CN113465628A - Inertial measurement unit data compensation method and system - Google Patents
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Abstract
The invention provides a data compensation method and a system for an inertial measurement unit, wherein the method comprises the following steps: denoising and preprocessing target data of an Inertial Measurement Unit (IMU) of a target vehicle to obtain first target data; acquiring a course angle and a vehicle speed of a target vehicle in a preset state according to navigation information of the target vehicle; determining a target function according to the first target data, the course angle and the vehicle speed; and determining the optimal solution of the objective function based on a multi-starting point simulated annealing algorithm, and determining the posture of the IMU relative to the vehicle system according to the optimal solution so as to obtain corrected objective data. The system is used for executing the method. The inertial measurement unit data compensation method and system provided by the invention realize estimation and compensation of the installation attitude error by combining the simulated annealing algorithm, can estimate and compensate the installation attitude error of the IMU under the state that the vehicle carrier moves linearly and has a certain speed, and improves the accuracy and stability of dead reckoning.
Description
Technical Field
The invention relates to the technical field of vehicle automation, in particular to a data compensation method and system for an inertial measurement unit.
Background
Vehicle automation is widely used in many fields as an active research field at present, and there are many documents providing methods for integrating a sensor suite of a navigation system. These approaches almost always require the use of many different types of sensors to adequately gather the available information in order to achieve the desired reliability and integrity. These sensors can be divided into two broad categories, dead reckoning sensors and external sensors. Dead reckoning sensors are generally very robust, but they accumulate errors over time, and external sensors provide absolute information by measuring known landmarks, so in practice they are often reset periodically using external sensors. The architecture of such a sensor assembly is shown in fig. 1.
Typical dead reckoning sensors are accelerometers, which measure acceleration relative to the inertial frame, and gyroscopes, which measure rotational angular velocity. An integrated Inertial Navigation System (INS) consists of at least three orthogonal axes of accelerometers and three orthogonal axes of gyroscopes to provide three-dimensional acceleration and rotation rates, and the single and double integrals of their outputs will provide velocity, position and attitude information, which collectively constitute an Inertial Measurement Unit (IMU). Due to the accumulated error of the IMU, additional information has to be used to perform calibration of the carrier in order to use the inertial navigation system for a long time. The Global Positioning System (GPS) is often used in conjunction with the IMU. The IMU unit provides high frequency information to generate a position estimate between the position and velocity corrections of the GPS. Furthermore, GPS sensor data may be lost for a long time, during which the IMU will provide critical information for navigation. The navigation mode of the combined IMU and GPS is combined navigation.
The IMU sensor is noisy in actual use, and filtering is carried out by using a proper method before data use, so that outliers (a small part of data points in a sequence of tracking data of a dynamic target, which are seriously deviated from the change trend of most data) and high-frequency noise are removed. Furthermore, in some commercial application scenarios, the installer does not mount the IMU on the carrier with a zero attitude error by professional means, so that there is a mounting attitude error (pitch angle error, roll angle error, heading angle error) between the IMU and the carrier, which has a great influence on dead reckoning. A good INS needs to estimate and compensate for this mounting angle because the dead reckoning algorithm uses the initial direction to update the attitude information. Because the pitch angle and the roll angle can be calculated by gravity decomposition, but the course can not be calculated because no gravity component exists, and the course of the platform is the key for obtaining accurate acceleration, then the speed and the position are updated by single integral and second integral, and if the course deviation is larger, the calculation accumulated error can be rapidly increased.
Calibration of ground vehicles has proven to be very difficult, and conventional calibration methods have mostly been performed using mechanical platforms or optical instruments, but in commercial applications such static calibration may incur unacceptable economic and time costs. The technology provides a static mounting angle calibration by using geomagnetic acquired course information, which is a good solution, but in some special application scenarios, geomagnetic signals are easily interfered or shielded, and calibration results caused by polluted geomagnetism can degrade an inertial navigation system instead, so that the stability of the system cannot be guaranteed. Therefore, the reliable dynamic automatic calibration system for the IMU installation attitude error has positive practical significance.
Disclosure of Invention
The inertial measurement unit data compensation method and system provided by the invention are used for overcoming the problems in the prior art, realize estimation and compensation of the installation attitude error by combining a simulated annealing algorithm, can estimate and compensate the installation attitude error of the IMU under the motion state of a vehicle carrier, and improve the accuracy and stability of dead reckoning.
The invention provides a data compensation method of an inertial measurement unit, which comprises the following steps:
denoising and preprocessing target data of an Inertial Measurement Unit (IMU) of a target vehicle to obtain first target data;
acquiring a course angle and a vehicle speed of the target vehicle in a preset state according to the navigation information of the target vehicle;
determining an objective function according to the first target data, the course angle and the vehicle speed;
determining an optimal solution of the objective function based on a multi-starting point simulated annealing algorithm, and determining the posture of the IMU relative to a vehicle system according to the optimal solution to obtain corrected objective data;
wherein the target data comprises angular velocity and acceleration;
the preset state is linear motion at a speed greater than a preset vehicle speed and less than a preset course angular speed.
According to the data compensation method for the inertial measurement unit provided by the invention, the target data denoising pretreatment of the inertial measurement unit IMU of the target vehicle is performed to obtain the first target data, and the method comprises the following steps:
determining the size of a window according to preset required time and preset sampling frequency;
cleaning target data of a current window according to a window dynamic threshold method;
and carrying out filtering processing on the cleaned target data based on a wavelet filtering method.
