CN116540274A - Self-adaptive integrated navigation method and device, electronic equipment and storage medium - Google Patents
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/393—Trajectory determination or predictive tracking, e.g. Kalman filtering
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/183—Compensation of inertial measurements, e.g. for temperature effects
- G01C21/188—Compensation of inertial measurements, e.g. for temperature effects for accumulated errors, e.g. by coupling inertial systems with absolute positioning systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
- G01S19/485—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an optical system or imaging system
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The application provides a self-adaptive integrated navigation method and device, electronic equipment and a storage medium, and belongs to the technical field of navigation systems. The method comprises the following steps: acquiring state data and measurement data of a target navigation system; constructing a state equation based on the state data, and constructing a measurement equation based on the measurement data; carrying out state prediction on the target navigation system to obtain predicted state data; carrying out noise matrix prediction on the target navigation system based on a Kalman filtering algorithm to obtain a first measurement noise variance matrix; carrying out noise matrix prediction on the target navigation system based on the fuzzy inference system to obtain a second measurement noise variance matrix; obtaining a target measurement noise variance matrix based on the first measurement noise variance matrix and the second measurement noise variance matrix; performing parameter estimation on the target navigation system based on the target measurement noise variance array to obtain state estimation parameters; and the feedback correction is carried out on the target navigation system based on the state estimation parameters, so that the estimation accuracy of the measurement noise variance array and the precision of the integrated navigation are improved.
Description
Technical Field
The present disclosure relates to the field of navigation systems, and in particular, to a method and apparatus for adaptive integrated navigation, an electronic device, and a storage medium.
Background
At present, when satellite navigation in the integrated navigation system is influenced by external environments such as shielding and electromagnetic interference, and the satellite navigation positioning accuracy is reduced, if a fixed measurement noise variance array is adopted to characterize the noise statistics characteristics of measurement information, the measurement accuracy of the integrated navigation system is greatly reduced and even the filtering divergence problem is caused due to the fact that the measurement noise model is inconsistent with the actual situation, and the estimation accuracy of the measurement noise variance array and the accuracy of the integrated navigation are greatly influenced.
Disclosure of Invention
The embodiment of the application mainly aims to provide a self-adaptive integrated navigation method and device, electronic equipment and storage medium, and aims to improve the estimation accuracy of a measurement noise variance matrix, so that the integrated navigation precision is improved.
To achieve the above object, a first aspect of an embodiment of the present application proposes an adaptive integrated navigation method, including:
acquiring state data and measurement data of a target navigation system;
Constructing a state equation based on the state data, and constructing a measurement equation based on the measurement data;
carrying out state prediction on the target navigation system to obtain predicted state data;
performing noise matrix prediction on the target navigation system based on a preset Kalman filtering algorithm to obtain a first measurement noise variance matrix;
performing noise matrix prediction on the target navigation system based on a preset fuzzy inference system to obtain a second measurement noise variance matrix;
obtaining a target measurement noise variance matrix based on the first measurement noise variance matrix and the second measurement noise variance matrix;
performing parameter estimation on the target navigation system based on the target measurement noise variance array to obtain state estimation parameters;
and carrying out feedback correction on the target navigation system based on the state estimation parameters.
In some embodiments, the performing noise matrix prediction on the target navigation system based on a preset kalman filtering algorithm to obtain a first measurement noise variance matrix includes:
calculating an innovation sequence of the target navigation system based on the predicted state data and the measurement equation;
and carrying out noise matrix prediction based on the Kalman filtering algorithm, the innovation sequence and a preset weighting coefficient to obtain the first measurement noise variance matrix.
In some embodiments, the predicting the noise matrix of the target navigation system based on the preset fuzzy inference system to obtain a second measurement noise variance matrix includes:
the fuzzy controller based on the fuzzy reasoning system predicts the noise regulating factor of the target navigation system to obtain a predicted regulating factor;
and weighting a preset reference measurement noise variance matrix based on the prediction adjusting factor to obtain the second measurement noise variance matrix.
In some embodiments, the predicting the noise adjustment factor by the fuzzy controller based on the fuzzy inference system to obtain a predicted adjustment factor includes:
acquiring an accuracy attenuation factor and a maximum carrier signal-to-noise ratio of the target navigation system;
blurring processing is carried out on the precision attenuation factors to obtain first membership data corresponding to the precision attenuation factors;
blurring processing is carried out on the maximum carrier signal-to-noise ratio, and second membership data corresponding to the maximum carrier signal-to-noise ratio is obtained;
based on a preset fuzzy rule of the fuzzy controller, the first membership data and the second membership data, carrying out working state analysis on the target navigation system to obtain the current working state of the target navigation system;
And obtaining the prediction regulating factor based on the current working state and a preset reference regulating factor.
In some embodiments, the first membership data includes a first membership state of the dilution of precision, the first membership state including one of good, medium, and bad, the second membership data includes a second membership state of the maximum carrier signal to noise ratio, the second membership state including one of good, medium, and bad, the preset fuzzy rule includes:
if the first membership state is good, the current working state is the same as the second membership state;
if the first membership state is medium, the current working state is the same as the second membership state;
and if the first membership state is poor, the current working state is poor.
In some embodiments, the estimating the parameters of the target navigation system based on the target measurement noise variance array to obtain state estimation parameters includes:
performing filtering gain calculation based on the target measurement noise variance matrix to obtain the Kalman filtering gain;
and carrying out state estimation on the target navigation system based on the predicted state data and the Kalman filtering gain to obtain the state estimation parameters.
