CN113791432A - Integrated navigation positioning method for improving AIS positioning accuracy - Google Patents

Integrated navigation positioning method for improving AIS positioning accuracy Download PDF

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CN113791432A
CN113791432A CN202110950348.7A CN202110950348A CN113791432A CN 113791432 A CN113791432 A CN 113791432A CN 202110950348 A CN202110950348 A CN 202110950348A CN 113791432 A CN113791432 A CN 113791432A
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positioning
ship
noise
representing
filtering
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刘洪丹
孙宏放
张兰勇
李芃
刘岩
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Heu Qingdao Ship Science And Technology Co ltd
Harbin Engineering University
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Heu Qingdao Ship Science And Technology Co ltd
Harbin Engineering University
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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
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/33—Multimode operation in different systems which transmit time stamped messages, e.g. GPS/GLONASS
    • 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
    • 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
    • G01S19/396—Determining accuracy or reliability of position or pseudorange measurements
    • 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
    • G01S19/40—Correcting position, velocity or attitude
    • 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
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • 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
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
    • G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial

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  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention relates to a combined navigation positioning method for improving AIS positioning accuracy. According to the state vector of the integrated navigation system, a state model, an observation model and an error model are determined; based on a UKF filtering algorithm, a Sage-Husa suboptimal unbiased large posterior estimator is additionally arranged to estimate unknown noise of the system, so that state estimation errors are reduced; the ship position provided by the satellite navigation receiver, the ship course from the gyrocompass and the ship-to-ground speed signal provided by the log are digitized by the interface circuit and transmitted to the monitor and the information processing part, and then the processed ship attitude positioning information is transmitted to a shore base or a ship through the VHF transceiver, so as to realize AIS positioning optimization on the ship.

Description

Integrated navigation positioning method for improving AIS positioning accuracy
Technical Field
The invention relates to the technical field of marine vessel state measurement, in particular to a combined navigation positioning method for improving AIS positioning accuracy.
Background
In order to promote marine application of the Beidou satellite navigation system in China, the research on the application of the Beidou satellite navigation system in AIS is of great significance. The traditional AIS navigation information is mostly provided by a GPS, but the GPS is a navigation system developed by the military in the United states, risks are inevitably existed in the use process, and the occurrence of the Beidou system breaks the impasse of the monopoly of the technology. However, no matter the Beidou system or the GPS, the navigation equipment has positioning defects to a certain extent, so that the positioning precision cannot meet higher requirements, therefore, the invention researches the specific application of the BDS \ GPS combined positioning system in the AIS and performs combined navigation filtering of the AIS system based on self-adaptive unscented Kalman filtering, thereby reducing the positioning error of the ship AIS and improving the precision of the ship AIS system.
The ship combined navigation system organically combines various navigation equipment or equipment which are independently used on ships together through a computer, applies various filtering data processing technologies, exerts the advantages of a single instrument, makes up for deficiencies of the instruments, and improves the precision, reliability and automation degree of final information parameters such as course, position, speed, attitude, water level and the like to the maximum extent. Therefore, the integrated navigation system has become one of the important development directions in the current navigation technology, and has also become a hot spot in the development of shipping causes of various countries in the world.
The ship integrated navigation in the 21 st century is mainly researched in the directions of an anti-interference technology, artificial intelligence, a filtering algorithm with small calculated amount and more obvious effect, and the research results inevitably promote the development of an integrated navigation system and make greater contribution to human beings. In the integrated navigation system, the filtering technology is one of the important factors influencing the navigation positioning accuracy. Since many observation models of navigation systems have different degrees of nonlinearity, research on nonlinear filtering theory and method suitable for the combined navigation system becomes a hot topic. The Carvalho rate firstly introduces a Particle Filter (PF) method into the research of the optimal nonlinear filtering problem in a GPS/INS integrated navigation system, solves the filtering stability problem when the number of visible satellites of a GPS is suddenly changed, and obtains the estimation precision superior to the conventional EKF. Dmitriyev provides a nonlinear filtering method of posterior probability segmentation Gaussian approximation for the height uncertainty of prior information in the initial alignment process of an inertial navigation system. The Larry provides a parallel structure of a generalized Kalman filter based on a terrain random linearization technology aiming at high nonlinearity of an observation equation in a terrain assisted inertial navigation system.
Currently, many filtering algorithms have been proposed according to the features of the integrated navigation system itself. However, these algorithms are often only suitable for some special cases, and have great limitations. For example, high-order terms are abandoned in the nonlinear processing process of Extended Kalman Filtering (EKF), so that the method cannot be applied to filtering objects with high nonlinear degree, and the applicable objects of Unscented Kalman Filtering (UKF) must have more accurate mathematical models. Therefore, although the two filter algorithms are provided with the nonlinear processing link on the basis of the Kalman filter algorithm, the problem of limited filter range still exists,
disclosure of Invention
The invention solves the space coverage capability of AIS equipment, provides a combined navigation positioning method for improving AIS positioning accuracy, and provides the following technical scheme:
an integrated navigation positioning method for improving AIS positioning accuracy comprises the following steps:
step 1: determining a state model, an observation model and an error model according to the state vector of the integrated navigation system;
step 2: based on a UKF filtering algorithm, a Sage-Husa suboptimal unbiased large posterior estimator is additionally arranged to estimate unknown noise of the system, so that state estimation errors are reduced;
and step 3: the ship position provided by the satellite navigation receiver, the ship course from the gyrocompass and the ship-to-ground speed signal provided by the log are digitized by the interface circuit and transmitted to the monitor and the information processing part, and then the processed ship attitude positioning information is transmitted to a shore base or a ship through the VHF transceiver, so as to realize AIS positioning optimization on the ship.
