CN102778230B - Gravity gradient auxiliary positioning method of artificial physical optimization particle filtering - Google Patents
Gravity gradient auxiliary positioning method of artificial physical optimization particle filtering Download PDFInfo
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Abstract
The invention provides a gravity gradient auxiliary positioning method of artificial physical optimization particle filtering. The method comprises the following steps of: determining the initial position parameter of a carrier through a GPS (global positioning system), and binding the initial position parameter into a navigation computer; warming up and preparing by a strapdown inertial navigation system, collecting data output by an optical fiber gyroscope and a quartz accelerometer and treating the data; and improving the particle distribution through introducing artificial physical optimization, overcoming the particle degradation problem of particle filtering, and reducing the calculation amount and the required iterations. Repulsive force is introduced by the artificial physical optimization particle filtering algorithm during the process of improving the particle distribution, particle sets are prevented from being excessively overlapped or crowded, and uniform covering capability is ensured to be formed by the posterior probability density by particle universality and optimized particle sets; through continuous updating and recursion of the artificial physical optimization particle filtering, the position error of an inertial navigation system is rectified after being estimated and calculated, thus the position error of the gravity gradient auxiliary positioning method tends to be zero gradually. The artificial physical optimization particle filtering algorithm disclosed by the invention enables the system to have good stability while the system estimation accuracy is improved.
Description
(1) technical field
What the present invention relates to is a kind of measuring method, and in particular a kind of artificial physics is optimized the gravity gradient assisted location method of particle filter.
(2) background technology
, good concealment completely autonomous owing to having, can provide continuously position, speed and attitude parameter, inertial navigation system is the basic navigation system of submarine navigation device always.But well-known, due to the intrinsic reason of inertia device, the positioning error of inertial navigation system accumulates in time, long-term accuracy is difficult to guarantee.The requirements at the higher level that modern war proposes navigation and positioning accuracy, continuous appearance and the development of various inertia base Combinated navigation methods have been promoted, as Terrain-aided Navigation, geomagnetic auxiliary navigation etc. combines with inertial navigation system, to overcome the deficiency of pure-inertial guidance system, effectively reduce the requirement to inertial navigation system.Gravity gradient assisted location method meets the development trend of gravity field measuring technique, make full use of the potential accuracy of gravity gradient auxiliary positioning and sourceless characteristic and provide hi-Fix parameter for optimal in structure, to improving the disguised and long-term autonomous of carrier, to navigate by water ability significant.
Because particle filter is applicable to the state estimation problem of nonlinear system, so be applied to the optimal State Estimation of non-linear non-Gauss's problems such as target following, navigator fix.But the defect that elementary particle filtering algorithm itself exists causes filtering result occurs Divergent Phenomenon, and particle degradation phenomena embodies obviously in gravity gradient auxiliary positioning, can not guarantee the stability of system.Particle filter lacks the ability that generates new particle, and when last particle can not represent real posterior probability density, particle filter algorithm will be failed.Therefore, the problem that particle is accelerated to degenerate is improved, and just can impel particle filter to be well used in gravity gradient auxiliary positioning.
The correlative study scheme that particle is degenerated is the popular problem that this area research is inquired into always, typically improves particle filter and has Auxiliary Particle Filter, without mark particle filter, canonical particle filter etc.In recent years, have scholar to propose swarm intelligence and particle filter to merge mutually, the core of improving particle filter due to swarm intelligence is to improve resampling, optimizes distribution of particles.Therefore effect and the realizability of these class methods depend on optimized algorithm used itself.Due to some swarm intelligence algorithm, conventionally there is the problems such as search capability is poor, early convergence, be therefore directly introduced into particle filter not only to fall flat, also may cause algorithm to become more complicated.For as above problem, patent of the present invention proposes a kind ofly artificial physics optimization method is introduced to particle filter to improve that particle is degenerated and the filtering algorithm of samples impoverishment problem, and by this algorithm application in gravity gradient auxiliary positioning.
(3) summary of the invention
Technology of the present invention is dealt with problems and is: overcome the deficiencies in the prior art, provide a kind of artificial physics to optimize the gravity gradient assisted location method of particle filter.
