CN111189441A - Multi-source self-adaptive fault-tolerant federal filtering combined navigation system and navigation method - Google Patents

Multi-source self-adaptive fault-tolerant federal filtering combined navigation system and navigation method Download PDF

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CN111189441A
CN111189441A CN202010027513.7A CN202010027513A CN111189441A CN 111189441 A CN111189441 A CN 111189441A CN 202010027513 A CN202010027513 A CN 202010027513A CN 111189441 A CN111189441 A CN 111189441A
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熊海良
卞若晨
麦珍珍
胡昌武
王广渊
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Abstract

The invention discloses a multi-source self-adaptive fault-tolerant federal filtering combined 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, wherein the main filter and the three sub-filters are in information connection with the system respectively; the three sub-filters are in information connection with the strapdown inertial navigation system and are connected with the main filter through a fault detection and isolation module, the strapdown inertial navigation system is connected with the fault detection and isolation module through a state propagator, and the output result of the fault detection and isolation module is input into the main filter after passing through an 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 propagator. The system and the method disclosed by the invention can more accurately track the state of each sub-filter and obtain a more accurate fusion result.

Description

Multi-source self-adaptive fault-tolerant federal filtering combined navigation system and navigation method
Technical Field
The invention relates to the technical field of navigation communication, in particular to a multi-source self-adaptive fault-tolerant federal filtering combined navigation system and a navigation method.
Background
With the continuous improvement of high precision, real-time, seamless navigation and positioning requirements, the traditional single-sensor navigation system cannot meet the actual requirements. Multi-sensor integrated navigation technology and some other emerging hybrid navigation technologies have become a focus of research. Global Navigation Satellite Systems (GNSS) are satellite-based radio navigation systems that can provide reliable location information for long periods of time in various situations. But in some signal-blocking environments, such as forests, canyons, tunnels, and urban areas, performance can drop dramatically. A Strapdown Inertial Navigation System (SINS) is an autonomous navigation system that is not disturbed by the external environment. It can provide navigation information without relying on external sensors. However, the SINS navigation error will accumulate over time and diverge after a long duration. Meanwhile, a long initial alignment time is required before using the SINS. The doppler velocity measurement system (DVL) is designed based on the doppler effect, and 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) can provide attitude information of a vehicle with a star as a beacon. The method has high navigation precision and no accumulated error, but is easily interfered by the atmospheric environment. Thus, accurate real-time navigation and positioning cannot be achieved using a single sensor. In order to obtain the ideal navigation positioning result, in recent years, the integrated navigation system has become a popular research field, and many researchers have proposed various combination methods to improve the navigation accuracy.
Meanwhile, in order to improve the stability of the integrated navigation system, it is necessary to establish a proper autonomous fault detection, isolation and recovery (FDIR) system. The FDIR system consists of three parts, fault detection, fault isolation and fault recovery.
In combined navigation, a correct filter estimation algorithm is also necessary. Classical Kalman Filters (KF) are widely used in integrated systems, but require rigorous system models and noise types. However, in practice, these requirements are often not met. Therefore, some filtering estimation algorithms more suitable for the actual environment need to be found to complete the estimation of the system.
In recent years, dispersive filtering techniques have been increasingly used in multi-sensor systems, and various dispersive filters have been proposed. The Federal Filter (FF) is a special dispersive filter that consists of a distributed filter structure as opposed to a lumped filter. The Federal filter adopts the information sharing principle of a local filter and a main filter, and eliminates the correlation between local estimates by using an upper-bound technology. The fault tolerance and accuracy of the federal filter are directly affected by the information sharing principle. However, the conventional information sharing coefficient cannot sufficiently reflect the difference of the state variables of the sub-filters, and cannot track the change of the state variables. Therefore, research on adaptive Information Sharing Factors (ISFs) is imminent.
Disclosure of Invention
In order to solve the technical problems, the invention provides a multi-source self-adaptive fault-tolerant federal filtering combined navigation system and a navigation method, so as to achieve the purposes of tracking the state of each sub-filter more accurately and obtaining a more accurate fusion result.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a multi-source self-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, a main filter, a sub-filter I, a sub-filter II and a sub-filter III, wherein the main filter, the sub-filter I, the sub-filter II and the sub-filter III are in information connection with the system respectively; the three sub-filters are in information connection with the strapdown inertial navigation system and are connected with the main filter through a fault detection and isolation module, the strapdown inertial navigation system is connected with the fault detection and isolation module through a state propagator, and the output result of the fault detection and isolation module is input into the main filter after passing through an 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 propagator.
In the above scheme, the fault detection and isolation module adopts a 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.
A multi-source self-adaptive fault-tolerant federated filtering combined navigation method adopts the multi-source self-adaptive fault-tolerant federated filtering combined navigation system, and comprises the following processes:
step one, a sensor arranged on a moving vehicle acquires data of the moving vehicle and transmits the data to three sub-filters;
step two, the three sub-filters respectively carry out filtering processing on the data and transmit the processed data to a fault detection and isolation module;
step three, the fault detection and isolation module calculates data, judges whether each sub-filter has a fault, and if the sub-filter has the fault, the sub-filter is isolated, 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, the result is input into the main filter and fed back to the three sub-filters;
and step five, the main filter performs information distribution and information fusion on the received data, outputs the result, and synchronizes the result with the three sub-filters and the state propagator to regulate and control the whole situation.
