CN114295122A - SINS _ GNSS time synchronization method and system for embedded system - Google Patents
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Abstract
The SINS _ GNSS time synchronization method and system for the embedded system read and store IMU data, perform inertial solution on the IMU data to obtain speed and position information of the SINS _ GNSS, and measure the position of a PPS signal through a DSP timer in a period of receiving an effective PPS signal; performing linear interpolation on IMU data and velocity and position information of the SINS-GNSS obtained through inertial solution to calculate inertial navigation velocity and position information at the position of the PPS; and filtering when receiving the speed and the position information of the GNSS valid at the position corresponding to the PPS, and updating the time and the measurement of the embedded system by adopting the speed and the position information of the GNSS at the position corresponding to the PPS at the same moment and the speed and the position information of inertial navigation. The time of the SINS and the GNSS at the filtering moment can be synchronized under the condition that the hardware cost and burden are not increased and inertial navigation resolving errors and model principle errors do not exist, and the precision of the SINS-GNSS combined navigation system is improved.
Description
Technical Field
The invention belongs to the technical field of inertial navigation, and particularly relates to a SINS _ GNSS time synchronization method and a system for an embedded system.
Background
A strap-down inertial navigation system (SINS) realizes autonomous navigation by a gyroscope, an accelerometer, a navigation computer and the like, angular velocity, acceleration, attitude, velocity and position information can be provided through output of a device and navigation calculation of the navigation computer, and final navigation errors are accumulated along with time due to errors of the device and an algorithm, so that the accuracy cannot be maintained for a long time. The GNSS system is mature and practical in the current environment, can continuously provide speed and position information of a carrier, can also provide course information under the condition of double antennas, but is low in updating frequency, easy to interfere and shield and easy to cause the condition of invalid data. The SINS-GNSS combined navigation system integrates and complements the characteristics of high GNSS long-term positioning precision, high SINS short-term positioning and orientation precision and high updating frequency, realizes a combined navigation system with high output frequency and high long-term precision, and is widely applied to various fields.
The integrated navigation adopts a mature Kalman filter to estimate various errors of an inertial navigation system, and then corrects the attitude, the speed, the position and the device errors of the system by error state estimation. In an actual embedded system, because hardware transmission communication delay and data update rate are different, a period of time is required for acquiring the GNSS data from the pulse per second to receive the SINS, and particularly under a high dynamic and high acceleration environment of inertial navigation for missile, if synchronous compensation is not performed, the accuracy of combined navigation is affected, and even filtering divergence may be caused in a severe case.
The existing time synchronization method mainly comprises hardware synchronization and combination of software and hardware, wherein the hardware synchronization needs to add extra hardware in practice, and hardware cost and burden are increased. In combination of software and hardware, the real-time filtering estimation is carried out by using a method of taking asynchronous time as a state variable of Kalman filtering, but the asynchronous time is not a random constant value in an actual system, so that the principle of an error model is not met, and the filtering estimation effect is not good; and based on the characteristic of high short-term precision of inertial navigation, the increment calculated by the navigation solution from the PPS time to the SINS receiving time is compensated into the GNSS, but the inertial navigation solution error still exists, and the reliability of the system can be reduced in the filtering process by the method. It is therefore desirable to accurately compensate for the time out-of-sync between the SINS and GNSS or to avoid time out-of-sync errors by other methods.
Disclosure of Invention
The invention overcomes one of the defects of the prior art, provides the SINS _ GNSS time synchronization method and the SINS _ GNSS time synchronization system for the embedded system, can avoid the asynchronous error between the PPS time and the received GNSS data on the basis of no inertial navigation resolving error and model principle error, and improves the SINS _ GNSS combined navigation precision.
According to an aspect of the present disclosure, the present invention provides a SINS _ GNSS time synchronization method for an embedded system, the method comprising:
step S1: reading and saving IMU data, carrying out inertial solution on the IMU data to obtain the speed information and the position information of the SINS-GNSS,
step S2: measuring the position of the PPS signal by a DSP timer in the period of receiving the effective PPS signal;
step S3: performing linear interpolation on the IMU data and the velocity information and the position information of the SINS-GNSS obtained by inertial solution to calculate the velocity information and the position information of inertial navigation at the PPS position;
step S4: and performing Kalman filtering when receiving the speed information and the position information of the GNSS valid at the position corresponding to the PPS, and performing time updating and measurement updating of the embedded system by adopting the speed information and the position information of the GNSS at the position corresponding to the PPS at the same moment and the speed information and the position information of inertial navigation.
In a possible implementation manner, the Kalman filtering the IMU data to obtain the speed information and the position information of the SINS _ GNSS includes:
establishing a system equation and a measurement equation for Kalman filtering of IMU data;
reading IMU data of the SINS _ GNSS navigation system, and performing Kalman filtering on the IMU data by using the Kalman filtering system equation and the measurement equation to obtain speed information and position information of the SINS _ GNSS.