Technical solution of the present invention is: a kind of artificial physics is optimized the gravity gradient assisted location method of particle filter, it is characterized in that particle filter completes after one-step prediction, artificial physics is optimized particle filter algorithm the candidate's particle collection obtaining is considered as to the individuality in artificial physics solution space, by optimization, generate new suggestion and distribute, newer particle collection is carried out to weights normalization and resampling.In this algorithm, fictitious force has determined particle motion track, for particle is assembled to high likelihood region, stipulates that all particles are subject to the gravitation of the maximum particle of weights, makes system have better stability, is conducive to alliance error and goes to zero gradually.Its concrete steps are as follows:
(1) by GPS, determine the initial position parameters of carrier, they are bound to navigational computer;
(2) strapdown inertial navitation system (SINS) is carried out preheating preparation, gathers the data of fibre optic gyroscope and quartz accelerometer output and data are processed;
(3) artificial physics is optimized the realization of particle filter algorithm;
1) initialization
From posterior probability Density Distribution, sample, weight is set to 1/N.Perception radius r is set simultaneously
s, the threshold value D of gravitation and repulsion
th, fictitious force coefficient K
a1, K
a2, the parameters such as iterations, perception matrix c, fictitious force matrix F and the zero setting of current iteration number of times.
2) prediction
Sample from the importance density function of sampling new particle collection calculate particle weights.
3) optimize distribution of particles
A) calculate particle s
iwith s
jbetween Euclidean distance d (s
i, s
j), i, j=1,2 ... N
B) calculate s
ito s
jperception matrix
I, j=1,2 ... N, r
sfor perception radius.
C) calculate particle s
ifictitious force F (s
i, s
j)
D) calculate particle s
ithe F (i) that makes a concerted effort being subject to
If F (i) > maximum displacement restriction L
max, F (i)=L
max; If F (i) < least displacement restriction L
min, F (i)=L
min.
E) new particle s more
iposition
x′
i＝x
i+F
i
Wherein, x '
iposition for after upgrading, is subject to the restriction of least displacement and maximum displacement, i.e. x '
i∈ [L
min, L
max].Meet after optimal conditions or iterations, optimized algorithm stops.
F) calculate new particle weights, upgrade the particle of weights maximum
4) obtain new particle collection, iteration optimization finishes.Recalculate new particle weights normalization
5) resample
If number of effective particles is less than the threshold value of setting, particle filter algorithm resamples
6) state estimation
Calculated single particle suffered close the renewal of carrying out particle position after fictitious force, with this, reduce calculated amount and required iterations, improve convergence of algorithm speed.Artificial physics is optimized particle algorithm by introduce repulsive force in improving the process of distribution of particles, avoids particle collection too overlapping or crowded, with this, realizes particle diversity and the distribution to posterior probability density.
(4) foundation of the auxiliary combined filter model of gravity gradient;
The combined filter model of deriving based on gravity gradient auxiliary positioning on INS errors equation basis, chooses longitude and latitude error, velocity error, attitude error, accelerometer bias and gyroscopic drift as state variable.The tight anabolic process of gravity gradient auxiliary positioning and inertial navigation system is using gravity gradient as observed quantity.Consider that gravity gradient observed reading is the function of carrier actual position, based on gravity gradient auxiliary positioning observation equation, can be expressed as:
Wherein, y
jk(j, k=x, y, z) is gravity gradient observed reading;
represent true carrier positions
the gravity gradient at place; V represents observation noise.Gravity gradient and carrier actual position are typical nonlinear relationship, because inertial navigation system can not directly provide carrier actual position
therefore, conventional linear-apporximation filtering method is by Nonlinear Vector function
at current time Filtering Estimation
near get single order Taylor series expansion:
Obtain the error observation equation of approximately linear:
Due to the general file layout that adopts discrete graticule mesh of benchmark gradient map of gravity gradient auxiliary positioning, there is corresponding relation in gravity gradient value and position coordinates, therefore based on gravity gradient auxiliary positioning observation equation, is difficult to represent with analytical function.By given system state initial value and initial variance battle array, the posteriority that particle filter can generate initial time site error distributes
with
therefore, two important parameters in the observation equation of gravity gradient auxiliary positioning obtain by particle filter method.Through one-step prediction, particle filter just can directly calculate from benchmark gradient map
as prediction probability density.As up-to-date observed reading y
jkduring arrival, particle filter, again by the importance density function, utilizes the difference between prediction Grad and observation Grad, and more new particle weights obtain system state.If the true posteriority that particle collection can not representative system state distributes, the difference between the prediction Grad of reading in benchmark gradient map so and observation Grad is larger, and weights are less, and vice versa.Particle filter is by constantly upgrading and recursion, proofreaies and correct after estimating the site error of inertial navigation system, thereby alliance error is gone to zero gradually.
The present invention's advantage is compared with prior art: the present invention optimizes by introducing artificial physics, improves distribution of particles, overcomes the particle degenerate problem of particle filter, has reduced calculated amount and required iterations.From artificial physics, optimizing particle filter implementation procedure can find out, this algorithm by having introduced repulsive force in improving the process of distribution of particles, avoided particle collection too overlapping or crowded, guaranteed that the particle set pair posterior probability density after particle diversity and optimization forms uniform covering power.Artificial physics is optimized particle algorithm when improving system estimation precision, makes system have better stability, is conducive to alliance error and goes to zero gradually.
The effect useful to the present invention is described as follows:
Under VC++ simulated conditions, the method is carried out to emulation experiment:
The artificial physics that patent of the present invention is carried is optimized particle filter algorithm and is applied to gravity gradient auxiliary positioning, adopt particle filter algorithm and particle group optimizing particle filter algorithm to contrast simultaneously, investigate feasibility and reliability that artificial physics is optimized particle algorithm.
Simulated conditions: strapdown inertial navigation system isolated operation 2 hours, then introduce gravity gradient auxiliary positioning.Artificial physics is optimized particle algorithm parameter: get perceived distance r
s=150 meters, threshold value D
th=10 meters, filtering initial value x
0=0.1, P
0=2, artificial physics is optimized particle algorithm and particle group optimizing particle filter algorithm population is all taken as 100, and site error initial value is 2000 meters, and iteration optimization number of times is 20 times.Particle algorithm population 2000,500 meters of site error initial values.The filtering cycle is taken as 10 seconds, observation noise in experimentation
Utilize method of the present invention to obtain three kinds of algorithms to the estimated result of site error (accompanying drawing 2) and each posteriority distribution of particles curve (accompanying drawing 3) constantly, result shows estimated result that artificial physics optimizes particle filter algorithm more steadily and estimated accuracy is better than particle filter algorithm and particle group optimizing particle filter algorithm.
(4) accompanying drawing explanation
Fig. 1 is the gravity gradient assisted location method process flow diagram that a kind of artificial physics of the present invention is optimized particle filter;
Fig. 2 is the estimated result curves of three kinds of particle filter methods of the present invention to inertial navigation system error;
Fig. 3 is the posteriority distribution of particles curve of each moment three kinds of particle filter methods of the present invention.
(5) embodiment
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in detail:
(1) by GPS, determine the initial position parameters of carrier, they are bound to navigational computer;
(2) strapdown inertial navitation system (SINS) is carried out preheating preparation, gathers the data of fibre optic gyroscope and quartz accelerometer output and data are processed;
(3) realization of artificial physics particle filter algorithm;
1) initialization
From posterior probability Density Distribution, sample, weight is set to 1/N.Perception radius r is set simultaneously
s, the threshold value D of gravitation and repulsion
th, fictitious force coefficient K
a1, K
a2, the parameters such as iterations, perception matrix c, fictitious force matrix F and the zero setting of current iteration number of times.
2) prediction
Sample from the importance density function of sampling new particle collection calculate particle weights.
3) optimize distribution of particles
A) calculate particle s
iwith s
jbetween Euclidean distance d (s
i, s
j), i, j=1,2 ... N
B) calculate s
ito s
jperception matrix
I, j=1,2 ... N, r
sfor perception radius.
C) calculate particle s
ifictitious force F (s
i, s
j)
D) calculate particle s
ithe F (i) that makes a concerted effort being subject to
If F (i) > maximum displacement restriction L
max, F (i)=L
max; If F (i) < least displacement restriction L
min, F (i)=L
min.
E) new particle s more
iposition
x′
i＝x
i+F
i (2)
Wherein, x '
iposition for after upgrading, is subject to the restriction of least displacement and maximum displacement, i.e. x '
i∈ [L
min, L
max].Meet after optimal conditions or iterations, optimized algorithm stops.
F) calculate new particle weights, upgrade the particle of weights maximum
4) obtain new particle collection, iteration optimization finishes.Recalculate new particle weights normalization
5) resample
If number of effective particles is less than the threshold value of setting, particle filter algorithm resamples
6) state estimation
Calculated single particle suffered close the renewal of carrying out particle position after fictitious force, with this, reduce calculated amount and required iterations, improve convergence of algorithm speed.Artificial physics is optimized particle algorithm by introduce repulsive force in improving the process of distribution of particles, avoids particle collection too overlapping or crowded, with this, realizes particle diversity and the distribution to posterior probability density.
(4) foundation of the auxiliary combined filter model of gravity gradient;
The combined filter model of deriving based on gravity gradient auxiliary positioning on INS errors equation basis, chooses longitude and latitude error, velocity error, attitude error, accelerometer bias and gyroscopic drift as state variable.The tight anabolic process of gravity gradient auxiliary positioning and inertial navigation system is using gravity gradient as observed quantity.Consider that gravity gradient observed reading is the function of carrier actual position, based on gravity gradient auxiliary positioning observation equation, can be expressed as:
Wherein, y
jk(j, k=x, y, z) is gravity gradient observed reading;
represent true carrier positions
the gravity gradient at place; V represents observation noise.Gravity gradient and carrier actual position are typical nonlinear relationship, because inertial navigation system can not directly provide carrier actual position
therefore, conventional linear-apporximation filtering method is by Nonlinear Vector function
at current time Filtering Estimation
near get single order Taylor series expansion:
Obtain the error observation equation of approximately linear:
Due to the general file layout that adopts discrete graticule mesh of benchmark gradient map of gravity gradient auxiliary positioning, there is corresponding relation in gravity gradient value and position coordinates, therefore based on gravity gradient auxiliary positioning observation equation, is difficult to represent with analytical function.By given system state initial value and initial variance battle array, the posteriority that particle filter can generate initial time site error distributes
with
therefore, two important parameters in the observation equation of gravity gradient auxiliary positioning obtain by particle filter method.Through one-step prediction, particle filter just can directly calculate from benchmark gradient map
as prediction probability density.As up-to-date observed reading y
jkduring arrival, particle filter, again by the importance density function, utilizes the difference between prediction Grad and observation Grad, and more new particle weights obtain system state.If the true posteriority that particle collection can not representative system state distributes, the difference between the prediction Grad of reading in benchmark gradient map so and observation Grad is larger, and weights are less, and vice versa.Particle filter is by constantly upgrading and recursion, proofreaies and correct after estimating the site error of inertial navigation system, thereby alliance error is gone to zero gradually.
Claims (1)
1. artificial physics is optimized a gravity gradient assisted location method for particle filter, it is characterized in that comprising the following steps:
(1) by GPS, determine the initial position parameters of carrier, they are bound to navigational computer;
(2) strapdown inertial navitation system (SINS) is carried out preheating preparation, gathers the data of fibre optic gyroscope and quartz accelerometer output and data are processed;
(3) artificial physics is optimized the realization of particle filter algorithm;
1) initialization
From posterior probability Density Distribution, sample, weight is set to 1/N, and perception radius r is set simultaneously
s, the threshold value D of gravitation and repulsion
th, fictitious force coefficient K
a1, K
a2, the parameters such as iterations, perception matrix c, fictitious force matrix F and the zero setting of current iteration number of times;
2) prediction
Sample from the importance density function of sampling new particle collection calculate particle weights;
3) optimize distribution of particles
A) calculate particle s
iwith s
jbetween Euclidean distance d (s
i, s
j), i, j=1,2 ... N;
B) calculate s
ito s
jperception matrix
I, j=1,2 ... N, r
sfor perception radius;
C) calculate particle s
ifictitious force F (s
i, s
j)
D) calculate particle s
ithe F that makes a concerted effort being subject to
i
If F
i> maximum displacement restriction L
max, F
i=L
max; If F
i< least displacement restriction L
min, F
i=L
min;
E) new particle s more
iposition
x′
i＝x
i+F
i
Wherein, x '
iposition for after upgrading, is subject to the restriction of least displacement and maximum displacement, i.e. x '
i∈ [L
min, L
max], meeting after optimal conditions or iterations, optimized algorithm stops;