Preferably, the state model determination process in step 1 specifically includes:
the state vector of the BDS \ GPS combined navigation specifically comprises the position, the speed and the acceleration of a receiver, clock difference parameters and clock drift parameters of a GPS and Beidou navigation system, wherein the position, the speed and the acceleration of the receiver are basically equivalent to the motion parameters of a ship in a navigation state by the following formula:
Figure BDA0003218198680000021
wherein,
Figure BDA0003218198680000022
respectively representing the three-position, the speed and the acceleration of the receiver carrier; c represents the speed of light;
Figure BDA0003218198680000023
respectively representing the clock error of the Beidou system and the GPS;
Figure BDA0003218198680000024
respectively representing the clock drifts of the Beidou system and the GPS;
and according to the mathematical motion model of the receiver carrier ship and the state relation of the system, expressing the state model of the system by the following formula:
Xk＝fk(Xk-1)+ΓkWk-1
wherein k represents the number of observation epochs; xk、Xk-1State vectors representing the kth and the k-1 th observation epoch, respectively; f. ofkRepresenting state vectorsXkAnd Xk-1The functional relationship of state transition between; gamma-shapedkRepresenting a noise driving matrix; wk-1Representing process noise.
Preferably, the observation model determination process in step 1 specifically includes:
the observation model of the combined system is mainly formed by combining pseudo-range single-point positioning models and Doppler single-point positioning models of two systems, and a pseudo-range single-point positioning equation is represented by the following formula:
ρ＝r+c[δts- rδt]+Iρ+Tρ+ερ
wherein, the rho pseudo range observed value; r represents the geometric distance between the satellite and the receiver; where δ ts、δtrRespectively representing clock differences of a satellite clock and a receiver clock relative to standard time of the satellite system; i isρ、TρTropospheric and ionospheric corrections are indicated separately; epsilonρRepresenting multipath observation noise, systematic errors;
the system of pseudorange single point location equations for the combined system is represented by:
Figure BDA0003218198680000031
wherein, 1 represents a GPS satellite, and 2 represents a Beidou satellite; delta t represents the time difference of the BDS \ GPS system;
from the doppler shift, that is, when the mobile station moves in a certain direction at a constant speed, the phase and frequency changes due to the propagation path difference, the kepler equation of the combined system is obtained, which is expressed by the following equation:
Figure BDA0003218198680000032
wherein,
Figure BDA0003218198680000033
receiver speed at time t;
Figure BDA0003218198680000034
is the satellite velocity at time t; r represents the true distance between the satellite and the receiver; delta RtThe frequency shift amount caused by phase change and code delay;
obtaining an observation model of the combined system according to the pseudo-range point location equation and the Doppler equation thereof, and expressing the observation model by the following formula:
Zk＝hk(Xk)+Vk
wherein Z iskRepresents an observation of the kth epoch; h iskAn observation Z describing the k epochkAnd a state variable XkFunctional relationship between; vkRepresenting the same observed noise.
Preferably, the error model determination process in step 1 specifically includes:
when observing the Beidou satellite n in a certain epochBGPS satellite nGBecause the pseudo-range measurement does not have ambiguity unknown numbers, the unknown parameters obtained by double differencing the pseudo-range positioning equations of the GPS and the Beidou only comprise 3 three-dimensional coordinate increments;
according to the principle of indirect adjustment, namely when determining the most probable value of a plurality of unknown quantities, selecting independent quantities without any relation as the unknown quantities, forming a function expression relation of the expression measurement of the unknown quantities, listing an error equation, and obtaining the most probable value of the unknown quantities according to a least square method; and (3) combining the two pseudorange positioning equations, linearizing the error model, and expressing the error model by the following formula:
V＝AX-L
wherein, the size of the unknown parameter array X is as follows: 3 x 1; the constant matrix L is of size: [ (n _ B-1) + (n _ G-1)]X 1; the size of the coefficient array A is as follows: [ (n)B-1)+(nG-1)]X 3; and finally, solving by using a least square method.