According to the data compensation method for the inertial measurement unit, provided by the invention, the target data of the current window is cleaned according to a window dynamic threshold method, and the method comprises the following steps:
determining a first variance of target data of a current window;
dividing the current window into preset sub-windows, and respectively acquiring a second variance of the target data of each sub-window to determine a variance deviation value between the second variance of each sub-window and the first variance;
if a first sub-window with the variance deviation value larger than a first preset threshold exists, determining a first threshold of the current window according to a second preset threshold and the first variance;
if the variance deviation values of all the sub-windows are smaller than the first preset threshold, determining a second threshold of the current window according to a third preset threshold and the first variance;
and performing data cleaning on the target data of the current window according to the first threshold or the second threshold.
According to the data compensation method for the inertial measurement unit, the course angle and the vehicle speed of the target vehicle in the preset state are obtained according to the navigation information of the target vehicle, and the method comprises the following steps:
acquiring longitude and latitude information of the current moment and a timestamp of the current moment according to the navigation information of the target vehicle;
determining a space rectangular coordinate corresponding to the longitude and latitude information according to the longitude and latitude information;
according to a reference space rectangular coordinate corresponding to longitude and latitude information of the previous moment, determining a station center coordinate corresponding to the space rectangular coordinate;
and determining the target vehicle to be in a moving state according to the station center coordinates and a preset distance threshold, determining the speed of the target vehicle in the preset state according to the station center coordinates, the timestamp of the current moment and the timestamp of the previous moment, and determining the course angle of the target vehicle in the moving state according to the station center coordinates.
According to the data compensation method for the inertial measurement unit, provided by the invention, the determining of the objective function according to the first target data, the course angle and the vehicle speed comprises the following steps:
respectively determining a target speed observation model and state equations of a course angle, a vehicle speed estimation value, an IMU attitude estimation value and a gyroscope zero offset estimation value of the IMU according to the first target data, the course angle and the vehicle speed;
determining a vehicle speed estimation value and a vehicle speed observation value according to the target speed observation model and the state equation, and taking the difference between the vehicle speed estimation value and the vehicle speed observation value as the target function;
the target speed observation model comprises an IMU speed observation model or a satellite navigation system GNSS speed observation model.
According to the data compensation method for the inertial measurement unit, provided by the invention, the optimal solution of the objective function is determined based on a multi-starting point simulated annealing algorithm, and the posture of the IMU relative to a vehicle system is determined according to the optimal solution so as to obtain corrected objective data, and the method comprises the following steps:
determining an initial solution group according to the starting point number, the initial temperature, the temperature attenuation factor, the search step factor, the maximum iteration number, the initial solution state, the suspension condition and the initial range boundary of the initial solution group, and generating a new solution group positioned in a solution space from the current solution group;
determining a first objective function value according to the initial solution group and the objective function;
determining a second objective function value according to the new solution group and the objective function;
determining whether to accept the new solution group according to the magnitude relation between the difference value of the second objective function value and the first objective function value and a preset threshold, and iteratively updating the current solution group by using the new solution group when accepting the new solution group until the iteration number reaches the maximum iteration number and meets the stopping condition, and stopping updating the current solution group;
determining an optimal solution group according to the updated current solution group, and acquiring the optimal solution according to the optimal solution group;
determining a target rotation matrix according to the optimal solution and a preset rotation matrix formula, and acquiring the corrected target data according to the target rotation matrix and a preset coordinate conversion matrix;
wherein the suspension condition is that none of the continuously preset new solution groups is accepted.
According to the data compensation method for the inertial measurement unit provided by the present invention, the determining whether to accept the new solution group according to the magnitude relationship between the difference between the second objective function value and the first objective function value and a preset threshold includes:
if the difference value between the second objective function value and the first objective function value is smaller than the preset threshold value, accepting the new solution;
and if the difference value between the second objective function value and the first objective function value is larger than the preset threshold value, determining an acceptance probability according to a Monte Carlo criterion, and accepting the new solution group according to the acceptance probability.
The invention also provides a data compensation system of the inertial measurement unit, which comprises: the device comprises a data processing module, a data acquisition module, an installation estimation module and a data compensation module;
the data processing module is used for denoising and preprocessing target data of an Inertial Measurement Unit (IMU) of a target vehicle to obtain first target data;
the data acquisition module is used for acquiring a course angle and a vehicle speed of the target vehicle in a preset state according to the navigation information of the target vehicle;
the installation estimation module is used for determining an objective function according to the first objective data, the course angle and the vehicle speed;
the data compensation module is used for determining an optimal solution of the objective function based on a multi-starting point simulated annealing algorithm and determining the posture of the IMU relative to the vehicle system according to the optimal solution so as to obtain corrected objective data;
wherein the target data comprises angular velocity and acceleration;
the preset state is linear motion at a speed greater than a preset vehicle speed and less than a preset course angular speed.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the data compensation method of the inertial measurement unit.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the inertial measurement unit data compensation method as described in any one of the above.
The inertial measurement unit data compensation method and system provided by the invention realize estimation and compensation of the installation attitude error by combining the simulated annealing algorithm, can estimate and compensate the installation attitude error of the IMU under the state that the vehicle carrier moves linearly and has a certain speed, and improves the accuracy and stability of dead reckoning.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a combined navigation system provided by the prior art;
FIG. 2 is a schematic flow chart of a data compensation method for an inertial measurement unit according to the present invention;
FIG. 3 is a schematic diagram of a system architecture for implementing the data compensation method of the inertial measurement unit provided by the present invention;
FIG. 4 is a schematic diagram of a vehicle system, an IMU system, a GNSS system and an inertial navigation coordinate system according to the present invention;
FIG. 5 is a schematic diagram of a simulated annealing algorithm solving process provided by the present invention;
FIG. 6 is a flow chart of an IMU installation attitude error estimation unit algorithm provided by the present invention;
FIG. 7 is a statistical chart of the error of the control group and the experimental group at each moment;
FIG. 8 is a schematic structural diagram of an inertial measurement unit data compensation system provided in the present invention;
fig. 9 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. 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.
