CN113987741A - Multi-target data tracking method and system - Google Patents

Multi-target data tracking method and system Download PDF

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CN113987741A
CN113987741A CN202111072136.XA CN202111072136A CN113987741A CN 113987741 A CN113987741 A CN 113987741A CN 202111072136 A CN202111072136 A CN 202111072136A CN 113987741 A CN113987741 A CN 113987741A
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王军德
费腾
张伟
张家豪
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Wuhan Kotei Informatics Co Ltd
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Abstract

The invention relates to a multi-target data tracking method and a system, wherein the method comprises the following steps: acquiring measurement data and a plurality of prediction data of a plurality of sensors, and matching each measurement target with each prediction target according to vector correlation consistency of the measurement data and the plurality of prediction data of each sensor; counting the successful matching times of each measurement target and each prediction target, and constructing an incidence matrix according to the successful matching times; and managing the measurement target and the prediction target according to a Kalman filtering method based on a kinematic model and the incidence matrix. According to the invention, through a matching method based on vector correlation consistency, the problem of error caused by a maximum matching algorithm is solved, and real-time multi-sensor multi-target data tracking and prediction are realized.

Description

Multi-target data tracking method and system
Technical Field
The invention belongs to the field of automatic driving and sensor data processing, and particularly relates to a multi-target data tracking method and system.
Background
Sensor fusion in the automatic driving field is a relatively important field, and in the multi-sensor target fusion stage, data association needs to be performed on different sensor output targets so as to judge whether multiple data belong to the same target.
In the field of automatic driving perception fusion, data association is generally performed by a Hungarian matching algorithm, but the Hungarian algorithm is a maximum matching algorithm, and under a special condition, two irrelevant targets can be matched, so that an error occurs in a fusion result.
Disclosure of Invention
In order to solve the problem that the maximum matching algorithm causes errors in multi-sensor data fusion, the invention provides a multi-target data tracking method in a first aspect, which comprises the following steps:
acquiring measurement data and a plurality of prediction data of a plurality of sensors, and matching each measurement target with each prediction target according to vector correlation consistency of the measurement data and the plurality of prediction data of each sensor;
counting the successful matching times of each measurement target and each prediction target, and constructing an incidence matrix according to the successful matching times;
and managing the measurement target and the prediction target according to a Kalman filtering method based on a kinematic model and the incidence matrix.
In some embodiments of the invention, the vector correlation consistency is calculated by:
Figure BDA0003260591900000011
wherein ρ (·) represents a vector correlation consistency index of the correlation matrix; s represents predicted target data, T represents measured target data, and E (means) represents a corresponding mean value; s denotes the ordinal number of the predicted target data, t denotes the ordinal number of the measured target data, and M and N denote the total number of measured targets and the total number of predicted targets, respectively.
Further, if there is a measure target equal to the vector correlation consistency indexes of the prediction targets, then: and selecting a group of predicted target data with the minimum Euclidean distance to the measuring target from the data of the plurality of predicted targets as matching data.
In some embodiments of the present invention, the managing the measurement target and the prediction target according to the kalman filtering method based on the kinematic model and the incidence matrix includes:
updating data of a measurement target and a prediction target according to a Kalman filtering method based on a kinematic model; and counting the updated predicted target and the matching times of the measured target, and managing the data of the measured target and the predicted target according to the updated predicted target and the updated measured target.
Further, the counting the updated matching times of the predicted target and the measurement target, and managing the data of the measurement target and the predicted target according to the statistics comprises: if the matching times of the data of the measurement target and the prediction target in the preset frame number are not less than the threshold value, the measurement target and the data thereof are reserved; otherwise, the measurement target is removed from the incidence matrix.
In the above embodiment, the incidence matrix is constructed by the following method: respectively taking the ordinal number of the measurement data and the predicted data of each sensor as row data and column data of the incidence matrix; performing vector correlation consistency calculation on the measurement data corresponding to the row data and the prediction data corresponding to the column data; and determining the values of elements in the incidence matrix corresponding to the row data and the column data according to the result of the vector correlation consistency calculation.
