Automotive synthetic aperture radar with radon transform

A method for using Synthetic Aperture Radar (SAR) to perform a maneuver in a land vehicle is provided. The method includes: receiving digitized radar return data from a radar transmission from a SAR onboard the 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 vehicle to generate transformed frames of data for each three-dimensional point, wherein the RADON transform is configured to perform coherent integration for 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 an area covered by the radar transmission from the SAR based on the transformed frames of data for each three-dimensional point; and performing a maneuver with the land vehicle by applying the generated two-dimensional map.

TECHNICAL FIELD

The technology described in this patent document relates generally to systems and methods for using Synthetic Aperture Radar (SAR) in an automotive setting and more particularly to systems and methods for near field use of SAR in an automotive setting.

Radar is useful 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 a pulsed radar. In pulsed radar, the radar sends signals in the form of pulses at fixed intervals. Obstacles scatter the transmitted pulses, and the scattered pulses are received by the radar. The time between sending a pulse and receiving a scattered pulse is proportional to the distance of the obstacle from the radar. Radar angular resolution can be limited by the physical antenna aperture. Radar angular resolution can be improved by creating a larger virtual aperture. By accumulating information from a moving radar, a large virtual aperture can be achieved. Synthetic Aperture Radar (SAR) can enable high angular resolution by creating a large synthetic antenna.

SAR uses pulse compression technology and the principle of synthetic aperture to achieve imaging of ground scenes. SAR is typically used on satellites or aircraft for far field applications such as environmental monitoring, resource exploration, surveying and mapping, and battlefield reconnaissance. The radar returns from SAR are typically processed using some form of a fast Fourier transform (FFT).

Conventional SAR processing requires the radar to travel at a straight path with a constant velocity, assumes far-field operation, and requires the unambiguous synthetic antenna with ½λ spacing, which limits maximal velocity to: v=F·A, where F is the frame rate, A is the antenna aperture and v is the maximal velocity. As an example, for a frame rate of 30 fps and an aperture of 4 cm (20 antennas), the maximal velocity is limited to 1.2 m/s. The automotive environment does not meet these assumptions, limiting the useability of conventional SAR implementations.

Accordingly, it is desirable to provide systems and methods for adapting SAR for near field application in an automotive environment. Furthermore, other desirable features and characteristics will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings.

SUMMARY

Systems and methods for adapting SAR for near field application in an automotive environment are provided. In one embodiment, a method for using Synthetic Aperture Radar (SAR) to perform a maneuver in a land vehicle is provided. The method includes: receiving digitized radar return data from a pulsed radar transmission from a SAR onboard the 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 transformed frames 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 for which a radar return exists from the pulsed radar transmission, project a radar trajectory 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 transmission from the SAR based on the transformed frames of data for each three-dimensional point; and performing an autonomous or semiautonomous maneuver with the land vehicle by applying the generated two-dimensional X-Y map.

In one embodiment, the RADON transform includes a range dimension, a Doppler dimension, and a phase dimension.

In one embodiment, the RADON transform is represented by:

In one embodiment, the range dimension of the RADON transform is represented by:

R⁡(m,f,x,y,z)=2⁢αc⁢(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)2+(Oz(t⁡(m,f))-z)2
wherein: c=speed of light, α=chirp slope, t(m,f)=mTc+fTf, Tc=Chirp repetition interval, Tf=Frame repetition interval, Ox=Odometry based vehicle position at the x-axis, Oy=Odometry based vehicle position at the y-axis, and Oz=Odometry based vehicle position at the z-axis.

In one embodiment, the Doppler dimension of the RADON transform is represented by:

D⁡(m,f,x,y,z)=2λ⁢Ox⁢(t⁢(m,f))-x)⁢OV⁢x+(Oy⁢(t⁢(m,f))-y)⁢OV⁢y+(Oz(t⁡(m,f))-z)⁢OV⁢z(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)2+(Oz(t⁡(m,f))-z)2
wherein: t(m,f)=mTc+fTf, λ=signal wavelength, Tc=Chirp repetition interval, Tf=Frame repetition interval, Ox=Odometry based vehicle position at the x-axis, Oy=Odometry based vehicle position at the y-axis, Oz=Odometry based vehicle position at the z-axis, OVx=Odometry based vehicle velocity at the x-axis, OVy=Odometry based vehicle velocity at the y-axis, and OVz=Odometry based vehicle velocity at the z-axis.