Preferably, the step 2 specifically comprises:
step 2.1: estimating unknown noise of a system by a secondary optimum unbiased maximum posterior estimator of Sage-Husa, combining a recursive estimation form with a UKF filtering algorithm, estimating in real time in the filtering process and correcting the statistical characteristics of the system noise so as to reduce state estimation errors, and expressing the system noise by the following formula:
Figure BDA0003218198680000041
wherein x iskRepresenting a system state vector at the k moment; f and h represent non-linear functions; w is ak-1Represents zero mean and variance of QkThe white noise sequence of (a); v. ofk-1Then zero mean gaussian white noise is represented;
noise q by estimatorkAnd QkAnd (3) estimation:
Figure BDA0003218198680000042
Figure BDA0003218198680000043
by filtering the estimates
Figure BDA0003218198680000044
Or forecast estimates
Figure BDA0003218198680000045
Approximation instead of smoothed estimates
Figure BDA0003218198680000046
Obtaining a suboptimal Sage-Husa estimator, and combining the suboptimal Sage-Husa estimator with UKF filtering to obtain an improved adaptive filter, q, suitable for a nonlinear system with time-varying noisekAnd QkRecursion is performed by the band time varying noise estimator by:
Figure BDA0003218198680000047
Figure BDA0003218198680000048
wherein d isk-1＝(1-b)(1-bk) Denotes a forgetting factor, j-0, 1<b<0.99, b can limit the memory length of filtering, and when the statistical change of noise is fast, b is properly larger; on the contrary, b should be properly small;
step 2.2: considering that divergence exists in the filtering process, the filtering divergence trend is judged by adopting a method based on covariance matching criterion, and the method is represented by the following formula:
Figure BDA0003218198680000049
wherein S represents a previously predetermined variable coefficient;
Figure BDA00032181986800000410
representing residual sequences, i.e.
Figure BDA00032181986800000411
If the formula is not satisfied, then pair Pk|k-1The correction is performed by the following formula:
Figure BDA00032181986800000412
wherein λ iskRepresents an adaptive weighting coefficient, determined by:
Figure BDA00032181986800000413
step 2.3: based on the realization of a suboptimal estimator, the self-adaptive UKF filtering algorithm:
selecting initial value of filter
Figure BDA0003218198680000051
Figure BDA0003218198680000052
Figure BDA0003218198680000053
Wherein,
Figure BDA0003218198680000054
representing an initial state variable estimation value; e (x)0) Indicating a state expectation; p0Representing an initial value of a variance matrix; q. q.s0、Q0Table noise variance initial value. In the above-mentioned formulae, the compound of formula,
Figure BDA0003218198680000055
representing an initial state variable estimation value; e (x)0) Indicating a state expectation; p0Representing an initial value of a variance matrix; q. q.s0、Q0A table noise initial value;
for a given mean value of model values
Figure BDA0003218198680000056
Sum variance Pk-1|k-1And solving a one-step prediction model value and an augmented sample point by using UT (user-terminal) transformation, wherein the prediction covariance is as follows:
Figure BDA0003218198680000057
making a divergence judgment of
Figure BDA0003218198680000058
]Judging whether the divergence occurs; if yes, indicating that the divergence is not generated, and entering the next step; if the formula is not true, it indicates divergence, for Pk|k-1Correcting;
calculating Pxy,PyyAnd a gain matrix KkThe calculated filter value is represented by the following formula:
Figure BDA0003218198680000059
Figure BDA00032181986800000510
and (3) estimating the statistical characteristic of the system noise by adopting filtering value recursion:
Figure BDA00032181986800000511
Figure BDA00032181986800000512
wherein phikRepresenting a state transition matrix; w is akRepresenting a noise sequence; pk|k、Pk-1|k-1Representing covariance matrices at different times;
Figure BDA00032181986800000513
estimating states at different moments;
and when the statistical characteristic of the system noise is accurate, finishing filtering and outputting a filtering value, otherwise, re-filtering until the statistical characteristic of the system is accurate.
Preferably, the step 3 specifically comprises:
when the Beidou receiver and the GPS receiver are based on the same ship carrier, the combined output is realized by utilizing two navigation systems to respectively carry out independent navigation positioning on the ship carrier, filtering processing is firstly carried out on the two groups of navigation information on the basis of comparison, then fusion processing is carried out, finally the navigation data which are positioned independently are compared, and more accurate navigation information is output to an AIS interface circuit for information processing and monitoring, so that the combined positioning is realized; in the filtering stage, an improved self-adaptive UKF filtering algorithm is selected to carry out filtering processing on the positioning signals, so that interference signals in the position signals are effectively filtered, and the positioning precision of the ship is improved.
The invention has the following beneficial effects:
the invention solves the space coverage capability of the AIS equipment. Because a plurality of navigation systems (GPS and Beidou) are overlapped in the space, the space coverage range is enlarged, compared with a single satellite navigation system, the space coverage range has obvious advantages, and the accuracy and the effectiveness of the AIS positioning data are improved.
The method has the advantages that the operation such as detection, judgment, reasoning and the like is carried out by adopting the information data of multiple systems, the measurement error is reduced, meanwhile, the improved adaptive unscented Kalman filtering algorithm is adopted to carry out filtering simulation on the ship longitude and latitude data obtained based on the GPS and the BDS, and then the two groups of filtering numerical values are subjected to fusion processing, so that the positioning accuracy of the AIS equipment is improved.
Due to the fact that the multi-system combination technology is used, more data can be obtained for positioning calculation, when one system cannot work effectively due to environmental changes, other systems reduce influences, and therefore the AIS system has strong adaptability to severe environments and improves robustness of the AIS system.
Drawings
FIG. 1 is a self-adaptive UKF flow;
FIG. 2 is an AIS device based on integrated navigational positioning;
FIG. 3 is a combined navigation output correction;
FIG. 4 is a diagram of a ship information transceiving platform;
FIG. 5 is a longitude data fusion;
FIG. 6 is a latitude data fusion.
