Signal modeling for unambiguous range rate estimation

Abstract

This document describes interleaved chirps for unambiguous range rate estimation. A signal model estimates the range rate of any chirp within a frame quickly and unambiguously. During each frame, transmission of radar signals is caused by interleaving two different groups of chirps (e.g., up chirps and down chirps) in an ordered sequence. This unique chirp pattern enables the signal model to calculate an ambiguity term (e.g., an ambiguity variable for each group) for each frame. The ambiguity term is generally non-zero. With the ambiguity of the frame taken into account, range rate estimates can be provided unambiguously. The ambiguity term allows for a quick and unambiguous result for any possible detection when applied to a scheme that estimates range rate. The ambiguity does not need to be resolved by analyzing multiple frames. This increase in accuracy and performance enables radar to be more widely adopted and can facilitate safe driving.

Description

Background Technology

[0001] Radar systems are used as perception systems in vehicles, for example, to enable autonomous control or advanced safety features. Radar signals reflect off objects in the field of view of such systems, and the reflections are processed into useful information about the relative position and movement of the objects. Many common driving environments involve numerous different objects moving at varying distances, directions, and speeds. Distance change rate estimates derived in such environments are often ambiguous; the estimated distance change rate may be reported too low due to folding. Dealiasing techniques can be used to eliminate this ambiguity; however, dealiasing operates across multiple frames, introducing latency and degrading performance. Radar systems need to be able to process returns quickly from many critical vehicle systems; vehicle functionality depends on immediate and accurate object reporting; if radar detection and its distance change rate estimation are delayed too long, unsafe and / or uncomfortable vehicle handling may occur. Summary of the Invention

[0002] This document describes signal modeling for unambiguous distance rate of change estimation. This invention is not intended to identify essential features of the claimed subject matter, nor is it intended to define the scope of the claimed subject matter.

[0003] In one example, the system includes a processor configured to transmit radar signals for frames from a radar system by interlacing chirps from two distinct groups into an ordered chirp sequence, such that each consecutive chirp pair includes an initial chirp from one group and a subsequent chirp from the other group. The processor can identify initial range and Doppler measurements for each of the two groups based on radar echoes obtained in response to the transmission of the radar signals. Based on the initial range and Doppler measurements for that group, the processor is configured to select a fuzzy term for each of the two groups from a plurality of fuzzy terms for unambiguously estimating the range change rate of the detection. The fuzzy term for each of the two groups is selected independently. The processor can use the fuzzy terms selected for both groups to estimate the unambiguous range change rate of the detection associated with either group.

[0004] In addition to this example and others, methods for signal modeling for unambiguous distance rate of change estimation are described. In some cases, a computer-readable medium (CRM) includes instructions that, when executed, configure a processor (e.g., a radar system) to perform these methods. This disclosure also contemplates systems including various means for performing these methods. By implementing these and other examples contemplated in this disclosure, signal modeling for unambiguous distance rate of change estimation can be achieved, thereby enabling safe and accurate driving decisions even in congested driving conditions. Attached Figure Description

[0005] This document describes the details of signal modeling for unambiguous distance rate of change estimation with reference to the accompanying figures. The figures may use the same numbers to refer to similar features and components, and may use hyphenated numbers to specify variations in these similar features and components. The figures are organized as follows: Figure 1 An example environment for signal modeling for unambiguous distance rate of change estimation is shown; Figure 2 An example system configured for modeling signals for unambiguous distance rate of change estimation is shown; Figure 3 and Figure 4 A diagram of an example signal waveform used in signal modeling for achieving unambiguous distance rate of change estimation is shown; Figure 5 An example procedure for signal modeling for unambiguous distance rate of change estimation is shown; and Figures 6 to 10 A diagram is shown to convey additional concepts to help understand the technique of interleaved chirping used for unambiguous distance rate of change estimation. Detailed Implementation Existing signal models

[0006] Radar systems employ signal models to estimate detection attributes such as range and rate of change of range. A typical signal model used by a radar system to perform range and rate of change of range estimation for detection employs a set of linear equations as shown below. Equation 1 is used to analyze detection in the fast time dimension of the frequency domain, and Equation 2 is used to analyze detection in the slow time dimension: In each of Equations 1 and 2, R represents the estimated detection distance, v represents the rate of change of the detection distance (e.g., velocity), M is the number of samples per chirp, K is the number of chirps per frame, and f s It is the sampling rate, and the Pulse Repetition Period (PRP) is the time delay between the first and second chirps within a frame. In Equation 1, p * This is a fractional bin estimation in the fast time dimension. In Equation 2, q * It is a fractional warehouse estimation in the slow time dimension.

[0007] Equations 3 and 4 define the ranging frequencies in the fast time dimension used to solve Equation 1. And the ranging frequency used to solve Equation 2 in the slow time dimension. In each of Equations 3 and 4, c is a constant representing the speed of light through air. The term S from Equation 3 is the slope of the chirp, and Δf in Equation 4 is the frequency step between chirps.

[0008] The modified Doppler frequencies used to solve Equation 1 in the fast time dimension. and the modified Doppler frequency in the slow time dimension used to solve Equation 2 The answers can be found by solving equations 5 and 6 respectively:

[0009] The Doppler frequency f used in each of Equations 5 and 6 D By etc. Formula 7 provides: The term f0 represents the starting frequency of the entire frame.

[0010] The term 'a' in equation 6 can be calculated by solving equation 8:

[0011] Finally, the fuzzy number N in Equation 2 模糊 (Also known as the fuzzy integer) represents how many times the distance change rate estimate was folded in this frame using the distance change rate coverage. Fuzzy number N 模糊 The ambiguity number N can change from frame to frame depending on the complexity of the driving environment, in which many objects move independently at different distances, directions, and speeds. As some examples, when ambiguity occurs in a radar system's field of view spanning multiple traffic lanes, or when the radar system has a field of view including busy highways or intersections, the ambiguity number N... 模糊 It can be non-zero. For targets with relatively high speeds, the ambiguity number N... 模糊 The ambiguity number N is usually greater than or less than zero, but not equal to zero. For targets with relatively low speeds, the ambiguity number N... 模糊 Approaching or equal to zero. Problems with existing signal models

[0012] Vehicle detection estimations relying on radar data require unambiguous range R and range change rate v, which can be solved quickly, especially when many objects move independently within the field of view at different distances, directions, and speeds. To reduce latency in the estimation process, some existing radar systems apply oversimplifications to the signal model used, allowing for faster solutions to Equations 1 and 2. Although this is generally an incorrect assumption, the number of frames N... 模糊 It is always set to zero, rather than its true value (e.g., based on peak detection for that frame). This greatly reduces the complexity of solving Equations 1 and 2, making it possible to quickly (e.g., even within a single frame) generate estimates of the distance R and the rate of change of distance v.

[0013] However, this simplification deliberately ignores the possibility that the detection peak of a frame could be associated with different objects appearing at similar angles within the field of view. Reducing the latency of generating radar data in this way decreases the accuracy and overall usefulness when used for other radar-related functions. This is achieved by assuming an ambiguity number N. 模糊 The rate of change of distance v, estimated as a constant of zero in each frame, is often ambiguous. When used by other radar or vehicle functions, ambiguity in radar data can lead to unsafe driving and / or uncomfortable vehicle handling.

