CN116520255A - Filtering method - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/021—Auxiliary means for detecting or identifying radar signals or the like, e.g. radar jamming signals
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention discloses a filtering method, which is used for performing rectangular coordinate conversion on radar data of a target vehicle to obtain converted radar data, wherein the converted radar data comprises first coordinates, and the first coordinates are the abscissa of the target vehicle under a rectangular coordinate system; filtering the first coordinate based on an alpha-beta filter to obtain a second coordinate of the target vehicle; and determining target filtering data of the target vehicle according to a preset motion model and data to be filtered, wherein the target filtering data is obtained by filtering the data to be filtered, and the data to be filtered comprises the second coordinates. According to the method, the first coordinate is subjected to filtering processing based on the alpha-beta filter, so that the shake of radar data in the y direction can be eliminated in advance, then, target filtering data of a target vehicle are determined according to a preset motion model and data to be filtered, the accuracy of the filtering data can be improved, and the safety of an automatic driving technology is further guaranteed.
Description
Technical Field
The invention relates to the technical field of radars, in particular to a filtering method.
Background
Automotive radar, one of the important members of automotive sensors, has been widely used in the automotive field as an autopilot service, and in order to ensure higher safety in autopilot, higher requirements are put forward on the accuracy of radar data.
The key point of realizing the accuracy of radar data is a data processing part, and the existing filtering processing method mainly carries out filtering processing on the radar data directly, so that the jitter error of the filtered data is larger, more accurate information feedback cannot be provided for an automatic driving technology, and a certain potential safety hazard is caused.
Disclosure of Invention
The invention provides a filtering method to reduce jitter error of filtering data, improve accuracy of the filtering data and further ensure safety of automatic driving technology.
According to an aspect of the present invention, there is provided a filtering method including:
performing rectangular coordinate conversion on radar data of a target vehicle to obtain converted radar data, wherein the converted radar data comprises first coordinates, and the first coordinates are abscissa coordinates of the target vehicle under a rectangular coordinate system;
filtering the first coordinate based on an alpha-beta filter to obtain a second coordinate of the target vehicle;
and determining target filtering data of the target vehicle according to a preset motion model and data to be filtered, wherein the target filtering data is obtained by filtering the data to be filtered, and the data to be filtered comprises the second coordinates.
According to another aspect of the present invention, there is provided a filtering apparatus including:
the coordinate conversion module is used for performing rectangular coordinate conversion on the radar data of the target vehicle to obtain converted radar data, wherein the converted radar data comprises first coordinates, and the first coordinates are the abscissa of the target vehicle under a rectangular coordinate system;
the processing module is used for carrying out filtering processing on the first coordinate based on an alpha-beta filter to obtain a second coordinate of the target vehicle;
the determining module is configured to determine target filtering data of the target vehicle according to a preset motion model and data to be filtered, where the target filtering data is filtering data obtained after the data to be filtered is filtered, and the data to be filtered includes the second coordinates.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the filtering method according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the filtering method according to any one of the embodiments of the present invention.
The embodiment of the invention provides a filtering method, which comprises the following steps: performing rectangular coordinate conversion on radar data of a target vehicle to obtain converted radar data, wherein the converted radar data comprises first coordinates, and the first coordinates are abscissa coordinates of the target vehicle under a rectangular coordinate system; filtering the first coordinate based on an alpha-beta filter to obtain a second coordinate of the target vehicle; and determining target filtering data of the target vehicle according to a preset motion model and data to be filtered, wherein the target filtering data is obtained by filtering the data to be filtered, and the data to be filtered comprises the second coordinates. By utilizing the technical scheme, the first coordinate is subjected to filtering processing based on the alpha-beta filter, so that the shake of radar data in the y direction can be eliminated in advance, then the target filtering data of the target vehicle is determined according to the preset motion model and the data to be filtered, the accuracy of the filtering data can be improved, and the safety of an automatic driving technology is further ensured.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a filtering method according to a first embodiment of the present invention;
fig. 2 is a flowchart of a filtering method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a filtering device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a filtering method according to a first embodiment of the present invention, where the method may be performed by a filtering device, and the filtering device may be implemented in hardware and/or software, and the filtering device may be configured in an electronic device.