In a further technical scheme, the first step is as follows:
setting the movement duration, the movement parameters and the movement environment parameter information of the moving vehicle through a track generator based on a strapdown inertial navigation system, generating the movement track information of the moving vehicle, and generating the original data of the strapdown inertial navigation system;
by a track generator, adding noise of a satellite navigation system by using the motion track information of a moving vehicle to generate position data of the satellite navigation system to the moving vehicle;
by the track generator, the moving track information of the moving vehicle is utilized, the noise of the Doppler velocimeter is added, and the speed data of the Doppler velocimeter on the moving vehicle is generated;
and by the track generator, the noise of the astronomical navigation system is added by utilizing the motion track information of the moving vehicle, and the attitude data of the astronomical navigation system to the moving vehicle is generated.
In a further technical scheme, the second step is specifically as follows:
(1) establishing a federal filtering integrated navigation system model under a navigation coordinate system:
an SINS error model is obtained by a perturbation method by taking an SINS as a reference system, and a linearized system state equation is described as follows:
Figure BDA0002363001030000031
where x represents the system state vector,
Figure BDA0002363001030000032
representing the system state vector at the next moment, F representing the state transfer function, and w representing the state noise;
in order to realize the filtering algorithm, firstly, the state equation is discretized to obtain a discrete-time state equation of which the system state vector x is propagated from the k-1 moment to the k moment:
xk＝Fk,k-1xk-1+wk
wherein ,Fk,k-1Representing the system transition matrix, xkRepresenting the system state vector at time k, xk-1Representing the system state vector at time k-1, wkRepresents process noise and satisfies the following statistical properties:
E[wk]＝0
Figure BDA0002363001030000033
wherein, E [. C]Meaning taking the mean of a matrix, T denotes taking the transpose of a matrix, δkjRepresenting a kronecker function, QkIs the covariance matrix of the process noise and the system state vector x is defined as
Figure BDA0002363001030000041
wherein ,δφEδφNδφUIndicating attitude error in east, north, delta vEδvNδvURepresenting velocity errors east, north, and up; δ L δ λ δ h represents a position error of latitude, longitude, and altitude; epsilonrxεryεrzErrors caused by gyro drift;
Figure BDA0002363001030000042
errors due to accelerometer bias;
(2) the three sub-filters respectively carry out filtering processing on the data:
(2.1) sub-filter-measurement equation for SINS/GNSS:
in a local filter coupled to the GNSS, the difference between the SINS position output and the GNSS is used as measurement information for a SINS/GNSS measurement equation expressed as:
Figure BDA0002363001030000043
wherein ,LSINS、λSINS、hSINSRespectively representing latitude, longitude and altitude, L, of measurements made by the SINS systemGNSS、λGNSS、hGNSSRespectively representing the latitude, longitude and altitude of the GNSS system measurement, respectively, δ L, δ λ, δ h respectively representing the error of the SINS system from the true position in latitude, longitude and altitude, v11、v12、v13Respectively representing GNSS systemsErrors from true position in latitude, longitude and altitude, which are independent zero mean white Gaussian noise processes, v1Is represented by v11、v12、v13A matrix of compositions;
H1expressed as:
H1＝[03×6diag[111]03×6]
wherein 0 represents an all-zero matrix, and diag [ ] represents a diagonal matrix;
(2.2) the second sub-filter has the measurement equation of SINS/DVL:
in the second sub-filter connected to the DVL, the difference between the velocity output of the SINS and the DVL is taken as the measurement information of the SINS/DVL measurement equation, which is expressed as:
Figure BDA0002363001030000044
wherein ,vE,SINS、vN,SINS、vU,SINSRespectively representing the east, north and upward velocities measured by the SINS system; v. ofE,DVL、vN,DVL、vU,DVLRespectively representing east, north and upward velocities measured by the DVL system; delta vE、δvN、δvURespectively representing the errors of the SINS system in east, north and upward speeds from the real speed; v. of21、v22、v23Respectively representing the errors of the DVL system from the true velocity in east, north and upward velocities, which are independent zero-mean white Gaussian noise processes, v2Is represented by v21、v22、v23A matrix of compositions;
H2expressed as:
H2＝[03×3diag[111]03×9]
wherein 0 represents an all-zero matrix, and diag [ ] represents a diagonal matrix;
(2.3) measurement equation of the sub-filter three about SINS/CNS:
in the third sub-filter connected with the CNS, the difference value between the posture output of the SINS and the CNS is used as the measurement information of the SINS/DVL measurement equation, and the measurement equation is expressed as:
Figure BDA0002363001030000051
wherein ,φE,SINS、φN,SINS、φU,SINSRespectively representing east, north and upward attitude angles measured by the SINS system; phi is aE,CNS、φN,CNS、φU,CNSRespectively representing east, north and upward attitude angles measured by the CNS system; delta vE、δvN、δvURespectively representing the errors of the SINS system on the east attitude angle, the north attitude angle and the upward attitude angle with the real attitude angle; v. of21、v22、v23Respectively representing errors of the CNS system on east, north and upward attitude angles and a real attitude angle; they are independent zero-mean Gaussian white noise processes, v2Is represented by v21、v22、v23A matrix of compositions;
H3shown as follows:
H3＝[diag[111]03×12]
where 0 represents an all-zero matrix and diag [ ] represents a diagonal matrix.