In one embodiment, the phase dimension of the RADON transform is represented by:

P⁡(m,h,f,x,y,z)=Oy(t⁡(m,f))-y)Ox(t⁡(m,f))-x)1+(Oy(t⁡(m,f))-y)Ox(t⁡(m,f))-x))2⁢1+(Oz(t⁡(m,f))-z)2(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)2⁢h⁢dhλ+Oz(t⁡(m,f))-z(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)21+(Oz(t⁡(m,f))-z)2(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)2⁢v⁢dvλ
wherein: t(m,f)=mTc+fTf, λ=signal wavelength, dh=antenna horizontal spacing, dv=antenna vertical spacing, Tc=Chirp repetition interval, Tf=Frame repetition interval, Ox=Odometry based vehicle position at the x-axis, Oy=Odometry based vehicle position at the y-axis, Oz=Odometry based vehicle position at the z-axis, OVx=Odometry based vehicle velocity at the x-axis, OVy=Odometry based vehicle velocity at the y-axis, and OVz=Odometry based vehicle velocity at the z-axis.

In one embodiment, the RADON transform is represented by:

In another embodiment, a system for applying Synthetic Aperture Radar (SAR) in a land vehicle to perform a maneuver is provided. The system includes a controller configured to: receive digitized radar return data from a pulsed radar transmission from a Synthetic Aperture Radar (SAR) onboard 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 transformed frames 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 for which a radar return exists from the pulsed radar transmission, project a radar trajectory 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 transmission from the SAR based on the transformed frames of data for each three-dimensional point; and perform an autonomous or semiautonomous maneuver with the land vehicle by applying the generated two-dimensional X-Y map.

In one embodiment, the RADON transform includes a range dimension, a Doppler dimension, and a phase dimension.

In one embodiment, the RADON transform is represented by:

In one embodiment, the range dimension of the RADON transform is represented by:

R⁡(m,f,x,y,z)=2⁢αc⁢(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)2+(Oz(t⁡(m,f))-z)2
wherein: c=speed of light, α=chirp slope, t(m,f)=mTc+fTf, Tc=Chirp repetition interval, Tf=Frame repetition interval, Ox=Odometry based vehicle position at the x-axis, Oy=Odometry based vehicle position at the y-axis, and Oz=Odometry based vehicle position at the z-axis.

In one embodiment, the Doppler dimension of the RADON transform is represented by:

D⁡(m,f,x,y,z)=2λ⁢Ox⁢(t⁢(m,f))-x)⁢OV⁢x+(Oy⁢(t⁢(m,f))-y)⁢OV⁢y+(Oz⁢(t⁢(m,f))-z)⁢OV⁢z(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)2+(Oz(t⁡(m,f))-z)2
wherein: t(m,f)=mTc+fTf, λ=signal wavelength, Tc=Chirp repetition interval, Tf=Frame repetition interval, Ox=Odometry based vehicle position at the x-axis, Oy=Odometry based vehicle position at the y-axis, Oz=Odometry based vehicle position at the z-axis, OVx=Odometry based vehicle velocity at the x-axis, OVy=Odometry based vehicle velocity at the y-axis, and OVz=Odometry based vehicle velocity at the z-axis.