The invention provides an algorithm for finding the installation attitude error in the vehicle running process, but most of the algorithms pay more attention to the convergence speed of the algorithm and pay less attention to the problem that the convergence falls into the local optimal solution.
Fig. 2 is a schematic flow chart of a data compensation method for an inertial measurement unit according to the present invention, and as shown in fig. 2, the method includes:
s1, carrying out denoising pretreatment on target data of an Inertial Measurement Unit (IMU) of the target vehicle to obtain first target data;
s2, acquiring a course angle and a vehicle speed of the target vehicle in a preset state according to the navigation information of the target vehicle;
s3, determining an objective function according to the first objective data, the course angle and the vehicle speed;
s4, determining an optimal solution of the objective function based on a multi-starting point simulated annealing algorithm, and determining the posture of the IMU relative to the vehicle system according to the optimal solution to obtain corrected objective data;
wherein the target data comprises angular velocity and acceleration;
the preset state is linear motion at a speed greater than a preset vehicle speed and less than a preset course angular rate.
It should be noted that the execution subject of the method may be a computer device, and the following description takes an example that the computer device deploys a system structural diagram shown in fig. 3 to implement the inertial measurement unit data compensation method provided by the present invention, and the system includes an IMU information acquisition unit (1), an IMU data processing unit (2), a GPS information acquisition unit (3), a GPS vehicle speed and heading angle resolving unit (4), an IMU installation attitude error estimation unit (5), and an IMU installation attitude error compensation unit (6).
Referring to fig. 4, the IMU is installed at a certain horizontal plane of the vehicle, and the GNSS antenna is installed at a position where the vehicle can receive the GPS signal in real time, where a is a GNSS system, b is an IMU coordinate system, the origin is located at the center of the IMU mapping unit, v is a carrier system, the origin coincides with b, and n is a navigation system, which is an east-north-sky coordinate system of the earth. After the IMU and a Global Navigation Satellite System (GNSS) are started to collect data, the vehicle is kept to run forward stably at a constant speed. In the driving process, an IMU acquires acceleration and angular velocity, a GNSS acquires navigation signals, and the two parts of information are respectively sent to an IMU data processing unit (2) and a GPS vehicle speed and course angle resolving unit (4).
Specifically, the IMU information acquisition unit (1) acquires acceleration information and angular velocity information, acquires the acquired analog signals as digital signals according to a certain sampling frequency, and obtains the acceleration f of the IMU of the target vehicle0And angular velocity ω0And the signal is transmitted to an IMU data processing unit (2), the IMU data processing unit (2) carries out denoising pretreatment on the digitized acceleration and angular velocity to obtain a denoised first acceleration f and a denoised first angular velocity omega, and the denoised first acceleration f and the denoised first angular velocity omega are transmitted to an IMU installation attitude error estimation unit (5).
The GPS information acquisition unit (3) acquires navigation information (longitude and latitude information and time information) of a vehicle carrier according to a set sampling frequency and transmits the navigation information to the GPS speed and course angle resolving unit (4).
The GPS vehicle speed and course angle resolving unit (4) judges the vehicle motion state according to longitude and latitude information, time information and a speed threshold value set in advance of a target vehicle, solves the vehicle speed and the course angle, outputs a vehicle state s to the IMU installation attitude error estimation unit (5), and outputs the course angle psi and the vehicle speed v when the vehicle is judged to be in a preset state (namely the vehicle carries out linear motion at a speed which is greater than the preset vehicle speed and less than the preset course angle speed, wherein the preset vehicle speed can be set according to experience and is generally equivalent to the walking speed of pedestrians, and the preset course angle speed is slightly different due to the filtering effect of original data and the application scene of the specific vehicle).
The IMU installation attitude error estimation unit (5) is started when the vehicle is in a motion state, determines an objective function by using the received first acceleration f, the first angular velocity omega, the heading angle psi and the vehicle speed v, and determines an optimal solution of the objective function based on a simulated annealing algorithm.
IMU installation attitude error compensation sheetThe element (6) re-calculates the IMU output after calibration according to the optimal solution of the target function, namely the corrected acceleration fcAnd corrected angular velocity ωc。
The data compensation method for the inertial measurement unit provided by the invention is combined with a simulated annealing algorithm to realize estimation and compensation of the installation attitude error, can estimate and compensate the installation attitude error of the IMU under the state that a vehicle carrier moves linearly and has a certain speed, and improves the accuracy and stability of dead reckoning.
Further, in an embodiment, the step S1 may specifically include:
s11, determining the window size according to the preset required time and the preset sampling frequency;
s12, cleaning the target data of the current window according to a window dynamic threshold method;
and S13, filtering the cleaned target data based on a wavelet filtering method.
