CN106384152B - PF space non-cooperative target orbital prediction methods based on firefly group's optimization - Google Patents
PF space non-cooperative target orbital prediction methods based on firefly group's optimization Download PDFInfo
- Publication number
- CN106384152B CN106384152B CN201610815269.4A CN201610815269A CN106384152B CN 106384152 B CN106384152 B CN 106384152B CN 201610815269 A CN201610815269 A CN 201610815269A CN 106384152 B CN106384152 B CN 106384152B
- Authority
- CN
- China
- Prior art keywords
- particle
- moment
- formula
- weights
- population
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
Abstract
A kind of PF space non-cooperative target orbital prediction methods based on firefly group's optimization are disclosed, including：Primary group is constructed according to the initial state value of systemAnd by primaryWeights be set toI=1,2 ... N；The population at k moment is calculated according to system state equationAnd calculate particleNormalization weightsThe population at k moment is optimized according to glowworm swarm algorithm, including：In each renewal process, each particle is calculatedTo the Attraction Degree β of other particlesi,j, and from βi,jMiddle selection particleMaximum Attraction Degree βi,max；Work as βi,maxMore than first threshold βthWhen, pair and βi,maxThe small particle of corresponding, weights carries out location updating and calculates its weights；When completing default update times m, the population after optimization is obtained；According to the population estimating system state parameter after optimization the k moment state averageThe present invention is optimized by using glowworm swarm algorithm to the resampling process of particle filter, and particle depletion issues are solved not giving up particle, retaining while low weights particle includes system information.
Description
Technical field
The present invention relates to orbital prediction technical field, more particularly to a kind of PF space non-cooperatives based on firefly group's optimization
Target track Forecasting Methodology.
Background technology
Noncooperative target, which refers to a class, can not provide the space object of effective cooperation information, including fault satellites, failure are defended
Star, space junk, enemy's spacecraft etc..Orbital prediction for noncooperative target refers to known noncooperative target at a time
State on the premise of, according to its track condition after in a period of time of dynamics of orbits model prediction.
In general, the orbital prediction technology for noncooperative target is the relative motion shape based on C-W equation analytic solutions
State equation, with reference to estimation theories such as EKF, Unscented kalman filtering, particle filters, to Space borne detection platform
Metrical information is tracked filtering process, to obtain the orbit information of noncooperative target.Specifically, based on the non-of particle filter
Cooperative target orbit prediction algorithm is mainly included the following steps that：Build primary group and the weights to particle carry out assignment；Profit
Estimate the population of subsequent time with state equation and calculate the weights of corresponding particle；Resampling, i.e., carry out evaluation to particle
Afterwards, the big particle of weights is replicated, to substitute the sample point that some are small to prediction contribution.Although the resampling process can be with
Alleviate sample degenerate problem, but repeatedly after circulation, may cause only to remain one or several big weights particles in population.So
One, although this method ensure that number of particles, but lose particle diversity, and then have impact on orbital prediction precision.
The particle depletion issues existed for the existing noncooperative target orbit prediction algorithm based on particle filter, are needed badly
It is a kind of not only to have can guarantee that number of particles but also can guarantee that the multifarious new noncooperative target orbit prediction algorithm of particle, to improve rail
Road precision of prediction.
The content of the invention
It is an object of the invention to propose a kind of new noncooperative target orbit prediction algorithm based on particle filter, with
Do not give up particle, retain solution particle depletion issues while system information is included in low weights particle.
The invention provides a kind of PF space non-cooperative target orbital prediction methods based on firefly group's optimization, including：
S1, according to the initial state value of system construct primary groupAnd by each primaryWeights be set toI=1,2 ... N；
S2, the state equation according to system's relative motionCalculate the population at k momentAnd each particle is calculated according to the measured value at the momentNormalization weightsI=1,2 ... N；
S3, according to glowworm swarm algorithm the population at k moment is optimized, including：In each renewal process, calculate every
Individual particleTo the Attraction Degree β of other particlesi,j, j=1,2 ... N and j ≠ i, and from βi,jMiddle selection particleMaximum Attraction Degree
βi,max；As maximum Attraction Degree βi,maxMore than default first threshold βthWhen, pair and βi,maxCorresponding small weights particle carries out position
Update and calculate its weights；After default update times m is completed, the population after optimization is obtained；
S4, according to the population estimating system state parameter after optimization the k moment state average
It is preferred that, in step s3, β is calculated according to formula 1i,j；
In formula, βi,jFor particleTo particleAttraction Degree,For the corresponding state variable norm of particle.