In one embodiment, the phase dimension of the RADON transform is represented by:

P⁡(m,h,f,x,y,z)=Oy(t⁡(m,f))-y)Ox(t⁡(m,f))-x)1+(Oy(t⁡(m,f))-y)Ox(t⁡(m,f))-x))21+(Oz(t⁡(m,f))-z)2(Ox(t⁡(m,f))-x)2+(Oy⁢(t⁢(m,f))-y)2⁢h⁢dhλ+Oz(t⁡(m,f))-z(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)21+(Oz(t⁡(m⁢f))-z)2(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)2⁢v⁢dvλ
wherein: t(m,f)=mTc+fTf, λ=signal wavelength, dh=antenna horizontal spacing, dv=antenna vertical spacing, Tc=Chirp repetition interval, Tf=Frame repetition interval, Ox=Odometry based vehicle position at the x-axis, Oy=Odometry based vehicle position at the y-axis, Oz=Odometry based vehicle position at the z-axis, OVx=Odometry based vehicle velocity at the x-axis, OVy=Odometry based vehicle velocity at the y-axis, and OVz=Odometry based vehicle velocity at the z-axis.

In one embodiment, the RADON transform is represented by:

In another embodiment, a non-transitory computer readable media encoded with programming instructions configurable to cause a processor in a land vehicle to perform a method for using Synthetic Aperture Radar (SAR) to perform a maneuver with the land vehicle. The method includes: 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 transformed frames 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 for which a radar return exists from the pulsed radar transmission, project a radar trajectory 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 transmission from the SAR based on the transformed frames of data for each three-dimensional point; and performing an autonomous or semiautonomous maneuver with the land vehicle by applying the generated two-dimensional X-Y map.

In one embodiment, the RADON transform includes a range dimension, a Doppler dimension, and a phase dimension.

In one embodiment, the RADON transform is represented by:

In one embodiment, the range dimension of the RADON transform is represented by:

R⁡(m,f,x,y,z)=2⁢αc⁢(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)2+(Oz(t⁡(m,f))-z)2
wherein: c=speed of light, α=chirp slope, t(m,f)=mTc+fTf, Tc=Chirp repetition interval, Tf=Frame repetition interval, Ox=Odometry based vehicle position at the x-axis, Oy=Odometry based vehicle position at the y-axis, and Oz=Odometry based vehicle position at the z-axis.

In one embodiment, the Doppler dimension of the RADON transform is represented by:

D⁡(m,f,x,y,z)=2λ⁢Ox⁢(t⁢(m,f))-x)⁢OV⁢x+(Oy⁢(t⁢(m,f))-y)⁢OV⁢y+(Oz⁢(t⁢(m,f))-z)⁢OV⁢z(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)2+(Oz(t⁡(m,f))-z)2
wherein: t(m,f)=mTc+fTf, λ=signal wavelength, Tc=Chirp repetition interval, Tf=Frame repetition interval, Ox=Odometry based vehicle position at the x-axis, Oy=Odometry based vehicle position at the y-axis, Oz=Odometry based vehicle position at the z-axis, OVx=Odometry based vehicle velocity at the x-axis, OVy=Odometry based vehicle velocity at the y-axis, and OVz=Odometry based vehicle velocity at the z-axis.

In one embodiment, the phase dimension of the RADON transform is represented by:

P⁡(m,h,f,x,y,z)=Oy(t⁡(m,f))-y)Ox(t⁡(m,f))-x)1+(Oy(t⁡(m,f))-y)Ox(t⁡(m,f))-x))21+(Oz(t⁡(m,f))-z)2(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)2⁢h⁢dhλ+Oz(t⁡(m,f))-z(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)21+(Oz(t⁡(m⁢f))-z)2(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)2⁢v⁢dvλ
wherein: t(m,f)=mTc+fTf, λ=signal wavelength, dh=antenna horizontal spacing, dv=antenna vertical spacing, Tc=Chirp repetition interval, Tf=Frame repetition interval, Ox=Odometry based vehicle position at the x-axis, Oy=Odometry based vehicle position at the y-axis, Oz=Odometry based vehicle position at the z-axis, OVx=Odometry based vehicle velocity at the x-axis, OVy=Odometry based vehicle velocity at the y-axis, and OVz=Odometry based vehicle velocity at the z-axis.

