CN107869993A - Small satellite attitude method of estimation based on adaptive iteration particle filter - Google Patents
Small satellite attitude method of estimation based on adaptive iteration particle filter Download PDFInfo
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- CN107869993A CN107869993A CN201711081317.2A CN201711081317A CN107869993A CN 107869993 A CN107869993 A CN 107869993A CN 201711081317 A CN201711081317 A CN 201711081317A CN 107869993 A CN107869993 A CN 107869993A
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- 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
Abstract
The invention discloses a kind of small satellite attitude method of estimation based on adaptive iteration particle filter, for solving the technical problem of existing small satellite attitude method of estimation poor practicability.Technical scheme is expectation and the variance of the Gaussian Profile when the importance density function, exceed certain threshold value by calculating, at this moment the method for utilizing correction term itself iteration, real-time update importance density function, particle is allowd to be advanced to high likelihood region rapidly, this also allows the weight of particle sample dispersed, improve the efficiency of sampling, while with simulated annealing, solve that particle weights are smaller, are not easy normalized problem, thus produce adaptive iteration particle filter (AIPF) method.The present invention is applied to this algorithm in the Attitude estimation of small satellite system, enabling the high-precision estimation attitude of satellite.
Description
Technical field
The present invention relates to a kind of small satellite attitude method of estimation, and adaptive iteration particle filter is based on more particularly to one kind
Small satellite attitude method of estimation.
Background technology
It is exactly the measured value according to attitude sensor to determine the attitude of satellite, obtains vector of the satellite relative to reference bearing
Information determines the posture of satellite.One of method that the attitude of satellite determines is state estimate, and this method is substantially exactly non-thread
Property filtering problem, the angle estimation optimal from probability statistics go out the posture of moonlet.But the attitude of satellite determine self model and
It is complicated, along with other reasons, causes the system equation of satellite to have strong nonlinearity.
Document " the microsatellite attitude estimation based on Unscented quaternary number particle filters, learn by BJ University of Aeronautics ＆ Astronautics
Report, 2007, Vol33 (5), P552-556 ", discloses a kind of microsatellite appearance based on Unscented quaternary number particle filters
State method of estimation.This method devises a kind of attitude estimator based on UPF, uses MEMS using error quaternion as attitude parameter
The random noise data of gyro collection have carried out computer semi-physical simulation.Unscented particle filters (UPF) are by non-linear
Conversion obtains the approximation state distribution of sampled point, so solves the nonlinear problem of system, the algorithm is that UKF is embedded into grain
In the framework of son filtering, the importance sampling density of particle filter is calculated by this method, due in the importance density function
Comprising newest measurement information, there is better performance compared with traditional PF methods.Its weak point of document methods described is not
Suitable for the system of noise non-gaussian distribution, also without adaptively, these all have an impact to the precision of acquired results.Because sample grain
The process of son has used UKF algorithms, so sampling efficiency is not also high, amount of calculation adds on the contrary.
The content of the invention
In order to overcome the shortcomings of existing small satellite attitude method of estimation poor practicability, the present invention provides a kind of based on adaptive
The small satellite attitude method of estimation of iteration particle filter.Expectation and side of this method when the Gaussian Profile of the importance density function
Difference, exceed certain threshold value by calculating, at this moment using the method for correction term itself iteration, real-time update importance density function,
Particle is allowd to be advanced to high likelihood region rapidly, this also allows the weight of particle sample dispersed, improves the effect of sampling
Rate, while with simulated annealing, solve that particle weights are smaller, are not easy normalized problem, thus produce adaptive
Answer iteration particle filter (AIPF) method.The present invention is applied to this algorithm in the Attitude estimation of small satellite system so that energy
Enough high-precision estimation attitudes of satellite.
The technical solution adopted for the present invention to solve the technical problems is：It is a kind of based on the small of adaptive iteration particle filter
Satellite Attitude Estimation method, it is characterized in comprising the following steps：
Step 1：State initial value and state initial variance value are initialized, give initial value x respectively1And P1。
Step 2：Time renewal is carried out, chooses a transitional provavility density as initial importance probability density function p
(xt+1|xt), and therefrom extract particle sampleWherein, N represents total number of particles, xtThe state of t is expressed as,
It is expressed as i-th of particle sample of t.