In some embodiments, the obtaining the target measurement noise variance matrix based on the first measurement noise variance matrix and the second measurement noise variance matrix includes:
acquiring preset weight data;
and weighting the first measurement noise variance matrix and the second measurement noise variance matrix based on the weight data to obtain the target measurement noise variance matrix.
To achieve the above object, a second aspect of the embodiments of the present application proposes an adaptive integrated navigation device, the device comprising:
the data acquisition module is used for acquiring state data and measurement data of the target navigation system;
the equation construction module is used for constructing a state equation based on the state data and constructing a measurement equation based on the measurement data;
the state prediction module is used for carrying out state prediction on the target navigation system to obtain predicted state data;
the first matrix prediction module is used for predicting the noise matrix of the target navigation system based on a preset Kalman filtering algorithm to obtain a first measurement noise variance matrix;
the second matrix prediction module is used for predicting the noise matrix of the target navigation system based on a preset fuzzy inference system to obtain a second measurement noise variance matrix;
The target matrix determining module is used for obtaining a target measurement noise variance matrix based on the first measurement noise variance matrix and the second measurement noise variance matrix;
the parameter estimation module is used for carrying out parameter estimation on the target navigation system based on the target measurement noise variance array to obtain state estimation parameters;
and the feedback correction module is used for carrying out feedback correction on the target navigation system based on the state estimation parameters.
To achieve the above object, a third aspect of the embodiments of the present application proposes an electronic device, which includes a memory, a processor, where the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.
To achieve the above object, a fourth aspect of the embodiments of the present application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method of the first aspect.
The application provides a self-adaptive integrated navigation method, a self-adaptive integrated navigation device, electronic equipment and a storage medium, wherein the self-adaptive integrated navigation device is used for acquiring state data and measurement data of a target navigation system; constructing a state equation based on the state data, and constructing a measurement equation based on the measurement data; carrying out state prediction on the target navigation system to obtain predicted state data; performing noise matrix prediction on the target navigation system based on a preset Kalman filtering algorithm to obtain a first measurement noise variance matrix; performing noise matrix prediction on the target navigation system based on a preset fuzzy inference system to obtain a second measurement noise variance matrix; obtaining a target measurement noise variance matrix based on the first measurement noise variance matrix and the second measurement noise variance matrix; performing parameter estimation on the target navigation system based on the target measurement noise variance array to obtain state estimation parameters; and performing feedback correction on the target navigation system based on the state estimation parameters. According to the method, the measuring noise variance matrix based on the Kalman filtering algorithm prediction and the measuring noise variance matrix based on the fuzzy inference system prediction can be combined to realize real-time estimation of the target measuring noise variance matrix, so that parameter estimation can be performed on the target navigation system more accurately and in real time, information fusion and error adjustment of the target navigation system are realized, instantaneity and accuracy of system noise statistical characteristic estimation can be better improved, estimation accuracy of Kalman filtering on state quantity is improved, measurement accuracy of the combined navigation system is further improved, and reliability of the combined navigation system in different environments is guaranteed.
Drawings
FIG. 1 is a flow chart of an adaptive integrated navigation method provided by an embodiment of the present application;
fig. 2 is a flowchart of step S104 in fig. 1;
fig. 3 is a flowchart of step S105 in fig. 1;
fig. 4 is a flowchart of step S301 in fig. 3;
fig. 5 is a flowchart of step S106 in fig. 1;
fig. 6 is a flowchart of step S107 in fig. 1;
fig. 7 is a schematic structural diagram of an adaptive integrated navigation device according to an embodiment of the present application;
fig. 8 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
It should be noted that although functional block division is performed in a device diagram and a logic sequence is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the block division in the device, or in the flowchart. The terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the present application.
First, several nouns referred to in this application are parsed:
machine learning: machine learning is a multidisciplinary cross-specialty covering probabilistic knowledge, statistical knowledge, approximate theoretical knowledge and complex algorithmic knowledge, uses a computer as a tool and aims at simulating human learning in real time, and performs knowledge structure division on existing content to effectively improve learning efficiency. Machine learning is classified based on a learning manner, and can be classified into supervised learning, unsupervised learning, and reinforcement learning. The input data of the supervised learning has a teacher signal, a probability function, an algebraic function or an artificial neural network is used as a basis function model, an iterative calculation method is adopted, and a learning result is used as a function. The input data of the unsupervised learning has no teacher signal, and a clustering method is adopted, so that the learning result is a category. Typical non-mentor learning is discovery learning, clustering, competitive learning, etc. Reinforcement learning is a learning method guided by statistical and dynamic planning techniques with environmental feedback (reward/punishment signals) as input.
Because inertial navigation has the advantages of comprehensive output navigation information, good autonomy, strong anti-interference capability and the like, but because errors can accumulate with time, the inertial navigation can only generally meet the short-time navigation requirement. The satellite navigation can realize global and all-weather speed measurement and positioning, has higher navigation precision, does not accumulate errors with time, has lower navigation information output frequency and is easy to be interfered by external environment. In the combined navigation system, inertial navigation information and satellite navigation information are fused by using an information fusion algorithm through the combination of inertial navigation and satellite navigation, so that the advantage complementation between the inertial navigation and the satellite navigation can be realized, and the measurement accuracy of the navigation system is improved.
The information fusion algorithm is a key for realizing complementary advantages of all subsystems of the integrated navigation system, wherein the Kalman filtering algorithm is most widely applied, when a system model and the error statistics characteristics can be accurately acquired, higher-precision estimation of the system state quantity can be realized through the Kalman filtering algorithm, but when the error statistics characteristics cannot be accurately determined or are changed in the application process, the estimation precision of the Kalman filtering algorithm is reduced, and the measurement precision of the integrated navigation system is affected. The measurement noise variance array represents the reliability of measurement information, the accuracy of the combined navigation output information is influenced by the accuracy of the measurement noise variance array, when satellite navigation is influenced by complex environments such as shielding, electromagnetic interference, multipath and the like, the positioning error of the satellite navigation is increased, the measurement noise variance array needs to be dynamically adjusted in real time according to the working state of the satellite navigation, the actual condition of the measurement noise is truly reflected, and the accuracy and the reliability of the combined navigation output information can be ensured.
The self-adaptive Kalman filtering algorithm utilizes the observation data to carry out online estimation adjustment on the noise statistical characteristics in the recursion process, reduces the dependence of the Kalman filtering algorithm on the initial noise statistical characteristics, ensures the estimation precision of the Kalman filtering algorithm when the noise statistical characteristics of the navigation system are changed, and improves the robustness of the algorithm.
At present, when satellite navigation in the integrated navigation system is influenced by external environments such as shielding and electromagnetic interference, and the satellite navigation positioning accuracy is reduced, if a fixed measurement noise variance array is adopted to characterize the noise statistics characteristics of measurement information, the measurement accuracy of the integrated navigation system is greatly reduced and even the filtering divergence problem is caused due to the fact that the measurement noise model is inconsistent with the actual situation, and the estimation accuracy of the measurement noise variance array and the accuracy of the integrated navigation are greatly influenced.
Based on this, the embodiment of the application provides a self-adaptive integrated navigation method, a self-adaptive integrated navigation device, electronic equipment and a storage medium, which aim to improve the estimation accuracy of a measurement noise variance matrix, thereby improving the accuracy of integrated navigation.
The embodiment of the application provides an adaptive integrated navigation method and device, an electronic device and a storage medium, and specifically, the following embodiment is used for explaining, and first describes the adaptive integrated navigation method in the embodiment of the application.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
The embodiment of the application provides a self-adaptive combined navigation method, and relates to the technical field of navigation. The self-adaptive integrated navigation method provided by the embodiment of the application can be applied to a terminal, a server side and software running in the terminal or the server side. In some embodiments, the terminal may be a smart phone, tablet, notebook, desktop, etc.; the server side can be configured as an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligent platforms and the like; the software may be an application or the like that implements the adaptive integrated navigation method, but is not limited to the above form.
The subject application is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
Fig. 1 is an optional flowchart of an adaptive integrated navigation method provided in an embodiment of the present application, where the method in fig. 1 may include, but is not limited to, steps S101 to S108.
Step S101, acquiring state data and measurement data of a target navigation system;
step S102, a state equation is built based on the state data, and a measurement equation is built based on the measurement data;
step S103, carrying out state prediction on the target navigation system to obtain predicted state data;
step S104, carrying out noise matrix prediction on the target navigation system based on a preset Kalman filtering algorithm to obtain a first measurement noise variance matrix;
step S105, carrying out noise matrix prediction on the target navigation system based on a preset fuzzy inference system to obtain a second measurement noise variance matrix;
step S106, a target measurement noise variance matrix is obtained based on the first measurement noise variance matrix and the second measurement noise variance matrix;
step S107, performing parameter estimation on the target navigation system based on the target measurement noise variance array to obtain state estimation parameters;
step S108, feedback correction is carried out on the target navigation system based on the state estimation parameters.
Step S101 to step S108 illustrated in the embodiment of the present application are performed by acquiring status data and measurement data of the target navigation system; constructing a state equation based on the state data, and constructing a measurement equation based on the measurement data; carrying out state prediction on the target navigation system to obtain predicted state data; performing noise matrix prediction on the target navigation system based on a preset Kalman filtering algorithm to obtain a first measurement noise variance matrix; performing noise matrix prediction on the target navigation system based on a preset fuzzy inference system to obtain a second measurement noise variance matrix; obtaining a target measurement noise variance matrix based on the first measurement noise variance matrix and the second measurement noise variance matrix; performing parameter estimation on the target navigation system based on the target measurement noise variance array to obtain state estimation parameters; and performing feedback correction on the target navigation system based on the state estimation parameters. According to the method, the measuring noise variance matrix based on the Kalman filtering algorithm prediction and the measuring noise variance matrix based on the fuzzy inference system prediction can be combined to realize real-time estimation of the target measuring noise variance matrix, so that parameter estimation can be performed on the target navigation system more accurately and in real time, information fusion and error adjustment of the target navigation system are realized, instantaneity and accuracy of system noise statistical characteristic estimation can be better improved, estimation accuracy of Kalman filtering on state quantity is improved, measurement accuracy of the combined navigation system is further improved, and reliability of the combined navigation system in different environments is guaranteed.
In step S101 of some embodiments, the target navigation system is set as a navigation system combining inertial navigation and satellite navigation according to the characteristics of the combined navigation system, and the position error [ δl δv δh of the target navigation system is selected]Misalignment angle error [ phi ] E Φ N Φ U ]Speed error [ delta V E δV N δV U ]Slowly varying drift [ epsilon ] of gyroscope rx ε ry ε rz ]Accelerometer slow-change driftAs state quantities of the target navigation system, these state quantities are integrated into state data. Wherein the state data X may represent the following, where T represents a transpose operation on the state quantity.
Further, the difference between the inertial navigation speed and the position and the satellite navigation speed and the position is selected as the measurement, and the measurement is integrated into the measurement data of the target navigation system, wherein the measurement data Z can be expressed as follows.
Wherein, the liquid crystal display device comprises a liquid crystal display device,east, north, and sky speeds, respectively, of inertial navigation, < >>The east direction, the north direction and the sky direction of satellite navigation are respectively the speed L INS ,λ INS ,h INS Longitude, latitude, altitude, L of inertial navigation respectively G ,λ G ,h G The longitude, latitude and altitude of satellite navigation respectively.
In step S102 of some embodiments, when a state equation is constructed based on the state data and a measurement equation is constructed based on the measurement data, a discretized state equation is established based on the state data, which may be expressed as shown in equation (1). Likewise, a discretized measurement equation is established based on the measurement data, which can be expressed as shown in equation (2).
X k ＝Φ k,k-1 X k-1 +Γ k,k-1 W k-1 Formula (1)
Z k ＝H k X k +V k Formula (2)
Wherein X is k Is the state data of the target navigation system at the moment k, phi k,k-1 State transition matrix W for target navigation system k-1 System noise sequence Γ for a target navigation system at time k-1 k, Driving matrix for noise of target navigation system, Z k Is the measurement data of the target navigation system at the moment k, H k For the measurement matrix at time k, V k The noise sequence is measured at the moment k; w (W) k-1 And V k The mean and variance of the system noise sequence and the measurement noise sequence of the target navigation system are respectively as follows: e [ W ] k ]＝0,E[V k ]＝0,/>Wherein Q is k R is a system noise variance matrix k For measuring the noise variance matrix, otherwise known as measuring the noise variance matrix, delta kj Is a delta function, i.e., a dirac function.
In step S103 of some embodiments, the state prediction is performed on the target navigation system to obtain predicted state dataThe specific procedure for updating the state one-step predictor can be expressed as shown in formula (3).
Wherein, the liquid crystal display device comprises a liquid crystal display device,phi is the predicted state data at time k k,k-1 For a state transition matrix of the target navigation system,is the predicted state data at time k-1.
Referring to fig. 2, in some embodiments, step S104 may include, but is not limited to, steps S201 to S202:
Step S201, calculating an innovation sequence of a target navigation system based on the prediction state data and the measurement equation;
step S202, noise matrix prediction is performed based on a Kalman filtering algorithm, an innovation sequence and a preset weighting coefficient, and a first measurement noise variance matrix is obtained.
In the embodiment, because of the relatively stable system noise statistics characteristic in the integrated navigation system, in order to reduce the calculation amount, the embodiment of the application mainly carries out self-adaptive adjustment on measured noise, is easy for engineering realization, simplifies the measuring noise variance matrix estimation part of the target navigation system in the Sage-Husa self-adaptive Kalman filtering algorithm, carries out noise matrix prediction by utilizing the simplified Sage-Husa self-adaptive Kalman filtering algorithm, and obtains a first measuring noise variance matrixThe simplified Sage-Husa adaptive Kalman filtering algorithm is the Kalman filtering algorithm claimed in the step S104.
Specifically, first, the data Z is measured at the time of k k And predicting the state value in one stepA new sequence and a first measurement noise variance matrix are calculated. Wherein the process of calculating the innovation sequence based on the prediction state data and the measurement equation can be expressed as shown in formula (4).
Wherein Z is k Is the measurement data at time k,is one-step predictive state value, H k Is the measurement matrix at the moment k.
Further, a weighting coefficient d is calculated k The process of calculating the weighting coefficient may be specifically expressed as shown in formula (5).
Wherein b is a constant weighting factor, and the value range is 0 to 1.
Further, noise matrix prediction is performed based on a Kalman filtering algorithm, an innovation sequence and a preset weighting coefficient, and a first measurement noise variance matrix is obtainedThe process of (2) may be expressed as shown in equation (6).
Wherein d k The weight coefficient is used to determine the weight coefficient,measuring noise variance matrix for target at k-1 moment, r k For the innovation sequence at time k, H k To measure matrix, P k,k-1 Is an error variance matrix.
The step S201 to the step S202 can be used for conveniently predicting the noise matrix by utilizing the simplified Sage-Husa self-adaptive Kalman filtering algorithm to obtain a first measurement noise variance matrix. Compared with the traditional Kalman filtering algorithm, the Kalman filtering algorithm in the method can effectively reduce dependence on priori knowledge of the statistical characteristics of the system noise, solves the problems that the measurement noise variance matrix estimation in the existing adaptive Kalman filtering algorithm is poor in instantaneity and is easily influenced by abnormal values, the adaptive capacity is gradually weakened after long-time filtering, the instantaneity and the accuracy of the statistical characteristics of the system noise are improved, the estimation accuracy of the Kalman filtering on state quantity is also improved, and therefore the measurement accuracy of the integrated navigation system is improved, and the working reliability of the integrated navigation system under different environments is guaranteed. In addition, the integrated navigation method of the embodiment of the application adopts a simplified Sage-Husa self-adaptive Kalman filtering algorithm, so that the calculated amount can be effectively reduced, and engineering realization is facilitated.
Referring to fig. 3, in some embodiments, step S105 may include, but is not limited to, steps S301 to S302:
step S301, a fuzzy controller based on a fuzzy reasoning system predicts a noise regulating factor of a target navigation system to obtain a predicted regulating factor;
step S302, weighting the preset reference measurement noise variance matrix based on the prediction adjustment factor to obtain a second measurement noise variance matrix.
In step S301 of some embodiments, the dilution of precision and the maximum carrier signal to noise ratio of the target navigation system are first obtained. And then carrying out blurring processing on the precision attenuation factor to obtain first membership data corresponding to the precision attenuation factor, and carrying out blurring processing on the maximum carrier signal-to-noise ratio to obtain second membership data corresponding to the maximum carrier signal-to-noise ratio. Further, working state analysis is carried out on the target navigation system based on a preset fuzzy rule, first membership data and second membership data of the fuzzy controller, and the current working state of the target navigation system is obtained; and finally, obtaining a predicted regulating factor beta based on the current working state and a preset reference regulating factor.
In step S302 of some embodiments, a predetermined reference measurement noise variance matrix R is obtained b The reference measurement noise variance matrix is pre-established according to measurement noise statistical characteristics and can be obtained according to the noise statistical characteristics under normal satellite navigation operation. Therefore, the fuzzy logic estimation can be conveniently obtained by weighting the preset reference measurement noise variance matrix based on the prediction adjusting factorSecond measurement noise variance matrix R of (2) l Wherein R is 1 ＝β*R b 。
The accuracy attenuation factor and the maximum carrier signal-to-noise ratio can be utilized to fully reflect the error of satellite navigation through the steps S301 to S302, and the accuracy and the rationality of fuzzy reasoning can be effectively improved by selecting the accuracy attenuation factor and the maximum carrier signal-to-noise ratio which can fully reflect the working state of the satellite navigation receiver as the input of the fuzzy controller of the fuzzy reasoning system.
Referring to fig. 4, in some embodiments, step S301 may include, but is not limited to, steps S401 to S405:
step S401, obtaining the precision attenuation factor and the maximum carrier signal-to-noise ratio of a target navigation system;
step S402, blurring processing is carried out on the precision attenuation factors, and first membership data corresponding to the precision attenuation factors are obtained;
step S403, blurring processing is carried out on the maximum carrier signal-to-noise ratio, and second membership data corresponding to the maximum carrier signal-to-noise ratio is obtained;
Step S404, analyzing the working state of the target navigation system based on the preset fuzzy rule, the first membership data and the second membership data of the fuzzy controller to obtain the current working state of the target navigation system;
step S405, obtaining a predicted adjustment factor based on the current working state and a preset reference adjustment factor.
In the specific implementation of this embodiment, first, the blurring process is performed on the precision attenuation factor DOP and the maximum carrier signal-to-noise ratio SNR output by the satellite navigation receiver of the target navigation system during the navigation process.
In step S402 of some embodiments, when the precision attenuation factor (DOP value) is subjected to blurring processing to obtain the first membership data corresponding to the precision attenuation factor, the precision attenuation factor is classified into good (H 1 ) Medium (M) 1 ) Difference (L) 1 ) The first membership state of the three fuzzy sets, namely the precision decay factors, comprises one of good, medium and bad, wherein eachMembership functions corresponding to fuzzy sets may be expressed asW M1 、W L1 。
DOP value was good (H 1 ) Membership function of (2) is
DOP value is medium (M 1 ) Membership function of (2):
DOP value is a difference (L 1 ) Membership function of (2):
where a1, a2, a3 are constants set according to actual conditions.
In step S403 of some embodiments, when the maximum carrier signal-to-noise ratio (SNR) is subjected to blurring processing to obtain the second membership data corresponding to the maximum carrier signal-to-noise ratio, the maximum carrier signal-to-noise ratio is classified as good according to the magnitude of the value (H 2 ) Medium (M) 2 ) Difference (L) 2 ) The third fuzzy set, namely the second membership state of the maximum carrier signal to noise ratio, comprises one of good, medium and bad, wherein the membership function corresponding to each fuzzy set can be expressed asW M2 、W L2 。
The carrier signal to noise ratio is good (H 2 ) Membership function of (2):
the carrier signal to noise ratio is medium (M 2 ) Membership function of (2):
the carrier signal to noise ratio is poor (L 2 ) Membership function of (2):
wherein b1, b2, b3 are constants set according to actual conditions.
In step S404 of some embodiments, a preset fuzzy rule is included in the fuzzy controller, where the preset fuzzy rule includes:
if the first membership state is good, the current working state is the same as the second membership state;
if the first membership state is medium, the current working state is the same as the second membership state;
if the first membership state is poor, the current working state is poor.
Specifically, the current operating state (ResCond) of the target navigation system includes good (H 3 ) Medium (M) 3 ) Difference (L) 3 ). When working state analysis is performed on the target navigation system based on a preset fuzzy rule, first membership data and second membership data of the fuzzy controller, the specific analysis process comprises the following steps:
case 1: if DOP epsilon H 1 And SNR ε H 2 ResCond e H 3 。
Case 2: if DOP epsilon H 1 And SNR εM 2 ResCond e M 3 。
Case 3: if DOP epsilon H 1 And SNR ε L 2 ResCond e L 3 。
Case 4: if DOP epsilon M 1 And SNR ε H 2 ResCond e H 3 。
Case 5: if DOP epsilon M 1 And SNR εM 2 ResCond e M 3 。
Case 6: if DOP epsilon M 1 And SNR ε L 2 ResCond e L 3 。
Case 7: if DOP epsilon L 1 And SNR ε H 2 ResCond e L 3 。
Case 8: if DOP epsilon L 1 And SNR εM 2 ResCond e L 3 。
Case 9: if DOP epsilon L 1 And SNR ε L 2 ResCond e L 3 。
In step S405 of some embodiments, after determining the current working state of satellite navigation in the target navigation system according to the above-mentioned fuzzy rule, the minimum value of two membership functions of DOM value and carrier signal-to-noise ratio is taken as the weight value W of each working state i . Further, a preset measuring noise statistical characteristic regulating factor under each working state of satellite navigation is obtained, namely a reference regulating factor under each working state. For example, the reference adjustment factors corresponding to the current working states of good, medium and poor respectively are beta 1 (H 3 )，β 2 (M 3 )，β 3 (L 3 ). And finally, determining a prediction regulating factor beta finally output by the fuzzy controller by adopting a weighted average judgment method. The calculation process of the prediction regulating factor is specifically shown in formula (7).
Through the steps S401 to S405, the measurement noise variance matrix can be predicted based on the fuzzy reasoning mode, so that the estimation accuracy and the instantaneity of the measurement noise variance matrix are effectively improved.
Referring to fig. 5, in some embodiments, step S106 may include, but is not limited to, steps S501 to S502:
step S501, obtaining preset weight data;
step S502, weighting the first measurement noise variance matrix and the second measurement noise variance matrix based on the weight data to obtain a target measurement noise variance matrix.
In step S501 of some embodiments, the preset weight data may be set according to the actual requirement, without limitation. For example, if the weight of the first measurement noise variance matrix is set to be a, the weight of the second measurement noise variance matrix is set to be 1-a, wherein the value of a ranges from 0 to 1.
In step S502 of some embodiments, weighting the first and second measurement noise variance arrays based on the weight data to obtain a target measurement noise variance array When this is the case, the specific process can be expressed as shown in formula (8).
Wherein, the liquid crystal display device comprises a liquid crystal display device,measuring a noise variance matrix for a target at time k, < >>For the first measuring noise variance matrix at k moment, R 1 And the second measurement noise variance matrix at the moment k.
The step S501 to the step S502 can be used for conveniently combining the first measuring noise variance matrix predicted by using the Kalman filtering algorithm and the second measuring noise variance matrix predicted by using the fuzzy reasoning system to estimate the target measuring noise variance matrix, so that the online real-time estimation of the target measuring noise variance matrix can be realized, the adjustment timeliness of the target measuring noise variance matrix can be improved, the problem that the measuring precision of the target navigation system is reduced or even diverged when the external environment is changed can be effectively solved, and the anti-interference performance of the target navigation system is improved. In addition, the integrated navigation method of the embodiment of the application can more accurately realize the estimation of the measurement noise by introducing the working state parameters of satellite navigation, and can adjust the measurement noise variance matrix on line in real time by combining two self-adaptive algorithms (namely a Kalman filtering algorithm and fuzzy reasoning), so that the reliability of the integrated navigation output information is improved when the satellite navigation is interfered.
Referring to fig. 6, in some embodiments, step S107 includes, but is not limited to, steps S601 to S602:
step S601, performing filtering gain calculation based on a target measurement noise variance matrix to obtain Kalman filtering gain;
step S602, performing state estimation on the target navigation system based on the predicted state data and the Kalman filtering gain to obtain state estimation parameters.
In the embodiment, the error variance matrix P of the k-1 moment is predicted first k,k-1 The prediction process may be expressed as shown in equation (9).
Further, filtering gain calculation is carried out based on the target measurement noise variance matrix to obtain Kalman filtering gain K at the moment K k The calculation process may be expressed as shown in equation (10).
Further, the state estimation is performed on the target navigation system based on the predicted state data and the Kalman filtering gain to obtain a state estimation parameter at the k moment, and the calculation process can be expressed as shown in a formula (11).
Finally, error variance matrix estimation is carried out on the target navigation system based on Kalman filtering gain and state estimation parameters, and a prediction error variance matrix P at the moment K is obtained k . The calculation process may be expressed as shown in equation (12).
P k ＝(I-K k H k )P k, Formula (11)
The step S601 to the step S602 can be used for conveniently carrying out real-time online estimation on the state estimation parameters, and estimation accuracy and instantaneity can be improved.
In step S108 of some embodiments, when the feedback correction is performed on the target navigation system based on the state estimation parameter, the above steps S102 to S107 may be repeated continuously, and the state estimation parameter may be estimated online in real time. After higher-precision estimation of the inertial navigation error is completed through a Kalman filtering algorithm, the navigation information of the inertial navigation system in the combined navigation system is corrected by utilizing the error estimation parameters in a feedback correction mode, so that the fusion between the satellite navigation information and the inertial navigation information is realized. According to the method, the target measurement noise variance matrix is estimated and adjusted in real time, so that the target navigation system can be well adapted to different working environments, and the system measurement accuracy of the integrated navigation is effectively improved.
In a specific embodiment, simulation verification is performed on the combined navigation method according to the embodiment of the application through matlab, and measurement errors of navigation information of the combined navigation system when the combined navigation method, the Sage-Husa self-adaptive Kalman filtering algorithm and the traditional Kalman filtering algorithm are applied to data fusion are compared. In the simulation process, the zero bias instability of the gyroscope is set to be 100 degrees/h, the angle random walk is set to be 0.5 degrees/v Hz, the zero bias instability of the accelerometer is set to be 1mg, the speed random walk is set to be 1.5 mg/v Hz, and the satellite navigation error is randomly changed in the simulation process. The simulation results are shown in table 1. According to the simulation result, the combined navigation method provided by the application can be used for estimating and adjusting the measurement noise variance array in real time, so that the combined navigation system can be well adapted to different working environments, and the measurement accuracy of the system is effectively improved.
TABLE 1
Referring to fig. 7, an embodiment of the present application further provides an adaptive integrated navigation device, which may implement the integrated navigation method, where the device includes:
a data acquisition module 701, configured to acquire status data and measurement data of the target navigation system;
an equation construction module 702 for constructing a state equation based on the state data and a measurement equation based on the measurement data;
the state prediction module 703 is configured to perform state prediction on the target navigation system to obtain predicted state data;
the first matrix prediction module 704 is configured to perform noise matrix prediction on the target navigation system based on a preset kalman filtering algorithm, so as to obtain a first measurement noise variance matrix;
the second matrix prediction module 705 is configured to perform noise matrix prediction on the target navigation system based on a preset fuzzy inference system, so as to obtain a second measurement noise variance matrix;
a target matrix determining module 706, configured to obtain a target measurement noise variance matrix based on the first measurement noise variance matrix and the second measurement noise variance matrix;
the parameter estimation module 707 is configured to perform parameter estimation on the target navigation system based on the target measurement noise variance array, so as to obtain a state estimation parameter;
A feedback correction module 708 for performing feedback correction on the target navigation system based on the state estimation parameters.
The specific implementation of the adaptive integrated navigation device is substantially the same as the specific embodiment of the adaptive integrated navigation method described above, and will not be described herein.
The embodiment of the application also provides electronic equipment, which comprises: the system comprises a memory, a processor, a program stored on the memory and capable of running on the processor, and a data bus for realizing connection communication between the processor and the memory, wherein the program realizes the self-adaptive combined navigation method when being executed by the processor. The electronic equipment can be any intelligent terminal including a tablet personal computer, a vehicle-mounted computer and the like.
Referring to fig. 8, fig. 8 illustrates a hardware structure of an electronic device according to another embodiment, the electronic device includes:
the processor 801 may be implemented by a general-purpose CPU (central processing unit), a microprocessor, an application-specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solutions provided by the embodiments of the present application;
the memory 802 may be implemented in the form of read-only memory (ReadOnlyMemory, ROM), static storage, dynamic storage, or random access memory (RandomAccessMemory, RAM). The memory 802 may store an operating system and other application programs, and when the technical solutions provided in the embodiments of the present application are implemented by software or firmware, relevant program codes are stored in the memory 802, and the processor 801 invokes an adaptive integrated navigation method to execute the embodiments of the present application;
An input/output interface 803 for implementing information input and output;
the communication interface 804 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g., USB, network cable, etc.), or may implement communication in a wireless manner (e.g., mobile network, WIFI, bluetooth, etc.);
a bus 805 that transfers information between the various components of the device (e.g., the processor 801, the memory 802, the input/output interface 803, and the communication interface 804);
wherein the processor 801, the memory 802, the input/output interface 803, and the communication interface 804 implement communication connection between each other inside the device through a bus 805.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to realize the adaptive integrated navigation method.
The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
According to the self-adaptive integrated navigation method, the self-adaptive integrated navigation device, the electronic equipment and the computer readable storage medium, the target measurement noise variance array is estimated by combining the first measurement noise variance array predicted by using the Kalman filtering algorithm and the second measurement noise variance array predicted by using the fuzzy reasoning system, so that the online real-time estimation of the target measurement noise variance array can be realized, the adjustment timeliness of the target measurement noise variance array can be improved, the problem that the measurement accuracy of the target navigation system is reduced or even diverged when the external environment is changed can be effectively solved, and the anti-interference performance of the target navigation system is improved. In addition, the integrated navigation method of the embodiment of the application can more accurately realize the estimation of the measurement noise by introducing the working state parameters of satellite navigation, and can adjust the measurement noise variance matrix on line in real time by combining two self-adaptive algorithms (namely a Kalman filtering algorithm and fuzzy reasoning), so that the reliability of the integrated navigation output information is improved when the satellite navigation is interfered. In addition, compared with the traditional Kalman filtering algorithm, the Kalman filtering algorithm in the application can effectively reduce the dependence on priori knowledge of the statistical characteristics of the system noise, solves the problems that the measurement noise variance matrix estimation in the existing adaptive Kalman filtering algorithm is poor in instantaneity and is easily influenced by abnormal values, the adaptive capacity is gradually weakened after long-time filtering, and the like, improves the instantaneity and accuracy of the statistical characteristics estimation of the system noise, and also improves the estimation precision of the Kalman filtering on state quantity, so that the measurement precision of the integrated navigation system can be improved, and the working reliability of the integrated navigation system under different environments is ensured. The integrated navigation method provided by the embodiment of the application has the advantages that the Sage-Husa self-adaptive Kalman filtering algorithm is simplified, the calculated amount is effectively reduced, and engineering realization is facilitated. According to the integrated navigation method, the measurement noise variance arrays are estimated and adjusted in real time, so that the integrated navigation system can be well adapted to different working environments, and the measurement accuracy of the system is effectively improved.
The embodiments described in the embodiments of the present application are for more clearly describing the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application, and as those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.
