CN103438886B - Determination method for attitudes of spinning stabilized meteorological satellite based on coarse-fine attitude relation model - Google Patents

Abstract

The invention relates to a determination method for attitudes of a spinning stabilized meteorological satellite based on a coarse-fine attitude relation model. The method is characterized by comprising the following steps that 1: the coarse attitude of the satellite is calculated; 2: a random error in the coarse attitude of the satellite is eliminated; and 3: the coarse attitude is modified by utilizing the coarse-fine attitude relation model. The method has the benefits that 1, the precision is high, and a calculated fine attitude result is basically consistent with the practical fine attitude of the satellite; 2, the applicability is high, and the fine attitude of the satellite can be calculated by the method to achieve accurate positioning of an observation cloud picture when the fine attitude cannot be obtained within 24h after attitude orbit control of the satellite or in an area observation mode; and 3, a processing procedure is distinct, the operand is not great, and implementation is facilitated.

Description

Spin stabilization meteorological satellite attitude determination method based on coarse and fine attitude relationship model

Technical Field

The invention belongs to the field of meteorological satellite observation image positioning, and particularly relates to a spin stabilization meteorological satellite attitude determination method based on a coarse and fine attitude relationship model.

Background

The accurate attitude determination is the basis for accurate positioning of meteorological satellite observation images and the basis for application of the meteorological satellite observation images. At present, the spin stabilization meteorological satellite in China relies on a disc image to deduce accurate attitude parameters of the satellite to position images in the future for 24 hours. The method is accurate in positioning and has been widely applied to services. However, when the method does not collect the images or the collected data of the disk images are not accumulated enough, the accurate attitude of the satellite cannot be deduced, and the accurate positioning of the satellite cloud images cannot be realized. When the satellite performs full-area map observation or within 48 hours after the satellite performs attitude control, no disc image exists or disc image data are too little, and the accurate attitude of the satellite is difficult to determine.

The satellite attitude can be calculated by utilizing the satellite remote measurement value, a certain error exists between the satellite attitude calculated by utilizing the satellite remote measurement value and the satellite actual attitude in actual service, and image grid deviation can be generated when an image is positioned by utilizing the attitude, so that the satellite attitude calculated by utilizing the satellite remote measurement value is called as a coarse attitude, and the satellite attitude reversely deduced by utilizing the image is called as a fine attitude. Since the remote measuring value of the spinning stable meteorological satellite is uninterrupted, the coarse attitude of the satellite can be calculated by satellite remote measuring at any time.

In order to improve the calculation accuracy of the satellite coarse attitude, a lot of experts such as Libra and the like make intensive research in this respect. Related researches indicate that the attitude determination results are often inconsistent due to the limitation of geometric observation conditions and different measurement errors and calculation methods. In order to eliminate the error, the relation between the normal included angle of the spin axis and the orbit and the geodesic chord width is determined by researching the change rule of the earth observation chord width of the on-satellite infrared earth sensor, and a calculation formula for calculating the normal included angle of the spin axis and the orbit by using the telemetering data difference value is deduced. However, the pose calculated by this method cannot be directly used for image localization, which requires determining the exact position of each pixel. Actual service operation shows that a certain relation exists between the coarse attitude and the fine attitude, and if a relation model of the coarse attitude and the fine attitude can be established, the fine attitude of the satellite can be accurately estimated according to the coarse attitude when the fine attitude cannot be calculated, so that the positioning of an observation image is realized.

Disclosure of Invention

The invention aims to provide a method for determining the attitude of a spin-stabilized meteorological satellite based on a coarse attitude and fine attitude relationship model, which is used for establishing the relationship model between the coarse attitude and the fine attitude of the satellite, deducing the accurate attitude of the satellite by using the relationship model when the satellite has no fine attitude, and realizing the accurate positioning of a cloud picture.

The purpose of the invention is realized by the following technical scheme:

a spin-stabilized meteorological satellite attitude determination method based on a coarse and fine attitude relationship model comprises the following steps:

step 1: and (3) calculating the coarse attitude of the satellite:

decoding the telemetry packet, extracting information such as horizon observation and the like in satellite telemetry data, realizing a satellite coarse attitude calculation algorithm, and outputting a coarse attitude result, wherein the decoding telemetry is used for realizing telemetry acquisition, telemetry format decoding and quality inspection functions, the adopted method is to judge the telemetry quality of each frame according to a frame header synchronization code of a telemetry frame and a sum check value of an end part of each frame, decode the telemetry frame which is subjected to quality inspection and meets requirements according to the definition of a telemetry format, then extract a plurality of T values in the telemetry, and calculate corresponding physical quantities according to a conversion formula;

the method adopted by the coarse attitude solution is to utilize sun-earth measurement information in satellite remote measurement to solve the attitude of a satellite, calculate the rotating speed of the satellite, the included angle between the spin axis of the satellite and the sun direction, the included angle between the spin axis of the satellite and the geocenter and dihedral angles through a T value, and then solve attitude parameters;

step 2: eliminating random errors in the satellite coarse attitude:

the satellite coarse attitude calculated according to the telemetering value is influenced by various factors to have random errors, an average composite filter based on a sliding median is adopted to correct the satellite coarse attitude, abnormal satellite coarse attitude data is judged through a weighted average composite filter based on the sliding median, and an attitude moving window with the length of m is adopted in an algorithmDetermining the validity of the satellite coarse attitude data by using m continuous attitude values, and outputting the satellite coarse attitude data if the filter judges that the satellite coarse attitude data is valid; otherwise, judging that the satellite coarse attitude data is abnormal data, and correcting by using a weighted mean value;

and step 3: and (3) correcting the coarse attitude by using a coarse attitude and fine attitude relation model:

the coarse attitude forecasting satellite attitude part is mainly used for researching the relation between a coarse attitude and a fine attitude and calculating and forecasting the attitude of the satellite by using the coarse attitude; generally, the precise satellite attitude comprises a satellite spin vector and a mismatch parameter of a scanning radiometer, and the coarse satellite attitude is the attitude of a satellite platform and does not contain the mismatch parameter of the satellite scanning radiometer. And calculating the system error by comparing the fine attitude and the coarse attitude in the historical data, and fitting the change rule by using a polynomial least square curve method, so far, finishing the calculation of the fine attitude of the spin-stabilized meteorological satellite.

The invention has the beneficial effects that:

1. the precision is high, and the calculated accurate attitude result is basically consistent with the actual accurate attitude of the satellite;

2. the method has strong applicability, and can be used for calculating the fine attitude of the satellite to realize the accurate positioning of the observed cloud picture when the fine attitude cannot be obtained within 24 hours after the attitude and orbit control of the satellite is carried out or in a regional observation mode;

3. the processing process is clear, the operation amount is not large, and the implementation is convenient.

Drawings

The invention is explained in further detail below with reference to the drawing.

FIG. 1 is a flowchart of a method for determining an attitude of a spin-stabilized meteorological satellite based on a coarse-fine attitude relationship model according to an embodiment of the present invention.

Detailed Description

As shown in fig. 1, a method for determining an attitude of a spin-stabilized meteorological satellite based on a coarse-fine attitude relationship model according to an embodiment of the present invention includes the following steps:

step 1: and (3) calculating the coarse attitude of the satellite:

step 1.1): performing quality inspection on the obtained satellite telemetering data by taking a frame as a unit, judging the quality of each frame of satellite telemetering data according to a frame header synchronous code and a frame tail check value, and discarding the frame header and the frame tail which do not meet the requirements if the quality is unqualified;

step 1.2): decoding the satellite telemetry data frame which is subjected to the quality inspection in the step 1.1 and meets the requirements according to the format definition of the satellite telemetry data, and storing each channel value into a designated array I;

step 1.3): extracting T from array I₁To T₁₁Then converted into the physical quantity P required for calculating the coarse attitude of the satellite₁To P₁₁；

Step 1.4): by P₁To P₁₁Deducing a unit sun vector S, a unit nadir vector E in the earth center direction and a unit vector Z in the satellite spin axis direction, and meanwhile, obtaining a satellite spin axis vectorIncluded angle theta between self-rotating axis and sun measured by sun sensor_sThe included angle theta between the spin axis and the nadir vector measured by the earth sensor_eIn the equatorial inertial frame, from the following attitude equation:

can calculate the included angle lambda between the plane of the sun-spin axis and the plane of the earth center-spin axis_se；

Step 1.5) based on the known position of the sun (α)_s，_s) From the following formula:

the right ascension α of the satellite's coarse attitude can be obtained_eAnd declination_eWherein:

=

=

step 1.6) based on the known position of the sun (α)_s，_s) Satellite orbit inclination angle i, true perigee angle f and elevation intersection right ascension omega, and S and E can be divided intoExpressed as:

S=

E=

thus, the position of the satellite is:

step 2: eliminating random errors in the satellite coarse attitude:

step 2, judging the abnormal satellite coarse attitude data through a weighted average composite filter based on a sliding median, wherein an attitude moving window with the length of m is adopted in the algorithmDetermining the validity of the satellite coarse attitude data by using m continuous attitude values, and outputting the satellite coarse attitude data if the filter judges that the satellite coarse attitude data is valid; otherwise, the satellite coarse attitude data is determined to be abnormal data, and is corrected by using the weighted mean, which further comprises:

step 2.1): assuming an initial satellite coarse attitude data sequenceThe median of Z, and the absolute deviation of the satellite coarse attitude median is used to construct a sequence=Hypothetical sequenceD, wherein the absolute deviation Q of the satellite coarse attitude median is 1.4826 × D, Q may replace the standard deviation;

step 2.2): if the mean absolute deviation sequence of the coarse attitude of the satelliteWhere there are k terms all having values greater than，Then according toValue of (2) is in sequenceIs disassembled intoAndtwo sequences of one or more of which,，；

step 2.3): sequence from step 2.2The value in (1) is a normal value, and the value is not corrected; sequence ofThe value in (1) is an abnormal value and needs to be corrected, and the satellite coarse attitude data sequenceThe final corrected result isAnd the following conditions are satisfied:

if it is notThen, then；

If it is not Then, there is equation 1, as follows:

step 2.4): in the algorithm, an initial satellite coarse attitude sequence is firstly accumulated, and sequences are respectively accumulated on x, y and z axes of the satellite coarse attitude、Andwhen the length of the sequence is m, correcting once according to the formula 1;

step 2.5): the fixed sequence length is m, and when a new coarse attitude value is calculated, the sliding sequence、Andand putting the new calculated value into the tail of the queue, removing one data of the original head of the queue, and then carrying out filtering correction on m values in the sequence according to a formula 1.

The characteristics of the weighted average composite filter based on the sliding median can be adjusted by three parameters, namely a window width m, a threshold L and a correction weight C. Wherein: m affects the overall consistency of the filter; the threshold parameter L directly determines the active access degree of the filter, the value of L is increased, the possibility that singular data is judged and replaced by a median value is reduced, and when L is equal to 0, the filter is always determined; and (3) directly determining the correction amplitude of the filter by using the correction weight value C, increasing the correction amplitude of the filter, and correcting the abnormal posture by using the mean value when C = 0.

And step 3: and (3) correcting the coarse attitude by using a coarse attitude and fine attitude relation model:

step 3, comparing the coarse attitude and the fine attitude without the random error, calculating the system error, and fitting the change rule of the two attitudes by using a polynomial least square curve method, further comprising the following steps:

step 3.1): representing the relationship of the fine and coarse poses on the x, y and z axes as three sets of discrete data points、Andrespectively combining the three relation functions、Andfitting to the given three sets of data;

step 3.2): for attitude data、Andat all times not exceedingFunction class formed by polynomialIn (1), respectively、Andmake an error、Andhas the smallest sum of squares, i.e.

Wherein,、、(i=0，1，…，m)；

、and，

make an error、Andat the minimum, the temperature of the mixture is controlled,，，it is the polynomial sought. Because of the relation between X, Y and ZThe fitted X, Y and Z values generally have difficulty satisfying this relationship. Therefore, only two items of X, Y and Z need to be selected for calculation in use, and the remaining one item is passedThis relationship is calculated. In actual use X, Y is typically selected for estimation and then Z is calculated X, Y.

The present invention is not limited to the above-mentioned preferred embodiments, and any other products in various forms can be obtained by anyone in the light of the present invention, but any changes in the shape or structure thereof, which have the same or similar technical solutions as those of the present application, fall within the protection scope of the present invention.

Claims (5)

1. A spin-stabilized meteorological satellite attitude determination method based on a coarse and fine attitude relationship model is characterized by comprising the following steps:

step 1: calculating a satellite coarse attitude;

step 2: eliminating random errors in the satellite coarse attitude, judging abnormal satellite coarse attitude data through a weighted average composite filter based on a sliding median, and adopting an attitude moving window { x with the length of m in the algorithm₀，x₁，...x_m-1Determining the existence of the satellite coarse attitude data by using m continuous attitude valuesEffectively, if the filter judges that the satellite coarse attitude data is valid, outputting; otherwise, judging that the satellite coarse attitude data is abnormal data, and correcting by using a weighted mean value; and

and step 3: and correcting the coarse attitude by using the coarse attitude and fine attitude relation model, comparing the coarse attitude without the random error with the fine attitude, calculating the system error, and fitting the change rule of the two attitudes by using a polynomial least square curve method.

2. The method for determining the attitude of a spin-stabilized meteorological satellite based on a coarse-fine attitude relationship model according to claim 1, wherein the step 1 further comprises:

step 1.1): performing quality inspection on the obtained satellite telemetering data by taking a frame as a unit, judging the quality of each frame of satellite telemetering data according to a frame header synchronous code and a frame tail check value, and discarding the satellite telemetering data value which does not meet the requirement as unqualified satellite telemetering data value;

step 1.2): decoding the satellite telemetry data frame which is subjected to the quality inspection in the step 1.1 and meets the requirements according to the format definition of the satellite telemetry data, and storing each channel value into a designated array I;

step 1.3): extracting binary original code values from T1 to T11 from the array I, and then converting the binary original code values into physical quantities P1 to P11 required for calculating the coarse attitude of the satellite;

step 1.4): according to the mature attitude calculation theory, deducing a unit sun vector S, a unit nadir vector E in the earth center direction and a unit vector Z in the satellite spin axis direction by using P1-P11, and meanwhile, according to an included angle thetas between a spin axis and the sun measured by a sun sensor and an included angle thetae between the spin axis and the nadir vector measured by an earth sensor, in an equator inertial coordinate system, according to the following attitude equation:

$\left\{\begin{array}{c}S\·Z={\mathrm{cos\θ}}_s;\\ E\·Z={\mathrm{cos\θ}}_e;\\ (S\×E)\·Z={\mathrm{sin\θ}}_s{\mathrm{sin\θ}}_e{\mathrm{sin\λ}}_{ie}\end{array}\right.$

the included angle lambda se between the solar-spin axis plane and the geocentric-spin axis plane can be calculated;

step 1.5): from the known position of the sun (α s, s), the following formula:

$\left\{\begin{array}{c}{\mathrm{\α}}_e={\mathrm{\α}}_s-{\mathrm{\α}}_o;\\ {\mathrm{\δ}}_e=\arcsin \[{\mathrm{sin\δ}}_s{\mathrm{cos\θ}}_s+{\mathrm{cos\δ}}_s{\mathrm{sin\θ}}_{s}cos({\mathrm{\ϵ}}_1+{\mathrm{\ϵ}}_2)\]\end{array}\right.$

obtaining the right ascension alpha e and the declination e of the satellite coarse attitude, wherein:

step 1.6): from the known sun position (α S, S), satellite orbit inclination i, true perigee angle f and elevation intersection declination Ω, S and E can be expressed as:

$S=\left|\begin{array}{cc}{\mathrm{cos\δ}}_s& {\mathrm{cos\α}}_s\\ {\mathrm{cos\δ}}_s& {\mathrm{sin\α}}_s\\ {\mathrm{sin\δ}}_s& 0\end{array}\right|$

$E=\left|\begin{array}{c}-\sin i\sin \mathrm{\Ω}\\ \sin i\cos \mathrm{\Ω}\\ -\cos i\end{array}\right|$

thus, the position of the satellite is:

3. the method for determining the attitude of a spin-stabilized meteorological satellite based on a coarse-fine attitude relationship model according to claim 2, wherein the step 2 comprises:

step 2.1): assume an initial satellite coarse attitude data sequence { X }₀，X₁，…，X_m-1The median value of the sequence is Z, and the absolute deviation of the median values of the coarse attitudes of the satellites is used for constructing a sequence d₀，d₁，...d_m-1}＝{|x₀-z|，|x₁-z|，...，|x_m-1-z | }, assuming the sequence d₀，d₁，...，d_m-1D, wherein the absolute deviation Q of the median of the coarse satellite attitude is 1.4826 × D, Q replaces the standard deviation;

step 2.2): if the value absolute deviation sequence { x (d) } ═ d in the coarse satellite attitude₀，d₁，...d_m-1The values of the k terms in the list are all greater than L Q, { d'₀，d′₁，...，d′_m-1-iAccording to the value of { x (d) }, the sequence { x is divided into a plurality of sequences₀，x₁，...，x_m-1Is disassembled into { x'₀，x′₁，...，x′_m-1-iAnd { x ″ }₀，x″₁，...，x″_iH.d. two sequences, wherein, x'_i(d)≤L×Q，x″_i(d) L is greater than L × Q, and L replaces the threshold;

