RADON Transform Automotive Synthetic Aperture Radar

Abstract

A method for performing maneuvering in a land vehicle using synthetic aperture radar (SAR) is provided. The method includes: receiving digitized radar echo data from a vehicle-mounted SAR radar transmission; accumulating multiple frames of the digitized radar echo data; applying a RADON transform to the accumulated multiple frames of digitized radar echo data and odometer data from the vehicle to generate a transformed frame of data for each three-dimensional point, wherein the RADON transform is configured to perform coherent accumulation on each three-dimensional point, project a radar trajectory onto each three-dimensional point, and project Doppler information onto each three-dimensional point; generating a two-dimensional map of the area covered by the radar transmission from the SAR radar based on the transformed frames of data for each three-dimensional point; and performing maneuvering using the land vehicle by applying the generated two-dimensional map.

Description

Technical Field

[0001] The technology described in this patent document generally relates to systems and methods for using synthetic aperture radar (SAR) in automotive environments, and more specifically, to systems and methods for using synthetic aperture radar in the near field of automotive environments. Background Technology

[0002] Radar is used in many vehicle applications, such as collision warning, blind spot warning, lane change assist, parking assist, and rear collision warning. One type of radar used is pulse radar. In pulse radar, the radar transmits signals in the form of pulses at fixed intervals. Obstacles scatter the emitted pulses, and the radar receives the scattered pulses. The time between transmitting and receiving the scattered pulses is proportional to the distance between the obstacle and the radar. Radar angular resolution is limited by the physical antenna aperture. Radar angular resolution can be improved by creating a larger virtual aperture. A large virtual aperture can be achieved by accumulating information from moving radar. Synthetic Aperture Radar (SAR) can achieve high angular resolution by creating a large synthetic antenna.

[0003] Synthetic Aperture Radar (SAR) uses pulse compression technology and the synthetic aperture principle to image ground scenes. SAR is typically used for far-field applications on satellites or aircraft, such as environmental monitoring, resource exploration, mapping, and battlefield reconnaissance. SAR radar echoes are usually processed using some form of Fast Fourier Transform (FFT).

[0004] Traditional SAR processing requires the radar to travel at a constant speed along a straight path, assumes far-field operation, and demands a defined synthetic antenna with a 1 / 2λ spacing. This limits the maximum speed to: v = F·A, where F is the frame rate, A is the antenna aperture, and v is the maximum speed. For example, for a frame rate of 30 fps and an aperture of 4 cm (20 antennas), the maximum speed is limited to 1.2 m / s. The automotive environment does not meet these assumptions, limiting the availability of traditional synthetic aperture radar implementations.

[0005] Therefore, it is desirable to provide systems and methods for adapting synthetic aperture radar to near-field applications in automotive environments. Furthermore, other desirable features and characteristics will become apparent from the following detailed description and appended claims, taken in conjunction with the accompanying drawings. Summary of the Invention

[0006] Systems and methods are provided for adapting synthetic aperture radar (SAR) to near-field applications in automotive environments. In one embodiment, a method for performing maneuvering in a land vehicle using SAR is provided. The method includes: receiving digitized radar echo data from pulse radar transmissions from a SAR on the land vehicle; accumulating multiple frames of the digitized radar echo data; applying a RADON transform to the accumulated multiple frames of odometer data from the land vehicle and the digitized radar echo data to generate a transformed frame of data for each three-dimensional (x, y, z) point, wherein the RADON transform is configured to perform coherent integration for each three-dimensional point where radar echoes exist due to the pulse radar transmissions, projecting radar trajectories onto each three-dimensional point, and projecting Doppler information onto each three-dimensional point; generating a two-dimensional XY map of the area covered by the pulse radar transmissions from the SAR based on the transformed frames of data for each three-dimensional point; and performing automatic or semi-automatic maneuvering using the land vehicle by applying the generated two-dimensional XY map.

[0007] In one embodiment, the RADON transform includes a distance dimension, a Doppler dimension, and a phase dimension.

[0008] In one embodiment, the RADON transformation is represented by the following equation:

[0009]

[0010] Where: R(m, f, x, y, z) represents the range dimension, D(m, f, x, y, z) represents the Doppler dimension, P(m, h, f, x, y, z) represents the phase dimension, m = sample index, n = chirp index, h = horizontal antenna index, v = vertical antenna index, f = frame index, x = x-axis position, y = y-axis position, z = z-axis position, s = sampled signal, N = number of samples, M = number of chirps, H = number of horizontal antennas, V = number of vertical antennas, F = number of frames.

