CN111189441B - Multi-source adaptive fault-tolerant federal filtering integrated navigation system and navigation method - Google Patents
Multi-source adaptive fault-tolerant federal filtering integrated navigation system and navigation method Download PDFInfo
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Abstract
The invention discloses a multi-source adaptive fault-tolerant federal filtering integrated navigation system and a navigation method, wherein the system comprises a strapdown inertial navigation system, a satellite navigation system, a Doppler velocity measurement system, an astronomical navigation system, a main filter and three sub filters which are respectively in information connection with the systems; the three sub-filters are in information connection with the strapdown inertial navigation system and are connected with the main filter through the fault detection and isolation module, the strapdown inertial navigation system is connected with the fault detection and isolation module through the state propagator, and the output result of the fault detection and isolation module is input into the main filter after passing through the information sharing factor calculation module; and the main filter outputs the fused information and synchronizes the fusion result with the three sub-filters and the state propagators. The system and the method disclosed by the invention can more accurately track the states of all the sub-filters and obtain more accurate fusion results.
Description
Technical Field
The invention relates to the technical field of navigation communication, in particular to a multi-source adaptive fault-tolerant federal filtering combined navigation system and a navigation method.
Background
With the continuous increase of high-precision, real-time and seamless navigation and positioning requirements, the traditional single-sensor navigation system cannot meet the actual requirements. Multi-sensor integrated navigation technologies and some other emerging hybrid navigation technologies have become a hotspot for research. Global Navigation Satellite Systems (GNSS) are satellite-based radio navigation systems capable of providing reliable location information over a long period of time in various situations. But in some signal blocking environments, such as forests, canyons, tunnels and urban areas, their performance can drop dramatically. The Strapdown Inertial Navigation System (SINS) is an autonomous navigation system and is not interfered by external environment. It can provide navigation information independent of external sensors. However, SINS navigation errors will accumulate over time and diverge over a long duration. Meanwhile, a longer initial alignment time is required before SINS is used. The Doppler velocity measurement system (DVL) is based on Doppler effect design, is an ideal velocity sensor with high precision and easy use, but the cost of the DVL is relatively high. An astronomical navigation system (CNS) may provide attitude information for a vehicle with a star as a beacon. It has high navigation accuracy and no accumulated error, but is easily interfered by the atmosphere. Thus, accurate real-time navigation and positioning cannot be achieved using a single sensor. In order to obtain an ideal navigation positioning result, in recent years, an integrated navigation system has become an popular research field, and many researchers have proposed various combination methods to improve navigation accuracy.
Meanwhile, in order to improve the stability of the integrated navigation system, an appropriate autonomous fault detection, isolation and restoration (FDIR) system must be established. The FDIR system consists of three parts, namely fault detection, fault isolation and fault recovery.
In integrated navigation, a correct filter estimation algorithm is also indispensable. Classical Kalman Filters (KF) find wide application in integrated systems, but require strict system models and noise types. However, in practice, these requirements are often not met. Therefore, it is necessary to find some filter estimation algorithms more suitable for the actual environment to complete the estimation of the system.
In recent years, the dispersion filter technology has been increasingly used in multi-sensor systems, and various dispersion filters have been proposed. Federal Filters (FFs) are a special type of dispersive filter, which consist of a distributed filter structure as opposed to a centralized filter. The federal filter adopts the information sharing principle of the local filter and the main filter, and eliminates the correlation between local estimation by using an upper bound technology. The error tolerance and the precision of the federal filter are directly affected by the principle of information sharing. However, conventional information sharing coefficients do not adequately reflect the differences in the state variables of the sub-filters and cannot track their changes. Thus, research on adaptive Information Sharing Factor (ISF) is being pursued.
Disclosure of Invention
In order to solve the technical problems, the invention provides a multi-source adaptive fault-tolerant federal filtering integrated navigation system and a navigation method, so as to achieve the purpose of tracking the states of all sub-filters more accurately and obtaining more accurate fusion results.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
a multi-source adaptive fault-tolerant federal filtering integrated navigation system comprises a strapdown inertial navigation system, a satellite navigation system, a Doppler velocity measurement system, an astronomical navigation system, and a main filter, a sub-filter I, a sub-filter II and a sub-filter III which are respectively in information connection with the systems; the three sub-filters are in information connection with the strapdown inertial navigation system and are connected with the main filter through the fault detection and isolation module, the strapdown inertial navigation system is connected with the fault detection and isolation module through the state propagator, and the output result of the fault detection and isolation module is input into the main filter after passing through the information sharing factor calculation module; and the main filter outputs the fused information and synchronizes the fusion result with the three sub-filters and the state propagators.