Further, in an embodiment, the step S12 may specifically include:
s121, determining a first variance of target data of a current window;
s122, dividing the current window into preset sub-windows, and respectively obtaining a second variance of the target data of each sub-window to determine a variance deviation value between the second variance and the first variance of each sub-window;
s123, if a first sub-window with the variance deviation value larger than a first preset threshold exists, determining a first threshold of the current window according to a second preset threshold and the first variance;
s124, if the variance deviation values of all the sub-windows are smaller than a first preset threshold, determining a second threshold of the current window according to a third preset threshold and the first variance;
and S125, performing data cleaning on the target data of the current window according to the first threshold or the second threshold.
Optionally, the IMU data processing unit (2) is configured by two parts, namely a window dynamic threshold value derefaction value and a wavelet filtering, and the following is an execution flow:
1) setting a window size W (tau F) according to a preset demand time tau and a preset sampling frequency F, and setting a first preset threshold value delta, a second preset threshold value A and a third preset threshold value B according to experience;
2) obtaining a first square difference between the acceleration and the angular velocity of the current window i through calculation
3) The current window i is divided into preset sub-windows, for example, the current window i can be divided into four segments equally, and the second variance is calculated for each segment of sub-window
Calculating variance deviation values between the four groups of sub-windows and the current period window i
Wherein,
representing the variance deviation value between the jth sub-window and the current window i, comparing the variance deviation values of all the obtained sub-windows with a preset first threshold value delta, if the sub-window larger than delta exists, representing that the IMU in the window i is a special window (the default window is narrow, one motion state can continue to a plurality of windows) for starting or ending the motion state change, and in order to avoid that the effective value is mistakenly eliminated, according to a second preset threshold value A and the first variance sigma2A first, more relaxed threshold is set for this window i: t ═ A σ2＝{a1σax′ 2,a2σay′ 2,a3σaz′ 2,a4σgx′ 2,a5σgy′ 2,a6σgz′ 2A in a second preset threshold A1,a2,a3,a4,a5,a6Is a threshold coefficient set empirically;
4) if all the sub-window variance deviation values are smaller than the set first preset threshold value deltaIf it is indicated that there is no large motion state change, a strict second threshold is set for this window i according to a third preset threshold B and the first difference: t ═ B σ2＝{b1σax′ 2,b2σay′ 2,b3σaz′ 2,b4σgx′ 2,b5σgy′ 2,b6σgz′ 2B in a third preset threshold B1,b2,b3,b4,b5,b6The threshold coefficient is also set according to experience, and the threshold coefficient in A is far larger than the threshold coefficient in B;
5) carrying out primary cleaning on the angular velocity and acceleration data of the current window, and identifying all data points outside the range of a first threshold value T or a second threshold value T' as wild points to be removed;
6) filtering the cleaned target data based on a wavelet filtering method, specifically, setting a wavelet to be db4 wavelet, setting a wavelet decomposition layer N to be 2, and setting a layering threshold value to be alpha to be 4 by selecting a proper strategy;
7) performing wavelet denoising and filtering on the cleaned acceleration and angular velocity data in the current window;
8) and moving the window forward by W points until the next window continues to repeat 2) until the acceleration and angular velocity of all windows are over, where "all" refers to all windows completing the installation angle estimation process.
According to the data compensation method for the inertial measurement unit, a wavelet filtering method is adopted for noise reduction, the effect is safer compared with that of traditional FFT filtering or smoothing device filtering, the method can better cope with application scenes with low signal to noise ratio (for example, data has larger vibration noise), meanwhile, a dynamic window is adopted for real-time threshold calculation for removing outliers, and the threshold is directly set in a non-traditional method, so that the dynamic performance of the system is better, and the adaptability to IMUs of different models is stronger.
Further, in an embodiment, the step S2 may specifically include:
s21, acquiring longitude and latitude information of the current moment and a timestamp of the current moment according to the navigation information of the target vehicle;
s22, determining a space rectangular coordinate corresponding to the longitude and latitude information according to the longitude and latitude information;
s23, according to the reference space rectangular coordinate corresponding to the longitude and latitude information of the previous moment, determining a station center coordinate corresponding to the space rectangular coordinate;
s24, determining that the target vehicle is in a moving state according to the station center coordinates and the preset distance threshold, determining the speed of the target vehicle in the preset state according to the station center coordinates, the timestamp of the current moment and the timestamp of the previous moment, and determining the course angle of the target vehicle in the moving state according to the station center coordinates.
Optionally, the GPS vehicle speed and heading angle resolving unit (4) mainly determines a vehicle motion state and outputs vehicle speed and heading angle information, and the following is an execution flow:
1) acquiring longitude and latitude information { lat) at current timek,lonkRecording time stamp t of longitude and latitude information timek；
2) Longitude and latitude information { lat) is obtained by utilizing the existing earth mathematical modelk,lonkConverting into space rectangular coordinates (x)k,yk)；
3) Recall the spatial rectangular coordinate (x) of the previous momentk-1,yk-1) Using the reference position as a reference space rectangular coordinate (x)k-1,yk-1) And will be (x)k,yk) Converting into a center of station coordinate (de, dn) of a center of station coordinate system;
4) judging the preset state of the vehicle, if so
If the vehicle state is smaller than the preset distance threshold T, the vehicle is judged to be in a parking state, and only the vehicle state s output by the IMU installation attitude error estimation unit (5) is 0, and the next round of data acquisition is carried out by jumping to 1); if the distance is larger than the preset distance threshold value T, the target vehicle is judged to be in a motion state, 5) is executed,
T＝D/F
wherein D is a constant set according to experience, and F represents the preset sampling frequency of the GPS.