[0011] In one embodiment, the distance dimension of the RADON transform is represented by the following equation:

[0012]

[0013] Where: c = speed of light, α = chirp slope, t(m,f) = mT c +fT f T c =Chirp repetition interval, T f =Frame repeat interval, O x = Vehicle position on the x-axis based on odometer reading, O y = Vehicle position on the y-axis based on odometer, and O z= Vehicle position on the z-axis based on odometer reading.

[0014] In one embodiment, the Doppler dimension of the RADON transform is represented by the following equation:

[0015]

[0016] Where: t(m,f)=mT c +fT f λ = signal wavelength, T c =Chirp repetition interval, T f =Frame repeat interval, O x = Vehicle position on the x-axis based on odometer reading, O y = Vehicle position on the y-axis based on odometer reading, O z = Vehicle position on the z-axis based on odometer, O Vx = Vehicle speed on the x-axis based on odometer reading, O Vy = Vehicle speed on the y-axis based on odometer readings, and O Vz = Vehicle speed on the z-axis based on odometer reading.

[0017] In one embodiment, the phase dimension of the RADON transform is represented by the following equation:

[0018]

[0019] Where: t(m, f) = mT c +fT f λ = signal wavelength, d h = Antenna horizontal spacing, d v = Antenna vertical spacing, T c =Chirp repetition interval, T f =Frame repeat interval, O x = Vehicle position on the x-axis based on odometer reading, O y = Vehicle position on the y-axis based on odometer reading, O z = Vehicle position on the z-axis based on odometer, O Vx = Vehicle speed on the x-axis based on odometer reading, O Vy = Vehicle speed on the y-axis based on odometer readings, and O Vz = Vehicle speed on the z-axis based on odometer reading.

[0020] In one embodiment, the RADON transformation is represented by the following equation:

[0021]

[0022] in:

[0023]

[0024]

[0025]

[0026] t(m, f) = mT c +fT f

[0027] m = sample index, n = chain ratio index, h = horizontal antenna index, v = vertical antenna index, f = frame index, x = x-axis position, y = y-axis position, z = z-axis position, s = sampled signal, N = number of samples, M = number of chirps, H = number of horizontal antennas, V = number of vertical antennas, F = number of frames, c = speed of light, α = chirp slope, λ = signal wavelength, d h = Antenna horizontal spacing, d v = Antenna vertical spacing, T c =Chirp repetition interval, T f =Frame repeat interval, O x = Vehicle position on the x-axis based on odometer reading, O y = Vehicle position on the y-axis based on odometer reading, O z = Vehicle position on the z-axis based on odometer, O Vx = Vehicle speed on the x-axis based on odometer reading, O Vy = Vehicle speed on the y-axis based on odometer readings, and O Vz = Vehicle speed on the z-axis based on odometer reading.

[0028] In another embodiment, a system is provided for performing maneuvering maneuvers using synthetic aperture radar (SAR) in a land vehicle. The system includes a controller configured to: receive digitized radar echo data from pulsed radar transmissions from the SAR on the ground vehicle; accumulate multiple frames of the digitized radar echo data; apply a RADON transform to the accumulated multiple frames of odometer data from the land vehicle and the digitized radar echo data to generate a transformed frame of data for each three-dimensional (x, y, z) point, wherein the RADON transform is configured to perform coherent accumulation on each three-dimensional point where radar echoes exist due to the pulsed radar transmissions, project a radar trajectory onto each three-dimensional point, and project Doppler information onto each three-dimensional point; generate a two-dimensional XY map of the area covered by the pulsed radar transmissions from the SAR based on the transformed frames of data for each three-dimensional point; and perform automatic or semi-automatic maneuvering maneuvers in conjunction with the land vehicle by applying the generated two-dimensional XY map.

[0029] In one embodiment, the RADON transform includes a distance dimension, a Doppler dimension, and a phase dimension.

[0030] In one embodiment, the RADON transformation is represented by the following equation:

[0031]

[0032] Where: R(m, F, x, y, z) represents the range dimension, D(m, F, x, y, z) represents the Doppler dimension, P(m, H, F, x, y, z) represents the phase dimension, m = sample index, n = ring ratio index, h = horizontal antenna index, v = vertical antenna index, f = frame index, x = x-axis position, y = y-axis position, z = z-axis position, s = sampled signal, N = number of samples, M = chirp number, H = number of horizontal antennas, V = number of vertical antennas, F = number of frames.

[0033] In one embodiment, the distance dimension of the RADON transform is represented by the following equation:

[0034]

[0035] Where: c = speed of light, α = chirp slope, t(m, f) = mT c +fT f T c =Chirp repetition interval, T f =Frame repeat interval, O x = Vehicle position on the x-axis based on odometer reading, O y = Vehicle position on the y-axis based on odometer, and O z = Vehicle position on the z-axis based on odometer reading.