It is considered that with the rapid development of the autopilot technology, a higher requirement is put on the accuracy of the automobile sensor, and the automobile radar is one of the important members of the automobile sensor, and has been widely applied to the autopilot service in the automobile field, so that the improvement of the accuracy of radar data is a problem to be solved. The key point for improving the accuracy of radar data is a filtering processing part, and the existing filtering processing method is to directly carry out filtering processing on the radar data, and does not carry out jitter elimination processing on the radar data before and after filtering, so that the jitter error of the filtered data is larger, and a certain potential safety hazard exists.
Based on the above, the embodiment of the invention provides a filtering method, which firstly carries out alpha-beta filtering on radar data to eliminate jitter, then carries out Kalman filtering on the radar data by combining a mixed motion model and measurement information covering Doppler speed, reduces jitter error of the filtering data, improves accuracy of the filtering data, and further ensures safety of an automatic driving technology. As shown in fig. 1, the method includes:
s110, performing rectangular coordinate conversion on radar data of a target vehicle to obtain converted radar data, wherein the converted radar data comprise first coordinates, and the first coordinates are abscissa coordinates of the target vehicle under a rectangular coordinate system.
The target vehicle may refer to a vehicle surrounding the current vehicle, such as a vehicle located in front of or beside the current vehicle, and the radar data of the target vehicle may be regarded as radar data of the target vehicle collected by the collecting device, for example, the radar data may include a doppler velocity of the target vehicle, and the like. The collection device may refer to a radar collection device, that is, a device for collecting radar data, for example, the collection device may include millimeter wave radar devices, the number and the installation position of the collection device are not limited, and the collection device may be set according to actual situations, for example, the collection device may be installed at the center position of the current vehicle.
In one embodiment, the radar data of the target vehicle includes a radial distance and/or an azimuth angle acquired by an acquisition device, the radial distance being a distance of the target vehicle relative to the acquisition device, the azimuth angle being an angle of the target vehicle relative to the acquisition device.
The radial distance may be understood as the distance of the target vehicle from the acquisition device and the azimuth angle may be understood as the angle of the target vehicle from the acquisition device.
The converted radar data may refer to data obtained by performing rectangular coordinate conversion on the radar data, for example, the converted radar data may include a first coordinate, where the first coordinate may be considered as an abscissa of the target vehicle in a rectangular coordinate system, in this embodiment, the rectangular coordinate system is defined by using the acquisition device as an origin, the vehicle traveling direction is an x-axis, and a direction perpendicular to the vehicle traveling direction is a y-axis, that is, the y-axis is a transverse direction, the y-coordinate may be considered as an abscissa of the target vehicle in the rectangular coordinate system, and the x-coordinate may be considered as an ordinate of the target vehicle in the rectangular coordinate system.
Specifically, in this embodiment, after radar data of the target vehicle is obtained, rectangular coordinate conversion may be performed on the radar data to obtain converted radar data. It is considered that the radar data may be data in a polar coordinate system, and the radar data may be understood as polar coordinate information of the target vehicle acquired by the acquisition device; the rectangular coordinate system may be any rectangular coordinate system, for example, the rectangular coordinate system may be a cartesian rectangular coordinate system. For example, the converted radar data may be calculated according to the following formula.
Wherein R represents the radial distance between the target vehicle and the acquisition device,representing azimuth angle of the target vehicle compared with the acquisition equipment, ρ representing Doppler speed of the target vehicle, x and y respectively representing ordinate and abscissa of the target vehicle in a rectangular coordinate system, v x And v y Represented as the speed of the target vehicle in the x and y directions, respectively. The radar data converted in this step may include a first coordinate y +>
And S120, filtering the first coordinate based on an alpha-beta filter to obtain a second coordinate of the target vehicle.
The alpha-beta filter can be regarded as a typical constant gain filter aiming at a uniform motion model, can be used for state estimation and data smoothing, and is different from a Kalman filter in terms of gain calculation, and has the advantage of being independent of a specific model of a system. The second coordinate may be understood as a coordinate obtained by performing an α - β filtering process on the first coordinate.
In this embodiment, the first coordinate may be filtered based on the α - β filter to obtain the second coordinate of the target vehicle, and the specific process of the filtering is not limited as long as the second coordinate of the target vehicle can be obtained.
