, mismatch calibrator 833 may be configured to determine a score function corresponding to a hypothetical antenna mismatch based on, for example, the following pseudocode algorithm: [Algorithm 1: Score function] TIFF0007632961000003.tif65154

[0262] In other embodiments, the score value may be implemented according to any other operation, algorithm, and / or calculation.

[0263] In some demonstrative embodiments, the score function may be implemented in a manner that may address one or more potential inefficiencies and / or computational difficulties.

[0264] In one example, in some cases, it may not be possible to find the analytical minimum of the score function using a histogram, for example because the histogram is not an analytical procedure, and accordingly, there may be no analytical derivative for the score function.

[0265] In another example, the score function may be non-convex and, correspondingly, it may not always be easy to find a global optimum.

[0266] In some demonstrative aspects, for example, as described below, a numerical gradient approach may be applied to the score function to determine the antenna mismatch calibration information 851 in an efficient and / or accurate manner.

[0267] In some demonstrative aspects, the mismatch calibrator 833 may be configured to determine the antenna mismatch calibration information 851, for example, by minimizing the numerical gradient of a score function, for example, as described below.

[0268] In some demonstrative aspects, the mismatch calibrator 833 may be configured to perform multiple calculation iterations to determine the antenna mismatch calibration information 851, for example, as described below.

[0269] In some demonstrative aspects, a calculation iteration of the multiple calculation iterations may include, for example, randomly selecting an initial hypothetical antenna mismatch, determining a minimum of a numerical gradient of a score function based on the initial hypothetical antenna mismatch, and updating antenna mismatch calibration information 851, for example, based on the minimum of the numerical gradient of the score function, as described below.

[0270] In other aspects, the calculation iterations may include any other additional or alternative operations.

[0271] In some demonstrative aspects, calibrator 833 may be configured to perform multiple calculation iterations, including at least 100 iterations.

[0272] In some demonstrative aspects, calibrator 833 may be configured to perform multiple calculation iterations, including at least 500 iterations.

[0273] In some demonstrative aspects, calibrator 833 may be configured to perform multiple calculation iterations, including at least 1000 iterations.

[0274] In other aspects, calibrator 833 may be configured to perform multiple calculation iterations, including any other number of iterations.

[0275] In some demonstrative aspects, for example, to find the AoA based on a radar Rx signal 811 from a single target, a DFT matrix may be implemented, for example, as follows: AoA=DFT(S) (2) Here, S denotes a vector of complex-valued signals coming from a single target.

[0276] In some demonstrative aspects, the radar signal received on one of the Rx antennas 816, for example, on each of the Rx antennas 816, may be corrupted by noise and mismatch, for example, as follows:

number

[0277] In some demonstrative embodiments, Equation 3 may be transformed in time, for example, as follows:

number

[0278] In some empirical aspects, it may be assumed that the additive noise N is small in absolute value and the antenna mismatch MM is constant in time.

[0279] In some demonstrative aspects, a score value may be determined for the antenna mismatch vector MM, for example, according to Algorithm 1 above.

[0280] In some demonstrative aspects, the mismatch calibrator 833 may be configured to implement a search algorithm to search for an antenna mismatch calibration based, for example, on a score value associated with a score function f, e.g., as described below.

[0281] In some empirical embodiments, the numerical gradient of the score function f may be defined as follows:

number

number

[0282] In some empirical aspects, mismatch calibrator 833 may start from some arbitrary initial point MM0 and calculate the numerical gradient ▽f(MM0) for some predefined step size △x, for example.

[0283] In some empirical aspects, the smallest entry of the numerical gradient ▽f(MM0) may represent the smallest score mismatch MM from the options considered.

[0284] In some empirical aspects, the mismatch calibrator 833 may determine the mismatch indicated by MM1 as the mismatch that results in the lowest cost, e.g., assuming that the smallest entry of the numerical gradient ▽f(MM0) is negative, as described below.

[0285] In some demonstrative aspects, the mismatch calibrator 833 performs multiple iterations to, for example:

number

[0286] In some demonstrative aspects, the mismatch calibrator 833 may update the step size, e.g., Δx=:Δx / 2 or any other step size update, and the numerical gradient

number

[0287] In some empirical aspects, mismatch calibrator 833 may repeat the procedure of updating the step size and calculating the numerical gradient, for example, until the step size Δx reaches some limit.

[0288] In some empirical embodiments, mismatch calibrator 833 may repeat the above procedure multiple times, eg, while using different initial values ​​of MM0, eg, because the cost function f may be non-convex.

[0289] In some empirical embodiments, for example, the value of MMO may be initialized multiple times, e.g., using randomness and / or based on any other settings or criteria, and all convergence points may be considered from their costs, e.g., the found MM for each MMO. The minimum of all converged costs may determine the MM.

