CN113984054A - Improved Sage-Husa self-adaptive fusion filtering method based on information anomaly detection and multi-source information fusion equipment - Google Patents
Improved Sage-Husa self-adaptive fusion filtering method based on information anomaly detection and multi-source information fusion equipment Download PDFInfo
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Abstract
The invention relates to an improved Sage-Husa self-adaptive fusion filtering method and multi-source information fusion equipment based on information anomaly detection, wherein 1, sensor measurement information and GPS longitude and latitude information are obtained; 2, establishing a GPS/INS integrated navigation system model, and establishing a state equation and a measurement equation; 3, an information anomaly detection process, namely constructing test statistics according to the prediction residual vector, judging whether abnormal observation exists, and adopting improved Sage-Husa adaptive filtering to set Kalman filtering gain to zero and introduce an exponential decay adaptive factor to adjust observation measurement noise when the system has abnormal measurement detection; and 4, carrying out filtering processing on the integrated navigation system by using the improved Sage-Husa self-adaptive filtering method, and predicting and correcting the Q array and the R array in real time on the basis of standard Kalman filtering. The multi-source information fusion equipment comprises a sensor, a processor, an information acquisition unit and a data transmission and receiving unit. Has the advantages that: and an information abnormity detection process is added, so that the navigation precision and fault tolerance of the system are improved.
Description
Technical Field
The invention belongs to the technical field of navigation positioning, and relates to an improved Sage-Husa self-adaptive fusion filtering method based on information anomaly detection and multi-source information fusion equipment.
Background
The multi-source information fusion is a technology for summarizing and integrating collected data output by each sensor and combining an optimal effect by adopting a certain rule. In the field of navigation and positioning of intelligent traffic systems, the most classical, efficient and feasible method for multi-source information fusion is the Kalman Filtering (KF). However, the algorithm has some limitations, and systematic errors are continuously accumulated in the calculation process of the Kalman filter, so that the positive nature of the error covariance matrix is affected, and the filtering estimation result is inaccurate, so that improvement is needed.
Disclosure of Invention
The invention aims to provide an improved Sage-Husa self-adaptive fusion filtering method based on information anomaly detection and multi-source information fusion equipment.
The technical scheme of the invention is as follows: an improved Sage-Husa self-adaptive fusion filtering method based on information anomaly detection comprises the following steps: acquiring measurement information of a sensor; a gyroscope and an accelerometer in the inertial measurement unit output corresponding measurement information of angular velocity and specific force, and a GPS outputs corresponding longitude and latitude measurement information;
step two: establishing a GPS/INS integrated navigation system model, determining a multidimensional state quantity formed by position, speed, attitude and deviation quantity, and establishing a state equation and a measurement equation;
step three: in the information anomaly detection process, test statistic is constructed according to the prediction residual vector, and whether anomaly observation exists is judged; when the system has no abnormal quantity measurement, the test statistic does not exceed the confidence limit, and standard Kalman filtering is adopted for prediction and correction, so that a fusion filtering result is output; when the system has abnormal measurement detection, adopting improved Sage-Husa adaptive filtering, setting Kalman filtering gain to zero, and introducing an exponential decay adaptive factor to adjust observation measurement noise;
step four: the improved Sage-Husa self-adaptive filtering method carries out filtering processing on the integrated navigation system, carries out real-time prediction and correction on a Q array and an R array on the basis of standard Kalman filtering, feeds back the self-adaptive adjusting process of adjusting the filtering gain K, and simultaneously sets the filtering gain to zero when the information is abnormal and restores the filtering gain to the standard Kalman, thereby realizing the purpose of inhibiting the influence of the information abnormality on the filtering; the improved Sage-Husa adaptive filtering algorithm flow is as follows:
initialized state estimates and covariance
Judgment ofIf the signal is in the confidence space, if so, standard Kalman filtering is carried out, otherwise,
then, performing robust adaptive filtering, the process is:
And K (: i) is 0, gives,
Pk=[I-KkHk]Pk,k-1
For one-step prediction of state, phik,k-1In order to be a state transition matrix,is the state estimator at time k-1,is the mean value of white noise of the system at the time k, Pk,k-1To predict the state covariance matrix, Φk,k-1Being a state transition matrix, Pk-1In order to observe the matrix, the system,in the form of a covariance matrix,for prediction residual estimation, ZkTo predict residual, HkIs the state quantity of the system, and the state quantity of the system,is rkThe estimated amount of (a) is,in order to predict the residual estimate, the residual estimate is,to predict the mean square error of the residual, dkIn order to be a factor for the adaptation,is rk-1The estimated amount of (a) is,as a noise covariance matrix, betakIs the same as defined in formula (16),for observing noise covariance matrix, RmaxFor observing noise covariance matrix minimum,Is the same as defined in formula (16),is ZkThe transpose matrix of (a) is,is HkTransposed matrix of (2), RmaxFor observing the maximum value of the noise covariance matrix, KkIn order to be a matrix of gains, the gain matrix,in order to perform a one-step pre-measurement of the state,is a covariance matrix of the state noise,in order to be a state estimator,for prediction residual estimation, PkIn order to observe the matrix, the system, white noise mean at time k, dkIn order to be a factor for the adaptation,is the average value of the white noise of the system at the moment k-1,for state estimators at time k, phik,k-1In order to be a state transition matrix,in the form of a covariance matrix,in order to predict the residual error(s),in order to be a transpose of the prediction residual,for transposing the gain matrix,. phik,k-1Being a state transition matrix, phik,k-1 TIs a transpose of the state transition matrix.