In the above scheme, the fault detection and isolation module adopts the BP neural network as a fault detection, isolation and recovery algorithm.
In the above scheme, the main filter is a non-reset federal filter, and the three sub-filters are strong tracking filters.
The multi-source adaptive fault-tolerant federal filtering integrated navigation method adopts the multi-source adaptive fault-tolerant federal filtering integrated navigation system, and comprises the following steps:
step one, a sensor arranged on a moving vehicle collects data of the moving vehicle and transmits the data to three sub-filters;
step two, the three sub-filters respectively carry out filtering treatment on the data, and the treated data are transmitted to a fault detection and isolation module;
step three, the fault detection and isolation module calculates data, judges whether each sub-filter has faults, if so, isolates the sub-filter, and information of the sub-filter cannot enter the main filter; if no fault occurs, the output result of each sub-filter is input into the main filter; meanwhile, the fault detection and isolation module sends the calculation result to the information sharing factor calculation module;
step four, after the information sharing factor calculation module calculates the information sharing factor, inputting the result into the main filter and feeding back to the three sub-filters;
and fifthly, the main filter performs information distribution and information fusion on the received data, outputs a result, and synchronizes the result with the three sub-filters and the state propagators to regulate and control the whole situation.
In a further technical scheme, the first step is specifically as follows:
setting the motion duration, motion parameters and motion environment parameter information of a moving vehicle through a track generator based on the strapdown inertial navigation system, generating motion track information of the moving vehicle, and generating original data of the strapdown inertial navigation system;
the method comprises the steps of adding noise of a satellite navigation system by using motion track information of a motion vehicle through a track generator, and generating position data of the satellite navigation system on the motion vehicle;
the method comprises the steps of adding noise of a Doppler velocimeter by using motion track information of a moving vehicle through a track generator, and generating speed data of the Doppler velocimeter on the moving vehicle;
and adding noise of the astronomical navigation system by using the motion track information of the moving vehicle through the track generator to generate posture data of the astronomical navigation system on the moving vehicle.
In a further technical scheme, the second step is specifically as follows:
(1) Establishing a federal filtering combined navigation system model under a navigation coordinate system:
the SINS is used as a reference system, an SINS error model is obtained through a perturbation method, and a linearized system state equation is described as follows:
where x represents a system state vector,
a system state vector representing the next moment, F representing a state transfer function, and w representing state noise;
in order to realize the filtering algorithm, the state equation is discretized firstly to obtain a discrete time state equation of the system state vector x transmitted from the k-1 moment to the k moment:
x k ＝F k,k-1 x k-1 +w k
wherein ,Fk,k-1 Representing a system transition matrix, x k System state vector x representing time k k-1 System state vector w representing time k-1 k Represents process noise and it satisfies the following statistical properties:
E[w k ]＝0
wherein E [. Cndot.]Representing the mean of a matrix, T representing the transpose, delta, of a matrix kj Represents a Cronecker function, Q k Is the covariance matrix of the process noise and the system state vector x is defined as
wherein ,δφE δφ N δφ U Representing the attitude errors in the east, north and in the north, δv E δv N δv U Indicating the speed error in east, north and up; δlδλδh represents the position error of latitude, longitude, and altitude; epsilon rx ε ry ε rz Errors caused by gyro drift;
errors caused by accelerometer bias;
(2) The three sub-filters respectively perform filtering processing on the data:
(2.1) sub-filter-measurement equation for SINS/GNSS:
in a local filter connected with the GNSS, taking the difference value between the position output of the SINS and the GNSS as measurement information of an SINS/GNSS measurement equation, wherein the measurement equation is expressed as:
wherein ,LSINS 、λ SINS 、h SINS Respectively represent latitude, longitude and altitude measured by SINS system, L GNSS 、λ GNSS 、h GNSS Respectively representing latitude, longitude and altitude measured by the GNSS system, and δL, δλ and δh respectively representing errors of the SINS system from the real position in terms of latitude, longitude and altitude, v 11 、v 12 、v 13 Representing errors of the GNSS system from the true position in terms of latitude, longitude and altitude, respectively, which are independent zero-mean Gaussian white noise processes, v 1 Represented by v 11 、v 12 、v 13 A matrix of components;