In a second aspect of the present invention, a multi-target data tracking system is provided, which includes an obtaining module, a matching module, and a management module, where the obtaining module is configured to obtain measurement data of a plurality of sensors and a plurality of prediction data, and match each measurement target with each prediction target according to vector correlation consistency of the measurement data of each sensor and the plurality of prediction data; the matching module is used for counting the successful matching times of each measurement target and each prediction target and constructing a correlation matrix according to the successful matching times; and the management module is used for managing the measurement target and the prediction target according to a Kalman filtering method based on a kinematic model and the incidence matrix.
Further, the management module comprises an updating unit and a management unit,
the updating unit is used for updating the data of the measurement target and the prediction target according to a Kalman filtering method based on a kinematic model;
and the management unit is used for counting the updated predicted target and the matching times of the measured target and managing the data of the measured target and the predicted target according to the updated predicted target and the updated measured target.
In a third aspect of the present invention, there is provided an electronic device comprising: one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors realize the multi-target data tracking method provided by the first aspect of the invention.
In a fourth aspect of the present invention, a computer-readable medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the multi-target data tracking method provided in the first aspect of the present invention.
The invention has the beneficial effects that:
1. according to the matching method based on the vector correlation consistency, the problem of error caused by a maximum matching algorithm is solved, and the matching precision is improved;
2. performing effective management through incidence matrix multi-sensor and multi-target data;
3. and updating data and counting the matching times by a Kalman filtering method, and realizing real-time multi-sensor multi-target data tracking and prediction.
Drawings
FIG. 1 is a basic flow diagram of a multi-target data tracking method in some embodiments of the invention;
FIG. 2 is a detailed flow diagram of a multi-target data tracking method in some embodiments of the invention;
FIG. 3 is a schematic representation of a correlation matrix in a multi-target data tracking method in some embodiments of the inventions;
FIG. 4 is a schematic diagram of a multi-target data tracking system in accordance with some embodiments of the invention;
fig. 5 is a schematic structural diagram of an electronic device in some embodiments of the invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1 and 2, in a first aspect of the present invention, a multi-target data tracking method is provided, including the following steps: s100, obtaining measurement data and a plurality of prediction data of a plurality of sensors, and matching each measurement target with each prediction target according to vector correlation consistency of the measurement data and the plurality of prediction data of each sensor;
s200, counting the number of times of successful matching of each measurement target and each prediction target, and constructing a correlation matrix according to the number of times;
s300, managing the measurement target and the prediction target according to a Kalman filtering method based on a kinematic model and the incidence matrix.
In steps S100 to S300 of some embodiments of the present invention, the vector correlation consistency is calculated by:
Figure BDA0003260591900000041
wherein ρ (·) represents a vector correlation consistency index of the correlation matrix; s represents predicted target data, T represents measured target data, and E (means) represents a corresponding mean value; s denotes the ordinal number of the prediction target data, t denotes the ordinal number of the measurement target data, and M and N denote the total number of the measurement targets and the total number of the prediction targets (corresponding to one frame data or multi-frame data), respectively.
It is understood that E () represents a mean value, which is calculated by methods including, but not limited to, arithmetic mean, geometric mean, variance, and the like. The measured target data or predicted target data may include one variable or a plurality of variables, and when the data is a plurality of variables, S or T may correspond to a multidimensional data type such as a matrix or a sequence. The vector correlation consistency index of the incidence matrix can also be calculated by adopting a matrix similarity method; the above s or t represents the ordinal number of the predicted target data or the ordinal number of the measured target data, which can be replaced by the ordinal number of the spatial frame or the temporal frame, respectively.
Further, if there is a measure target equal to the vector correlation consistency indexes of the prediction targets, then: and selecting a group of predicted target data with the minimum Euclidean distance to the measuring target from the data of the plurality of predicted targets as matching data. For example, in the data of a certain predicted target, both the inertial navigation sensor and the satellite positioning sensor can be used for measuring or predicting the pose information of the target, so that the data generated by the inertial navigation sensor and the satellite positioning sensor need to be matched.