It is preferred that, in step s3, location updating is carried out to particle according to formula 2；
In formula,For to particleProduce maximum Attraction Degree βi,maxParticle, εiFor the random number produced by Gaussian Profile
Vector, K updates step-length, and K=β for often stepi,max, α is the arbitrary width factor.
It is preferred that, in step s 2, each particle is calculated according to formula 3,4,5Normalization weights
In formula,For important density function,For the particle at k momentState value take observation
ykProbability,For the weights at the k moment after renewal.
It is preferred that, in step s 4, calculated according to formula 6
In formula,For system status parameters the k moment state average.
It is preferred that, step S4 also includes：According to the population estimating system state parameter after optimization the k moment covariance
Pk；
In formula,ForTransposition.
It is preferred that, first threshold βthMeet：βth=(rand (1)+1)/N；In formula, rand (1) is the random number within 1.
It is preferred that, N is met:100≤N≤500.
It is preferred that, default update times m is met：50≤m≤100.
As can be seen from the above technical solutions, the PF space non-cooperative target orbital prediction methods based on firefly group's optimization
Mainly include the following steps that：Primary group is constructed according to the initial state value of system, and the weights of each primary are set
For 1/N；The population at k moment is calculated according to system state equation, and calculates the normalization weights of each k moment particleRoot
The population at k moment is optimized according to glowworm swarm algorithm, including：In each renewal process, each particle is calculatedMost
Big Attraction Degree βi,max；Work as βi,maxMore than first threshold βthWhen, pair and βi,maxCorresponding small weights particle carries out location updating, simultaneously
Calculate its weights；When completing default update times m, the population after optimization is obtained；According to the population estimation after optimization
State average of the system status parameters at the k momentResampling of the present invention by using glowworm swarm algorithm to particle filter
Journey is optimized, and particle depletion issues are solved not giving up particle, retaining while low weights particle includes system information.
Brief description of the drawings
By the embodiment part of offer referring to the drawings, the features and advantages of the present invention will become more
It is readily appreciated that, in the accompanying drawings：
Fig. 1 is the PF space non-cooperative target orbital prediction methods based on firefly group's optimization in the embodiment of the present invention
Schematic flow sheet；
Fig. 2 is the site error schematic diagram according to existing Forecasting Methodology；
Fig. 3 is the site error schematic diagram according to Forecasting Methodology of the present invention；
Fig. 4 is the velocity error schematic diagram according to existing Forecasting Methodology；
Fig. 5 is the velocity error schematic diagram according to Forecasting Methodology of the present invention.
Embodiment
The illustrative embodiments to the present invention are described in detail with reference to the accompanying drawings.Illustrative embodiments are retouched
State merely for the sake of demonstration purpose, and be definitely not to the present invention and its application or the limitation of usage.
Due to there are particle depletion issues, significantly shadow in the existing noncooperative target orbit prediction algorithm based on particle filter
Precision of prediction is rung.In consideration of it, the present inventor proposes a kind of PF space non-cooperative mesh based on firefly group's optimization
Mark orbital prediction method.The present invention main thought be：By the particle analogy of selection into the firefly for meeting the worm cluster characteristics of motion
Fireworm individual, using optimizing mode individual in population, enables the relatively low particle of weights to be moved to higher particle.Such one
Come, particle depletion issues are solved while particle, the system information that the low weights particle of reservation is included is not given up.
