CN113551669A - Short baseline-based combined navigation positioning method and device - Google Patents

Short baseline-based combined navigation positioning method and device Download PDF

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Publication number
CN113551669A
CN113551669A CN202110841082.2A CN202110841082A CN113551669A CN 113551669 A CN113551669 A CN 113551669A CN 202110841082 A CN202110841082 A CN 202110841082A CN 113551669 A CN113551669 A CN 113551669A
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positioning
information
ins
carrier
value
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CN113551669B (en
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陈永秀
赵宪臣
李鸿伟
李建志
李美谚
宋红艳
方瑞银
蔡学福
刘文慧
周熙军
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Shandong Quanqing Communication Co ltd
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Shandong Quanqing Communication Co ltd
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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
    • 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
    • G01C21/20—Instruments for performing navigational calculations
    • 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
    • G01S19/49—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 inertial position system, e.g. loosely-coupled

Abstract

The invention provides a short baseline-based combined navigation positioning method and a short baseline-based combined navigation positioning device, wherein the method comprises the steps of acquiring a PPS signal of a multimode receiver and positioning in an RTK (real time kinematic) mode; taking the positioning information in the PPS signal as an initial value of the INS, and performing INS calculation to obtain INS information; and combining the PPS signal corresponding to the successful positioning and the INS information through Kalman filtering to obtain the optimal attitude and position information. The invention adopts a short base line, the length of the base line is only 320mm, the weight of the two receiving antennas is only 5kg, the equipment is small and exquisite, the movement is convenient, the GNSS and the INS are combined for navigation, the INS can be used for fast positioning when a carrier is shielded, a high-precision INS inertial element is adopted, the error of the inertial element is optimized by Kalman filtering, the positioning information of the GNSS can correct the deviation of the INS inertial element, and the positioning precision and the reliability of the system are ensured.

Description

Short baseline-based combined navigation positioning method and device
Technical Field
The invention relates to the technical field of inertial navigation, in particular to a short baseline-based integrated navigation positioning method and device.
Background
The existing Inertial Navigation System is mainly a GPS/INS (Global Positioning System, INS, Inertial Navigation System) integrated Navigation System. The system is a frameless system and comprises three gyroscopes, three accelerometers and a microcomputer, wherein the gyroscopes and the accelerometers are directly and fixedly connected to a carrier, and attitude data is obtained by calculating output values of the gyroscopes and the accelerometers through the microcomputer.
As shown in fig. 1, the accelerometer measures accelerations along three axes of a carrier coordinate system, performs coordinate transformation calculation after error compensation calculation, sums and integrates the accelerations and the acceleration of gravity to obtain a velocity, and obtains a position value of the carrier by double integration of the accelerations. The gyroscope measures angular velocities along three axes of a carrier coordinate system, coordinate system conversion and attitude matrix calculation are carried out after error compensation calculation, the attitude matrix changes continuously along with the change of time, and the attitude angle of the carrier can be determined from the attitude matrix. And finally, obtaining three direction angles, speeds and position information of the carrier.
Because the inertia element directly bears the environmental conditions of vibration, impact, temperature fluctuation and the like of the carrier, the output information of the inertia element can generate serious dynamic errors which are difficult to compensate, and the errors can be accumulated after long-term use, so that the errors are larger, and the precision of a navigation system is reduced. In order to ensure high positioning accuracy, an existing combined navigation system usually adopts a plurality of antennas, and a base line between antenna receivers is usually larger than 1m, and the weight of the antenna receivers is larger than 10kg, so that the problems of large volume, heavy weight, inconvenience in movement and the like of equipment are caused.