[0014] Other radar processing techniques exist that can mitigate ambiguity after the signal model incorrectly assumes the ambiguity number is zero; one such post-signal model processing technique is dealiasing. These techniques are typically used in conjunction with the radar tracking process; therefore, they are applied across multiple frames. Eliminating ambiguity through dealiasing relies on the analysis of detections observed over a duration longer than just one frame. While dealiasing can eliminate ambiguity in range rate of change estimates, this improvement in accuracy can introduce excessive delays to certain radar-related functions, especially considering the frequency with which detected objects may change position, orientation, and / or velocity. Many critical vehicle functions (e.g., safety, control) depend on immediate and accurate reporting of detection estimates; any delays from dealiasing or inaccuracies caused by other techniques can lead to unsafe and / or uncomfortable vehicle behavior. Overview

[0015] This document describes interleaved chirps for unambiguous range rate of change estimation. The signal model estimates the range and range rate of change detected by radar quickly and unambiguously, without ignoring ambiguity. The signal model enables the transmission of a radar signal that sequentially interleaves two distinct sets of chirps (e.g., upward chirps and downward chirps) during each frame. Half of the chirps in the sequence are upward chirp sets, and the other half are downward chirp sets. The upward chirp increases in frequency from the beginning to the end of the chirp; the downward chirp decreases in frequency from the beginning to the end of the chirp. The radar system interleaves these two types of chirps within an ordered sequence, for example, alternating between the chirp from the first set at each odd position in the sequence (e.g., positions 1, 3, 5) and the chirp from the other set at the remaining even positions in the ordered sequence of that frame (e.g., positions 2, 4, 6). Each chirp frame consists of an alternating sequence of chirps up and down, or chirps down and up.

[0016] The properties of upward chirps can be analyzed in conjunction with downward chirps to compute a fuzzy term for the frame, which is uniquely chosen for each identified detection peak. This unique chirp pattern allows the signal model to consider (rather than ignore) the fuzzy term as an important, usually non-zero, variable for correctly estimating the distance and rate of change of distance of the detections in that frame. For each set of chirps, the fuzzy variable is determined to be equal to one of a plurality of integers, including at least one integer, and more commonly several integers greater than or less than zero. Each set of chirps may have different peak detections, and the signal model computes a unique fuzzy variable for each set given the peak detections of that set. The fuzzy term is determined from these fuzzy variables; this approach appropriately applies fuzzy considerations to the detections obtained for each set of chirps.

[0017] The fuzzy term can be modified before being compared to a set of possible values. For example, the fuzzy term can be truncated, rounded, or otherwise factored (including offsets or other modifications) to help determine the correct possible value to use as the fuzzy term for that set of chirps. The signal model can select the possible value from the possible values ​​that is most similar to the calculated fuzzy variable.

[0018] The ambiguity term is fed into a linear equation that can be solved quickly to estimate the distance and rate of change of distance associated with the detection in that frame without ambiguity. Ambiguity can be resolved without analyzing multiple frames. By applying more accurate assumptions to the example radar system's estimation of distance and rate of change of distance, the example radar system can quickly output unambiguous radar data. Ambiguity in the radar data is resolved much faster than using multi-frame processing techniques (e.g., dealiasing). This improvement in radar accuracy and performance enables wider adoption of radar, thereby promoting safer driving. Example Environment

[0019] Figure 1 An example environment 100 for signal modeling for unambiguous distance rate of change estimation is shown. In the depicted environment 100, a vehicle 102 is traveling on a road. Although shown as a passenger car, vehicle 102 could represent other types of motorized vehicles (e.g., cars, motorcycles, buses, tractors, semi-trailers), non-motorized vehicles (e.g., bicycles), rail vehicles (e.g., trains), water vehicles (e.g., boats), aircraft (e.g., airplanes), or spacecraft (e.g., satellites), etc. Vehicle 102 relies on a perception system (including radar system 104) to generate radar data used to detect and avoid other objects as vehicle 102 travels among other objects in environment 100.

[0020] Radar system 104 outputs radar data indicating the presence, distance, and / or rate of change of distance of an object (or part of an object) detected in field of view 106. Radar system 104 can be mounted on, integrated into, or incorporated into any part of vehicle 102, such as the front, rear, top, bottom, or sides, bumpers, mirrors, light modules, rails, fenders, frames, panels, or glass, including any internal or external location of vehicle 102 where object detection using radar is desired. Radar system 104 may include multiple radar devices or multiple radar systems that can be coordinated to focus field of view 106, influence the size of field of view 106, or adjust the range of field of view 106.

[0021] Radar data can take various forms, including data cubes, detection lists, and object tracking. During each stage of the radar processing pipeline, radar data can be transformed in different ways; some stages may perform steps to perform signal processing, detection estimation, object classification, object tracking, etc. Radar data can be output from radar system 104 during any one or more of these radar processing stages. This enables the radar logic of radar system 104 (e.g., one or more processing units, processing cores, chips, programmable logic units, or other hardware) to perform a wide range of complex radar functions (e.g., object trackers, object classifiers); furthermore, access to various forms of radar data enables other functions performed on or outside vehicle 102 (e.g., safety, advanced security, semi-autonomous / autonomous control, sensor fusion sensing, occupant detection, satellite-independent positioning / navigation, home automation, commercial delivery, vehicle-to-vehicle, vehicle-to-external) to improve their performance in performing actions based on radar information about objects (e.g., object 108) that are present in the vicinity of vehicle 102 when vehicle 102 is operating in environment 100.

[0022] As previously suggested, radar system 104 may include various radar logic, circuitry, or elements, including combinations of hardware components and software components executing thereon. Some hardware and / or software components of radar system 104... Figure 1 As shown in the simplified illustration, radar system 104 may include other functional hardware and / or software elements beyond those shown. The computer-readable medium (CRM) of radar system 104 may include a non-transient computer-readable storage device storing computer-executable instructions that, when executed by hardware components (e.g., a processor), configure radar system 104 to generate radar data including information about objects detected in field of view 106. Radar system 104 includes a monolithic microwave integrated circuit (MMIC) 112, which is integrated (e.g., on-chip) or otherwise operatively coupled to at least one processor 114. Waveform generator 116, radar processing unit 118, and antenna array 120 are also part of radar system 104 and may each operate in conjunction with MMIC 112, processor 114, or both.

[0023] Antenna array 120 includes a transmit-receive antenna array; as an example, this is a multiple-input multiple-output (MIMO) antenna array. Other simpler or more complex antenna array designs can be used to perform the ambiguity cancellation techniques described herein for range rate of change estimation; the techniques described herein are not necessarily limited to MIMO radar systems and can be used by other radar systems using other antenna designs for ambiguity cancellation of range rate of change estimation. Antenna array 120 may include elements of a virtual array, a physical array, or a combination of physical and virtual arrays. MMIC 112 includes an interface to antenna array 120 and interfaces to one or more of processor 114, waveform generator 116, and radar processing unit 118.

[0024] In each frame, MMIC 112 is configured to transmit radar signal 110-1 via antenna array 120 to radiate objects in field of view 106. In response to these transmissions, MMIC 112 is configured to obtain radar echo 110-2 from antenna array 120 corresponding to a reflected version of the radar signal 110-1 transmitted earlier in the frame. One or more waveguide elements can couple antenna array 120 to MMIC 112 to improve the reception of radar echo 110-2 or the transmission of radar signal 110-1.

