CN108957499B - Accompanying target relative navigation method based on observed quantity spectral analysis and optimal estimation - Google Patents

Accompanying target relative navigation method based on observed quantity spectral analysis and optimal estimation Download PDF

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CN108957499B
CN108957499B CN201810417623.7A CN201810417623A CN108957499B CN 108957499 B CN108957499 B CN 108957499B CN 201810417623 A CN201810417623 A CN 201810417623A CN 108957499 B CN108957499 B CN 108957499B
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angle offset
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CN108957499A (en
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张磊
王大轶
黄美丽
邹元杰
史文华
赵峭
刘德成
周静
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Beijing Institute of Spacecraft System Engineering
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • 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
    • G01S19/03—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers
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Abstract

The invention discloses a method and a system for accompanying flying target relative navigation based on observed quantity spectral analysis and optimal estimation, wherein the method comprises the following steps: determining a relative motion orbit of the target according to the observed quantity of the target line-of-sight angle; extracting a target line-of-sight angle offset linear regression coefficient matched with the determined target relative motion orbit from a line-of-sight angle offset linear regression model coefficient database; determining the actual observation line-of-sight angle offset of the target by utilizing an optimal estimation method for the observation value of the target line-of-sight angle in the observation time period; and substituting the target line-of-sight angle offset linear regression coefficient and the target actual observation line-of-sight angle offset into a line-of-sight angle offset regression model, and solving to obtain a target flat latitude amplitude angle so as to finish the flight-following track improvement. According to the method, on the basis of the traditional unscented Kalman filtering algorithm, the real-time correction of the horizontal latitude argument is realized by using an optimal estimation method according to the spectral characteristics of the observed quantity, and the problem that only the measured angle of the accompanying target is determined relative to the navigation horizontal latitude argument is solved.

Description

Accompanying target relative navigation method based on observed quantity spectral analysis and optimal estimation
Technical Field
The invention belongs to the technical field of navigation, and particularly relates to a flight-accompanying target relative navigation method and system based on observed quantity spectral analysis and optimal estimation.
Background
The non-cooperative accompanying target relative navigation problem can be related to the fields of space attack and defense, situation awareness, on-orbit service and the like. Due to the non-cooperative characteristic of the target, only the target line-of-sight angle observed quantity is often available in the relative navigation calculation, so the accompanying target relative navigation technology based on the angle measurement information only becomes a key technology in the field, and has wide and important application.
Due to the problem that only angle measurement of the space base is poor relative to the inherent observability of navigation, the problem that the horizontal latitude breadth angle (lambda is omega + M, omega is the breadth angle of the near place, and M is the horizontal near point angle) is difficult to determine exists when the relative orbit calculation is carried out on the accompanying target. The traditional solution generally utilizes orbit maneuver to change observation geometry so as to extract the sight line distance information of the accompanying target, or adopts a high-precision relative navigation model, combines the perturbation characteristic of the accompanying orbit, and utilizes a system noise adaptive filtering algorithm to identify tiny distance perturbation change. The methods either need to consume extra spacecraft fuel and need multiple orbital maneuvers to avoid filtering divergence, or have extremely high requirements on model precision, stability and the like of a filtering algorithm, and the calculation amount is relatively large, so that the method is not beneficial to implementation of an on-satellite algorithm.
In consideration of the periodic relative motion characteristics of the flying target, the target line-of-sight angle observed quantity also has the characteristics of a periodic frequency spectrum, and parameters such as amplitude, offset, frequency and initial phase of the line-of-sight angle spectrum are closely related to the relative orbit of the flying target. By analyzing the change relation of the accompanying target sight angle frequency spectrum parameter along with the horizontal latitude argument, a brand new technical means can be provided for solving the problem of determining the horizontal latitude argument of the accompanying target, and the method has important significance and application prospect.