In one embodiment, the converted radar data includes at least two first coordinates, the filtering processing is performed on the first coordinates based on an α - β filter to obtain second coordinates of the target vehicle, including:
and filtering the first coordinates based on the alpha-beta filter to obtain second coordinates corresponding to the first coordinates.
In one embodiment, the converted radar data may include the first coordinates of the target vehicle at a plurality of time points, and then in this step, the first coordinates may be filtered based on the α - β filter, so as to obtain second coordinates corresponding to each first coordinate, and the specific manner of determining each second coordinate may be determined according to a prediction equation and an update equation of the α - β filter.
In one embodiment, the predictive equation for the α - β filter is X k+1/k ＝X k Update equation to X k+1/k+1 ＝α*X k+1/k +β X y, where α and β are filter coefficients, y is the first coordinate, and state quantity X k ＝y k 。
Where α and β may be regarded as filter coefficients of the α - β filter, α and β satisfy α+β=1, and a specific value may be determined by an empirical value, which is not limited in this embodiment.
In this embodiment, the transformed radar data may include a first coordinate y, and the first coordinate y may be α - β filtered, so that the state vector only includes y values, i.e., the state quantity X k ＝y k . Then the state variable at the k+1 moment can be obtained through the state variable at the k moment, and the state variable at the subsequent moment can be obtained through recursion in sequence, wherein a prediction equation at the k+1 moment can be expressed as X k+1/k ＝X k The state variable at time k+1 may be updated by updating equation X k+1/k+1 ＝α*X k+1/k +β×y.
For example, the converted radar data may include y 1 、y 2 、y 3 Then the second coordinate at time k=1 is Y 1 ＝y k ＝y 1 The second coordinate at time k=2 is Y 2 ＝α*Y 1 +β*y 2 The second coordinate at time k=3 is Y 3 ＝α*Y 2 +β*y 3 Therefore, the filtering process can be performed on each first coordinate based on the steps to obtain the second coordinate corresponding to each first coordinate.
S130, determining target filtering data of the target vehicle according to a preset motion model and data to be filtered, wherein the target filtering data are filtering data obtained after the data to be filtered are filtered, and the data to be filtered comprise the second coordinates.
The preset motion model may be regarded as a preset motion model for determining target filtering data of the target vehicle, and the specific content of the preset motion model is not limited.
The target filtering data is finally determined filtering data, for example, the target filtering data may be data obtained after filtering the data to be filtered, the data to be filtered may refer to data to be filtered, for example, the data to be filtered may include second coordinates, may further include other data except the second coordinates, for example, other data may be the speed of the target vehicle relative to the collecting device, and the like.
In one embodiment, the preset motion model is a uniform acceleration motion model in the positive X-axis direction of the rectangular coordinate system, and the preset motion model is a uniform motion model in the positive Y-axis direction of the rectangular coordinate system.
In one embodiment, a preset motion model may be established, for example, the motion model of the target vehicle is decomposed in the positive direction along the X-axis and the positive direction along the Y-axis, that is, the preset motion model is a uniform acceleration motion model in the positive direction along the X-axis of the rectangular coordinate system, so as to control the target vehicle to perform uniform acceleration motion in the positive direction along the X-axis, and the preset motion model is a uniform motion model in the positive direction along the Y-axis of the rectangular coordinate system, so as to control the target vehicle to perform uniform motion in the positive direction along the Y-axis.
For example, the state vector of the target vehicle may be x= [ X, v x ,a x ,y,v y ]The state transition matrix of the preset motion model may be
Specifically, the target filtering data of the target vehicle may be determined based on the established preset motion model and the data to be filtered, the specific determining process is not further expanded, and different filtering modes may correspond to different filtering processes.
According to the filtering method provided by the embodiment of the invention, rectangular coordinate conversion is carried out on radar data of a target vehicle to obtain converted radar data, wherein the converted radar data comprises first coordinates, and the first coordinates are the abscissa of the target vehicle under a rectangular coordinate system; filtering the first coordinate based on an alpha-beta filter to obtain a second coordinate of the target vehicle; and determining target filtering data of the target vehicle according to a preset motion model and data to be filtered, wherein the target filtering data is obtained by filtering the data to be filtered, and the data to be filtered comprises the second coordinates. According to the method, the first coordinate is subjected to filtering processing based on the alpha-beta filter, so that the shake of radar data in the y direction can be eliminated in advance, then, target filtering data of a target vehicle are determined according to a preset motion model and data to be filtered, the accuracy of the filtering data can be improved, and the safety of an automatic driving technology is further guaranteed.