[0290] In some demonstrative aspects, the mismatch calibrator 833 may be configured to search the antenna mismatch MM to determine the mismatch calibration information 851 to provide a minimum score value of a cost function f, for example, according to the following gradient descent algorithm: [Algorithm 2: Gradient descent] JPEG0007632961000010.jpg204131

[0291] In some demonstrative aspects, mismatch calibrator 833 may be configured to perform some or all of the operations of Algorithm 2 and / or one or more additional or alternative operations.

[0292] Referring to FIG. 11, this figure shows a graph 1100 illustrating a comparison of three simulated AoA images, in accordance with some demonstrative embodiments.

[0293] In some demonstrative embodiments, the AoA image of FIG. 11 may be simulated for three targets at a range of 200 meters.

[0294] As shown in FIG. 11 , AoA image 1102 may be based on radar Rx signals received by a MIMO antenna array without misalignment, AoA image 1106 may be based on radar Rx signals received by a MIMO antenna array corrupted with misalignment, and AoA image 1104 may be based on radar Rx signals received by a MIMO antenna array corrupted with misalignment, where the misalignment may be mitigated by applying misalignment calibration, e.g., according to a cost function f, as described above.

[0295] In some demonstrative aspects, one or more weights of the cost function f may be determined and / or defined, e.g., to improve and / or optimize mismatch calibration, e.g., for one or more scenarios and / or use cases, e.g., as described below.

[0296] In some empirical embodiments, the cost function f may be defined such that, for example, the weights are of a monotonically increasing nature, eg, as described below.

[0297] In some empirical embodiments, the cost function f may be defined, e.g., such that the weighting is not increased for high levels of energy and / or any other levels of energy, e.g., as described below.

[0298] In some demonstrative aspects, the weights of the cost function f may be defined according to a first weighting parameter, denoted a, which represents, for example, the curvature of the monotonically increasing portion of the cost function f, as described below.

[0299] In some demonstrative aspects, the weights of the cost function f may be defined according to a second weighting parameter, denoted b, that represents, e.g., a cutoff bin of the cost function f, e.g., a bin from which the cost function f is to be kept constant, e.g., as described below.

[0300] In other embodiments, the cost function f may be defined using any other additional or alternative parameters and / or weights.

[0301] In some empirical aspects, the weighting parameter a representing the curvature, e.g., right curvature, of the weighting function 1000 (FIG. 10) and / or the weighting parameter a representing the cutoff portion of the weighting function 1000 (FIG. 10) may be identified experimentally, e.g., as described below.

[0302] In some demonstrative aspects, the curvature of the weighting function may be defined, for example, as follows:

number

[0303] In some empirical embodiments, the weighting function cutoff b may be defined, for example, as follows: For b ∈ 1...101, weight[b:end] = weight[b] (7)

[0304] In some demonstrative aspects, the weights may be normalized, for example, as follows:

number

[0305] Referring to FIG. 12, this figure illustrates a simplified map 1200 showing simulated sidelobe levels as a function of weighting function parameters, in accordance with some empirical aspects.

[0306] In some demonstrative aspects, as shown in FIG. 12, a graph 1200 shows four maximum sidelobe level maps for different combinations of weighting parameter a (curvature) and weighting parameter b (cutoff bin) for various ranges and numbers of targets.

[0307] For example, graph 1210 shows a maximum side lobe level map for one target within a 300m range, graph 1220 shows a maximum side lobe level map for three targets within an 80m range, graph 1230 shows a maximum side lobe level map for ten targets within a 40m range, and graph 1240 shows a maximum side lobe level map for ten targets within a 300m range.

[0308] In some demonstrative embodiments, as shown in FIG. 12, the minima of maps 1210, 1220, 1230, and 1240 fall within the same region.

[0309] For example, as shown in FIG. 12, a minimum level of side lobes may be maintained, for example, for all scenarios in FIG. 12 when setting the curvature parameter, for example, within a range of about 7 to about 12 and / or when setting the cutoff bin, for example, within a range of about 50 to about 65.

[0310] 13, which diagrammatically illustrates a simulated heat map 1300 of simulated side lobe levels resulting from weighting function parameters, in accordance with some empirical aspects. For example, the simulated heat map 1300 may include maximum side lobe levels at different ranges and target numbers, which may be based on a combination of the results of the graphs in FIG.

[0311] In some empirical aspects, an optimum point in the heat map 1300 may be found around a=9 and b=60, as shown in FIG.

[0312] Referring to FIG. 14, this figure illustrates a schematic of simulated MIMO radar performance results in accordance with some empirical aspects.

[0313] In some empirical aspects, as shown in FIG. 14, graph 1410 shows performance results in a single range bin with no mismatch calibration.

[0314] In some demonstrative aspects, as shown in FIG. 14, a graph 1420 shows performance results with mismatch calibration, eg, according to a cost function f, eg, as described above.