In aIn a possible implementation manner, the Kalman filtering system equation is
X is a system error state variable, W is a system noise variable, F is a system state transition matrix, and G is a system noise transition matrix;
the Kalman filtering measurement equation is Z ═ HX + V, wherein Z is a measurement vector, H is a measurement matrix, and V is a measurement noise vector.
According to another aspect of the present disclosure, a SINS _ GNSS time synchronization system for an embedded system is proposed, the system comprising:
the IMU data resolving module is used for reading and storing IMU data and carrying out inertial resolution on the IMU data to obtain speed information and position information of the SINS-GNSS;
the PPS signal position measuring module is used for measuring the position of the PPS signal through the DSP timer in the period of receiving the effective PPS signal;
the linear interpolation calculation module is used for performing linear interpolation on the IMU data and the velocity information and the position information of the SINS _ GNSS obtained through inertial solution to calculate the velocity information and the position information of inertial navigation at the PPS position;
and the filtering updating module is used for performing Kalman filtering when receiving the speed information and the position information of the GNSS which are effective at the position corresponding to the PPS, and performing time updating and measurement updating of the embedded system by adopting the speed information and the position information of the GNSS at the position corresponding to the PPS at the same moment and the speed information and the position information of inertial navigation.
In one possible implementation, the filtering update module includes:
establishing a system equation and a measurement equation for Kalman filtering of IMU data;
reading IMU data of the SINS _ GNSS navigation system, and performing Kalman filtering on the IMU data by using the Kalman filtering system equation and the measurement equation to obtain speed information and position information of the SINS _ GNSS.
In one possible implementation, the method comprisesThe Kalman filtering system equation is
X is a system error state variable, W is a system noise variable, F is a system state transition matrix, and G is a system noise transition matrix;
the Kalman filtering measurement equation is Z ═ HX + V, wherein Z is a measurement vector, H is a measurement matrix, and V is a measurement noise vector.
According to the SINS _ GNSS time synchronization method for the embedded system, the integrated navigation is carried out at the moment when the GNSS information is received by storing the inertial navigation information at the PPS moment, and under the condition that the hardware cost and burden are not increased and inertial navigation resolving errors and model principle errors do not exist, the SINS and the GNSS do not have time asynchronous errors at the filtering moment, the optimal filtering effect is obtained, and finally the precision of the integrated navigation system is improved.
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The accompanying drawings are included to provide a further understanding of the technology or prior art of the present application and are incorporated in and constitute a part of this specification. The drawings expressing the embodiments of the present application are used for explaining the technical solutions of the present application, and should not be construed as limiting the technical solutions of the present application.
Fig. 1 illustrates a flowchart of a SINS _ GNSS time synchronization method for an embedded system according to an embodiment of the present disclosure;
FIG. 2 illustrates a flow chart of a SINS _ GNSS time synchronization method for an embedded system according to another embodiment of the present disclosure;
FIG. 3 illustrates a schematic diagram of SINS and GNSS signal reception according to an embodiment of the present disclosure;
fig. 4 shows a schematic block diagram of a SINS _ GNSS time synchronization system for an embedded system according to an embodiment of the present disclosure.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the accompanying drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the corresponding technical effects can be fully understood and implemented. The embodiments and the features of the embodiments can be combined without conflict, and the technical solutions formed are all within the scope of the present invention.
Fig. 1 shows a flowchart of a SINS _ GNSS time synchronization method for an embedded system according to an embodiment of the present disclosure. As shown in fig. 1, the method may include:
step S1: reading and saving IMU data, and performing inertial solution on the IMU data to obtain speed information and position information of the SINS-GNSS;
step S2: measuring the position of the PPS signal by a DSP timer in the period of receiving the effective PPS signal;
step S3: performing linear interpolation on the IMU data and the velocity information and the position information of the SINS-GNSS obtained through inertial solution to calculate the velocity information and the position information of inertial navigation at the PPS position;
step S4: and performing Kalman filtering when receiving the speed information and the position information of the GNSS valid at the position corresponding to the PPS, and performing time updating and measurement updating of the embedded system by adopting the speed information and the position information of the GNSS at the position corresponding to the PPS at the same moment and the speed information and the position information of inertial navigation.
FIG. 2 illustrates a flow chart of a SINS _ GNSS time synchronization method for an embedded system according to another embodiment of the present disclosure; fig. 3 illustrates a schematic diagram of SINS and GNSS signal reception according to an embodiment of the disclosure.