Technical scheme is described in detail with specific embodiment below in conjunction with the accompanying drawings.Fig. 1 shows this hair
The schematic flow sheet of the PF space non-cooperative target orbital prediction methods based on firefly group's optimization in bright embodiment.From Fig. 1
It can be seen that, this method mainly includes step S1~S4.
Before orbital prediction is carried out, it is necessary first to it is determined that emulation system used.In particular it is necessary to determine system phase
To the state equation of motionAnd the observational equation of system.In this embodiment it is assumed that two star distances
Much smaller than star ground distance and reference orbit is circular orbit, ignore the influence that perturbation is performed to satellite, and carry out first-order linear,
The C-W equations of classics can then be obtained.If further ignoring controling power, C-W non trivial solutions analysis solution can be released, formula 9 is referred to.
Wherein, x, y, z is relative position,For relative velocity, n is tracking star mean orbit angular speed.If will
C-W non trivial solutions analysis solution is write as the form of state matrix, then the state equation of system's relative motion can be expressed as：X (k+1)=Φ
(T)X(k).Wherein, state-transition matrix Φ (T) is：
In addition, in this embodiment, the observational equation of system can be set to Yk=hk(xk,t)+vk, refer to formula 10, system
Noise may be set to：wk~(0, Qk), vk~(0, Rk)。
After the system used in emulation is determined, step S1~S4 is illustrated below for the system.
Step S1, according to the initial state value of system construct primary groupAnd will be each initial
ParticleWeights be set to 1/N, i=1,2 ... N.
Specifically, in step sl, the initial state value of system refers to the input data by pretreatment, such as position,
Speed data etc..In actual track forecasting research, the measuring cell that space-based platform observation is taken is angle measurement camera and laser
Rangefinder.Angle information of the extraterrestrial target for observation platform can be obtained by angle measurement camera, can be with by laser range finder
Obtain range information of the extraterrestrial target relative to observation platform.Measurement mould is set up according to the characteristic of the two measuring cells respectively
Type, can obtain input data., it is necessary to take some algorithms to pre-process input data after input data is obtained,
To improve the efficiency and precision of tracking filter algorithm, such as improved Laplace methods.When constructing primary group, particle
Number N can COMPREHENSIVE CALCULATING amount and required precision chosen.Such as, N can take whole more than or equal to 100 and less than or equal to 500
Number.
Step S2, the state equation according to system's relative motionCalculate the population at k momentAnd each particle is calculated according to the measured value at the momentNormalization weightsI=1,2 ... N.
Specifically, in step s 2, the particle at k-1 moment is substituted into system's relative motion state equation, you can when drawing k
The particle at quarter.In addition, the measured value at the moment can be drawn according to observational equation, and then each grain can be calculated according to formula 3,4,5
SonNormalization weights
In formula,For important density function,For the particle at k momentState value take observation
ykProbability,For the weights at the k moment after renewal.
Can be seen that from step S2 can obtain a priori value of particle by particle substitution state equation, through observational equation
An evaluation to the particle can be obtained again.So, just by information of forecasting fusion in the distribution of particle, and observation is believed
Breath has been integrated into the weight of each particle.
Step S3, according to glowworm swarm algorithm the population at k moment is optimized, including：In each renewal process, meter
Calculate each particleTo the Attraction Degree β of other particlesi,j, j=1,2 ... N and j ≠ i, and from βi,jMiddle selection particleMaximum suction
Degree of drawing βi,max；As maximum Attraction Degree βi,maxMore than default first threshold βthWhen, pair and βi,maxCorresponding small weights particle is carried out
Location updating simultaneously calculates its weights；After default update times m is completed, the population after optimization is obtained.Wherein, update secondary
Number m can be determined according to the actual requirements, such as, m can be taken more than or equal to the 50, integer less than or equal to 100.