[0025] MMIC 112 converts the analog signal transmitting radar echo 110-2 into a digital format, which can be used by processor 114 and other components of radar system 104 to perform radar-based functions. Inputs to MMIC 112 can be parameters or instructions received from processor 114 that cause MMIC 112 to apply specific waveforms when transmitting radar signal 110-1 and / or receiving radar echo 110-2. Outputs from MMIC 112 can provide processor 114 with a set of complex values ​​representing the collection of all chirped samples of radar echo 110-2 acquired at antenna array 120 across one frame.

[0026] Processor 114 may include a single processor or multiple processors. A processor may be one or more processing cores, integrated circuits, microchips, or other processing units, such as a graphics processing unit (GPU). Processor 114 may be integrated into a system-on-a-chip (SoC) including an MMIC 112 and memory or other hardware (e.g., a CRM) storing instructions that, when executed by the processor, perform operations associated with waveform generator 116 and / or radar processing unit 118.

[0027] When waveform generator 116 is enabled, it determines specific waveforms for MMIC 112 to transmit radar signal 110-1 and receive radar echo 110-2. Waveform generator 116 may be implemented separately in hardware (e.g., a system-on-a-chip, a programmable logic unit) or in a combination of hardware and computer-readable instructions (e.g., software and / or firmware) executed thereon (e.g., by processor 114). Waveform generator 116 may send signal waveforms determined by MMIC 112 for use during the transmission of radar signal 110-1 to processor 114. MMIC 112 may perform modulation, multiplexing, or any other signal processing operations based on commands and parameters provided from processor 114, including instructions that enable waveform generator 116 to modify how MMIC 112 handles the transmission of radar signal 110-1 and / or the reception of radar echo 110-2.

[0028] Radar processing unit 118, activated by radar system 104, transforms radar data for each frame output by MMIC 112 from the time domain to the frequency domain (e.g., in multiple dimensions). In the frequency domain, radar echo 110-2 can be represented using a fast time dimension (e.g., samples collected on a single chirp) and a slow time dimension (e.g., samples collected on multiple chirs). The two-dimensional Fourier transform applied to radar echo 110-2 can include Discrete Fourier Transform (DFT) or Fast Fourier Transform (FFT).

[0029] The energy associated with each of the radar echoes 110-2 becomes apparent after this transformation, allowing identification of the detection 122. Some of the two-dimensional data may indicate false detections or noise; this data can be filtered, thereby reducing complexity or the amount of radar data ultimately processed. Based on the detection 122 (which is a digitized two-dimensional representation of the radar echo 110-2 obtained from the MMIC 112), the radar processing unit 118 can configure the processor 114 to filter the detection 122 by applying thresholding and / or non-maximum suppression (e.g., by searching in the range-Doppler plane) to the energy attributed to the detection 122. Performing energy peak lookup in the digitized two-dimensional radar data makes it easier to separate the detection 122 for real objects from false detections and noise. For example, peaks can be found in the two-dimensional representation of the digitized radar echo 110-2 based on the location of the detection 122 in both the fast time dimension (e.g., samples collected on a single chirp) and the slow time dimension (e.g., samples collected on multiple chirs). Low-energy echoes can be ignored (e.g., discarded as noise); groups of high-energy echoes can indicate the detection of radar reflection portions attributed to an object during a frame 122.

[0030] After being filtered to reduce the complexity of further radar data processing (e.g., estimating range and rate of change of range), radar processing unit 118 can use the found peaks to generate a list of detections 122 that are considered effective for analysis and reporting in the output from radar system 104. For example, one or more of the detections 122 can indicate the range and radial rate of change of range attributable to radar waves 110-2 (or scattering) that are effective when reflected from an object 108 in environment 100. Although the detections 122 in the list may have errors in precise location (e.g., azimuth), high-quality estimates of the range and radial rate of change of range can be accurately derived.

[0031] Analyzing the attributes of detection 122 across multiple frames enables other complex radar functions, such as object classification and object tracking. For example, a radar tracker can generate object data derived from detection 122 by grouping and analyzing the estimates from detection 122, for instance, using detection-based object recognition techniques. Ensuring that the attributes of detection 122 (including range and rate of change of range estimates) are accurate and computationally fast (e.g., within a single frame) can significantly improve the performance of other vehicle functions that rely on the efficiency of detection 122.

[0032] The radar processing unit 118 can execute a signal model to estimate the range and range change rate of each of the detectors 122 considered to be of high energy. The signal model specifies the transmission of the radar signal 110-1, the analysis of the radar echo 110-2, and the mathematical scheme used to estimate the properties of the detectors 122. The signal model of the radar processing unit 118 defines a set of linear equations to be applied to the detectors 122 to estimate the range and range change rate associated with any detector 122 corresponding to a found peak. The solution to the complex system of equations can be obtained using the frequency of the peak value of each detector 122 in its respective frequency domain. Example System

[0033] Figure 2 An example system configured to model a signal for unambiguous distance rate of change estimation is shown. Figure 2 The system in the figure represents a part of vehicle 102, and in this context, the system is referred to as vehicle 102-1. For ease of explanation and brevity of the figures, many other parts of vehicle 102-1 are omitted; these other components of vehicle 102-1 that depend on radar data can improve the performance of their own functions by receiving an unambiguous estimate of the rate of change of range determined by interleaved chirp according to the described technique.

[0034] Included in vehicle 102-1 is radar system 104-1, which is an example of radar system 104 shown in more detail. Vehicle 102-1 includes vehicle-based system 200, which is operatively and / or communicatively coupled to radar system 104-1 via link 202. Link 202 can be one or more wired and / or wireless links, including vehicle-based network communications for interconnecting components of vehicle 102-1. In some examples, link 202 is a vehicle communication bus.

[0035] Vehicle-based system 200 uses radar data provided by radar system 104-1 on link 202 to perform specific functions. Examples of vehicle-based system 200 include systems for autonomous control 206-1, safety 206-2, positioning 206-3, vehicle-to-vehicle communication 206-4, occupant interface 206-5, and multi-sensor tracker 206-6. Other examples of vehicle-based system 200 may be conceived as including its vehicle subsystem (e.g., components, circuitry). For example, detection 122 may include information about the movement of an object in the field of view, such as the distance and rate of change of distance of object 108, so that vehicle-based system 200 can control vehicle 102-1 to perform emergency braking, lane changes, or adjust the direction and / or speed of the vehicle.

[0036] The radar data output to the vehicle-based system 200 on link 202 includes information about an object or part thereof detected in the field of view 106. The radar data may include detection 122, or object tracking derived from detection 122; detection 122 and / or tracking may be provided to the vehicle-based system 200 to enable other vehicle functions. Upon being received by the vehicle-based system 200, detection 122 or other radar data may enable vehicle 102-1 to drive safely by avoiding object 108. For example, object detection of vehicles in adjacent lanes of vehicle 102-1 may be used for blind spot monitoring.

[0037] As an additional example, the autonomous control 206-1 provided by the vehicle-based system 200 can autonomously or semi-autonomously control the vehicle 102-1 and drive it safely on the road using radar data received via link 202. The vehicle-based system 200 can perform other functions in response to detection 122 or tracking, which are not necessarily for vehicle control. For example, detection 122 can be output to the occupant interface 206-5 to allow the operator or controller to take action and avoid objects. Detection 122 can be provided to other vehicles using vehicle-to-vehicle communication 206-4 to allow the operator or controller of the other vehicle to also avoid the tracked object, or to ensure that vehicle 102-1 is aware of the presence of other vehicles. Radar data enables enhanced situational awareness, allowing vehicle 102-1 to drive more safely, avoiding object 108 and other objects in the environment 100.

[0038] Radar system 104-1 includes MMIC 112-1 (as an example of MMIC 112). MMIC 112-1 includes transmitter / receiver element 210, timing / control element 212, and analog-to-digital converter 214. For the sake of brevity in the figures, Figure 2 Antenna array 120, which is also part of radar system 104-1 and is operatively coupled to transmitter / receiver element 210, is omitted here.

[0039] Transmitter / receiver element 210 is configured to transmit electromagnetic (EM) signals as radar signals (e.g., radar signal 110-1) and receive reflected versions of the EM signals (e.g., radar echo 110-2). In the MIMO example, transmitter / receiver element 210 enables the reception of radar echoes at a higher resolution compared to, for example, multi-state radar. Transmitter / receiver element 210 includes one or more components for transmitting EM signals and one or more components for detecting EM signals. Transmitter / receiver element 210 enables the transmission of radar signals radiating a field of view 106 and manages the reception of radar echoes, where a plurality of radar echoes correspond to object reflections of the transmitted radar signals. Transmitter / receiver element 210 can be configured as a transceiver implemented as a single component (e.g., a chip) to perform both transmission and reception. Alternatively, transmitter / receiver element 210 can be implemented as a separate transmitter and a separate receiver.

[0040] Timing / control element 212 performs operations to adjust the characteristics (e.g., frequency, gain, phase, period) of the transmitted radar signal in a manner efficient for radar processing, or to capture the reflected radar echo. For example, according to existing MIMO technology, timing / control element 212 causes transmitter / receiver element 210 to adjust the size, shape, antenna pattern, or other characteristics of radar signal 110-1 so that radar echo 110-2 captures high-resolution information about environment 100.

[0041] Analog-to-digital converter 214 converts the radar echo received from transmitter / receiver element 210 into a digital form that can be used to generate detection 122 and other radar processing. MMIC 112-1 samples the signal at an appropriate sampling rate (e.g., at least a Nyquist frequency). MMIC 112-1 outputs the digitized radar echo via link 208, which represents an internal communication link between components of radar system 104-1. Link 208 can be wired or wireless and allows the internal representation of radar data to be exchanged within radar system 104-1, and then presented as detection 122 and / or tracking output on link 202.

[0042] Radar system 104-1 is operatively coupled to MMIC 112-1 and link 208. Radar system 104-1 also includes at least one processor 114-1, which is an example of processor 114. Some examples of processor 114-1 include controllers, control circuitry, microprocessors, chips, systems, system-on-a-chip (SoC), devices, processing units, digital signal processing units, graphics processing units, and central processing units. Processor 114-1 can be any component configured to process frames of digitized radar echoes obtained from MMIC 112-1 to transmit objects in the field of view based on information present in detection 122. Processor 114-1 may include multiple processors, one or more cores, embedded memory storing software or firmware, caches, or any other computer element enabling processor 114-1 to execute machine-readable instructions for generating detection 122.

[0043] Machine-readable instructions executed by processor 114-1 may be stored in a computer-readable medium (CRM 204) of radar system 104-1. CRM 204 may also be used to store data managed by processor 114-1 during instruction execution. In some examples, CRM 204 and processor 114-1 are a single component, such as a SoC including CRM 204, which is configured as dedicated memory for processor 114-1. In some examples, access to CRM 204 is shared by other components of radar system 104-1 connected to CRM 204, for example via link 208 (e.g., MMIC 112-1). Processor 114-1 obtains instructions from CRM 204, and the execution of these instructions configures processor 114-1 to perform radar operations. Example radar operations include signal processing that results in detection 122 being transmitted via link 202 to vehicle-based system 200 and other components of vehicle 102-1.

[0044] In this example, CRM 204 may include instructions for configuring processor 114-1 to generate detection 122 and resolve ambiguities in estimates of the range and rate of change of range associated with detection 122. For example, CRM 204 includes instructions for executing radar processing unit 118-1 (which is an example of radar processing unit 118). CRM 204 also includes an instruction set that, when executed by processor 114-1, implements waveform generator 116-1 (which is an example of waveform generator 116).

[0045] When executed by processor 114-1, waveform generator 116-1 generates, or instructs MMIC 112-1 to generate, a radar signal with a waveform based on defined parameters. In various aspects, waveform generator 116-1 can provide instructions for generating the waveform from MMIC 112-1, where the waveform can undergo any number of signal operations, such as modulation, multiplexing, filtering, etc. The waveform can then be output by MMIC 112-1 to a transmitting antenna array that transmits the radar signal within the target's field of view.

[0046] The transmitted radar signal may include a waveform generated using waveform generator 116-1, and therefore, radar echoes reflected from objects in the field of view can be processed to include unambiguous range and range change rate estimates. It should be understood that although processor 114-1 is shown as executing waveform generator 116-1, waveform generator 116-1 and processor 114-1 can be implemented together, for example, as a SoC. Furthermore, waveform generator 116-1 may include any other combination of fixed logic circuitry and can be implemented wholly or partially in hardware, or a combination of hardware and software or firmware.

[0047] When executed by processor 114-1, radar processing unit 118-1 determines a two-dimensional representation of the digitized radar echo output by MMIC 112-1 in the frequency domain. For example, radar processing unit 118-1 can use a Fourier transform to transform the digitized radar echo output by MMIC 112-1 in the time domain into a two-dimensional representation of the radar echo in the frequency domain. The two-dimensional representation may include a fast time dimension corresponding to samples collected on a single chirp and a slow time dimension corresponding to samples collected on multiple chirs.

[0048] Once the two-dimensional representation of the radar echo in the frequency domain is determined, the radar processing unit 118-1 can manipulate the peaks of the two-dimensional representation, for example, by peak search. In doing so, the radar processing unit 118-1 can use the frequencies of the peaks found in the fast time dimension and the frequencies of the peaks found in the slow time dimension to solve a system of two-dimensional equations relating the range and the rate of change of range to the found peaks. The radar processing unit 118-1 can output a detection 122 including the range and the rate of change of range. The vehicle-based system 200 can directly receive the detection 122 via link 202 to assist in performing the functions of the vehicle 102-1.

[0049] In order to generate detection 122 (including unambiguous range and range change rate estimates), radar system 104-1 employs signal model 218, which prevents ambiguity according to the described techniques to improve the accuracy of detection 122. Example signal model

[0050] Signal model 218 is an example of the aforementioned new signal model, which takes into account, rather than ignores, potential ambiguities in its detection based on range rate of change estimation. Signal model 218 interacts with waveform generator 116-1 to configure MMIC 112 for transmitting radar signal 110-1 as an ordered chirped sequence across each frame.