5) Obtaining a timestamp t of a previous timek-1And according to the station center coordinates (de, dn) and the time stamp t of the current timekDetermining the vehicle speed v when the target vehicle is in a motion state;
6) determining a heading angle beta when the target vehicle is in a moving state according to the station center coordinates, and if the beta is less than 0, replacing psi with psi 'as the heading angle, wherein psi' is phi +360 degrees;
ψ＝tan-1(de/dn)
7) and outputting the target vehicle in a moving state to an IMU installation attitude error estimation unit (5), namely s is 1, the vehicle speed and the course angle jump back to 1) and carrying out data acquisition of the next round.
The mathematical model of the earth model is as follows:
the major semi-axis p of the earth ellipsoid is 6378137m
Short semi-axis q of the earth ellipsoid 6356752.3142m
Eccentricity ratio
Second eccentricity
Coefficient of proportionality
Space rectangular abscissa X ═ K (lon-lon)0)
Spatial rectangular ordinate
Wherein lat, lon, lat0,lon0Respectively, the latitude at the time k, the longitude at the time k and the time k-1Latitude, longitude at time k-1.
The inertial measurement unit data compensation method provided by the invention can acquire the course angle and the speed of the vehicle in a motion state, and lays a foundation for compensating IMU installation attitude errors based on the course angle and the speed subsequently.
Further, in an embodiment, the step S3 may specifically include:
s31, respectively determining a target speed observation model and a state equation of a heading angle, a vehicle speed estimation value, an IMU attitude estimation value and a gyroscope zero offset estimation value of the IMU according to the first target data, the heading angle and the vehicle speed;
s32, determining a vehicle speed estimation value and a vehicle speed observation value according to the target speed observation model and the state equation, and taking the difference between the vehicle speed estimation value and the vehicle speed observation value as a target function;
wherein the target velocity observation object may typically be selected from an IMU or a satellite navigation system GNSS.
The IMU installation attitude error estimation unit (5) is started when the vehicle is in the preset state, an IMU speed observation model based on vehicle body motion constraint assumption is established by utilizing the received first angular speed, the received first acceleration, the received course angle and the vehicle speed, a state equation of the course angle and the vehicle speed estimation value, the IMU attitude estimation value and the gyroscope zero offset estimation value is established, and the vehicle speed estimation value of the IMU at the k moment is calculated. And using the vehicle speed observed value of the IMU as a target value, and using the difference between the vehicle speed estimated value and the vehicle speed observed value as a target function.
Kalman linear discrete dynamic space model state transition equation:
xk＝φkxk-1+uk
kalman Observation equation:
zk＝Hkxk+vk
xkis the state vector of the system at the time k, and the covariance matrix is Pk，ukIs the state noise at time k, zkIs the observation vector at time k, and vk is the observation noise at time k. Phi is akIs a matrix of the state transitions of the system,Hkis an observation matrix.
The vehicle motion constraint refers to the observation matrix HkAnd a measurement vector zkAppropriate selection or modification is made to conform to the empirical rule, so that the state update of the system is more expected. For example, the vehicle body does not have large lateral sliding and vertical displacement in the running process, so that the H can be properly modifiedkAnd zkThe lateral and the sky speed are measured to be 0, etc.
The model corresponding to the system is a weak nonlinear system, and should be modified appropriately based on kalman filtering, and the extended kalman filtering is applied to estimate, since the formula and derivation process are complicated and well known to those skilled in the relevant arts, it is not demonstrated here.
Setting state dimensions (generally comprising speed, position, attitude, accelerometer dynamic bias, gyroscope dynamic bias, carrier antenna lever arm, vehicle body antenna lever arm and the like) of extended Kalman filtering, determining a transfer matrix of the states according to error propagation variance and discretizing the transfer matrix to obtain phikSetting a priori state x before starting Kalman filtering0And a priori covariance matrix P0，x0The states include 15-21 dimensions (each state quantity is x, y and z dimensions), including the initial IMU installation attitude angle psibvStarting extended Kalman filtering can calculate estimated value of vehicle body speed at k moment in iterative process
Vehicle body attitude estimation value
GNSS antenna lever arm estimation
IMU attitude estimation
And gyroscope dynamic zero offset estimation
The process is briefly described as follows:
wherein
Can be approximately regarded as
a is GNSS system, e is EFEC system, b is IMU system, and v is vehicle system.
Velocity observation equation:
is a GNSS velocity observation,
is the velocity observation of the IMU we are looking at,
is the filtered angular velocity read by the IMU.
Optionally, the same purpose can be achieved by using the IMU or the GNSS as an observation target, except that the observation and estimation equations are changed by the lever arm, if the lever arm between the two is ignored, the velocity observation of the two will be the same, the GNSS antenna is used as the target for observation and estimation, and in the system with the antenna as the target, the observation velocity of the antenna is the actual antenna velocity
The estimated speed of the antenna (considering the lever arm) is based on the IMU speed estimated by kalman filtering as follows:
at the moment, the convergence process becomes an optimization process of the difference between the observed speed and the estimated speed of the antenna by taking the IMU installation posture as an independent variable.
No matter who is the observation target, the objective function is:
starting a multi-starting-point simulated annealing algorithm, and iteratively solving the optimal coordinate transformation matrix between the carrier system (v system) and the IMU system (b system) on the premise of meeting the set minimum convergence time
And then transmitted to an IMU mounting attitude error compensation unit (6).