As shown in fig. 2, the specific implementation manner of the SINS _ GNSS time synchronization method for the embedded system is as follows:
firstly, establishing a Kalman filtering system equation and a measurement equation for inertial navigation solution, wherein the Kalman filtering system equation is
X is the system error state variable, W is the system noise variable, F is the system state transition matrix, and G is the system noise transition matrix. The measurement equation is Z ═ HX + V, where Z is the measurement vector, H is the measurement matrix, and V is the measurement noise vector. In using Kalman filtering systemInitializing the state transition matrix phi during equation filteringk,k-1,fitAs an identity matrix, a system noise matrix Qk-1,fitA zero matrix is set.
The computer of the SINS-GNSS navigation system reads IMU data to carry out pure inertia calculation to obtain speed information and position information of the SINS-GNSS, caches the speed and position information of the SINS-GNSS before IMU data calculation, and simultaneously carries out a state transition matrix phik,k-1Of the computation and system noise matrix Qk-1The calculation of (2):
wherein, FiFor the system state transition matrix in each cycle, TsFor resolving the period, Q is the system noise variance matrix, GiAnd n is the number of the periods of the DSP in the interval time of the PPS time of this time and the PPS time of the last time.
In actual operation, the PPS frequency is strictly 1Hz, the PPS frequency is considered to be absolutely accurate when the error is nanosecond, a GNSS has the condition that a certain frame is invalid or the checksum error cannot be received due to error codes, and the GPS frequency is less than or equal to 1 Hz. Every time in a period of receiving an effective PPS signal, measuring the position of the PPS signal in the period through a DSP timer, calculating the speed information and the position information of inertial navigation at the PPS moment through linear interpolation of the calculated speed information and the position information of the SINS _ GNSS and the cached IMU data information of the previous period, latching the speed information and the position information of the inertial navigation at the moment and then using the latched speed information and the latched position information for subsequent Kalman filtering, and updating a step state transfer matrix and a system noise matrix of the Kalman filtering:
φk′,k-1,fit＝φk,k-1,fit+φk,k-1,
Qk′-1,fit＝Qk-1,fit+Qk-1,
wherein phi isk,k-1For a one-step state transition matrix within this PPS interval, phik,k-1,fitIs a one-step state transition matrix at the last PPS moment, and is an identity matrix phi if the GNSS is effective and frames are not lostk′,k-1,fitFor a one-step state transition matrix, Q, at the moment of the updated PPSk-1Is the system noise matrix, Q, within this PPS intervalk-1,fitIs the system noise matrix at the last PPS moment, and if the GNSS is effective and no frame is lost, the matrix is a zero matrix, Qk′-1,fitThe updated system noise matrix at the time of the PPS.
As shown in fig. 2, filtering is performed in a period of receiving effective GNSS velocity information and position information corresponding to PPS to obtain GNSS velocity information and position information corresponding to PPS time, time updating and measurement updating are performed with information that is the same time as the inertial navigation velocity position latched at the corresponding PPS time, model principle errors and short-time calculation errors of inertial navigation are avoided, and phi is reset after filtering is completedk,k-1,fitIs an identity matrix, Qk-1,fitAnd 5, a zero matrix is formed, and the next filtering is waited.
Wherein the time update comprises: state one-step predictive vector updating and one-step predictive mean square error matrix updating. State one-step predictor vector update: xk/k-1＝φk,k-1,fitXk-1And updating a one-step prediction mean square error matrix:
the measurement update includes filter gain, state estimation calculation, and estimation of the mean square error matrix. Wherein, the filtering gain is:
wherein R iskIs a covariance matrix of the measured noise vector. And (3) state estimation calculation: xk＝Xk/k-1+Kk(Zk-HkXk/k-1) Estimating a mean square error matrix:
the SINS _ GNSS time synchronization and the update of the latest state of the embedded system can be realized through the formula.
Fig. 4 shows a schematic block diagram of a SINS _ GNSS time synchronization system for an embedded system according to an embodiment of the present disclosure.
According to another aspect of the present disclosure, a SINS _ GNSS time synchronization system for an embedded system is provided, as shown in fig. 4, the system may include:
the IMU data calculation module 41 is configured to read and store IMU data, and perform inertial calculation on the IMU data to obtain speed information and position information of the SINS _ GNSS;
a PPS signal position measurement module 42, configured to measure a position of the PPS signal by using the DSP timer in a period in which the valid PPS signal is received;
a linear interpolation calculation module 43, configured to perform linear interpolation on the IMU data and the velocity information and the position information of the SINS _ GNSS obtained through inertial solution to calculate velocity information and position information of inertial navigation at the PPS position;
and the filtering updating module 44 is configured to perform filtering when receiving the speed information and the position information of the GNSS valid at the corresponding PPS position, and perform time updating and measurement updating of the embedded system using the speed information and the position information of the GNSS at the PPS position at the same time and the speed information and the position information of the inertial navigation.