[0315] In some empirical aspects, implementing mismatch calibration as shown in FIG. 14 may reduce side lobes and may provide improved resolution of targets, for example, even at full range.

[0316] Referring to FIG. 15, this figure illustrates generally a graph 1500 showing simulated AoA images for multiple range / target scenarios in accordance with some demonstrative aspects.

[0317] 15, by implementing mismatch calibration, e.g., according to a cost function f, as described above, the AoA image may be improved to a level 1502 that is comparable to a “true” AoA image 1504 in the absence of mismatch. This improved image 1502 may be significantly better than, e.g., a “corrupted” image 1506 in the absence of mismatch calibration.

[0318] With reference to FIG. 16, that figure illustrates a graph showing simulated performance grades in accordance with certain demonstrative embodiments.

[0319] In some demonstrative aspects, as shown in FIG. 16, graph 1610 shows the average difference between a true AoA image, e.g., without misalignment, and a calibrated AoA image, implementing misalignment calibration, e.g., according to a cost function f, e.g., as described above, for different ranges and numbers of targets.

[0320] In some demonstrative aspects, as shown in FIG. 16, a graph 1620 shows the root mean square (RMS) of the true AoA image and the calibrated AoA image, e.g., without misalignment, for different ranges and numbers of targets, implementing misalignment calibration, e.g., according to a cost function f, e.g., as described above.

[0321] In some empirical aspects, as shown in FIG. 16, graph 1630 shows the maximum sidelobe levels achievable by implementing mismatch calibration, e.g., according to a cost function f, e.g., as described above, for different ranges and numbers of targets.

[0322] 17, which illustrates generally a method of radar antenna calibration in accordance with some demonstrative embodiments. For example, one or more of the operations of the method of FIG. 17 may be performed by one or more elements of a system, such as one or more vehicles, such as vehicle 100 (FIG. 1), a radar device, such as radar device 101 (FIG. 1), a mismatch calibrator, such as mismatch calibrator 833 (FIG. 8), a radar processor, such as radar processor 836 (FIG. 8) and / or radar processor 834 (FIG. 8).

[0323] As shown in block 1702, the method may include determining antenna mismatch calibration information to calibrate antenna mismatch of a radar antenna array including multiple Rx antennas. For example, the mismatch calibrator 833 (FIG. 8) may determine antenna mismatch calibration information 851 (FIG. 8) to calibrate antenna mismatch of a radar antenna array 881 (FIG. 8) including an Rx antenna 816 (FIG. 8), e.g., as described above.

[0324] As shown in block 1704, the method may include determining radar information based on radar Rx data and antenna mismatch calibration information, where the radar Rx data is based on radar signals received at multiple Rx antennas. For example, the processor 836 (FIG. 8) may determine radar information 813 (FIG. 8) based on the radar Rx data 811 (FIG. 8) and antenna mismatch calibration information 851 (FIG. 8), e.g., as described above.

[0325] 18, which diagrammatically illustrates a product 1800 in accordance with certain demonstrative aspects. Product 1800 may include one or more tangible computer-readable ("machine-readable") non-transitory storage media 1802 that may include computer-executable instructions, for example, embodied by logic 1804, that when executed by at least one computer processor, cause the at least one computer processor to control a vehicle, such as vehicle 100 (FIG. 1), a radar device, such as radar device 101 (FIG. 1), a mismatch calibrator, such as mismatch calibrator 833 (FIG. 8), a radar processor, such as radar processor 836 (FIG. 8) and / or radar processor 834 (FIG. 8). 17 and / or 18. The mismatch calibrator may be operable to implement one or more operations at a processor, to cause a vehicle, such as vehicle 100 (FIG. 1), a radar device, such as radar device 101 (FIG. 1), a mismatch calibrator, such as mismatch calibrator 833 (FIG. 8), a radar processor, such as radar processor 836 (FIG. 8) and / or radar processor 834 (FIG. 8) to perform, trigger, and / or implement one or more operations and / or functions, and / or to perform, trigger, and / or implement one or more operations and / or functions described with reference to FIGS. 1 through 17 and / or one or more operations described herein. The phrases "non-transitory machine-readable medium" and "computer-readable non-transitory storage medium" may be directed to include all machine-readable media and / or computer-readable media with the sole exception being transitory, propagating signals.