In step S300 of some embodiments of the present invention, the managing the measurement target and the prediction target according to the kalman filtering method based on the kinematic model and the incidence matrix includes:
s301, updating data of a measurement target and a prediction target according to a Kalman filtering method based on a kinematic model; s302, counting the updated predicted target and the matching times of the measured target, and managing the data of the measured target and the predicted target according to the data. The kinematic model includes: constant Velocity model (CV), Constant Acceleration model (CA), Constant Turn Rate and Velocity magnitude model (CTRV), Constant Turn Rate and Acceleration (CTRA), Constant Steering Angle and Velocity (CSAV) Constant Curvature and Acceleration (CCA).
Specifically, in step S301, the step of updating the data of the measurement target and the prediction target based on the kalman filter method is as follows:
the CV model based kalman procedure is as follows:
the prediction equation:
Figure BDA0003260591900000051
(Linear CV model of motion)
Figure BDA0003260591900000052
(State covariance matrix)
Updating an equation:
Figure BDA0003260591900000053
(calculation of Kalman gain)
Figure BDA0003260591900000061
(updating the estimate by measurement)
Pt＝(I-KtH)Pt -Fifthly (update error covariance)
Where x (t) ═ P in equation (r)x,Py,Vx,Vy)TX is a state vector (state vector), p is position information of the target (x and y are components on corresponding coordinate axes), and v is the relative speed of the target; the superscript ^ represents the measured value of the corresponding variable, and the superscript-represents the predicted value of the corresponding variable; and:
Figure BDA0003260591900000062
vt＝vt-1+ut×Δt；
arranging the above into a matrix form:
Figure BDA0003260591900000063
p of the equation II is a state covariance matrix, Q is noise of a prediction model, and F represents a state transition equation and describes uncertainty of target prediction; equation III, where K is the computational Kalman gain, where H is the transfer matrix and R is the measurement (sensing)The observer observes the input) covariance matrix of the noise, t is the time or frame number, Δ t is the time interval, utIs the acceleration or rate of change of velocity.
Further, in step S302 of some embodiments, the counting the updated predicted target and the number of times of matching of the measured target, and managing the data of the measured target and the predicted target according to the counted number includes: if the matching times of the data of the measurement target and the prediction target in the preset frame number are not less than the threshold value, the measurement target and the data thereof are reserved; otherwise, the measurement target is removed from the incidence matrix.
Specifically, the number of times of matching between the prediction target and the measurement target in the multi-frame data is counted, if the data is matched for 3 times in the 5-frame data, the data is considered to be valid data, and if the data is not matched for 3 times in the 5-frame data, the data is considered to be invalid data, and the invalid data needs to be removed from the incidence matrix.
Referring to fig. 3, in the above embodiment, the correlation matrix is constructed by the following method: respectively taking the ordinal number of the measurement data and the predicted data of each sensor as row data and column data of the incidence matrix; performing vector correlation consistency calculation on the measurement data corresponding to the row data and the prediction data corresponding to the column data; and determining the values of elements in the incidence matrix corresponding to the row data and the column data according to the result of the vector correlation consistency calculation. And the value of the corresponding element of the successfully matched row data and column data in the incidence matrix is 1, otherwise, the value is 0.
Example 2
Referring to fig. 4, in a second aspect of the present invention, there is provided a multi-target data tracking system 1, including an obtaining module 11, a matching module 12, and a management module 13, where the obtaining module is configured to obtain measurement data of a plurality of sensors and a plurality of prediction data, and match each measurement target with each prediction target according to vector correlation consistency of the measurement data of each sensor and the plurality of prediction data; the matching module is used for counting the successful matching times of each measurement target and each prediction target and constructing a correlation matrix according to the successful matching times; and the management module is used for managing the measurement target and the prediction target according to a Kalman filtering method based on a kinematic model and the incidence matrix.
Further, the management module 13 includes an updating unit and a management unit, where the updating unit is configured to update data of the measurement target and the prediction target according to a kalman filtering method based on a kinematic model;
and the management unit is used for counting the updated predicted target and the matching times of the measured target and managing the data of the measured target and the predicted target according to the updated predicted target and the updated measured target.