[0036] In one embodiment, the Doppler dimension of the RADON transform is represented by the following equation:

[0037]

[0038] Where: t(m, f) = mT c +fT f λ = signal wavelength, T c =Chirp repetition interval, T f =Frame repeat interval, O x = Vehicle position on the x-axis based on odometer reading, O y = Vehicle position on the y-axis based on odometer reading, O z = Vehicle position on the z-axis based on odometer, O Vx = Vehicle speed on the x-axis based on odometer reading, O Vy = Vehicle speed on the y-axis based on odometer readings, and O Vz = Vehicle speed on the z-axis based on odometer reading.

[0039] In one embodiment, the phase dimension of the RADON transform is represented by the following equation:

[0040]

[0041] Where: t(m, f) = mT c +fT f λ = signal wavelength, d h = Antenna horizontal spacing, d v = Antenna vertical spacing, T c =Chirp repetition interval, T f =Frame repeat interval, O x = Vehicle position on the x-axis based on odometer reading, O y = Vehicle position on the y-axis based on odometer reading, O z = Vehicle position on the z-axis based on odometer, O Vx = Vehicle speed on the x-axis based on odometer reading, O Vy = Vehicle speed on the y-axis based on odometer readings, and O Vz = Vehicle speed on the z-axis based on odometer reading.

[0042] In one embodiment, the RADON transformation is represented by the following equation:

[0043]

[0044] in:

[0045]

[0046]

[0047]

[0048] t(m, f) = mT c +fT f

[0049] m = sample index, n = chain ratio index, h = horizontal antenna index, v = vertical antenna index, f = frame index, x = x-axis position, y = y-axis position, z = z-axis position, s = sampled signal, N = number of samples, M = number of chirps, H = number of horizontal antennas, V = number of vertical antennas, F = number of frames, c = speed of light, α = chirp slope, λ = signal wavelength, d h = Antenna horizontal spacing, d v = Antenna vertical spacing, T c =Chirp repetition interval, T f =Frame repeat interval, O x = Vehicle position on the x-axis based on odometer reading, O y = Vehicle position on the y-axis based on odometer reading, O z = Vehicle position on the z-axis based on odometer, O Vx = Vehicle speed on the x-axis based on odometer reading, O Vy = Vehicle speed on the y-axis based on odometer readings, and O Vz= Vehicle speed on the z-axis based on odometer reading.

[0050] In another embodiment, a non-transitory computer-readable medium encoded with programming instructions can be configured to cause a processor in a land vehicle to execute a method for performing land vehicle maneuvering using synthetic aperture radar (SAR). The method includes: accumulating multiple frames of digitized radar echo data; applying a RADON transform to the accumulated multiple frames of odometer data and digitized radar echo data from the land vehicle to generate a transformed frame of data for each three-dimensional (x, y, z) point, wherein the RADON transform is configured to perform coherent accumulation for each three-dimensional point where radar echoes exist due to pulse radar transmission, projecting a radar trajectory onto each three-dimensional point, and projecting Doppler information onto each three-dimensional point; generating a two-dimensional XY map of the area covered by the pulse radar transmission from the SAR based on the transformed frame of data for each three-dimensional point; and performing automatic or semi-automatic maneuvering using the land vehicle by applying the generated two-dimensional XY map.

[0051] In one embodiment, the RADON transform includes a distance dimension, a Doppler dimension, and a phase dimension.

[0052] In one embodiment, the RADON transformation is represented by the following equation:

[0053]

[0054] Where: R(m, f, x, y, z) represents the range dimension, D(m, f, x, y, z) represents the Doppler dimension, P(m, h, f, x, y, z) represents the phase dimension, m = sample index, n = ring ratio index, h = horizontal antenna index, v = vertical antenna index, f = frame index, x = x-axis position, y = y-axis position, z = z-axis position, s = sampled signal, N = number of samples, M = chirp number, H = number of horizontal antennas, V = number of vertical antennas, F = number of frames.

[0055] In one embodiment, the distance dimension of the RADON transform is represented by the following equation:

[0056]

[0057] Where: c = speed of light, α = chirp slope, t(m, f) = mT c +fT f T c =Chirp repetition interval, T f =Frame repeat interval, O x = Vehicle position on the x-axis based on odometer reading, O y = Vehicle position on the y-axis based on odometer, and O z = Vehicle position on the z-axis based on odometer reading.