It will be appreciated by those skilled in the art that the solutions shown in fig. 1-6 are not limiting to embodiments of the present application and may include more or fewer steps than shown, or may combine certain steps, or different steps.
The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the present application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in this application, "at least one" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the above-described division of units is merely a logical function division, and there may be another division manner in actual implementation, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including multiple instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing a program.
Preferred embodiments of the present application are described above with reference to the accompanying drawings, and thus do not limit the scope of the claims of the embodiments of the present application. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the embodiments of the present application shall fall within the scope of the claims of the embodiments of the present application.
Claims (10)
1. An adaptive integrated navigation method, the method comprising:
acquiring state data and measurement data of a target navigation system;
constructing a state equation based on the state data, and constructing a measurement equation based on the measurement data;
carrying out state prediction on the target navigation system to obtain predicted state data;
performing noise matrix prediction on the target navigation system based on a preset Kalman filtering algorithm to obtain a first measurement noise variance matrix;
performing noise matrix prediction on the target navigation system based on a preset fuzzy inference system to obtain a second measurement noise variance matrix;
obtaining a target measurement noise variance matrix based on the first measurement noise variance matrix and the second measurement noise variance matrix;
performing parameter estimation on the target navigation system based on the target measurement noise variance array to obtain state estimation parameters;
and carrying out feedback correction on the target navigation system based on the state estimation parameters.
2. The adaptive integrated navigation method according to claim 1, wherein the performing noise matrix prediction on the target navigation system based on a preset kalman filtering algorithm to obtain a first measurement noise variance matrix includes:
Calculating an innovation sequence of the target navigation system based on the predicted state data and the measurement equation;
and carrying out noise matrix prediction based on the Kalman filtering algorithm, the innovation sequence and a preset weighting coefficient to obtain the first measurement noise variance matrix.
3. The adaptive integrated navigation method according to claim 1, wherein the performing noise matrix prediction on the target navigation system based on the preset fuzzy inference system to obtain a second measurement noise variance matrix includes:
the fuzzy controller based on the fuzzy reasoning system predicts the noise regulating factor of the target navigation system to obtain a predicted regulating factor;
and weighting a preset reference measurement noise variance matrix based on the prediction adjusting factor to obtain the second measurement noise variance matrix.
4. The adaptive integrated navigation method of claim 3, wherein the predicting the noise adjustment factor for the target navigation system by the fuzzy controller based on the fuzzy inference system to obtain a predicted adjustment factor comprises:
acquiring an accuracy attenuation factor and a maximum carrier signal-to-noise ratio of the target navigation system;
Blurring processing is carried out on the precision attenuation factors to obtain first membership data corresponding to the precision attenuation factors;
blurring processing is carried out on the maximum carrier signal-to-noise ratio, and second membership data corresponding to the maximum carrier signal-to-noise ratio is obtained;
based on a preset fuzzy rule of the fuzzy controller, the first membership data and the second membership data, carrying out working state analysis on the target navigation system to obtain the current working state of the target navigation system;
and obtaining the prediction regulating factor based on the current working state and a preset reference regulating factor.
5. The adaptive integrated navigation method of claim 4, wherein the first membership data comprises a first membership state of the dilution of precision, the first membership state comprising one of good, medium, and bad, the second membership data comprises a second membership state of the maximum carrier signal to noise ratio, the second membership state comprising one of good, medium, and bad, the preset fuzzy rule comprising:
if the first membership state is good, the current working state is the same as the second membership state;
If the first membership state is medium, the current working state is the same as the second membership state;
and if the first membership state is poor, the current working state is poor.
6. The adaptive integrated navigation method according to claim 1, wherein the performing parameter estimation on the target navigation system based on the target measurement noise variance matrix to obtain a state estimation parameter includes:
performing filtering gain calculation based on the target measurement noise variance matrix to obtain the Kalman filtering gain;
and carrying out state estimation on the target navigation system based on the predicted state data and the Kalman filtering gain to obtain the state estimation parameters.
7. The adaptive integrated navigation method according to any one of claims 1 to 6, wherein the obtaining a target measurement noise variance matrix based on the first measurement noise variance matrix and the second measurement noise variance matrix includes:
acquiring preset weight data;
and weighting the first measurement noise variance matrix and the second measurement noise variance matrix based on the weight data to obtain the target measurement noise variance matrix.
8. An adaptive integrated navigation device, the device comprising:
the data acquisition module is used for acquiring state data and measurement data of the target navigation system;
the equation construction module is used for constructing a state equation based on the state data and constructing a measurement equation based on the measurement data;
the state prediction module is used for carrying out state prediction on the target navigation system to obtain predicted state data;
the first matrix prediction module is used for predicting the noise matrix of the target navigation system based on a preset Kalman filtering algorithm to obtain a first measurement noise variance matrix;
the second matrix prediction module is used for predicting the noise matrix of the target navigation system based on a preset fuzzy inference system to obtain a second measurement noise variance matrix;
the target matrix determining module is used for obtaining a target measurement noise variance matrix based on the first measurement noise variance matrix and the second measurement noise variance matrix;
the parameter estimation module is used for carrying out parameter estimation on the target navigation system based on the target measurement noise variance array to obtain state estimation parameters;
and the feedback correction module is used for carrying out feedback correction on the target navigation system based on the state estimation parameters.
9. An electronic device comprising a memory storing a computer program and a processor implementing the adaptive integrated navigation method of any one of claims 1 to 7 when the computer program is executed by the processor.
10. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the adaptive integrated navigation method of any one of claims 1 to 7.
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