CN111982106A - Navigation method, navigation device, storage medium and electronic device - Google Patents
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- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
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Abstract
The application discloses a navigation method, a navigation device, a storage medium and an electronic device. The method comprises the following steps: acquiring first attitude data of a target object, wherein the first attitude data is obtained by resolving based on inertial data, and the inertial data is acquired when an inertial navigation system observes the target object; acquiring second position and posture data of the target object, wherein the second position and posture data are position and posture data obtained when the inertial navigation system observes the target object and the satellite navigation system observes the target object; calculating a data difference value of the first posture data and the second posture data; inputting the data difference value into a Kalman filter, and processing to obtain covariance; and if the covariance is smaller than the preset value, navigating the target object based on the first attitude data. By the method and the device, the problem that navigation and positioning errors are large by adopting satellite measurement navigation and inertial measurement elements in the related technology is solved.
Description
Technical Field
The present application relates to the field of navigation technologies, and in particular, to a navigation method, an apparatus, a storage medium, and an electronic apparatus.
Background
The navigation positioning, the path planning, the auxiliary driving and other functions are main development directions of the vehicle, wherein the high-precision navigation positioning is the core and the foundation of other development directions, so that the navigation positioning system is required to have the capability of continuously working for a long time with high precision, and meanwhile, the system has higher cost performance.
At present, a single navigation system is more or less deficient in performance, and the reliability of the navigation system is limited by the limitation of information acquisition, so that the requirement of people on navigation accuracy cannot be met. In particular, in a vehicle navigation system. The Global Positioning System (GPS) is widely used, but the anti-interference capability is poor, and satellite signals are easily weakened under the conditions of severe weather influence, high buildings or plant sheltering, and the like, so that autonomous positioning cannot be achieved. With the development of technology, Inertial Navigation Systems (INS) are gradually mature and widely used, but the system is seriously influenced by the accumulation of sensor errors
Aiming at the problem that navigation and positioning errors are large by adopting a satellite measurement navigation and an inertial measurement element in the related technology, an effective solution is not provided at present.
Disclosure of Invention
The application provides a navigation method, a navigation device, a storage medium and an electronic device, which are used for solving the problem of large navigation positioning errors caused by satellite measurement navigation and inertial measurement elements in the related technology.
According to one aspect of the present application, a navigation method is provided. The method comprises the following steps: acquiring first attitude data of a target object, wherein the first attitude data is obtained by resolving based on inertial data, and the inertial data is acquired when an inertial navigation system observes the target object; acquiring second position and posture data of the target object, wherein the second position and posture data are position and posture data obtained when the inertial navigation system observes the target object and the satellite navigation system observes the target object; calculating a data difference value of the first posture data and the second posture data; inputting the data difference value into a Kalman filter, and processing to obtain covariance; and if the covariance is smaller than the preset value, navigating the target object based on the first attitude data.
Optionally, the acquiring the first pose data of the target object comprises: determining initial position data and initial velocity data of the target object based on the inertial data; inputting the inertial data into an error model of an inertial navigation system to obtain a position error when determining initial position data of a target object and a speed error when determining initial speed data of the target object; determining position data of the target object based on the initial position data and the position error, and determining velocity data of the target object based on the initial velocity data and the velocity error; the position data and velocity data of the target object are determined as first pose data of the target object.
Optionally, the acquiring the first pose data of the target object further comprises: determining an attitude matrix of the target object according to a quaternion algorithm based on triaxial gyroscope data and triaxial accelerometer data in the inertial data; determining attitude angle data of the target object based on the attitude matrix of the target object; the pose angle data of the target object is determined as first pose data of the target object.
Optionally, the method is preceded by inputting the inertial data into an error model of the inertial navigation system, resulting in a position error in determining the initial position data of the target object, and a velocity error in determining the initial velocity data of the target object
The method also comprises the following steps: determining a state equation of the inertial navigation system:
wherein x (t) is a system state matrix for characterizing system errors, the system errors at least including system errors in determining pose data of the target object; f (t) is a system state transition matrix for representing the correction quantity of the system error related to the time; g (t)
Is a system noise distribution matrix; w (t) is a noise vector;
is the updated system state matrix; determining an observation equation of the inertial navigation system: z (t) hx (t) + V, where z (t) is a system measurement matrix used to characterize the viewMeasuring errors, wherein the observation errors at least comprise observation errors when the pose data of the target object are determined; h is a system measurement updating matrix used for representing the correction quantity of the correlation between the observation error and the time; v is the observation noise; an error model of the inertial navigation system is determined based on the state equation and the observation equation.