Disclosure of Invention
The technical problem of the invention is solved: the method and the system overcome the defects of the prior art, realize real-time correction of the horizontal latitude argument by utilizing the optimal estimation method according to the spectral characteristics of the observed quantity on the basis of the traditional unscented Kalman filtering algorithm and solve the problem that the horizontal latitude argument of the flying target is determined only by measuring the angle relative to the navigation horizontal latitude argument. The method realizes the self-adaptive correction of the horizontal latitude argument according to the frequency spectrum difference of the target sight angles of different satellite relative motion orbits, wherein the statistic analysis of the sight angle bias characteristics of the satellite flying targets with larger calculation amount can be completed off line, and the real-time correction of the horizontal latitude argument parameters is completed by utilizing an optimal estimation algorithm according to prior information during on-line calculation, thereby not only improving the determination precision of the target horizontal latitude argument, but also facilitating the application of autonomous relative navigation on the satellite.
In order to solve the technical problem, the invention discloses a flight-accompanying target relative navigation method based on observed quantity spectral analysis and optimal estimation, which comprises the following steps:
(1) determining a relative motion track of the target by adopting an unscented Kalman filtering algorithm according to the observed quantity of the target line-of-sight angle;
(2) extracting a target line-of-sight angle offset linear regression coefficient matched with the determined target relative motion orbit from a line-of-sight angle offset linear regression model coefficient database;
(3) determining the actual observation line-of-sight angle offset of the target by utilizing an optimal estimation method for the observation value of the target line-of-sight angle in the observation time period;
(4) and substituting the target line-of-sight angle offset linear regression coefficient and the target actual observation line-of-sight angle offset into a line-of-sight angle offset regression model, and solving to obtain a target flat latitude amplitude angle so as to finish the flight-following track improvement.
In the method for the companion flight target relative navigation based on the observed quantity spectral analysis and the optimal estimation, a line-of-sight angle offset linear regression model coefficient database is established through the following steps:
aiming at different accompanying relative motion tracks, carrying out target sight angle calculation by preset step length to obtain a target sight angle change curve in an accompanying track period;
determining the sight angle frequency spectrum parameters corresponding to the accompanying relative motion tracks by using an optimal estimation method according to the target sight angle change curve;
respectively extracting a latitude-average argument and a corresponding line-of-sight angle offset from each accompanying flight relative motion orbit parameter and a line-of-sight angle frequency spectrum parameter;
and determining the corresponding line-of-sight angle offset linear regression coefficient of each accompanying relative motion orbit by using a least square estimation method according to the extracted horizontal latitude argument and the corresponding line-of-sight angle offset, and storing to obtain a line-of-sight angle offset linear regression model coefficient database.
In the method for the accompanying target relative navigation based on the observed quantity spectrum analysis and the optimal estimation, the target line-of-sight angle is calculated by preset step length according to different accompanying relative motion tracks to obtain a target line-of-sight angle change curve in an accompanying track period, and the method comprises the following steps:
let the orbital number of the observation satellite be sigmao＝[a,e,i,Ω,ω,M]TWherein a, e, i, omega and M respectively represent the orbit half-length diameter, eccentricity, orbit inclination, ascension of ascending intersection point, argument of near place and argument of near point of observation satellite; delta e, delta i, delta omega and delta M respectively represent the deviation of the eccentricity, the orbit inclination, the ascension of the ascending intersection point, the amplitude angle of the perigee and the angle of the perigee of the observation satellite; the orbital element of the satellite is sigmat＝σo+ δ σ, where δ σ ═ 0, δ e, δ i, δ Ω, δ ω, δ M]T(ii) a Representing the deviation of the orbit numbers corresponding to the satellite and the observation satellite;
by σoAnd σtThrough ephemeris calculation, state vectors X of the observation satellite and the satellite in the earth center J2000 coordinate system are respectively obtainedo＝[ro,vo]TAnd Xt＝[rt,vt]TWherein r iso,voRespectively representing the position and velocity vectors, r, of the observed satellitet,vtRespectively representing the position and the velocity vector of the satellite;
for a given σoAnd delta sigma combination, which respectively takes delta t as step length to calculate the target sight angle in a period to obtain a target sight angle change curve in the accompanying orbit period.
In the above-described flying target relative navigation method based on the observation quantity spectrum analysis and the optimal estimation,
and (4) establishing a line-of-sight angle offset linear regression model coefficient database through offline calculation.