In one embodiment, the RADON transform is represented by:

DETAILED DESCRIPTION

The following detailed description is merely exemplary in nature and is not intended to limit the application and uses. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, summary, or the following detailed description. As used herein, the term “module” refers to any hardware, software, firmware, electronic control component, processing logic, and/or processor device, individually or in any combination, including without limitation: application specific integrated circuit (ASIC), a field-programmable gate-array (FPGA), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.

The subject matter described herein discloses apparatus, systems, techniques, and articles for adapting SAR for near field application in an automotive environment. The following disclosure provides example systems and methods for applying a novel SAR processing approach using a Radon transform. The described subject matter discloses apparatus, systems, techniques, and articles for accumulating multiple radar frames from a moving vehicle and projecting the gathered information according to the vehicle path to estimate the environment. In the described subject matter, a novel Radon-SAR transform is performed on the accumulated data, coherently integrating multiple frames to generate high resolution detection.

In the described subject matter, in addition to the large spatial aperture generated from the vehicle movement, localization accuracy is increased by exploiting Doppler information. In the described subject matter, the Doppler information reduces angular ambiguity, lowering the FPS requirement. The described apparatus, systems, techniques, and articles adapt SAR for near field application by taking the vehicle trajectory into account. In the described subject matter, the adaptation to near field application is accomplished by projecting the vehicle path to each position independently and calculating the Radon-SAR transform for each projection. In the described subject matter, the near field environment increases accuracy and reduces ambiguity due to the multiple observation projections. The described apparatus, systems, techniques, and articles can provide a two-dimensional X-Y map resulting from the described process.

FIG.1depicts an example vehicle100that includes a SAR module102for using Synthetic Aperture Radar (SAR) to assist the vehicle with performing a maneuver. As depicted inFIG.1, the vehicle100generally includes a chassis12, a body14, front wheels16, and rear wheels18. The body14is arranged on the chassis12and substantially encloses components of the vehicle100. The body14and the chassis12may jointly form a frame. The wheels16-18are each rotationally coupled to the chassis12near a respective corner of the body14.

In various embodiments, the vehicle100may be an autonomous vehicle or a semi-autonomous vehicle. An autonomous vehicle100is, for example, a vehicle that is automatically controlled to carry passengers from one location to another. The vehicle100is depicted in the illustrated embodiment as a passenger car, but other vehicle types, including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), marine vessels, aircraft, etc., may also be used.

As shown, the vehicle100generally includes a propulsion system20, a transmission system22, a steering system24, a brake system26, a sensor system28, an actuator system30, at least one data storage device32, at least one controller34, and a communication system36. The propulsion system20may, in various embodiments, include an internal combustion engine, an electric machine such as a traction motor, and/or a fuel cell propulsion system. The transmission system22is configured to transmit power from the propulsion system20to the vehicle wheels16and18according to selectable speed ratios. According to various embodiments, the transmission system22may include a step-ratio automatic transmission, a continuously-variable transmission, or other appropriate transmission.

The brake system26is configured to provide braking torque to the vehicle wheels16and18. Brake system26may, in various embodiments, include friction brakes, brake by wire, a regenerative braking system such as an electric machine, and/or other appropriate braking systems.

The steering system24influences a position of the vehicle wheels16and/or18. While depicted as including a steering wheel25for illustrative purposes, in some embodiments contemplated within the scope of the present disclosure, the steering system24may not include a steering wheel.

The sensor system28includes one or more sensing devices40a-40nthat sense observable conditions of the exterior environment and/or the interior environment of the vehicle100(such as the state of one or more occupants) and generate sensor data relating thereto. Sensing devices40a-40nmight include, but are not limited to, radars (e.g., long-range, medium-range-short range, SAR), lidars, global positioning systems, optical cameras (e.g., forward facing, 360-degree, rear-facing, side-facing, stereo, etc.), thermal (e.g., infrared) cameras, ultrasonic sensors, odometry sensors (e.g., encoders) and/or other sensors that might be utilized in connection with systems and methods in accordance with the present subject matter.