Step 3：Carry out measurement renewal.
1. whether the particle sample that judgment step two extracts is effective；
The particle sample of extraction is brought into measurement equationDraw probability density function p (yt|z1：t-1)
Normal Distribution Represent that i-th of particle of t brings the measuring value obtained by measurement equation into,Represent
I-th of particle of t carries out NONLINEAR CALCULATION in measurement equation；
Wherein For the weight of the i-th -1 particle of t-1 moment；
What if above formula calculatedAnd ptMeetρ is given critical value, then the grain
It is sub effective；If not satisfied, correction term c (z are calculated by the successive ignition of particle filtert)；
Wherein,It is xtGained estimate, z after k-1 iterationtIt is actual measured value.As long as ck(zt), it is known that just
Use ck(zt) replace c (zt).Solve the problems, such as that likelihood function is in kurtosis using simulated annealing method simultaneously, expand measurement and make an uproar
The variance of sound is α R, and α >=1, wherein R are the variances for measuring noise, and α is the coefficient for expanding noise.Draw c (zt) after, obtain one
Individual new importance density functionNew particle sample is therefrom obtained again
ThisWherein pvThe probability density function obeyed for system noise, xtRepresent the state of t, f (xt-1) represent t-1 when
The state at quarter carries out NONLINEAR CALCULATION in system equation.
2. calculate weightsWeights normalizeWherein,For likelihood function,For probability density function,For important probability density
Function.
Step 4：When particle is degenerated, resampling is carried out to it.
Step 5：The estimate of computing system stateCalculate state variance
Step 6：T=t+1 is made, returns step 2, carries out the circulation at next moment, until t=T, T is what is set
Time cycle-index.
The beneficial effects of the invention are as follows：Expectation and variance of this method when the Gaussian Profile of the importance density function, pass through
Calculating exceeds certain threshold value, at this moment utilizes the method for correction term itself iteration, real-time update importance density function so that particle
High likelihood region can be advanced to rapidly, this also allows the weight of particle sample dispersed, improves the efficiency of sampling, and together
Shi Yunyong simulated annealings, solve that particle weights are smaller, are not easy normalized problem, thus produce adaptive iteration particle
(AIPF) method of filtering.The present invention is applied to this algorithm in the Attitude estimation of small satellite system, enabling high-precision
Estimate the attitude of satellite.
The present invention improves sampling efficiency using itself iteration real-time update and amendment importance density function；Utilize simultaneously
It is small that annealing algorithm solves particle weights, is not easy normalized problem.By increasing a correction term and use to measurement equation
Simulated annealing, which expands, to be measured noise and obtains new sample particles, and obtained new sample particles are closer to likelihood area, more
It is higher added with effect, accuracy.
The present invention is elaborated with reference to embodiment.
Embodiment
Small satellite attitude method of estimation of the invention based on adaptive iteration particle filter comprises the following steps that：
The system equation and measurement equation to state description are initially set up, state vector is expressed asWherein ρ
=[ρ1 ρ2 ρ3]TModified discrete chirp-Fourier transform is represented, represents roll angle, the angle of pitch and the yaw angle of satellite respectively；ω=[ω1
ω2 ω3]TFor angular speed, the pace of change of three attitude angles is corresponded respectively to.Measurement model uses focal plane model, wherein
And ykIt is observation and actual value respectively.System equation and measurement equation are expressed as：
xk+1=f (xk)+vk
vtThe process noise of expression system, it is 6 × 1 vector,This
Locating value iswt+1It is 2 × 1 column vector for the measurement noise of system,Value is hereinProvide modified discrete chirp-Fourier transformAnd angular speedN=100 sample of the state initial value of initial value, i.e. systemStep
One：Modified discrete chirp-Fourier transform is setAnd angular speedInitial value, be exactly the state initial value of system N
Individual sampleThe initial true value of MRPs and estimate (rad)：
The initial true value of angular speed and estimate (rad/s)：
MRPs and angular speed initial error covariance：
Moment of inertia matrix
Step 2：Choose a state transition function p (xt+1|xt) importance density function is used as, therefrom extract N
Individual particle sampleFurther according to the t obtained
Sample particles, the t+1 moment is obtained by system equation
Step 3：Carry out measurement renewal；
1. whether the particle sample extracted firstly the need of judgment step two is effective；
The particle sample of extraction is brought into measurement equationDraw probability density function p (yt|z1：t-1)
Gaussian distributed
Wherein
What if above formula calculatedAnd ptMeet(ρ is given critical value), the then grain
It is sub effective；If not satisfied, correction term c (z are calculated by the successive ignition of particle filtert)；
Wherein,It is xtThe gained estimate after k-1 iteration.As long as ck(zt) known, it is possible to use ck(zt) generation
For c (zt).Solve the problems, such as that likelihood function is in kurtosis using simulated annealing method simultaneously, the variance for expanding measurement noise is
α R, wherein usual α >=1, R are the variances for measuring noise.Draw c (zt) after, obtain a new importance density functionNew particle sample is therefrom obtained againWherein pvFor system noise
The probability density function of obedience.
