CN103438879B - A kind of atomic spin gyroscope based on ant colony PF algorithm and gaussmeter tight integration method for determining posture - Google Patents

Abstract

A kind of atomic spin gyroscope based on ant colony PF algorithm/gaussmeter tight integration method for determining posture, the present invention relates to a kind of SERF atomic spin gyroscope/gaussmeter integrated attitude determination method。The method obtains inertia angular velocity information first with atomic spin gyroscope, then carries out gyro error compensation, is obtained the attitude information of carrier by attitude algorithm；Next utilizes atom magnetometer to obtain earth magnetism measurement information, and it is carried out geomagnetic matching, obtains earth magnetism Vector Message；Ant colony (Ant Colony) particle filter (Particle Filter) algorithm is finally utilized to adopt tight integration mode earth magnetism Vector Message and attitude of carrier information to be blended, solve mission nonlinear and noise non-gaussian problem, solve high accuracy attitude of carrier information, estimate gyroscopic drift, and feedback compensation attitude of carrier and compensate gyroscopic drift；Final realization, based on the on-line amending of SERF atomic spin gyroscope/gaussmeter tight integration attitude determination system Gyro Random error, completes long-time, high accuracy integrated attitude determination to motion carrier。

Description

A kind of atomic spin gyroscope based on ant colony PF algorithm and gaussmeter tight integration method for determining posture

Technical field

The present invention relates to a kind of atomic spin gyroscope based on ant colony PF algorithm and gaussmeter tight integration method for determining posture, full region, round-the-clock, autonomous attitude determination round-the-clock, long-time, high-precision can be realized, and autonomy is strong, good concealment, cost are low, can be widely used for multiple fields such as underwater detectoscope, aircraft, guided missile, spacecraft。

Background technology

For meeting the urgent needs in space-based earth observation, weapon precision strike and space, undersea search exploitation, all kinds of spacecrafts, underwater detectoscope must possess autonomous operation and operating capability, and high-precision autonomous attitude determination is the core technology bottleneck of spacecraft autonomous operation and management。Owing to utilizing atomic spin effect can realize superhigh precision inertia and magnetic-field measurement under without white rotation exchange relaxation (SERF) state simultaneously so that SERF atomic gyroscope/gaussmeter integrated attitude determination becomes and realizes one of long-time high-precision independent important development direction determining appearance。Gyroscope/gaussmeter integrated attitude determination has can provide appearance information of determining continuous, comprehensive independently, in real time, and is not affected by the feature of the impacts such as time, region, time。

At present, the high-precision independent of spacecraft determines appearance, it is impossible to relies on and any determine appearance means and independently realize。Pure inertia attitude determination system, precision is high in short-term, but its error accumulated with the working time, it is difficult to meet the long-time high-precision fixed appearance requirement of spacecraft；Earth magnetism is determined appearance and can be measured geomagnetic field intensity in real time and realize the location of carrier, and error does not accumulate in time, but is limited to geomagnetic matching precision and magnetic-field measurement precision etc. so that earth magnetism accuracy of attitude determination is still relatively low at present；Both is combined, has complementary advantages, constitute gyroscope/gaussmeter integrated attitude determination system, be realize that spacecraft is long-time, the maximally efficient means of high-precision fixed appearance。

At gyroscope/gaussmeter integrated attitude determination technical elements, all adopted EKF EKF (ExtendedKalmanFilter) method in the past, but EKF was only applicable to filtering error and the only small situation of forecast error。The Unscented Kalman filtering UKF (UnscentedKalmanFilter) proposed in recent years is the innovatory algorithm of a kind of EKF, effectively solves the nonlinear problem of system, but its deficiency is the system not being suitable for noise non-gaussian distribution。Particle filter (ParticleFilter, PF) owing to adopting Monte Carlo (MonteCarlosampling) structure to embody increasing superiority on non-linear, non-Gaussian filtering status tracking, but its shortcoming is to there is degradation phenomena, eliminates degradation phenomena and depend on two key technologies: suitably choose importance density function and carry out resampling。For the former improved method, EKPF (ExtentedKalmanParticleFilter) can be used, carry out the selection of importance density function without mark particle filter (UnscentedParticleFilter, UPF)。Improved method for the latter, conventional resampling methods has cumulative distribution resampling (Binarysearch), system resampling (Systematicresampling), residual gravity sampling (Residualresampling) etc., these algorithms solve the degenerate problem of particle by increasing the effectiveness of particle, but in actual applications can the robustness of influential system, after resampling completes, the particle that importance degree is high is repeatedly chosen by resampling, this lost the multiformity of particle to a certain extent, the consequence thereby resulted in is once track rejection or tracking accuracy are inadequate, the probability that system restrains automatically is only small, for this, a lot of scholars propose genetic particle filtering (GPF) algorithm, although GPF algorithm adds again the multiformity of particle while ensureing particle effectiveness, yet suffer from that filtering speed is slow and the problem of poor robustness。