IMU installation attitude error compensation unit (6) will utilize
Modifying the coordinate transformation matrix of the existing inertial navigation and recalculating the calibrated IMU output, i.e. the corrected acceleration fcAnd corrected angular velocity ωc。
According to the data compensation method for the inertial measurement unit, the difference between the vehicle speed estimation value and the vehicle speed observation value is used as the objective function, so that a foundation is laid for solving an optimal solution based on the objective function subsequently and compensating IMU data through the optimal solution.
Further, in an embodiment, the step S4 may specifically include:
s41, determining an initial solution group according to the starting point number, the initial temperature, the temperature attenuation factor, the search step factor, the maximum iteration number, the initial solution state, the suspension condition and the initial range boundary of the initial solution group, and generating a new solution group positioned in a solution space from the current solution group;
s42, determining a first objective function value according to the initial solution group and the objective function;
s43, determining a second objective function value according to the new solution group and the objective function;
s44, determining whether to accept a new solution group according to the size relation between the difference value of the second objective function value and the first objective function value and a preset threshold, and iteratively updating the current solution group by using the new solution group when accepting the new solution group, and stopping updating the current solution group until the iteration number reaches the maximum iteration number and meets the stopping condition;
s45, determining an optimal solution group according to the updated current solution group, and acquiring an optimal solution according to the optimal solution group;
s46, determining a target rotation matrix according to the optimal solution and a preset rotation matrix formula, and acquiring corrected target data according to the target rotation matrix and a preset coordinate conversion matrix;
wherein the suspension condition is that the continuous preset new solution groups are not accepted.
Further, in an embodiment, the step S44 may specifically include:
s441, if the difference value between the second objective function value and the first objective function value is smaller than a preset threshold value, accepting new group solving;
and S442, if the difference value between the second objective function value and the first objective function value is greater than a preset threshold value, determining an acceptance probability according to a Monte Carlo criterion, and accepting a new solution group according to the acceptance probability.
Optionally, the simulated annealing algorithm is derived from a cooling process of the crystal, if the solid is not in the lowest energy state, the solid is heated and cooled again, and as the temperature slowly decreases, atoms in the solid are arranged according to a certain shape to form a high-density low-energy regular crystal, which corresponds to a globally optimal solution in the algorithm. If the temperature is lowered too quickly, it may result in atoms lacking sufficient time to align into a crystalline structure, resulting in an amorphous with higher energy, which is a locally optimal solution. Therefore, according to the annealing process, a little more energy can be added to the annealing process, then the annealing process is cooled again, the local optimal solution is jumped out, and finally the local optimal solution falls into the optimal solution slowly along with the repeated cooling process, as shown in fig. 5.
The search result of the simulated annealing possibly falls into the local optimal solution adjacent to the optimal solution, so that the simulated annealing with multiple starting points is adopted, the system has multiple starting points, and the solution (range solution) with the highest occurrence frequency is found in the results corresponding to the multiple starting points, so that the misjudgment caused by one-time simulated annealing is avoided.
The IMU installation attitude error estimation unit (5) calculates the installation attitude error of the IMU module by using a multi-starting point simulated annealing algorithm, namely the equivalent rotation vector between the vehicle carrier and the IMU, and the specific flow refers to FIG. 6, and the following is an execution flow:
1) setting the number of starting points N of an initial solution group, setting an initial temperature T, setting a temperature attenuation factor, setting a search step factor, setting a maximum iteration number L, setting an initial solution state Q, setting a suspension condition and setting an initial range boundary;
2) randomly generating an initial solution group
Calculating a first objective function value of an objective function f (Ψ);
3) generating a new solution group Ψ 'located in the solution space from the current solution group, and calculating a second objective function value of the objective function f (Ψ');
4) calculating a target function difference delta f (Ψ') -f (Ψ) corresponding to the current solution group and the new solution group;
5) if Δ f is less than 0, accepting the new solution group, replacing Ψ 'with original Ψ, and f (Ψ') with original f (Ψ), and if Δ f is greater than 0, determining the acceptance probability P of accepting the new solution group according to the Monte Carlo Metropolis criterionkAnd accepting a new solution group according to the acceptance probability;
wherein k isBIs the Boltzmann constant, generally taken as 8.6173324X 10-5The probability that the particle will tend to equilibrate at temperature T is
6) Judging whether the iteration times reach the maximum iteration times L or not, and if not, jumping back to the step 3);
7) judging whether the termination condition is met, if the termination condition is not met, reducing the initial temperature T, at the moment, if the maximum iteration time is not enough, properly increasing and resetting the maximum iteration time, and jumping back to the step 3)
8) If the suspension condition is met, the optimal solution group is obtained according to the updated current solution group, and an optimal solution is found out from the optimal solution group
That is, the target rotation matrix is obtained according to the preset rotation matrix formula
And then transferred to an IMU mounting attitude error compensation unit (6).
Wherein the objective function f (Ψ) is:
wherein,
is the estimation of the speed of the vehicle,
is to observe the speed of the vehicle,
θ, ψ denote roll angle, pitch angle, and heading angle, respectively.
Target rotation matrix formula:
an IMU installation attitude error compensation unit (6) converts the sensor output of the IMU into a carrier coordinate system by using the obtained IMU, a target rotation matrix and a preset coordinate conversion matrix (the coordinate conversion matrix of the existing inertial navigation), and outputs the corrected acceleration fcAnd angular velocity ωc。
Fig. 7 is an error statistical chart of each time of two experiments, the GPS and IMU used in the experiments are consumer-grade products, the abscissa in the chart is time, the ordinate is distance error of the GPS and the dead reckoning at each time, the upper chart in fig. 7 is a control group, the total error of the whole course of the experiment is 6530 meters, the lower chart in fig. 7 is an experiment group, and the total error of the whole course of the experiment is 2322 meters, thereby quantifying the effect of the IMU data processing unit (2) of the present invention on reducing the track error to a certain extent.
According to the data compensation method for the inertial measurement unit, the initial solution group is random, the capacity of resisting external unstable factors is achieved, the robustness of the system is enhanced, meanwhile, the problem of trapping in local solution can be avoided, the global optimal solution is found, in addition, the accidental deviation of single particle solving is avoided through multi-starting-point searching, the convergence is finished in advance by setting the iteration number, and the rapidity of solving is guaranteed.
The following describes the data compensation system of the inertial measurement unit provided by the present invention, and the data compensation system of the inertial measurement unit described below and the data compensation method of the inertial measurement unit described above may be referred to correspondingly.
Fig. 8 is a schematic structural diagram of a data compensation system of an inertial measurement unit according to the present invention, as shown in fig. 8, including: a data processing module 810, a data acquisition module 811, a mounting estimation module 812, and a data compensation module 813;
the data processing module 810 is used for denoising and preprocessing target data of an inertial measurement unit IMU of a target vehicle to obtain first target data;
the data acquisition module 811 is used for acquiring a course angle and a vehicle speed of the target vehicle in a preset state according to the navigation information of the target vehicle;
an installation estimation module 812 for determining an objective function based on the first target data, the heading angle, and the vehicle speed;
the data compensation module 813 is used for determining an optimal solution of the objective function based on a multi-starting point simulated annealing algorithm, and determining the posture of the IMU relative to the vehicle system according to the optimal solution so as to obtain corrected objective data;
wherein the target data comprises angular velocity and acceleration;
the preset state is linear motion at a speed greater than a preset vehicle speed and less than a preset course angular rate.
The inertial measurement unit data compensation system provided by the invention realizes estimation and compensation of the installation attitude error by combining the simulated annealing algorithm, can estimate and compensate the installation attitude error of the IMU under the state that the vehicle carrier moves linearly and has a certain speed, and improves the accuracy and stability of dead reckoning.
Fig. 9 is a schematic structural diagram of an electronic device provided in the present invention, and as shown in fig. 9, the electronic device may include: a processor (processor)910, a communication interface 911, a memory 912 and a bus 913, wherein the processor 910, the communication interface 911 and the memory 912 complete the communication with each other through the bus 913. Processor 910 may call logic instructions in memory 912 to perform the following method:
denoising and preprocessing target data of an Inertial Measurement Unit (IMU) of a target vehicle to obtain first target data;
acquiring a course angle and a vehicle speed of a target vehicle in a preset state according to navigation information of the target vehicle;
determining a target function according to the first target data, the course angle and the vehicle speed;
determining an optimal solution of a target function based on a multi-starting point simulated annealing algorithm, and determining the posture of the IMU relative to a vehicle system according to the optimal solution so as to obtain corrected target data;
wherein the target data comprises angular velocity and acceleration;
the preset state is linear motion at a speed greater than a preset vehicle speed and less than a preset course angular rate.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention or a part thereof, which essentially contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer power screen (which may be a personal computer, a server, or a network power screen, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.
Further, the present invention discloses a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the inertial measurement unit data compensation method provided by the above-mentioned method embodiments, for example, comprising:
denoising and preprocessing target data of an Inertial Measurement Unit (IMU) of a target vehicle to obtain first target data;
acquiring a course angle and a vehicle speed of a target vehicle in a preset state according to navigation information of the target vehicle;
determining a target function according to the first target data, the course angle and the vehicle speed;
determining an optimal solution of a target function based on a multi-starting point simulated annealing algorithm, and determining the posture of the IMU relative to a vehicle system according to the optimal solution so as to obtain corrected target data;
wherein the target data comprises angular velocity and acceleration;
the preset state is linear motion at a speed greater than a preset vehicle speed and less than a preset course angular rate.
In another aspect, the present invention also provides a non-transitory computer readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to execute the inertial measurement unit data compensation method provided in the foregoing embodiments, for example, including:
denoising and preprocessing target data of an Inertial Measurement Unit (IMU) of a target vehicle to obtain first target data;
acquiring a course angle and a vehicle speed of a target vehicle in a preset state according to navigation information of the target vehicle;
determining a target function according to the first target data, the course angle and the vehicle speed;
determining an optimal solution of a target function based on a multi-starting point simulated annealing algorithm, and determining the posture of the IMU relative to a vehicle system according to the optimal solution so as to obtain corrected target data;
wherein the target data comprises angular velocity and acceleration;
the preset state is linear motion at a speed greater than a preset vehicle speed and less than a preset course angular rate.
The above-described system embodiments are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on such understanding, the above technical solutions may be essentially or partially implemented in the form of software products, which may be stored in computer readable storage media, such as ROM/RAM, magnetic disk, optical disk, etc., and include instructions for causing a computer power supply screen (which may be a personal computer, a server, or a network power supply screen, etc.) to execute the method according to the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A data compensation method for an inertial measurement unit is characterized by comprising the following steps:
denoising and preprocessing target data of an Inertial Measurement Unit (IMU) of a target vehicle to obtain first target data;
acquiring a course angle and a vehicle speed of the target vehicle in a preset state according to the navigation information of the target vehicle;
determining an objective function according to the first target data, the course angle and the vehicle speed;
determining an optimal solution of the objective function based on a multi-starting point simulated annealing algorithm, and determining the posture of the IMU relative to a vehicle system according to the optimal solution to obtain corrected objective data;
wherein the target data comprises angular velocity and acceleration;
the preset state is linear motion at a speed greater than a preset vehicle speed and less than a preset course angular speed.