In the second step, the established state equation is as follows:
in the formula (1), X (k) is a state variable, F (k) is a system state transition matrix, and G (k) is a system noise transition matrix; w (k) is the system noise vector, X (k) is the state variable,
selecting a state variable X as:
in the formula (2), [ phi ]E φN φU]The attitude misalignment angles in the east, north and sky directions of the inertial platform are shown, and the unit is an angle division; [ Delta VEδVN δVU]The unit of the speed error is meter/second, wherein the unit of the speed error is east, north and sky; [ Delta L Delta Lambda Delta h]Errors representing latitude, longitude, altitude, in meters; [ epsilon ]x εy εz]The constant drift error of the gyroscope is unit degree/hour;is the drift error of the accelerometer, in ug,
the measurement equation established in the second step is as follows:
in the formula (9), Zv(t) is a velocity measurement vector, Zp(t) is the position measurement vector, V (t) is the observation noise, and the velocity measurement vector is:
in the formula (10), Hv=[03×3 diag(1 1 1) 03×9],Vv=[vGE vGN vGU]T,vGE、vGN、vGUAre speed errors of the GNSS along the east, north and sky directions respectively,
the position measurement vector is:
in the formula (11), Hp=[03×6 diag(1 1 1) 03×6],Vp=[NGE NGN NGU]T,NGE、NGN、NGUThe position errors of the GNSS in the east, north and sky directions are respectively.
In the information anomaly detection process in the third step, the prediction residual vector is used for constructing test statistic, so as to judge whether an observation anomaly error exists or not,
prediction residualActual measurement value Z representing time kkAnd measure one-step predictionThe error between the two is defined as:
in the formula (12), X (k) is a state vector at the time k, H (k) is a system measurement moment, VkIn order to observe the noise, it is,in order to predict the state in one step,
In the formula (13), the reaction mixture is, Hkmeasuring the noise matrix, V, for the systemkIn order to observe the noise matrix,
the measurement information prediction residual error is a white noise sequence, the obedient mean value is zero, and the variance isIs normally distributed, i.e.
Normalizing the test data to obtain test statistic as follows:
in the formula (15), the reaction mixture is, the ith row element of the observation matrix representing the time instant at time k,represents the diagonal elements of the observed noise covariance matrix,
assume confidence level ofIf the test statistic does not exceed the confidence limit, the observation is not abnormal; if the confidence limit is exceeded, an exponential decay adaptive factor is introduced to adjust the observation noise covariance matrix, and the purpose of identifying and inhibiting the abnormal influence of the observed quantity is achieved.
The exponential decay adaptive factor automatically adjusts the observation noise process: the variance of the prediction residual can be derived from equation (13)The expression of (a) is:
the variance of the prediction residual represents the lumped average of the random sequence, and can be replaced by the time average in the discretization equation, and equation (14) is shifted by terms, and the observed noise covariance matrix can be rewritten as:
considering that the observation anomaly error may be large, the noise covariance calculated by equation (16) will increase the effect of anomaly observationEquation (16) can be expressed as:
when an exponential decay adaptive factor is introduced to update the filter, observation noise is automatically adjusted according to the prediction residual error, the upper limit and the lower limit of the noise variance are set, the filtering precision is prevented from being reduced when the matrix inversion is negative, and meanwhile, if the difference between two adjacent iterations does not exceed the limit, the iteration is stopped.
A multi-source information fusion device includes a memory unit for storing a computer program;
a processor unit for implementing the improved Sage-Husa adaptive fusion filtering method as described above when executing a computer program; the processor unit receives external information to be processed.