Summary of the invention

The technology of the present invention solves problem: overcome the deficiencies in the prior art, the white gyro spin instrument of a kind of atom based on ant colony PF algorithm and gaussmeter tight integration method for determining posture are proposed, solve mission nonlinear and noise non-gaussian problem, quickly to obtain high-precision attitude information, and gyroscopic drift can be estimated exactly, it is achieved all kinds spacecraft integrated attitude determination long-time, high-precision。

The technical solution of the present invention is: a kind of gyroscope based on atomic spin effect and gaussmeter integrated attitude determination method, the particularly a kind of integrated attitude determination method of filtering algorithm based on ant colony PF, it is characterized in that: utilize the moment of inertia measurement information and earth magnetism measurement information, by ant colony (AntColonyAlgorithm) PF (particle filter) method, realizing spacecraft Rapid Combination long-time, high-precision and determine appearance, implementation step is as follows:

(1) utilize the white gyro spin instrument of atom to obtain inertia angular velocity information, then carry out gyro error compensation, obtained the attitude information of carrier by attitude algorithm；

(2) utilize atom magnetometer to obtain earth magnetism measurement information, and it is carried out geomagnetic matching, obtain earth magnetism Vector Message；

(3) utilize the geomagnetic matching data that geomagnetic database provides and the attitude information obtained by step (1) to match corresponding earth magnetism Vector Message, then the attitude information that the earth magnetism Vector Message (1) obtained with step (2) obtains compares, and comparative result is as measured value；

(4) the correspondingly magnetic vector information matched that step (3) obtains by ant colony (AntColony) particle filter (ParticleFilter) algorithm and the earth magnetism Vector Message that step (2) atom magnetometer provides is utilized to compare filtering, obtain the optimal estimation of error, utilize this estimation respectively the data of the white gyro spin instrument of atom and gaussmeter to be corrected。

(5) atomic spin gyroscope and atom magnetometer adopt tight integration mode: utilize the Geomagnetism Information that atom magnetometer matches to go to revise the drift of gyro, be embodied in: attitude of carrier information that the magnetic declination recorded with atom magnetometer and magnetic dip angle information go correction wave filter to export and gyroscopic drift；Go to revise the Magnetic Field of atom magnetometer with the attitude information of atomic spin gyroscope output, it is embodied in: remove to revise corresponding magnetic declination and the magnetic dip angle of the atom magnetometer that geomagnetic matching obtains with the attitude of carrier angle information of wave filter output, the metrical information making atomic gyroscope and atom magnetometer is revised mutually, it is achieved the tight integration of atomic gyroscope and atom magnetometer determines appearance。

Principles of the invention is: obtains inertia angular velocity information first with the white gyro spin instrument of atom, then carries out gyro error compensation, is obtained the attitude information of carrier by attitude algorithm；Next utilizes atom magnetometer to obtain earth magnetism measurement information, and adopts the matching process based on " the Hausdorff distance based on standard variance of geomagnetic entropy and a kind of improvement " to carry out earth magnetism vector matching it, it is thus achieved that the earth magnetism Vector Message of carrier position；Tight integration mode is adopted Geomagnetism Information and attitude of carrier information to be blended again with ant colony (AntColony) particle filter (ParticleFilter) algorithm, solve mission nonlinear and noise non-gaussian problem, solve high accuracy attitude of carrier information, estimate gyroscopic drift, and feedback compensation attitude of carrier, compensate gyroscopic drift and correct magnetic declination and the magnetic dip angle that atom magnetometer records；Finally realize the on-line amending of the white gyro spin instrument of atom/gaussmeter tight integration attitude determination system Gyro Random error, complete long-time, high accuracy integrated attitude determination to spacecraft。