In a further technical scheme, 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, the BP neural network consists of an input layer, a hidden layer and an output layer, and the hidden layer has one or more layers;
firstly, training a BP neural network through fault information and normal information, wherein the network selects an S-shaped transfer function
Figure BDA0002363001030000061
By back-propagation of error functions
Figure BDA0002363001030000062
Continuously adjusting the network weight and the threshold value to make the error function F extremely small, wherein tiTo a desired output, OiIs the computational output of the network;
then, whether the sub-filters have faults is detected by inputting difference values of the state propagator and the sub-filters, in the BP neural network, a Sigmoid function is adopted as an excitation function of the network, when an output result of the excitation function is greater than 0.5, the sub-filters have faults, and when the output result of the excitation function is less than 0.5, the sub-filters normally operate;
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 solution, the method for calculating the information sharing factor in the fourth step is as follows:
Figure BDA0002363001030000063
Figure BDA0002363001030000064
Figure BDA0002363001030000065
wherein ,β1、β2、β3Information sharing factors of the first sub-filter, the second sub-filter and the third sub-filter are respectively; y is1、y2、y3And the outputs of the BP artificial neural network excitation functions of the first sub-filter, the second sub-filter and the third sub-filter are respectively.
In a further technical scheme, the concrete method of the step five is as follows:
the information distribution process is as follows:
Figure BDA0002363001030000066
Figure BDA0002363001030000067
Figure BDA0002363001030000068
wherein ,
Figure BDA0002363001030000071
representing the process noise covariance, Q, of the ith sub-filter at time kkRepresenting the process noise covariance of the main filter at time k,
Figure BDA0002363001030000072
covariance matrix of estimation errors representing the ith sub-filter at time k, Pk|kRepresenting the estimation error covariance matrix of the main filter at time k,
Figure BDA0002363001030000073
representing the state estimate of the ith sub-filter at time k,
Figure BDA0002363001030000074
representing the state estimate of the main filter at time k, βiIs an information sharing factor of the ith sub-filter, and satisfies:
Figure BDA0002363001030000075
wherein I represents an identity matrix;
the information fusion process is as follows:
Figure BDA0002363001030000076
Figure BDA0002363001030000077
wherein ,PgCovariance matrix, P, representing the estimation error of the main filteriRepresents the estimated error covariance matrix of the ith sub-filter at time k,
Figure BDA0002363001030000078
which represents the state estimate of the main filter,
Figure BDA0002363001030000079
representing the state estimate of the ith sub-filter at time k.
Compared with the prior art, the multi-source self-adaptive fault-tolerant federal filtering combined navigation system and the navigation method provided by the invention have the remarkable advantages that:
(1) by adopting a federal filtering algorithm and taking an inertial navigation system (SINS) as a common reference system, the combination of a Doppler velocity measurement system, an astronomical navigation system and a Global Navigation Satellite System (GNSS) and the inertial navigation system (SINS) is realized, each sub-filter automatically judges the current working state of the system, and information distribution factors are updated in a self-adaptive manner to obtain a local optimal solution, so that the flexible selection of a combination mode is realized, the global optimal estimation of the SINS error state of the common reference system is finally synthesized, and the positioning accuracy of the multisource fusion combined navigation system is improved;
(2) the main filter selects a non-reset federal filter, adopts a self-adaptive federal information distribution factor, can isolate faults when a certain sub information source fails, does not affect the normal filtering of the filter, enables the system to keep better stability and robustness, and improves the reliability and the anti-interference capability of the multi-source fusion combined navigation system.
(3) The sub-filters adopt strong tracking filters, so that more accurate estimation results can be obtained, and the reliability and the anti-interference capability of the multi-source fusion combined navigation system are improved.
(4) The BP neural network has the capacity of associative memory of external stimulation and input information, strong recognition and classification capacity of external input samples, and optimized calculation capacity; 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, nonlinearity is better introduced, the state of each sub-filter can be tracked more accurately, and a more accurate fusion result is obtained.
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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 structural diagram of a multi-source adaptive fault-tolerant federated filtering integrated navigation system of the present invention;
FIG. 2 is a flow diagram of a multi-source adaptive fault-tolerant federated filtering combination navigation method of the present invention;
fig. 3 is a schematic structural diagram of a BP neural network.
Detailed Description
The technical solution 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, a multi-source adaptive fault-tolerant federal filtering integrated navigation system includes 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 in information connection with the above systems respectively; the three sub-filters are in information connection with the strapdown inertial navigation system and are connected with the main filter through a fault detection and isolation module, the strapdown inertial navigation system is connected with the fault detection and isolation module through a state propagator, and after an output result of the fault detection and isolation module passes through an information sharing factor calculation module, a calculation result is input into the main filter; the main filter outputs the fused information and synchronizes the fusion result with the three sub-filters and the state propagator.
The fault detection and isolation module adopts a BP neural network as a fault detection, isolation and recovery algorithm.