[0051] Signal model 218 commands waveform generator 116-1 to include interleaved or alternating patterns of chirps from two different groups in the ordered chirp sequence of each frame. One group of chirps includes upward chirps, and the other group includes downward chirps. A frame can begin with either downward chirps or upward chirps. Although for ease of description, there are two sets of available chirp types (including upward and downward chirps) in the ordered sequence of each frame, it is possible to use more than two sets of chirps.

[0052] Signal model 218 divides the ordered chirp sequence into odd-position groups and even-position groups. Waveform generator 116-1 receives instructions from signal model 218 to generate radar signal 110-1, which has one type of chirp emitted at each odd position (e.g., 1, 3, 5) of the sequence and another type of chirp emitted at each even position (e.g., 2, 4, 6). This results in each frame comprising many consecutive pairs of upward and downward chirps. The upward and downward chirps are interleaved such that no two consecutive chirps are of the same type. Detailed aspects of the radar signal 110-1 generated by the execution of signal model 218 can be derived from... Figure 3 and Figure 4 This will become even more apparent in the following description.

[0053] By interleaving the upward and downward chirps in waveform generator 116-1 during each frame, signal model 218 can account for and eliminate ambiguity in estimating the range and rate of change of range for each chirp (which causes radar response during that frame). This is in contrast to incorrectly assuming the ambiguity term N. 模糊 Unlike typical signal models where the value is always zero, signal model 218 dynamically calculates the ambiguity term N for each frame. 模糊 220, including the corresponding fuzzy terms for each chirp group.

[0054] Chirps from the upward chirping group are assigned corresponding fuzzy terms. It can also be called or The downward chirping in the other group was assigned a corresponding fuzzy term. It can also be grouped based on its location rather than its chirping property, and is referred to as such. or The ambiguity term is not necessarily the same between different chirp groups within each frame. Each chirp group can have a different ambiguity term. More typically, the ambiguity term is greater than or less than zero, but not equal to zero. The corresponding ambiguity term for each chirp group is calculated independently using initial distance and Doppler measurements of the detections (e.g., peaks) associated with that chirp group. Signal model 218 estimates the ambiguity term by applying a unique mathematical solution to the peak detections within that group through chirp analysis across the entire frame. and Each of them.

[0055] Equation 9 represents a set of four linear equations, which signal model 218 applies to determine each of the four unknowns: In Equation 9, each group of chirps (e.g., odd chirps, even chirps) is analyzed as a whole. Signal model 218 operates based on parameters (which can be reprogrammable), including a parameter (M) for the number of samples per chirp, a parameter (K) for the number of chirps per frame, and a parameter (f) for the sampling rate. s ), parameters for the time delay between the first odd chirp and the second odd chirp within a frame (PRP) 奇数 ), and the parameter (PRP) for the time delay between the first and second even chirps within a frame. 偶数 In addition, the parameter (p) * 奇数 ) is the fast time dimension fractional warehouse estimate of the first group of chirps (e.g., all odd chirps), with parameters (p) * 偶数 ) is a fast-time dimension fractional warehouse estimate for another set of chirps (e.g., all even chirps). Similarly, the parameter (q) * 奇数 ) is the first set of chirped slow-time dimension fractional warehouse estimates, with parameters (q) * 偶数 ) is a fractional warehouse estimate of the slow time dimension of the second set of chirps. Another parameter includes the time delay Δt between the first chirp (e.g., odd) and the second chirp (e.g., even) of the frame to compensate for target movement on consecutive chirps.

[0056] During radar processing of the radar echo, the initial range and Doppler measurements are determined; odd chirps have ranging frequencies in a fast time dimension. Ranging frequency in slow time dimension Odd-numbered chirps also possess Doppler frequencies in the fast time dimension. Doppler frequency in slow time dimension Even-numbered chirps similarly have ranging frequencies in the fast time dimension. Ranging frequency in slow time dimension Doppler frequencies in the fast time dimension Doppler frequency in slow time dimension

[0057] The fuzzy term N is calculated using each set of chirps for each frame. 模糊 220, the distance R and the rate of change of distance v from the detection of each chirp can be estimated without ambiguity. For example, the distance R and the rate of change of distance v from the detection of each chirp can be calculated by applying Equations 1 and 2. However, instead of applying the zero constant of ambiguity, the correct ambiguity term N is considered. 模糊 220 (for example, and Using the correct blur term N for each chirp in the frame. 模糊 Solving Equations 1 and 2 in equation 220 enables signal model 218 to quickly calculate the unambiguous distance change rate v based on detection. Figure 5 The text further details how signal model 218 configures radar system 104-1 for estimating unambiguous range change rate v when solving equation 9. Example waveform

[0058] Figure 3 and Figure 4 A diagram of an example signal waveform used in signal modeling for achieving unambiguous distance rate of change estimation is shown. Figure 3 and Figure 4 The example frames shown are just two examples; other waveforms may be used depending on the specific application and the radar technology applied.

[0059] Figure 3 Each of the figures in Figures 300-1 and 300-2 represents a different example waveform (showing the signal frequency over time) used by signal model 218 during each frame. Signal model 218 is configured to interleave upward chirp with downward chirp in either case.

[0060] Figure 4 Figure 400-1 in the figure shows the corresponding PRP response at each chirp index (e.g., each position in an ordered chirp sequence) of the waveform depicted in Figure 300-1. Figure 4 Figure 400-2 in the figure shows the corresponding PRP response at each chirp index (e.g., each position in an ordered chirp sequence) of the waveform depicted in Figure 300-2.

[0061] Figures 300-1 and 400-1 illustrate example down-frames. In a down-frame, the frequency range of the ordered chirp sequence shifts to lower frequencies throughout the frame. A down-frame consists of an ordered chirp sequence. The first chirp is a down chirp, and every other odd-numbered position in the sequence also includes a down chirp. The second chirp in the ordered sequence is an up chirp, not a down chirp. Every other even-numbered position in the sequence also includes an up chirp.

[0062] Figures 300-2 and 400-2 provide example upward frames. In contrast to downward frames, the frequency range of the ordered chirp sequence in the upward frame shifts to higher frequencies over the duration of the frame. The upward frame also includes an ordered chirp sequence. However, the first chirp position of the sequence and every other odd position of the sequence include an upward chirp. The second chirp position of the sequence and every other even position of the sequence include a downward chirp instead of an upward chirp. Example process

[0063] Figure 5 An example procedure 500 for signal modeling for unambiguous distance rate of change estimation is shown. Operations (or steps) 502 to 510 are performed, but are not limited to the order or combination of these operations shown herein. Furthermore, any one or more operations may be repeated, combined, or rearranged to provide other operations.

[0064] For ease of description, process 500 is described with reference to the elements in the preceding figures. Process 500 can be performed by radar system 104, radar system 104-1, and other example radar systems that utilize a signal model (such as signal model 218) to calculate the ambiguity term N. 模糊 220, and achieves a more accurate range change rate v estimate without ambiguity. For ease of description, process 500 is described in the context of radar processing unit 118-1 (including signal model 218 executed on processor 114-1).

[0065] Consider an example where a vehicle 102 is in motion and a radar system 104-1, as part of a vehicle system 102-1, provides radar data as perception information to other parts of the vehicle 102. This radar data can help the radar system 104-1 or other systems of the vehicle 102 perform driving-related operations (e.g., autonomous control, semi-autonomous control, advanced safety emergency stop).