Example 3
Referring to fig. 5, in a third aspect of the present invention, there is provided an electronic apparatus comprising: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method provided by the first aspect of the invention. Alternatively, the electronic device may be used for an electronic control unit ECU in an on-board device, an on-board device or an aircraft, and a control unit implementing target predictive management.
The electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following devices may be connected to the I/O interface 505 in general: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; a storage device 508 including, for example, a hard disk; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of embodiments of the present disclosure. It should be noted that the computer readable medium described in the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, however, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more computer programs which, when executed by the electronic device, cause the electronic device to:
computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, Python, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. A multi-target data tracking method is characterized by comprising the following steps:
acquiring measurement data and a plurality of prediction data of a plurality of sensors, and matching each measurement target with each prediction target according to vector correlation consistency of the measurement data and the plurality of prediction data of each sensor;
counting the successful matching times of each measurement target and each prediction target, and constructing an incidence matrix according to the successful matching times;
and managing the measurement target and the prediction target according to a Kalman filtering method based on a kinematic model and the incidence matrix.
2. The multi-target data tracking method according to claim 1, wherein the vector correlation consistency is calculated by:
Figure FDA0003260591890000011
wherein ρ (·) represents a vector correlation consistency index of the correlation matrix; s represents predicted target data, T represents measured target data, and E (means) represents a corresponding mean value; s denotes the ordinal number of the predicted target data, t denotes the ordinal number of the measured target data, and M and N denote the total number of measured targets and the total number of predicted targets, respectively.
3. The multi-target data tracking method according to claim 2, wherein if there is one measurement target equal to the vector correlation consistency indexes of the plurality of prediction targets: and selecting a group of predicted target data with the minimum Euclidean distance to the measuring target from the data of the plurality of predicted targets as matching data.
4. The multi-target data tracking method according to claim 1, wherein the managing the measurement targets and the prediction targets according to the kinematics model-based kalman filtering method and the correlation matrix comprises:
updating data of a measurement target and a prediction target according to a Kalman filtering method based on a kinematic model;
and counting the updated predicted target and the matching times of the measured target, and managing the data of the measured target and the predicted target according to the updated predicted target and the updated measured target.
5. The multi-target data tracking method according to claim 4, wherein the counting the updated predicted targets and the updated matching times of the measured targets, and the managing the data of the measured targets and the predicted targets according to the counting comprises:
if the matching times of the data of the measurement target and the prediction target in the preset frame number are not less than the threshold value, the measurement target and the data thereof are reserved; otherwise, the measurement target is removed from the incidence matrix.
6. The multi-target data tracking method according to any one of claims 1 to 5, wherein the correlation matrix is constructed by:
respectively taking the ordinal number of the measurement data and the predicted data of each sensor as row data and column data of the incidence matrix;
performing vector correlation consistency calculation on the measurement data corresponding to the row data and the prediction data corresponding to the column data;
and determining the values of elements in the incidence matrix corresponding to the row data and the column data according to the result of the vector correlation consistency calculation.
7. A multi-target data tracking system is characterized by comprising an acquisition module, a matching module and a management module,
the acquisition module is used for acquiring the measurement data and the plurality of prediction data of the plurality of sensors and matching each measurement target with each prediction target according to the vector correlation consistency of the measurement data and the plurality of prediction data of each sensor;
the matching module is used for counting the successful matching times of each measurement target and each prediction target and constructing a correlation matrix according to the successful matching times;
and the management module is used for managing the measurement target and the prediction target according to a Kalman filtering method based on a kinematic model and the incidence matrix.
8. The multi-target data tracking system of claim 7, wherein the management module includes an update unit, a management unit,
the updating unit is used for updating the data of the measurement target and the prediction target according to a Kalman filtering method based on a kinematic model;
and the management unit is used for counting the updated predicted target and the matching times of the measured target and managing the data of the measured target and the predicted target according to the updated predicted target and the updated measured target.
9. An electronic device, comprising: one or more processors; a storage device to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the multi-target data tracking method of any one of claims 1 to 5.
10. A computer-readable medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the multi-target data tracking method according to any one of claims 1 to 5.
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