CN114743326A - Intelligent manufacturing workshop anti-theft early warning system with intelligent identification function - Google Patents
Intelligent manufacturing workshop anti-theft early warning system with intelligent identification function Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
- G08B13/19602—Image analysis to detect motion of the intruder, e.g. by frame subtraction
- G08B13/19613—Recognition of a predetermined image pattern or behaviour pattern indicating theft or intrusion
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/10—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using wireless transmission systems
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B7/00—Signalling systems according to more than one of groups G08B3/00 - G08B6/00; Personal calling systems according to more than one of groups G08B3/00 - G08B6/00
- G08B7/06—Signalling systems according to more than one of groups G08B3/00 - G08B6/00; Personal calling systems according to more than one of groups G08B3/00 - G08B6/00 using electric transmission, e.g. involving audible and visible signalling through the use of sound and light sources
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Abstract
The invention discloses an intelligent manufacturing workshop anti-theft early warning system with intelligent identification, and particularly relates to the technical field of intelligent manufacturing. According to the invention, through calculation and analysis of image data, the related algorithm can realize matching of images of related objects, judge the moving track of an object, match the moving track of related personnel, simply predict the moving track of the object, judge the transfer operation process of the object, conveniently and accurately judge whether the object is stolen or not in time, and accurately judge the moving track of the object and whether the object is stolen or not in time, and then give an alarm prompt in time, so that accurate discovery and theft deterrence are realized.
Description
Technical Field
The invention relates to the technical field of intelligent manufacturing, in particular to an intelligent manufacturing workshop anti-theft early warning system with intelligent identification.
Background
Intelligent manufacturing is a man-machine integrated intelligent system composed of intelligent machines and human experts, which can perform intelligent activities such as analysis, reasoning, judgment, conception and decision making during the manufacturing process. By the cooperation of human and intelligent machine, the mental labor of human expert in the manufacturing process is enlarged, extended and partially replaced. The concept of manufacturing automation is updated, and the manufacturing automation is expanded to flexibility, intellectualization and high integration.
The DNC is only used as a network platform for solving the communication of numerical control equipment in the early period, and with the continuous development and growth of customers, the problem of only solving the equipment networking is far from meeting the requirements of modern manufacturing enterprises. As early as the 90 s, the Predator Software INC in the United states endows DNC with wider connotation-production equipment and station intelligent networking management systems, which is the earliest and most mature technology of 'Internet of things' in the world, namely 'Internet of things' in workshops, and makes DNC a necessary underlying platform for MES systems in discrete manufacturing industry. DNC must be able to carry more information. Meanwhile, the DNC system must be capable of effectively combining advanced digital data entry or reading technologies, such as a bar code technology, a radio frequency technology, a touch screen technology and the like, so as to help enterprises realize production station digitization.
The intelligent manufacturing workshop is in the production course of working and probably appear that partial product or raw materials are carried out the workshop by the staff privately, have the intention of stealing, but present control is mostly to be brought back and transferred after discovering the condition of stealing, carry out the condition and investigate or look back, be difficult to in time discover the appearance of the condition of stealing, can't play the early warning effect, cause comparatively abominable condition and influence easily, consequently need one kind be provided with intelligent recognition's intelligent manufacturing workshop theftproof early warning system and solve above-mentioned problem.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides an intelligent manufacturing workshop anti-theft early warning system with intelligent identification, and the invention aims to solve the technical problems that: the intelligent manufacturing workshop is in the production course of working and probably appears partial product or raw materials and is carried out the workshop by the staff privately, has the intention of stealing, but present control is mostly to be brought back and retrieved after discovering the theft condition, carries out the condition and grope or look back, is difficult to in time discover the appearance of the theft condition, can't play the early warning effect, leads to the fact the problem of comparatively abominable condition and influence easily.
In order to achieve the purpose, the invention provides the following technical scheme: an intelligent manufacturing workshop anti-theft early warning system with intelligent identification comprises an image acquisition unit, the output end of the image acquisition unit is electrically connected with the input end of the data analysis module, the data analysis module comprises a data storage unit, a judgment unit and a data processing unit, the output end of the image acquisition unit is electrically connected with the input ends of the data storage unit and the data processing unit, the output end of the data storage unit is electrically connected with the input end of the judging unit, the input end of the judging unit is electrically connected with the output end of the data processing unit, the output end of the judging unit is electrically connected with the input end of the early warning module, the output end of the early warning module is electrically connected with the input end of the terminal module, the output end of the judging unit is electrically connected with the input end of the terminal module, and the input end of the terminal module is electrically connected with the output end of the image acquisition unit.