The system further comprises an information acquisition unit and a data transmission and receiving unit, wherein the information acquisition unit comprises an IMU inertial navigation sensor and a GPS satellite receiver and is used for acquiring the measurement information of the angular velocity and the specific force output by a gyroscope and an accelerometer and outputting the corresponding longitude and latitude measurement information by the GPS;
and the data transmission and receiving unit is used for transmitting the information obtained by the information acquisition unit to the processor unit.
The invention has the beneficial effects that: 1. compared with the traditional Sage-Husa self-adaptive fusion filtering, the method adds an information anomaly detection process, and improves the navigation precision and fault tolerance of the system. 2. The method solves the problems that in Sage-Husa self-adaptive fusion filtering, information is continuously set to zero when continuous information abnormity occurs, and the system navigation resolving error is increased, adaptively adjusts the measurement noise variance, and effectively controls the influence of the measurement abnormity on the filtering result.
Drawings
FIG. 1 is a process diagram of an improved Sage-Husa adaptive fusion filtering scheme based on information anomaly detection;
FIG. 2 is a graph of experimental simulation traces;
FIG. 3 is a comparison plot of the positioning error of the prior Sage-Husa method and the modified Sage-Husa method.
Detailed Description
An improved Sage-Husa self-adaptive fusion filtering method based on information anomaly detection comprises the following steps: acquiring measurement information of a sensor; a gyroscope and an accelerometer in the inertial measurement unit output corresponding measurement information of angular velocity and specific force, and a GPS outputs corresponding longitude and latitude measurement information;
step two: establishing a GPS/INS integrated navigation system model, determining a multidimensional state quantity formed by position, speed, attitude and deviation quantity, and establishing a state equation and a measurement equation;
step three: in the information anomaly detection process, test statistic is constructed according to the prediction residual vector, and whether anomaly observation exists is judged; when the system has no abnormal quantity measurement, the test statistic does not exceed the confidence limit, and standard Kalman filtering is adopted for prediction and correction, so that a fusion filtering result is output; when the system has abnormal measurement detection, adopting improved Sage-Husa adaptive filtering, setting Kalman filtering gain to zero, and introducing an exponential decay adaptive factor to adjust observation measurement noise;
step four: the improved Sage-Husa self-adaptive filtering method carries out filtering processing on the integrated navigation system, carries out real-time prediction and correction on a Q array and an R array on the basis of standard Kalman filtering, feeds back the self-adaptive adjusting process of adjusting the filtering gain K, and simultaneously sets the filtering gain to zero when the information is abnormal and restores the filtering gain to the standard Kalman, thereby realizing the purpose of inhibiting the influence of the information abnormality on the filtering; the algorithm flow is shown in fig. 1.
In the second step, the established state equation is as follows:
in the formula (1), X (k) is a state variable, F (k) is a system state transition matrix, and G (k) is a system noise transition matrix; w (k) is the system noise vector,
selecting a state variable X as:
in the formula (2), [ phi ]E φN φU]The attitude misalignment angles in the east, north and sky directions of the inertial platform are shown, and the unit is an angle division; [ Delta VEδVN δVU]The unit of the speed error is meter/second, wherein the unit of the speed error is east, north and sky; [ Delta L Delta Lambda Delta h]Errors representing latitude, longitude, altitude, in meters; [ epsilon ]x εy εz]The constant drift error of the gyroscope is unit degree/hour;is the drift error of the accelerometer, in ug,
the measurement equation established in the second step is as follows:
in the formula (9), Zv(t) is a velocity measurement vector, Zp(t) is the position measurement vector, V (t) is the observation noise, and the velocity measurement vector is:
in the formula (10), Hv=[03×3 diag(1 1 1) 03×9],Vv=[vGE vGN vGU]T,vGE、vGN、vGUAre speed errors of the GNSS along the east, north and sky directions respectively,
the position measurement vector is:
in the formula (11), Hp=[03×6 diag(1 1 1) 03×6],Vp=[NGE NGN NGU]T,NGE、NGN、NGUThe position errors of the GNSS in the east, north and sky directions are respectively.
In the information anomaly detection process in the third step, the prediction residual vector is used for constructing test statistic, so as to judge whether an observation anomaly error exists or not,
prediction residualActual measurement value Z representing time kkAnd measure one-step predictionThe error between the two is defined as:
The measurement information prediction residual error is a white noise sequence, the obedient mean value is zero, and the variance isIs normally distributed, i.e.
Normalizing the test data to obtain test statistic as follows:
in the formula (15), the reaction mixture is, the ith row element of the observation matrix representing the time instant at time k,represents the diagonal elements of the observed noise covariance matrix,
assume confidence level ofIf the test statistic does not exceed the confidence limit, the observation is not abnormal; if the confidence limit is exceeded, an exponential decay adaptive factor is introduced to adjust the observation noise covariance matrix, and the purpose of identifying and inhibiting the abnormal influence of the observed quantity is achieved.
The exponential decay adaptive factor automatically adjusts the observation noise process: the variance of the prediction residual can be derived from equation (13)The expression of (a) is:
the variance of the prediction residual represents the lumped average of the random sequence, and can be replaced by the time average in the discretization equation, and equation (14) is shifted by terms, and the observed noise covariance matrix can be rewritten as:
considering that the observation anomaly error may be large, the noise covariance calculated by equation (16) will increase the effect of anomaly observationEquation (16) can be expressed as:
when an exponential decay adaptive factor is introduced to update the filter, observation noise is automatically adjusted according to the prediction residual error, the upper limit and the lower limit of the noise variance are set, the filtering precision is prevented from being reduced when the matrix inversion is negative, and meanwhile, if the difference between two adjacent iterations does not exceed the limit, the iteration is stopped.
The algorithm flow of the improved Sage-Husa adaptive filtering in the fourth step is shown in FIG. 1.
The method is realized by using a multi-source information fusion device which comprises a memory unit and a multi-source information fusion unit, wherein the memory unit is used for storing a computer program; the processor unit is used for realizing the improved Sage-Husa self-adaptive fusion filtering method when executing a computer program; the processor unit receives external information to be processed. The system further comprises an information acquisition unit and a data transmission and receiving unit, wherein the information acquisition unit comprises an IMU inertial navigation sensor and a GPS satellite receiver and is used for acquiring the measurement information of the angular velocity and the specific force output by a gyroscope and an accelerometer and outputting the corresponding longitude and latitude measurement information by the GPS; and the data transmission and receiving unit is used for transmitting the information obtained by the information acquisition unit to the processor unit.
As seen from FIG. 3, RAKF fluctuation is obviously smaller than AKF, which shows that the method has obvious improvement effect compared with the original method, and the maximum error value is reduced from about 3.8m to about 2.8 m.
Claims (7)
1. An improved Sage-Husa self-adaptive fusion filtering method based on information anomaly detection is characterized by comprising the following steps: the method comprises the following steps: acquiring measurement information of a sensor; a gyroscope and an accelerometer in the inertial measurement unit output corresponding measurement information of angular velocity and specific force, and a GPS outputs corresponding longitude and latitude measurement information;
step two: establishing a GPS/INS integrated navigation system model, determining a multidimensional state quantity formed by position, speed, attitude and deviation quantity, and establishing a state equation and a measurement equation;
step three: in the information anomaly detection process, test statistic is constructed according to the prediction residual vector, and whether anomaly observation exists is judged; when the system has no abnormal quantity measurement, the test statistic does not exceed the confidence limit, and standard Kalman filtering is adopted for prediction and correction, so that a fusion filtering result is output; when the system has abnormal measurement detection, adopting improved Sage-Husa adaptive filtering, setting Kalman filtering gain to zero, and introducing an exponential decay adaptive factor to adjust observation measurement noise;
step four: the improved Sage-Husa self-adaptive filtering method carries out filtering processing on the integrated navigation system, carries out real-time prediction and correction on a Q array and an R array on the basis of standard Kalman filtering, feeds back the self-adaptive adjusting process of adjusting the filtering gain K, and simultaneously sets the filtering gain to zero when the information is abnormal and restores the filtering gain to the standard Kalman, thereby realizing the purpose of inhibiting the influence of the information abnormality on the filtering; the improved Sage-Husa adaptive filtering algorithm flow is as follows:
initialized state estimates and covariance
Judgment ofIf the signal is in the confidence space, if so, standard Kalman filtering is carried out, otherwise,
then, performing robust adaptive filtering, the process is:
To obtain
And K (: i) is 0, gives
Pk=[I-KkHk]Pk,k-1
2. The improved Sage-Husa adaptive fusion filtering method based on information anomaly detection according to claim 1, characterized in that: in the second step, the established state equation is as follows:
in the formula (1), X (k) is a state variable, F (k) is a system state transition matrix, and G (k) is a system noise transition matrix; w (k) is the system noise vector, X (k) is the state variable,
selecting a state variable X as:
in the formula (2), [ phi ]E φN φU]The attitude misalignment angles in the east, north and sky directions of the inertial platform are shown, and the unit is an angle division; [ Delta VE δVN δVU]The unit of the speed error is meter/second, wherein the unit of the speed error is east, north and sky; [ Delta L Delta Lambda Delta h]Errors representing latitude, longitude, altitude, in meters; [ epsilon ]x εy εz]The constant drift error of the gyroscope is unit degree/hour;the drift error of the accelerometer is in ug.