Example two
Fig. 2 is a flowchart of a filtering method according to a second embodiment of the present invention, where the second embodiment is optimized based on the above embodiments. In this embodiment, determining the target filtering data of the target vehicle according to the preset motion model and the data to be filtered is further specified as: and carrying out Kalman filtering on the data to be filtered by adopting the preset motion model to obtain target filtering data of the target vehicle.
For details not yet described in detail in this embodiment, refer to embodiment one.
As shown in fig. 2, the method includes:
and S210, performing rectangular coordinate conversion on the radar data of the target vehicle to obtain converted radar data.
S220, filtering the first coordinate based on an alpha-beta filter to obtain a second coordinate of the target vehicle.
S230, carrying out Kalman filtering on the data to be filtered by adopting the preset motion model to obtain target filtering data of the target vehicle.
In one embodiment, the data to be filtered further includes a third coordinate and/or a radial velocity, the third coordinate being an ordinate of the target vehicle in a rectangular coordinate system, the radial velocity being a velocity of the target vehicle relative to the acquisition device.
The third coordinate may be considered as the ordinate of the target vehicle in a rectangular coordinate system and the radial velocity may refer to the velocity of the target vehicle relative to the acquisition device.
In this embodiment, a preset motion model may be used to perform kalman filtering on the data to be filtered to obtain target filtered data of the target vehicle. Specifically, the data to be filtered includes not only target position information but also doppler velocity, that is, measurement vector z= [ x, y, ρ ]', so that extended kalman filtering is needed to filter the data to be filtered, the idea of extended kalman filtering is to perform approximate linearization processing on a nonlinear function, that is, approximate linearization processing is performed by using a first-order taylor series expansion mode to obtain a measurement model, and the mapping relation between radar polar coordinate system measurement information and a cartesian coordinate system can be known, wherein the jacobian matrix after conversion of the nonlinear measurement function used in the nonlinear kalman filtering process is:
in this embodiment, the kalman filtering may be performed on the data to be filtered according to a state transition matrix of a preset motion model formed by fusing a state transition matrix of uniform acceleration motion and a state transition matrix of uniform motion, so as to obtain target filtering data of the target vehicle.
According to the filtering method provided by the second embodiment of the invention, rectangular coordinate conversion is carried out on radar data of a target vehicle, so that converted radar data are obtained; filtering the first coordinate based on an alpha-beta filter to obtain a second coordinate of the target vehicle; and carrying out Kalman filtering on the data to be filtered by adopting the preset motion model to obtain target filtering data of the target vehicle. By using the method, the Kalman filtering is carried out on the data to be filtered by adopting the preset motion model, so that the target filtering data of the target vehicle is obtained, the jitter error of the target filtering data can be further reduced, and the accuracy of the filtering data is improved.
The filtering method provided in this embodiment is described below as an example:
first, a millimeter wave radar apparatus is mounted on a current vehicle, and polar coordinate data (i.e., radar data) of a target vehicle is collected. Wherein, the measurement information collected by the millimeter wave radar is a two-dimensional vector, namelyR represents the distance (i.e. radial distance) of the target vehicle, -a target vehicle>Representing the azimuth (i.e., azimuth) of the target vehicle. The Doppler velocity ρ (i.e., radial velocity) is added to the measurement vector, the measurement vector of the target vehicle is +.>
And then, converting the acquired polar coordinate data into Cartesian coordinate system data (namely, performing rectangular coordinate conversion on the radar data of the target vehicle to obtain converted radar data). Specifically, according to the mapping relation between the radar polar coordinate system measurement information and the cartesian coordinate system, the data of the target position information (i.e. radar data) in the cartesian coordinate system can be obtained, namely:
then, the measured y value (i.e., the first coordinate) of the target vehicle in the rectangular coordinate system is subjected to alpha-beta filtering (i.e., the first coordinate is subjected to filtering based on an alpha-beta filter), so as to obtain the second coordinate of the target vehicle. Since only the measured y value is alpha-beta filtered, the target state vector at this time contains only the y value, i.e., the state quantity X k ＝y k The prediction equation of the alpha-beta filter can be expressed as X k+1/k ＝X k The update equation may be X k+1/k+1 ＝α*X k+1/k +β x y, where α and β are parameters of the filter, which can be set by themselvesIts value is large and small.