As a further scheme of the invention: the early warning module comprises a voice prompt unit, a light prompt unit and a timing unit, wherein the input ends of the voice prompt unit and the light prompt unit are electrically connected with the output end of the judging unit, and the output ends of the voice prompt unit and the light prompt unit are electrically connected with the input end of the timing unit.
As a further scheme of the invention: the terminal module comprises a display unit and a remote communication unit, the output end of the display unit is electrically connected with the output ends of the judging unit and the image acquisition unit, and the output end of the timing unit is electrically connected with the in-out section of the remote communication unit.
As a further scheme of the invention: the image acquisition unit is used for acquiring image information of each processing position in the workshop;
the data processing unit is used for calculating and analyzing the acquired image data and sending the calculated data to the judging unit;
the data storage unit is used for storing the image information acquired by the image acquisition unit and storing judgment standard data for theft;
the judging unit is used for calling the theft judgment standard data in the data storage unit and comparing and judging the theft judgment standard data with the calculated data to obtain the result of whether the theft occurs or not.
As a further scheme of the invention: the display unit is used for displaying the acquired image information and the judgment result;
the voice prompt unit is used for sending out a warning voice prompt to prompt a worker to check and dispose when the judgment result is that the burglary is taken;
the light prompting unit is used for sending out warning light to prompt a worker to check and dispose when the judgment result is that the burglary is caused;
the timing unit is used for timing the working time of the voice prompt unit and the light prompt unit;
and the remote communication unit is used for remotely sending the judgment result to the mobile terminal of the worker when the timing unit times for more than three minutes.
As a further scheme of the invention: the image data calculation and analysis comprises the following specific steps:
moving the target template image in the image to be matched, calculating error cumulant of pixels between the image and the window to be matched for each reference point, if the cumulant reaches a threshold value before pixel points are accumulated, mismatching is carried out, and the next point needs to be detected; and if the accumulation amount slowly rises, overlapping the accumulation times, and taking the maximum accumulation times as a matching window.
First, let M × M be the pixels of the template image T (i, j), move the pixels point by point on the image, and set the search subgraph as f (i, j), where the absolute error is defined:
wherein, the first and the second end of the pipe are connected with each other,
and
the method comprises the following steps of respectively searching a sub-image and a template image for pixel gray average values:
taking a threshold value K;
randomly selecting pixel points in f (i, j), calculating epsilon of the pixel points and corresponding points in the template image T (i, j), accumulating and counting epsilon, and if the number reaches r, satisfying r
Then it is not accumulated and r is recorded, defining the SSDA detection surface I (I, j) as follows:
predicting the motion trail:
if the point (I, j) maximizes the value of I (I, j), then it is taken as the matching point;
the discrete dynamic system is divided into an n-dimensional dynamic system and an m-dimensional (m is less than or equal to n) observation system (adopting a linear ground cabinet filter plate technology and adopting a minimum mean square error estimation method), and the state and observation equations are as follows:
Xt＝AXt-1+Wt-1
Zt＝HXt+Vt
wherein XtAnd Xt-1Respectively is a system state vector at the t moment and the t-1 moment; ztA system observation value vector at the time t; a is a system state transition matrix; h is a system observation matrix; process noise vector Wt-1And an observation noise vector VtHave not much relation between them, and both values are 0, Wt-1Has a covariance matrix of Qt，VtHas a covariance matrix of Rt。
Assuming that the motion state parameters of the target are the position and the speed of the target at the moment t, in the tracking process, the delta t of the adjacent 2 frames is shorter, and the target is positioned at the moment tThe motion state does not change much, and the motion state is regarded as uniform motion within unit time interval delta t, and a system state vector X is assumedt＝(Sx,Sy,Vx,Vy) In which S isxAnd SyThe position of the target in the x and y axes, respectively, where VxAnd VyAnalyzing the speed of the target in the x and Y axes respectively to obtain the position information of the target, and defining an observation state vector YtIs (S)sx,Ssy) Definitions a and H are:
Wtis normal white noise with a mean value of 0, and is set to WtOf the covariance matrix QtComprises the following steps:
Vtset V as normal white noise with a mean value of 0tOf the covariance matrix RtComprises the following steps:
the target motion estimation process is as follows:
initializing a target position x0The value is obtained by detection, if the speed cannot be obtained, the value is set to 0, and the moment is recorded;
the acquired covariance is received, the position of the target at the next position is predicted and judged, and the speed is estimated to obtain a prior estimated value;
by means of a feedback method and the obtained observation values, a posterior estimate is further obtained and taken as a target position from which specific speed information is fed back.
The invention has the beneficial effects that:
1. according to the invention, through calculation and analysis of image data, a related algorithm can realize matching of images of related objects, judge the moving track of an object, match the moving track of related personnel, simply predict the moving track of the object, judge the transfer operation process of the object, conveniently and accurately judge whether the object is stolen or not, judge the moving track of the object and whether the object is stolen or not in time, and alarm prompt is carried out in time subsequently, so that accurate discovery and theft prevention are realized;
2. according to the invention, by arranging the timing unit and the remote communication unit, when the relevant post personnel are not on duty, under the condition that the voice prompt unit and the light prompt unit work for more than three minutes and are not closed by people, the remote communication unit sends the burglary behavior to the mobile terminal of the staff in time, so that the staff can be prompted timely and accurately.
Drawings
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a schematic structural view of the present invention;
in the figure: 1. an image acquisition unit; 2. a data analysis module; 21. a data storage unit; 22. a data processing unit; 23. a judgment unit; 3. a terminal module; 31. a display unit; 32. a remote communication unit; 4. a voice prompt unit; 42. a light prompting unit; 43. a timing unit.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in FIGS. 1-2, the invention provides an intelligent manufacturing workshop anti-theft early warning system with intelligent identification, which comprises an image acquisition unit 1, wherein the output end of the image acquisition unit 1 is electrically connected with the input end of a data analysis module 2, the data analysis module 2 comprises a data storage unit 21, a judgment unit 23 and a data processing unit 22, the output end of the image acquisition unit 1 is electrically connected with the input ends of the data storage unit 21 and the data processing unit 22, the output end of the data storage unit 21 is electrically connected with the input end of the judgment unit 23, the input end of the judgment unit 23 is electrically connected with the output end of the data processing unit 22, the output end of the judgment unit 23 is electrically connected with the input end of an early warning module 4, the output end of the early warning module 4 is electrically connected with the input end of a terminal module 3, the output end of the judgment unit 23 is electrically connected with the input end of the terminal module 3, the input end of the terminal module 3 is electrically connected with the output end of the image acquisition unit 1.
The early warning module 4 comprises a voice prompt unit 41, a light prompt unit 42 and a timing unit 43, wherein the input ends of the voice prompt unit 41 and the light prompt unit 42 are electrically connected with the output end of the judging unit 23, and the output ends of the voice prompt unit 41 and the light prompt unit 42 are electrically connected with the input end of the timing unit 43.
The terminal module 3 comprises a display unit 31 and a remote communication unit 32, wherein the output end of the display unit 31 is electrically connected with the output ends of the judgment unit 23 and the image acquisition unit 1, and the output end of the timing unit 43 is electrically connected with the entrance and exit section of the remote communication unit 32.
The image acquisition unit 1 is used for acquiring image information of each processing position in a workshop;
the data processing unit 22 performs calculation analysis on the acquired image data and sends the calculated data to the judging unit 23;
the data storage unit 21 is used for storing the image information acquired by the image acquisition unit 1 and storing judgment standard data of theft;
the judging unit 23 is configured to compare the theft judgment standard data in the data storage unit 21 with the calculated data to obtain a result of whether a theft occurs.
The display unit 31 is used for displaying the acquired image information and the judgment result;
the voice prompt unit 41 is used for sending out a warning voice to prompt a worker to check and dispose when the judgment result is that the burglary is taken;
the light prompting unit 42 is used for sending out warning light to prompt a worker to check and dispose when the judgment result is that the burglary is caused;
the timing unit 43 is used for timing the working time of the voice prompt unit 41 and the light prompt unit 42;
the remote communication unit 32 is configured to remotely transmit the determination result to the mobile terminal of the staff member when the time counted by the time counting unit 43 exceeds three minutes.
The image data calculation and analysis method comprises the following specific steps:
moving the target template image in the image to be matched, calculating error cumulant of pixels between the image and the window to be matched for each reference point, if the cumulant reaches a threshold value before pixel points are accumulated, mismatching is carried out, and the next point needs to be detected; and if the accumulation amount slowly rises, overlapping the accumulation times, and taking the maximum accumulation time as a matching window.
First, let M × M be the pixels of the template image T (i, j), move the pixels point by point on the image, and set the search subgraph as f (i, j), where the absolute error is defined:
wherein the content of the first and second substances,
and
the method comprises the following steps of respectively searching a pixel gray average value of a sub image and a pixel gray average value of a template image, and specifically comprises the following steps:
taking a threshold value K;
randomly selecting pixel points in f (i, j), calculating the epsilon of the pixel points and corresponding points in the template image T (i, j), accumulating and counting the epsilon, and if the epsilon reaches r times and meets the requirement
Then it is not accumulated and r is recorded, defining the SSDA detection surface I (I, j) as follows:
predicting the motion trail:
if the point (I, j) maximizes the value of I (I, j), then it is taken as the matching point;
the discrete dynamic system is divided into an n-dimensional dynamic system and an m-dimensional (m is less than or equal to n) observation system (adopting a linear ground cabinet filter plate technology and adopting a minimum mean square error estimation method), and the state and observation equations are as follows:
Xt＝AXt-1+Wt-1
Zt＝HXt+Vt
wherein XtAnd Xt-1Respectively is a system state vector at the t moment and the t-1 moment; ztA system observation value vector at the time t; a is a system state transition matrix; h is a system observation matrix; process noise vector Wt-1And an observation noise vector VtHave not much relation between them, and both values are 0, Wt-1Has a covariance matrix of Qt，VtHas a covariance matrix of Rt。
Assuming that the motion state parameters of the target are the position and the speed of the target at the moment t, in the tracking process, the adjacent 2 frames are short in delta t, the motion state of the target is not changed greatly, the target is regarded as uniform motion in a unit time interval delta t, and a system state vector X is assumedt＝(Sx,Sy,Vx,Vy) In which S isxAnd SyThe position of the target in the x and y axes, respectively, where VxAnd VyAnalyzing the speed of the target in the x and y axes respectively to obtain the position information of the target, and defining the observation state vectorYtIs (S)sx,Ssy) Definitions a and H are:
Wtis normal white noise with a mean value of 0, and is set to WtOf the covariance matrix QtComprises the following steps:
Vtset V as normal white noise with a mean value of 0tOf the covariance matrix RtComprises the following steps:
the target motion estimation process is as follows:
initializing a target position x0The value is obtained by detection, if the speed cannot be obtained, the value is set to 0, and the moment is recorded;
the acquired covariance is received, the position of the target at the next position is predicted and judged, and the speed is estimated to obtain a prior estimated value;
by means of a feedback method and the obtained observation values, a posterior estimate is further obtained and taken as a target position from which specific speed information is fed back.
In summary, the present invention:
according to the invention, through calculation and analysis of image data, the related algorithm can realize matching of images of related objects, judge the moving track of an object, match the moving track of related personnel, simply predict the moving track of the object, judge the transfer operation process of the object, conveniently and accurately judge whether the object is stolen or not in time, and accurately judge the moving track of the object and whether the object is stolen or not in time, and then give an alarm prompt in time, so that accurate discovery and theft deterrence are realized.
According to the invention, by arranging the timing unit 43 and the remote communication unit 32, when the relevant post personnel are not on duty, under the condition that the voice prompt unit 411 and the light prompt unit 42 work for more than three minutes and are not closed by people, the remote communication unit 32 sends the burglary behavior to the mobile terminal of the worker remotely in time, so that the worker can be prompted accurately in time.
Secondly, the method comprises the following steps: in the drawings of the disclosed embodiments of the invention, only the structures related to the disclosed embodiments are referred to, other structures can refer to common designs, and the same embodiment and different embodiments of the invention can be combined with each other without conflict;
and finally: 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 are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (6)
1. The utility model provides a be provided with intelligent manufacturing shop theftproof early warning system of intelligent recognition, includes image acquisition unit (1), its characterized in that: the output end of the image acquisition unit (1) is electrically connected with the input end of the data analysis module (2), the data analysis module (2) comprises a data storage unit (21), a judgment unit (23) and a data processing unit (22), the output end of the image acquisition unit (1) is electrically connected with the input ends of the data storage unit (21) and the data processing unit (22), the output end of the data storage unit (21) is electrically connected with the input end of the judgment unit (23), the input end of the judgment unit (23) is electrically connected with the output end of the data processing unit (22), the output end of the judgment unit (23) is electrically connected with the input end of the early warning module (4), the output end of the early warning module (4) is electrically connected with the input end of the terminal module (3), and the output end of the judgment unit (23) is electrically connected with the input end of the terminal module (3), the input end of the terminal module (3) is electrically connected with the output end of the image acquisition unit (1).
2. The intelligent manufacturing shop anti-theft early warning system provided with intelligent identification according to claim 1, characterized in that: early warning module (4) are including voice prompt unit (41), light prompt unit (42) and timing unit (43), the input of voice prompt unit (41) and light prompt unit (42) is connected with the output electricity of judging unit (23), the output of voice prompt unit (41) and light prompt unit (42) is connected with the input electricity of timing unit (43).
3. The intelligent manufacturing shop anti-theft early warning system provided with intelligent identification according to claim 2, characterized in that: the terminal module (3) comprises a display unit (31) and a remote communication unit (32), the output end of the display unit (31) is electrically connected with the judging unit (23) and the output end of the image acquisition unit (1), and the output end of the timing unit (43) is electrically connected with the access section of the remote communication unit (32).
4. The intelligent manufacturing shop anti-theft early warning system provided with intelligent identification according to claim 1, characterized in that: the image acquisition unit (1) is used for acquiring image information of each processing position in a workshop;
the data processing unit (22) performs calculation analysis on the acquired image data and sends the calculated data to the judging unit (23);
the data storage unit (21) is used for storing the image information acquired by the image acquisition unit (1) and storing judgment standard data for theft;
the judging unit (23) is used for calling the theft judgment standard data in the data storage unit (21) to compare with the calculated data for judgment, and obtaining the result of whether the theft occurs or not.
5. The intelligent manufacturing shop anti-theft early warning system provided with intelligent identification according to claim 4, characterized in that: the display unit (31) is used for displaying the acquired image information and the judgment result;
the voice prompt unit (41) is used for sending out a warning voice prompt to prompt staff to check and dispose when the judgment result is that the burglary is caused;
the light prompting unit (42) is used for sending out warning light to prompt staff to check and dispose when the judgment result is that the burglary is taken;
the timing unit (43) is used for timing the working time of the voice prompt unit (41) and the light prompt unit (42);
the remote communication unit (32) is used for remotely transmitting the judgment result to the mobile terminal of the staff when the time counted by the time counting unit (43) exceeds three minutes.
6. The intelligent manufacturing shop anti-theft early warning system with intelligent recognition according to claim 5, wherein the image data is calculated and analyzed by the following specific steps:
moving the target template image in the image to be matched, calculating error cumulant of pixels between the image and the window to be matched for each reference point, if the cumulant reaches a threshold value before pixel points are accumulated, mismatching is carried out, and the next point needs to be detected; and if the accumulation amount slowly rises, overlapping the accumulation times, and taking the maximum accumulation times as a matching window.
First, let M × M be the pixels of the template image T (i, j), move the pixels point by point on the image, and set the search subgraph as f (i, j), where the absolute error is defined:
wherein the content of the first and second substances,
and
the method comprises the following steps of respectively searching a sub-image and a template image for pixel gray average values:
taking a threshold value K;
randomly selecting pixel points in f (i, j), calculating epsilon of the pixel points and corresponding points in the template image T (i, j), accumulating and counting epsilon, and if the number reaches r, satisfying r
Then it is not accumulated and r is recorded, defining the SSDA detection surface I (I, j) as follows:
predicting the motion trail:
if the point (I, j) maximizes the value of I (I, j), then it is taken as the matching point;
the discrete dynamic system is divided into an n-dimensional dynamic system and an m-dimensional (m is less than or equal to n) observation system, and the state and observation equations are as follows:
Xt＝AXt-1+Wt-1
Zt＝HXt+Vt
wherein XtAnd Xt-1Respectively a system state vector at the t moment and the t-1 moment; ztA system observation value vector at the time t; a is a system state transition matrix; h is a system observation matrix; process noise vector Wt-1And an observation noise vector VtHave not much relation between them, and both values are 0, Wt-1Has a covariance matrix of Qt，VtHas a covariance matrix of Rt。
Assuming that the motion state parameters of the target are the position and the speed of the target at the moment t, in the tracking process, the delta t of the adjacent 2 frames is shorter, the motion state of the target is not changed greatly, the target is regarded as uniform motion within the unit time interval delta t,suppose a system state vector Xt＝(Sx,Sy,Vx,Vy) In which S isxAnd SyThe position of the target in the x and y axes, respectively, where VxAnd VyAnalyzing the speed of the target in the x and Y axes respectively to obtain the position information of the target, and defining an observation state vector YtIs (S)sx,Ssy) Definitions a and H are:
Wtis normal white noise with a mean value of 0, and is set to WtOf (2) covariance matrix QtComprises the following steps:
Vtset V as normal white noise with a mean value of 0tOf the covariance matrix RtComprises the following steps:
the target motion estimation process is as follows:
initializing target position x0The value is obtained by detection, if the speed cannot be obtained, the value is set to 0, and the moment is recorded;
the acquired covariance is received, the position of the target at the next position is predicted and judged, and the speed is estimated to obtain a prior estimated value;
by means of a feedback method and the obtained observation values, a posterior estimate is further obtained and taken as a target position from which specific speed information is fed back.
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