Optionally, before inputting the data difference value into the kalman filter and processing the data difference value to obtain the covariance, the method further includes: determining a state prediction equation: xk＝FXk-1Wherein k is a discrete time, XkIs a combined system state matrix, X, at time kk-1The method comprises the steps that a combined system state matrix at the moment k-1 is used for representing combined system errors, the combined system errors at least comprise errors between position data obtained through calculation based on inertial data and position data observed by a satellite navigation system and errors between speed data obtained through calculation based on the inertial data and speed data observed by the satellite navigation system, and F is a system state conversion matrix under discrete time; determining a covariance prediction equation: pk＝FPk-1FT+ Q, formula (I), PkIs the covariance matrix of time k, Pk-1Covariance matrix, F, at time k-1TIs the transpose of F, Q is the noise vector; determining a system state update equation: xk′＝Xk+K(Zk-H), in which Xk' combining the optimization results of the system state matrix for time k, ZkIs the system measurement matrix at time K, H is the system measurement update matrix, K is the Kalman gain matrix, K is PkHT/(HPk-1HT+ R), wherein HTTranspose for H, R represents measurement variance; determining a covariance update equation: pk′＝(I-KH)PkIn the formula, Pk' is covariance matrix updated at time k, and I is identity matrix; a Kalman filter is determined based on a state prediction equation, a covariance prediction equation, a system state update equation, and a covariance update equation.
Optionally, acquiring the initial inertial data in the coordinate system adopted by the inertial navigation system includes: and observing the target object by adopting a micro inertial navigation measuring unit, and acquiring initial inertial data.
According to another aspect of the present application, a navigation device is provided. The device includes: the first acquisition unit is used for acquiring first attitude data of the target object, wherein the first attitude data is obtained by resolving based on inertial data, and the inertial data is acquired when the inertial navigation system observes the target object; the second acquisition unit is used for acquiring second position and posture data of the target object, wherein the second position and posture data are position and posture data obtained by observing the target object by the satellite navigation system when the target object is observed by the inertial navigation system; the calculating unit is used for calculating a data difference value of the first position and attitude data and the second position and attitude data; the processing unit is used for inputting the data difference value into a Kalman filter and processing the data difference value to obtain covariance; and the navigation unit is used for navigating the target object based on the first attitude data if the covariance is smaller than a preset value.
According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium including a stored program, wherein the program controls a device in which the non-volatile storage medium is located to perform a navigation method when running.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory has computer readable instructions stored therein and the processor is configured to execute the computer readable instructions, wherein the computer readable instructions when executed perform a navigation method.
Through the application, the following steps are adopted: acquiring first attitude data of a target object, wherein the first attitude data is obtained by resolving based on inertial data, and the inertial data is acquired when an inertial navigation system observes the target object; acquiring second position and posture data of the target object, wherein the second position and posture data are position and posture data obtained when the inertial navigation system observes the target object and the satellite navigation system observes the target object; calculating a data difference value of the first posture data and the second posture data; inputting the data difference value into a Kalman filter, and processing to obtain covariance; if the covariance is smaller than the preset value, the target object is navigated based on the first attitude data, and the problem of large navigation positioning error caused by satellite measurement navigation and inertial measurement element navigation in the related technology is solved. Error data between first position and attitude data obtained by inertial solution and second position and attitude data obtained by satellite navigation are calculated, and the error data is processed by adopting a Kalman filter to determine the optimal navigation data of the integrated navigation, so that the effect of improving the positioning accuracy of the integrated navigation system obtained by satellite measurement navigation and inertial measurement element navigation is achieved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
FIG. 1 is a flow chart of a navigation method provided according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a target coordinate system in a navigation method provided according to an embodiment of the present application;
FIG. 3 is a flow chart of another navigation method provided in accordance with an embodiment of the present application; and
fig. 4 is a schematic diagram of a navigation device provided according to an embodiment of the application.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the related art, since the navigation performance of both the satellite navigation and the inertial navigation is good and bad, the two navigation systems are combined to obtain a combined navigation system which has better navigation characteristics than a single navigation system.
However, the accuracy of the GPS/INS integrated navigation system is limited by the accuracy of the sensor, and a high-accuracy sensor is required to obtain a high-accuracy position, and the high-accuracy sensor generally has high cost, which limits the development and commercial application of the system. In addition, the GPS/INS integrated navigation system also has the problems of the anti-interference performance of the navigation system and the accumulation of INS navigation errors along with time. The problem of difficult realization of the accurate positioning of the integrated navigation under the condition of losing lock of the GPS signal.
Based on this, the present application intends to provide a solution to the above technical problem, the details of which will be explained in the following embodiments.
According to an embodiment of the present application, a navigation method is provided.
Fig. 1 is a flow chart of a navigation method according to an embodiment of the present application. As shown in fig. 1, the method comprises the steps of:
step S101, first attitude data of the target object is obtained, wherein the first attitude data is obtained through calculation based on inertial data, and the inertial data is acquired when the inertial navigation system observes the target object.