Correspondingly, the invention also discloses a flight accompanying target relative navigation system based on observed quantity spectrum analysis and optimal estimation, which comprises:
the determining module is used for determining a relative motion track of the target by adopting an unscented Kalman filtering algorithm according to the observed quantity of the target line-of-sight angle;
the extraction module is used for extracting a target line-of-sight angle offset linear regression coefficient matched with the determined target relative motion track from a line-of-sight angle offset linear regression model coefficient database;
the calculation module is used for determining the actual observation line angle offset of the target by utilizing an optimal estimation method for the observation value of the target line angle in the observation time period;
and the correction module is used for substituting the target line-of-sight angle offset linear regression coefficient and the target actual observation line-of-sight angle offset into the line-of-sight angle offset regression model, and solving to obtain a target horizontal latitude argument so as to complete the accompanying flight track improvement.
The invention has the following advantages:
(1) the method is based on the spectral characteristic analysis of the observed quantity, combines least squares (OLS) estimation and an unscented Kalman filtering algorithm, and effectively solves the problem that only the angle measurement of the accompanying target is difficult to determine relative to the navigation latitude and longitude.
(2) The method adopts a simplex-simulated annealing mixed algorithm to complete the optimal estimation of the view angle spectrum parameters, and has the characteristics of high calculation efficiency and high convergence rate.
(3) According to the off-line established target line-of-sight angle offset regression model, the adaptive correction of the horizontal latitude breadth angle can be realized only by calculating the line-of-sight angle offset obtained by actual observation on line, the calculation load is small, and the on-satellite algorithm is favorably realized.
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Fig. 1 is a flowchart illustrating steps of a method for navigating a flying target relative to each other based on observation quantity spectrum analysis and optimal estimation according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a target line-of-sight angle in a local horizontal coordinate system of satellite velocity according to an embodiment of the present invention;
fig. 3 is a flowchart of calculating an optimal viewing-angle spectrum parameter according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Referring to fig. 1, a flowchart illustrating steps of a method for navigating a flying target relative to each other based on observation quantity spectrum analysis and optimal estimation in an embodiment of the present invention is shown. In this embodiment, the method for navigating the accompanying target relative to each other based on observation quantity spectrum analysis and optimal estimation includes:
and step 101, determining a relative motion orbit of the target by adopting an unscented Kalman filtering algorithm according to the observed quantity of the target line-of-sight angle.
And 102, extracting a target line-of-sight angle offset linear regression coefficient matched with the determined target relative motion orbit from a line-of-sight angle offset linear regression model coefficient database.
And 103, determining the actual observation line-of-sight angle offset of the target by utilizing an optimal estimation method for the observation value of the target line-of-sight angle in the observation time period.
And step 104, substituting the target line-of-sight angle offset linear regression coefficient and the target actual observation line-of-sight angle offset into a line-of-sight angle offset regression model, and solving to obtain a target horizontal latitude argument so as to complete the flight following track improvement.
In a preferred embodiment of the present invention, the database of line-of-sight angle biased linear regression model coefficients may be built by: aiming at different accompanying relative motion tracks, carrying out target sight angle calculation by preset step length to obtain a target sight angle change curve in an accompanying track period; determining the sight angle frequency spectrum parameters corresponding to the accompanying relative motion tracks by using an optimal estimation method according to the target sight angle change curve; respectively extracting a latitude-average argument and a corresponding line-of-sight angle offset from each accompanying flight relative motion orbit parameter and a line-of-sight angle frequency spectrum parameter; and determining the corresponding line-of-sight angle offset linear regression coefficient of each accompanying relative motion orbit by using a least square estimation method according to the extracted horizontal latitude argument and the corresponding line-of-sight angle offset, and storing to obtain a line-of-sight angle offset linear regression model coefficient database.
Preferably, in this embodiment, the performing target line-of-sight angle calculation with preset step lengths for different accompanying relative movement orbits to obtain a target line-of-sight angle variation curve in one accompanying orbit period may specifically include:
let the orbital number of the observation satellite be sigmao＝[a,e,i,Ω,ω,M]TWherein a, e, i, omega and M respectively represent the orbit half-length diameter, eccentricity, orbit inclination, ascension of ascending intersection point, argument of near place and argument of near point of observation satellite; delta e, delta i, delta omega and delta M respectively represent the deviation of the eccentricity, the orbit inclination, the ascension of the ascending intersection point, the amplitude angle of the perigee and the angle of the perigee of the observation satellite; the orbital element of the satellite is sigmat＝σo+ δ σ, where δ σ ═ 0, δ e, δ i, δ Ω, δ ω, δ M]T(ii) a And the deviation of the orbit number corresponding to the satellite and the observation satellite is shown.