Before step S3 is discussed in detail, the glowworm swarm algorithm (Firefly Algorithm, i.e. FA) first to standard is entered
Row is introduced.In glowworm swarm algorithm, the Attraction Degree that individual is subject to determines single individual moving direction and distance.By to this
The renewal of two key elements, is taken so as to realize to seeking for optimal value, and its major parameter is defined as follows：Firefly is fixed by Attraction Degree
Justice isThe location updating formula that each iteration is used is defined as：si=si×(1-β)+β×sj+αεi。
Wherein, γ approximately represents the fixed absorption coefficient of light, its typical value scope (0.1,10), rijRepresent firefly away from it is another each and every one
The distance of body, andBehalf firefly position coordinates.
During research glowworm swarm algorithm and particle filter, inventors have seen that：Can will be selected
Particle analogy into meet the worm cluster characteristics of motion firefly individual, using optimizing mode individual in population, make weights compared with
Low particle can be moved to higher particle, to improve the weight of itself, it is to avoid degradation phenomena occur.In consideration of it, of the invention
Inventor some amendments are carried out to glowworm swarm algorithm the characteristics of combine particle filter algorithm, and by revised glowworm swarm algorithm
During particle filtering resampling.
Specifically, in this embodiment, the Attraction Degree formula redefined is as follows：
In formula, βi,jFor particleTo particleAttraction Degree,For the corresponding state variable norm of particle.βi,j
It is bigger, illustrate that the weights difference of two particles is bigger.Work as βi,jDuring less than 0, illustrate the particle in observation scopeWeights be less than
ParticleWithout to particleIt is updated.
In addition, in step s3, the particle movement formula redefined is as follows:
In formula,For to particleProduce maximum Attraction Degree βI, maxParticle, K updates step-length, and K=β for often stepi,max。
To avoid glowworm swarm algorithm with being absorbed in local extremum phenomenon other intelligent optimization algorithms, α ε are introducedi, wherein, α is random
Step factor, general span is [0,1]；εiUsually by Gaussian Profile, be uniformly distributed or other distribution produce it is random
Number vector.
After being redefined to Attraction Degree, particle movement, the population at k moment can be optimized based on step S3.
In the specific implementation, first threshold βthIt can be determined according to actual conditions.Such as, when initial population is 100, and it is final effective
, can be by β when population is 10~20thIt is defined as：βth=(rand (1)+1)/N.In formula, rand (1) is the random number within 1.
By observing Attraction Degree calculation formula as can be seen that the actual numerical value magnitude of Attraction Degree is 10-2, it is even more small.Defined by above formula
βth, ensure that most particles are moved to more excellent position.In addition, the weights of the particle after to location updating are counted
During calculation, the probability density function that the particle can be directly substituted into existing white noise is calculated.
Step S4, according to the population estimating system state parameter after optimization the k moment state averageAnd, root
According to the population estimating system state parameter after optimization the k moment covariance Pk；
Specifically, in step s 4, being calculated according to formula 6P is calculated according to formula 7k；
In formula,For system status parameters the k moment state average.
In formula,ForTransposition.
In the specific implementation, step S2~S4 can be circulated repeatedly according to demand.After primary particle filtering terminates, under
Last time treated population is directly inputted in state equation by primary particle filtering, into circulating again.In the present invention
In embodiment, the resampling process of particle filter is optimized by using glowworm swarm algorithm, number of particles is not only ensure that,
And ensure that the diversity of particle.Further, the precision of orbital position, prediction of speed is improved.
The technique effect of the embodiment of the present invention is carried out specifically with reference to the specific simulation processes of Fig. 2 to Fig. 5 and one
It is bright.In this simulation process, emulation data are as follows：In platform track parameter, semi-major axis of orbit is 17178.1km, centrifugation
Rate is 0.001, and orbit inclination angle is 5 °, and the argument of perigee is 0 °, and the red footpath of ascending node is 0 °, and true anomaly is 100 °；In target track
In road parameter, semi-major axis of orbit is 17128.1km, and eccentricity is 0.012, and orbit inclination angle is 10 °, and the argument of perigee is 30 °, is risen
The red footpath of intersection point is 0 °, and true anomaly is 70 °.Simulation time chooses 200S, and initial measured error variance matrix isSimulation result is referring to Fig. 2 to Fig. 5.It is and existing from Fig. 2 to Fig. 5
Particle filter orbital prediction method compare, the present invention largely improve position, the precision of prediction of speed.