Specifically, the target object may be a vehicle, the vehicle is observed by an inertial navigation system to obtain inertial data, and first attitude data of the vehicle is calculated based on the inertial data, where the first attitude data may specifically include data such as a position, a speed, and an attitude angle of the vehicle.
And S102, acquiring second position and posture data of the target object, wherein the second position and posture data are obtained when the inertial navigation system observes the target object and the satellite navigation system observes the target object.
It should be noted that, in the embodiment of the present application, a system obtained by combining an inertial navigation system and a satellite navigation system is used for navigation, the inertial navigation system is used for observing a target object, the satellite navigation system is also used for observing the target object, and the satellite navigation system can directly obtain the second position and orientation data of the target object during observation.
For convenience of calculation, optionally, in the navigation method provided in this embodiment of the present application, before acquiring the first pose data of the target object, the method further includes: acquiring initial inertial data under a coordinate system adopted by an inertial navigation system; and converting the initial inertial data into data in a target coordinate system to obtain inertial data.
Specifically, the target coordinate system may be a northeast navigation coordinate system, as shown in fig. 2, which is a schematic diagram of the northeast navigation coordinate system, and the coordinate system adopted by the inertial navigation system includes at least one of the following: the system comprises a space inertial coordinate system, a carrier coordinate system and a navigation coordinate system, wherein the coordinate system adopted by an inertial navigation system is unified to a northeast navigation coordinate system through a geocentric inertial reference coordinate system, and meanwhile, inertial navigation data under the coordinate system adopted by the inertial navigation system are subjected to coordinate conversion, wherein the inertial navigation data at least comprise accelerometer data, gyroscope data, earth basic parameters and the like.
It should be noted that, the northeast navigation coordinate system is used as the reference coordinate system, the rectangular coordinate system uses the distance from the origin of coordinates as the navigation parameter, the position result is in the order of millimeters, and compared with the traditional navigation system, the position information error of the error state variable is given in the form of longitude and latitude height, the error variable will involve about 8 bits of decimal point, and the error in the calculation angle is reduced.
For convenience of calculation, optionally, in the navigation method provided in this embodiment of the present application, before acquiring the second pose data of the target object, the method further includes: acquiring initial pose data under a coordinate system adopted by a satellite navigation system; and converting the initial pose data into data in a target coordinate system to obtain second pose data.
Specifically, the target coordinate system may be a northeast navigation coordinate system, and the coordinate system adopted by the satellite navigation system includes at least one of the following: the satellite navigation system comprises a geospatial direct coordinate system, a local horizontal coordinate system, a geographic coordinate system, a spatial inertial coordinate system, a carrier coordinate system and a navigation coordinate system which are adopted by inertial measurement, the coordinate system adopted by the satellite navigation system is also unified under a northeast navigation coordinate system, and simultaneously, the satellite navigation data under the coordinate system adopted by the satellite navigation system is subjected to coordinate conversion, so that the fusion of the satellite navigation data and the inertial navigation data is realized, and the precision of navigation calculation is improved.
And step S103, calculating a data difference value of the first position and attitude data and the second position and attitude data.
Specifically, the first attitude data obtained by inertial navigation solution and the second attitude data obtained by satellite measurement may respectively include velocity data and position data, and an error between the position velocity information obtained by inertial navigation solution and the position velocity information obtained by satellite measurement is determined and used as an input of the kalman filter information fusion device.
And step S104, inputting the data difference value into a Kalman filter, and processing to obtain covariance.
Specifically, the kalman filter recurs a navigation system including gaussian noise to obtain a corresponding navigation state estimate, and continuously updates data by using its adaptive characteristic, so that the covariance corresponds to the optimal prediction result of the combined navigation in the case of obtaining a covariance smaller than a preset value. Therefore, the precision positioning of the vehicle under the condition that the GPS signal is unlocked is realized, and the problem that the navigation precision error of the inertial measurement element is accumulated along with the time is solved.
And S105, if the covariance is smaller than a preset value, navigating the target object based on the first attitude data.
Specifically, the covariance is smaller than a preset value, which means that the error of pose data obtained by inertial solution and satellite measurement is small, and the covariance at this time corresponds to the best prediction result of the combined navigation, and the first pose data at this time can be used for navigating the target object.
According to the navigation method provided by the embodiment of the application, the first attitude data of the target object is obtained by obtaining the first attitude data, wherein the first attitude data is obtained by resolving based on inertial data, and the inertial data is acquired when the inertial navigation system observes the target object; acquiring second position and posture data of the target object, wherein the second position and posture data are position and posture data obtained when the inertial navigation system observes the target object and the satellite navigation system observes the target object; calculating a data difference value of the first posture data and the second posture data; inputting the data difference value into a Kalman filter, and processing to obtain covariance; if the covariance is smaller than the preset value, the target object is navigated based on the first attitude data, and the problem of large navigation positioning error caused by satellite measurement navigation and inertial measurement element navigation in the related technology is solved. Error data between first position and attitude data obtained by inertial solution and second position and attitude data obtained by satellite navigation are calculated, and the error data is processed by adopting a Kalman filter to determine the optimal navigation data of the integrated navigation, so that the effect of improving the positioning accuracy of the integrated navigation system obtained by satellite measurement navigation and inertial measurement element navigation is achieved.