Disclosure of Invention
The invention provides a short baseline-based combined navigation positioning method and device, which are used for solving the problems of large size and inconvenient movement of the conventional equipment.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a short baseline-based integrated navigation positioning method in a first aspect, which comprises the following steps:
acquiring a PPS signal of the multimode receiver, and positioning in an RTK mode;
taking the positioning information in the PPS signal as an initial value of the INS, and performing INS calculation to obtain INS information;
and combining the PPS signal corresponding to the successful positioning and the INS information through Kalman filtering to obtain the optimal attitude and position information.
Furthermore, the multi-mode receiver comprises a front antenna receiver and a rear antenna receiver, the front antenna receiver sends the observation data to the rear antenna receiver, and the rear antenna receiver establishes a carrier double-difference equation to perform relative positioning by using the own observation data and difference information.
Further, the specific process of positioning by the RTK method is as follows:
assuming that the co-view satellite of the front antenna receiver and the rear antenna receiver has k +1 satellites, the linearized double difference equation of the carrier phase is xi ═ a η + BN + v, where: xi is in the middle of R2kIs a vector formed by a carrier phase double-difference observed value and a pseudo-range double-difference observed value, and eta belongs to R3Denotes the baseline vector, N ∈ ZkRepresenting the carrier phase double difference integer ambiguity vector, v ∈ R2kRepresenting observation noise, A ∈ R2k×3Representing the base-line constant coefficient matrix, B ∈ R2k×kRepresenting a fuzzy constant coefficient matrix;
solving the carrier phase double-difference equation if the coordinate of the front antenna receiver is known as etaaThen the post-antenna receiver coordinate ηbHas a difference estimation value of
Figure BDA0003177137500000021
Figure BDA0003177137500000022
Is a baseline coordinate vector.
Further, the INS information includes longitude and latitude, altitude information, and attitude angle of the carrier.
Further, the specific process of performing the INS calculation to obtain the INS information is as follows:
sum of angular velocities of a carrier relative to an inertial frame based on gyroscope output
Figure BDA0003177137500000023
Obtaining the angular velocity of the carrier coordinate system relative to the navigation coordinate system
Figure BDA0003177137500000024
Multiplying the angular velocity by sampling time to obtain an angular vector value of the current rotation, and carrying out normalization processing on the angular vector values in the three directions of the northeast to obtain an attitude angle;
the output value of the accelerometer is multiplied by sampling time to obtain a current velocity vector value, quaternion transformation is carried out on the velocity vector value to obtain the velocity of the carrier relative to a navigation coordinate system, the angular velocity and the velocity of the carrier relative to a geographic coordinate system are subjected to normalization processing through coordinate transformation, velocity increment caused by Coriolis force is corrected, the velocities in three directions of the northeast are calculated, the velocity increments of the two times of the previous velocity are calculated to obtain position increments, and the position increments are added with the previous position to obtain current longitude, latitude and altitude information.
Further, the specific process of obtaining the optimal attitude and position information through kalman filtering combination is as follows:
taking the positioning information as a positioning information state of a Kalman filtering estimation system, and establishing a Kalman filtering equation of INS information to obtain a state vector x and a state equation x (t) of the integrated navigation system;
taking carrier speed information and position information in the PPS signal as measurement to obtain a measurement equation of the integrated navigation system, and performing discretization processing to obtain an observation equation;
and performing Kalman filtering calculation on the random state variable X at the moment k to obtain the optimal estimation of the position information of the carrier at the moment k + 1.
Further, the specific process of performing kalman filter calculation is as follows:
obtaining a group of state acquisition points and corresponding weights by utilizing UT conversion;
obtaining a one-step prediction and a covariance matrix of the system state by weighting and summing the predicted values of the state acquisition points;
carrying out UT transformation on the predicted value in the one step to generate a new state acquisition point;
substituting the new state acquisition point into an observation equation to obtain a predicted observation value, and performing weighted summation on the observation value to obtain a predicted mean value and a predicted covariance;
and calculating a Kalman gain matrix, and updating a state and covariance, wherein the updated state is the optimal estimation of the position information of the carrier at the moment of k + 1.