Present invention advantage compared with prior art is in that: instant invention overcomes the deficiency that conventional gyro/gaussmeter integrated attitude determination method accuracy of attitude determination is low and filtering performance is poor, ant colony (AntColonyAlgorithm) PF (particle filter) algorithm is utilized to efficiently solve mission nonlinear and the problem of noise non-gaussian, utilize ant group algorithm that PF (particle filter) is optimized, effectively raise the precision of integrated attitude determination；The matching process based on the Hausdorff distance of standard variance based on geomagnetic entropy and a kind of improvement is adopted to carry out earth magnetism vector matching；Adopt atomic gyroscope/atom magnetometer tight integration mode, the moment of inertia measurement information and earth magnetism measurement information are blended, further increase the precision of integrated attitude determination, it is achieved that the accurate estimation to gyroscopic drift, meet spacecraft requirement long-time, integrated attitude determination in high precision。

Accompanying drawing explanation

Fig. 1 is the integrated attitude determination Method And Principle figure of a kind of filtering algorithm based on ant colony PF of the present invention；

Fig. 2 is the geomagnetic matching algorithm flow chart based on standard variance Hausdorff distance based on geomagnetic entropy and a kind of improvement in the present invention；

Fig. 3 is the flow chart optimizing particle in the present invention with ant group algorithm。

Detailed description of the invention

As it is shown in figure 1, specific embodiment of the invention step is as follows:

Step 1, the moment of inertia measurement information first atomic gyroscope recorded by attitude algorithm, obtain attitude of carrier information after compensating gyro output data, and flow process is as follows:

Step 1, inertia measurement information, namely utilize atomic gyroscope measurement to obtain and the data that export, utilizes information fusion algorithm to estimate the gyroscopic drift obtainedCarry out the ε in rectification building-out gyro output data ω=K × V+ ε+ζ, afterwards by attitude algorithm, obtain attitude of carrier information；Described step 1 specifically includes following steps:

Step 1.1. set initial attitude asCalculate initial attitude quaternary number q (0) battle array:

Step 1.2. updates attitude quaternion matrix:

$q(n+1)=\{\cos \frac{\mathrm{\Δ\φ}}{2}I+\frac{\sin \frac{\mathrm{\Δ\φ}}{2}}{\mathrm{\Δ\φ}}[\mathrm{\Δ\Φ}\left]\right\}q\left(n\right)$

Wherein, n was the n-th moment, and I is unit quaternary number, Δ φ=[Δ φ_XΔφ_YΔφ_Z] for gyro output angle increment, [ΔΦ] is:

$\left[\mathrm{\Δ\Φ}\right]=\left[\begin{array}{cccc}0& -{\mathrm{\Δ\φ}}_X& -{\mathrm{\Δ\φ}}_Y& -{\mathrm{\Δ\φ}}_Z\\ {\mathrm{\Δ\φ}}_X& 0& {\mathrm{\Δ\φ}}_Z& -{\mathrm{\Δ\φ}}_Y\\ {\mathrm{\Δ\φ}}_Y& -{\mathrm{\Δ\φ}}_Z& 0& {\mathrm{\Δ\φ}}_X\\ {\mathrm{\Δ\φ}}_Z& {\mathrm{\Δ\φ}}_Y& -{\mathrm{\Δ\φ}}_X& 0\end{array}\right];$

Step 1.3. is by q (n+1)=[q₁q₂q₃q₄]^T, calculating attitude cosine battle array C is:

$C=\left[\begin{array}{ccc}{C}_{11}& C_{12}& C_{13}\\ C_{21}& C_{22}& C_{23}\\ C_{31}& C_{32}& C_{33}\end{array}\right]=\left[\begin{array}{ccc}{q}_4^2+q_1^2-q_2^2-q_3^2& 2(q_1{q}_2+q_4{q}_3)& 2(q_1{q}_3-q_4{q}_2)\\ 2(q_1{q}_2-q_4{q}_3)& q_4^2-q_1^2+q_2^2-q_3^2& 2(q_2{q}_3+q_4{q}_1)\\ 2(q_1{q}_3+q_4{q}_2)& 2(q_2{q}_3-q_4{q}_1)& q_4^2-q_1^2-q_2^2+q_3^2\end{array}\right];$