[0058] In one embodiment, the Doppler dimension of the RADON transform is represented by the following equation:

[0059]

[0060] Where: t(m, f) = mT c +fT f λ = signal wavelength, T c =Chirp repetition interval, T f =Frame repeat interval, O x = Vehicle position on the x-axis based on odometer reading, O y = Vehicle position on the y-axis based on odometer reading, O z = Vehicle position on the z-axis based on odometer, O Vx = Vehicle speed on the x-axis based on odometer reading, O Vy = Vehicle speed on the y-axis based on odometer readings, and O Vz = Vehicle speed on the z-axis based on odometer reading.

[0061] In one embodiment, the phase dimension of the RADON transform is represented by the following equation:

[0062]

[0063] Where: t(m, f) = mT c +fT f λ = signal wavelength, d h = Antenna horizontal spacing, d v = Antenna vertical spacing, T c =Chirp repetition interval, T f =Frame repeat interval, O x = Vehicle position on the x-axis based on odometer reading, O y = Vehicle position on the y-axis based on odometer reading, O z = Vehicle position on the z-axis based on odometer, O Vx = Vehicle speed on the x-axis based on odometer reading, O Vy = Vehicle speed on the y-axis based on odometer readings, and O Vz = Vehicle speed on the z-axis based on odometer reading.

[0064] In one embodiment, the RADON transformation is represented by the following equation:

[0065]

[0066] in:

[0067]

[0068]

[0069]

[0070] t(m, f) = mT c +fT f

[0071] m = sample index, n = chain ratio index, h = horizontal antenna index, v = vertical antenna index, f = frame index, x = x-axis position, y = y-axis position, z = z-axis position, s = sampled signal, N = number of samples, M = number of chirps, H = number of horizontal antennas, V = number of vertical antennas, F = number of frames, c = speed of light, α = chirp slope, λ = signal wavelength, d h = Antenna horizontal spacing, d v = Antenna vertical spacing, T c =Chirp repetition interval, T f =Frame repeat interval, O x = Vehicle position on the x-axis based on odometer reading, O y = Vehicle position on the y-axis based on odometer reading, O z = Vehicle position on the z-axis based on odometer, O Vx = Vehicle speed on the x-axis based on odometer reading, O Vy = Vehicle speed on the y-axis based on odometer readings, and O Vz = Vehicle speed on the z-axis based on odometer reading. Attached Figure Description

[0072] Exemplary embodiments will now be described in conjunction with the following accompanying drawings, wherein the same reference numerals denote the same elements, and wherein:

[0073] Figure 1 This is a block diagram of an example vehicle implementing synthetic aperture radar according to various embodiments;

[0074] Figure 2 This is a process flowchart describing an example process, according to various embodiments, for processing radar echoes from a synthetic aperture radar implemented on a vehicle to generate a map for use by the vehicle; and

[0075] Figure 3 This is a process flowchart describing an example process of performing maneuvering in a land vehicle using synthetic aperture radar (SAR) according to various embodiments. Detailed Implementation

[0076] The following detailed description is merely exemplary in nature and is not intended to limit application and use. Furthermore, it is not intended to be bound by any express or implied theory presented in the foregoing technical field, background art, summary of the invention, or the following detailed description. As used herein, the term "module" refers to any hardware, software, firmware, electronic control components, processing logic, and / or processor device, individually or in any combination, including, but not limited to: application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), electronic circuits, processors (shared, dedicated, or grouped), and memory executing one or more software or firmware programs, combinational logic circuitry, and / or other suitable components providing the described functionality.

[0077] Embodiments of this disclosure can be described herein based on functional and / or logical block components and various processing steps. It should be understood that such block components can be implemented by any number of hardware, software, and / or firmware components configured to perform specified functions. For example, embodiments of this disclosure can employ various integrated circuit components, such as memory elements, digital signal processing elements, logic elements, lookup tables, etc., which can perform various functions under the control of one or more microprocessors or other control devices. Furthermore, those skilled in the art will understand that embodiments of this disclosure can be practiced in conjunction with any number of systems, and the systems described herein are merely exemplary embodiments of this disclosure.

[0078] For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, machine learning models, radar, laser detectors, image analysis, and other functional aspects of the system (as well as the various operating components of the system) will not be described in detail here. Furthermore, the connecting lines shown in the various figures included herein are intended to represent exemplary functional relationships and / or physical couplings between various elements. It should be noted that many alternative or additional functional relationships or physical connections may exist in the embodiments of this disclosure.

[0079] This document describes apparatuses, systems, techniques, and articles for adapting synthetic aperture radar (SAR) to near-field applications in automotive environments. The following disclosures provide example systems and methods for applying novel SAR processing methods using the RADON transform. The described subject matter discloses apparatuses, systems, techniques, and articles for accumulating multiple radar frames from a moving vehicle and estimating the environment based on information collected from the vehicle's path projection. In the described subject matter, a novel Radon-SAR transform is performed on the accumulated data, and multiple frames are coherently accumulated to generate high-resolution detections.