Further, the successful determination of the positioning specifically includes:
the received PPS signal contains complete positioning information, and the number of satellites visible to the antenna receiver is greater than 4.
The invention provides a short baseline-based combined navigation positioning device, which comprises a combined navigation system formed by a multimode receiver and an INS (inertial navigation system), and a microcomputer, wherein the multimode receiver is used for acquiring a PPS (polyphenylene sulfide) signal, the microcomputer is used for positioning by utilizing the PPS signal in an RTK (real time kinematic) mode and calculating INS information, and the PPS signal corresponding to successful positioning and the INS information are combined through Kalman filtering to obtain optimal attitude and position information.
Further, the length of the base line of the multimode receiver is 320 mm.
The effect provided in the summary of the invention is only the effect of the embodiment, not all the effects of the invention, and one of the above technical solutions has the following advantages or beneficial effects:
the invention adopts a short base line, the length of the base line is only 320mm, the weight of the two receiving antennas is only 5kg, the equipment is small and exquisite, the movement is convenient, the GNSS and the INS are combined for navigation, the INS can be used for fast positioning when a carrier is shielded, a high-precision INS inertial element is adopted, the error of the inertial element is optimized by Kalman filtering, the positioning information of the GNSS can correct the deviation of the INS inertial element, and the positioning precision and the reliability of the system are ensured.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of a prior art combined inertial navigation system;
FIG. 2 is a schematic flow diagram of the process of the present invention;
FIG. 3 is a schematic view of the integrated navigation system of the present invention;
FIG. 4 is a schematic flow chart of an embodiment of the method of the present invention;
FIG. 5 is a comparison of simulated position values for the present invention;
FIG. 6 is a graph showing a comparison of the deviation values of the present invention;
FIG. 7 is a graph of a course angle output value of a conventional inertial navigation system;
FIG. 8 is a graph of the course angle output of the system of the present invention;
FIG. 9 is a graph of a pitch angle output value of a conventional inertial navigation system;
fig. 10 is a graph of pitch angle output for a system according to the present invention.
Detailed Description
In order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
As shown in fig. 2, the short baseline-based integrated navigation positioning method of the present invention includes the following steps:
s1, acquiring PPS signals of the multimode receiver, and positioning in an RTK mode;
s2, taking the positioning information in the PPS signal as an initial value of the INS, and performing INS calculation to obtain INS information;
and S3, combining the PPS signals corresponding to successful positioning and the INS information through Kalman filtering to obtain the optimal attitude and position information.
As shown in fig. 3 and 4, the microcomputer receives a PPS (pulse per second) signal through a GPS/BDS multimode receiver, acquires position information by using an RTK positioning mode after receiving the PPS signal, assigns the acquired speed, longitude, latitude and altitude information to initial values of INS information (including UTC time, longitude, latitude, altitude, heading angle and angular velocity in three directions of north and east), determines the number of satellites of a received satellite according to the strength of the satellite signal received by the receiver, determines that the satellite signal is valid when the signal strength is greater than 15dBm, determines that the positioning information is valid when the satellite visible number of the GPS/BDS is greater than 4, otherwise re-receives the PPS signal for re-positioning, and combines the GPS/BDS information and the calculated INS information through filtering to obtain optimal attitude and position information after successful positioning.
In step S1, the multimode receiver includes a front antenna receiver and a rear antenna receiver, the front antenna receiver sends the observation data to the rear antenna receiver, and the rear antenna receiver establishes a carrier double-difference equation to perform relative positioning by using its own observation data and difference information.