In one example, the filter update module 44 includes:
establishing a system equation and a measurement equation for Kalman filtering of IMU data;
reading IMU data of the SINS _ GNSS navigation system, and performing Kalman filtering on the IMU data by using the Kalman filtering system equation and the measurement equation to obtain speed information and position information of the SINS _ GNSS.
In one example, the Kalman filtering system equation is
X is the system error state variable and W is the system noiseThe acoustic variable F is a system state transition matrix, and G is a system noise conversion matrix;
the Kalman filtering measurement equation is Z ═ HX + V, wherein Z is a measurement vector, H is a measurement matrix, and V is a measurement noise vector.
The SINS _ GNSS time synchronization method and system for the embedded system read and store IMU data, perform inertial solution on the IMU data to obtain speed information and position information of the SINS _ GNSS, and measure the position of a PPS signal through a DSP timer in a period of receiving an effective PPS signal; performing linear interpolation on the IMU data and the velocity information and the position information of the SINS-GNSS obtained by inertial solution to calculate the velocity information and the position information of inertial navigation at the PPS position; and performing Kalman filtering when receiving the speed information and the position information of the GNSS valid at the position corresponding to the PPS, and performing time updating and measurement updating of the embedded system by adopting the speed information and the position information of the GNSS at the position corresponding to the PPS at the same moment and the speed information and the position information of inertial navigation. Under the conditions that hardware cost and burden are not increased, inertial navigation resolving errors and model principle errors do not exist, the SINS and the GNSS do not have time asynchronous errors at the filtering moment, the optimal filtering effect is obtained, and finally the precision of the integrated navigation system is improved.
Although the embodiments of the present invention have been described above, the above descriptions are only for the convenience of understanding the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. A SINS _ GNSS time synchronization method for an embedded system, the method comprising:
reading and saving IMU data, and performing inertial solution on the IMU data to obtain speed information and position information of the SINS-GNSS;
measuring the position of the PPS signal by a DSP timer in the period of receiving the effective PPS signal;
performing linear interpolation on the IMU data and the velocity information and the position information of the SINS-GNSS obtained through inertial solution to calculate the velocity information and the position information of inertial navigation at the PPS position;
and performing Kalman filtering when receiving the speed information and the position information of the GNSS valid at the position corresponding to the PPS, and performing time updating and measurement updating of the embedded system by adopting the speed information and the position information of the GNSS at the position corresponding to the PPS at the same moment and the speed information and the position information of inertial navigation.
2. The method of claim 1, wherein the Kalman filtering the IMU data to obtain the velocity information and the position information of the SINS GNSS comprises:
establishing a system equation and a measurement equation for Kalman filtering of IMU data;
reading IMU data of the SINS _ GNSS navigation system, and performing Kalman filtering on the IMU data by using the Kalman filtering system equation and the measurement equation to obtain speed information and position information of the SINS _ GNSS.
3. The SINS _ GNSS time synchronization method of claim 2, wherein the Kalman filtering system equation is
X is a system error state variable, W is a system noise variable, F is a system state transition matrix, and G is a system noise transition matrix;
the Kalman filtering measurement equation is Z ═ HX + V, wherein Z is a measurement vector, H is a measurement matrix, and V is a measurement noise vector.
4. A SINS _ GNSS time synchronization system for an embedded system, the system comprising:
the IMU data resolving module is used for reading and storing IMU data and carrying out inertial resolution on the IMU data to obtain speed information and position information of the SINS-GNSS;
the PPS signal position measuring module is used for measuring the position of the PPS signal through the DSP timer in the period of receiving the effective PPS signal;
the linear interpolation calculation module is used for performing linear interpolation on the IMU data and the velocity information and the position information of the SINS _ GNSS obtained through inertial solution to calculate the velocity information and the position information of inertial navigation at the PPS position;
and the filtering updating module is used for performing Kalman filtering when receiving the speed information and the position information of the GNSS which are effective at the position corresponding to the PPS, and performing time updating and measurement updating of the embedded system by adopting the speed information and the position information of the GNSS at the position corresponding to the PPS at the same moment and the speed information and the position information of inertial navigation.
5. The SINS _ GNSS time synchronization system of claim 4, wherein the filter update module comprises:
establishing a system equation and a measurement equation for Kalman filtering of IMU data;
reading IMU data of the SINS _ GNSS navigation system, and performing Kalman filtering on the IMU data by using the Kalman filtering system equation and the measurement equation to obtain speed information and position information of the SINS _ GNSS.
6. The SINS _ GNSS time synchronization system of claim 5, wherein the Kalman filtering system equation is
X is a system error state variable, W is a system noise variable, F is a system state transition matrix, and G is a system noise transition matrix;
the Kalman filtering measurement equation is Z ═ HX + V, wherein Z is a measurement vector, H is a measurement matrix, and V is a measurement noise vector.
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