Optionally, in the navigation method provided in this embodiment of the present application, the obtaining the first pose data of the target object further includes: determining an attitude matrix of the target object according to a quaternion algorithm based on triaxial gyroscope data and triaxial accelerometer data in the inertial data; determining attitude angle data of the target object based on the attitude matrix of the target object; the pose angle data of the target object is determined as first pose data of the target object.
Specifically, the attitude matrix may be obtained by using three-axis gyroscope data and three-axis accelerometer data measured by the inertial navigation system according to a quaternion algorithm, so as to obtain a corresponding attitude angle.
Meanwhile, specific force data are required to be used for resolving the speed data and the position data, so that the specific force data can be obtained through the attitude rotation matrix and the triaxial accelerometer data obtained in the attitude angle resolving process, and a data base is laid for resolving the speed data and the position data.
Optionally, in the navigation method provided in the embodiment of the present application, the obtaining the first pose data of the target object includes: determining initial position data and initial velocity data of the target object based on the inertial data; inputting the inertial data into an error model of an inertial navigation system to obtain a position error when determining initial position data of a target object and a speed error when determining initial speed data of the target object; determining position data of the target object based on the initial position data and the position error, and determining velocity data of the target object based on the initial velocity data and the velocity error; the position data and velocity data of the target object are determined as first pose data of the target object.
Specifically, an initial speed and an initial position are determined, and then an error model of the inertial navigation system is adopted to solve a speed error and a position error, so that the initial speed and the initial position are corrected, and speed data and position data of the target object are obtained.
It should be noted that the error model of the inertial navigation system includes two parts, one is a state equation of the navigation system, and the other is a navigation system observation equation, and optionally, in the navigation method provided in the embodiment of the present application, before inputting inertial data into the error model of the inertial navigation system, obtaining a position error when determining initial position data of the target object, and determining a velocity error when determining initial velocity data of the target object, the method further includes:
an error model of the inertial navigation system is determined based on the state equation and the observation equation.
Wherein, the state equation of the inertial navigation system is determined as follows: establishing an 18-dimensional state equation through accelerometer data of an inertial device, gyro modeling zero offset, white noise superposition data and the like:
in particular, the amount of the solvent to be used,
wherein the elements in the matrix
F42＝-fu，F43＝fn，
F51＝fu，
F53＝-fe，
F61＝-fn，F62＝fe，
In the formula, vu、vE、vNThe speed of the carrier is the speed of the carrier in the sky direction, the speed of the carrier in the east direction and the speed of the carrier in the north direction; rn、Rm、ReRespectively the meridian curvature radius, the prime circle curvature radius and the earth long radius of the point where the carrier is located; w, L are the longitude and latitude of the point where the carrier is located, respectively; f. ofu、fe、fnRespectively the specific force of the carrier navigation system in the lower direction, the specific force of the east direction and the specific force of the north direction; g0For acceleration of earth gravity, Marix3×3And updating the attitude matrix in three dimensions.
Wherein X (t) [ phi ]x φy φz vx vy vz x y z x y z ▽x ▽y ▽z]
In the formula, phix、φy、φzMisalignment angle, v, of mathematical platform for strapdown inertial navigationx、vy、vzFor a starting point inertial system with a velocity error in 3 axes,x、y、zin order to be a position error,x、y、zis a constant drift of the gyroscopex、▽y、▽zIs normally biased for the accelerometer.
Determining an observation equation of the inertial navigation system: z (t) hx (t) + V, where z (t) is a system measurement matrix used to characterize observation errors, and the observation errors at least include observation errors in determining pose data of the target object; h is a system measurement updating matrix used for representing the correction quantity of the correlation between the observation error and the time; v is observation noise, which can be set as a reference actual device error in the embodiment of the application;
specifically, the observation equation is composed of three parts, i.e., a position observation equation, a velocity observation equation, and a satellite phase observation equation, and Z (t) ═ Z (Z)P ZV ZB)T，ZPAs a position observation equation, ZVFor the equation of velocity observation, ZBFor the satellite phase observation equation, in the embodiment of the present application, ZPAnd ZVIs a non-zero element, ZBIs zero-element.