The specific process of positioning by the RTK mode is as follows:
assuming that the co-view satellite of the front antenna receiver and the rear antenna receiver has k +1 satellites, the linearized double difference equation of the carrier phase is xi ═ a η + BN + v, where: xi is in the middle of R2kIs a vector formed by a carrier phase double-difference observed value and a pseudo-range double-difference observed value, and eta belongs to R3Denotes the baseline vector, N ∈ ZkRepresenting the carrier phase double difference integer ambiguity vector, v ∈ R2kRepresenting observation noise, A ∈ R2k×3Representing the base-line constant coefficient matrix, B ∈ R2k×kRepresenting a fuzzy constant coefficient matrix;
solving the carrier phase double-difference equation if the coordinate of the front antenna receiver is known as etaaThen the post-antenna receiver coordinate ηbHas a difference estimation value of
Figure BDA0003177137500000061
Figure BDA0003177137500000062
Is a baseline coordinate vector.
The specific process of solving the carrier phase double-difference equation is as follows:
firstly, solving a floating solution of eta and N (the precision of the floating solution is between 0.1 and 0.5 meter) by using a least square method, namely
Figure BDA0003177137500000063
The covariance matrix of the floating-point solution is
Figure BDA0003177137500000064
Four quantities in the matrix represent the covariance between two of η and N.
Second, fix the ambiguity float solution (the precision of the fixed solution can reach 1 cm, the precision is improved), that is
Figure BDA0003177137500000065
A third step according to
Figure BDA0003177137500000066
Sum covariance
Figure BDA0003177137500000067
Finding the base line vector coordinates, i.e.
Figure BDA0003177137500000068
According to the RTK difference algorithm, if the front antenna coordinate is known to be etaaPosterior antenna coordinate ηbHas a difference estimation value of
Figure BDA0003177137500000069
In step S2, the INS information includes longitude and latitude, altitude information, and attitude angle of the carrier.
The specific process of performing the INS calculation to obtain the INS information is as follows:
s21, angular velocity sum of carrier relative to inertial coordinate system based on gyroscope output
Figure BDA0003177137500000071
Obtaining the angular velocity of the carrier coordinate system relative to the navigation coordinate system
Figure BDA0003177137500000072
Multiplying the angular velocity by sampling time to obtain an angular vector value of the current rotation, and carrying out normalization processing on the angular vector values in the three directions of the northeast to obtain an attitude angle;
and S22, multiplying the output value of the accelerometer by the sampling time to obtain a current velocity vector value, carrying out quaternion transformation on the velocity vector value to obtain the velocity of the carrier relative to a navigation coordinate system, carrying out coordinate transformation on the angular velocity and the velocity of the carrier relative to a geographic coordinate system, carrying out normalization processing on the angular velocity and the velocity of the carrier relative to the geographic coordinate system, correcting the velocity increment caused by Coriolis force, calculating the velocities in three directions of the northeast and the north, carrying out integral calculation on the velocities in the two times to obtain a position increment, and adding the position increment and the previous position to obtain current longitude and latitude and altitude information.
The specific implementation process of step S21 is as follows:
obtaining output value of gyroscope
Figure BDA0003177137500000073
Namely the angular velocity of the carrier relative to an inertial coordinate system, and the angular velocity of the earth rotation under the carrier navigation coordinate system is obtained according to the latitude obtained by GPS positioning
Figure BDA0003177137500000074
Figure BDA0003177137500000075
Figure BDA0003177137500000076
Wherein ω isieThe earth rotation angular velocity at the position of the equator, and lambda is the latitude of the carrier;
position rate
Figure BDA0003177137500000077
The earth curvature radius R in the meridian plane and the plane perpendicular to the meridian plane is calculated according to the latitude of the carrier position determined by the ground speed of the carrier along the navigation coordinate systemMAnd RNThe speed, the curvature radius, the latitude and the height of the carrier in the northeast direction can be calculated according to the speed, the curvature radius, the latitude and the height of the carrier in the northeast direction
Figure BDA0003177137500000078
Figure BDA0003177137500000079
veIs the east velocity of the vector, vnIs the north velocity of the carrierH is the altitude of the carrier;
Figure BDA00031771375000000710
obtaining the angular velocity value of the carrier in a geographic coordinate system;
Figure BDA00031771375000000711
obtaining the angular velocity of the navigation coordinate system relative to the inertial coordinate system under the navigation coordinate system;
calculating roll angle and pitch angle of the carrier according to the output value of the accelerometer and the gravity accelerometer, obtaining the course angle of the carrier according to GPS positioning, converting the three angles into quaternions q0, q1, q2 and q3, and converting the three angles into angular velocity of a navigation coordinate system relative to an inertial coordinate system through attitude coordinates
Figure BDA00031771375000000712
Figure BDA00031771375000000713
q denotes the conjugate of a quaternion.
Obtaining the angular velocity of the carrier coordinate system relative to the navigation coordinate system
Figure BDA0003177137500000081
ωnbb＝ωibb-ωibn。
According to
Figure BDA0003177137500000082
And obtaining the angle vector value of the rotation at the moment by multiplying the sampling time, wherein the higher the sampling frequency is, the higher the angle precision is, and the high-precision attitude angle is obtained after the angles in the three directions are subjected to normalization processing calculation.
The specific implementation process of step S22 is as follows:
the output value of the accelerometer is multiplied by the sampling time to obtain the velocity vector value at the moment, the higher the sampling frequency is, the higher the precision of the velocity is, the quaternion transformation is carried out on the velocity vector value to obtain the carrierVelocity v of a body relative to a navigation coordinate systemn＝q×vb×q*；
Calculating the gravity acceleration of the position of the carrier according to the curvature radius of the earth and the altitude of the latitude and the elevation of the carrier,
g0＝9.780318×(1+(5.3024×10-3)×sin2(λ)-(5.9×10-6)×sin2(2λ))；
g＝g0/((1+h/RN)2)；
g0is the gravity acceleration value of the position of the equator, lambda is the latitude of the position of the carrier, and h is the altitude of the carrier.
The speed vector of the sky direction needs to remove the influence error of the gravity acceleration, so the speed u of the sky directiond＝ud+ g × t (t is the sampling time).
The carrier normalizes the angular velocity and the velocity of a geographic coordinate system, calculates and corrects velocity increment caused by Coriolis force, calculates the velocities in three directions, calculates the velocity integral of the previous time and the next time to obtain position increment, and adds the position increment and the previous position to obtain longitude and latitude altitude information at the moment.
In step S3, the specific process of obtaining the optimal attitude and position information through kalman filtering combination is as follows:
s31, taking the positioning information as the positioning information state of the Kalman filtering estimation system, establishing a Kalman filtering equation of INS information, and obtaining a state vector x and a state equation x (t) of the integrated navigation system.
The state vector x of the integrated navigation system is:
Figure BDA0003177137500000083
ve、vn、vnwhich represents the speed in three directions and,
Figure BDA0003177137500000084
respectively representing longitude and latitude and height, phi e, phi n and phi u representing angles in three directions, de、dn、duRepresents the moving distance in three directions, ae、an、auRepresenting accelerations in three directions.
The state equation of the integrated navigation system is as follows:
Figure BDA0003177137500000094
wherein f [. C]And w (t) is system noise.
And S32, taking the carrier speed information and the position information in the PPS signal as measurement to obtain a measurement equation of the integrated navigation system, and obtaining an observation equation through discretization.
Selecting carrier speed information v output by GPS/BDSegvngvugPosition information
Figure BDA0003177137500000091
As a measure of the amount of Z,
Figure BDA0003177137500000092
then the measurement equation of the integrated navigation system is obtained as follows: zk＝H(xk,vk)
Discretizing the system model, and obtaining a system equation and an observation equation after processing respectively as follows:
X(k+1)＝ΦX(k)+ΓW(k)
Z(k)＝HX(k)+V(k)
wherein k is a discrete time, and the state of the system at time k is X (k) epsilon Rn；Z(k)∈RmAn observed signal that is a corresponding state; w (k) ε RrIs the input white noise; v (k) ε RmFor observing noise, Φ is the state transition matrix, Γ is the noise drive matrix, and H is the observation matrix.