H 1 expressed as:
H 1 ＝[0 3×6 diag[111]0 3×6 ]
wherein 0 represents an all-zero matrix, diag [ ] represents a diagonal matrix;
(2.2) measurement equation for SINS/DVL for sub-filter two:
in a second sub-filter connected with the DVL, taking the difference between the velocity output of the SINS and the DVL as measurement information of a SINS/DVL measurement equation, the measurement equation is expressed as:
wherein ,vE,SINS 、v N,SINS 、v U,SINS Respectively representing the east, north and upward speeds measured by the SINS system; v E,DVL 、v N,DVL 、v U,DVL Respectively representing the east, north and upward velocity measured by the DVL system; δv E 、δv N 、δv U The errors of the SINS system in east, north and upward speeds and the true speed are respectively represented; v 21 、v 22 、v 23 Representing the error of the DVL system from the true velocity in the east, north and upward velocities, respectively, which are independent zero-mean GaussWhite noise process, v 2 Represented by v 21 、v 22 、v 23 A matrix of components;
H 2 expressed as:
H 2 ＝[0 3×3 diag[111]0 3×9 ]
wherein 0 represents an all-zero matrix, diag [ ] represents a diagonal matrix;
(2.3) subfilter three measurement equations for SINS/CNS:
in a third sub-filter connected to the CNS, the difference between the attitude output of the SINS and the CNS is taken as measurement information of the SINS/DVL measurement equation, which is expressed as:
wherein ,φE,SINS 、φ N,SINS 、φ U,SINS Respectively representing the east, north and upward attitude angles measured by the SINS system; phi (phi) E,CNS 、φ N,CNS 、φ U,CNS Representing the east, north and upward attitude angles, respectively, measured by the CNS system; δv E 、δv N 、δv U Respectively representing errors of the SINS system in the east, north and upward attitude angles and the true attitude angles; v 21 、v 22 、v 23 Representing errors of the CNS system from the true attitude angles in the east, north and upward attitude angles, respectively; they are independent zero-mean Gaussian white noise processes, v 2 Represented by v 21 、v 22 、v 23 A matrix of components;
H 3 the method is shown as follows:
H 3 ＝[diag[111]0 3×12 ]
where 0 represents an all-zero matrix and diag [ ] represents a diagonal matrix.
In a further technical scheme, the third step is specifically as follows:
the fault detection and isolation module adopts a BP neural network as a fault detection, isolation and recovery algorithm, wherein the BP neural network consists of an input layer, a hidden layer and an output layer, and the hidden layer is provided with one or more layers;
firstly, training BP neural network by fault information and normal information, wherein the network adopts S-shaped transfer function
By means of a counter error function->
Continuously adjusting the network weight and the threshold value to enable the error function F to be extremely small, wherein t i To desired output, O i The calculation output of the network;
then detecting whether the sub-filter has faults or not through the difference value of the input state propagator and each sub-filter, adopting a Sigmoid function as an excitation function of the network in the BP neural network, when the output result of the excitation function is more than 0.5, the sub-filter has faults, and when the output result of the excitation function is less than 0.5, the sub-filter normally operates;
if a sub-filter fails, then this sub-filter is isolated and the output of the main filter is updated to the sub-filter at the next time.
In a further technical scheme, the method for calculating the information sharing factor in the fourth step is as follows:
wherein ,β1 、β 2 、β 3 The information sharing factors of the first sub-filter, the second sub-filter and the third sub-filter are respectively; y is 1 、y 2 、y 3 The method is respectively the output of BP artificial neural network excitation functions of a first sub-filter, a second sub-filter and a third sub-filter.
In a further technical scheme, the specific method of the fifth step is as follows:
the information distribution process is as follows:
wherein ,
representing the process noise covariance of the ith sub-filter at time k, Q k Process noise covariance representing the main filter at time k,/->
An estimation error covariance matrix representing the ith sub-filter at k time, P k|k An estimation error covariance matrix representing the main filter at time k +.>
State estimation representing the ith sub-filter at time k,/, for>
Representing the state estimate of the main filter at time k, beta i Is the information sharing factor of the i-th sub-filter, and satisfies:
wherein I represents an identity matrix;
the information fusion process is as follows:
wherein ,Pg Representing the estimation error covariance matrix of the main filter, P i Representing the estimated error covariance matrix of the ith sub-filter at time k,
state estimation representing the main filter, +.>
Representing the state estimate of the ith sub-filter at time k.
Through the technical scheme, the multi-source adaptive fault-tolerant federal filtering integrated navigation system and the navigation method provided by the invention have the remarkable advantages compared with the prior art that:
(1) The combination of the Doppler velocity measurement system, the astronomical navigation system, the global navigation satellite system and the inertial navigation system SINS is realized by adopting a federal filtering algorithm and taking the inertial navigation system SINS as a public reference system, each sub-filter automatically judges the current working state of the system, the self-adaptive updating information distribution factor obtains a local optimal solution, the flexible selection of a combination mode is realized, the global optimal estimation of the error state of the public reference system SINS is finally synthesized, and the positioning precision of the multi-source fusion combined navigation system is improved;
(2) The main filter is a non-reset federal filter, and the self-adaptive federal information distribution factor is adopted, so that when a certain sub information source fails, the fault can be isolated, the normal filtering of the filter is not affected, the system keeps better stability and robustness, and the reliability and the anti-interference capability of the multi-source fusion integrated navigation system are improved.
(3) The sub-filter adopts a strong tracking filter, so that a more accurate estimation result can be obtained, and the reliability and the anti-interference capability of the multi-source fusion integrated navigation system are improved.
(4) The BP neural network has the capability of associatively memorizing external stimulus and input information, has strong recognition and classification capability on external input samples, and has optimization computing capability; the BP neural network is used as a fault detection, isolation and recovery algorithm, so that the anti-interference capability of the system is better improved; meanwhile, the error detection result is introduced into the self-adaptive information distribution factor, the nonlinearity is better introduced, the state of each sub-filter can be tracked more accurately, and a more accurate fusion result is obtained.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
FIG. 1 is a schematic diagram of a multi-source adaptive fault-tolerant federal filtered integrated navigation system according to the present invention;
FIG. 2 is a flow chart of the multi-source adaptive fault-tolerant federal filtering integrated navigation method of the present invention;
fig. 3 is a schematic structural diagram of a BP neural network.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
As shown in FIG. 1, the multi-source adaptive fault-tolerant federal filtering integrated navigation system comprises a strapdown inertial navigation system, a satellite navigation system, a Doppler velocity measurement system, an astronomical navigation system, and a main filter, a sub-filter I, a sub-filter II and a sub-filter III which are respectively in information connection with the systems; the three sub-filters are in information connection with the strapdown inertial navigation system, and are connected with the main filter through the fault detection and isolation module, the strapdown inertial navigation system is connected with the fault detection and isolation module through the state propagator, and the output result of the fault detection and isolation module is input into the main filter after passing through the information sharing factor calculation module; the main filter outputs the fused information and synchronizes the fusion result with the three sub-filters and the state propagators.
The fault detection and isolation module adopts BP neural network as fault detection, isolation and recovery algorithm.
In this embodiment, the main filter is a no-reset federal filter, and the three sub-filters are strong tracking filters.
As shown in fig. 2, a multi-source adaptive fault-tolerant federal filtering integrated navigation method includes the following steps:
step one, a sensor arranged on a moving vehicle collects data of the moving vehicle and transmits the data to three sub-filters;
based on the strapdown inertial navigation system, the motion duration, motion parameters and motion environment parameter information of the moving vehicle are set through the track generator, the motion track information of the moving vehicle is generated, and the original data of the strapdown inertial navigation system are generated, wherein the method comprises the following specific steps:
according to a physical model of the moving vehicle during movement, setting movement parameters and movement duration of each stage, generating movement tracks of the moving vehicle, including straight forward, backward, turning, acceleration, deceleration and the like, and generating corresponding SINS data.
The method comprises the steps of adding noise of a satellite navigation system by utilizing motion track information of a motion vehicle through a track generator to generate position data of the satellite navigation system on the motion vehicle, wherein the position data of the satellite navigation system on the motion vehicle are specifically as follows:
generating three-dimensional position information of the moving vehicle by utilizing a track generator according to the movement track information of the moving vehicle, adding noise according to the generation reason of errors of a satellite navigation system, and obtaining the position information of the moving vehicle;
by using motion trail information of a moving vehicle through a trail generator, adding noise of a Doppler velocimeter to generate speed data of the Doppler velocimeter on the moving vehicle, the method comprises the following steps:
generating three-dimensional speed information of the moving vehicle by utilizing a track generator according to the movement track information of the moving vehicle, adding noise according to the generation reason of the Doppler velocimeter error, and obtaining the speed information of the moving vehicle;
by using the motion track information of the moving vehicle, the track generator adds noise of the astronomical navigation system to generate posture data of the astronomical navigation system to the moving vehicle, and the method comprises the following steps of:
according to the motion track information of the moving vehicle, generating three-dimensional posture information of the moving vehicle by utilizing a track generator, adding noise according to the generation reason of the astronomical navigation system error, and obtaining the posture information of the moving vehicle.
Step two, the three sub-filters respectively carry out filtering treatment on the data, and the treated data are transmitted to a fault detection and isolation module;
(1) Establishing a federal filtering combined navigation system model under a navigation coordinate system:
the SINS is used as a reference system, an SINS error model is obtained through a perturbation method, and a linearized system state equation is described as follows:
where x represents a system state vector,
a system state vector representing the next moment, F representing a state transfer function, and w representing state noise;
in order to realize the filtering algorithm, the state equation is discretized firstly to obtain a discrete time state equation of the system state vector x transmitted from the k-1 moment to the k moment:
x k ＝F k,k-1 x k-1 +w k
wherein ,Fk,k-1 Representing a system transition matrix, x k System state vector x representing time k k-1 System state vector w representing time k-1 k Represents process noise and it satisfies the following statistical properties:
E[w k ]＝0
wherein E [. Cndot.]Representing the mean of a matrix, T representing the transpose, delta, of a matrix kj Represents a Cronecker function, Q k Is the covariance matrix of the process noise and the system state vector x is defined as
wherein ,δφE δφ N δφ U Representing the attitude errors in the east, north and in the north, δv E δv N δv U Indicating the speed error in east, north and up; δlδλδh represents the position error of latitude, longitude, and altitude; epsilon rx ε ry ε rz Errors caused by gyro drift;
errors caused by accelerometer bias;
(2) The three sub-filters respectively perform filtering processing on the data:
(2.1) sub-filter-measurement equation for SINS/GNSS:
in a local filter connected with the GNSS, taking the difference value between the position output of the SINS and the GNSS as measurement information of an SINS/GNSS measurement equation, wherein the measurement equation is expressed as:
wherein ,LSINS 、λ SINS 、h SINS Respectively represent latitude, longitude and altitude measured by SINS system, L GNSS 、λ GNSS 、h GNSS Respectively representing latitude, longitude and altitude measured by the GNSS system, and δL, δλ and δh respectively representing errors of the SINS system from the real position in terms of latitude, longitude and altitude, v 11 、v 12 、v 13 Representing errors of the GNSS system from the true position in terms of latitude, longitude and altitude, respectively, which are independent zero-mean Gaussian white noise processes, v 1 Represented by v 11 、v 12 、v 13 A matrix of components;
H 1 expressed as:
H 1 ＝[0 3×6 diag[111]0 3×6 ]
wherein 0 represents an all-zero matrix, diag [ ] represents a diagonal matrix;
(2.2) measurement equation for SINS/DVL for sub-filter two:
in a second sub-filter connected with the DVL, taking the difference between the velocity output of the SINS and the DVL as measurement information of a SINS/DVL measurement equation, the measurement equation is expressed as:
wherein ,vE,SINS 、v N,SINS 、v U,SINS Respectively representing the east, north and upward speeds measured by the SINS system; v E,DVL 、v N,DVL 、v U,DVL Respectively representing the east, north and upward velocity measured by the DVL system; δv E 、δv N 、δv U The errors of the SINS system in east, north and upward speeds and the true speed are respectively represented; v 21 、v 22 、v 23 Representing the error of the DVL system from the true velocity in the east, north and upward velocities, respectively, which are independent zero-mean Gaussian white noise processes, v 2 Represented by v 21 、v 22 、v 23 A matrix of components;
H 2 expressed as:
H 2 ＝[0 3×3 diag[111]0 3×9 ]
wherein 0 represents an all-zero matrix, diag [ ] represents a diagonal matrix;
(2.3) subfilter three measurement equations for SINS/CNS:
in a third sub-filter connected to the CNS, the difference between the attitude output of the SINS and the CNS is taken as measurement information of the SINS/DVL measurement equation, which is expressed as:
wherein ,φE,SINS 、φ N,SINS 、φ U,SINS Respectively representing the east, north and upward attitude angles measured by the SINS system; phi (phi) E,CNS 、φ N,CNS 、φ U,CNS Representing the east, north and upward attitude angles, respectively, measured by the CNS system; δv E 、δv N 、δv U Respectively representing errors of the SINS system in the east, north and upward attitude angles and the true attitude angles; v 21 、v 22 、v 23 Representing errors of the CNS system from the true attitude angles in the east, north and upward attitude angles, respectively; they are independent zero-mean Gaussian white noise processes, v 2 Represented by v 21 、v 22 、v 23 A matrix of components;
H 3 the method is shown as follows:
H 3 ＝[diag[111]0 3×12 ]
where 0 represents an all-zero matrix and diag [ ] represents a diagonal matrix.
Step three, the fault detection and isolation module calculates data, judges whether each sub-filter has faults, if so, isolates the sub-filter, and information of the sub-filter cannot enter the main filter; if no fault occurs, the output result of each sub-filter is input into the main filter; meanwhile, the fault detection and isolation module sends the calculation result to the information sharing factor calculation module;
the fault detection and isolation module adopts a BP neural network as a fault detection, isolation and recovery algorithm, and as shown in figure 3, the BP neural network consists of an input layer, a hidden layer and an output layer, wherein the hidden layer is provided with one or more layers;
firstly, training BP neural network by fault information and normal information, wherein the network adopts S-shaped transfer function
By means of a counter error function->
Continuously adjusting the network weight and the threshold value to enable the error function F to be extremely small, wherein t i To desired output, O i The calculation output of the network;
then detecting whether the sub-filter has faults or not through the difference value of the input state propagator and each sub-filter, adopting a Sigmoid function as an excitation function of the network in the BP neural network, when the output result of the excitation function is more than 0.5, the sub-filter has faults, and when the output result of the excitation function is less than 0.5, the sub-filter normally operates;
if a sub-filter fails, then this sub-filter is isolated and the output of the main filter is updated to the sub-filter at the next time.
Step four, after the information sharing factor calculation module calculates the information sharing factor, inputting the result into the main filter and feeding back to the three sub-filters;
the information sharing factor calculation method is as follows:
wherein ,β1 、β 2 、β 3 The information sharing factors of the first sub-filter, the second sub-filter and the third sub-filter are respectively; y is 1 、y 2 、y 3 The method is respectively the output of BP artificial neural network excitation functions of a first sub-filter, a second sub-filter and a third sub-filter.
And fifthly, the main filter performs information distribution and information fusion on the received data, outputs a result, and synchronizes the result with the three sub-filters and the state propagators to regulate and control the whole situation.
The information distribution process is as follows:
wherein ,
representing the process noise covariance of the ith sub-filter at time k, Q k Process noise covariance representing the main filter at time k,/->
An estimation error covariance matrix representing the ith sub-filter at k time, P k|k An estimation error covariance matrix representing the main filter at time k +.>
State estimation representing the ith sub-filter at time k,/, for>
Representing the main filter at time kState estimation, beta i Is the information sharing factor of the i-th sub-filter, and satisfies:
wherein I represents an identity matrix;
the information fusion process is as follows:
wherein ,Pg Representing the estimation error covariance matrix of the main filter, P i Representing the estimated error covariance matrix of the ith sub-filter at time k,
state estimation representing the main filter, +.>
Representing the state estimate of the ith sub-filter at time k.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. The multi-source adaptive fault-tolerant federal filtering integrated navigation method is characterized in that the integrated navigation system comprises a strapdown inertial navigation system, a satellite navigation system, a Doppler speed measurement system, an astronomical navigation system, and a main filter, a sub-filter I, a sub-filter II and a sub-filter III which are respectively in information connection with the strapdown inertial navigation system, the satellite navigation system, the Doppler speed measurement system and the astronomical navigation system; the three sub-filters are in information connection with the strapdown inertial navigation system and are connected with the main filter through the fault detection and isolation module, the strapdown inertial navigation system is connected with the fault detection and isolation module through the state propagator, and the output result of the fault detection and isolation module is input into the main filter after passing through the information sharing factor calculation module; the main filter outputs the fused information and synchronizes the fusion result with the three sub-filters and the state propagators;
the integrated navigation method comprises the following steps:
step one, a sensor arranged on a moving vehicle collects data of the moving vehicle and transmits the data to three sub-filters;
step two, the three sub-filters respectively carry out filtering treatment on the data, and the treated data are transmitted to a fault detection and isolation module;
step three, the fault detection and isolation module calculates data, judges whether each sub-filter has faults, if so, isolates the sub-filter, and information of the sub-filter cannot enter the main filter; if no fault occurs, the output result of each sub-filter is input into the main filter; meanwhile, the fault detection and isolation module sends the calculation result to the information sharing factor calculation module;
step four, after the information sharing factor calculation module calculates the information sharing factor, inputting the result into the main filter and feeding back to the three sub-filters;
step five, the main filter performs information distribution and information fusion on the received data, outputs a result, synchronizes the result with the three sub-filters and the state propagators, and regulates the overall situation;
the third step is as follows:
the fault detection and isolation module adopts a BP neural network as a fault detection, isolation and recovery algorithm, wherein the BP neural network consists of an input layer, a hidden layer and an output layer, and the hidden layer is provided with one or more layers;
firstly, training BP neural network by fault information and normal information, wherein the network adopts S-shaped transfer function
By means of a counter error function->
Continuously adjusting the network weight and the threshold value to enable the error function F to be extremely small, wherein t i To desired output, O i The calculation output of the network;
then detecting whether the sub-filter has faults or not through the difference value of the input state propagator and each sub-filter, adopting a Sigmoid function as an excitation function of the network in the BP neural network, when the output result of the excitation function is more than 0.5, the sub-filter has faults, and when the output result of the excitation function is less than 0.5, the sub-filter normally operates;
if the sub-filter fails, then isolating the sub-filter and updating the output of the main filter to the sub-filter at the next time;
the method for calculating the information sharing factor in the fourth step is as follows:
wherein ,β1 、β 2 、β 3 The information sharing factors of the first sub-filter, the second sub-filter and the third sub-filter are respectively; y is 1 、y 2 、y 3 The method is respectively the output of BP artificial neural network excitation functions of a first sub-filter, a second sub-filter and a third sub-filter.
2. The multi-source adaptive fault-tolerant federal filtering integrated navigation method according to claim 1, wherein the fault detection and isolation module employs a BP neural network as a fault detection, isolation and recovery algorithm.
3. A multi-source adaptive fault-tolerant federal filtering integrated navigation method according to claim 1 or 2, wherein the main filter is a no-reset federal filter and the three sub-filters are strong tracking filters.
4. The multi-source adaptive fault-tolerant federal filtering integrated navigation method according to claim 1, wherein the first step is as follows:
setting the motion duration, motion parameters and motion environment parameter information of a moving vehicle through a track generator based on the strapdown inertial navigation system, generating motion track information of the moving vehicle, and generating original data of the strapdown inertial navigation system;
the method comprises the steps of adding noise of a satellite navigation system by using motion track information of a motion vehicle through a track generator, and generating position data of the satellite navigation system on the motion vehicle;
the method comprises the steps of adding noise of a Doppler velocimeter by using motion track information of a moving vehicle through a track generator, and generating speed data of the Doppler velocimeter on the moving vehicle;
and adding noise of the astronomical navigation system by using the motion track information of the moving vehicle through the track generator to generate posture data of the astronomical navigation system on the moving vehicle.