By σoAnd σtThrough ephemeris calculation, state vectors X of the observation satellite and the satellite in the earth center J2000 coordinate system are respectively obtainedo＝[ro,vo]TAnd Xt＝[rt,vt]TWherein r iso,voRespectively representing the position and velocity vectors, r, of the observed satellitet,vtRespectively, the position and velocity vectors of the satellite.
For a given σoAnd delta sigma combination, which respectively takes delta t as step length to calculate the target sight angle in a period to obtain a target sight angle change curve in the accompanying orbit period.
Wherein, it should be noted that the establishment of the line-of-sight angle offset linear regression model coefficient database can be realized by off-line calculation; the steps 101-104 may be an online calculation process.
Based on the above embodiments, the following description will be made with reference to a specific example.
(1) Let the orbital number of the observation satellite be sigmao＝[a,e,i,Ω,ω,M]TThe orbital element of the satellite is sigmat＝σo+δσ, fromoAnd σtObtaining the position vector X of the observation satellite and the satellite in the earth center J2000 coordinate system through ephemeris calculationo＝[ro,vo]TVelocity vector Xt＝[rt,vt]T。
In the present embodiment, δ σ ═ 0, δ e, δ i, δ Ω, δ ω, δ M]T. For a given σoAnd δ σ combinations, each of which calculates a target line-of-sight angle in one cycle with Δ t as a step. The target line-of-sight angular observation is represented here by the azimuth angle α and the elevation angle β of the target line-of-sight vector in the observed satellite velocity local level (VVLH) coordinate system (which is defined as shown in fig. 2).
According to vector XoAnd XtThe target sight line vector r in the VVLH coordinate system can be calculated by the following formulaVVLH：
Figure BDA0001649785610000061
In the formula (I), the compound is shown in the specification,
Figure BDA0001649785610000062
x, y, z represent coordinate values of the observation satellite.
The expression for the target line-of-sight angle observation can be written as:
Figure BDA0001649785610000063
(2) according to the periodic relative orbital motion characteristics of the accompanying target, the line-of-sight angle observed quantity can be expressed in the form of a periodic function as follows:
Figure BDA0001649785610000064
wherein alpha is0、β0Represents the line of sight angle offset; δ α, δ β represent the amplitude of the change in the viewing angle; n represents a viewLine angle variation frequency; thetaα、θβIndicating the line-of-sight angle initial phase.
According to the change of the line-of-sight angle of the accompanying flying target in a period, solving the spectrum parameters in the formula by using an optimal estimation method, so that the residual error of the line-of-sight angle of the target in the period is extremely small, namely:
Figure BDA0001649785610000071
Figure BDA0001649785610000072
here, a simplex-simulated annealing hybrid algorithm is used to solve the optimal solution of the above problem. The simplex-simulated annealing hybrid algorithm combines the characteristics of high convergence rate of the simplex algorithm and high quality prevention of the simulated annealing algorithm, and has the characteristics of high calculation efficiency and high convergence rate. The specific calculation flow of the algorithm is shown in fig. 3, and the principles and algorithm implementation of the simplex algorithm and the simulated annealing algorithm can be found in sections 2.1 and 8.1 of the book "intelligent optimization algorithm and its application" (royal press), which is not described herein again.
(3) For different companion flight tracks, the line-of-sight angular spectrum parameters are respectively solved according to the process to obtain the angle-of-sight spectral parameters of each group [ sigma ]o,δσ]TCorresponding optimum [ alpha ]0,β0,δα,δβ,n,θα,θβ]TAnd (4) combining.