In this embodiment, the main filter is a non-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 combined navigation method includes the following processes:
step one, a sensor arranged on a moving vehicle acquires data of the moving vehicle and transmits the data to three sub-filters;
based on the strapdown inertial navigation system, the motion duration, the motion parameters and the 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 specifically comprises the following steps:
and setting motion parameters and motion duration of each stage according to a physical model of the moving vehicle during motion, generating motion tracks of the moving vehicle including straight forward, backward, turning, accelerating, decelerating and the like, and generating corresponding SINS data.
By the track generator, the noise of the satellite navigation system is added by utilizing the motion track information of the moving vehicle, and the position data of the satellite navigation system to the moving vehicle is generated, which is specifically as follows:
generating three-dimensional position information of the moving vehicle by using a track generator according to the motion track information of the moving vehicle, and adding noise according to the error generation reason of a satellite navigation system to obtain the position information of the moving vehicle;
through the track generator, utilize the motion track information of moving vehicle, add the noise of doppler velocimeter, generate doppler velocimeter to the velocity data of moving vehicle, specifically as follows:
generating three-dimensional speed information of the moving vehicle by using a track generator according to the moving track information of the moving vehicle, and adding noise according to the error generation reason of the Doppler velocimeter to obtain the speed information of the moving vehicle;
by the track generator, the noise of the astronomical navigation system is added by utilizing the motion track information of the moving vehicle, and the attitude data of the astronomical navigation system to the moving vehicle is generated, which specifically comprises the following steps:
and generating three-dimensional attitude information of the moving vehicle by using a trajectory generator according to the movement trajectory information of the moving vehicle, and adding noise according to the error generation reason of the astronomical navigation system to obtain the attitude information of the moving vehicle.
Step two, the three sub-filters respectively carry out filtering processing on the data and transmit the processed data to a fault detection and isolation module;
(1) establishing a federal filtering integrated navigation system model under a navigation coordinate system:
an SINS error model is obtained by a perturbation method by taking an SINS as a reference system, and a linearized system state equation is described as follows:
Figure BDA0002363001030000091
where x represents the system state vector,
Figure BDA0002363001030000092
representing the system state vector at the next moment, F representing the state transfer function, and w representing the state noise;
in order to realize the filtering algorithm, firstly, the state equation is discretized to obtain a discrete-time state equation of which the system state vector x is propagated from the k-1 moment to the k moment:
xk＝Fk,k-1xk-1+wk
wherein ,Fk,k-1Representing the system transition matrix, xkRepresenting the system state vector at time k, xk-1Representing the system state vector at time k-1, wkRepresents process noise and satisfies the following statistical properties:
E[wk]＝0
Figure BDA0002363001030000101
wherein, E [. C]Meaning taking the mean of a matrix, T denotes taking the transpose of a matrix, δkjRepresenting a kronecker function, QkIs the covariance matrix of the process noise and the system state vector x is defined as
Figure BDA0002363001030000102
wherein ,δφEδφNδφUIndicating attitude error in east, north, delta vEδvNδvURepresenting velocity errors east, north, and up; δ L δ λ δ h represents a position error of latitude, longitude, and altitude; epsilonrxεryεrzErrors caused by gyro drift;
Figure BDA0002363001030000103
errors due to accelerometer bias;
(2) the three sub-filters respectively carry out filtering processing on the data:
(2.1) sub-filter-measurement equation for SINS/GNSS:
in a local filter coupled to the GNSS, the difference between the SINS position output and the GNSS is used as measurement information for a SINS/GNSS measurement equation expressed as:
Figure BDA0002363001030000104
wherein ,LSINS、λSINS、hSINSRespectively representing latitude, longitude and altitude, L, of measurements made by the SINS systemGNSS、λGNSS、hGNSSRespectively representing the latitude, longitude and altitude of the GNSS system measurement, respectively, δ L, δ λ, δ h respectively representing the error of the SINS system from the true position in latitude, longitude and altitude, v11、v12、v13Respectively representing the errors of the GNSS system from the true position in latitude, longitude and altitude, which are independent zero mean Gaussian white noise processes, v1Is represented by v11、v12、v13A matrix of compositions;
H1expressed as:
H1＝[03×6diag[111]03×6]
wherein 0 represents an all-zero matrix, and diag [ ] represents a diagonal matrix;
(2.2) the second sub-filter has the measurement equation of SINS/DVL:
in the second sub-filter connected to the DVL, the difference between the velocity output of the SINS and the DVL is taken as the measurement information of the SINS/DVL measurement equation, which is expressed as:
Figure BDA0002363001030000111
wherein ,vE,SINS、vN,SINS、vU,SINSRespectively representing the east, north and upward velocities measured by the SINS system; v. ofE,DVL、vN,DVL、vU,DVLRespectively representing east, north and upward velocities measured by the DVL system; delta vE、δvN、δvURespectively representing the errors of the SINS system in east, north and upward speeds from the real speed; v. of21、v22、v23Respectively representing the errors of the DVL system from the true velocity in east, north and upward velocities, which are independent zero-mean white Gaussian noise processes, v2Is represented by v21、v22、v23A matrix of compositions;
H2expressed as:
H2＝[03×3diag[111]03×9]
wherein 0 represents an all-zero matrix, and diag [ ] represents a diagonal matrix;
(2.3) measurement equation of the sub-filter three about SINS/CNS:
in the third sub-filter connected with the CNS, the difference value between the posture output of the SINS and the CNS is used as the measurement information of the SINS/DVL measurement equation, and the measurement equation is expressed as:
Figure BDA0002363001030000112
wherein ,φE,SINS、φN,SINS、φU,SINSRespectively representing east, north and upward attitude angles measured by the SINS system; phi is aE,CNS、φN,CNS、φU,CNSRespectively representing east, north and upward attitude angles measured by the CNS system; delta vE、δvN、δvURespectively representing the errors of the SINS system on the east attitude angle, the north attitude angle and the upward attitude angle with the real attitude angle; v. of21、v22、v23Respectively representing errors of the CNS system on east, north and upward attitude angles and a real attitude angle; they are independent zero-mean Gaussian white noise processes, v2Is represented by v21、v22、v23A matrix of compositions;
H3shown as follows:
H3＝[diag[111]03×12]
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 a fault, and if the sub-filter has the fault, the sub-filter is isolated, 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, as shown in FIG. 3, the BP neural network is composed of an input layer, a hidden layer and an output layer, and the hidden layer has one or more layers;
firstly, training a BP neural network through fault information and normal information, wherein the network selects an S-shaped transfer function
Figure BDA0002363001030000121
By back-propagation of error functions
Figure BDA0002363001030000122
Continuously adjusting the network weight and the threshold value to make the error function F extremely small, wherein tiTo a desired output, OiIs the computational output of the network;
then, whether the sub-filters have faults is detected by inputting difference values of the state propagator and the sub-filters, in the BP neural network, a Sigmoid function is adopted as an excitation function of the network, when an output result of the excitation function is greater than 0.5, the sub-filters have faults, and when the output result of the excitation function is less than 0.5, the sub-filters normally operate;
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, the result is input into the main filter and fed back to the three sub-filters;
the information sharing factor is calculated as follows:
Figure BDA0002363001030000123
Figure BDA0002363001030000124
Figure BDA0002363001030000131
wherein ,β1、β2、β3Information sharing factors of the first sub-filter, the second sub-filter and the third sub-filter are respectively; y is1、y2、y3And the outputs of the BP artificial neural network excitation functions of the first sub-filter, the second sub-filter and the third sub-filter are respectively.
And step five, the main filter performs information distribution and information fusion on the received data, outputs the result, and synchronizes the result with the three sub-filters and the state propagator to regulate and control the whole situation.
The information distribution process is as follows:
Figure BDA0002363001030000132
Figure BDA0002363001030000133
Figure BDA0002363001030000134
wherein ,
Figure BDA0002363001030000135
representing the process noise covariance, Q, of the ith sub-filter at time kkRepresenting the process noise covariance of the main filter at time k,
Figure BDA0002363001030000136
covariance matrix of estimation errors representing the ith sub-filter at time k, Pk|kRepresenting the estimation error covariance matrix of the main filter at time k,
Figure BDA0002363001030000137
representing the state estimate of the ith sub-filter at time k,
Figure BDA0002363001030000138
representing the state estimate of the main filter at time k, βiIs an information sharing factor of the ith sub-filter, and satisfies:
Figure BDA0002363001030000139
wherein I represents an identity matrix;
the information fusion process is as follows:
Figure BDA00023630010300001310
Figure BDA00023630010300001311
wherein ,PgCovariance matrix, P, representing the estimation error of the main filteriRepresents the estimated error covariance matrix of the ith sub-filter at time k,
Figure BDA00023630010300001312
which represents the state estimate of the main filter,
Figure BDA00023630010300001313
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 (9)

1. A multi-source self-adaptive fault-tolerant federal filtering integrated navigation system is characterized by comprising a strapdown inertial navigation system, a satellite navigation system, a Doppler velocity measurement system, an astronomical navigation system, a main filter, a sub-filter I, a sub-filter II and a sub-filter III, wherein the main filter, the sub-filter I, the sub-filter II and the sub-filter III are in information connection with the systems respectively; the three sub-filters are in information connection with the strapdown inertial navigation system and are connected with the main filter through a fault detection and isolation module, the strapdown inertial navigation system is connected with the fault detection and isolation module through a state propagator, and the output result of the fault detection and isolation module is input into the main filter after passing through an 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 propagator.
2. The multi-source adaptive fault-tolerant federated filtering combination navigation system 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. The combined navigation system of claim 1 or 2, wherein the main filter is a no-reset federal filter, and the three sub-filters are strong tracking filters.
4. A multi-source self-adaptive fault-tolerant federated filtering combination navigation method adopts the multi-source self-adaptive fault-tolerant federated filtering combination navigation system of claim 1, which is characterized by comprising the following processes:
step one, a sensor arranged on a moving vehicle acquires data of the moving vehicle and transmits the data to three sub-filters;
step two, the three sub-filters respectively carry out filtering processing on the data and transmit the processed data to a fault detection and isolation module;
step three, the fault detection and isolation module calculates data, judges whether each sub-filter has a fault, and if the sub-filter has the fault, the sub-filter is isolated, 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, the result is input into the main filter and fed back to the three sub-filters;
and step five, the main filter performs information distribution and information fusion on the received data, outputs the result, and synchronizes the result with the three sub-filters and the state propagator to regulate and control the whole situation.