[0326] In some demonstrative aspects, product 1800 and / or storage medium 1802 may include one or more types of computer-readable storage media capable of storing data, including volatile memory, non-volatile memory, removable or non-removable memory, erasable or non-erasable memory, writeable or rewriteable memory, and the like. For example, the storage medium 1802 may include RAM, DRAM, double data rate DRAM (DDR-DRAM), SDRAM, static RAM (SRAM), ROM, programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), compact disc ROM (CD-ROM), compact disc recordable (CD-R), compact disc rewriteable (CD-RW), flash memory (e.g., NOR or NAND flash memory), content addressable memory (CAM), polymer memory, phase change memory, ferroelectric memory, silicon oxide nitride oxide silicon (SONOS) memory, disk, floppy disk, hard drive, optical disk, magnetic disk, card, magnetic card, optical card, tape, cassette, and the like. The computer readable storage medium may include any suitable medium involved in downloading or transferring a computer program carried by a plurality of data signals embodied in a carrier wave or other propagation medium from a remote computer to a requesting computer via a communications link, e.g., a modem, wireless, or network connection.

[0327] In some demonstrative aspects, logic 1804 may include instructions, data, and / or code that, when executed by a machine, may cause the machine to perform methods, processes, and / or operations as described herein. The machine may include, for example, any suitable processing platform, computing platform, computing device, processing device, computing system, processing system, computer, processor, or the like, and may be implemented using any suitable combination of hardware, software, firmware, and the like.

[0328] In some demonstrative aspects, logic 1804 may include or be implemented as software, software modules, applications, programs, subroutines, instructions, instruction sets, computing code, words, values, symbols, and the like. Instructions may include any suitable type of code, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, and the like. Instructions may be implemented according to a predefined computer language, style, or syntax to instruct a processor to perform a particular function. Instructions may be implemented using any suitable high-level, low-level, object-oriented, visual, compiled, and / or interpreted programming language, such as C, C++, Java, BASIC, Matlab, Pascal, Visual Basic, assembly language, machine code, and the like.

[0329] [example] The following examples relate to further embodiments.

[0330] Example 1 includes an apparatus comprising: a mismatch calibrator configured to determine antenna mismatch calibration information to calibrate antenna mismatch of a radar antenna array including a plurality of receive (Rx) antennas; and a radar processor that processes radar Rx data and generates radar information based on the radar Rx data and the antenna mismatch calibration information, where the radar Rx data is based on Rx radar signals received at the plurality of Rx antennas.

[0331] Example 2 includes the subject matter of example 1, and optionally, the mismatch calibrator is configured to determine the antenna mismatch calibration information based on a score function, the score function configured to provide a score value corresponding to a hypothetical antenna mismatch based on the hypothetical antenna mismatch and hypothetical radar information provided by the radar Rx data.

[0332] Example 3 includes the subject matter of example 2, and optionally, the mismatch calibrator is configured to determine an identified antenna mismatch that minimizes the score function; and determine the antenna mismatch calibration information based on the identified antenna mismatch.

[0333] Example 4 includes the subject matter of examples 2 or 3, optionally wherein the mismatch calibrator is configured to determine the antenna mismatch calibration information by minimizing a numerical gradient of the score function.

[0334] Example 5 includes the subject matter of example 4, and optionally, the mismatch calibrator is configured to perform multiple calculation iterations, a calculation iteration including randomly selecting an initial hypothetical antenna mismatch, determining a minimum of a numerical slope of the scoring function based on the initial hypothetical antenna mismatch, and updating the antenna mismatch calibration information based on the minimum of the numerical slope of the scoring function.

[0335] Example 6 includes the subject matter of example 5, optionally wherein the multiple calculation iterations include at least 100 iterations.

[0336] Example 7 includes the subject matter of example 5, optionally wherein the multiple calculation iterations include at least 500 iterations.

[0337] Example 8 includes the subject matter of example 5, optionally wherein the multiple calculation iterations include at least 1000 iterations.

[0338] Example 9 includes the subject matter of any one of Examples 2-8, and optionally, the mismatch calibrator is configured to determine the score value corresponding to the hypothetical antenna mismatch based on a histogram of hypothetical noise values ​​corresponding to the hypothetical antenna mismatch and the hypothetical radar information provided by the radar Rx data.

[0339] Example 10 includes the subject matter of Example 9, and optionally, the score function is configured to provide a first score value for a first hypothetical antenna mismatch and a second score value for a second hypothetical antenna mismatch, the first hypothetical antenna mismatch resulting in a first histogram including a highest histogram value in a first range of hypothetical noise values ​​and the second hypothetical antenna mismatch resulting in a second histogram including a highest histogram value in a second range of hypothetical noise values, the first range of hypothetical noise values ​​being higher than the second range of hypothetical noise values, and the first score value being higher than the second score value.

[0340] Example 11 includes the subject matter of any one of Examples 2-10, and optionally, the mismatch calibrator is configured to determine the score value corresponding to the hypothetical antenna mismatch based on a histogram of the hypothetical antenna mismatch and a hypothetical noise value in an angle-of-arrival (AoA) image resulting from the radar Rx data.

[0341] Example 12 includes the subject matter of example 11, and optionally, the mismatch calibrator is configured to determine the AoA image based on the hypothetical antenna mismatch and a Discrete Fourier Transform (DFT) applied to the radar Rx data.