Wherein the non-zero element
In the formula, Wlast、Llast、hlastLatitude, longitude and altitude, W, respectively, of the moment of the carrier's locationcom、Lcom、hcomRespectively latitude, longitude and altitude of the next moment of the carrier point; v. ofnlast、velast、vulastRespectively the north, east and sky velocities at the moment of the carrier's pointncom、vecom、vucomRespectively the north, east and sky velocities at the moment of the carrier's point.
Wherein, H ═ diag (H)P HV HB)TWherein H isPIs a location measurement update matrix, HVIs a velocity measurement update matrix, HBIs the phase measurement update matrix.
Further, after an error model of the inertial navigation system is established, inertial navigation data after coordinate transformation is input into the error model of the inertial navigation system, and a speed error and a position error are solved by adopting the model.
Optionally, in the navigation method provided in the embodiment of the present application, before the data difference is input to the kalman filter and the covariance is obtained by processing, the method further includes:
determining a state prediction equation: xk＝FXk-1Wherein k is a discrete time, XkIs a combined system state matrix, X, at time kk-1A combined system state matrix for time k-1, the combined system state matrix being used to characterize a combined system error, the combined system error comprising at leastAnd calculating to obtain an error between the position data and the position data observed by the satellite navigation system based on the inertial data, and calculating to obtain an error between the velocity data and the velocity data observed by the satellite navigation system based on the inertial data, wherein F is a system state transition matrix under discrete time.
Determining a covariance prediction equation: pk＝FPk-1FT+ Q, formula (I), PkIs the covariance matrix of time k, Pk-1Covariance matrix, F, at time k-1TIs the transpose of F and Q is the noise vector.
Determining a system state update equation: xk′＝Xk+K(Zk-H), in which Xk' combining the optimization results of the system state matrix for time k, ZkIs the system measurement matrix at time K, H is the system measurement update matrix, K is the Kalman gain matrix, K is PkHT/(HPk-1HT+ R), wherein HTTransposed to H, R represents the measurement variance.
Determining a covariance update equation: pk′＝(I-KH)PkIn the formula, Pk' is the covariance matrix updated at time k, and I is the identity matrix.
A Kalman filter is determined based on a state prediction equation, a covariance prediction equation, a system state update equation, and a covariance update equation.
It should be noted that the initial value of the kalman filter includes five aspects: the method comprises the following steps of firstly, parameters such as a long radius, an earth oblateness and an angular velocity of earth rotation in a geophysical model; secondly, time updating of a continuous system state conversion array F; thirdly, error parameters of the inertial measurement unit, namely a combined system state conversion array, including speed information and position information of the inertial measurement system; fourthly, speed, position and height information of the satellite navigation system; and fifthly, updating the input matrix of the continuous system and the measurement matrix of the continuous system, correspondingly inputting a covariance prediction equation, a covariance update equation and a system state update equation into the initial value of the Kalman filter, so as to obtain corresponding covariance by recursion of the system state, continuously updating data, obtaining the covariance smaller than a preset value, and further obtaining the optimal prediction result of the combined navigation.
By the aid of the GPS/MINS integrated navigation mode and the information fusion method of Kalman filtering, the defect of long-time error accumulation of a strapdown inertial navigation system can be overcome, and the purpose of effectively inhibiting long-time drift of an inertial device is achieved; meanwhile, the defects of large positioning error and large azimuth deviation of the traditional satellite navigation equipment under the condition of lock losing can be overcome; the navigation system is particularly suitable for road condition vehicle navigation in high-rise zones with unstable satellite signals and special geographic environments.
In order to reduce the cost while ensuring the accuracy, optionally, in the navigation method provided in the embodiment of the present application, acquiring initial inertial data in a coordinate system adopted by an inertial navigation system includes: and observing the target object by adopting a micro inertial navigation measuring unit, and acquiring initial inertial data.
Specifically, on the basis of the GPS/INS integrated navigation, the embodiment of the application selects a low-cost and medium-precision micro inertial navigation unit (MIMU) for data acquisition, combines a navigation algorithm of inertial measurement and satellite measurement integration, introduces state feedback, and designs a Kalman filtering information fusion method, thereby realizing an all-weather navigation system with low cost, stable navigation performance and strong reliability under the condition of ensuring higher precision.
Fig. 3 is a schematic diagram of a navigation method according to an embodiment of the application. As shown in fig. 3, the method includes:
the method comprises the steps of carrying out coordinate transformation on inertial data of a target object acquired by an Inertial Navigation System (INS), specifically, carrying out coordinate transformation on accelerometer data measured by accelerometer combination to provide a data base for navigation calculation, meanwhile, determining an initial value of an attitude matrix according to the accelerometer data, carrying out attitude matrix calculation by combining gyroscope data measured by the gyroscope combination to obtain attitude data (namely attitude angle data) and specific force data of the target object, and providing a data base for navigation calculation.