Secondly, a hybrid motion model (namely a preset motion model) of the target is built, an X-direction target vehicle is a uniform acceleration linear motion model, and a Y-direction target vehicle is a uniform velocity linear motion model (namely the preset motion model is a uniform acceleration motion model in the positive X-axis direction of the rectangular coordinate system, and the preset motion model is a uniform velocity motion model in the positive Y-axis direction of the rectangular coordinate system).
Specifically, the target vehicle makes uniform acceleration linear motion in the X direction and makes uniform linear motion in the Y direction, and the state vector of the target is x= [ X, v x ,a x ,y,v y ]The kalman filtered target state transition matrix F is then composed of two parts, namely
Then, a measurement model of the target is built, the target measurement information not only has the target position information but also contains Doppler velocity, namely, the measurement vector is Z= [ x, y and rho ]', so that the measurement model part is nonlinear, an extended kalman filter is needed to be selected for filtering, and the idea of the extended kalman filter is to approximate linearization of a nonlinear function, namely, approximate linearization by using a first-order Taylor series expansion mode is utilized to obtain the target measurement model.
And finally, performing Kalman filtering to obtain target position and speed information (namely, performing Kalman filtering on the data to be filtered by adopting the preset motion model to obtain target filtering data of the target vehicle). Wherein, when Kalman filtering is performed, the measurement value y obtained by alpha-beta filtering is needed α-β (i.e., the second coordinate) instead of the measured value y (i.e., the first coordinate) after coordinate conversion, and the doppler velocity is replaced with the radial velocity V measured by the radar, i.e., the measured vector of the target becomes z= [ x, y α-β ,V]’。
It can be seen that, after the radar device collects the position data of the target vehicle, the embodiment of the invention firstly performs α - β filtering on the measured y value converted from the polar coordinate to the cartesian coordinate system, then establishes the target hybrid motion model and the measurement model including doppler measurement, and finally performs Kalman filtering to obtain the processed target position information.
Example III
Fig. 3 is a schematic structural diagram of a filtering device according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes:
the coordinate conversion module 310 is configured to perform rectangular coordinate conversion on radar data of a target vehicle to obtain converted radar data, where the converted radar data includes a first coordinate, and the first coordinate is an abscissa of the target vehicle in a rectangular coordinate system;
a processing module 320, configured to perform filtering processing on the first coordinate based on an α - β filter, so as to obtain a second coordinate of the target vehicle;
the determining module 330 is configured to determine target filtering data of the target vehicle according to a preset motion model and data to be filtered, where the target filtering data is filtering data obtained after filtering the data to be filtered, and the data to be filtered includes the second coordinates.
In the filtering device provided by the third embodiment of the present invention, rectangular coordinate conversion is performed on radar data of a target vehicle by using the coordinate conversion module 310, so as to obtain converted radar data, where the converted radar data includes a first coordinate, and the first coordinate is an abscissa of the target vehicle in a rectangular coordinate system; filtering the first coordinate based on an alpha-beta filter by a processing module 320 to obtain a second coordinate of the target vehicle; and determining target filtering data of the target vehicle according to a preset motion model and data to be filtered by a determining module 330, wherein the target filtering data is obtained by filtering the data to be filtered, and the data to be filtered comprises the second coordinates. By utilizing the device, the first coordinate is subjected to filtering processing based on the alpha-beta filter, so that the shake of radar data in the y direction can be eliminated in advance, then the target filtering data of the target vehicle is determined according to the preset motion model and the data to be filtered, the accuracy of the filtering data can be improved, and the safety of an automatic driving technology is further ensured.
Optionally, the converted radar data includes at least two first coordinates, and the processing module 320 is specifically configured to:
and filtering the first coordinates based on the alpha-beta filter to obtain second coordinates corresponding to the first coordinates.