[0342] Example 13 includes the subject matter of any one of Examples 9 to 12, and optionally, the mismatch calibrator is configured to determine the score value corresponding to the hypothetical antenna mismatch based on a predefined weighting function applied to the histogram.

[0343] Example 14 includes the subject matter of example 13, and optionally, the weighting function is monotonically increasing over a predefined range of hypothetical noise values.

[0344] Example 15 includes the subject matter of examples 13 or 14, optionally wherein the weighting function is monotonically increasing for hypothetical noise values ​​between -70 dB and -40 dB.

[0345] Example 16 includes the subject matter of any one of examples 13-15, optionally wherein the weighting function is constant for hypothetical noise values ​​above -20 dB.

[0346] Example 17 includes the subject matter of any one of Examples 1 to 16, and optionally, the mismatch calibrator is configured to determine the antenna mismatch calibration information to calibrate at least one of a gain mismatch or a phase mismatch of the radar antenna array.

[0347] Example 18 includes the subject matter of any one of Examples 1-17, and optionally, the mismatch calibrator is configured to determine the antenna mismatch calibration information comprising a vector of a plurality of complex numbers, a complex number of the plurality of complex numbers representing a gain mismatch and a phase mismatch corresponding to an Rx antenna of the plurality of Rx antennas.

[0348] Example 19 includes the subject matter of any one of Examples 1-18, and optionally, the mismatch calibrator is configured to dynamically calibrate the antenna mismatch of the radar antenna array by updating the antenna mismatch calibration information in real time.

[0349] Example 20 includes the subject matter of any one of Examples 1-19, and optionally, the mismatch calibrator is configured to dynamically calibrate the antenna mismatch of the radar antenna array by iteratively determining the antenna mismatch calibration information based on a calibration update criterion.

[0350] Example 21 includes the subject matter of any one of examples 1 to 20, and optionally, the radar information includes angle-of-arrival (AoA) information.

[0351] Example 22 includes the subject matter of any one of Examples 1 to 21, and optionally, the radar antenna array includes a multiple-input multiple-output (MIMO) radar antenna array including a plurality of transmit (Tx) antennas transmitting Tx radar signals, and the Rx radar signal is based on the radar Tx signal.

[0352] Example 23 includes the subject matter of any one of Examples 1 to 22, and optionally including a vehicle including the radar antenna array.

[0353] Example 24 includes an apparatus including means for performing any of the recited operations of Examples 1-23.

[0354] Example 25 includes a machine-readable medium storing instructions for execution by a processor to perform any of the recited operations of Examples 1-23.

[0355] Example 26 includes an apparatus including a memory and a processing circuit configured to perform any of the recited operations of Examples 1-23.

[0356] Example 27 includes a method including any of the recited acts of examples 1-23.

[0357] Any features, operations, components, and / or features described herein with reference to one or more embodiments may be combined or utilized in combination with one or more other features, operations, components, and / or features described herein with reference to one or more other embodiments, or vice versa.