5. The multi-source adaptive fault-tolerant federated filtering combination navigation method according to claim 4, wherein the first step is specifically as follows:
setting the movement duration, the movement parameters and the movement environment parameter information of the moving vehicle through a track generator based on a strapdown inertial navigation system, generating the movement track information of the moving vehicle, and generating the original data of the strapdown inertial navigation system;
by a track generator, adding noise of a satellite navigation system by using the motion track information of a moving vehicle to generate position data of the satellite navigation system to the moving vehicle;
by the track generator, the moving track information of the moving vehicle is utilized, the noise of the Doppler velocimeter is added, and the speed data of the Doppler velocimeter on the moving vehicle is generated;
and by the track generator, the noise of the astronomical navigation system is added by utilizing the motion track information of the moving vehicle, and the attitude data of the astronomical navigation system to the moving vehicle is generated.
6. The multi-source adaptive fault-tolerant federated filtering combination navigation method according to claim 4, wherein the second step is specifically as follows:
(1) establishing a federal filtering integrated navigation system model under a navigation coordinate system:
an SINS error model is obtained by a perturbation method by taking an SINS as a reference system, and a linearized system state equation is described as follows:
Figure FDA0002363001020000021
where x represents the system state vector,
Figure FDA0002363001020000022
representing the system state vector at the next moment, F representing the state transfer function, and w representing the state noise;
in order to realize the filtering algorithm, firstly, the state equation is discretized to obtain a discrete-time state equation of which the system state vector x is propagated from the k-1 moment to the k moment:
xk＝Fk,k-1xk-1+wk
wherein ,Fk,k-1Representing the system transition matrix, xkRepresenting the system state vector at time k, xk-1Representing the system state vector at time k-1, wkRepresents process noise and satisfies the following statistical properties:
E[wk]＝0
Figure FDA0002363001020000023
wherein, E [. C]Meaning taking the mean of a matrix, T denotes taking the transpose of a matrix, δkjRepresenting a kronecker function, QkIs the covariance matrix of the process noise and the system state vector x is defined as
x＝[δφEδφNδφUδvEδvNδvUδL δλ δh εrxεryεrz▽E▽N▽U]
wherein ,δφEδφNδφUIndicating attitude error in east, north, delta vEδvNδvURepresenting velocity errors east, north, and up; δ L δ λ δ h represents a position error of latitude, longitude, and altitude; epsilonrxεryεrzError due to gyro drift ▽E▽N▽UErrors due to accelerometer bias;
(2) the three sub-filters respectively carry out filtering processing on the data:
(2.1) sub-filter-measurement equation for SINS/GNSS:
in a local filter coupled to the GNSS, the difference between the SINS position output and the GNSS is used as measurement information for a SINS/GNSS measurement equation expressed as:
Figure FDA0002363001020000031
wherein ,LSINS、λSINS、hSINSRespectively representing latitude, longitude and altitude, L, of measurements made by the SINS systemGNSS、λGNSS、hGNSSRespectively representing the latitude, longitude and altitude of the GNSS system measurement, respectively, δ L, δ λ, δ h respectively representing the error of the SINS system from the true position in latitude, longitude and altitude, v11、v12、v13Respectively representing the latitude and longitude of the GNSS systemError in degree and altitude from true position, which are independent zero mean white Gaussian noise processes, v1Is represented by v11、v12、v13A matrix of compositions;
H1expressed as:
H1＝[03×6diag[111]03×6]
wherein 0 represents an all-zero matrix, and diag [ ] represents a diagonal matrix;
(2.2) the second sub-filter has the measurement equation of SINS/DVL:
in the second sub-filter connected to the DVL, the difference between the velocity output of the SINS and the DVL is taken as the measurement information of the SINS/DVL measurement equation, which is expressed as:
Figure FDA0002363001020000032
wherein ,vE,SINS、vN,SINS、vU,SINSRespectively representing the east, north and upward velocities measured by the SINS system; v. ofE,DVL、vN,DVL、vU,DVLRespectively representing east, north and upward velocities measured by the DVL system; delta vE、δvN、δvURespectively representing the errors of the SINS system in east, north and upward speeds from the real speed; v. of21、v22、v23Respectively representing the errors of the DVL system from the true velocity in east, north and upward velocities, which are independent zero-mean white Gaussian noise processes, v2Is represented by v21、v22、v23A matrix of compositions;
H2expressed as:
H2＝[03×3diag[111]03×9]
wherein 0 represents an all-zero matrix, and diag [ ] represents a diagonal matrix;
(2.3) measurement equation of the sub-filter three about SINS/CNS:
in the third sub-filter connected with the CNS, the difference value between the posture output of the SINS and the CNS is used as the measurement information of the SINS/DVL measurement equation, and the measurement equation is expressed as:
Figure FDA0002363001020000041
wherein ,φE,SINS、φN,SINS、φU,SINSRespectively representing east, north and upward attitude angles measured by the SINS system; phi is aE,CNS、φN,CNS、φU,CNSRespectively representing east, north and upward attitude angles measured by the CNS system; delta vE、δvN、δvURespectively representing the errors of the SINS system on the east attitude angle, the north attitude angle and the upward attitude angle with the real attitude angle; v. of21、v22、v23Respectively representing errors of the CNS system on east, north and upward attitude angles and a real attitude angle; they are independent zero-mean Gaussian white noise processes, v2Is represented by v21、v22、v23A matrix of compositions;
H3shown as follows:
H3＝[diag[111]03×12]
where 0 represents an all-zero matrix and diag [ ] represents a diagonal matrix.