[0066] At 502, a radar signal for a frame is transmitted by interleaving two distinct groups of chirps into an ordered chirp sequence, such that each consecutive chirp pair includes an initial chirp from one of the two groups and a subsequent chirp from the other group. For example, radar system 104-1 transmits a frame of radar signal 110-1 into environment 100. The signal model 218 employed by radar system 104-1 configures MMIC 112-1 and / or waveform generator 116-1 to induce the transmission of radar signal 110-1, which then takes the form of a waveform similar to that shown in Figures 300-1 and 300-2. During each frame, radar signal 110-1 includes an ordered chirp sequence in which chirps from two distinct groups are transmitted alternately. For example, one of the groups is an odd-numbered chirp group, which includes an upward chirp with increasing frequency during transmission. The other group is the even-numbered chirp group, which includes downward chirps with decreasing frequency during transmission. Each consecutive chirp pair in the ordered sequence includes one chirp from each of the two groups.

[0067] Odd-numbered chirp groups may include downward chirps, while even-numbered chirp groups may include upward chirps. In other examples, odd-numbered chirp groups include upward chirps, while even-numbered chirp groups include downward chirps. Referring to step 502, the initial chirp in each consecutive chirp pair may be a chirp from the upward chirp group, while subsequent chirps in each consecutive chirp pair may be chirps from the downward chirp group; this is illustrated in Figure 300-2. In other examples, the initial chirp is a chirp from the downward chirp group, while subsequent chirps come from the upward chirp group, as shown in Figure 300-1.

[0068] Signal model 218 operates based on parameters (which may be reprogrammable), including a parameter (M) for the number of samples per chirp, a parameter (K) for the number of chirps per frame, and a parameter (f) for the sampling rate. s ), parameters for the time delay between the first odd chirp and the second odd chirp within a frame (PRP) 奇数 ), and the parameter (PRP) for the time delay between the first and second even chirps within a frame. 偶数 Another parameter includes the time delay Δt between the first chirp (e.g., odd) and the second chirp (e.g., even) of the frame to compensate for target movement on two consecutive chirs in the sequence.

[0069] At position 504, based on the radar echoes obtained in response to the transmission of radar signals, the initial range and Doppler measurements for each of the two groups are identified. For example, radar system 104-1 transforms the digitized radar echoes 110-2 output from MMIC 112-1 in the time domain into their two-dimensional representations in the frequency domain. The two-dimensional representations may include a fast time dimension corresponding to samples collected on a single chirp and a slow time dimension corresponding to samples collected on multiple chirs. Once the two-dimensional representation of the radar echoes 110-1 in the frequency domain is determined, the initial range and Doppler estimates associated with each group of chirs can be inferred.

[0070] Each of the two chirp sets is associated with an initial range and Doppler measurement derived from sampling the radar echo 110-2 attributed to that chirp set. For example, the radar processing unit 118-1 can independently generate initial range and Doppler measurements identifiable from the radar echo 110-2 for each of the two chirp sets. The initial range and Doppler measurements for the odd-numbered chirp sets can have a ranging frequency in a fast time dimension. Ranging frequency in slow time dimension Doppler frequency in fast time dimension And the Doppler frequency in the slow time dimension Similarly, even-numbered chirp groups can have ranging frequencies that include the fast time dimension. Ranging frequency in slow time dimension Doppler frequency in fast time dimension Doppler frequency in slow time dimension The initial distance and Doppler measurements. The initial distance and Doppler measurements for odd-numbered chirp groups in the frame may also include parameters (p...). * 奇数 ), the parameter (p * 奇数 ) is a fractional bin estimate of the fast time dimension for the odd-chirped groups in this frame. The initial distance and Doppler measurement for the even-chirped groups may include parameters (p * 偶数 ), the parameter (p * 偶数 ) is the fast time dimension fractional bin estimate of the even-numbered chirping groups in this frame. Parameter (q) * 奇数 ) and parameters (q) * 偶数 ) can be included in the initial distance and Doppler measurements for that frame identifier, where the parameter (q) * 奇数 ) is the slow-time dimension fractional bin estimate of odd-numbered chirping groups in a frame, (q) * 偶数) is the fractional warehouse estimate of the slow time dimension of even-numbered chirping groups in a frame.

[0071] At point 506, a fuzzy term is selected for each of the two groups from a plurality of fuzzy terms used to unambiguously estimate the rate of change of the detection range. Based on the initial range and Doppler measurement of the detection identified for that group, a fuzzy term is selected independently for each of the two groups. The plurality of fuzzy terms can include zero and non-zero terms. They can include at least one integer greater than or less than zero. In some cases, one or more of the fuzzy terms are selected as zero. That is, unlike other radar systems that ignore possible fuzziness, fuzzy terms from odd or even chirps can be non-zero.

[0072] To generate unambiguous distance and distance change rate estimates for detection 122 in a frame, signal model 218 is configured to compute ambiguity term 220 (N) for each frame. 模糊 Instead of ignoring it, signal model 218 generates a solution to Equation 9 for each chirped frame, which is then used to select the ambiguity term N for that particular frame. 模糊 220.

[0073] Signal model 218 configures radar system 104-1 to generate a solution to Equation 9 to compute ambiguity variables for each set of chirps based on the initial range and Doppler measurements of detection 122. The ambiguity variables for each set can be used to select ambiguity terms from multiple ambiguity terms to be applied to estimate the rate of change of detection range inferred from that set of chirps in the frame.

[0074] For example, the fuzzy term N 模糊 220 may include an ambiguity term for the odd-numbered chirps of the frame and another ambiguity term for the even-numbered chirps of the frame. An ambiguity variable is calculated for each set of chirps. An ambiguity term for a set of chirps can be selected as the ambiguity term from a plurality of ambiguity terms that is most closely related in value to the ambiguity variable calculated for that set of chirps. In some examples, the ambiguity variable for a set can be rounded up or down to the nearest integer value corresponding to the ambiguity term applied to that set. In this way, signal model 218 configures radar system 104-1 to handle ambiguity term N. 模糊 220 is correctly applied to each group of chirps.

[0075] Radar system 104-1 can determine the first fuzzy variable for the first set of chirps (e.g., odd-numbered chirps). The first fuzzy variable can be used to select the first fuzzy term from multiple fuzzy terms that is most numerically close to the first fuzzy variable. For example, the first fuzzy variable can be calculated as a decimal value and rounded to the nearest positive or negative integer (e.g., zero, one, two, negative one, negative two) to derive the first fuzzy term.

[0076] A second fuzzy variable is also calculated for the second set of chirps (e.g., even chirps). This second fuzzy variable can be used to select a second fuzzy term from multiple fuzzy terms that is most closely related in value to one of the available fuzzy terms.

[0077] In rearranging Equation 9, Equation 10 can be used to solve for the fuzzy variables of each chirp: Solving equation 10 allows us to select the correct fuzzy term N for each of the two sets of chirps. 模糊 220. Based in part on the parameters obtained from radar echo 110-2 as described above, as well as the initial range and Doppler measurements, signal model 218 configures radar system 104-1 to solve equation 10 to correct the ambiguity N of each set of chirps in the frame. 模糊 220 is set as two unknowns and Any one of them.

[0078] At point 508, the fuzzy term chosen for both groups is used to estimate the unfuzzy distance change rate of the detection associated with either group. For example, instead of ignoring its contribution, the correct fuzzy term N is used. 模糊 Input 220 into Equation 10 to solve for the distance R and distance change rate v of the detection 122 in this group to prevent ambiguity in the distance change rate estimation. In this way, the estimates of distance R and distance change rate v can be determined without ambiguity.

[0079] As mentioned above, odd chirps can be either upward or downward chirps. If an odd chirp is upward, then an even chirp is downward, or if an odd chirp is downward, then an even chirp is upward. Equation 11 shows the solution for unambiguously estimating the distance R and the rate of change v of an upward chirp according to Equation 10. Equation 12 shows the solution for unambiguously estimating the distance R and the rate of change v of a downward chirp.

[0080] In each of equations 11 and 12, the fuzzy term and This corresponds to the fuzzy variables derived from solving Equation 10. To select the correct fuzzy term... and Each fuzzy variable is rounded to the nearest integer. Then, when solving equations 11 and 12, the distance R and the rate of change of distance v of the detection 122 from each set of chirps (whether chirping upwards or downwards) can be estimated without fuzziness.

[0081] At 510, ambiguity terms selected for the two groups are used to estimate the unambiguous rate of change of the detection associated with either group. For example, the range rate of change estimate of detection 122 inferred from the two groups of chirps is output from radar signal processing unit 118-1. Radar processing unit 118-1 can output detection 122 (including range and range rate of change) so that vehicle-based system 200 can receive detection 122 to help perform the functions of vehicle 102-1.

[0082] Other components of these vehicle systems or radar systems 104-1 (e.g., trackers) can use this accurate and unambiguous radar data to improve the performance of vehicle functions that rely on radar data. Therefore, this unambiguous technique can be used to calculate the rate of change of distance v, as it is unambiguous because the correct ambiguity term is used for each set of chirps. Further details of signal modeling by eliminating ambiguity

[0083] Figures 6 to 10 A diagram is shown to convey additional concepts to help understand the technique of interleaved chirping used for unambiguous distance rate of change estimation. Figure 6 Figures 600-1 to 600-4 each illustrate an example of using an upward chirp or a downward chirp alone in an upward or downward frame of a radar signal. Figure 600-1 includes a downward frame with a downward chirp, Figure 600-2 includes an upward chirp in a downward frame, Figure 600-3 includes an upward frame with an upward chirp, and Figure 600-4 includes a downward chirp in an upward frame. Figure 7 Including Figures 700-1 to 700-4, Figures 700-1 to 700-4 and Figure 6 The example radar signal shown corresponds to the PRP chart. Figure 8 The results are shown using any one of these four different example radar signal waveforms, resulting in ambiguity at different speeds. The aliasing is evident in Figures 800-1 and 800-2, where the estimation depends on the variable vehicle speed rather than a constant distance or rate of change of distance.

[0084] In contrast to Figures 800-1 and 800-2, Figure 9The results of applying the techniques of this disclosure to eliminate ambiguity in the estimation of distance and rate of change of distance are shown. Each of Figures 900-1 and 900-2 demonstrates a consistent and accurate estimation of distance even as the speed of the object or vehicle changes. As shown in Figure 11, Figures 1000-1 and 1000-2 show that the amount of error in the rate of change of distance estimated using the described techniques is significantly reduced. Regardless of the speed of the radar system 104, the vehicle 102, or the detected object 108, the magnitude of the error in the estimation of the rate of change of distance exhibits limitation to a predictable narrow range. Additional examples

[0085] Some additional examples are as follows:

[0086] Example 1: A method comprising: transmitting a radar signal for a frame from a radar system by interlacing chirps from two distinct groups into an ordered chirp sequence such that each consecutive chirp pair includes an initial chirp from one of the two groups and a subsequent chirp from the other of the two groups; identifying initial range and Doppler measurements for each of the two groups based on radar echoes obtained in response to the transmission of the radar signal; selecting a fuzzy term for each of the two groups from a plurality of fuzzy terms for unfuzzy estimation of the range change rate of the detection, the fuzzy term for each of the two groups being independently selected based on the initial range and Doppler measurements of that group; and having the radar system use the fuzzy term selected for the two groups to estimate an unfuzzy range change rate of the detection associated with either of the two groups.

[0087] Example 2: Any other example of the method, wherein one of the two groups includes an upward chirp with an increasing frequency during transmission, and the other of the two groups includes a downward chirp with a decreasing frequency during transmission.

[0088] Example 3: Any other example of a method where: one of the two groups consists of an odd number of chirps and the initial chirp is an up chirp, and the other of the two groups consists of an even number of chirps and the subsequent chirps are down chirps; or the initial chirp is a down chirp and the subsequent chirps are up chirps.

[0089] Example 4: Any other example of a method where the frame includes an up frame that shifts the ordered chirp sequence higher in frequency during frame transmission.

[0090] Example 5: Any other example of a method where the frame includes a downframe that shifts the ordered chirp sequence lower in frequency during frame transmission.

[0091] Example 6: A method for any other example, where multiple fuzzy items include at least one integer that is greater than zero or less than zero.

[0092] Example 7: A method for any other example, wherein selecting a fuzzy term for each of the two groups includes: calculating a fuzzy variable for the first group based on the initial distance and Doppler measurement of the first group; and selecting a corresponding fuzzy term from a plurality of fuzzy terms that is most closely related in value to the fuzzy variable of the first group for the first group.

[0093] Example 8: A method for any other example, wherein selecting a fuzzy term for each of the two groups includes: calculating a first fuzzy variable for the first group based on the initial distance and Doppler measurements of the first group in the two different groups; and calculating a second fuzzy variable for the second group based on the initial distance and Doppler measurements of the second group in the two different groups.

[0094] Example 9: Any other example method further includes: selecting a first fuzzy term from a plurality of fuzzy terms that is most closely related in value to the fuzzy variable of the first group for the first group; and selecting a second fuzzy term from a plurality of fuzzy terms that is most closely related in value to the fuzzy variable of the second group for the second group.

[0095] Example 10: A method for any other example, wherein at least one of the selected first fuzzy term or the selected second fuzzy term is not zero.

[0096] Example 11: Any other example of the method further includes: outputting unambiguous distance change rates of at least some of the detected data from a radar system to a component of a vehicle to enable tracking or other radar-related functions performed on the vehicle.

[0097] Example 12: A computer-readable storage medium including instructions that, when executed, cause a processor to perform a method of any of the preceding examples.

[0098] Example 13: A system including a processor configured to perform a method of any of the previous examples.

[0099] Example 14: A system as described in any of the previous examples, comprising a radar system for vehicles.

[0100] Example 15: As described in any of the previous examples, the system includes vehicles.

[0101] Example 16: A system comprising methods for performing as described in any of the previous examples. Conclusion

[0102] While various embodiments of this disclosure have been described in the foregoing description and illustrated in the accompanying drawings, it should be understood that this disclosure is not limited thereto, but can be practiced in various ways within the scope of the following claims. It will be apparent from the foregoing description that various modifications can be made without departing from the spirit and scope of this disclosure. Although described with regard to specific radar system elements, the techniques, apparatus, and systems of this disclosure provide plug-and-play compatibility with other radar systems.

[0103] Unless the context explicitly states otherwise, the use of "or" and grammatically related terms indicates an unrestricted, non-exclusive alternative. As used herein, the phrase referring to "at least one" of a list of items means any combination of those items, including a single member. As an example, "at least one of a, b, or c" is intended to cover: a, b, c, ab, ac, bc, and abc, as well as any combination with multiple identical elements (e.g., aa, aaa, aab, aac, abb, acc, bb, bbb, bbc, cc, and ccc, or any other ordering of a, b, and c).

Claims

1. A method, the method comprising: Radar signals for frames are transmitted from a radar system by interlacing chirps from two different groups, including a first group and a second group, into an ordered chirp sequence, such that each consecutive chirp pair includes an initial chirp from the first group and a subsequent chirp from the second group, one of the two groups including an upward chirp with increasing frequency during transmission, and the other of the two groups including a downward chirp with decreasing frequency during transmission. Based on the radar echo obtained in response to the transmission of the radar signal, identify the initial range and Doppler measurements of peak detection associated with each of the two groups of the frame; A first fuzzy term associated with a first set of chirps and a second fuzzy term associated with a second set of chirps are selected from a plurality of fuzzy terms used for unfuzzy estimation of the rate of change of distance detected. The first fuzzy term and the second fuzzy term are selected independently, and the first fuzzy term is selected based on the initial distance and Doppler measurements from peak detection only from the first set of chirps, while the second fuzzy term is selected based on the initial distance and Doppler measurements from the second set of chirps only. as well as The radar system uses the ambiguity term selected for each of the two groups to estimate the unambiguous range change rate of the detection associated with each of the two groups.

2. The method as described in claim 1, characterized in that: One of the two groups includes an odd number of chirps and the initial chirp is an upward chirp, and the other of the two groups includes an even number of chirps and the subsequent chirps are downward chirps; or The initial chirp is the downward chirp, and the subsequent chirps are the upward chirps.

3. The method as described in claim 1, characterized in that, The frame includes an up frame that shifts the ordered chirp sequence higher in frequency during the transmission of the frame.

4. The method as described in claim 1, characterized in that, The frame includes a downframe that shifts the ordered chirp sequence to a lower frequency during the transmission of the frame.

5. The method as described in claim 1, characterized in that, The plurality of fuzzy terms includes at least one integer that is greater than zero or less than zero.

6. The method as described in claim 1, characterized in that, Selecting the fuzzy item for each of the two groups includes: Based on the initial distance and Doppler measurements of the first group of the two groups, the fuzzy variables of the first group are calculated; and From the plurality of fuzzy terms, select the corresponding fuzzy term for the first group that is closest in value to the fuzzy variable of the first group.

7. The method as described in claim 1, characterized in that, Selecting the fuzzy item for each of the two groups includes: Based on the initial distance and Doppler measurements of the first group from the two different groups, calculate the first fuzzy variable for the first group; and Based on the initial distance and Doppler measurements of the second group of the two different groups, the second fuzzy variable of the second group is calculated.

8. The method of claim 7, further comprising: From the plurality of fuzzy terms, select the first fuzzy term that is most closely related in value to the fuzzy variable of the first group for the first group; as well as From the plurality of fuzzy terms, select the second fuzzy term that is closest in value to the fuzzy variable of the second group for the second group.

9. The method as described in claim 8, characterized in that, At least one of the selected first fuzzy term or the selected second fuzzy term is not zero.

10. The method of claim 1, further comprising: The radar system outputs at least some of the detected unambiguous distance change rates from the radar to components of the vehicle to enable tracking or other radar-related functions performed on the vehicle.

11. A computer-readable storage medium comprising instructions that, when executed, cause a processor to: Radar signals for frames are transmitted from a radar system by interlacing chirps from two different groups, including a first group and a second group, into an ordered chirp sequence, such that each consecutive chirp pair includes an initial chirp from the first group and a subsequent chirp from the second group, one of the two groups including an upward chirp with increasing frequency during transmission, and the other of the two groups including a downward chirp with decreasing frequency during transmission. Based on the radar echo obtained in response to the transmission of the radar signal, identify the initial range and Doppler measurements of peak detection associated with each of the two groups of the frame; A first fuzzy term associated with a first set of chirps and a second fuzzy term associated with a second set of chirps are selected from a plurality of fuzzy terms used for unfuzzy estimation of the rate of change of distance detected. The first fuzzy term and the second fuzzy term are selected independently, and the first fuzzy term is selected based on the initial distance and Doppler measurements from peak detection only from the first set of chirps, while the second fuzzy term is selected based on the initial distance and Doppler measurements from the second set of chirps only. as well as The ambiguity term selected for each of the two groups is used to estimate the unambiguous distance change rate of the detection associated with each of the two groups.

12. A system comprising a processor configured to: Radar signals for frames are transmitted by interlacing chirps from two different groups, including a first group and a second group, into an ordered chirp sequence, such that each consecutive chirp pair includes an initial chirp from the first group and a subsequent chirp from the second group, one of the two groups including an upward chirp with increasing frequency during transmission, and the other of the two groups including a downward chirp with decreasing frequency during transmission. Based on the radar echo obtained in response to the transmission of the radar signal, identify the initial range and Doppler measurements of peak detection associated with each of the two groups of the frame; A first fuzzy term associated with a first set of chirps and a second fuzzy term associated with a second set of chirps are selected from a plurality of fuzzy terms used for unfuzzy estimation of the rate of change of distance detected. The first fuzzy term and the second fuzzy term are selected independently, and the first fuzzy term is selected based on the initial distance and Doppler measurements from peak detection only from the first set of chirps, while the second fuzzy term is selected based on the initial distance and Doppler measurements from the second set of chirps only. as well as The ambiguity term selected for each of the two groups is used to estimate the unambiguous distance change rate of the detection associated with each of the two groups.

13. The system of claim 12, wherein: The initial chirp is the upward chirp, and the subsequent chirps are the downward chirps; or The initial chirp is the downward chirp, and the subsequent chirps are the upward chirps.

14. The system as described in claim 12, characterized in that, The processor is further configured to: Based on the initial distance and Doppler measurements of the first group of the two groups, the fuzzy variables of the first group are calculated; and From the plurality of fuzzy terms, select the corresponding fuzzy term for the first group that is closest in value to the fuzzy variable of the first group.

15. The system as described in claim 12, characterized in that, The processor is further configured to select the fuzzy item for each of the two groups in the following manner: Based on the initial distance and Doppler measurement of the first group in the two different groups, calculate the first fuzzy variable of the first group; as well as Based on the initial distance and Doppler measurements of the second group of the two different groups, the second fuzzy variable of the second group is calculated.

16. The system as described in claim 15, characterized in that, The processor is further configured to: From the plurality of fuzzy terms, select the first fuzzy term that is most closely related in value to the fuzzy variable of the first group for the first group; as well as From the plurality of fuzzy terms, select the second fuzzy term that is closest in value to the fuzzy variable of the second group for the second group.

17. The system as claimed in claim 16, characterized in that, At least one of the selected first fuzzy term or the selected second fuzzy term is not zero.

18. The system as claimed in claim 12, characterized in that, The processor is further configured to: Output the unambiguous distance change rate of at least some of the detected data to components of the vehicle to enable tracking or other radar-related functions performed on the vehicle.

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