During navigation calculation, on one hand, a position initial value and a speed initial value of the target object are determined according to the inertial data, on the other hand, a position error value and a speed error value are determined according to the inertial data and the error model, so that the position initial value and the speed initial value are corrected to obtain corrected position data and speed data of inertial navigation, and the corrected position data and speed data can be recorded as first attitude data.
And simultaneously, carrying out coordinate transformation on position data and speed data of a target object observed by a satellite navigation system (GPS), recording the transformed data as second position and attitude data, calculating errors of the first position and attitude data, inputting the errors into a KaLman filter for filtering, and carrying out feedback correction on input parameters of an error model in navigation calculation according to a filtering result in the filtering process so as to continuously iterate to obtain a filtering result. And judging whether the filtering result meets a preset requirement, wherein the corresponding position data and speed data are position data and speed data which can be used in navigation under the condition of meeting the preset requirement.
Further, the target object is navigated in conjunction with the available position data, velocity data, and pose angle data determined at the time.
By the aid of the GPS/MINS integrated navigation mode and the information fusion method of Kalman filtering, the defect of long-time error accumulation of a strapdown inertial navigation system can be overcome, and the purpose of effectively inhibiting long-time drift of an inertial device is achieved; meanwhile, the defects of large positioning error and large azimuth deviation of the traditional satellite navigation equipment under the condition of lock losing can be overcome; the all-weather navigation system with low cost, stable navigation performance, strong reliability and higher precision can be realized, and the system is particularly suitable for the navigation of road condition vehicles in high-rise zones with unstable satellite signals and special geographic environments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
The embodiment of the present application further provides a navigation device, and it should be noted that the navigation device in the embodiment of the present application can be used to execute the method for navigation provided in the embodiment of the present application. The following describes a navigation device provided in an embodiment of the present application.
Fig. 4 is a schematic diagram of a navigation device according to an embodiment of the application. As shown in fig. 4, the apparatus includes: a first acquisition unit 10, a second acquisition unit 20, a calculation unit 30, a processing unit 40 and a navigation unit 50.
Specifically, the first obtaining unit 10 is configured to obtain first pose data of the target object, where the first pose data is obtained by calculation based on inertial data, and the inertial data is data collected when the inertial navigation system observes the target object.
And a second obtaining unit 20, configured to obtain second pose data of the target object, where the second pose data is pose data obtained when the satellite navigation system observes the target object when the inertial navigation system observes the target object.
And a calculating unit 30, configured to calculate a data difference between the first posture data and the second posture data.
And the processing unit 40 is used for inputting the data difference value into the Kalman filter and processing the data difference value to obtain the covariance.
And the navigation unit 50 is used for navigating the target object based on the first position data if the covariance is smaller than the preset value.
The navigation device provided by the embodiment of the application acquires first attitude data of a target object through the first acquisition unit 10, wherein the first attitude data is obtained by resolving based on inertial data, and the inertial data is acquired when the inertial navigation system observes the target object; the second obtaining unit 20 obtains second pose data of the target object, where the second pose data is pose data obtained by the satellite navigation system observing the target object when the inertial navigation system observes the target object; the calculation unit 30 calculates a data difference value between the first and second posture data; the processing unit 40 inputs the data difference value into a Kalman filter, and covariance is obtained through processing; if the covariance is smaller than the preset value, the navigation unit 50 navigates the target object based on the first position data, so that the problem of large positioning errors caused by satellite measurement navigation and inertial measurement element navigation in the related technology is solved, error data between the first position data obtained by calculating inertial solution and the second position data obtained by satellite navigation is calculated, and the error data is processed by adopting a kalman filter to determine the optimal navigation data of the integrated navigation, thereby achieving the effect of improving the positioning accuracy of the integrated navigation system obtained by satellite measurement navigation and inertial measurement element navigation.
Optionally, in the navigation device provided in the embodiment of the present application, the first obtaining unit 10 includes: a first determination module to determine initial position data and initial velocity data of a target object based on inertial data; the processing module is used for inputting the inertial data into an error model of the inertial navigation system to obtain a position error when determining initial position data of the target object and a speed error when determining initial speed data of the target object; a second determination module to determine position data of the target object based on the initial position data and the position error, and to determine velocity data of the target object based on the initial velocity data and the velocity error; and the third determining module is used for determining the position data and the speed data of the target object as the first attitude data of the target object.
Optionally, in the navigation device provided in the embodiment of the present application, the first obtaining unit 10 further includes: the fourth determination module is used for determining the attitude matrix of the target object according to a quaternion algorithm based on the triaxial gyroscope data and the triaxial accelerometer data in the inertial data; a fifth determination module for determining attitude angle data of the target object based on the attitude matrix of the target object; and the sixth determining module is used for determining the attitude angle data of the target object as the first attitude data of the target object.