Optionally, the prediction equation of the α - β filter is X k+1/k ＝X k Update equation to X k+1/k+1 ＝α*X k+1/k +β X y, where α and β are filter coefficients, y is the first coordinate, and state quantity X k ＝y k 。
Optionally, the radar data of the target vehicle includes a radial distance and/or an azimuth angle acquired by an acquisition device, where the radial distance is a distance between the target vehicle and the acquisition device, and the azimuth angle is an angle between the target vehicle and the acquisition device.
Optionally, the data to be filtered further includes a third coordinate and/or a radial speed, where the third coordinate is an ordinate of the target vehicle in a rectangular coordinate system, and the radial speed is a speed of the target vehicle relative to the acquisition device.
Optionally, the determining module 330 is specifically configured to:
and carrying out Kalman filtering on the data to be filtered by adopting the preset motion model to obtain target filtering data of the target vehicle.
Optionally, the preset motion model is a uniform acceleration motion model in the positive direction of the X axis of the rectangular coordinate system, and the preset motion model is a uniform motion model in the positive direction of the Y axis of the rectangular coordinate system.
The filtering device provided by the embodiment of the invention can execute the filtering method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as filtering methods.
In some embodiments, the filtering method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the filtering method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the filtering method in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of filtering comprising:
performing rectangular coordinate conversion on radar data of a target vehicle to obtain converted radar data, wherein the converted radar data comprises first coordinates, and the first coordinates are abscissa coordinates of the target vehicle under a rectangular coordinate system;
filtering the first coordinate based on an alpha-beta filter to obtain a second coordinate of the target vehicle;
and determining target filtering data of the target vehicle according to a preset motion model and data to be filtered, wherein the target filtering data is obtained by filtering the data to be filtered, and the data to be filtered comprises the second coordinates.
2. The method of claim 1, wherein the converted radar data includes at least two first coordinates, the filtering the first coordinates based on an α - β filter to obtain second coordinates of the target vehicle, comprising:
and filtering the first coordinates based on the alpha-beta filter to obtain second coordinates corresponding to the first coordinates.
3. The method of claim 2, wherein the prediction equation of the α - β filter is X k+1/k ＝X k Update equation to X k+1/k+1 ＝α*X k+1/k +β X y, where α and β are filter coefficients, y is the first coordinate, and state quantity X k ＝y k 。
4. The method of claim 1, wherein the radar data of the target vehicle comprises a radial distance and/or azimuth angle acquired by an acquisition device, the radial distance being a distance of the target vehicle relative to the acquisition device, the azimuth angle being an angle of the target vehicle relative to the acquisition device.
5. The method of claim 4, wherein the data to be filtered further comprises a third coordinate and/or a radial velocity, the third coordinate being an ordinate of the target vehicle in a rectangular coordinate system, the radial velocity being a velocity of the target vehicle relative to the acquisition device.
6. The method of claim 5, wherein determining target filtered data for the target vehicle based on the pre-set motion model and the data to be filtered comprises:
and carrying out Kalman filtering on the data to be filtered by adopting the preset motion model to obtain target filtering data of the target vehicle.
7. The method of claim 1, wherein the predetermined motion model is a uniform acceleration motion model in an X-axis positive direction of the rectangular coordinate system, and the predetermined motion model is a uniform velocity motion model in a Y-axis positive direction of the rectangular coordinate system.
8. A filtering apparatus, comprising:
the coordinate conversion module is used for performing rectangular coordinate conversion on the radar data of the target vehicle to obtain converted radar data, wherein the converted radar data comprises first coordinates, and the first coordinates are the abscissa of the target vehicle under a rectangular coordinate system;
the processing module is used for carrying out filtering processing on the first coordinate based on an alpha-beta filter to obtain a second coordinate of the target vehicle;
the determining module is configured to determine target filtering data of the target vehicle according to a preset motion model and data to be filtered, where the target filtering data is filtering data obtained after the data to be filtered is filtered, and the data to be filtered includes the second coordinates.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the filtering method of any one of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement the filtering method of any one of claims 1-7 when executed.
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| CN202310487733.1A CN116520255A (en) | 2023-04-28 | 2023-04-28 | Filtering method |
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