Detailed Description
The present invention will be described in detail with reference to specific examples.
The first embodiment is as follows:
according to the AIS positioning method based on the ship combination navigation of the adaptive UKF, which is disclosed by the invention, the ship AIS combination system is positioned and analyzed according to the port ship position information, as shown in FIGS. 1 to 6. According to the combined navigation output correction mode, an improved self-adaptive unscented Kalman filtering algorithm is adopted to carry out filtering simulation on the ship longitude and latitude data obtained based on the GPS and the BDS, and then two groups of filtering numerical values are subjected to fusion processing, so that the ship AIS positioning information is output. The improved self-adaptive UKF algorithm is characterized in that a Sage-Husa suboptimal unbiased large posterior estimator is additionally arranged on the basis of a standard UKF to estimate unknown noise of a system, and real-time estimation is carried out and the statistical characteristic of the noise of the system is corrected in the filtering process by combining the recursive estimation form of the estimator with the UKF filtering algorithm so as to reduce the state estimation error. The method comprises the following specific implementation processes:
the invention provides a combined navigation positioning method for improving AIS positioning accuracy, which comprises the following steps:
an integrated navigation positioning method for improving AIS positioning accuracy comprises the following steps:
step 1: determining a state model, an observation model and an error model according to the state vector of the integrated navigation system;
the state model determination process in the step 1 specifically includes:
the state vector of the BDS \ GPS combined navigation specifically comprises the position, the speed and the acceleration of a receiver, clock difference parameters and clock drift parameters of a GPS and Beidou navigation system, wherein the position, the speed and the acceleration of the receiver are basically equivalent to the motion parameters of a ship in a navigation state by the following formula:
Figure BDA0003218198680000071
wherein,
Figure BDA0003218198680000072
respectively representing the three-position, the speed and the acceleration of the receiver carrier; c represents the speed of light;
Figure BDA0003218198680000073
respectively representing the clock error of the Beidou system and the GPS;
Figure BDA0003218198680000074
respectively representing the clock drifts of the Beidou system and the GPS;
and according to the mathematical motion model of the receiver carrier ship and the state relation of the system, expressing the state model of the system by the following formula:
Xk＝fk(Xk-1)+ΓkWk-1
wherein k represents the number of observation epochs; xk、Xk-1State vectors representing the kth and the k-1 th observation epoch, respectively; f. ofkRepresents a state vector XkAnd Xk-1The functional relationship of state transition between; gamma-shapedkRepresenting a noise driving matrix; wk-1Representing process noise.
The observation model determination process in the step 1 specifically comprises the following steps:
the observation model of the combined system is mainly formed by combining pseudo-range single-point positioning models and Doppler single-point positioning models of two systems, and a pseudo-range single-point positioning equation is represented by the following formula:
ρ＝r+c[δts- rδt]+Iρ+Tρ+ερ
wherein, the rho pseudo range observed value; r represents the geometric distance between the satellite and the receiver; where δ ts、δtrRespectively representing clock differences of a satellite clock and a receiver clock relative to standard time of the satellite system; i isρ、TρTropospheric and ionospheric corrections are indicated separately; epsilonρRepresenting multipath observation noise, systematic errors;
the system of pseudorange single point location equations for the combined system is represented by:
Figure BDA0003218198680000075
wherein, 1 represents a GPS satellite, and 2 represents a Beidou satellite; delta t represents the time difference of the BDS \ GPS system;
from the doppler shift, that is, when the mobile station moves in a certain direction at a constant speed, the phase and frequency changes due to the propagation path difference, the kepler equation of the combined system is obtained, which is expressed by the following equation:
Figure BDA0003218198680000081
wherein,
Figure BDA0003218198680000082
receiver speed at time t;
Figure BDA0003218198680000083
is the satellite velocity at time t; r represents the true distance between the satellite and the receiver; delta RtThe frequency shift amount caused by phase change and code delay;
obtaining an observation model of the combined system according to the pseudo-range point location equation and the Doppler equation thereof, and expressing the observation model by the following formula:
Zk＝hk(Xk)+Vk
wherein Z iskRepresents an observation of the kth epoch; h iskAn observation Z describing the k epochkAnd a state variable XkFunctional relationship between; vkRepresenting the same observed noise.
The error model determination process in the step 1 specifically includes:
when observing the Beidou satellite n in a certain epochBGPS satellite nGBecause the pseudo-range measurement does not have ambiguity unknown numbers, the unknown parameters obtained by double differencing the pseudo-range positioning equations of the GPS and the Beidou only comprise 3 three-dimensional coordinate increments;
according to the principle of indirect adjustment, namely when determining the most probable value of a plurality of unknown quantities, selecting independent quantities without any relation as the unknown quantities, forming a function expression relation of the expression measurement of the unknown quantities, listing an error equation, and obtaining the most probable value of the unknown quantities according to a least square method; and (3) combining the two pseudorange positioning equations, linearizing the error model, and expressing the error model by the following formula:
V＝AX-L
wherein, the size of the unknown parameter array X is as follows: 3 x 1; the constant matrix L is of size: [ (n _ B)-1)+(n_G-1)]X 1; the size of the coefficient array A is as follows: [ (n)B-1)+(nG-1)]X 3; and finally, solving by using a least square method.
Step 2: based on a UKF filtering algorithm, a Sage-Husa suboptimal unbiased large posterior estimator is additionally arranged to estimate unknown noise of the system, so that state estimation errors are reduced;
the step 2 specifically comprises the following steps:
step 2.1: estimating unknown noise of a system by a secondary optimum unbiased maximum posterior estimator of Sage-Husa, combining a recursive estimation form with a UKF filtering algorithm, estimating in real time in the filtering process and correcting the statistical characteristics of the system noise so as to reduce state estimation errors, and expressing the system noise by the following formula:
Figure BDA0003218198680000084
wherein x iskRepresenting a system state vector at the k moment; f and h represent non-linear functions; w is ak-1Represents zero mean and variance of QkThe white noise sequence of (a); v. ofk-1Then zero mean gaussian white noise is represented;
noise q by estimatorkAnd QkAnd (3) estimation:
Figure BDA0003218198680000091
Figure BDA0003218198680000092
by filtering the estimates
Figure BDA0003218198680000093
Or forecast estimates
Figure BDA0003218198680000094
Approximation instead of smoothed estimates
Figure BDA0003218198680000095
Obtaining a suboptimal Sage-Husa estimator, and combining the suboptimal Sage-Husa estimator with UKF filtering to obtain an improved adaptive filter, q, suitable for a nonlinear system with time-varying noisekAnd QkRecursion is performed by the band time varying noise estimator by:
Figure BDA0003218198680000096
Figure BDA0003218198680000097
wherein d isk-1＝(1-b)(1-bk) Denotes a forgetting factor, j-0, 1<b<0.99, b can limit the memory length of filtering, and when the statistical change of noise is fast, b is properly larger; on the contrary, b should be properly small;
step 2.2: considering that divergence exists in the filtering process, the filtering divergence trend is judged by adopting a method based on covariance matching criterion, and the method is represented by the following formula:
Figure BDA0003218198680000098
wherein S represents a previously predetermined variable coefficient;
Figure BDA0003218198680000099
representing residual sequences, i.e.
Figure BDA00032181986800000910
If the formula is not satisfied, then pair Pk|k-1The correction is performed by the following formula:
Figure BDA00032181986800000911
wherein λ iskRepresents an adaptive weighting coefficient, determined by:
Figure BDA00032181986800000912
step 2.3: based on the realization of a suboptimal estimator, the self-adaptive UKF filtering algorithm:
selecting initial value of filter
Figure BDA00032181986800000913
Figure BDA00032181986800000914
Figure BDA00032181986800000915
Wherein,
Figure BDA00032181986800000916
representing an initial state variable estimation value; e (x)0) Indicating a state expectation; p0Representing an initial value of a variance matrix; q. q.s0、Q0Table noise variance initial value. In the above-mentioned formulae, the compound of formula,
Figure BDA00032181986800000917
representing an initial state variable estimation value; e (x)0) Indicating a state expectation; p0Representing an initial value of a variance matrix; q. q.s0、Q0A table noise initial value;
for a given mean value of model values
Figure BDA0003218198680000101
Sum variance Pk-1|k-1And solving a one-step prediction model value and an augmented sample point by using UT (user-terminal) transformation, wherein the prediction covariance is as follows:
Figure BDA0003218198680000102
making a divergence judgment of
Figure BDA0003218198680000103
]Judging whether the divergence occurs; if yes, indicating that the divergence is not generated, and entering the next step; if the formula is not true, it indicates divergence, for Pk|k-1Correcting;
calculating Pxy,PyyAnd a gain matrix KkThe calculated filter value is represented by the following formula:
Figure BDA0003218198680000104
Figure BDA0003218198680000105
and (3) estimating the statistical characteristic of the system noise by adopting filtering value recursion:
Figure BDA0003218198680000106
Figure BDA0003218198680000107
wherein phikRepresenting a state transition matrix; w is akRepresenting a noise sequence; pk|k、Pk-1|k-1Representing covariance matrices at different times;
Figure BDA0003218198680000108
estimating states at different moments;
and when the statistical characteristic of the system noise is accurate, finishing filtering and outputting a filtering value, otherwise, re-filtering until the statistical characteristic of the system is accurate.
And step 3: the ship position provided by the satellite navigation receiver, the ship course from the gyrocompass and the ship-to-ground speed signal provided by the log are digitized by the interface circuit and transmitted to the monitor and the information processing part, and then the processed ship attitude positioning information is transmitted to a shore base or a ship through the VHF transceiver, so as to realize AIS positioning optimization on the ship.
Preferably, the step 3 specifically comprises:
when the Beidou receiver and the GPS receiver are based on the same ship carrier, the combined output is realized by utilizing two navigation systems to respectively carry out independent navigation positioning on the ship carrier, filtering processing is firstly carried out on the two groups of navigation information on the basis of comparison, then fusion processing is carried out, finally the navigation data which are positioned independently are compared, and more accurate navigation information is output to an AIS interface circuit for information processing and monitoring, so that the combined positioning is realized; in the filtering stage, an improved self-adaptive UKF filtering algorithm is selected to carry out filtering processing on the positioning signals, so that interference signals in the position signals are effectively filtered, and the positioning precision of the ship is improved.
During the navigation of the ship, signals such as ship position provided by a satellite navigation receiver, ship course from a gyrocompass, ship to ground speed provided by a log and the like are digitized by an interface circuit and then transmitted to a monitor and an information processing part, and then the processed ship attitude positioning information is transmitted to a shore base or other ships through a VHF transceiver, so that real-time grasping of ship dynamic data is realized, namely AIS positioning is realized on the ship, wherein the AIS composition is shown in figure 2.
Satellite navigation equipment in AIS generally is the GPS receiver, but considers that GPS can have the deviation when receiving factors such as environment influence, receives influence such as sky satellite state and used positioning device type difference, produces positioning effect difference shortcoming such as great, can directly lead to the boats and ships position information that AIS received to have very big error. Therefore, on the basis of a single GPS navigation system, the Beidou system can be applied to the AIS, and the position of the ship is positioned through the dual-system satellite navigation equipment, so that the AIS combined positioning of the ship is realized.
The AIS that is equipped with two navigation equipment when carrying out boats and ships location theory of operation is unanimous basically with traditional AIS, and the main difference lies in when boats and ships navigation in-process fixes a position it, not only includes the positional information that the GPS receiver provided, still includes the positional information that the big dipper receiver provided. Although the positioning of the two navigation systems is different, the difference value of the positioning information of the two navigation systems is not large compared with the real advancing track of the ship, the normal positioning of the ship cannot be interfered by the difference value of the positioning information of the two navigation systems, and the positioning information of the two navigation systems needs to be processed after the two receivers provide the position information, so that the influence of different navigation system receiver equipment on the positioning accuracy of the ship is reduced to a great extent. The AIS integrated navigation is realized according to the output correction of the BDS \ GPS integrated navigation, and is shown in figure 3.
Taking the Beidou system as an example, the whole AIS transceiving platform is specifically realized by acquiring ship attitude information including position, course and speed information during ship sailing through a ship sailing state acquisition cabinet, transmitting the ship attitude information to a ship monitoring PC and a transmitting unit through a field bus, and transmitting the ship attitude information to a Beidou commanding machine through a satellite to realize remote positioning guarantee, wherein accessories of the ship information transceiving platform are shown in figure 4. And selecting longitude and latitude data of the medium-sea oil ship berthing at the large-connection port for data processing. Firstly, two groups of data are filtered based on an improved adaptive filtering algorithm, and the specific simulation result is shown in the attached figures 5 and 6: and performing fusion processing on the two groups of filtered data, wherein the ratio of the standard deviation and the range of the statistics of the fused data and the filtered data is shown in tables 1 and 2.
TABLE 1 statistical comparison of longitude fusion data
Figure BDA0003218198680000111
TABLE 2 statistical comparison of latitude fusion data
Figure BDA0003218198680000121
The standard variance of the longitude and latitude fusion number is smaller than the data after difference filtering, and is closer to the standard variance and range of the GPS filtering number, so that the standard variance and range can be obtained; on the premise of error uncertainty, the fused data is more stable than the filtered data and is closer to the real longitude and latitude data.
Therefore, compared with a single navigation positioning system, the BDS \ GPS combined positioning system has more advantages in ship-out AIS positioning, namely better positioning accuracy and positioning stability. In addition, after the superiority of the combined positioning system in AIS positioning is verified, the problem of positioning accuracy thereof has to be considered.
For the ship AIS combined positioning system, the positioning accuracy improvement method of the combined navigation positioning system can be used, and AIS calculation efficiency, anti-interference performance and filtering effect of a combined positioning algorithm can be further improved. The method comprises the following specific steps:
(1) the AIS information resolving rate is determined by a main information processing module, namely a computer system. Therefore, the AIS computer control system can be continuously upgraded and improved to improve the computing efficiency, and the improvement mode is basically consistent with the development direction of computers under the condition of a certain degree, so that the AIS resolving speed in the future should be greatly broken through to the existing degree.
(2) In terms of system anti-interference, the specific environment, climate, ship type and specific task of the ship must be comprehensively considered. When a ship carries out navigation information interaction in the process of sailing, whether a carrier of the ship is provided with a stable information receiving and transmitting platform or not can greatly affect a receiver for receiving satellite signals and transmitting information to different ships or base stations, including AIS positioning of the ship. The stronger the anti-interference performance of the system is, the stronger the stability of the system is, so that the whole system has a better navigation and positioning environment, and the positioning accuracy is higher compared with a system with weaker anti-interference performance. Thus, enhancing AIS system immunity indirectly increases system accuracy.
(3) In the aspect of the combined positioning algorithm, the filtering effect of the combined positioning algorithm directly influences the positioning signal. The better the filtering effect, the less interference signals and noise signals in the system positioning signals, and the higher the positioning accuracy. Therefore, the specific analysis can be carried out according to the specific problems existing in the ship positioning on the basis of the traditional filtering algorithm,
based on above several aspects, can carry out the precision to boats and ships AIS combined positioning and improve to satisfy the boats and ships location of higher requirement.