7. The multi-source adaptive fault-tolerant federated filtering combination navigation method according to claim 4, wherein 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, the BP neural network consists of an input layer, a hidden layer and an output layer, and the hidden layer has one or more layers;
firstly, training a BP neural network through fault information and normal information, wherein the network selects an S-shaped transfer function
Figure FDA0002363001020000051
By back-propagation of error functions
Figure FDA0002363001020000052
Constantly adjusting network rightsThe values and thresholds minimize the error function F, where tiTo a desired output, OiIs the computational output of the network;
then, whether the sub-filters have faults is detected by inputting difference values of the state propagator and the sub-filters, in the BP neural network, a Sigmoid function is adopted as an excitation function of the network, when an output result of the excitation function is greater than 0.5, the sub-filters have faults, and when the output result of the excitation function is less than 0.5, the sub-filters normally operate;
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.
8. The multi-source adaptive fault-tolerant federated filtering combination navigation method according to claim 7, wherein the calculation method of the information sharing factor in the fourth step is as follows:
Figure FDA0002363001020000053
Figure FDA0002363001020000054
Figure FDA0002363001020000055
wherein ,β1、β2、β3Information sharing factors of the first sub-filter, the second sub-filter and the third sub-filter are respectively; y is1、y2、y3And the outputs of the BP artificial neural network excitation functions of the first sub-filter, the second sub-filter and the third sub-filter are respectively.
9. The multi-source adaptive fault-tolerant federal filter combined navigation method according to claim 7, wherein the concrete method of the fifth step is as follows:
the information distribution process is as follows:
Figure FDA0002363001020000056
Figure FDA0002363001020000057
Figure FDA0002363001020000058
wherein ,
Figure FDA0002363001020000061
representing the process noise covariance, Q, of the ith sub-filter at time kkRepresenting the process noise covariance of the main filter at time k,
Figure FDA0002363001020000062
covariance matrix of estimation errors representing the ith sub-filter at time k, PkkRepresenting the estimation error covariance matrix of the main filter at time k,
Figure FDA0002363001020000063
representing the state estimate of the ith sub-filter at time k,
Figure FDA0002363001020000064
representing the state estimate of the main filter at time k, βiIs an information sharing factor of the ith sub-filter, and satisfies:
Figure FDA0002363001020000065
wherein I represents an identity matrix;
the information fusion process is as follows:
Figure FDA0002363001020000066
Figure FDA0002363001020000067
wherein ,PgCovariance matrix, P, representing the estimation error of the main filteriRepresents the estimated error covariance matrix of the ith sub-filter at time k,
Figure FDA0002363001020000068
which represents the state estimate of the main filter,
Figure FDA0002363001020000069
representing the state estimate of the ith sub-filter at time k.
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Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111811512A (en) * 2020-06-02 2020-10-23 北京航空航天大学 Federal smoothing-based MPOS offline combined estimation method and device
CN111829508A (en) * 2020-07-24 2020-10-27 中国人民解放***箭军工程大学 Fault-tolerant federated filtering method and system based on innovation
CN111854742A (en) * 2020-07-15 2020-10-30 中南大学 Speed measurement positioning method and system of moving object based on multi-source information fusion
CN112180361A (en) * 2020-09-30 2021-01-05 南京航空航天大学 Vehicle-mounted radar target tracking method based on dynamic federal filtering
CN112525188A (en) * 2020-12-15 2021-03-19 上海交通大学 Combined navigation method based on federal filtering
CN112577485A (en) * 2020-11-26 2021-03-30 江西省科学院应用物理研究所 Positioning system and method based on inertial navigation
CN112711055A (en) * 2020-12-08 2021-04-27 重庆邮电大学 Indoor and outdoor seamless positioning system and method based on edge calculation
CN113375666A (en) * 2021-06-23 2021-09-10 北京超星未来科技有限公司 Sensor fusion positioning system and method
CN113776527A (en) * 2021-09-13 2021-12-10 中国民用航空飞行学院 Integrated navigation system and navigation method for civil aircraft full time and space
CN113819911A (en) * 2021-09-13 2021-12-21 北京理工大学 Navigation method based on adaptive fault-tolerant filtering under GNSS unlocking
WO2022037340A1 (en) * 2020-08-20 2022-02-24 华为技术有限公司 Fault detection method, apparatus, and system