[0358] While several specific features have been illustrated and described herein, many modifications, substitutions, changes, and equivalents may occur to those skilled in the art. It is therefore to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the present disclosure. [Other possible items] (Item 1) 1. An apparatus comprising: a mismatch calibrator configured to determine antenna mismatch calibration information to calibrate antenna mismatch of a radar antenna array including a plurality of receive (Rx) antennas; a processor for processing radar Rx data and generating radar information based on the radar Rx data and the antenna mismatch calibration information, the radar Rx data being based on Rx radar signals received at the plurality of Rx antennas; An apparatus comprising: (Item 2) 2. The apparatus of claim 1, wherein the mismatch calibrator is configured to determine the antenna mismatch calibration information based on a score function, the score function configured to provide a score value corresponding to a hypothetical antenna mismatch based on the hypothetical antenna mismatch and hypothetical radar information provided by the radar Rx data. (Item 3) 3. The apparatus of claim 2, wherein the mismatch calibrator is configured to determine an identified antenna mismatch that minimizes the score function and to determine the antenna mismatch calibration information based on the identified antenna mismatch. (Item 4) Item 3. The apparatus of item 2, wherein the mismatch calibrator is configured to determine the antenna mismatch calibration information by minimizing a numerical gradient of the score function. (Item 5) 5. The apparatus of claim 4, wherein the mismatch calibrator is configured to perform a plurality of calculation iterations, a calculation iteration of the plurality of calculation iterations including: randomly selecting an initial hypothetical antenna mismatch; determining a minimum of a numerical slope of the score function based on the initial hypothetical antenna mismatch; and updating the antenna mismatch calibration information based on the minimum of the numerical slope of the score function. (Item 6) 6. The apparatus of claim 5, wherein the multiple calculation iterations include at least 100 iterations. (Item 7) Item 6. The apparatus of item 5, wherein the multiple calculation iterations include at least 500 iterations. (Item 8) 3. The apparatus of claim 2, wherein the mismatch calibrator is configured to determine the score value corresponding to the hypothetical antenna mismatch based on a histogram of hypothetical noise values ​​corresponding to the hypothetical antenna mismatch and the hypothetical radar information provided by the radar Rx data. (Item 9) 9. The apparatus of claim 8, wherein the score function is configured to provide a first score value for a first hypothetical antenna mismatch and a second score value for a second hypothetical antenna mismatch, the first hypothetical antenna mismatch resulting in a first histogram including a highest histogram value in a first range of hypothetical noise values ​​and the second hypothetical antenna mismatch resulting in a second histogram including a highest histogram value in a second range of hypothetical noise values, the first range of hypothetical noise values ​​being higher than the second range of hypothetical noise values ​​and the first score value being higher than the second score value. (Item 10) 3. The apparatus of claim 2, wherein the mismatch calibrator is configured to determine the score value corresponding to the hypothetical antenna mismatch based on a histogram of the hypothetical antenna mismatch and a hypothetical noise value in an angle-of-arrival (AoA) image resulting from the radar Rx data. (Item 11) Item 11. The apparatus of item 10, wherein the mismatch calibrator is configured to determine the AoA image based on the hypothetical antenna mismatch and a Discrete Fourier Transform (DFT) applied to the radar Rx data. (Item 12) Item 11. The apparatus of item 10, wherein the mismatch calibrator is configured to determine the score value corresponding to the hypothetical antenna mismatch based on a predefined weighting function applied to the histogram. (Item 13) Item 13. The apparatus of item 12, wherein the weighting function is monotonically increasing over a predefined range of hypothetical noise values. (Item 14) Item 13. The apparatus of item 12, wherein the weighting function is monotonically increasing for hypothetical noise values ​​between -70 dB and -40 dB. (Item 15) Item 13. The apparatus of item 12, wherein the weighting function is constant for hypothetical noise values ​​above -20 dB. (Item 16) Item 16. The apparatus of any one of items 1 to 15, wherein the mismatch calibrator is configured to determine the antenna mismatch calibration information to calibrate at least one of gain mismatch or phase mismatch of the radar antenna array. (Item 17) Item 16. The apparatus of any one of items 1 to 15, wherein the mismatch calibrator is configured to determine the antenna mismatch calibration information comprising a vector of a plurality of complex numbers, a complex number among the plurality of complex numbers representing a gain mismatch and a phase mismatch corresponding to a Rx antenna among the plurality of Rx antennas. (Item 18) Item 16. The apparatus of any one of items 1 to 15, wherein the mismatch calibrator is configured to dynamically calibrate the antenna mismatch of the radar antenna array by updating the antenna mismatch calibration information in real time. (Item 19) 1. An article of manufacture comprising one or more tangible computer-readable non-transitory storage media having computer-executable instructions that, when executed by at least one processor, cause the at least one processor to: determining antenna mismatch calibration information to calibrate antenna mismatch of a radar antenna array including a plurality of receive (Rx) antennas; processing radar Rx data to generate radar information based on the radar Rx data and the antenna mismatch calibration information, the radar Rx data being based on Rx radar signals received at the plurality of Rx antennas; The product is operable to enable the execution of (Item 20) 20. The article of manufacture of claim 19, wherein the instructions, when executed, cause the radar device to determine the antenna mismatch calibration information based on a score function, the score function being configured to provide a score value corresponding to a hypothetical antenna mismatch based on the hypothetical antenna mismatch and hypothetical radar information provided by the radar Rx data. (Item 21) 21. The article of manufacture of claim 20, wherein the instructions, when executed, cause the radar device to determine an identified antenna mismatch that minimizes the score function and to determine the antenna mismatch calibration information based on the identified antenna mismatch. (Item 22) 21. The article of manufacture of claim 20, wherein the instructions, when executed, cause the radar device to determine the score value corresponding to the hypothetical antenna mismatch based on a histogram of hypothetical noise values ​​corresponding to the hypothetical antenna mismatch and the hypothetical radar information provided by the radar Rx data. (Item 23) A vehicle, the vehicle comprising: a system controller configured to control one or more vehicle systems of the vehicle based on the radar information; a radar device configured to provide the radar information to the system controller; and The radar device comprises: a radar antenna array including a plurality of transmit (Tx) antennas for transmitting Tx radar signals and a plurality of receive (Rx) antennas for receiving Rx radar signals based on the Tx radar signals; a mismatch calibrator configured to determine antenna mismatch calibration information to calibrate antenna mismatch of the radar antenna array; a processor for processing radar Rx data and generating the radar information based on the radar Rx data and the antenna mismatch calibration information, the radar Rx data being based on the Rx radar signals received at the multiple Rx antennas; having vehicle. (Item 24) 24. The vehicle of claim 23, wherein the mismatch calibrator is configured to determine the antenna mismatch calibration information based on a score function, the score function configured to provide a score value corresponding to a hypothetical antenna mismatch based on the hypothetical antenna mismatch and hypothetical radar information provided by the radar Rx data. (Item 25) 25. The vehicle of claim 24, wherein the mismatch calibrator is configured to determine the score value corresponding to the hypothetical antenna mismatch based on a histogram of the hypothetical antenna mismatch and a hypothetical noise value in an angle-of-arrival (AoA) image resulting from the radar Rx data.