[0080] In the described subject matter, in addition to the large spatial aperture generated by vehicle motion, positioning accuracy is improved by utilizing Doppler information. In the described subject matter, Doppler information reduces angular ambiguity and lowers FPS requirements. The described devices, systems, technologies, and articles make synthetic aperture radar suitable for near-field applications by considering vehicle trajectories. In the described subject matter, adaptation to near-field applications is achieved by independently projecting the vehicle path to each location and calculating the Radon-SAR transform for each projection. In the described subject matter, near-field environment accuracy is increased and ambiguity is reduced due to multiple observation projections. The described devices, systems, technologies, and articles can provide a two-dimensional XY map generated by the described process.

[0081] Figure 1 An example vehicle 100 is depicted, which includes a synthetic aperture radar (SAR) module 102 for using SAR to assist the vehicle in performing maneuvering operations. Figure 1 As shown, vehicle 100 typically includes a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is mounted on the chassis 12 and substantially encloses the components of vehicle 100. The body 14 and chassis 12 may together form a frame. Each of the wheels 16-18 is rotatably connected to the chassis 12 near a corresponding corner of the body 14.

[0082] In various embodiments, vehicle 100 may be an autonomous or semi-autonomous vehicle. An autonomous vehicle 100 is, for example, a vehicle automatically controlled to transport passengers from one location to another. Vehicle 100 is depicted as a passenger car in the illustrated embodiment, but other vehicle types may also be used, including motorcycles, trucks, SUVs, recreational vehicles (RVs), boats, aircraft, etc.

[0083] As shown in the figure, vehicle 100 typically includes a propulsion system 20, a transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a communication system 36. In various embodiments, the propulsion system 20 may include an internal combustion engine, an electric motor such as a traction motor, and / or a fuel cell propulsion system. The transmission system 22 is configured to transmit power from the propulsion system 20 to the wheels 16 and 18 according to a selectable speed ratio. According to various embodiments, the transmission system 22 may include a stepped transmission, a continuously variable transmission (CVT), or other suitable transmission.

[0084] Braking system 26 is configured to provide braking torque to wheels 16 and 18. In various embodiments, braking system 26 may include friction brakes, brake-by-wire brakes, regenerative braking systems such as electric motors, and / or other suitable braking systems.

[0085] The steering system 24 affects the position of the wheels 16 and / or 18. Although depicted as including a steering wheel 25 for illustrative purposes, in some embodiments contemplated within the scope of this disclosure, the steering system 24 may not include a steering wheel.

[0086] Sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the external and / or internal environment of vehicle 100 (e.g., the state of one or more occupants) and generate associated sensor data. Sensing devices 40a-40n may include, but are not limited to, radar (e.g., long-range, medium-to-short-range, SAR), lidar, global positioning system, optical cameras (e.g., forward, 360-degree, rearward, lateral, stereo, etc.), thermal (e.g., infrared) cameras, ultrasonic sensors, odometer sensors (e.g., encoders), and / or other sensors that may be used in combination with systems and methods according to this subject matter.

[0087] The actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle features, such as, but not limited to, the propulsion system 20, the transmission system 22, the steering system 24, and the braking system 26. In various embodiments, the vehicle 100 may also include… Figure 1 Interior and / or exterior vehicle features not shown, such as various doors, trunk and cabin features, such as air, music, lighting, touch screen display components (e.g., components used in conjunction with a navigation system), etc.

[0088] Data storage device 32 stores data used for automatically controlling vehicle 100. Data storage device 32 may be part of controller 34, separate from controller 34, or partly part of controller 34 and partly part of a separate system. In various embodiments, controller 34 implements SAR module 102 configured to perform SAR processing.

[0089] The controller 34 includes at least one processor 44 and a computer-readable storage device or medium 46. The processor 44 can be any custom or commercially available processor, central processing unit (CPU), graphics processing unit (GPU), application-specific integrated circuit (ASIC) (e.g., a custom ASIC implementing a neural network), field-programmable gate array (FPGA), an auxiliary processor among several processors associated with the controller 34, a semiconductor-based microprocessor (in the form of a microchip or chipset), any combination thereof, or any device typically used for executing instructions. The computer-readable storage device or medium 46 can include volatile and non-volatile memory, such as read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is persistent or non-volatile memory that can be used to store various operational variables when the processor 44 is powered off. The computer-readable storage device or medium 46 may be implemented using any of a variety of known storage devices, such as PROM (programmable read-only memory), ePROM (electric PROM), EEPROM (electrically erasable PROM), flash memory, or any other electrical, magnetic, optical, or combined storage device capable of storing data, some of which represent executable instructions used by the controller 34 when controlling the vehicle 100.

[0090] The instructions may include one or more separate programs, each including an ordered list of executable instructions for implementing logical functions. When executed by processor 44, the instructions receive and process signals (e.g., sensor data) from sensor system 28, execute logic, calculations, methods, and / or algorithms for automatically controlling components of vehicle 100, and generate control signals transmitted to actuator system 30 to automatically control components of vehicle 100 based on logic, calculations, methods, and / or algorithms. Although in Figure 1 Only one controller 34 is shown, but embodiments of vehicle 100 may include any number of controllers 34 that communicate via any suitable communication medium or combination of communication media and cooperate to process sensor signals, execute logic, calculations, methods and / or algorithms, and generate control signals to automatically control the features of vehicle 100.

[0091] Figure 2 This is a process flowchart describing an example process 200 for processing radar echoes (e.g., SAR module 102 implemented by controller 34) from SAR implemented on a vehicle (e.g., vehicle 100) (e.g., from sensor system 28 including one or more SAR-implementing sensing devices 40a-40n) to generate a map for use by the vehicle. The sequence of operations within process 200 is not limited to... Figure 2 The execution may be performed in the order shown, or in one or more different orders depending on the content of this disclosure.

[0092] Example process 200 includes (operation 302) performing analog-to-digital conversion (ADC) on radar echoes from SAR in the vehicle to generate digital data frames for each converted radar echo. ADC can be performed using conventional methods used in radar systems in vehicles.

[0093] Example process 200 includes multiple frames of accumulated digitized data (operation 204). Each frame may include returned radar data collected from pulse transmissions, identified by x, y, z coordinates (hereinafter referred to as (x, y, z) positions). The x, y, z coordinates are based on a coordinate system having an x-axis direction in the vehicle's direction of travel, a y-axis direction 90 degrees to the left of the x-axis, and a z-axis direction 90 degrees to the right of the vehicle's direction of travel. Multiple frames may include all or almost all of the data frames collected from the pulse transmissions.

[0094] Example procedure 200 includes processing frames of accumulated digitized data using a RADON-SAR transform (operation 206) and vehicle odometer data 207. The vehicle odometer data may include vehicle position and speed data. The RADON-SAR transform is a RADON transform specifically designed for SAR. The RADON-SAR transform is configured to perform coherent accumulation for each (x, y, z) point (e.g., the (x, y, z) position of a radar echo due to pulse transmission). The RADON-SAR transform is configured to characterize the radar trajectory based on the vehicle path by projecting the radar trajectory onto each three-dimensional (x, y, z) point and coherently accumulating the accumulated frames. This allows SAR to support arbitrary radar trajectories in addition to straight-line trajectories with constant velocity. The RADON-SAR transform is configured to consider Doppler information to improve accuracy and reduce ambiguity. The RADON-SAR transform includes range, Doppler, and phase dimensions. This configures the RADON-SAR transform to support intra-frame and inter-frame range, Doppler, and spatial offsets caused by radar motion. The example RADON-SAR transform assumes a static object. The coherent accumulation performed in the RADON-SAR transform improves the target signal-to-noise ratio.

[0095] An example of the RADON-SAR transform is represented by the following equation:

[0096]

[0097] in:

[0098]

[0099]

[0100]

[0101] t(m,f)=mT c +fT f

[0102] m - Sample index

[0103] n-month-on-month index

[0104] h-Horizontal Antenna Index

[0105] v-Vertical Antenna Index

[0106] f-frame index

[0107] xx axis position

[0108] yy axis position

[0109] z-axis position

[0110] s-sampled signal

[0111] c - speed of light

[0112] N - Number of samples

[0113] M-Chirp Count

[0114] Number of H-horizontal antennas

[0115] Number of V-vertical antennas

[0116] F-frame count

[0117] α-Chirp Slope

[0118] λ - signal wavelength

[0119] d h - Antenna horizontal spacing

[0120] d v - Antenna vertical spacing

[0121] T c -Chirp repeat interval

[0122] T f - Frame repetition interval

[0123] O x - Odometer-based x-axis vehicle position

[0124] O y - Vehicle y-axis position based on odometer

[0125] O z -Z-axis vehicle position based on odometer

[0126] O Vx - Vehicle speed on the x-axis based on odometer

[0127] O Vy -Y-axis vehicle speed based on odometer

[0128] O Vz -Z-axis vehicle speed based on odometer

[0129] Example procedure 200 includes generating a two-dimensional XY map (operation 208) of the area covered by pulse radar transmission from synthetic aperture radar based on transformed data frames for each three-dimensional (X, Y, z) point. The two-dimensional XY map can be generated using conventional techniques for generating maps from radar echo data.

[0130] After generating a 2D XY map, the vehicle can use the data from the 2D XY map for autonomous or semi-autonomous driving features, such as parking space detection for automated parking assistance. The ability of a vehicle to enter smaller parking spaces can be improved by using the higher resolution provided by SAR and RADON-SAR transforms, as the boundaries of the parking space will be known in more detail.

[0131] Figure 3 This is a flowchart describing an example process 300 for performing maneuvering maneuvers in a land vehicle (e.g., vehicle 100) using synthetic aperture radar (SAR). The exemplary process 300 includes receiving digitized radar echo data (e.g., from a sensor system 28 including one or more sensing devices 40a-40n implementing SAR) from a pulse radar transmission of a SAR on the land vehicle (operation 302) and accumulating multiple frames of the digitized radar echo data (operation 304).

[0132] Example process 300 includes applying a RADON transform (e.g., SAR module 102 implemented by controller 34) to multiple frames accumulated from odometer data and digitized radar echo data from a land vehicle to generate a transformed frame of data for each 3D point (operation 306). The RADON transform is configured to perform coherent accumulation for each 3D point where radar echoes exist due to pulsed radar transmissions, projecting the radar trajectory onto each 3D point and projecting Doppler information onto each 3D point. The example RADON-SAR transform given above can be used as a RADON transform.

[0133] Example process 300 includes transforming frames based on data from each 3D point to generate a 2D XY map of the area covered by pulse radar transmissions from synthetic aperture radar (Operation 308), and performing automatic or semi-automatic maneuvering using a land vehicle by applying the generated 2D XY map (Operation 310). The 2D XY map can be generated using conventional techniques for generating maps from radar echo data. Automatic or semi-automatic maneuvering may include parking-assisted maneuvering or other maneuvering that can benefit from the high-precision position data provided by synthetic aperture radar.

[0134] The foregoing has outlined features of several embodiments to enable those skilled in the art to better understand aspects of this disclosure. Those skilled in the art should understand that they can readily use this disclosure as the basis for designing or modifying other processes and structures to achieve the same purposes and / or advantages as the embodiments described herein. Those skilled in the art should also recognize that such equivalent constructions do not depart from the spirit and scope of this disclosure, and that various changes, substitutions, and alterations can be made herein without departing from the spirit and scope of this disclosure.

Claims

1. A method of performing a maneuvering maneuver in a land vehicle using synthetic aperture radar (SAR), the method comprising: receiving digitized radar return data from pulsed radar transmissions from a synthetic aperture radar (SAR) on a land vehicle; accumulating a plurality of frames of the digitized radar return data; applying a RADON transform to the accumulated plurality of frames of the digitized radar return data and odometry data from the land vehicle to generate a transformed frame of data for each three-dimensional point, wherein the RADON transform is configured to perform coherent accumulation for each three-dimensional point for which a radar return exists due to the pulsed radar transmissions, project a radar track onto each three-dimensional point, and project Doppler information onto each three-dimensional point; generating a two-dimensional X-Y map of an area covered by the pulsed radar transmissions from the SAR based on the transformed frame of data for each three-dimensional point; and performing an automatic or semi-automatic maneuvering maneuver with the land vehicle by applying the generated two-dimensional X-Y map; wherein a formula of the RADON transform includes a range dimension, a Doppler dimension, and a phase dimension; wherein the RADON transform is represented by the following: where: R(m,f,x,y,z) represents the range dimension, D(m,f,x,y,z) represents the Doppler dimension, P(m,h,f,x,y,z) represents the phase dimension, m = sample index, n = ring ratio index, h = horizontal antenna index, v = vertical antenna index, f = frame index, x = x-axis position, y = y-axis position, z = z-axis position, s = sample signal, N = number of samples, M = number of chirps, H = number of horizontal antennas, V = number of vertical antennas, F = number of frames.

2. The method of claim 1, wherein the range dimension of the RADON transform is represented by the following: wherein: c = speed of light, a = chirp slope, t(m,f) = mT c +fT f , T c = chirp repetition interval, T f = frame repetition interval, O x = odometry-based x-axis vehicle position, O y = odometry-based y-axis vehicle position, and O z = odometry-based z-axis vehicle position.

3. The method of claim 1, wherein the Doppler dimension of the RADON transform is represented by the following: wherein: t(m, f) = mT c + fT f , λ = signal wavelength, T c = chirp repetition interval, T f = frame repetition interval, O x = odometry-based x-axis vehicle position, O y = odometry-based y-axis vehicle position, O z = odometry-based z-axis vehicle position, O Vx = odometry-based x-axis vehicle velocity, O Vy = odometry-based y-axis vehicle velocity, and O Vz = odometry-based z-axis vehicle velocity.

4. The method of claim 1, wherein the phase dimension of the RADON transform is represented by the following: wherein: t(m, f) = mT c + fT f , λ = signal wavelength, d h = antenna horizontal spacing, d v = antenna vertical spacing, T c = chirp repetition interval, T f = frame repetition interval, O x = vehicle position in x-axis based on odometry, O y = vehicle position in y-axis based on odometry, O z = vehicle position in z-axis based on odometry, O Vx = vehicle velocity in x-axis based on odometry, O Vy = vehicle velocity in y-axis based on odometry, and O Vz = vehicle velocity in z-axis based on odometry.

5. The method of claim 1, wherein the RADON transform is represented by the following: where: t(m,f) = mT c +fT f m = sample index, n = ring index, h = horizontal antenna index, v = vertical antenna index, f = frame index, x = x-axis position, y = y-axis position, z = z-axis position, s = sample signal, N = number of samples, M = number of chirps, H = number of horizontal antennas, V = number of vertical antennas, F = number of frames, c = speed of light, a = chirp slope, l = signal wavelength, d h = antenna horizontal spacing, d v = antenna vertical spacing, T c = chirp repetition interval, T f = frame repetition interval, O x = vehicle position in x-axis based on odometry, O y = vehicle position in y-axis based on odometry, O z = vehicle position in z-axis based on odometry, O Vx = vehicle velocity in x-axis based on odometry, O Vy = vehicle velocity in y-axis based on odometry, and O Vz = vehicle velocity in z-axis based on odometry.

6. A system for applying synthetic aperture radar (SAR) to perform a maneuvering maneuver in a land vehicle, the system comprising a controller configured to: receive digitized radar return data from pulsed radar transmissions from a synthetic aperture radar (SAR) on a land vehicle; accumulate a plurality of frames of the digitized radar return data; apply a RADON transform to the accumulated plurality of frames of the digitized radar return data and odometry data from the land vehicle to generate a transformed frame of data for each three-dimensional point, wherein the RADON transform is configured to perform coherent accumulation for each three-dimensional point for which a radar return exists due to the pulsed radar transmissions, project a radar track onto each three-dimensional point, and project Doppler information onto each three-dimensional point; generate a two-dimensional X-Y map of an area covered by the pulsed radar transmissions from the SAR based on the transformed frame of data for each three-dimensional point; and perform an automatic or semi-automatic maneuvering maneuver with the land vehicle using the generated two-dimensional X-Y map; wherein a formula of the RADON transform includes a range dimension, a Doppler dimension, and a phase dimension; wherein the RADON transform is represented by the following: where: R(m,f,x,y,z) represents the range dimension, D(m,f,x,y,z) represents the Doppler dimension, P(m,h,f,x,y,z) represents the phase dimension, m = sample index, n = ring ratio index, h = horizontal antenna index, v = vertical antenna index, f = frame index, x = x-axis position, y = y-axis position, z = z-axis position, s = sample signal, N = number of samples, M = number of chirps, H = number of horizontal antennas, V = number of vertical antennas, F = number of frames. wherein the formula of the RADON transform includes a range dimension, a Doppler dimension, and a phase dimension; wherein the RADON transform is represented by: where: R(m, f, x, y, z) represents the range dimension, D(m, f, x, y, z) represents the Doppler dimension, P(m, h, f, x, y, z) represents the phase dimension, m = sample index, n = chirp index, h = horizontal antenna index, v = vertical antenna index, f = frame index, x = x-axis position, y = y-axis position, z = z-axis position, s = sample signal, N = number of samples, M = number of chirps, H = number of horizontal antennas, V = number of vertical antennas, F = number of frames.

7. The system of claim 6, wherein the RADON transform is represented by: wherein: t(m,f) = mT c +fT f m = sample index, n = ring index, h = horizontal antenna index, v = vertical antenna index, f = frame index, x = x-axis position, y = y-axis position, z = z-axis position, s = sample signal, N = number of samples, M = number of chirps, H = number of horizontal antennas, V = number of vertical antennas, F = number of frames, c = speed of light, a = chirp slope, l = signal wavelength, d h = antenna horizontal spacing, d v = antenna vertical spacing, T c = chirp repetition interval, T f = frame repetition interval, O x = vehicle position in x-axis based on odometry, O y = vehicle position in y-axis based on odometry, O z = vehicle position in z-axis based on odometry, O Vx = vehicle velocity in x-axis based on odometry, O Vy = vehicle velocity in y-axis based on odometry, and O Vz = vehicle velocity in z-axis based on odometry.

Images