5. The multi-source adaptive fault-tolerant federal filtering integrated navigation method according to claim 1, wherein the second step is specifically as follows:
(1) Establishing a federal filtering combined navigation system model under a navigation coordinate system:
the SINS is used as a reference system, an SINS error model is obtained through a perturbation method, and a linearized system state equation is described as follows:
where x represents a system state vector,
a system state vector representing the next moment, F representing a state transfer function, and w representing state noise;
in order to realize the filtering algorithm, the state equation is discretized firstly to obtain a discrete time state equation of the system state vector x transmitted from the k-1 moment to the k moment:
x k ＝F k,k-1 x k-1 +w k
wherein ,Fk,k-1 Representing a system transition matrix, x k System state vector x representing time k k-1 System state vector w representing time k-1 k Represents process noise and it satisfies the following statistical properties:
E[w k ]＝0
wherein E [. Cndot.]Representing the mean of a matrix, T representing the transpose, delta, of a matrix kj Represents a Cronecker function, Q k Is the covariance matrix of the process noise and the system state vector x is defined as
wherein ,δφE δφ N δφ U Representing the attitude errors in the east, north and in the north, δv E δv N δv U Indicating the speed error in east, north and up; δlδλδh represents the position error of latitude, longitude, and altitude; epsilon rx ε ry ε rz Errors caused by gyro drift;
errors caused by accelerometer bias;
(2) The three sub-filters respectively perform filtering processing on the data:
(2.1) sub-filter-measurement equation for SINS/GNSS:
in a local filter connected with the GNSS, taking the difference value between the position output of the SINS and the GNSS as measurement information of an SINS/GNSS measurement equation, wherein the measurement equation is expressed as:
wherein ,LSINS 、λ SINS 、h SINS Respectively represent latitude, longitude and altitude measured by SINS system, L GNSS 、λ GNSS 、h GNSS Respectively representing latitude, longitude and altitude measured by the GNSS system, and δL, δλ and δh respectively representing errors of the SINS system from the real position in terms of latitude, longitude and altitude, v 11 、v 12 、v 13 Representing errors of the GNSS system from the true position in terms of latitude, longitude and altitude, respectively, which are independent zero-mean Gaussian white noise processes, v 1 Represented by v 11 、v 12 、v 13 A matrix of components;
H 1 expressed as:
H 1 ＝[0 3×6 diag[1 1 1] 0 3×6 ]
wherein 0 represents an all-zero matrix, diag [ ] represents a diagonal matrix;
(2.2) measurement equation for SINS/DVL for sub-filter two:
in a second sub-filter connected with the DVL, taking the difference between the velocity output of the SINS and the DVL as measurement information of a SINS/DVL measurement equation, the measurement equation is expressed as:
wherein ,vE,SINS 、v N,SINS 、v U,SINS Respectively representing the east, north and upward speeds measured by the SINS system; v E,DVL 、v N,DVL 、v U,DVL Respectively representing the east, north and upward velocity measured by the DVL system; δv E 、δv N 、δv U The errors of the SINS system in east, north and upward speeds and the true speed are respectively represented; v 21 、v 22 、v 23 Representing the error of the DVL system from the true velocity in the east, north and upward velocities, respectively, which are independent zero-mean Gaussian white noise processes, v 2 Represented by v 21 、v 22 、v 23 A matrix of components;
H 2 expressed as:
H 2 ＝[0 3×3 diag[1 1 1] 0 3×9 ]
wherein 0 represents an all-zero matrix, diag [ ] represents a diagonal matrix;
(2.3) subfilter three measurement equations for SINS/CNS:
in a third sub-filter connected to the CNS, the difference between the attitude output of the SINS and the CNS is taken as measurement information of the SINS/DVL measurement equation, which is expressed as:
wherein ,φE,SINS 、φ N,SINS 、φ U,SINS Respectively representing the east, north and upward attitude angles measured by the SINS system; phi (phi) E,CNS 、φ N,CNS 、φ U,CNS Representing the east, north and upward attitude angles, respectively, measured by the CNS system; delta phi E 、δφ N 、δφ U Respectively representing errors of the SINS system in the east, north and upward attitude angles and the true attitude angles; v 31 、v 32 、v 33 Representing errors of the CNS system from the true attitude angles in the east, north and upward attitude angles, respectively; they are independent zero-mean Gaussian white noise processes, v 3 Represented by v 31 、v 32 、v 33 A matrix of components;
H 3 the method is shown as follows:
H 3 ＝[diag[111]0 3×12 ]
where 0 represents an all-zero matrix and diag [ ] represents a diagonal matrix.
6. The multi-source adaptive fault-tolerant federal filtering integrated navigation method according to claim 1, wherein the specific method of the fifth step is as follows:
the information distribution process is as follows:
wherein ,
representing the process noise covariance of the ith sub-filter at time k, Q k Process noise covariance representing the main filter at time k,/->
An estimation error covariance matrix representing the ith sub-filter at time k,/th sub-filter>
An estimation error covariance matrix representing the main filter at time k +.>
State estimation representing the ith sub-filter at time k,/, for>
Representing the state estimate of the main filter at time k, beta i Is the information sharing factor of the i-th sub-filter, and satisfies:
wherein I represents an identity matrix;
the information fusion process is as follows:
wherein ,Pg Representing the estimation error covariance matrix of the main filter, P i Representing the estimated error covariance matrix of the ith sub-filter at time k,
representing the shape of the main filterState estimation, ->
Representing the state estimate of the ith sub-filter at time k. />
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