Claims

1. An apparatus for a radar device, comprising: a mismatch calibrator configured to determine antenna mismatch calibration information to calibrate antenna mismatch of a radar antenna array including a plurality of receive (Rx) antennas; a processor for processing radar Rx data and generating radar information based on the radar Rx data and the antenna mismatch calibration information, the radar Rx data being based on Rx radar signals received at the plurality of Rx antennas; Equipped with the mismatch calibrator is configured to determine the antenna mismatch calibration information based on a score function, the score function being configured to provide a score value corresponding to a hypothetical antenna mismatch based on a histogram of hypothetical noise values ​​corresponding to the hypothetical antenna mismatch and hypothetical radar information provided by the radar Rx data. Device.

2. 2. The apparatus of claim 1, wherein the mismatch calibrator is configured to determine an identified antenna mismatch that minimizes the score function and to determine the antenna mismatch calibration information based on the identified antenna mismatch.

3. The apparatus of claim 1 or 2, wherein the mismatch calibrator is configured to determine the antenna mismatch calibration information by minimizing a numerical gradient of the score function.

4. 4. The apparatus of claim 3, wherein the mismatch calibrator is configured to perform a plurality of calculation iterations, a calculation iteration of the plurality of calculation iterations including: randomly selecting an initial hypothetical antenna mismatch; determining a minimum of a numerical slope of the scoring function based on the initial hypothetical antenna mismatch; and updating the antenna mismatch calibration information based on the minimum of the numerical slope of the scoring function.

5. The apparatus of claim 4 , wherein the multiple calculation iterations comprises at least 100 iterations.

6. 6. The apparatus of claim 1, wherein the score function is configured to provide a first score value for a first hypothetical antenna mismatch and a second score value for a second hypothetical antenna mismatch, the first hypothetical antenna mismatch resulting in a first histogram including a highest histogram value in a first range of hypothetical noise values ​​and the second hypothetical antenna mismatch resulting in a second histogram including a highest histogram value in a second range of hypothetical noise values, the first range of hypothetical noise values ​​being higher than the second range of hypothetical noise values ​​and the first score value being higher than the second score value.

7. 7. The apparatus of claim 1, wherein the mismatch calibrator is configured to determine the score value corresponding to the hypothetical antenna mismatch based on a histogram of the hypothetical antenna mismatch and a hypothetical noise value in an Angle of Arrival (AoA) image resulting from the radar Rx data.

8. An apparatus for a radar device, comprising: a mismatch calibrator configured to determine antenna mismatch calibration information to calibrate antenna mismatch of a radar antenna array including a plurality of receive (Rx) antennas; a processor for processing radar Rx data and generating radar information based on the radar Rx data and the antenna mismatch calibration information, the radar Rx data being based on Rx radar signals received at the plurality of Rx antennas; Equipped with the mismatch calibrator is configured to determine the antenna mismatch calibration information based on a score function, the score function being configured to provide a score value corresponding to a hypothetical antenna mismatch based on the hypothetical antenna mismatch and a histogram of hypothetical noise values ​​in an Angle of Arrival (AoA) image resulting from the radar Rx data. Device.

9. 9. The apparatus of claim 7 or 8, wherein the mismatch calibrator is configured to determine the AoA image based on the hypothetical antenna mismatch and a Discrete Fourier Transform (DFT) applied to the radar Rx data.

10. the mismatch calibrator is configured to determine the score value corresponding to the hypothetical antenna mismatch based on a predefined weighting function applied to the histogram; the weighting function is monotonically increasing over a predefined range of hypothetical noise values; the weighting function is monotonically increasing for hypothetical noise values ​​between -70 dB and -40 dB; and / or The weighting function is constant for hypothetical noise values ​​above −20 dB.

10. Apparatus according to any one of claims 7 to 9.

11. 11. The apparatus of claim 1, wherein the mismatch calibrator is configured to determine the antenna mismatch calibration information to calibrate at least one of a gain mismatch or a phase mismatch of the radar antenna array.

12. 12. The apparatus of claim 1, wherein the mismatch calibrator is configured to determine the antenna mismatch calibration information comprising a vector of a plurality of complex numbers, a complex number among the plurality of complex numbers representing a gain mismatch and a phase mismatch corresponding to a Rx antenna among the plurality of Rx antennas.

13. 13. The apparatus of claim 1, wherein the mismatch calibrator is configured to dynamically calibrate the antenna mismatch of the radar antenna array by updating the antenna mismatch calibration information in real time.

14. 1. A method implemented in a radar device, comprising: determining antenna mismatch calibration information to calibrate antenna mismatch of a radar antenna array including a plurality of receive (Rx) antennas; processing radar Rx data to generate radar information based on the radar Rx data and the antenna mismatch calibration information, the radar Rx data being based on Rx radar signals received at the plurality of Rx antennas; Equipped with The step of calibrating the antenna mismatch comprises: determining the antenna mismatch calibration information based on a score function, the score function configured to provide a score value corresponding to a hypothetical antenna mismatch based on a histogram of hypothetical noise values ​​corresponding to the hypothetical antenna mismatch and hypothetical radar information provided by the radar Rx data; The method comprising:

15. The method of claim 14 , comprising determining an identified antenna mismatch that minimizes the score function, and determining the antenna mismatch calibration information based on the identified antenna mismatch.

16. 16. The method of claim 14 or 15, comprising determining the score value corresponding to the hypothetical antenna mismatch based on a histogram of the hypothetical antenna mismatch and hypothetical noise values ​​in an Angle of Arrival (AoA) image resulting from the radar Rx data.

17. 1. A method implemented in a radar device, comprising: determining antenna mismatch calibration information to calibrate antenna mismatch of a radar antenna array including a plurality of receive (Rx) antennas; processing radar Rx data to generate radar information based on the radar Rx data and the antenna mismatch calibration information, the radar Rx data being based on Rx radar signals received at the plurality of Rx antennas; Equipped with The step of calibrating the antenna mismatch comprises: determining the antenna mismatch calibration information based on a score function, the score function configured to provide a score value corresponding to a hypothetical antenna mismatch based on the hypothetical antenna mismatch and a histogram of hypothetical noise values ​​in an Angle of Arrival (AoA) image resulting from the radar Rx data. The method comprising:

18. determining the score value corresponding to the hypothetical antenna mismatch based on a predefined weighting function applied to the histogram; the weighting function is monotonically increasing over a predefined range of hypothetical noise values; the weighting function is monotonically increasing for hypothetical noise values ​​between -70 dB and -40 dB; and / or The weighting function is constant for hypothetical noise values ​​above −20 dB.

18. A method according to claim 16 or 17, comprising the step of determining.

19. A computer program causing a processor to carry out the method of any one of claims 14 to 18.

20. 19. An arrangement of a radar device, comprising processing means for carrying out the method according to any one of claims 14 to 18.

21. A vehicle, the vehicle comprising: a system controller configured to control one or more vehicle systems of the vehicle based on the radar information; a radar device configured to provide the radar information to the system controller; and The radar device comprises: a radar antenna array including a plurality of transmit (Tx) antennas for transmitting Tx radar signals and a plurality of receive (Rx) antennas for receiving Rx radar signals based on the Tx radar signals; a mismatch calibrator configured to determine antenna mismatch calibration information to calibrate antenna mismatch of the radar antenna array; a processor for processing radar Rx data and generating the radar information based on the radar Rx data and the antenna mismatch calibration information, the radar Rx data being based on the Rx radar signals received at the multiple Rx antennas; having the mismatch calibrator is configured to determine the antenna mismatch calibration information based on a score function, the score function being configured to provide a score value corresponding to a hypothetical antenna mismatch based on a histogram of hypothetical noise values ​​corresponding to the hypothetical antenna mismatch and hypothetical radar information provided by the radar Rx data. vehicle.

22. A vehicle, the vehicle comprising: a system controller configured to control one or more vehicle systems of the vehicle based on the radar information; a radar device configured to provide the radar information to the system controller; and The radar device comprises: a radar antenna array including a plurality of transmit (Tx) antennas for transmitting Tx radar signals and a plurality of receive (Rx) antennas for receiving Rx radar signals based on the Tx radar signals; a mismatch calibrator configured to determine antenna mismatch calibration information to calibrate antenna mismatch of the radar antenna array; a processor for processing radar Rx data and generating the radar information based on the radar Rx data and the antenna mismatch calibration information, the radar Rx data being based on the Rx radar signals received at the multiple Rx antennas; having the mismatch calibrator is configured to determine the antenna mismatch calibration information based on a score function, the score function being configured to provide a score value corresponding to a hypothetical antenna mismatch based on the hypothetical antenna mismatch and a histogram of hypothetical noise values ​​in an Angle of Arrival (AoA) image resulting from the radar Rx data. vehicle.

Images