2. calculate weightsWeights normalize
Step 4：When particle is degenerated, resampling is carried out to it.
Step 5：The estimate of computing system stateCalculate state variance
Step 6：T=t+1 is made, returns step 2, the circulation at next moment is carried out, until t=T.
Claims (1)
1. a kind of small satellite attitude method of estimation based on adaptive iteration particle filter, it is characterised in that comprise the following steps：
Step 1：State initial value and state initial variance value are initialized, give initial value x respectively1And P1；
Step 2：Time renewal is carried out, chooses a transitional provavility density as initial importance probability density function p (xt+1|
xt), and therefrom extract particle sampleWherein, N represents total number of particles, xtThe state of t is expressed as,Represent
For i-th of particle sample of t；
Step 3：Carry out measurement renewal；
1. whether the particle sample that judgment step two extracts is effective；
The particle sample of extraction is brought into measurement equationDraw probability density function p (yt|z1：t-1) obey
Normal distribution Represent that i-th of particle of t brings the measuring value obtained by measurement equation into,Represent in t
Carve i-th of particle and NONLINEAR CALCULATION is carried out in measurement equation；
Wherein For the weight of the i-th -1 particle of t-1 moment；
What if above formula calculatedAnd PtMeetρ is given critical value, then the particle has
Effect；If not satisfied, correction term c (z are calculated by the successive ignition of particle filtert)；
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Wherein,It is xtGained estimate, z after k-1 iterationtIt is actual measured value；As long as ck(zt), it is known that just using ck
(zt) replace c (zt)；Solve the problems, such as that likelihood function is in kurtosis using simulated annealing method simultaneously, expand and measure noise
Variance is α R, and α >=1, wherein R are the variances for measuring noise, and α is the coefficient for expanding noise；Draw c (zt) after, obtain one newly
Importance density functionNew particle sample is therefrom obtained againWherein pvThe probability density function obeyed for system noise, xtRepresent the state of t, f (xt-1) represent the t-1 moment
State carries out NONLINEAR CALCULATION in system equation；
2. calculate weightsWeights normalizeWherein,For seemingly
Right function,For probability density function,For important probability density function；
Step 4：When particle is degenerated, resampling is carried out to it；
Step 5：The estimate of computing system stateCalculate state variance
Step 6：T=t+1 is made, returns step 2, carries out the circulation at next moment, until t=T, T is the time set
Cycle-index.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109084751A (en) * | 2018-06-08 | 2018-12-25 | 西北工业大学 | A kind of high energy efficiency attitude of satellite based on box particle filter determines algorithm |
| CN112257346A (en) * | 2020-10-29 | 2021-01-22 | 重庆科技学院 | Novel UPFNN aluminum electrolysis energy consumption calculation method based on self-adaptive MCMC sampling |
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| CN104066179A (en) * | 2014-07-10 | 2014-09-24 | 浙江银江研究院有限公司 | Improved method for positioning WSN nodes through adaptive iterative UKF |
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| CN112257346A (en) * | 2020-10-29 | 2021-01-22 | 重庆科技学院 | Novel UPFNN aluminum electrolysis energy consumption calculation method based on self-adaptive MCMC sampling |
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