CN113311191A - On-line calibration method and device for vehicle-mounted accelerometer - Google Patents
On-line calibration method and device for vehicle-mounted accelerometer Download PDFInfo
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Abstract
The invention provides an on-line calibration method of a vehicle-mounted accelerometer, which comprises the following steps: acquiring the detection speed of a vehicle and the longitudinal acceleration detected by the vehicle-mounted accelerometer; differentiating the detected speed of the vehicle to determine a calculated acceleration; and determining an acceleration factor based on the longitudinal acceleration and the calculated acceleration, the acceleration factor being used to calibrate the on-board accelerometer.
Description
Technical Field
The invention relates to the field of vehicle information acquisition, in particular to an on-line calibration method and device for a vehicle-mounted accelerometer.
Background
The medium-low speed maglev train has the speed per hour of one hundred to one hundred twenty kilometers, has the characteristics of energy conservation, environmental protection, low noise, small turning radius, high climbing capacity and the like, has the manufacturing cost slightly higher than that of a light rail and far lower than that of a subway, is an advanced technology in the urban rail traffic at present, and has great advantages in the process of solving the traffic jam and urban fixed diseases.
The speed measurement method of the medium-low speed maglev train mainly depends on a technical sleeper method at present, and the method has the defects of high-speed pulse loss, poor low-speed measurement precision and the like, so that the method for measuring the speed by adopting the fusion accelerometer becomes a mainstream solution. Meanwhile, the acceleration is also an important data source for researching the safety of the train.
Due to the problems of manufacturing process, equipment installation and the like, different acceleration sensors have different parameters, and larger accumulated errors can be caused in analysis and calculation to cause analysis and calculation errors, so that the accuracy of solving the driving track, the attitude angle and the like is influenced. Therefore, the mounting error of the accelerometer needs to be calibrated and calibrated, so that the calculation error caused by the error of the accelerometer is reduced to the maximum extent, and the accuracy of the calculation result is improved. Most of the existing accelerometer calibration methods are static calibration operation for one time or limited times, and the deviation change which possibly occurs in the running process of a train cannot be adjusted, so that the method has certain limitation.
In order to solve the above problems, the present invention aims to provide an on-line calibration method for a vehicle-mounted accelerometer, which can improve the speed detection precision of a counting pillow method integrated accelerometer, and further, can further calibrate the static installation error of the vehicle-mounted accelerometer on line.
Disclosure of Invention
The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.
According to an aspect of the invention, an online calibration method for an on-board accelerometer is provided, which includes: acquiring the detection speed of a vehicle and the longitudinal acceleration detected by the vehicle-mounted accelerometer; differentiating the detected speed of the vehicle to determine a calculated acceleration; and determining an acceleration factor based on the longitudinal acceleration and the calculated acceleration, the acceleration factor being used to calibrate the on-board accelerometer.
Still further, said determining an acceleration factor based on said longitudinal acceleration and said calculated acceleration comprises: and in response to the fact that the vehicle is in a uniform acceleration stage within preset time and the fluctuation of the longitudinal acceleration detected by the vehicle-mounted accelerometer is smaller than a preset threshold value, determining the acceleration factor based on the average value of the calculated acceleration determined within the preset time and the average value of the longitudinal acceleration within the preset time.
Further, the line calibration method further comprises: judging whether the detected speed of the vehicle is greater than a preset speed or not; and responding to the fact that the detected speed is larger than the preset speed, and judging that the vehicle is in a uniform acceleration stage.
Further, the line calibration method further comprises: calculating the standard deviation of the longitudinal acceleration detected by the vehicle-mounted accelerometer within the preset time; and responding to the fact that the standard deviation is smaller than a preset threshold value, and judging that the fluctuation of the longitudinal acceleration detected by the vehicle-mounted accelerometer is smaller than the preset threshold value.
Still further, the determining an acceleration factor includes: calculating a formula using the acceleration factor
ComputingDeriving the acceleration factor, where k is the acceleration factor,
is the average value of the calculated acceleration within the preset time,
and the average value of the longitudinal acceleration in the preset time is obtained.
Further, the line calibration method further comprises: filtering the detected velocity and the longitudinal acceleration; and said differentiating the detected speed of the vehicle to determine the calculated acceleration comprises: differentiating the filtered detected velocity to determine the calculated acceleration; and said determining an acceleration factor based on said longitudinal acceleration and said calculated acceleration comprises: and determining the acceleration factor by using the calculated acceleration determined by the filtered detection speed and the filtered longitudinal acceleration.
Further, the line calibration method further comprises: and carrying out installation error calibration on the original longitudinal acceleration detected by the vehicle-mounted accelerometer to determine the longitudinal acceleration.
Further, the calibrating the installation error of the original longitudinal acceleration detected by the vehicle-mounted accelerometer comprises: mounting the on-board accelerometer on the vehicle; acquiring an acceleration original value output by the vehicle-mounted accelerometer under a static condition, wherein the acceleration original value comprises an original longitudinal acceleration, an original roll acceleration and an original pitch acceleration; determining the mounting declination angle of the accelerometer relative to the vehicle by using the theoretical value of the three-axis acceleration of the vehicle in a vehicle coordinate system under a static condition and the original value of the acceleration output by the vehicle-mounted accelerometer; and calibrating a raw longitudinal acceleration of the on-board accelerometer using the stagger angle to determine the longitudinal acceleration.
Still further, the determining the mounting declination angle of the accelerometer relative to the vehicle by using the theoretical value of the three-axis acceleration of the vehicle in the vehicle coordinate system and the original value of the acceleration output by the on-board accelerometer under the static condition comprises: determining a transformation matrix of the accelerometer coordinate system to a vehicle coordinate system of the vehicle, the transformation matrix employing mounting declination identification of the accelerometer relative to the vehicle; establishing an equation set about the triaxial acceleration theoretical value and an acceleration original value output by the vehicle-mounted accelerometer by using the conversion matrix; and solving the system of equations by using an adaptive particle swarm algorithm to determine the installation declination.
Still further, said solving said system of equations using an adaptive particle swarm algorithm comprises: setting a population size N searched by the self-adaptive particle swarm algorithm; randomly initializing initial positions and initial speeds of the N particles; setting evolution iteration times; determining fitness of the N particles; and repeating the iteration of the adaptive particle swarm algorithm until the iteration times are larger than the evolution iteration times, and taking the iteration result as the optimal solution of the equation set.
Still further, the determining the fitness of the N particles includes: determining fitness function of the N particles according to least square thought
Wherein α, β, γ are the three-axis mounting declination of the accelerometer coordinate system relative to the vehicle coordinate system of the vehicle, respectively, fiAnd (alpha, beta, gamma) is a conversion equation of any axis, and alpha, beta and gamma of the F (alpha, beta and gamma) which takes the minimum value are the optimal solutions of the installation deflection angle.
Further, the acceleration factor is multiplied by the longitudinal acceleration output by the on-board accelerometer to be used as the actual longitudinal acceleration of the vehicle.
According to another aspect of the present invention, there is also provided an on-line calibration apparatus for an on-vehicle accelerometer, including: a memory; and a processor configured to: acquiring the detection speed of a vehicle and the longitudinal acceleration detected by the vehicle-mounted accelerometer; differentiating the detected speed of the vehicle to determine a calculated acceleration; and determining an acceleration factor based on the longitudinal acceleration and the calculated acceleration, the acceleration factor being used to calibrate the on-board accelerometer.
Still further, the processor is further configured to: and in response to the fact that the vehicle is in a uniform acceleration stage within preset time and the fluctuation of the longitudinal acceleration detected by the vehicle-mounted accelerometer is smaller than a preset threshold value, determining the acceleration factor based on the average value of the calculated acceleration determined within the preset time and the average value of the longitudinal acceleration within the preset time.
Still further, the processor is further configured to: judging whether the detected speed of the vehicle is greater than a preset speed or not; and responding to the fact that the detected speed is larger than the preset speed, and judging that the vehicle is in a uniform acceleration stage.
Still further, the processor is further configured to: calculating the standard deviation of the longitudinal acceleration detected by the vehicle-mounted accelerometer within the preset time; and responding to the fact that the standard deviation is smaller than a preset threshold value, and judging that the fluctuation of the longitudinal acceleration detected by the vehicle-mounted accelerometer is smaller than the preset threshold value.
Still further, the processor is further configured to: calculating a formula using the acceleration factor
Calculating the acceleration factor, wherein k is the acceleration factor,
is the average value of the calculated acceleration within the preset time,
and the average value of the longitudinal acceleration in the preset time is obtained.
Still further, the processor is further configured to: filtering the detected velocity and the longitudinal acceleration; differentiating the filtered detected velocity to determine the calculated acceleration; and determining the acceleration factor using the calculated acceleration determined from the filtered detected velocity and the filtered longitudinal acceleration.
Still further, the processor is further configured to: and carrying out installation error calibration on the original longitudinal acceleration detected by the vehicle-mounted accelerometer to determine the longitudinal acceleration.
Still further, the processor is further configured to: mounting the on-board accelerometer on the vehicle; acquiring an acceleration original value output by the vehicle-mounted accelerometer under a static condition, wherein the acceleration original value comprises an original longitudinal acceleration, an original roll acceleration and an original pitch acceleration; determining the mounting declination angle of the accelerometer relative to the vehicle by using the theoretical value of the three-axis acceleration of the vehicle in a vehicle coordinate system under a static condition and the original value of the acceleration output by the vehicle-mounted accelerometer; and calibrating a raw longitudinal acceleration of the on-board accelerometer using the stagger angle to determine the longitudinal acceleration.
Still further, the processor is further configured to: determining a transformation matrix of the accelerometer coordinate system to a vehicle coordinate system of the vehicle, the transformation matrix employing mounting declination identification of the accelerometer relative to the vehicle; establishing an equation set about the triaxial acceleration theoretical value and an acceleration original value output by the vehicle-mounted accelerometer by using the conversion matrix; and solving the system of equations by using an adaptive particle swarm algorithm to determine the installation declination.
Still further, the processor is further configured to: setting a population size N searched by the self-adaptive particle swarm algorithm; randomly initializing initial positions and initial speeds of the N particles; setting evolution iteration times; determining fitness of the N particles; and repeating the iteration of the adaptive particle swarm algorithm until the iteration times are larger than the evolution iteration times, and taking the iteration result as the optimal solution of the equation set.
Still further, the processor is further configured to: determining fitness function of the N particles according to least square thought
Wherein α, β, γ are the three-axis mounting declination of the accelerometer coordinate system relative to the vehicle coordinate system of the vehicle, respectively, fiAnd (alpha, beta, gamma) is a conversion equation of any axis, and alpha, beta and gamma of the F (alpha, beta and gamma) which takes the minimum value are the optimal solutions of the installation deflection angle.
Still further, the processor is further configured to: and multiplying the acceleration factor by the longitudinal acceleration output by the vehicle-mounted accelerometer to serve as the actual longitudinal acceleration of the vehicle.
According to yet another aspect of the present invention, there is also provided a computer storage medium having a computer program stored thereon, the computer program when executed implementing the steps of the online calibration method as described in any of the above embodiments.
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The above features and advantages of the present disclosure will be better understood upon reading the detailed description of embodiments of the disclosure in conjunction with the following drawings.
FIG. 1 is a flow chart diagram illustrating a method for on-line calibration of an on-board accelerometer according to one embodiment of the invention;
FIG. 2 is a partial flow diagram of a method for on-line calibration of an on-board accelerometer according to one embodiment of the invention;
FIG. 3 is a partial flow diagram of a method for on-line calibration of an on-board accelerometer according to one embodiment of the invention;
FIG. 4 is a partial flow diagram of a method of on-line calibration of an on-board accelerometer according to one embodiment depicted in accordance with an aspect of the invention;
FIG. 5 is a partial flow diagram of a method of on-line calibration of an on-board accelerometer according to one embodiment depicted in accordance with an aspect of the invention;
FIG. 6 is a partial flow diagram of a method of on-line calibration of an on-board accelerometer according to one embodiment depicted in accordance with an aspect of the invention;
FIG. 7 is a schematic block diagram of an online calibration apparatus for an on-board accelerometer according to an embodiment depicted in another aspect of the invention.
Detailed Description
The following description is presented to enable any person skilled in the art to make and use the invention and is incorporated in the context of a particular application. Various modifications, as well as various uses in different applications will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to a wide range of embodiments. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
In the following detailed description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. It will be apparent, however, to one skilled in the art that the practice of the invention may not necessarily be limited to these specific details. In other instances, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the present invention.
The reader's attention is directed to all papers and documents which are filed concurrently with this specification and which are open to public inspection with this specification, and the contents of all such papers and documents are incorporated herein by reference. All the features disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features.
Note that where used, the designations left, right, front, back, top, bottom, positive, negative, clockwise, and counterclockwise are used for convenience only and do not imply any particular fixed orientation. In fact, they are used to reflect the relative position and/or orientation between the various parts of the object. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
It is noted that, where used, further, preferably, still further and more preferably is a brief introduction to the exposition of the alternative embodiment on the basis of the preceding embodiment, the contents of the further, preferably, still further or more preferably back band being combined with the preceding embodiment as a complete constituent of the alternative embodiment. Several further, preferred, still further or more preferred arrangements of the belt after the same embodiment may be combined in any combination to form a further embodiment.
The invention is described in detail below with reference to the figures and specific embodiments. It is noted that the aspects described below in connection with the figures and the specific embodiments are only exemplary and should not be construed as imposing any limitation on the scope of the present invention.
According to one aspect of the invention, an online calibration method for an on-board accelerometer is provided, which can realize online calibration of acceleration detected by the on-board accelerometer.
In the speed calculation of the medium and low speed maglev train, the acceleration in the running direction of the train is focused, so the on-line calibration method focuses on the dynamic on-line calibration of the longitudinal acceleration of the medium and low speed maglev train. The acceleration factor is the ratio of the actual longitudinal acceleration of the medium-low speed magnetic levitation train to the longitudinal acceleration output by the accelerometer, and the actual longitudinal acceleration of the train can be determined by combining the data of the longitudinal acceleration output by the accelerometer only by determining the acceleration factor on line.
In one embodiment, as shown in FIG. 1, the on-line calibration method 100 for an on-board accelerometer includes steps S110-S130.
Wherein, step S110 is: and acquiring the detected speed of the vehicle and the longitudinal acceleration detected by the vehicle-mounted accelerometer.
The detected speed of the vehicle is the vehicle operating speed determined by the counting tie method expected to be calibrated. It can be understood that when the vehicle running speed determined by the counting sleeper method is used for online calibration of the vehicle-mounted accelerometer, the detection data when the vehicle meets the detection requirement of the counting sleeper method can be selected for online calibration. It will be appreciated that screening test data may improve calibration accuracy.
It can be understood that when the vehicle adopts other speed detection methods to determine the vehicle speed, the detection data under the running condition that the vehicle meets the speed detection methods with higher precision can be selected for calibration, so as to improve the online calibration precision.
The longitudinal acceleration detected by the vehicle-mounted accelerometer is longitudinal acceleration data output by the vehicle-mounted accelerometer.
Step S120 is: differentiating the detected speed of the vehicle to determine a calculated acceleration.
The calculated acceleration refers to a longitudinal acceleration calculated based on the running speed of the vehicle. If the vehicle satisfies the operating condition of high detection accuracy of the counting sleeper method, the vehicle detection speed detected by the counting sleeper method is differentiated to obtain high-accuracy calculated acceleration. When the detection accuracy of the count tie method is 100%, the calculated acceleration determined based on the detected vehicle speed data is the actual acceleration of the train.
In particular, when the vehicle is in a level acceleration stage, even if there is a certain speed detection error in the count tie method, the calculated acceleration determined based on the continuously collected detected speeds can be regarded as the actual acceleration of the train. Therefore, to improve calibration accuracy, the speed data of the vehicle in the smooth acceleration phase may be selected for calibration.
Step S130 is: an acceleration factor is determined based on the longitudinal acceleration and the calculated acceleration.
Assuming that the calculated acceleration is the actual acceleration of the train, an acceleration factor can be determined based on the longitudinal acceleration output by the vehicle-mounted accelerometer and the calculated acceleration.
Assuming that the acceleration factor is k, a is the calculated acceleration, and a is the longitudinal acceleration output by the on-vehicle accelerometer, the acceleration factor can be determined by the following equation (1).
After the acceleration factor is determined, the longitudinal acceleration output by the vehicle-mounted accelerometer can be corrected. Specifically, the determined acceleration factor may be multiplied by the longitudinal acceleration output by the onboard accelerometer as the actual longitudinal acceleration of the vehicle.
Further, to improve the accuracy of the determined acceleration factor, data screening may be performed on the calculated acceleration used to determine the acceleration factor and the longitudinal acceleration output by the onboard accelerometer. Correspondingly, step S130 may be optimized as: and in response to the fact that the vehicle is in a uniform acceleration stage within preset time and the fluctuation of the longitudinal acceleration detected by the vehicle-mounted accelerometer is smaller than a preset threshold value, determining the acceleration factor based on the average value of the calculated acceleration determined within the preset time and the average value of the longitudinal acceleration within the preset time.
It is understood that when the vehicle is in a level acceleration phase for a period of time, the calculated acceleration determined based on the detected vehicle speed data may be considered as the actual longitudinal acceleration of the vehicle. Meanwhile, if the fluctuation of the longitudinal acceleration detected by the vehicle-mounted accelerometer in the period of time is small, the data error of the longitudinal acceleration detected by the vehicle-mounted accelerometer is small. The accuracy of the acceleration factor determined based on the data over the period of time is also higher.
It is understood that the fluctuation range of the longitudinal acceleration data output by the vehicle-mounted accelerometer can be represented by the variance, standard deviation or other indexes which can be used for representing the data fluctuation condition of the longitudinal acceleration data output by the vehicle-mounted accelerometer within the preset time. Correspondingly, the preset threshold is set as a threshold of the variance, standard deviation or other indexes which can be used for representing the data fluctuation condition of the longitudinal acceleration data.
Preferably, the acceleration factor is determined based on the average calculated acceleration and the average longitudinal acceleration over the period of time, further reducing data glitches. Equation (1) can be optimized to equation (2).
Wherein k is an acceleration factor,
is the average value of the calculated acceleration within the preset time,
and the average value of the longitudinal acceleration in the preset time is obtained.
Further, step S130 may further include the step of determining that the vehicle is in the level-up acceleration phase, and specifically, as shown in fig. 2, step S130 may include steps S131 to S132.
Step S131 is: and judging whether the detected speed of the vehicle is greater than a preset speed or not.
It can be understood that when a maglev train is in a starting stage, it generally needs to accelerate to a target speed of a road section in a shortest time, in the process, the maglev train firstly passes through a variable acceleration stage, the acceleration is increased to a maximum acceleration in the variable acceleration stage, and then passes through a uniform acceleration stage, and the uniform acceleration stage is accelerated to the target speed by the maximum acceleration. Thus, based on empirical or performance design data of the train, the maximum speed that can be achieved in the variable acceleration phase, after which the train is in the uniform acceleration phase, can be determined. Therefore, the maximum speed that the train can reach in the variable acceleration stage can be set as the preset speed.
Step S132 is: and responding to the fact that the detected speed is larger than the preset speed, and judging that the vehicle is in a uniform acceleration stage.
Particularly, when the train meets a special condition and needs to decelerate after reaching the preset speed in the variable acceleration stage, the speed of the train may be lower than the preset speed, so that all the detection speed values detected in the preset time can be compared with the preset speed, and when all the detection speeds meet the requirement of being higher than the preset speed, the train is judged to be in the uniform acceleration stage.
Preferably, the timing may be started from any time, and it may be determined whether all the vehicle speed data detected within a preset time after the start time is greater than a preset speed, and when all the vehicle speeds detected within the preset time after a certain time is greater than the preset speed, the speed data from the time to the next preset time is set as valid data.
When the detected speed data in a preset time is judged to be valid data, the detected speed at the acquisition moment of any data in the preset time can be differentiated, and a calculated acceleration can be obtained based on each detected speed. Let v be the detected speed at time i (any time within a preset time period)iThen the calculated acceleration at that time
Assuming that there are N detected speeds within the preset time, the average calculated acceleration of the preset time is
Further, step S130 may further include a step of determining that the longitudinal acceleration data output by the vehicle-mounted accelerometer has small fluctuation, and specifically, as shown in fig. 3, step S130 may further include steps S133 to S134.
Step S133 is: and calculating the standard deviation of the longitudinal acceleration detected by the vehicle-mounted accelerometer within the preset time.
Assuming that the preset threshold is a standard deviation, the standard deviation of all longitudinal acceleration data output by the vehicle-mounted accelerometer within a preset time from any moment can be calculated.
The standard deviation calculation formula can be represented by formula (3):
wherein N is the number of the longitudinal acceleration output by the vehicle-mounted accelerometer collected in the preset time period,
the average value of the N longitudinal accelerations output by the vehicle-mounted accelerometer and collected in the preset time period,
Aiis the ith longitudinal acceleration value of the N longitudinal accelerations.
Step S134 is: and responding to the condition that the standard deviation is smaller than a preset threshold value, and judging that the fluctuation of the longitudinal acceleration detected by the vehicle-mounted accelerometer is smaller than the preset threshold value.
When the standard deviation of the longitudinal acceleration data output by the vehicle-mounted accelerometer from a certain moment to a preset time later is smaller than a preset threshold value, the longitudinal acceleration data output in the period can be set as valid data.
It is to be understood that, when both the detected speed data of the vehicle and the longitudinal acceleration data output from the on-vehicle accelerometer are valid data within a preset time from a certain time, the acceleration factor may be determined based on the detected speed data within the preset time from the time and the longitudinal acceleration data output from the on-vehicle accelerometer.
It is understood that, in order to reduce the amount of calculation, a calculation cycle may be set, and the validity of the detected speed data in the calculation cycle and the validity of the longitudinal acceleration data output by the on-vehicle accelerometer may be determined every other calculation cycle from the start of the vehicle.
Preferably, in order to remove noise from the detected velocity data and the longitudinal acceleration data output by the vehicle-mounted accelerometer, the online calibration method 100 may further include the step of filtering the data: and filtering the acquired detection speed and the acquired longitudinal acceleration.
Correspondingly, step S120 may be configured to: differentiating the filtered detected velocity to determine the calculated acceleration.
Correspondingly, step S130 may be configured to: and determining the acceleration factor by using the calculated acceleration determined by the filtered detection speed and the filtered longitudinal acceleration.
In a preferred embodiment, the online calibration method 100 may further include online calibration of mounting errors of the onboard accelerometer.
Assuming that the coordinate system of the vehicle-mounted accelerometer is oxyz, the coordinate system of the carrier vehicle is ox 'y' z ', and the coordinate system oxyz is rotated to ox' y 'z', the counterclockwise rotation angle around each coordinate axis is defined as positive, and the counterclockwise rotation angles of the x, y and z axes are respectively assumed as alpha, beta and gamma.
According to the rotation sequence of x → y → z, the transformation matrix from the accelerometer coordinate system oxyz to the carrier coordinate system ox ' y ' z ' is shown as equation (4):
the three-axis acceleration of the carrier coordinate system can thus be expressed as:
Av＝CAs (5)
wherein A isv＝[Avx Avy Avz]TIs the three-axis acceleration, A, in the carrier coordinate system ox 'y' zs＝[Asx Asy Asz]TThe three-axis acceleration is the three-axis acceleration under the coordinate system oxyz of the vehicle-mounted accelerometer, namely the longitudinal acceleration, the roll acceleration and the pitch acceleration which are generally understood.
Therefore, three angles alpha, beta and gamma in the conversion matrix are solved, and the conversion matrix C is used for calibrating and calibrating the triaxial acceleration values output by the vehicle-mounted accelerometer, so that the mounting error of the vehicle-mounted accelerometer can be overcome.
To achieve online calibration of the mounting error of the on-board accelerometer, as shown in fig. 4, the online calibration method 100 may further include steps S140 to S160.
Step S140 is: mounting the on-board accelerometer on the vehicle.
It will be appreciated that the on-board accelerometer may be onboard the vehicle or otherwise. When the on-board accelerometer installed on the vehicle is calibrated for installation errors, step S140 may be skipped.
Step S150 is: acquiring acceleration raw values output by the vehicle-mounted accelerometer under a static condition, wherein the acceleration raw values comprise original longitudinal acceleration, original roll acceleration and original pitch acceleration.
The static condition means that the vehicle is still on a horizontal plane, at the moment, the carrier coordinate system is considered to be coincident with the geodetic coordinate system, namely the theoretical value A of the three-axis acceleration in the carrier coordinate systemv＝[0 0 g]TAnd g is the local gravitational acceleration value. At this time, the vehicle-mounted accelerometer outputs the detected triaxial acceleration, and an equality relation which is satisfied between the triaxial acceleration in the vehicle-mounted coordinate system under the static condition and the triaxial acceleration in the vehicle-mounted accelerometer coordinate system can be listed according to the formula (5).
Step S160 is: and determining the mounting declination angle of the accelerometer relative to the vehicle by using the theoretical value of the three-axis acceleration of the vehicle in a vehicle coordinate system under a static condition and the original value of the acceleration output by the vehicle-mounted accelerometer.
It can be understood that based on the equation relation satisfied between the three-axis acceleration in the vehicle-mounted coordinate system under the static condition and the three-axis acceleration in the vehicle-mounted accelerometer coordinate system, it is obvious that three mounting declination angles α, β and γ of the vehicle-mounted accelerometer relative to the vehicle coordinate system can be solved.
Specifically, as shown in fig. 5, step S160 may include steps S161 to S163.
Step S161 is: determining a transformation matrix of the accelerometer coordinate system to a vehicle coordinate system of the vehicle.
The corresponding transformation matrix may be listed based on a preset rotation order of the three axes, and the rotation order of the rotation of equation (4) and the setting rule of the positive direction are not necessarily limited. The present invention will be explained by taking the rotation order and the setting rule of the positive direction set by the equation (4) as an example.
Step S162 is: and establishing an equation set about the three-axis acceleration theoretical value and the acceleration original value output by the vehicle-mounted accelerometer by using the conversion matrix.
Downloading the theoretical value A of three-axis acceleration value in a body coordinate system under static conditionv＝[0 0 g]TAnd the acquired triaxial acceleration original value A output by the vehicle-mounted accelerometers＝[Asx Asy Asz]TA non-linear system of equations for the three angles α, β, γ can be obtained by substituting equation (5):
wherein, cij(i ═ 1,2,3, j ═ 1,2,3) is the corresponding matrix element in the conversion matrix C of equation (4). Solving the nonlinear equation system (6) to obtain three mounting deflection angles alpha, beta and gamma of the vehicle-mounted accelerometer relative to the carrier coordinate system.
Step S163 is: solving the system of equations using an adaptive particle swarm algorithm to determine the installation declination.
The self-adaptive particle swarm algorithm is a method for solving problems by simulating the predation behavior of a bird swarm. Assuming that there is a food and a group of birds in an area, and each bird does not know where the food is but how far away it is from the food at its current location, the optimal strategy to find the food is to search the surrounding area of the bird that is closest to the food. Based on the strategy of the bird group predation behavior, it can be assumed that the solution of each problem is a bird, which is called a particle in the particle swarm algorithm. Each particle has an adaptive value determined by the fitness function and a velocity that determines the direction and distance they fly, and the particles search in solution space following the current optimal particle.
Specifically, as shown in FIG. 6, step S163 may include steps S1631-S1635.
Wherein, step S1631 is: and setting the population size N searched by the self-adaptive particle swarm algorithm.
The population size is related to the size of the calculated amount, and generally speaking, the larger the population size is, the closer the obtained optimal solution is to the true solution. But need to be set in harmony based on the accuracy requirements required for the on-line calibration, the hardware conditions, and the scale of calculations that can be achieved.
Step S1632 is: the initial positions X and the initial velocities V of the N particles are randomly initialized.
Step S1633 is: setting the number k of evolution iterationsmax。
Step S1634 is: and determining the fitness of the N particles.
The least squares method is a mathematical optimization technique that finds the best functional match of the data by minimizing the sum of the squares of the errors. Unknown data can be easily obtained by the least square method, and the sum of squares of errors between these obtained data and actual data is minimized. Therefore, the least squares concept can be employed to determine the fitness function of the N particles in the adaptive particle swarm optimization.
Specifically, the fitness function of the N particles can be determined using equation (7) according to the least squares concept:
wherein α, β and γ are the three-axis mounting declination of the accelerometer coordinate system relative to the vehicle coordinate system of the vehicle, respectively, fiAnd the (alpha, beta, gamma) is a conversion equation of any axis, and the alpha, the beta and the gamma when the F (alpha, beta, gamma) takes the minimum value are the optimal solution of the installation deflection angle.
Step S1635 is: and repeating the iteration of the adaptive particle swarm algorithm until the iteration times are larger than the evolution iteration times, and taking the iteration result as the optimal solution of the equation set.
In the k-th iterationIn the generation process, the velocity X of each particle i is updated according to the equations (8) and (9)iAnd position ViThe current optimal position P of each particleiComparing to obtain the maximum PiAs global optimum position GiAnd updates the population.
Vi k+1＝ωkVi k+c1r1(Pi-Xi k)+c2r2(Gi-Xi k) (8)
Xi k+1＝Xi k+ζk+1Vi k+1 (9)
Wherein, c1、c2Is the acceleration coefficient of the particle, r1、r2Is [0,1 ]]Random number over interval, PiFor the optimal particle position, G, of the kth iteration of the ith particleiIs the global optimum particle position, ω, for the kth iterationkLinear inertia factor, ζ, for the kth iterationk+1The velocity weighting factor for the (k + 1) th iteration.
Linear inertia factor omegakAnd velocity weighting factor ζk+1The calculation method of (2) is shown in the formula (10) and the formula (11):
where k is the number of iterations, kmaxTo maximum number of iterations, ζkVelocity weighting factor, ζ, for the kth iterationmaxMaximum velocity weighting factor, ζminMinimum velocity weighting factor, ω1Is an initial inertia factor, ω2Is the inertia factor iterated to the maximum number.
K is greater than k when the number of iterations k is greater thanmaxThen, the output obtained in the k iteration processAnd the optimal position is the optimal solution under the set conditions.
It is understood that the above solving process is a process of solving the mounting declination angles α, β, and γ, and after the solving is completed, the obtained transformation matrix C may be used to calibrate the original value of the longitudinal acceleration output by the on-board accelerometer to be the longitudinal acceleration detected by the on-board accelerometer obtained in step S110.
And calibrating the static installation error of the longitudinal acceleration original value output by the vehicle-mounted accelerometer by using the determined installation deflection angle, and calibrating the dynamic measurement error of the vehicle-mounted accelerometer by using the acceleration factor.
While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood by one skilled in the art.
According to another aspect of the present invention, an on-line calibration apparatus for an on-board accelerometer is further provided, which is capable of performing on-line calibration of the acceleration detected by the on-board accelerometer.
In one embodiment, as shown in FIG. 7, the online calibration device 700 may include a memory 710 and a processor 720.
The memory 710 is used to store computer programs.
The processor 720 is coupled to the memory 710 for executing computer programs on the memory 710. The processor 720 may be configured to implement the steps of the online calibration method as described in any of the embodiments above.
According to yet another aspect of the present invention, there is also provided a computer storage medium having a computer program stored thereon, the computer program when executed implementing the steps of the online calibration method as described in any of the above embodiments.
Those of skill in the art would understand that information, signals, and data may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits (bits), symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The various illustrative logical modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk (disk) and disc (disc), as used herein, includes Compact Disc (CD), laser disc, optical disc, Digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks (disks) usually reproduce data magnetically, while discs (discs) reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. It is to be understood that the scope of the invention is to be defined by the appended claims and not by the specific constructions and components of the embodiments illustrated above. Those skilled in the art can make various changes and modifications to the embodiments within the spirit and scope of the present invention, and these changes and modifications also fall within the scope of the present invention.
Claims (25)
1. An on-line calibration method of an on-board accelerometer, comprising:
acquiring the detection speed of a vehicle and the longitudinal acceleration detected by the vehicle-mounted accelerometer;
differentiating the detected speed of the vehicle to determine a calculated acceleration; and
determining an acceleration factor based on the longitudinal acceleration and the calculated acceleration, the acceleration factor being used to calibrate the on-board accelerometer.
2. The online calibration method of claim 1, wherein said determining an acceleration factor based on said longitudinal acceleration and said calculated acceleration comprises:
and in response to the fact that the vehicle is in a uniform acceleration stage within preset time and the fluctuation of the longitudinal acceleration detected by the vehicle-mounted accelerometer is smaller than a preset threshold value, determining the acceleration factor based on the average value of the calculated acceleration determined within the preset time and the average value of the longitudinal acceleration within the preset time.
3. The on-line calibration method of claim 2, further comprising:
judging whether the detected speed of the vehicle is greater than a preset speed or not; and
and responding to the fact that the detected speed is larger than the preset speed, and judging that the vehicle is in a uniform acceleration stage.
4. The on-line calibration method of claim 2, further comprising:
calculating the standard deviation of the longitudinal acceleration detected by the vehicle-mounted accelerometer within the preset time; and
and responding to the condition that the standard deviation is smaller than a preset threshold value, and judging that the fluctuation of the longitudinal acceleration detected by the vehicle-mounted accelerometer is smaller than the preset threshold value.
5. The online calibration method of claim 2, wherein the determining the acceleration factor comprises:
calculating a formula using the acceleration factor
Calculating the acceleration factor, wherein k is the acceleration factor,
is the average value of the calculated acceleration within the preset time,
and the average value of the longitudinal acceleration in the preset time is obtained.
6. The on-line calibration method of claim 1, further comprising:
filtering the detected velocity and the longitudinal acceleration; and
the differentiating the detected speed of the vehicle to determine a calculated acceleration includes:
differentiating the filtered detected velocity to determine the calculated acceleration; and
said determining an acceleration factor based on said longitudinal acceleration and said calculated acceleration comprises:
and determining the acceleration factor by using the calculated acceleration determined by the filtered detection speed and the filtered longitudinal acceleration.
7. The on-line calibration method of claim 1, further comprising:
and carrying out installation error calibration on the original longitudinal acceleration detected by the vehicle-mounted accelerometer to determine the longitudinal acceleration.
8. The on-line calibration method of claim 7, wherein said calibrating for installation error of the raw longitudinal acceleration detected by the on-board accelerometer comprises:
mounting the on-board accelerometer on the vehicle;
acquiring an acceleration original value output by the vehicle-mounted accelerometer under a static condition, wherein the acceleration original value comprises an original longitudinal acceleration, an original roll acceleration and an original pitch acceleration;
determining the mounting declination angle of the accelerometer relative to the vehicle by using the theoretical value of the three-axis acceleration of the vehicle in a vehicle coordinate system under a static condition and the original value of the acceleration output by the vehicle-mounted accelerometer; and
calibrating a raw longitudinal acceleration of the on-board accelerometer using the stagger angle to determine the longitudinal acceleration.
9. The on-line calibration method of claim 8 wherein said determining the mounting offset angle of the accelerometer relative to the vehicle using the theoretical values of the three-axis acceleration of the vehicle in the vehicle coordinate system and the raw values of the acceleration output by the on-board accelerometer under static conditions comprises:
determining a transformation matrix of the accelerometer coordinate system to a vehicle coordinate system of the vehicle, the transformation matrix employing mounting declination identification of the accelerometer relative to the vehicle;
establishing an equation set about the triaxial acceleration theoretical value and an acceleration original value output by the vehicle-mounted accelerometer by using the conversion matrix; and
solving the system of equations using an adaptive particle swarm algorithm to determine the installation declination.
10. The on-line calibration method of claim 9 wherein said solving said system of equations using an adaptive particle swarm algorithm comprises:
setting a population size N searched by the self-adaptive particle swarm algorithm;
randomly initializing initial positions and initial speeds of the N particles;
setting evolution iteration times;
determining fitness of the N particles; and
and repeating the iteration of the adaptive particle swarm algorithm until the iteration times are larger than the evolution iteration times, and taking the iteration result as the optimal solution of the equation set.
11. The on-line calibration method of claim 10, wherein said determining a fitness of the N particles comprises:
determining fitness function of the N particles according to least square thought
Wherein α, β, γ are the three-axis mounting declination of the accelerometer coordinate system relative to the vehicle coordinate system of the vehicle, respectively, fiAnd (alpha, beta, gamma) is a conversion equation of any axis, and alpha, beta and gamma of the F (alpha, beta and gamma) which takes the minimum value are the optimal solutions of the installation deflection angle.
12. The on-line calibration method according to any one of claims 1 to 11, further comprising:
and multiplying the acceleration factor by the longitudinal acceleration output by the vehicle-mounted accelerometer to serve as the actual longitudinal acceleration of the vehicle.
13. An on-line calibration device for an on-board accelerometer, comprising:
a memory; and
a processor configured to:
acquiring the detection speed of a vehicle and the longitudinal acceleration detected by the vehicle-mounted accelerometer;
differentiating the detected speed of the vehicle to determine a calculated acceleration; and
determining an acceleration factor based on the longitudinal acceleration and the calculated acceleration, the acceleration factor being used to calibrate the on-board accelerometer.
14. The online calibration device of claim 13, wherein the processor is further configured to:
and in response to the fact that the vehicle is in a uniform acceleration stage within preset time and the fluctuation of the longitudinal acceleration detected by the vehicle-mounted accelerometer is smaller than a preset threshold value, determining the acceleration factor based on the average value of the calculated acceleration determined within the preset time and the average value of the longitudinal acceleration within the preset time.
15. The online calibration device of claim 14, wherein the processor is further configured to:
judging whether the detected speed of the vehicle is greater than a preset speed or not; and
and responding to the fact that the detected speed is larger than the preset speed, and judging that the vehicle is in a uniform acceleration stage.
16. The online calibration device of claim 14, wherein the processor is further configured to:
calculating the standard deviation of the longitudinal acceleration detected by the vehicle-mounted accelerometer within the preset time; and
and responding to the condition that the standard deviation is smaller than a preset threshold value, and judging that the fluctuation of the longitudinal acceleration detected by the vehicle-mounted accelerometer is smaller than the preset threshold value.
17. The online calibration device of claim 14, wherein the processor is further configured to:
using said accelerationFormula for calculating factor
Calculating the acceleration factor, wherein k is the acceleration factor,
is the average value of the calculated acceleration within the preset time,
and the average value of the longitudinal acceleration in the preset time is obtained.
18. The online calibration device of claim 13, wherein the processor is further configured to:
filtering the detected velocity and the longitudinal acceleration;
differentiating the filtered detected velocity to determine the calculated acceleration; and
and determining the acceleration factor by using the calculated acceleration determined by the filtered detection speed and the filtered longitudinal acceleration.
19. The online calibration device of claim 13, wherein the processor is further configured to:
and carrying out installation error calibration on the original longitudinal acceleration detected by the vehicle-mounted accelerometer to determine the longitudinal acceleration.
20. The online calibration device of claim 19, wherein the processor is further configured to:
mounting the on-board accelerometer on the vehicle;
acquiring an acceleration original value output by the vehicle-mounted accelerometer under a static condition, wherein the acceleration original value comprises an original longitudinal acceleration, an original roll acceleration and an original pitch acceleration;
determining the mounting declination angle of the accelerometer relative to the vehicle by using the theoretical value of the three-axis acceleration of the vehicle in a vehicle coordinate system under a static condition and the original value of the acceleration output by the vehicle-mounted accelerometer; and
calibrating a raw longitudinal acceleration of the on-board accelerometer using the stagger angle to determine the longitudinal acceleration.
21. The online calibration device of claim 20, wherein the processor is further configured to:
determining a transformation matrix of the accelerometer coordinate system to a vehicle coordinate system of the vehicle, the transformation matrix employing mounting declination identification of the accelerometer relative to the vehicle;
establishing an equation set about the triaxial acceleration theoretical value and an acceleration original value output by the vehicle-mounted accelerometer by using the conversion matrix; and
solving the system of equations using an adaptive particle swarm algorithm to determine the installation declination.
22. The online calibration device of claim 21, wherein the processor is further configured to:
setting a population size N searched by the self-adaptive particle swarm algorithm;
randomly initializing initial positions and initial speeds of the N particles;
setting evolution iteration times;
determining fitness of the N particles; and
and repeating the iteration of the adaptive particle swarm algorithm until the iteration times are larger than the evolution iteration times, and taking the iteration result as the optimal solution of the equation set.
23. The online calibration device of claim 22, wherein the processor is further configured to:
determining fitness function of the N particles according to least square thought
Wherein α, β, γ are the three-axis mounting declination of the accelerometer coordinate system relative to the vehicle coordinate system of the vehicle, respectively, fiAnd (alpha, beta, gamma) is a conversion equation of any axis, and alpha, beta and gamma of the F (alpha, beta and gamma) which takes the minimum value are the optimal solutions of the installation deflection angle.
24. The online calibration device of any of claims 13-23, wherein the processor is further configured to:
and multiplying the acceleration factor by the longitudinal acceleration output by the vehicle-mounted accelerometer to serve as the actual longitudinal acceleration of the vehicle.
25. A computer storage medium having a computer program stored thereon, wherein the computer program when executed implements the steps of a method for online calibration of an on-board accelerometer of any of claims 1-12.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113928327A (en) * | 2021-09-29 | 2022-01-14 | 深圳市麦谷科技有限公司 | Three-emergency event detection method and system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5483452A (en) * | 1993-11-25 | 1996-01-09 | Sumitomo Electric Industries, Ltd. | Apparatus for detecting failures of longitudinal acceleration sensor |
| CN101509936A (en) * | 2008-02-16 | 2009-08-19 | 威伯科有限公司 | Method and apparatus for calibrating wheel speeds |
| CN101718546A (en) * | 2009-12-10 | 2010-06-02 | 清华大学 | Method and system for measuring vehicle-mounted road profile |
| CN105807095A (en) * | 2016-03-10 | 2016-07-27 | 同济大学 | Three-axis acceleration sensor mounting error correcting method |
| CN106382946A (en) * | 2016-09-14 | 2017-02-08 | 邹红斌 | Parameter calibration method and parameter calibration device |
| CN109085381A (en) * | 2018-09-14 | 2018-12-25 | 上海移为通信技术股份有限公司 | Vehicle-mounted acceleration transducer direction calibration method |
-
2020
- 2020-02-26 CN CN202010120193.XA patent/CN113311191A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5483452A (en) * | 1993-11-25 | 1996-01-09 | Sumitomo Electric Industries, Ltd. | Apparatus for detecting failures of longitudinal acceleration sensor |
| CN101509936A (en) * | 2008-02-16 | 2009-08-19 | 威伯科有限公司 | Method and apparatus for calibrating wheel speeds |
| CN101718546A (en) * | 2009-12-10 | 2010-06-02 | 清华大学 | Method and system for measuring vehicle-mounted road profile |
| CN105807095A (en) * | 2016-03-10 | 2016-07-27 | 同济大学 | Three-axis acceleration sensor mounting error correcting method |
| CN106382946A (en) * | 2016-09-14 | 2017-02-08 | 邹红斌 | Parameter calibration method and parameter calibration device |
| CN109085381A (en) * | 2018-09-14 | 2018-12-25 | 上海移为通信技术股份有限公司 | Vehicle-mounted acceleration transducer direction calibration method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113928327A (en) * | 2021-09-29 | 2022-01-14 | 深圳市麦谷科技有限公司 | Three-emergency event detection method and system |
| CN113928327B (en) * | 2021-09-29 | 2024-04-05 | 深圳市麦谷科技有限公司 | Method and system for detecting three emergency events |
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