The actuator system30includes one or more actuator devices42a-42nthat control one or more vehicle features such as, but not limited to, the propulsion system20, the transmission system22, the steering system24, and the brake system26. In various embodiments, vehicle100may also include interior and/or exterior vehicle features not illustrated inFIG.1, such as various doors, a trunk, and cabin features such as air, music, lighting, touch-screen display components (such as those used in connection with navigation systems), and the like.

The data storage device32stores data for use in automatically controlling the vehicle100. The data storage device32may be part of the controller34, separate from the controller34, or part of the controller34and part of a separate system. In various embodiments, controller34implements a SAR module102that is configured to implement SAR processing.

The instructions may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. The instructions, when executed by the processor44, receive and process signals (e.g., sensor data) from the sensor system28, perform logic, calculations, methods and/or algorithms for automatically controlling the components of the vehicle100, and generate control signals that are transmitted to the actuator system30to automatically control the components of the vehicle100based on the logic, calculations, methods, and/or algorithms. Although only one controller34is shown inFIG.1, embodiments of the vehicle100may include any number of controllers34that communicate over any suitable communication medium or a combination of communication mediums and that cooperate to process the sensor signals, perform logic, calculations, methods, and/or algorithms, and generate control signals to automatically control features of the vehicle100.

FIG.2is a process flow chart depicting an example process200for processing radar returns (e.g., by SAR module102implemented by controller34) from a SAR (e.g., from a sensor system28that includes one or more sensing devices40a-40nthat implements SAR) implemented on a vehicle (e.g., vehicle100) to generate a map for use by the vehicle. The order of operation within process200is not limited to the sequential execution as illustrated in theFIG.2but may be performed in one or more varying orders as applicable and in accordance with the present disclosure.

The example process200includes performing analog to digital conversion (ADC) on radar returns from a SAR in the vehicle (operation302) to produce a frame of digital data for each converted radar return. The ADC may be performed using conventional methods used in radar systems in a vehicle.

The example process200includes accumulating multiple frames of the digitized data (operation204). Each frame may include radar data from the return for a specific three-dimensional location identified by x,y,z coordinates (hereinafter referred to as (x,y,z) location) gathered from a pulsed transmission. The x,y,z coordinates are based on a coordinate system with an x-axis direction in the direction of vehicle travel, a y-axis direction that is 90 degrees to the left of the x-axis, and a z-axis direction that is 90 degrees in a vertical direction to vehicle travel. The multiple frames may include all or almost all the frames of data gathered from a pulsed transmission.

The example process200includes processing the accumulated frames of the digitized data using a RADON-SAR transform (operation206) and vehicle odometry data207. The vehicle odometry data may include vehicle position and velocity data. The RADON-SAR transform is a type of Radon transform that has been specially adapted for use with SAR. The RADON-SAR transform is configured to perform coherent integration for every (x,y,z) point (e.g., (x,y,z) location for which a radar return exists from the pulsed transmission). The RADON-SAR transform is configured to account for a radar trajectory, according to the vehicle path, by projecting the radar trajectory onto each three-dimensional (x,y,z) point and coherently integrate the accumulated frames. This allows the SAR to support arbitrary radar trajectory, in addition to straight line trajectory with a constant velocity. The RADON-SAR transform is configured to account for Doppler information to increase accuracy and reduce ambiguity. The RADON-SAR transform includes a range dimension, a Doppler dimension, and a phase dimension. This configures the RADON-SAR transform to support range, Doppler, and spatial migration within frames and between of frames, caused from radar movement. The example RADON-SAR transform assumes static objects. The coherent integration performed in the RADON-SAR transform increase target signal-to-noise-ratio (SNR).