Optionally, in the navigation device provided in the embodiment of the present application, the device further includes: a first determination unit for determining a state equation of the inertial navigation system before inputting the inertial data into an error model of the inertial navigation system, obtaining a position error when determining initial position data of the target object, and a velocity error when determining initial velocity data of the target object:
wherein x (t) is a system state matrix for characterizing system errors, the system errors at least including system errors in determining pose data of the target object; f (t) is a system state transition matrix for representing the correction quantity of the system error related to the time; g (t) is a system noise distribution matrix; w (t) is a noise vector;
is the updated system state matrix; a second determination unit for determining an observation equation of the inertial navigation system: z (t) hx (t) + V, where z (t) is a system measurement matrix used to characterize observation errors, and the observation errors at least include observation errors in determining pose data of the target object; h is a system measurement updating matrix used for representing the correction quantity of the correlation between the observation error and the time; v is the observation noise; and the third determination unit is used for determining an error model of the inertial navigation system based on the state equation and the observation equation.
Optionally, in the navigation device provided in the embodiment of the present application, the device further includes: a fourth determination unit configured to determine a state prediction equation: xk＝FXk-1Wherein k is a discrete time, XkIs a combined system state matrix, X, at time kk-1The method comprises the steps that a combined system state matrix at the moment k-1 is used for representing combined system errors, the combined system errors at least comprise errors between position data obtained through calculation based on inertial data and position data observed by a satellite navigation system and errors between speed data obtained through calculation based on the inertial data and speed data observed by the satellite navigation system, and F is a system state conversion matrix under discrete time; a fifth determination unit for determining a covariance prediction equation: pk＝FPk-1FT+ Q, formula (I), PkIs the covariance matrix of time k, Pk-1Covariance matrix, F, at time k-1TIs the transpose of F, Q is the noise vector; a sixth determining unit, configured to determine a system state update equation: xk′＝Xk+K(Zk-H), in which Xk' combining the optimization results of the system state matrix for time k, ZkIs the system measurement matrix at time K, H is the system measurement update matrix, K is the Kalman gain matrix, K is PkHT/(HPk-1HT+ R), wherein HTTranspose for H, R represents measurement variance; a seventh determining unit configured to determine a covariance update equation: pk′＝(I-KH)PkIn the formula, Pk' is covariance matrix updated at time k, and I is identity matrix; and the eighth determining unit is used for determining the Kalman filter based on the state prediction equation, the covariance prediction equation, the system state updating equation and the covariance updating equation.
Optionally, in the navigation device provided in the embodiment of the present application, the device further includes: the third acquisition unit is used for acquiring initial inertial data under a coordinate system adopted by an inertial navigation system before acquiring the first attitude data of the target object; and the first conversion unit is used for converting the initial inertial data into data in a target coordinate system to obtain inertial data.
Optionally, in the navigation device provided in the embodiment of the present application, the device further includes: a fourth acquiring unit, configured to acquire initial pose data in a coordinate system adopted by the satellite navigation system before acquiring second pose data of the target object; and the second conversion unit is used for converting the initial pose data into data in a target coordinate system to obtain second pose data.
Optionally, in the navigation device provided in the embodiment of the present application, the third obtaining unit includes: and the acquisition module is used for observing the target object by adopting the micro inertial navigation measurement unit and acquiring initial inertial data.
The navigation device comprises a processor and a memory, wherein the first acquisition unit 10, the second acquisition unit 20, the calculation unit 30, the processing unit 40, the navigation unit 50 and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, and the problem of large navigation and positioning errors caused by satellite measurement navigation and inertial measurement elements in the related technology is solved by adjusting kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
The embodiment of the application also provides a nonvolatile storage medium, wherein the nonvolatile storage medium comprises a stored program, and the program controls the equipment where the nonvolatile storage medium is located to execute a navigation method when running.
The embodiment of the application also provides an electronic device, which comprises a processor and a memory; the memory has computer readable instructions stored therein and the processor is configured to execute the computer readable instructions, wherein the computer readable instructions when executed perform a navigation method. The electronic device herein may be a server, a PC, a PAD, a mobile phone, etc.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (11)
1. A navigation method, comprising:
acquiring first attitude data of a target object, wherein the first attitude data is obtained by calculation based on inertial data, and the inertial data is acquired when an inertial navigation system observes the target object;
acquiring second position and posture data of the target object, wherein the second position and posture data are position and posture data obtained by observing the target object by a satellite navigation system when the inertial navigation system observes the target object;
calculating a data difference value of the first position and attitude data and the second position and attitude data;
inputting the data difference value into a Kalman filter, and processing to obtain covariance;
and if the covariance is smaller than a preset value, navigating the target object based on the first attitude data.