2. The inertial measurement unit data compensation method of claim 1, wherein the denoising pre-processing of the target data of the inertial measurement unit IMU of the target vehicle to obtain the first target data comprises:
determining the size of a window according to preset required time and preset sampling frequency;
cleaning target data of a current window according to a window dynamic threshold method;
and carrying out filtering processing on the cleaned target data based on a wavelet filtering method.
3. The inertial measurement unit data compensation method of claim 2, wherein the cleaning of the target data of the current window according to the window dynamic threshold method comprises:
determining a first variance of target data of a current window;
dividing the current window into preset sub-windows, and respectively acquiring a second variance of the target data of each sub-window to determine a variance deviation value between the second variance of each sub-window and the first variance;
if a first sub-window with the variance deviation value larger than a first preset threshold exists, determining a first threshold of the current window according to a second preset threshold and the first variance;
if the variance deviation values of all the sub-windows are smaller than the first preset threshold, determining a second threshold of the current window according to a third preset threshold and the first variance;
and performing data cleaning on the target data of the current window according to the first threshold or the second threshold.
4. The data compensation method for the inertial measurement unit according to claim 1, wherein the obtaining of the course angle and the vehicle speed of the target vehicle in a preset state according to the navigation information of the target vehicle comprises:
acquiring longitude and latitude information of the current moment and a timestamp of the current moment according to the navigation information of the target vehicle;
determining a space rectangular coordinate corresponding to the longitude and latitude information according to the longitude and latitude information;
according to a reference space rectangular coordinate corresponding to longitude and latitude information of the previous moment, determining a station center coordinate corresponding to the space rectangular coordinate;
and determining the target vehicle to be in a moving state according to the station center coordinates and a preset distance threshold, determining the speed of the target vehicle in the preset state according to the station center coordinates, the timestamp of the current moment and the timestamp of the previous moment, and determining the course angle of the target vehicle in the moving state according to the station center coordinates.
5. The inertial measurement unit data compensation method of claim 1, wherein determining an objective function based on the first target data, the heading angle, and the vehicle speed comprises:
respectively determining a target speed observation model and state equations of a course angle, a vehicle speed estimation value, an IMU attitude estimation value and a gyroscope zero offset estimation value of the IMU according to the first target data, the course angle and the vehicle speed;
determining a vehicle speed estimation value and a vehicle speed observation value according to the target speed observation model and the state equation, and taking the difference between the vehicle speed estimation value and the vehicle speed observation value as the target function;
the target speed observation model comprises an IMU speed observation model or a satellite navigation system GNSS speed observation model.
6. The inertial measurement unit data compensation method of claim 1, wherein the determining an optimal solution for the objective function based on a multi-start simulated annealing algorithm and determining the pose of the IMU relative to the vehicle body from the optimal solution to obtain the corrected objective data comprises:
determining an initial solution group according to the starting point number, the initial temperature, the temperature attenuation factor, the search step factor, the maximum iteration number, the initial solution state, the suspension condition and the initial range boundary of the initial solution group, and generating a new solution group positioned in a solution space from the current solution group;
determining a first objective function value according to the initial solution group and the objective function;
determining a second objective function value according to the new solution group and the objective function;
determining whether to accept the new solution group according to the magnitude relation between the difference value of the second objective function value and the first objective function value and a preset threshold, and iteratively updating the current solution group by using the new solution group when accepting the new solution group until the iteration number reaches the maximum iteration number and meets the stopping condition, and stopping updating the current solution group;
determining an optimal solution group according to the updated current solution group, and acquiring the optimal solution according to the optimal solution group;
determining a target rotation matrix according to the optimal solution and a preset rotation matrix formula, and acquiring the corrected target data according to the target rotation matrix and a preset coordinate conversion matrix;
wherein the suspension condition is that none of the continuously preset new solution groups is accepted.
7. The method for compensating data of an inertial measurement unit according to claim 6, wherein the determining whether to accept the new solution group according to a magnitude relation between a difference value between the second objective function value and the first objective function value and a preset threshold value comprises:
if the difference value between the second objective function value and the first objective function value is smaller than the preset threshold value, accepting the new solution;
and if the difference value between the second objective function value and the first objective function value is larger than the preset threshold value, determining an acceptance probability according to a Monte Carlo criterion, and accepting the new solution group according to the acceptance probability.
8. An inertial measurement unit data compensation system, comprising: the device comprises a data processing module, a data acquisition module, an installation estimation module and a data compensation module;
the data processing module is used for denoising and preprocessing target data of an Inertial Measurement Unit (IMU) of a target vehicle to obtain first target data;
the data acquisition module is used for acquiring a course angle and a vehicle speed of the target vehicle in a preset state according to the navigation information of the target vehicle;
the installation estimation module is used for determining an objective function according to the first objective data, the course angle and the vehicle speed;
the data compensation module is used for determining an optimal solution of the objective function based on a multi-starting point simulated annealing algorithm and determining the posture of the IMU relative to the vehicle system according to the optimal solution so as to obtain corrected objective data;
wherein the target data comprises angular velocity and acceleration;
the preset state is linear motion at a speed greater than a preset vehicle speed and less than a preset course angular speed.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the inertial measurement unit data compensation method according to any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the inertial measurement unit data compensation method of any one of claims 1 to 7.
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