Step 1.4. is solved the real-time attitude information of carrier by attitude cosine battle array C, and the real-time attitude information of described carrier includes course, pitching and roll three attitude angle information:

Course, pitching and roll three attitude angleSolution formula as follows:

Pitching angle theta value is: θ=sin^-1(C₂₃)

Course angleValue be calculated as follows shown in table:

Roll angle γ-value be calculated as follows shown in table:

C13Value judges	C33Value judges	Roll angle γ-value
			=0	<0	-π
>0	<0	atan-1(-C13/C33)-π
			>0	=0	-π/2
Arbitrary value	>0	atan-1(-C13/C33)
			<0	=0	π/2
<0	<0	atan-1(-C13/C33)+π

Step 2, utilize atom magnetometer to obtain earth magnetism measurement information, and it is carried out earth magnetism vector matching。A kind of method based on the Hausdorff distance of standard variance based on geomagnetic entropy and improvement is utilized to carry out geomagnetic matching, as shown in Figure 2, for the matching process flow chart based on geomagnetic entropy and the Hausdorff distance based on standard variance of a kind of improvement of the present invention, step is as follows:

A. magnetic chart in real time is recorded according to carrier current location atom magnetometer；

B. the geomagnetic entropy of magnetic chart in real time is calculated according to below equation；

$\left\{\begin{array}{c}{H}_f=-\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{p}_i(p_i-1)\\ p_i=\frac{g\left(i\right)}{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}g\left(i\right)}\end{array}\right.$

Wherein, the probability that p (i) occurs for each ground magnetic value, g (i) is the field strength values of i-th point;

C. judge the decision threshold of the index whether contentedly magnetic entropy algorithm of thick coupling, if meeting, obtaining thick match point, performing step 4), being unsatisfactory for, continue executing with step 3)；

D. scope and the step-length of essence coupling are determined；

E. calculate the Hausdorff distance of the improvement based on standard variance according to magnetic chart in real time and benchmark magnetic chart, by introducing the standard variance of point set spacing, add the distributed intelligence between point set, define as follows:

$h_{\mathrm{STMHD}}(A,B)=\frac{1}{N_A}\underset{a\∈A}{\mathrm{\Σ}}\underset{b\∈B}{\min }\left|\right|a-b\left|\right|+\mathrm{kS}(A,B)$

$h_{\mathrm{STMHD}}(B,A)=\frac{1}{N_B}\underset{a\∈B}{\mathrm{\Σ}}\underset{b\∈A}{\min }\left|\right|b-a\left|\right|+\mathrm{kS}(B,A)$

Wherein, a is point setIn element, b is point setIn element, k is weight coefficient, for the proportion that the segment information of point of adjustment is shared in distance calculates；S (A, B) represent in point set A a bit to the standard variance of the distance in solstics in point set B, S (B, A) represents the standard variance to the closest approach in point set A of solstics in point set B, then can be tried to achieve the Hausdorff distance based on standard variance by following formula:

$S(A,B)=\sqrt{\underset{a\∈A}{\mathrm{\Σ}}{\left[\underset{b\∈B}{\min }\right||a-b||-\frac{1}{N_A}\underset{a\∈A}{\mathrm{\Σ}}\underset{b\∈B}{\min }||a-b|\left|\right]}^2}$

$S(B,A)=\sqrt{\underset{b\∈B}{\mathrm{\Σ}}{\left[\underset{b\∈B}{\min }\right||b-a||-\frac{1}{N_B}\underset{b\∈B}{\mathrm{\Σ}}\underset{a\∈A}{\min }||b-a|\left|\right]}^2}$

H_STMHD(A, B)=max{h_STMHD(A, B), h_STMHD(B, A) }

F. the Hausdorff distance based on standard variance is sorted from small to large, get rid of indivedual higher value to the remaining Hausdorff distance value averaged based on standard variance, so that it is determined that the distance between 2 is based on the Hausdorff distance of the improvement of standard variance；

G. final earth magnetism Vector Message is exported。

Step 3, atomic spin gyroscope and atom magnetometer adopt tight integration mode:

1) utilize the Geomagnetism Information that atom magnetometer matches to go to revise the drift of gyro, be embodied in: attitude of carrier angle information that the magnetic declination recorded with atom magnetometer and magnetic dip angle information go correction wave filter to export and gyroscopic drift；