CN114370870A (en) * 2022-01-05 2022-04-19 中国兵器工业计算机应用技术研究所 Filter updating information screening method suitable for pose measurement Kalman filtering
CN116105743A (en) * 2023-04-17 2023-05-12 山东大学 Information factor distribution method of federal filtering system and underwater navigation system
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CN117452448A (en) * 2023-12-25 2024-01-26 合众新能源汽车股份有限公司 High-precision positioning jump joint fault diagnosis method and vehicle positioning control system

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050012660A1 (en) * 2002-11-15 2005-01-20 Lockheed Martin Corporation All-weather precision guidance and navigation system
US20060074558A1 (en) * 2003-11-26 2006-04-06 Williamson Walton R Fault-tolerant system, apparatus and method
CN103323007A (en) * 2013-06-17 2013-09-25 南京航空航天大学 Robust federated filtering method based on time-variable measurement noise
CN103697894A (en) * 2013-12-27 2014-04-02 南京航空航天大学 Multi-source information unequal interval federated filtering method based on filter variance matrix correction
CN104913781A (en) * 2015-06-04 2015-09-16 南京航空航天大学 Unequal interval federated filter method based on dynamic information distribution
CN106197408A (en) * 2016-06-23 2016-12-07 南京航空航天大学 A kind of multi-source navigation data fusion method based on factor graph
WO2017071140A1 (en) * 2015-10-30 2017-05-04 中国空间技术研究院 Menu-type design method for geo satellite control system based on optimized information integration
CN106679693A (en) * 2016-12-14 2017-05-17 南京航空航天大学 Fault detection-based vector information distribution adaptive federated filtering method
CN110095800A (en) * 2019-04-19 2019-08-06 南京理工大学 A kind of self-adapted tolerance federated filter Combinated navigation method of multi-source fusion
US10466362B1 (en) * 2019-03-27 2019-11-05 Beihang University Integrated navigation integrity monitoring system for unmanned aerial vehicles
CN110579740A (en) * 2019-09-17 2019-12-17 大连海事大学 unmanned ship integrated navigation method based on adaptive federal Kalman filtering
WO2019242336A1 (en) * 2018-06-22 2019-12-26 东南大学 Navigation and positioning system for underwater glider, and floating precision correction method

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050012660A1 (en) * 2002-11-15 2005-01-20 Lockheed Martin Corporation All-weather precision guidance and navigation system
US20060074558A1 (en) * 2003-11-26 2006-04-06 Williamson Walton R Fault-tolerant system, apparatus and method
CN103323007A (en) * 2013-06-17 2013-09-25 南京航空航天大学 Robust federated filtering method based on time-variable measurement noise
CN103697894A (en) * 2013-12-27 2014-04-02 南京航空航天大学 Multi-source information unequal interval federated filtering method based on filter variance matrix correction
CN104913781A (en) * 2015-06-04 2015-09-16 南京航空航天大学 Unequal interval federated filter method based on dynamic information distribution
WO2017071140A1 (en) * 2015-10-30 2017-05-04 中国空间技术研究院 Menu-type design method for geo satellite control system based on optimized information integration
CN106197408A (en) * 2016-06-23 2016-12-07 南京航空航天大学 A kind of multi-source navigation data fusion method based on factor graph
CN106679693A (en) * 2016-12-14 2017-05-17 南京航空航天大学 Fault detection-based vector information distribution adaptive federated filtering method
WO2019242336A1 (en) * 2018-06-22 2019-12-26 东南大学 Navigation and positioning system for underwater glider, and floating precision correction method
US10466362B1 (en) * 2019-03-27 2019-11-05 Beihang University Integrated navigation integrity monitoring system for unmanned aerial vehicles
CN110095800A (en) * 2019-04-19 2019-08-06 南京理工大学 A kind of self-adapted tolerance federated filter Combinated navigation method of multi-source fusion
CN110579740A (en) * 2019-09-17 2019-12-17 大连海事大学 unmanned ship integrated navigation method based on adaptive federal Kalman filtering

Non-Patent Citations (11)

* Cited by examiner, † Cited by third party
Title
HAILIANG XIONG ET AL.: "A Robust Single GPS Navigation and Positioning Algorithm Based on Strong Tracking Filtering", IEEE SENSORS JOURNAL *
YUEQIAN LIANG ET AL.: "A fault-tolerant integrated navigation for UAV systems using time-variant vector distribution coefficients", 2014 AMERICAN CONTROL CONFERENCE *
李佩娟;徐晓苏;王立辉;李瑶;: "水下无源组合导航***智能容错方法设计", 中国惯性技术学报 *
熊智;邵慧;华冰;方峥;: "改进故障隔离的容错联邦滤波", 航空学报 *
熊智等: "改进故障隔离的容错联邦滤波", 《航空学报》 *
熊海良等: "一种认知超宽带无线通信***的设计与实现", ***工程理论与实践 *
王其等: "***容错技术在水下航行器组合导航***中的应用", 《中国惯性技术学报》 *
王春霞;赵伟;刘瑞华;刘建业;: "动态捷联惯导/多卫星组合导航自适应联邦滤波算法研究", 信息与控制 *
蒋文: "基于BDS/SINS的组合导航***研究", 中国优秀硕士学位论文全文数据库 信息科技辑 *
陆建新,赵大炜,缪栋: "基于神经网络的SINS/GPS/TAN容错组合导航***设计", 上海航天 *
高尚;祝小平;: "基于联邦滤波的无人机组合导航***设计", 科学技术与工程 *

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