Although with reference to illustrative embodiments, invention has been described, but it is to be understood that the present invention does not limit to
The embodiment that Yu Wenzhong is described in detail and shown, in the case of without departing from claims limited range, this
Art personnel can make various changes to the illustrative embodiments.
Claims (7)
1. a kind of PF space non-cooperative target orbital prediction methods based on firefly group's optimization, it is characterised in that methods described
Including：
S1, according to the initial state value of system construct primary groupAnd by each primary's
Weights are set to
S2, the state equation according to system's relative motionCalculate the population at k momentAnd each particle is calculated according to the measured value at the momentNormalization weightsI=1,2 ... N；
S3, according to glowworm swarm algorithm the population at k moment is optimized, including：In each renewal process, each grain is calculated
SonTo the Attraction Degree β of other particlesi,j, j=1,2 ... N and j ≠ i, and from βi,jMiddle selection particleMaximum Attraction Degree
βi,max；As maximum Attraction Degree βi,maxMore than default first threshold βthWhen, location updating is carried out to the particle and its power is calculated
Value；After default update times m is completed, the population after optimization is obtained；
S4, according to the population estimating system state parameter after optimization the k moment state average
Wherein, β is calculated according to formula 1i,j；
In formula, βi,jFor particleTo particleAttraction Degree,For the corresponding state variable norm of particle；
Location updating is carried out to particle according to formula 2；
In formula,For to particleProduce maximum Attraction Degree βi,maxParticle, εiFor the random number vector produced by Gaussian Profile,
K updates step-length, and K=β for often stepi,max, α is the arbitrary width factor.
2. each particle the method for claim 1, wherein in step s 2, is calculated according to formula 3,4,5Normalizing
Change weights
In formula,For important density function,For the particle at k momentState value take observation yk
Probability,For the weights at the k moment after renewal.
3. the method for claim 1, wherein in step s 4, calculated according to formula 6
In formula,For system status parameters the k moment state average.
4. the method for claim 1, wherein step S4 also includes：According to the population estimating system state after optimization
Covariance P of the parameter at the k momentk；
In formula,ForTransposition.
5. the method as described in claim 1-4 is any, wherein, first threshold βthMeet：
βth=(rand (1)+1)/N；
In formula, rand (1) is the random number within 1.
6. the method as described in claim 1-4 is any, wherein, N is met:
100≤N≤500。
7. the method as described in claim 1-4 is any, wherein, default update times m is met：
50≤m≤100。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610815269.4A CN106384152B (en) | 2016-09-09 | 2016-09-09 | PF space non-cooperative target orbital prediction methods based on firefly group's optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610815269.4A CN106384152B (en) | 2016-09-09 | 2016-09-09 | PF space non-cooperative target orbital prediction methods based on firefly group's optimization |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106384152A CN106384152A (en) | 2017-02-08 |
| CN106384152B true CN106384152B (en) | 2017-09-26 |
Family
ID=57935580
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610815269.4A Active CN106384152B (en) | 2016-09-09 | 2016-09-09 | PF space non-cooperative target orbital prediction methods based on firefly group's optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106384152B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108958064B (en) * | 2017-05-17 | 2021-10-01 | 上海微小卫星工程中心 | Attitude guidance law error judgment method and system and electronic equipment |
| CN110263905B (en) * | 2019-05-31 | 2021-03-02 | 上海电力学院 | Robot positioning and mapping method and device based on firefly optimized particle filtering |