The nonlinear system is constructed as follows:
X(k+1)＝f(x(k),W(k))
Z(k)＝h(x(k),V(k))
f is a non-linear equation of state function; h is a nonlinear observation equation function.
And S33, performing Kalman filtering calculation on the random state variable X at the moment k to obtain the optimal estimation of the position information of the carrier at the moment k + 1.
Using the UT transform, a nonlinear transform y ═ f (x) is set, the state vector x is an n-dimensional random variable, and its mean value is known
Figure BDA0003177137500000093
And variance P, 2n +1 sigma points X and corresponding weights omega are obtained through the following UT transformation to calculate the statistical characteristics of y:
2n +1 sigma points, i.e. sample points, are calculated, n being the system state dimension.
Figure BDA0003177137500000101
Figure BDA0003177137500000102
Figure BDA0003177137500000103
In the formula (I), the compound is shown in the specification,
Figure BDA0003177137500000104
the ith column representing the square root of the matrix.
Figure BDA0003177137500000105
Is the mean value of the 2n +1 sigma point sets X after UT conversion is carried out on the state vector X.
Calculating the corresponding weight of the sampling points
Figure BDA0003177137500000106
Figure BDA0003177137500000107
Figure BDA0003177137500000108
In the formula, the subscript m is the mean, c is the covariance, and the subscript is the ith sample point. Parameter λ ═ a2The (n + k) -ns is a scaling parameter for reducing the total prediction error, the selection of a controls the distribution state of the sampling points, k is a parameter to be selected, the specific value has no limit, and the matrix (n + λ) P is usually guaranteed to be a semi-positive definite matrix. The candidate parameter beta is a non-negative weight coefficient, and the moments of the high-order terms in the equation can be combined, so that the influence of the high-order terms can be calculated.
A set of state sampling points (referred to as sigma point set) and corresponding weights are obtained using the UT transform.
Figure BDA0003177137500000109
Calculate a prediction of the next time instant of the set of 2n +1 sigma points, i ═ 1,2
X(i)(k+1|k)＝f[k,X(i)(k|k)]
Calculating a one-step prediction and covariance matrix of the system state, and obtaining the prediction value of the sigma point set by weighted summation, wherein the weight omega(i)The calculation mode of the UT transformation can be obtained.
Figure BDA00031771375000001010
Figure BDA00031771375000001011
And carrying out UT conversion again according to the predicted value of the one step to generate a new sigma point set.
Figure BDA0003177137500000111
Figure BDA0003177137500000112
Figure BDA0003177137500000113
And substituting the new sigma point set into an observation equation to obtain a predicted observed quantity, wherein i is 1, 2.
Z(i)(k+1|k)＝h[X(i)(k+1|k)]
Therefore, an observation predicted value of the sigma point set is obtained, and the mean value and the covariance of system prediction are obtained through weighted summation.
Figure BDA0003177137500000114
Figure BDA0003177137500000115
Figure BDA0003177137500000116
And calculating a Kalman gain matrix.
Figure BDA0003177137500000117
State update and covariance update for computing systems
Figure BDA0003177137500000118
Figure BDA0003177137500000119
P (k +1| k +1) is the covariance obtained after kalman filtering,
Figure BDA00031771375000001110
the method is the optimal estimation of the position information of the carrier at the moment k +1 obtained after Kalman filtering, and the optimal carrier attitude position information is obtained through the calculation of the steps.
The following is a simulation calculation of the above method.