2) go to revise the Magnetic Field of atom magnetometer by attitude angle information, be embodied in: remove to revise corresponding magnetic declination and the magnetic dip angle of the atom magnetometer that geomagnetic matching obtains with the attitude of carrier angle information of wave filter output；

The metrical information making atomic gyroscope and atom magnetometer is revised mutually, it is achieved the tight integration of atomic gyroscope and atom magnetometer determines appearance。

Step 4 as it is shown on figure 3, be the present invention ant group algorithm optimize particle flow chart, utilize the PF algorithm of ant group algorithm optimization astronomy attitude information and attitude of carrier information to be blended, completing spacecraft is long-time, high-precision integrated attitude determination step is:

(1), during t=0, initialize:

To initial priori probability density p (x₀) sample, generate N number of obedience p (x₀) particle that is distributedIts average and variance meet

$P_0^{\left(i\right)}=E\left[{(x_0^{\left(i\right)}-{\stackrel{\‾}{x}}_0^{\left(i\right)})(x_0^{\left(i\right)}-{\stackrel{\‾}{x}}_0^{\left(i\right)})}^T\right];$

(2), during t >=1, step is as follows:

1. sampling step

With Kalman filtering more new particleObtainSampling ${\hat{x}}_k^{\left(i\right)}~$ $q\left(x_k^{\left(i\right)}\right|x_{0:k-1}^{\left(i\right)},y_{1:k})=N({\stackrel{\‾}{x}}_k^{\left(i\right)},P_k^{\left(i\right)}),i=1,...,N;$

2. weight is calculated

${\tilde{w}}_k^{\left(i\right)}={\tilde{w}}_{k-1}^{\left(i\right)}\frac{p\left(y_k\right|{\hat{x}}_k^{\left(i\right)})p\left({\hat{x}}_k^{\left(i\right)}\right|x_{k-1}^{\left(i\right)})}{q\left({\hat{x}}_k^{\left(i\right)}\right|x_{0:k-1,}^{\left(i\right)}{y}_{1:k})}$

Normalized weight: ${w_k}^{\left(i\right)}={{\tilde{w}}_k}^{\left(i\right)}/\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{{\tilde{w}}_k}^{\left(i\right)};$

3. resampling steps

From Discrete DistributionIn carry out n times resampling, obtain particle one group newIt is stillApproximate representation；

4. from this new particle of group obtained, choose excellent particle by ant group algorithm, pick out low etc. particle, to solve particle exhaustion problem, utilize the step that ant group algorithm is optimized as follows:

A initializes

Make time t=0, iterations N=0, pheromone τ_ij(0)=C, C is normal number, τ_ij(0) it is 0 moment node (i, pheromone intensity j)；

M Formica fusca is placed in starting point, each Formica fusca by b, according to following transition probability formula, adopts roulette wheel selection mode to move,

$p_{\mathrm{ij}}^k=\left\{\begin{array}{cc}\frac{{\mathrm{\τ}}_{\mathrm{ij}}^{\mathrm{\α}}\left(t\right){\mathrm{\η}}_{\mathrm{ij}}^{\mathrm{\β}}\left(t\right)}{\underset{{s\∈\mathrm{allowed}}_k}{\mathrm{\Σ}}{\mathrm{\τ}}_{\mathrm{is}}^{\mathrm{\α}}\left(t\right){\mathrm{\η}}_{\mathrm{is}}^{\mathrm{\β}}\left(t\right)}& {j\∈\mathrm{allowed}}_k\\ 0& \mathrm{otherwise}\end{array}\right.$

allowed_kNext step allows the set of the path point passed by represent Formica fusca k；For t node (i, pheromone intensity j)；α is heuristic greedy method, represents the relative importance of track；β is expected heuristic value, represents the relative importance of visibility；η_ijT () is node (i, the visibility on j)；

C is according to the target function value F of each Formica fusca_k(select particle weights as object function F_k), and record the optimal solution of this circulation；

D is according to below equation update information element intensity:

${\mathrm{\τ}}_{\mathrm{ij}}(t+n)={\mathrm{\ρ\τ}}_{\mathrm{ij}}\left(t\right)+(1-\mathrm{\ρ})\mathrm{\Δ}{\mathrm{\τ}}_{\mathrm{ij}},\mathrm{\Δ}{\mathrm{\τ}}_{\mathrm{ij}}=\underset{k=1}{\overset{m}{\mathrm{\Σ}}}\mathrm{\Δ}{\mathrm{\τ}}_{\mathrm{ij}}^k$

In formula, parameter ρ (0≤ρ≤1) remains the factor for pheromone, represents the persistency of track；1-ρ represents the pheromone dough softening；Represent that kth Formica fusca stays node (i, the quantity of information on j) in this circulation；Δ τ_ijRepresent that this circulation collects node (i, the increment of the pheromone on j)。Constant Q is pheromone intensity, embodies the stayed tracking quantity of Formica fusca；

E makes t ← t+n, N ← N+1；

If f is N < N_Cmax, then turn b, otherwise turn f, wherein N_CmaxFor maximum iteration time；

G exports optimal solution。

5. export

According to minimum variance principle, the optimal estimation value of attitude of carrier is exactly the average of condition distribution, it may be assumed that

${\hat{x}}_k=\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{w}_k^i{x}_k^i$

$p_k=\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{w}_k^i(x_k^i-{\hat{x}}_k){(x_k^i-{\hat{x}}_k)}^T.$

The content not being described in detail in description of the present invention belongs to the known prior art of professional and technical personnel in the field。

Claims (4)

1. the atomic spin gyroscope based on ant colony PF algorithm and gaussmeter tight integration method for determining posture, it is characterised in that comprise the following steps:

(1) utilize atomic spin gyroscope to obtain inertia angular velocity information, then carry out gyro error compensation, obtained the attitude information of carrier by attitude algorithm；

(2) utilize atom magnetometer to obtain earth magnetism measurement information, and it is carried out geomagnetic matching, obtain earth magnetism Vector Message；

(3) utilize the geomagnetic matching data that geomagnetic database provides and the attitude information obtained by step (1) to match corresponding earth magnetism Vector Message, then the earth magnetism Vector Message obtained with step (2) compares, and comparative result is as measured value；

(4) the correspondingly magnetic vector information matched that step (3) obtains by ant colony (AntColony) particle filter (ParticleFilter) algorithm and the earth magnetism Vector Message that step (2) atom magnetometer provides is utilized to compare filtering, obtain the optimal estimation of error, utilize this estimation respectively the data of atomic spin gyroscope and gaussmeter to be corrected；

(5) atomic spin gyroscope and atom magnetometer adopt tight integration mode: utilize the Geomagnetism Information that atom magnetometer matches to go to revise the drift of gyro, be embodied in: attitude of carrier information that the magnetic declination recorded with atom magnetometer and magnetic dip angle information go correction wave filter to export and gyroscopic drift；Go to revise the Magnetic Field of atom magnetometer with the attitude information of atomic spin gyroscope output, it is embodied in: remove to revise corresponding magnetic declination and the magnetic dip angle of the atom magnetometer that geomagnetic matching obtains with the attitude of carrier angle information of wave filter output, the metrical information making atomic gyroscope and atom magnetometer is revised mutually, it is achieved the tight integration of atomic gyroscope and atom magnetometer determines appearance；

Described step (2) Atom spin gyroscope and atom magnetometer adopt tight integration mode:

(A) utilize the Geomagnetism Information that atom magnetometer matches to go to revise the drift of gyro, be embodied in: attitude of carrier information that the magnetic declination recorded with atom magnetometer and magnetic dip angle information go correction wave filter to export and gyroscopic drift；

(B) go to revise the Magnetic Field of atom magnetometer with the attitude information of atomic spin gyroscope output, be embodied in: remove to revise corresponding magnetic declination and the magnetic dip angle of the atom magnetometer that geomagnetic matching obtains with the attitude of carrier angle information of wave filter output；

The metrical information making atomic gyroscope and atom magnetometer is revised mutually, it is achieved the tight integration of atomic gyroscope and atom magnetometer determines appearance。

2. a kind of atomic spin gyroscope based on ant colony PF algorithm according to claim 1 and gaussmeter tight integration method for determining posture, it is characterized in that: described step (2) utilizes a kind of method based on the Hausdorff distance of standard variance based on geomagnetic entropy and improvement carry out geomagnetic matching, concretely comprise the following steps:

(21) magnetic chart in real time is recorded according to carrier current location atom magnetometer；

(22) the absolute force data recorded in a period of time are lined up an array according to the priority of the time of acquisition, calculate the geomagnetic entropy of magnetic chart in real time according to below equation:

$\left\{\begin{array}{c}{H}_f=-\underset{i=1}{\overset{n}{\&Sigma;}}{p}_i(p_i-1)\\ p_i=\frac{g\left(i\right)}{\underset{i=1}{\overset{n}{\&Sigma;}}g\left(i\right)}\end{array}\right.$

Wherein, the probability that p (i) occurs for each ground magnetic value, g (i) is the field strength values of i-th point；

(23) judge the decision threshold of the index whether contentedly magnetic entropy algorithm of thick coupling, if meeting, obtaining thick match point, performing step (24)；It is unsatisfactory for, continues executing with step (23)；

(24) scope and the step-length of essence coupling are determined；

(25) calculate the Hausdorff distance of the improvement based on standard variance according to magnetic chart in real time and benchmark magnetic chart, by introducing the standard variance of point set spacing, add the distributed intelligence between point set, define as follows:

$h_{STMHD}(A,B)=\frac{1}{N_A}\underset{a\&Element;A}{\&Sigma;}\underset{b\&Element;B}{min}\left|\right|a-b\left|\right|+kS(A,B)$

$h_{STMHD}(B,A)=\frac{1}{N_B}\underset{a\&Element;B}{\&Sigma;}\underset{b\&Element;A}{min}\left|\right|b-a\left|\right|+kS(B,A)$

Wherein, a is point setIn element, b is point setIn element, k is weight coefficient, for the proportion that the segment information of point of adjustment is shared in distance calculates；S (A, B) represent in point set A a bit to the standard variance of the distance in solstics in point set B, S (B, A) represents the standard variance to the closest approach in point set A of solstics in point set B, then can be tried to achieve the Hausdorff distance based on standard variance by following formula:

$S(A,B)=\sqrt{\underset{a\&Element;A}{\&Sigma;}{\&lsqb;\underset{b\&Element;B}{min}\left|\right|a-b\left|\right|-\frac{1}{N_A}\underset{a\&Element;A}{\&Sigma;}\underset{b\&Element;B}{min}\left|\right|a-b\left|\right|\&rsqb;}^2}$

$S(B,A)=\sqrt{\underset{b\&Element;B}{\&Sigma;}{\&lsqb;\underset{b\&Element;B}{min}\left|\right|b-a\left|\right|-\frac{1}{N_B}\underset{b\&Element;B}{\&Sigma;}\underset{a\&Element;A}{min}\left|\right|b-a\left|\right|\&rsqb;}^2}$

H_STMHD(A, B)=max{h_STMHD(A,B),h_STMHD(B,A)}

(26) the Hausdorff distance based on standard variance is sorted from small to large, get rid of indivedual higher value to the remaining Hausdorff distance value averaged based on standard variance, so that it is determined that the distance between 2 is based on the Hausdorff distance of the improvement of standard variance；

(27) finally magnetic vector information is exported。

3. a kind of atomic spin gyroscope based on ant colony PF algorithm according to claim 1 and gaussmeter tight integration method for determining posture, it is characterised in that: described step (3) utilizes ant colony particle filter algorithm realize step to be:

(31) as initial sampling instant t=0, initialize:

To initial priori probability density p (x₀) sample, generate N number of obedience p (x₀) particle that is distributedIts averageAnd varianceMeet:

${\stackrel{\&OverBar;}{x}}_0^{\left(i\right)}=E\&lsqb;x_0^{\left(i\right)}\&rsqb;$

$P_0^{\left(i\right)}=E\&lsqb;(x_0^{\left(i\right)}-{\stackrel{\&OverBar;}{x}}_0^{\left(i\right)}){(x_0^{\left(i\right)}-{\stackrel{\&OverBar;}{x}}_0^{\left(i\right)})}^T\&rsqb;;$

(32), during t >=1, step is as follows:

1. sampling step

With Kalman filtering more new particleObtainSampling ${\hat{x}}_k^{\left(i\right)}~$ $q\left(x_k^{\left(i\right)}\right|x_{0:k-1}^{\left(i\right)},y_{1:k})=N({\stackrel{\&OverBar;}{x}}_k^{\left(i\right)},P_k^{\left(i\right)}),i=1,...,N;$