F) calculate new particle weights, upgrade the particle of weights maximum;
4) obtain new particle collection, iteration optimization finishes, and recalculates new particle weights normalization;
5) resample
If number of effective particles is less than the threshold value of setting, particle filter algorithm resamples;
6) state estimation
Calculated single particle suffered close the renewal of carrying out particle position after fictitious force, with this, reduce calculated amount and required iterations, improve convergence of algorithm speed, artificial physics is optimized particle algorithm by introduce repulsive force in improving the process of distribution of particles, avoid particle collection too overlapping or crowded, with this, realize particle diversity and the distribution to posterior probability density;
(4) foundation of the auxiliary combined filter model of gravity gradient;
The combined filter model of deriving based on gravity gradient auxiliary positioning on INS errors equation basis, choose longitude and latitude error, velocity error, attitude error, accelerometer bias and gyroscopic drift as state variable, the tight anabolic process of gravity gradient auxiliary positioning and inertial navigation system is using gravity gradient as observed quantity, consider that gravity gradient observed reading is the function of carrier actual position, based on gravity gradient auxiliary positioning observation equation, can be expressed as:
Wherein, y
jk(j, k=x, y, z) is gravity gradient observed reading;
represent true carrier positions
the gravity gradient at place; V represents observation noise, and gravity gradient and carrier actual position are typical nonlinear relationship, because inertial navigation system can not directly provide carrier actual position
therefore, conventional linear-apporximation filtering method is by Nonlinear Vector function
at current time Filtering Estimation
near get single order Taylor series expansion:
Obtain the error observation equation of approximately linear:
The general file layout that adopts discrete graticule mesh of benchmark gradient map due to gravity gradient auxiliary positioning, there is corresponding relation in gravity gradient value and position coordinates, therefore based on gravity gradient auxiliary positioning observation equation, be difficult to represent with analytical function, by given system state initial value and initial variance battle array, the posteriority that particle filter can generate initial time site error distributes
with
therefore, two important parameters in the observation equation of gravity gradient auxiliary positioning obtain by particle filter method, and through one-step prediction, particle filter just can directly calculate from benchmark gradient map
as prediction probability density, as up-to-date observed reading y
jkduring arrival, particle filter passes through the importance density function again, utilize the difference between prediction Grad and observation Grad, more new particle weights obtain system state, if the true posteriority that particle collection can not representative system state distributes, difference between the prediction Grad of reading in benchmark gradient map so and observation Grad is larger, weights are less, vice versa, particle filter is by constantly upgrading and recursion, after estimating the site error of inertial navigation system, proofread and correct, thereby alliance error is gone to zero gradually.
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| CN105180938B (en) * | 2015-06-03 | 2018-02-02 | 北京理工大学 | A kind of gravity sample vector matching locating method based on particle filter |
| CN105157704A (en) * | 2015-06-03 | 2015-12-16 | 北京理工大学 | Bayesian estimation-based particle filter gravity-assisted inertial navigation matching method |
| CN105021197A (en) * | 2015-06-26 | 2015-11-04 | 四川理工学院 | ILOPF-based quadrotor attitude estimation method |
| CN105737831B (en) * | 2016-01-29 | 2019-02-12 | 北京理工大学 | Mutative scale based on particle filter is changed direction gravity sample vector matching locating method |
| CN105716605B (en) * | 2016-03-30 | 2018-07-10 | 北京理工大学 | A kind of Method in Gravity Aided INS system matches method |
| CN108073742B (en) * | 2016-11-17 | 2021-08-10 | 北京机电工程研究所 | Method for estimating flight state of intercepted missile tail section based on improved particle filter algorithm |
| CN107063249A (en) * | 2017-04-17 | 2017-08-18 | 重庆邮电大学 | A kind of gravity gradient aids in INS field positioner |
| CN107024206A (en) * | 2017-04-17 | 2017-08-08 | 重庆邮电大学 | A kind of integrated navigation system based on GGI/GPS/INS |
| CN108415096B (en) * | 2018-02-08 | 2019-06-28 | 武汉科技大学 | Subaqueous gravity gradient object detection method based on Newton iteration method |
| CN110567441B (en) * | 2019-07-29 | 2021-09-28 | 广东星舆科技有限公司 | Particle filter-based positioning method, positioning device, mapping and positioning method |
| CN111238483A (en) * | 2020-02-12 | 2020-06-05 | 上海海事大学 | Low-power-consumption terrain auxiliary navigation system and method based on SIR particle filtering method |
| CN113534222A (en) * | 2020-04-17 | 2021-10-22 | 宝马股份公司 | Method for vehicle positioning, device for vehicle positioning and vehicle |
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