step 2.3): sequence { x 'from step 2.2'₀，x′₁，...，x′_m-1-iThe values in (f) are normal values, such values are not corrected; sequence { x ″)₀，x″₁，...，x″_iThe value in the sequence is an abnormal value and needs to be corrected, and the satellite coarse attitude data sequence { x }₀，x₁，...，x_m-1The final corrected result isAnd the following conditions are satisfied:

if x_i∈{x′₀，x′₁，...，x′_m-1-i}, then

If x_i∈{x″₀，x″₁，...，x″_iThere is formula 1, as follows:

step 2.4): in the algorithm, an initial satellite coarse attitude sequence is firstly accumulated, and sequences { x, y and z axes of the satellite coarse attitude are respectively accumulated₀，x₁，...，x_m-1}、{y₀，y₁,...,y_m-1And { z }₀，z₁，...，z_m-1When the length of the sequence is m, correcting once according to the formula 1;

step 2.5): the fixed sequence length is m, and when a new coarse attitude value is calculated, the sliding sequence { x }₀，x₁，...，x_m-1}、{x₀，x₁，...，x_m-1And { x }₀，x₁，...，x_m-1And putting the new calculated value into the tail of the queue, removing one data of the original head of the queue, and then carrying out filtering correction on m values in the sequence according to a formula 1.

4. The method for determining the attitude of a spin-stabilized meteorological satellite based on a coarse-fine attitude relationship model according to claim 3, wherein: the characteristics of the weighted average composite filter based on the sliding median are adjusted by three parameters, namely a window width m, a threshold L and a correction weight C, wherein: m affects the overall consistency of the filter; the threshold L directly determines the active access degree of the filter, the value of L is increased, the possibility that singular data is judged and replaced by a median value is reduced, and when L is equal to 0, the filter is always determined; the correction weight value C directly determines the correction amplitude of the filter, the value C is increased, the larger the correction amplitude of the filter is, and when C is equal to 0, the abnormal posture is corrected by using the average value.

5. The method for determining the attitude of a spin-stabilized meteorological satellite based on a coarse-fine attitude relationship model according to claim 4, wherein the step 3 comprises:

step 3.1): representing the relationship of the fine and coarse poses on the x, y and z axes as three discrete data points { (x)_i，x′_i)，i＝0，1，2，...，m}、{(y_i，y′_i) I { (z) } 0, 1, 2_i，z′_i) I is 0, 1, 2,.. times.m.three relation functions X ″ -X (X) are respectively set as_i)、y″＝Y(y_i) And Z ″ ═ Z (Z)_i) Fitting to the given three sets of data;

step 3.2): for attitude data { (x)_i，x′_i)，i＝0，1，2，...，m}、{(y_i，y′_i) I { (z) } 0, 1, 2_i，z′_i) I is 0, 1, 2,.. multidot.m, and in the function class Φ formed by the polynomial with all the degree not exceeding n, where n is less than or equal to m, the values are respectively solvedAndlet the error r_xi、r_yiAnd r_ziHas the smallest sum of squares, i.e.

$\underset{i=0}{\overset{m}{\Σ}}{r}_{ri}^2=\underset{i=0}{\overset{m}{\Σ}}{\left(\underset{k=0}{\overset{n}{\Σ}}{X}_n\right(x_i)-x^n)}^2=\min$

$\underset{i=0}{\overset{m}{\Σ}}{r}_{yi}^2=\underset{i=0}{\overset{m}{\Σ}}{\left(\underset{k=0}{\overset{n}{\Σ}}{Y}_n\right(y_i)-y^n)}^2=\min$

$\underset{i=0}{\overset{m}{\Σ}}{r}_{zi}^2=\underset{i=0}{\overset{m}{\Σ}}{\left(\underset{k=0}{\overset{n}{\Σ}}{Z}_n\right(z_i)-z^n)}^2=\min$

Wherein r is_xi＝X(x_i)-x_i、r_yi＝Y(y_i)-y_i、z_yi＝Z(z_i)-z_i(i＝0，1，…，m)；

Let the error r_xi、r_yiAnd r_ziMinimum, X_n(x_i)，Y_n(y_i)，Z_n(z_i) It is the polynomial sought.