The above is only a preferred embodiment of the integrated navigation positioning method for improving the AIS positioning accuracy, and the protection scope of the integrated navigation positioning method for improving the AIS positioning accuracy is not limited to the above embodiments, and all technical solutions belonging to the idea belong to the protection scope of the present invention. It should be noted that modifications and variations which do not depart from the gist of the invention will be those skilled in the art to which the invention pertains and which are intended to be within the scope of the invention.

Claims (6)

1. An integrated navigation positioning method for improving AIS positioning accuracy is characterized in that: the method comprises the following steps:
step 1: determining a state model, an observation model and an error model according to the state vector of the integrated navigation system;
step 2: based on a UKF filtering algorithm, a Sage-Husa suboptimal unbiased large posterior estimator is additionally arranged to estimate unknown noise of the system, so that state estimation errors are reduced;
and step 3: the ship position provided by the satellite navigation receiver, the ship course from the gyrocompass and the ship-to-ground speed signal provided by the log are digitized by the interface circuit and transmitted to the monitor and the information processing part, and then the processed ship attitude positioning information is transmitted to a shore base or a ship through the VHF transceiver, so as to realize AIS positioning optimization on the ship.
2. The integrated navigation positioning method for improving the AIS positioning accuracy as claimed in claim 1, wherein: the state model determination process in the step 1 specifically includes:
the state vector of the BDS \ GPS combined navigation specifically comprises the position, the speed and the acceleration of a receiver, clock difference parameters and clock drift parameters of a GPS and Beidou navigation system, wherein the position, the speed and the acceleration of the receiver are basically equivalent to the motion parameters of a ship in a navigation state by the following formula:
Figure FDA0003218198670000011
wherein,
Figure FDA0003218198670000012
respectively representing the three-position, the speed and the acceleration of the receiver carrier; c represents the speed of light;
Figure FDA0003218198670000013
respectively representing the clock error of the Beidou system and the GPS;
Figure FDA0003218198670000014
Figure FDA0003218198670000015
respectively representing the clock drifts of the Beidou system and the GPS;
and according to the mathematical motion model of the receiver carrier ship and the state relation of the system, expressing the state model of the system by the following formula:
Xk＝fk(Xk-1)+ΓkWk-1
wherein k represents the number of observation epochs; xk、Xk-1State vectors representing the kth and the k-1 th observation epoch, respectively; f. ofkRepresents a state vector XkAnd Xk-1The functional relationship of state transition between; gamma-shapedkRepresenting a noise driving matrix; wk-1Representing process noise.
3. The integrated navigation positioning method for improving the AIS positioning accuracy as claimed in claim 2, wherein: the observation model determination process in the step 1 specifically comprises the following steps:
the observation model of the combined system is mainly formed by combining pseudo-range single-point positioning models and Doppler single-point positioning models of two systems, and a pseudo-range single-point positioning equation is represented by the following formula:
ρ＝r+c[δts- rδt]+Iρ+Tρ+ερ
wherein, the rho pseudo range observed value; r represents the geometric distance between the satellite and the receiver; where δ ts、δtrRespectively representing clock differences of a satellite clock and a receiver clock relative to standard time of the satellite system; i isρ、TρTropospheric and ionospheric corrections are indicated separately; epsilonρRepresenting multipath observation noise, systematic errors;
the system of pseudorange single point location equations for the combined system is represented by:
Figure FDA0003218198670000021
wherein, 1 represents a GPS satellite, and 2 represents a Beidou satellite; delta t represents the time difference of the BDS \ GPS system;
from the doppler shift, that is, when the mobile station moves in a certain direction at a constant speed, the phase and frequency changes due to the propagation path difference, the kepler equation of the combined system is obtained, which is expressed by the following equation:
Figure FDA0003218198670000022
wherein,
Figure FDA0003218198670000023
receiver speed at time t;
Figure FDA0003218198670000024
is the satellite velocity at time t; r represents the true distance between the satellite and the receiver; delta RtThe frequency shift amount caused by phase change and code delay;
obtaining an observation model of the combined system according to the pseudo-range point location equation and the Doppler equation thereof, and expressing the observation model by the following formula:
Zk＝hk(Xk)+Vk
wherein Z iskRepresents an observation of the kth epoch; h iskAn observation Z describing the k epochkAnd a state variable XkFunctional relationship between; vkRepresenting the same observed noise.
4. The integrated navigation positioning method for improving the AIS positioning accuracy as claimed in claim 3, wherein: the error model determination process in the step 1 specifically includes:
when observing the Beidou satellite n in a certain epochBGPS satellite nGBecause the pseudo-range measurement does not have ambiguity unknown numbers, the unknown parameters obtained by double differencing the pseudo-range positioning equations of the GPS and the Beidou only comprise 3 three-dimensional coordinate increments;
according to the principle of indirect adjustment, namely when determining the most probable value of a plurality of unknown quantities, selecting independent quantities without any relation as the unknown quantities, forming a function expression relation of the expression measurement of the unknown quantities, listing an error equation, and obtaining the most probable value of the unknown quantities according to a least square method; and (3) combining the two pseudorange positioning equations, linearizing the error model, and expressing the error model by the following formula:
V＝AX-L
wherein, the size of the unknown parameter array X is as follows: 3 x 1; the constant matrix L is of size: [ (n _ B-1) + (n _ G-1)]X 1; the size of the coefficient array A is as follows: [ (n)B-1)+(nG-1)]X 3; and finally, solving by using a least square method.
5. The integrated navigation positioning method for improving AIS positioning accuracy according to claim 4, wherein: the step 2 specifically comprises the following steps:
step 2.1: estimating unknown noise of a system by a secondary optimum unbiased maximum posterior estimator of Sage-Husa, combining a recursive estimation form with a UKF filtering algorithm, estimating in real time in the filtering process and correcting the statistical characteristics of the system noise so as to reduce state estimation errors, and expressing the system noise by the following formula:
Figure FDA0003218198670000031
wherein x iskRepresenting a system state vector at the k moment; f and h represent non-linear functions; w is ak-1Represents zero mean and variance of QkThe white noise sequence of (a); v. ofk-1Then zero mean gaussian white noise is represented;
noise q by estimatorkAnd QkAnd (3) estimation:
Figure FDA0003218198670000041
Figure FDA0003218198670000042
by filtering the estimates
Figure FDA0003218198670000043
Or forecast estimates
Figure FDA0003218198670000044
Approximation instead of smoothed estimates
Figure FDA0003218198670000045
Obtaining a suboptimal Sage-Husa estimator, and combining the suboptimal Sage-Husa estimator with UKF filtering to obtain an improved adaptive filter, q, suitable for a nonlinear system with time-varying noisekAnd QkRecursion is performed by the band time varying noise estimator by:
Figure FDA0003218198670000046
Figure FDA0003218198670000047
wherein d isk-1＝(1-b)(1-bk) Denotes a forgetting factor, j-0, 1<b<0.99, b can limit the memory length of filtering, and when the statistical change of noise is fast, b is properly larger; on the contrary, b should be properly small;
step 2.2: considering that divergence exists in the filtering process, the filtering divergence trend is judged by adopting a method based on covariance matching criterion, and the method is represented by the following formula:
Figure FDA0003218198670000048
wherein S represents a previously predetermined variable coefficient;
Figure FDA0003218198670000049
representing residual sequences, i.e.
Figure FDA00032181986700000410
If the formula is not satisfied, then pair Pk|k-1The correction is performed by the following formula:
Figure FDA00032181986700000411
wherein λ iskRepresents an adaptive weighting coefficient, determined by:
Figure FDA00032181986700000412
step 2.3: based on the realization of a suboptimal estimator, the self-adaptive UKF filtering algorithm:
selecting initial value of filter
Figure FDA00032181986700000413
Figure FDA00032181986700000414
Figure FDA00032181986700000415
Wherein,
Figure FDA0003218198670000051
representing an initial state variable estimation value; e (x)0) Indicating a state expectation; p0Representing an initial value of a variance matrix; q. q.s0、Q0Table noise variance initial value;
Figure FDA0003218198670000052
representing an initial state variable estimation value; e (x)0) Indicating a state expectation; p0Representing an initial value of a variance matrix; q. q.s0、Q0A table noise initial value;
for a given mean value of model values
Figure FDA0003218198670000053
Sum variance Pk-1|k-1And solving a one-step prediction model value and an augmented sample point by using UT (user-terminal) transformation, wherein the prediction covariance is as follows:
Figure FDA0003218198670000054
making a divergence judgment of
Figure FDA0003218198670000055
Judging whether the divergence occurs; when the two conditions are satisfied, the divergence is not shown, and the next step is carried out(ii) a If the formula is not true, it indicates divergence, for Pk|k-1Correcting;
calculating Pxy,PyyAnd a gain matrix KkThe calculated filter value is represented by the following formula:
Figure FDA0003218198670000056
Figure FDA0003218198670000057
and (3) estimating the statistical characteristic of the system noise by adopting filtering value recursion:
Figure FDA0003218198670000058
Figure FDA0003218198670000059
wherein phikRepresenting a state transition matrix; w is akRepresenting a noise sequence; pk|k、Pk-1|k-1Representing covariance matrices at different times;
Figure FDA00032181986700000510
estimating states at different moments;
and when the statistical characteristic of the system noise is accurate, finishing filtering and outputting a filtering value, otherwise, re-filtering until the statistical characteristic of the system is accurate.
6. The integrated navigation positioning method for improving the AIS positioning accuracy as claimed in claim 5, wherein: the step 3 specifically comprises the following steps:
when the Beidou receiver and the GPS receiver are based on the same ship carrier, the combined output is realized by utilizing two navigation systems to respectively carry out independent navigation positioning on the ship carrier, filtering processing is firstly carried out on the two groups of navigation information on the basis of comparison, then fusion processing is carried out, finally the navigation data which are positioned independently are compared, and more accurate navigation information is output to an AIS interface circuit for information processing and monitoring, so that the combined positioning is realized; in the filtering stage, an improved self-adaptive UKF filtering algorithm is selected to carry out filtering processing on the positioning signals, so that interference signals in the position signals are effectively filtered, and the positioning precision of the ship is improved.
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