An example RADON-SAR transform is represented by:

R⁡(m,f,x,y,z)=2⁢αc⁢(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)2+(Oz(t⁡(m,f))-z)2D⁡(m,f,x,y,z)=2λ⁢Ox⁢(t⁢(m,f))-x)⁢OV⁢x+(Oy⁢(t⁢(m,f))-y)⁢OV⁢y+(Oz⁢(t⁢(m,f))-z)⁢OV⁢z(Ox(t⁡(m,f))-x)2+(Oy(t⁡(m,f))-y)2+(Oz(t⁡(m,f))-z)2P⁡(m,h,f,x,y,z)=Oy(t⁡(m,f))-y)Ox(t⁡(m,f))-x)1+(Oy(t⁡(m⁢f))-y)Ox(t⁡(m⁢f))-x))21+(Oz(t⁡(m⁢f))-z)2(Ox(t⁡(m⁢f))-x)2+(Oy(t⁡(m⁢f))-y)2⁢h⁢dhλ+Oz(t⁡(m⁢f))-z(Ox(t⁡(m⁢f))-x)2+(Oy(t⁡(m⁢f))-y)21+(Oz(t⁡(m⁢f))-z)2(Ox(t⁡(m⁢f))-x)2+(Oy(t⁡(m⁢f))-y)2⁢v⁢dvλt⁡(m,f)=m⁢Tc+f⁢Tfm—sample indexn—chirp indexh—horizontal antenna indexv—vertical antenna indexf—frame indexx—x-axis locationy—y-axis locationz—z-axis locations—sampled signalc—speed of lightN—number of samplesM—number of chirpsH—number of horizontal antennasV—number of vertical antennasF—number of framesα—chirp slopeλ—signal wavelengthdh—antenna horizontal spacingdv—antenna vertical spacingTc—Chirp repetition intervalTf—Frame repetition intervalOx—Odometry based vehicle position at the x-axisOy—Odometry based vehicle position at the y-axisOz—Odometry based vehicle position at the z-axisOVx—Odometry based vehicle velocity at the x-axisOVy—Odometry based vehicle velocity at the y-axisOVz—Odometry based vehicle velocity at the z-axis

The example process200includes generating a two-dimensional X-Y map of the area covered by the pulsed radar transmission from the SAR based on the transformed frames of data for each three-dimensional (x,y,z) point (operation208). The two-dimensional X-Y map may be generated using conventional techniques for generating a map from radar return data.

After generation of a two-dimensional X-Y map, the vehicle may use the data from the two-dimensional X-Y map for autonomous or semi-autonomous driving features such as parking spot detection for automatic park assist. The increased resolution provided by use of SAR and the RADON-SAR transform can improve a vehicle's ability to maneuver into smaller parking spaces because the boundaries of a parking space will be known with greater detail.

FIG.3is a process flow chart depicting an example process300for using Synthetic Aperture Radar (SAR) to perform a maneuver in a land vehicle (e.g., vehicle100). The example process300includes receiving digitized radar return data (e.g., from a sensor system28that includes one or more sensing devices40a-40nthat implements SAR) from a pulsed radar transmission from a SAR onboard the land vehicle (operation302) and accumulating a plurality of frames of the digitized radar return data (operation304).

The example process300includes applying a RADON transform (e.g., by SAR module102implemented by controller34) to the accumulated plurality of frames of the digitized radar return data and odometry data from the land vehicle to generate transformed frames of data for each three-dimensional point (operation306). The RADON transform is configured to perform coherent integration for each three-dimensional point for which a radar return exists from the pulsed radar transmission, project a radar trajectory onto each three-dimensional point, and project Doppler information onto each three-dimensional point. The example RADON-SAR transform presented above may be used as the RADON transform.

The example process300includes generating a two-dimensional X-Y map of an area covered by the pulsed radar transmission from the SAR based on the transformed frames of data for each three-dimensional point (operation308) and performing an autonomous or semiautonomous maneuver with the land vehicle by applying the generated two-dimensional X-Y map (operation310). The two-dimensional X-Y map may be generated using conventional techniques for generating a map from radar return data. The autonomous or semiautonomous maneuver may include a parking assist maneuver or some other maneuver that could benefit from highly accurate position data that can be provided from SAR.