By combining with the orbit dynamics characteristic analysis, the accompanying target line-of-sight angle offset can be expressed as a function of delta omega, delta omega and delta M:
Figure BDA0001649785610000073
when δ Ω is determined, [ α ]0,β0]TChanges along with delta M and delta omega approximately satisfy a linearization relation, so [ alpha ] can be adjusted0,β0]TFurther expressed as a linear function of δ M, δ ω:
Figure BDA0001649785610000074
from the above relationship, the sum of the sum and the arbitrary group (σ) can be determined by OLS estimationoδ σ) corresponding to the regression coefficient a0、A1、A2、B0、B1、B2
Figure BDA0001649785610000075
Figure BDA0001649785610000076
In the formula (I), the compound is shown in the specification,
Figure BDA0001649785610000081
respectively represent alpha0、β0Sample means of δ M, δ ω. According to the process, the view angle offset regression coefficient of different companion flight tracks can be calculated off line, so that the method is convenient to use when the latitude argument is corrected on line later.
(4) And (3) resolving relative navigation on line: and according to the target line-of-sight angle observed quantity, completing initial estimation of the relative motion orbit of the accompanying target by adopting an Unscented Kalman Filtering (UKF) algorithm. For the specific calculation process of UKF algorithm, refer to "optimal state estimation-Kalman, H∞And section 14.3 of the non-linear filtering (Zhang Yonggang, Lining, Pengyouyang translation), which is not repeated here. The target state vector to be estimated adopts the relative track number in the following form so as to be suitable for tracks with any eccentricity.
Figure BDA0001649785610000082
Wherein [ a ]t,et,it,Ωt,ωt,Mt]TDenotes the number of orbits of the satellite, [ a ]o,eo,io,Ωo,ωo,Mo]TIndicating the number of orbits of the observed satellite.
(5) Determining the number [ delta a, delta i, delta omega, delta e ] of the target relative orbit according to the UKF algorithmx,δey]TCombined with observation satellite reference orbit sigmaoScreening a target line-of-sight angle offset coefficient C (sigma) under the corresponding track from the line-of-sight angle offset regression coefficient library established in the step 3o,δa,δi,δΩ,δex,δey)＝[A0,A1,A2,B0,B1,B2]T。
(6) For the observed value of the target line-of-sight angle in a given time period, determining the offset [ alpha ] of the accompanying target line-of-sight angle by utilizing a simplex-simulated annealing hybrid algorithm according to the same process as the step (2)0,β0]opt；
(7) Biasing the viewing angle by [ alpha ]0,β0]optAnd the coefficient of linearization [ A ]0,A1,A2,B0,B1,B2]TThe modified calculation result of the altitude argument of the flight accompanying target is obtained by substituting into an equation system (8)
Figure BDA0001649785610000083
Based on the above embodiment, the present invention also discloses a flight-accompanying target relative navigation system based on observed quantity spectrum analysis and optimal estimation, including: the determining module is used for determining a relative motion track of the target by adopting an unscented Kalman filtering algorithm according to the observed quantity of the target line-of-sight angle; the extraction module is used for extracting a target line-of-sight angle offset linear regression coefficient matched with the determined target relative motion track from a line-of-sight angle offset linear regression model coefficient database; the calculation module is used for determining the actual observation line angle offset of the target by utilizing an optimal estimation method for the observation value of the target line angle in the observation time period; and the correction module is used for substituting the target line-of-sight angle offset linear regression coefficient and the target actual observation line-of-sight angle offset into the line-of-sight angle offset regression model, and solving to obtain a target horizontal latitude argument so as to complete the accompanying flight track improvement.
For the system embodiment, since it corresponds to the method embodiment, the description is relatively simple, and for the relevant points, refer to the description of the method embodiment section.
The embodiments in the present description are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The above description is only for the best mode of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Those skilled in the art will appreciate that the invention may be practiced without these specific details.