3. The improved Sage-Husa adaptive fusion filtering method based on information anomaly detection according to claim 1, characterized in that: the measurement equation established in the second step is as follows:
in the formula (9), Zv(t) is a velocity measurement vector, Zp(t) is a position measurement vector, Hv、HpRespectively, a system state parameter, X (t) is a state vector, Vv(t) velocity observation noise, Vp(t) position observation noise, H (t) system measurement matrix, and V (t) observation noise;
the velocity measurement vector is:
in the formula (10), Hv=[03×3 diag(1 1 1) 03×9],Vv=[vGE vGN vGU]T,vGE、vGN、vGUVelocity errors of GNSS along east, north and sky directions, vIE、vIN、vIUVelocity errors of IMU along east, north and sky directions, X (t) is a state vector, Vv(t) velocity observation noise;
the position measurement vector is:
4. The improved Sage-Husa adaptive fusion filtering method based on information anomaly detection according to claim 1, characterized in that: in the information anomaly detection process in the third step, the prediction residual vector is used for constructing test statistic, so as to judge whether an observation anomaly error exists or not,
prediction residualActual measurement value Z representing time kkAnd measure one-step predictionThe error between the two is defined as:
in the formula (12), X (k) is a state vector at the time k, H (k) is a system measurement moment, VkIn order to observe the noise, it is,in order to predict the state in one step,
In the formula (13), the reaction mixture is, Hkmeasuring the noise matrix, V, for the systemkIn order to observe the noise matrix,
the measurement information prediction residual error is a white noise sequence, the obedient mean value is zero, and the variance isIs normally distributed, i.e.
Normalizing the test data to obtain test statistic as follows:
in the formula (15), the reaction mixture is, the ith row element of the observation matrix representing the time instant at time k,is composed ofThe transpose matrix of (a) is,represents the diagonal elements of the observed noise covariance matrix,
assume confidence level ofIf the test statistic does not exceed the confidence limit, the observation is not abnormal; if the confidence limit is exceeded, an exponential decay adaptive factor is introduced to adjust the observation noise covariance matrix, and the purpose of identifying and inhibiting the abnormal influence of the observed quantity is achieved.
5. The improved Sage-Husa adaptive fusion filtering method based on information anomaly detection according to claim 4, characterized in that: the exponential decay adaptive factor automatically adjusts the observation noise process: the variance of the prediction residual is obtained by equation (13)The expression of (a) is:
the variance of the prediction residual represents the lumped average of the random sequence, and is replaced by the time average in the discretization equation, equation (14) is shifted, and the observed noise covariance matrix can be rewritten as:
considering that the observation anomaly error may be large, the noise covariance calculated by equation (16) will increase the effect of anomaly observationEquation (16) is therefore expressed as:
when an exponential decay adaptive factor is introduced to update the filter, observation noise is automatically adjusted according to the prediction residual error, the upper limit and the lower limit of the noise variance are set, the filtering precision is prevented from being reduced when the matrix inversion is negative, and meanwhile, if the difference between two adjacent iterations does not exceed the limit, the iteration is stopped.
6. A multi-source information fusion device is characterized in that: comprises a memory unit for storing a computer program;
a processor unit for implementing the improved Sage-Husa adaptive fusion filtering method of claim 1 or 2 or 3 or 4 or 5 when executing a computer program; the processor unit receives external information to be processed.
7. The multi-source information fusion device of claim 6, wherein: the system comprises an information acquisition unit and a data transmission and receiving unit, wherein the information acquisition unit comprises an IMU inertial navigation sensor and a GPS satellite receiver and is used for acquiring the measurement information of angular velocity and specific force output by a gyroscope and an accelerometer and outputting the corresponding longitude and latitude measurement information by the GPS;
and the data transmission and receiving unit is used for transmitting the information obtained by the information acquisition unit to the processor unit.
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CN115342820A (en) * | 2022-10-18 | 2022-11-15 | 深圳市诚王创硕科技有限公司 | Navigation positioning system for automobile driving at night |
CN116086466A (en) * | 2022-12-28 | 2023-05-09 | 淮阴工学院 | Method for improving INS error precision |
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