As shown in fig. 5 and fig. 6, fig. 5 is a true value of the simulation position, a measured value obtained by direct calculation of the inertial navigation, and an optimal estimated value obtained by the inertial navigation after kalman filtering, respectively, and fig. 6 is a deviation of the inertial navigation from an actual value and a deviation after the kalman filtering. Compared with the value directly calculated by inertial navigation, the Kalman filtering is greatly reduced in deviation and is closer to the true value. Although the error does not disappear completely after Kalman filtering, the error approaches to a true value as far as possible, and the influence of the error is reduced, so that the precision of the navigation positioning system is improved.
Taking the course angle obtained by orientation as an example for comparison, fig. 7 is a graph of the long-time output value of the course angle of the common inertial navigation system, and fig. 8 is a graph of the course angle output value of the combined navigation system adopted by the scheme. As can be seen from the figure, the common inertial navigation system has low orientation precision, the maximum course angle deviation can reach 15 degrees, the course angle deviation obtained by the algorithm adopted by the scheme is only more than 1 degree, the precision is very high, meanwhile, large deviation can not occur in long-time measurement, and the reliability is good.
Fig. 9 and 10 are graphs of pitch angle output values of inertial navigation during sinusoidal motion of a swing table. Fig. 9 is an output value of a common inertial navigation system, and fig. 10 is an output value of a combined navigation system pitch angle according to the scheme. The curve comparison in the graph can show that the dynamic precision of the algorithm adopted by the scheme is high, and the system equipment can be ensured to be accurate in orientation in the motion process.
The invention also provides a short baseline-based combined navigation positioning device, which comprises a combined navigation system formed by a multimode receiver and an INS, and a microcomputer, wherein the multimode receiver is used for acquiring a PPS signal, the microcomputer is used for positioning by utilizing the PPS signal in an RTK mode and calculating INS information, and the PPS signal corresponding to successful positioning and the INS information are combined through Kalman filtering to obtain optimal attitude and position information. The length of the base line of the multimode receiver is 320 millimeters.
The invention overcomes the defects of large volume, unstable positioning, easy interference and poor reliability of the traditional inertial navigation equipment, and has the advantages of small volume, easy carrying, short distance between the antennas, small errors between the longitude and latitude and the altitude of the positioning and the position of the carrier and relatively simple calculation because of adopting the short base line design. The invention adopts GNSS/INS combined navigation positioning, has rapid positioning, can independently position according to INS information under the condition that a carrier is shielded and can not receive satellite signals to carry out GPS/BDS positioning, has high reliability relative to a common inertial navigation system, adopts Kalman filtering to synthesize the positioning information of the GPS/BDS and the INS to obtain optimal position information, and adopts an algorithm to correct the deviation of a gyroscope and an accelerometer, so that the positioning precision of the system is higher than that of the common inertial navigation system.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (10)

1. The combined navigation positioning method based on the short baseline is characterized by comprising the following steps:
acquiring a PPS signal of the multimode receiver, and positioning in an RTK mode;
taking the positioning information in the PPS signal as an initial value of the INS, and performing INS calculation to obtain INS information;
and combining the PPS signal corresponding to the successful positioning and the INS information through Kalman filtering to obtain the optimal attitude and position information.
2. The short baseline-based integrated navigational positioning method of claim 1, wherein the multi-mode receiver comprises a front antenna receiver and a rear antenna receiver, the front antenna receiver transmits the observation data to the rear antenna receiver, and the rear antenna receiver uses the own observation data and the differential information to establish a carrier double difference equation for relative positioning.
3. The short baseline-based integrated navigation positioning method of claim 2, wherein the positioning in the RTK mode comprises the following specific steps:
assuming that the co-view satellite of the front antenna receiver and the rear antenna receiver has k +1 satellites, the linearized double difference equation of the carrier phase is xi ═ a η + BN + v, where: xi is in the middle of R2kIs a vector formed by a carrier phase double-difference observed value and a pseudo-range double-difference observed value, and eta belongs to R3Denotes the baseline vector, N ∈ ZkRepresenting the carrier phase double difference integer ambiguity vector, v ∈ R2kRepresenting observation noise, A ∈ R2k×3Representing the base-line constant coefficient matrix, B ∈ R2k×kRepresenting a fuzzy constant coefficient matrix;
solving the carrier phase double-difference equation if the coordinate of the front antenna receiver is known as etaaThen the post-antenna receiver coordinate ηbHas a difference estimation value of
Figure FDA0003177137490000011
Figure FDA0003177137490000012
Is a baseline coordinate vector.
4. The short baseline-based integrated navigational positioning method of claim 1, wherein the INS information comprises latitude and longitude of the carrier, altitude information, and attitude angle.
5. The integrated navigation positioning method based on short baseline as claimed in claim 4, wherein the specific process of performing INS calculation to obtain INS information is as follows:
carrier based gyroscope output relative toAngular velocity of inertial frame and
Figure FDA0003177137490000013
obtaining the angular velocity of the carrier coordinate system relative to the navigation coordinate system
Figure FDA0003177137490000021
Multiplying the angular velocity by sampling time to obtain an angular vector value of the current rotation, and carrying out normalization processing on the angular vector values in the three directions of the northeast to obtain an attitude angle;
the output value of the accelerometer is multiplied by sampling time to obtain a current velocity vector value, quaternion transformation is carried out on the velocity vector value to obtain the velocity of the carrier relative to a navigation coordinate system, the angular velocity and the velocity of the carrier relative to a geographic coordinate system are subjected to normalization processing through coordinate transformation, velocity increment caused by Coriolis force is corrected, the velocities in three directions of the northeast are calculated, the velocity increments of the two times of the previous velocity are calculated to obtain position increments, and the position increments are added with the previous position to obtain current longitude, latitude and altitude information.
6. The integrated navigation positioning method based on the short baseline of claim 4, wherein the specific process of obtaining the optimal attitude and position information through Kalman filtering combination is as follows:
taking the positioning information as a positioning information state of a Kalman filtering estimation system, and establishing a Kalman filtering equation of INS information to obtain a state vector x and a state equation x (t) of the integrated navigation system;
taking carrier speed information and position information in the PPS signal as measurement to obtain a measurement equation of the integrated navigation system, and performing discretization processing to obtain an observation equation;
and performing Kalman filtering calculation on the random state variable X at the moment k to obtain the optimal estimation of the position information of the carrier at the moment k + 1.
7. The integrated navigation positioning method based on short baseline according to claim 6, wherein the kalman filtering calculation is performed by the following steps:
obtaining a group of state acquisition points and corresponding weights by utilizing UT conversion;
obtaining a one-step prediction and a covariance matrix of the system state by weighting and summing the predicted values of the state acquisition points;
carrying out UT transformation on the predicted value in the one step to generate a new state acquisition point;
substituting the new state acquisition point into an observation equation to obtain a predicted observation value, and performing weighted summation on the observation value to obtain a predicted mean value and a predicted covariance;
and calculating a Kalman gain matrix, and updating a state and covariance, wherein the updated state is the optimal estimation of the position information of the carrier at the moment of k + 1.
8. The short baseline-based integrated navigation positioning method according to claim 1, wherein the successful positioning judgment specifically comprises:
the received PPS signal contains complete positioning information, and the number of satellites visible to the antenna receiver is greater than 4.
9. The combined navigation positioning device based on the short baseline is characterized by comprising a combined navigation system formed by a multimode receiver and an INS (inertial navigation system), and a microcomputer, wherein the multimode receiver is used for acquiring a PPS (polyphenylene sulfide) signal, the microcomputer is used for positioning by utilizing the PPS signal in an RTK (real time kinematic) mode, calculating INS (inertial navigation system) information, and combining the PPS signal corresponding to successful positioning and the INS information through Kalman filtering to obtain optimal attitude position information.
10. The short baseline-based integrated navigational positioning apparatus of claim 9, wherein the multimode receiver has a baseline length of 320 mm.
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