2. weight is calculated ${\tilde{w}}_k^{\left(i\right)}={\tilde{w}}_{k-1}^{\left(i\right)}\frac{p\left(y_k|{\hat{x}}_k^{\left(i\right)}\right)p\left({\hat{x}}_k^{\left(i\right)}|x_{k-1}^{\left(i\right)}\right)}{q\left({\hat{x}}_k^{\left(i\right)}|x_{0:k-1}^{\left(i\right)},y_{1:k}\right)},$ Normalized weight: ${w_k}^{\left(i\right)}={{\tilde{w}}_k}^{\left(i\right)}/\underset{i=1}{\overset{N}{\&Sigma;}}{{\tilde{w}}_k}^{\left(i\right)};$

3. resampling steps

From Discrete DistributionIn carry out n times resampling, obtain particle one group newIt is still p (x_k|y_0:k) approximate representation；

4. from this new particle of group obtained, choose excellent particle by ant group algorithm, pick out low etc. particle, to solve particle exhaustion problem；

5. export

According to minimum variance principle, the optimal estimation of attitude of carrier is exactly the average of condition distribution, it may be assumed that

${\hat{x}}_k=\underset{i=1}{\overset{N}{\&Sigma;}}{w}_k^i{x}_k^i$

$p_k=\underset{i=1}{\overset{N}{\&Sigma;}}{w}_k^i(x_k^i-{\hat{x}}_k){(x_k^i-{\hat{x}}_k)}^T.$

4. a kind of atomic spin gyroscope based on ant colony PF algorithm according to claim 3 and gaussmeter tight integration method for determining posture, it is characterised in that: it is as follows that 4. described step organizes, from this obtaining, the step choosing excellent particle new particle by ant group algorithm:

A initializes

Make time t=0, iterations N=0, pheromone τ_ij(0)=C, C is normal number, τ_ij(0) it is 0 moment node (i, pheromone intensity j)；

M Formica fusca is placed in starting point, each Formica fusca by b, according to following transition probability formula, adopts roulette wheel selection mode to move,

$p_{ij}^k=\left\{\begin{array}{cc}\frac{{\mathrm{\&tau;}}_{ij}^{\mathrm{\&alpha;}}\left(t\right){\mathrm{\&eta;}}_{ij}^{\mathrm{\&beta;}}\left(t\right)}{\underset{s\&Element;{\mathrm{allowed}}_k}{\&Sigma;}{\mathrm{\&tau;}}_{is}^{\mathrm{\&alpha;}}\left(t\right){\mathrm{\&eta;}}_{is}^{\mathrm{\&beta;}}\left(t\right)}& j\&Element;{\mathrm{allowed}}_k\\ 0& otherwise\end{array}\right.$

allowed_kNext step allows the set of the path point passed by represent Formica fusca k；For t node (i, pheromone intensity j)；α is heuristic greedy method, and β is expected heuristic value, η_ijT () is node (i, the visibility on j)；

C is according to the target function value F of each Formica fusca_k, select particle weights as target function value F_k, and record the optimal solution of this circulation；

D is according to below equation update information element intensity:

τ_ij(t+n)=ρ τ_ij(t)+(1-ρ)Δτ_ij,

In formula, parameter ρ (0≤ρ≤1) remains the factor for pheromone, and 1-ρ represents the pheromone dough softening；Represent that kth Formica fusca stays node (i, the quantity of information on j) in this circulation；Δ τ_ijRepresent that this circulation collects node (constant Q is pheromone intensity for i, the increment of the pheromone on j)；

E makes t ← t+n, N ← N+1；

If f is N < N_Cmax, then turn b, otherwise turn f, wherein N_CmaxFor maximum iteration time；

G exports optimal solution；

5. export

According to minimum variance principle, the optimal estimation of attitude of carrier is exactly the average of condition distribution, it may be assumed that

${\hat{x}}_k=\underset{i=1}{\overset{N}{\&Sigma;}}{w}_k^i{x}_k^i$

$p_k=\underset{i=1}{\overset{N}{\&Sigma;}}{w}_k^i(x_k^i-{\hat{x}}_k){(x_k^i-{\hat{x}}_k)}^T.$