Claims (4)

1. A method for accompanying target relative navigation based on observed quantity spectral analysis and optimal estimation is characterized by comprising the following steps:
(1) determining a relative motion track of the target by adopting an unscented Kalman filtering algorithm according to the observed quantity of the target line-of-sight angle;
(2) extracting a target line-of-sight angle offset linear regression coefficient matched with the determined target relative motion orbit from a line-of-sight angle offset linear regression model coefficient database;
(3) determining the actual observation line-of-sight angle offset of the target by utilizing an optimal estimation method for the observation value of the target line-of-sight angle in the observation time period;
(4) substituting the target line-of-sight angle offset linear regression coefficient and the target actual observation line-of-sight angle offset into a line-of-sight angle offset regression model, and solving to obtain a target flat latitude amplitude angle so as to complete the flight following track improvement;
wherein:
establishing a line-of-sight angle offset linear regression model coefficient database by the following steps:
aiming at different accompanying relative motion tracks, carrying out target sight angle calculation by preset step length to obtain a target sight angle change curve in an accompanying track period;
determining the sight angle frequency spectrum parameters corresponding to the accompanying relative motion tracks by using an optimal estimation method according to the target sight angle change curve;
respectively extracting a latitude-average argument and a corresponding line-of-sight angle offset from each accompanying flight relative motion orbit parameter and a line-of-sight angle frequency spectrum parameter;
and determining the corresponding line-of-sight angle offset linear regression coefficient of each accompanying relative motion orbit by using a least square estimation method according to the extracted horizontal latitude argument and the corresponding line-of-sight angle offset, and storing to obtain a line-of-sight angle offset linear regression model coefficient database.
2. The accompanying target relative navigation method based on observation quantity spectral analysis and optimal estimation according to claim 1, wherein for different accompanying relative motion orbits, target line-of-sight angle calculation is performed with preset step length to obtain a target line-of-sight angle variation curve in an accompanying orbit period, including:
let the orbital number of the observation satellite be sigmao＝[a,e,i,Ω,ω,M]TWherein a, e, i, omega and M respectively represent the orbit half-length diameter, eccentricity, orbit inclination, ascension of ascending intersection point, argument of near place and argument of near point of observation satellite; delta e, delta i, delta omega and delta M respectively represent the deviation of the eccentricity, the orbit inclination, the ascension of the ascending intersection point, the amplitude angle of the perigee and the angle of the perigee of the observation satellite; the orbital element of the satellite is sigmat＝σo+ δ σ, where δ σ ═ 0, δ e, δ i, δ Ω, δ ω, δ M]T(ii) a Representing the deviation of the orbit numbers corresponding to the satellite and the observation satellite;
by σoAnd σtThrough ephemeris calculation, state vectors X of the observation satellite and the satellite in the earth center J2000 coordinate system are respectively obtainedo＝[ro,vo]TAnd Xt＝[rt,vt]TWherein r iso,voRespectively show the viewMeasuring the position, velocity vector, r, of the satellitet,vtRespectively representing the position and the velocity vector of the satellite;
for a given σoAnd delta sigma combination, which respectively takes delta t as step length to calculate the target sight angle in a period to obtain a target sight angle change curve in the accompanying orbit period.
3. The method for companion target relative navigation based on observation quantity spectral analysis and optimal estimation according to claim 2,
and (4) establishing a line-of-sight angle offset linear regression model coefficient database through offline calculation.
4. A companion target relative navigation system based on observed quantity spectral analysis and optimal estimation is characterized by comprising:
the determining module is used for determining a relative motion track of the target by adopting an unscented Kalman filtering algorithm according to the observed quantity of the target line-of-sight angle;
the extraction module is used for extracting a target line-of-sight angle offset linear regression coefficient matched with the determined target relative motion track from a line-of-sight angle offset linear regression model coefficient database; the method comprises the following steps of establishing a line-of-sight angle offset linear regression model coefficient database: aiming at different accompanying relative motion tracks, carrying out target sight angle calculation by preset step length to obtain a target sight angle change curve in an accompanying track period; determining the sight angle frequency spectrum parameters corresponding to the accompanying relative motion tracks by using an optimal estimation method according to the target sight angle change curve; respectively extracting a latitude-average argument and a corresponding line-of-sight angle offset from each accompanying flight relative motion orbit parameter and a line-of-sight angle frequency spectrum parameter; determining and storing a line-of-sight angle offset linear regression coefficient corresponding to each accompanying relative motion orbit by using a least square estimation method according to the extracted horizontal latitude argument and the corresponding line-of-sight angle offset to obtain a line-of-sight angle offset linear regression model coefficient database;
the calculation module is used for determining the actual observation line angle offset of the target by utilizing an optimal estimation method for the observation value of the target line angle in the observation time period;
and the correction module is used for substituting the target line-of-sight angle offset linear regression coefficient and the target actual observation line-of-sight angle offset into the line-of-sight angle offset regression model, and solving to obtain a target horizontal latitude argument so as to complete the accompanying flight track improvement.
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