2. The method of claim 1, wherein obtaining first pose data for a target object comprises:
determining initial position data and initial velocity data of the target object based on the inertial data;
inputting the inertial data into an error model of an inertial navigation system to obtain a position error when determining the initial position data of the target object and a velocity error when determining the initial velocity data of the target object;
determining position data of the target object based on the initial position data and the position error, and determining velocity data of the target object based on the initial velocity data and the velocity error;
determining position data and velocity data of the target object as the first pose data of the target object.
3. The method of claim 1, wherein obtaining first pose data for a target object further comprises:
determining an attitude matrix of the target object according to a quaternion algorithm based on triaxial gyroscope data and triaxial accelerometer data in the inertial data;
determining pose angle data of the target object based on the pose matrix of the target object;
determining pose angle data of the target object as the first pose data of the target object.
4. The method of claim 2, wherein prior to inputting the inertial data into an error model of an inertial navigation system, resulting in a position error in determining the initial position data of the target object, and a velocity error in determining the initial velocity data of the target object, the method further comprises:
determining an equation of state of the inertial navigation system:
wherein x (t) is a system state matrix characterizing systematic errors including at least systematic errors in determining pose data of the target object; f (t) is a system state transition matrix for characterizing the correction quantity of the system error in relation to time; g (t) is a system noise distribution matrix; w (t) is a noise vector;
is the updated system state matrix;
determining an observation equation of the inertial navigation system:
Z(t)＝HX(t)+V
wherein Z (t) is a system measurement matrix for characterizing observation errors, the observation errors at least including observation errors in determining pose data of the target object; h is a system measurement updating matrix used for representing the correction quantity of the observation error related to time; v is the observation noise;
an error model of the inertial navigation system is determined based on the state equation and the observation equation.
5. The method of claim 4, wherein before inputting the data difference values into a Kalman filter and processing the data difference values into covariance, the method further comprises:
determining a state prediction equation:
Xk＝FXk-1
wherein k is a discrete time, XkIs a combined system state matrix, X, at time kk-1A combined system state matrix at a time k-1, wherein the combined system state matrix is used for representing combined system errors, the combined system errors at least comprise errors between position data obtained by calculation based on the inertial data and position data observed by the satellite navigation system, and errors between velocity data obtained by calculation based on the inertial data and velocity data observed by the satellite navigation system, and F is the system state transition matrix at discrete time;
determining a covariance prediction equation:
Pk＝FPk-1FT+Q
in the formula, PkIs the covariance matrix of time k, Pk-1Covariance matrix, F, at time k-1TIs the transpose of F, Q is the noise vector;
determining a system state update equation:
Xk′＝Xk+K(Zk-H)
in the formula, Xk' optimization of the State matrix of the Combined System, Z, for time kkThe system measurement matrix at time K, H is the system measurement update matrix, K is the Kalman gain matrix, K is PkHT/(HPk-1HT+ R), wherein HTTranspose for H, R represents measurement variance;
determining a covariance update equation:
Pk′＝(I-KH)Pk
in the formula, Pk' is covariance matrix updated at time k, and I is identity matrix;
determining the Kalman filter based on the state prediction equation, the covariance prediction equation, the system state update equation, and the covariance update equation.
6. The method of claim 1, wherein prior to acquiring the first pose data of the target object, the method further comprises:
acquiring initial inertial data under a coordinate system adopted by the inertial navigation system;
and converting the initial inertial data into data in a target coordinate system to obtain the inertial data.
7. The method of claim 6, wherein prior to acquiring the second pose data of the target object, the method further comprises:
acquiring initial pose data under a coordinate system adopted by the satellite navigation system;
and converting the initial pose data into data in the target coordinate system to obtain the second pose data.
8. The method of claim 6, wherein acquiring initial inertial data in a coordinate system employed by the inertial navigation system comprises:
and observing the target object by adopting a micro inertial navigation measuring unit, and acquiring the initial inertial data.
9. A navigation device, comprising:
the first acquisition unit is used for acquiring first attitude data of a target object, wherein the first attitude data is obtained by calculation based on inertial data, and the inertial data is acquired when an inertial navigation system observes the target object;
the second acquisition unit is used for acquiring second position and posture data of the target object, wherein the second position and posture data are position and posture data obtained by observing the target object by a satellite navigation system when the inertial navigation system observes the target object;
the calculating unit is used for calculating a data difference value of the first position and attitude data and the second position and attitude data;
the processing unit is used for inputting the data difference value into a Kalman filter and processing the data difference value to obtain covariance;
and the navigation unit is used for navigating the target object based on the first attitude data if the covariance is smaller than a preset value.
10. A non-volatile storage medium, comprising a stored program, wherein the program when executed controls a device in which the non-volatile storage medium is located to perform the navigation method of any one of claims 1 to 8.
11. An electronic device comprising a processor and a memory, the memory having computer-readable instructions stored therein, the processor being configured to execute the computer-readable instructions, wherein the computer-readable instructions are configured to execute the navigation method according to any one of claims 1 to 8.
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