| CN110348560A (en) * | 2019-07-02 | 2019-10-18 | 河北科技大学 | A method of based on the trajectory predictions for improving glowworm swarm algorithm optimized particle filter |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102360214A (en) * | 2011-09-02 | 2012-02-22 | 哈尔滨工程大学 | Naval vessel path planning method based on firefly algorithm |
| CN103901432A (en) * | 2012-12-25 | 2014-07-02 | 中国科学院声学研究所 | Disoperative target trajectory tracking method and system under multiple observation nodes |
| CN103968841A (en) * | 2014-06-03 | 2014-08-06 | 哈尔滨工程大学 | Improved fireflyalgorithm based AUV (autonomous underwater vehicle) three-dimensional track planning method |
-
2016
- 2016-09-09 CN CN201610815269.4A patent/CN106384152B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102360214A (en) * | 2011-09-02 | 2012-02-22 | 哈尔滨工程大学 | Naval vessel path planning method based on firefly algorithm |
| CN103901432A (en) * | 2012-12-25 | 2014-07-02 | 中国科学院声学研究所 | Disoperative target trajectory tracking method and system under multiple observation nodes |
| CN103968841A (en) * | 2014-06-03 | 2014-08-06 | 哈尔滨工程大学 | Improved fireflyalgorithm based AUV (autonomous underwater vehicle) three-dimensional track planning method |
Non-Patent Citations (3)
| Title |
|---|
| 一种基于人工萤火虫群优化的改进粒子滤波算法;朱文超，许德章;《计算机应用研究》;20141031;第31卷(第10期);2920-2924 * |
| 基于萤火虫算法改进移动机器人定位方法研究;朱奇光，肖亚昆，陈卫东，倪春香，陈颖;《仪器仪表学报》;20160229;第37卷(第2期);323-329 * |
| 萤火虫算法智能优化粒子滤波;田梦楚，薄煜明，陈志敏，吴盘龙，赵高鹏;《自动化学报》;20160131;第42卷(第1期);89-97 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106384152A (en) | 2017-02-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wu et al. | Path planning for solar-powered UAV in urban environment | |
| CN107145161B (en) | Flight path planning method and device for unmanned aerial vehicle to access multiple target points | |
| CN106441308B (en) | A kind of Path Planning for UAV based on adaptive weighting dove group's algorithm | |
| CN105426970B (en) | A kind of meteorological intimidation estimating method based on discrete dynamic Bayesian network | |
| CN105205313B (en) | Fuzzy Gaussian sum particle filtering method and device and target tracking method and device | |
| CN106970648A (en) | Unmanned plane multi-goal path plans combined method for searching under the environment of city low latitude | |
| US20220326664A1 (en) | Improved machine learning for technical systems | |
| CN108958238B (en) | Robot point-to-area path planning method based on covariant cost function | |
| CN105333879A (en) | Synchronous positioning and map building method | |
| CN106384152B (en) | PF space non-cooperative target orbital prediction methods based on firefly group's optimization | |
| CN101852615A (en) | Improved mixed Gaussian particle filtering method used in inertial integrated navigation system | |
| CN110311652A (en) | A kind of increment under deficient observation condition is quadratured kalman filter method | |
| CN108022045A (en) | Distribution estimation method | |
| Chang et al. | A new fuzzy strong tracking cubature Kalman filter for INS/GNSS | |
| CN113741486A (en) | Space robot intelligent motion planning method and system based on multiple constraints | |
| CN114167295B (en) | Lithium ion battery SOC estimation method and system based on multi-algorithm fusion | |
| Li et al. | Wind power prediction based on PSO-Kalman | |
| Zhao et al. | Fusing vehicle trajectories and gnss measurements to improve gnss positioning correction based on actor-critic learning | |
| CN116518982B (en) | Low-altitude forestry monitoring remote sensing unmanned aerial vehicle path multi-target planning method | |
| CN108037986A (en) | Target observation method for double-star cluster | |
| CN110231619B (en) | Radar handover time forecasting method and device based on Enk method | |
| CN111798494A (en) | Maneuvering target robust tracking method under generalized correlation entropy criterion | |
| CN111711432A (en) | Target tracking algorithm based on UKF and PF hybrid filtering | |
| CN115143970A (en) | Obstacle avoidance method and system of underwater vehicle based on threat degree evaluation | |
| Tian et al. | Novel hybrid of strong tracking Kalman filter and improved radial basis function neural network for GPS/INS integrated navagation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |