CN103235327A - GNSS/MINS (global navigation satellite system/micro-electro-mechanical systems inertial navigation system) super-deep combination navigation method, system and device - Google Patents

Abstract

The invention provides a GNSS/MINS (global navigation satellite system/micro-electro-mechanical systems inertial navigation system) super-deep combination navigation method, aiming to realize super-deep combination of a GNSS and an MINS. The GNSS uses a direct location estimation method to estimate location errors, speed error and clock errors according to location and speed auxiliary information provided by an INS (inertial navigation system) to close loops; a system takes an MIMU (MINS inertial measurement unit) and a base band correlator as sensors to realize all the navigation functions in a top combinational algorithm, in other words, the algorithm takes the GNSS base band correlator as the sensor of sensitive space-time locating fields and takes the MIMU as the sensor of sensitive inertance fields to realize integral combination of the GNSS and the MINS; and by the aid of the INS, the GNSS combines multichannel information to perform vector phase discrimination and vector locating. The invention further provides super-deep combination system and device applied to the method. The method, the system and the device have the advantages of high navigation accuracy, good dynamic property, high GNSS tracking sensitivity, high GNSS anti-interference performance, wide GNSS dynamic pulling range and the like; and in theory, the dynamic range is limited by trends of the MINS, and -160dBM (decibels above one milliwatt in 600 ohms) signal tracking can be realized by the GNSS by the aid of the MINS.

Description

The super deep integrated navigation method, system and device of a kind of GNSS/MINS

Technical field

The invention belongs to the integrated navigation technical field, relate in particular to the super deep integrated navigation method, system and device of a kind of GNSS/MINS.

Background technology

GNSS/MINS(Global Navigation Satellite System GPS (Global Position System)/Micro-Electro-Mechanical Systems Inertial Navigation System micro electro mechanical inertia navigational system) integrated navigation system refers to MIMU(MEMS Inertial Measurement Unit micro electro mechanical inertia measuring unit) as sensor, proofread and correct the MINS error by reference informations such as GNSS receiver outgoing position/speed, by the navigational system of MINS outgoing position/speed assisted GNSS receiver acquisition tracking.

The GNSS/MINS integrated navigation system is low because of its cost, volume is little, error advantages such as accumulation in time, high sensitivity, anti-high dynamic stress not, and good prospect is arranged in following aviation, vehicular applications.The integrated navigation algorithm has pine combination, tight combination, makes up three kinds deeply at present, and position-based/speed, pseudorange/pseudorange rates, I/Q accumulated value aspect make up respectively.

So far, no matter pine makes up, tightly makes up still dark the combination, and GNSS and MINS are relatively independent between the two.MINS is sensor with MIMU, and additional strapdown resolves realizes the inertial navigation function; GNSS is sensor with the baseband correlators, and additional navigation calculation algorithm is realized the satellite navigation function; GNSS and MINS realize merging again in the top layer composite module.

The INS that is used for integrated navigation generally adopts strapdown to resolve algorithm.The GNSS loop tracks can be divided into scalar loop and vector loop (VDFLL).Use independently DLL(delay lock loop in the scalar loop) and the PLL(phase-locked loop) every satellite of tracking, the observed reading of every satellite uses Kalman filter or least square method of recursion to carry out the PVT(Position, Velocity and Time in input navigation calculation module) resolve; Vector operation at first calculates satellite pseudorange, pseudorange rates according to predicted position and speed, calculate carrier frequency and code phase according to pseudorange, pseudorange rates again, phase detector output is as observed quantity, the poor of pseudorange, pseudorange rates and true pseudorange, pseudorange rates predicted in representative, feed back to Kalman filter customer location is predicted again, thus closed circuit.As seen, the scalar loop tracks is conciliate point counting and is driven row into, and the vector loop tracks is finished with resolving in a Kalman filter.

Each passage of scalar algorithm works alone, and can't utilize the redundant information between each passage, so tracking sensitivity is low and dynamic property is bad, can not influence each other but advantage is each passage.

A plurality of channel informations are followed the tracks of and are located although the vector loop is coupled, its tracking sensitivity is better than the scalar loop, but the phase detector that uses in the vector loop is non-linear phase detector, and descend with carrier-to-noise ratio, phase demodulation error non-linear growth, cause following the tracks of and positioning performance decline, so the vector loop is unsuitable for following the tracks of the GNSS weak signal.

Summary of the invention

The present invention one of is intended to solve the problems of the technologies described above at least to a certain extent or provides a kind of useful commerce to select at least.For this reason, first purpose of the present invention is to propose a kind of super deep integrated navigation method of GNSS/MINS that the fusion degree is good, navigation accuracy is high that has.

According to the super deep integrated navigation method of GNSS/MINS of the embodiment of the invention, be sensor with MIMU and GNSS baseband correlators, carry out integrated navigation and calculate, comprising: step S01, the integrated navigation system initiation parameter arranges; Step S02, GNSS catch satellite and obtain navigation message, and INS finishes big misalignment initial alignment under GNSS is auxiliary; Step S03, float according to the position of INS output and the inclined to one side and initial clock of initial clock of speed and GNSS output, carrying out first grid divides, and finish from the position, speed, clock is floated to clock partially and carries the mapping of frequency and code phase mutually, realize the division of second grid, corresponding each second grid element center point is set up correlator; Step S04 carries out the coherent integration of 1 bit text length, correlator output correlation; Step S05 judges whether to reach T integral time, if do not reach, enters step S06, otherwise enters step S07; Step S06, system carries out noncoherent accumulation and strap-down navigation resolves, and adjusts described second grid when resolving fructufy according to described strap-down navigation and divide the center, returns step S05; Step S07 carries out second grid to the inverse mapping of first grid with the noncoherent accumulation correlation of every satellite, carries out peaked search then, and the inclined to one side sum of errors clock of site error, velocity error, clock that obtains in the T time floats error; Step S08 with the observed quantity as the integrated kalman filter device of each error of obtaining among the step S07, carries out INS error update and clock error update, then with the kalman filter state zero clearing; Step S09, the search grid size adaptation is adjusted; Step S10, strap-down navigation resolve and upgrade after customer location, the speed, enter next algorithm circulation.

In an embodiment of method of the present invention, described first grid be divided at three-dimensional position, three-dimensional velocity, clock float, clock octuple grid on the upper side divides, the two-dimensional grid that second grid is divided on GNSS carrier frequency, pseudo-code phase is divided.

In an embodiment of method of the present invention, described super deep integrated navigation method utilizes pressure altimeter to introduce barometer altitude data assisting navigation.

In an embodiment of method of the present invention, described super deep integrated navigation method is used the high precision crystal oscillator.

In an embodiment of method of the present invention, described super deep integrated navigation method is used maximum Likelihood, and the maximal possibility estimation formula is:

Wherein, r (t) is GNSS receiving baseband signal plus noise, S _k(t) be k the satellite baseband signal that receiver produces, N is satellites in view quantity, T ₀Be the initial moment of pre-detection integration, T is pre-detection integral time, and t is that receiver generates the baseband signal time series.Suppose S _k(t) cross over D bit navigation message, the D value 50～150 in typical case;

In the n data bit, k the local baseband signal s of satellite _k(t) be:

s _k(t)=a _k,nm _k(t)exp[j(ω _kt+φ _k,n)]

A wherein _{K, n}Be signal amplitude, relevant with carrier-to-noise ratio; m _k(t) be the CA sign indicating number, determined partially by customer location, clock; ω _kBe the carrier wave circular frequency, float decision by user velocity, clock; φ _{K, n}Be carrier phase;

The maximal possibility estimation formula is launched, is obtained:

Because R is only relevant with the reception signal, therefore minimizes [R-S], is equivalent to maximization [S], that is:

I wherein _{K, n}Be the time span of k satellite n data bit, inner summation number be noncoherent accumulation, and outside summation number adds up for each satellite channel fusion;

CA sign indicating number sequence waveform m _k(t) finding the solution formula is:

$m_k\left(t\right)\≅C_k[t-b\left(T_0\right)-{\mathrm{\τ}}_k\left(T_0\right)-(t-T_0){\mathrm{\τ}}_k^{\′}\left(T_0\right)]$

C wherein _k(t _GNSS) for the GNSS time being the satellite CA sign indicating number generation function of parameter, C _k(t _GNSS) known; τ _k(T ₀) be T ₀K the satellite-signal transmission delay that arrives; τ ' _k(T ₀) be rate of change time delay that is caused by Doppler frequency; B (T ₀) be that clock is inclined to one side, provided by navigation message, Several Parameters is calculated as follows:

${\mathrm{\τ}}_k\left(T_0\right)\≅\frac{|p_k\left(T_0\right)-p\left(T_0\right)|}{c+v_k\left(T_0\right)\·u_k\left(T_0\right)}\sec$

${\mathrm{\τ}}_k^{\′}\left(T_0\right)\≅\frac{1}{c}\left\{\right[v_k\left(T_0\right)-v\left(T_0\right)]\·u_k\left(T_0\right)\}\sec /\sec$

P wherein _k(T ₀) be k satellite position; P (T ₀) be customer location; v _k(T ₀) be k satellite velocities; u _k(T ₀) be that the user is to the projection vector of k satellite; V (T ₀) be user velocity; C is the light velocity;

Base band Doppler frequency ω _kCalculating formula is:

${\mathrm{\ω}}_k=2\mathrm{\π}\left\{\frac{1}{\mathrm{\λ}}\right[v\left(T_0\right)-v_k\left(T_0\right)]\·u_k\left(T_0\right)-\stackrel{\·}{b}\left(T_0\right)f\}$

Wherein f is carrier frequency, and λ is carrier wavelength.As seen, under the situation that known navigation message, user velocity, clock float, can find the solution base band Doppler frequency ω _k

In an embodiment of method of the present invention, in step S07, according to horizontal velocity, vertical speed, clock float, horizontal level, highly, the inclined to one side order of clock carries out peaked search, the horizontal velocity error, vertical speed error, the clock that obtain in the corresponding T time float error, horizontal position error, height error, the inclined to one side error of clock.

In an embodiment of method of the present invention, described horizontal velocity error searching method is: suppose that the initial moment INS of n data bit outgoing position, speed are

Clock partially and clock float for

Carry out 8 dimension grids and divide centered by this group parameter, the quantity that each dimension is divided grid is followed successively by n ₁～n ₈, and n ₁～n ₈Be odd number, at every satellite, be divided into 9 parts in code phase, two dimensions of carrier doppler:

$\left[\begin{array}{ccccc}{\tilde{p}}_{n,x}-\frac{n_1-1}{2}{\mathrm{\&Delta;p}}_x& ...& {\tilde{p}}_{n,x}& ...& {\tilde{p}}_{n,x}+\frac{n_1-1}{2}{\mathrm{\&Delta;p}}_x\\ {\tilde{p}}_{n,y}-\frac{n_2-1}{2}{\mathrm{\&Delta;p}}_y& ...& {\tilde{p}}_{n,y}& ...& {\tilde{p}}_{n,y}+\frac{n_2-1}{2}{\mathrm{\&Delta;p}}_y\\ {\tilde{p}}_{n,z}-\frac{n_3-1}{2}{\mathrm{\&Delta;p}}_z& ...& {\tilde{p}}_{n,z}& ...& {\tilde{p}}_{n,z}+\frac{n_3-1}{2}{\mathrm{\&Delta;p}}_z\\ {\tilde{b}}_n-\frac{n_7-1}{2}\mathrm{\&Delta;b}& ...& {\tilde{b}}_n& ...& {\tilde{b}}_n+\frac{n_7-1}{2}\mathrm{\&Delta;b}\end{array}\right]\&DoubleRightArrow;\left[\begin{array}{ccccc}{m}_1-{4\mathrm{\&Delta;<figure-callout\; id="1"\; label="m"\; filenames="HDA00003018026600032.png"\; state="\{\{state\}\}">m</figure-callout>}}_1& <figure-callout\; id="1"\; label="...\; m"\; filenames="HDA00003018026600032.png"\; state="\{\{state\}\}">.</figure-callout>..& m_{}{}_1& <figure-callout\; id="1"\; label="...\; m"\; filenames="HDA00003018026600032.png"\; state="\{\{state\}\}">.</figure-callout>..& m_{}{}_1+4{\mathrm{\&Delta;<figure-callout\; id="1"\; label="m"\; filenames="HDA00003018026600032.png"\; state="\{\{state\}\}">m</figure-callout>}}_1\\ .& & .& & .\\ .& ...& .& ...& .\\ .& & .& & .\\ m_k-4{\mathrm{\&Delta;m}}_k& ...& m_k& ...& m_k+4{\mathrm{\&Delta;m}}_k\\ .& & .& & .\\ .& ...& .& ...& .\\ .& & .& & .\\ m_N-4{\mathrm{\&Delta;m}}_N& ...& m_N& ...& m_N+4{\mathrm{\&Delta;m}}_N\end{array}\right]$

$\left[\begin{array}{ccccc}{\tilde{v}}_{n,x}-\frac{n_4-1}{2}{\mathrm{\&Delta;v}}_x& ...& {\tilde{v}}_{n,x}& ...& {\tilde{v}}_{n,x}+\frac{n_4-1}{2}{\mathrm{\&Delta;v}}_x\\ {\tilde{v}}_{n,y}-\frac{n_5-1}{2}{\mathrm{\&Delta;v}}_y& ...& {\tilde{v}}_{n,y}& ...& {\tilde{v}}_{n,y}+\frac{n_5-1}{2}{\mathrm{\&Delta;v}}_y\\ {\tilde{v}}_{n,z}-\frac{n_6-1}{2}{\mathrm{\&Delta;v}}_z& ...& {\tilde{v}}_{n,z}& ...& {\tilde{v}}_{n,z}+\frac{n_6-1}{2}{\mathrm{\&Delta;v}}_z\\ {\widetilde{\stackrel{\&CenterDot;}{b}}}_n-\frac{n_8-1}{2}\mathrm{\&Delta;}\stackrel{\&CenterDot;}{b}& ...& {\widetilde{\stackrel{\&CenterDot;}{b}}}_n& ...& {\widetilde{\stackrel{\&CenterDot;}{b}}}_n+\frac{n_8-1}{2}\mathrm{\&Delta;}\stackrel{\&CenterDot;}{b}\end{array}\right]\&DoubleRightArrow;\left[\begin{array}{ccccc}{\mathrm{\&omega;}}_1-{4\mathrm{\&Delta;\&omega;}}_1& ...& {\mathrm{\&omega;}}_1& ...& {\mathrm{\&omega;}}_1+4{\mathrm{\&Delta;\&omega;}}_1\\ .& & .& & .\\ .& ...& .& ...& .\\ .& & .& & .\\ {\mathrm{\&omega;}}_k-4{\mathrm{\&Delta;\&omega;}}_k& ...& {\mathrm{\&omega;}}_k& ...& {\mathrm{\&omega;}}_k+4{\mathrm{\&Delta;\&omega;}}_k\\ .& & .& & .\\ .& ...& .& ...& .\\ .& & .& & .\\ {\mathrm{\&omega;}}_N-4{\mathrm{\&Delta;\&omega;}}_N& ...& {\mathrm{\&omega;}}_N& ...& {\mathrm{\&omega;}}_N+4{\mathrm{\&Delta;\&omega;}}_N\end{array}\right]$

For satellite k, with each grid element center correspondence code phase place, carrier doppler frequency correlator is set, then every satellite need arrange 81 correlators, behind pre-detection T integral time, obtains 81 correlations, enters the search phase; At first according to search center fixed position, vertical speed, clock partially and clock float, with 81 correlation inverse mappings of satellite k in horizontal velocity search two dimensional surface; Successively the correlation inverse mapping of other satellites is searched in the two dimensional surface to horizontal velocity, according to each satellite carrier-to-noise ratio weight addition, maximum of points is corresponding horizontal velocity error afterwards.

In an embodiment of method of the present invention, after horizontal velocity search finishes, the fixing horizontal velocity error, according to identical method carry out successively that vertical speed, clock are floated, horizontal level, highly, the inclined to one side error search of clock.

In an embodiment of method of the present invention, Kalman filtering error model vector is:

Wherein, geographic coordinate system is elected east northeast ground coordinate system as, and subscript N, E, D represent north orientation respectively, east orientation, ground to,

Be the inertial navigation attitude error,

Be the inertial navigation velocity error,

Be the inertial navigation gyroscope constant value drift,

Be the inertial navigation accelerometer constant value drift, δ L, δ λ and δ h are respectively inertial navigation latitude error, longitude error and height error, and b is clock jitter,

Be clock drift;

Kalman filtering state equation is:

Wherein:

$F=\left[\begin{array}{cccccc}{F}_{11}& F_{12}& F_{13}& F_{14}& 0_{3\&times;3}& 0_{3\&times;2}\\ F_{21}& F_{22}& F_{23}& 0_{3\&times;3}& F_{25}& 0_{3\&times;2}\\ 0_{3\&times;3}& F_{32}& F_{33}& 0_{3\&times;3}& 0_{3\&times;3}& 0_{3\&times;2}\\ 0_{6\&times;3}& 0_{6\&times;3}& 0_{6\&times;3}& 0_{6\&times;3}& 0_{6\&times;3}& 0_{3\&times;2}\\ 0_{2\&times;3}& 0_{2\&times;3}& 0_{2\&times;3}& 0_{2\&times;3}& 0_{2\&times;3}& F_{46}\end{array}\right],$ $W=\left[\begin{array}{c}-C_{b_s}^n{\mathrm{\&epsiv;}}_w^{b_s}\\ C_{b_s}^n{\&dtri;}_w^{b_s}\\ 0_{11\&times;1}\end{array}\right]$

$F_{11}=(-{\mathrm{\&omega;}}_{\mathrm{in}}^n\&times;)=\left[\begin{array}{ccc}0& -({\mathrm{\&omega;}}_{\mathrm{ie}}\sin L+\frac{V_E}{R_N+h}\tan L)& \frac{V_N}{R_M+h}\\ {\mathrm{\&omega;}}_{\mathrm{ie}}\sin L+\frac{V_E}{R_N+h}\tan L& 0& {\mathrm{\&omega;}}_{\mathrm{ie}}\cos L+\frac{V_E}{R_N+h}\\ -\frac{V_N}{R_M+h}& -({\mathrm{\&omega;}}_{\mathrm{ie}}\cos L+\frac{V_E}{R_N+h})& 0\end{array}\right]$

$F_{12}=\left[\begin{array}{ccc}0& \frac{1}{R_N+h}& 0\\ -\frac{1}{R_M+h}& 0& 0\\ 0& -\frac{\tan L}{R_N+h}& 0\end{array}\right]$

$F_{13}=\left[\begin{array}{ccc}-{\mathrm{\&omega;}}_{\mathrm{ie}}\sin L& 0& 0\\ 0& 0& 0\\ -{\mathrm{\&omega;}}_{\mathrm{ie}}\cos L-\frac{V_E}{R_N+h}{\sec }^{2}L& 0& 0\end{array}\right]$

$F_{14}=-\left[\begin{array}{ccc}\cos \mathrm{\&theta;}\cos \mathrm{\&psi;}& -\cos \mathrm{\&gamma;}\sin \mathrm{\&psi;}+\sin \mathrm{\&gamma;}\sin \mathrm{\&theta;}\cos \mathrm{\&psi;}& \sin \mathrm{\&gamma;}\sin \mathrm{\&psi;}+\cos \mathrm{\&gamma;}\sin \mathrm{\&theta;}\cos \mathrm{\&psi;}\\ \cos \mathrm{\&theta;}\sin \mathrm{\&psi;}& \cos \mathrm{\&gamma;}\cos \mathrm{\&psi;}+\sin \mathrm{\&gamma;}\sin \mathrm{\&theta;}\sin \mathrm{\&psi;}& -\sin \mathrm{\&gamma;}\cos \mathrm{\&psi;}+\cos \mathrm{\&gamma;}\sin \mathrm{\&theta;}\sin \mathrm{\&psi;}\\ -\sin \mathrm{\&theta;}& \sin \mathrm{\&gamma;}\cos \mathrm{\&theta;}& \cos \mathrm{\&gamma;}\cos \mathrm{\&theta;}\end{array}\right]$

$F_{21}=(f^n\&times;)=\left[\begin{array}{ccc}0& {-f}_z^n& f_y^n\\ f_z^n& 0& {-f}_x^n\\ {-f}_y^n& f_x^n& 0\end{array}\right]$

$F_{22}=\left[\begin{array}{ccc}\frac{V_D}{R_M+h}& -2({\mathrm{\&omega;}}_{\mathrm{ie}}\sin L+\frac{V_E}{R_N+h}\tan L)& \frac{V_N}{R_M+\mathrm{<figure-callout\; id="2"\; label="h"\; filenames="HDA00003018026600012.png"\; state="\{\{state\}\}">h</figure-callout>}}\\ {2\mathrm{\&omega;}}_{\mathrm{ie}}\sin L+\frac{V_E}{R_N+h}\tan L& \frac{V_D}{R_N+h}+\frac{V_N}{R_N+h}\tan \mathrm{<figure-callout\; id="2"\; label="L"\; filenames="HDA00003018026600012.png"\; state="\{\{state\}\}">L</figure-callout>}& 2{\mathrm{\&omega;}}_{\mathrm{ie}}\cos L+\frac{V_E}{R_N+h}\\ -\frac{2V_N}{R_M+h}& -2({\mathrm{\&omega;}}_{\mathrm{ie}}\cos L+\frac{V_E}{R_N+h})& 0\end{array}\right]$

$F_{23}=\left[\begin{array}{ccc}-({2V}_E{\mathrm{\&omega;}}_{\mathrm{ie}}\cos L+\frac{V_E^2}{R_N+h}{\sec }^{2}L)& 0& 0\\ 2V_N{\mathrm{\&omega;}}_{\mathrm{ie}}\cos L-{2V}_E{\mathrm{\&omega;}}_{\mathrm{ie}}\sin L+\frac{V_N{V}_E}{R_N+h}{\sec }^{2}L& 0& 0\\ {2V}_E{\mathrm{\&omega;}}_{\mathrm{ie}}\sin L& 0& -\frac{2g}{R_M}\end{array}\right]$

$F_{25}=\left[\begin{array}{ccc}\cos \mathrm{\&theta;}\cos \mathrm{\&psi;}& -\cos \mathrm{\&gamma;}\sin \mathrm{\&psi;}+\sin \mathrm{\&gamma;}\sin \mathrm{\&theta;}\cos \mathrm{\&psi;}& \sin \mathrm{\&gamma;}\sin \mathrm{\&psi;}+\cos \mathrm{\&gamma;}\sin \mathrm{\&theta;}\cos \mathrm{\&psi;}\\ \cos \mathrm{\&theta;}\sin \mathrm{\&psi;}& \cos \mathrm{\&gamma;}\cos \mathrm{\&psi;}+\sin \mathrm{\&gamma;}\sin \mathrm{\&theta;}\sin \mathrm{\&psi;}& -\sin \mathrm{\&gamma;}\cos \mathrm{\&psi;}+\cos \mathrm{\&gamma;}\sin \mathrm{\&theta;}\sin \mathrm{\&psi;}\\ -\sin \mathrm{\&theta;}& \sin \mathrm{\&gamma;}\cos \mathrm{\&theta;}& \cos \mathrm{\&gamma;}\cos \mathrm{\&theta;}\end{array}\right]$

$F_{32}=\left[\begin{array}{ccc}\frac{1}{R_M+h}& 0& 0\\ 0& \frac{1}{(R_N+h)\cos L}& 0\\ 0& 0& -1\end{array}\right]F_{33}=\left[\begin{array}{ccc}0& 0& -\frac{V_N}{{(R_M+h)}^2}\\ \frac{V_E\tan L}{(R_N+h)\cos L}& 0& -\frac{V_E}{{(R_N+h)}^2\cos L}\\ 0& 0& 0\end{array}\right]F_{46}=\left[\begin{array}{cc}0& 1\\ 0& 0\end{array}\right]$

Wherein, V _N, V _EAnd V _DBe respectively north orientation, east orientation and the ground of carrier to speed, L is the geographic latitude of carrier positions, and h is the carrier sea level elevation, R _MBe earth radius of curvature of meridian, R _NBe earth radius of curvature in prime vertical, ω _IeBe the spin velocity of the earth,

With

Be the specific force of inertial navigation,

Be the noise of gyro,

Noise for accelerometer;

The Kalman filter observation vector is: $Z=[\mathrm{\&Delta;x},\mathrm{\&Delta;y},\mathrm{\&Delta;z},{\mathrm{\&Delta;v}}_N,{\mathrm{\&Delta;v}}_E,{\mathrm{\&Delta;v}}_D,\mathrm{\&Delta;b},\mathrm{\&Delta;}\stackrel{\&CenterDot;}{b}];$

The Kalman filter observation equation is: Z=HX+V;

Wherein,

$H=\left[\begin{array}{cccc}{0}_{3\&times;3}& 0_{3\&times;3}& I_{3\&times;3}& 0_{3\&times;2}\\ 0_{3\&times;3}& I_{3\&times;3}& 0_{3\&times;3}& 0_{3\&times;2}\\ 0_{2\&times;3}& 0_{2\&times;3}& 0_{2\&times;3}& {\mathrm{<figure-callout\; id="2"\; label="I"\; filenames="HDA00003018026600012.png"\; state="\{\{state\}\}">I</figure-callout>}}_{2\&times;2}\end{array}\right]$ Be observing matrix, V is observation noise.

In an embodiment of method of the present invention, in step S06, inertial navigation resolves speed and equates or be the multiple relation with the navigation message code check.

In an embodiment of method of the present invention, it is characterized in that, in step 07, adjust the search grid size according to kalman filter state covariance matrix self-adaptation.

In an embodiment of method of the present invention, described GNSS system is GPS, big-dipper satellite positioning system, glonass system or Galileo navigational system.

Second purpose of the present invention is to propose a kind of super deep integrated navigation system of GNSS/MINS that the fusion degree is good, navigation accuracy is high that has.

The super deep integrated navigation system of GNSS/MINS according to the embodiment of the invention, comprise: the GNSS system, by antenna (601) receiving satellite signal, through radio-frequency front-end (602), with described satellite-signal down coversion sampling back output intermediate-freuqncy signal, and receive satellite navigation message; The MINS system passes through MIMU(603) obtain measurement data, after resolving, strapdown inertial navitation system (SINS) (604) obtains carrier positions, speed and attitude; Carrier frequency and code phase computing unit (605) are used for GNSS signal(-) carrier frequency and code phase are calculated in conjunction with described satellite navigation message in speed and the position of the output of described MINS system, to produce local signal; And integrated kalman filter device (609), state error is fed back to the MINS system carry out error correction; It is characterized in that, also comprise: correlator bank (606) is used for carrying out relevant with intermediate-freuqncy signal described local signal; Projection mapping from second grid to first grid is finished with the output result of described correlator bank (606) in projection mapping unit (607); And search unit (608), the output result of described projection mapping unit (607) is searched for, obtain maximal value, namely position error, velocity error, the inclined to one side sum of errors clock of clock float error, as the observed quantity of described integrated kalman filter device.

In an embodiment of system of the present invention, described search unit (608) according to horizontal velocity, vertical speed, clock float, horizontal level, highly, the inclined to one side order of clock carries out peaked search, the horizontal velocity error, vertical speed error, the clock that obtain in the corresponding T time float error, horizontal position error, height error, the inclined to one side error of clock.

In an embodiment of system of the present invention, described GNSS system is GPS, big-dipper satellite positioning system, glonass system or Galileo navigational system.

In an embodiment of system of the present invention, described system also comprises pressure altimeter, for system introduces the barometer altitude data.

In an embodiment of system of the present invention, described system also comprises the high precision crystal oscillator.

The 3rd purpose of the present invention is to propose a kind of super deep integrated navigation system of GNSS/MINS that the fusion degree is good, navigation accuracy is high that has.

Combined navigation device according to the embodiment of the invention comprises: antenna (701) and radio-frequency module (702), pass through FPGA(706) interface is GNSS intermediate frequency data drawing-in system; MIMU(704), pass through FPGA(706) interface is MIMU(704) the measurement data drawing-in system; And power module (705), DSP(708) and host computer (707); It is characterized in that, described combined navigation device also comprises correlator bank (709), described DSP(708) will calculate code phase, carrier frequency and projection vector, and write FPGA(706 by the EMIF bus) inside, FPGA(706) divide grid automatically and be mapped to described correlator bank (709) according to the search grid size, behind pre-detection T integral time, FPGA(706) output valve and the maximizing of processing correlator bank (709), it is the site error in the corresponding T time, velocity error, clock partially and clock float error, described DSP(708) read in described site error, velocity error, clock partially and clock float the error observed reading and the User Status amount upgraded.

In an embodiment of device of the present invention, also comprise pressure altimeter (703), passing through FPGA(706) interface is barometer altitude data drawing-in system.

In an embodiment of device of the present invention, described GNSS system is GPS, big-dipper satellite positioning system, glonass system or Galileo navigational system.

The vector operations that adopt in the prior art more, owing to utilizing multi-satellite combined signal location, its navigation performance is better than the scalar algorithm, but when the satellite-signal carrier-to-noise ratio was low, the vector operation phase detector error sharply increased, navigation performance decline.The present invention mainly adopts direct position algorithm for estimating (DPE) and does not use phase detector, uses the Bayesian Estimation algorithm, can reach the highest precision in theory, realizes more profound GNSS/INS combination.It is good that the super deep integrated navigation method, system and device of GNSS/MINS of the present invention have the fusion degree, the advantage that navigation accuracy is high.

Additional aspect of the present invention and advantage part in the following description provide, and part will become obviously from the following description, or recognize by practice of the present invention.

Description of drawings

Above-mentioned and/or additional aspect of the present invention and advantage are from obviously and easily understanding becoming the description of embodiment in conjunction with following accompanying drawing, wherein:

Fig. 1 is direct position algorithm for estimating schematic diagram.

Fig. 2 is that first grid is to the second mesh mapping synoptic diagram.

Fig. 3 is the super deep integrated navigation method flow diagram of embodiment one.

Fig. 4 is the fundamental diagram of a kind of employed horizontal velocity error searching method of present embodiment.

The schematic diagram that grid was divided and shone upon when Fig. 5 searched for for horizontal velocity.

Fig. 6 is the structural drawing of the super deep integrated navigation system of embodiment two.

Fig. 7 is the structural drawing of the super deep integrated navigation device of embodiment three.

Embodiment

Describe embodiments of the invention below in detail, the example of described embodiment is shown in the drawings, and wherein identical or similar label is represented identical or similar elements or the element with identical or similar functions from start to finish.Be exemplary below by the embodiment that is described with reference to the drawings, be intended to for explaining the present invention, and can not be interpreted as limitation of the present invention.

In description of the invention, it will be appreciated that, term " " center "; " vertically "; " laterally "; " length "; " width "; " thickness ", " on ", D score, " preceding ", " back ", " left side ", " right side ", " vertically ", " level ", " top ", " end " " interior ", " outward ", " clockwise ", close the orientation of indications such as " counterclockwise " or position is based on orientation shown in the drawings or position relation, only be that the present invention for convenience of description and simplification are described, rather than device or the element of indication or hint indication must have specific orientation, with specific orientation structure and operation, therefore can not be interpreted as limitation of the present invention.

In addition, term " first ", " second " only are used for describing purpose, and can not be interpreted as indication or hint relative importance or the implicit quantity that indicates indicated technical characterictic.Thus, one or more these features can be expressed or impliedly be comprised to the feature that is limited with " first ", " second ".In description of the invention, the implication of " a plurality of " is two or more, unless clear and definite concrete restriction is arranged in addition.

In the present invention, unless clear and definite regulation and restriction are arranged in addition, broad understanding should be done in terms such as term " installation ", " linking to each other ", " connection ", " fixing ", for example, can be fixedly connected, also can be to removably connect, or connect integratedly; Can be mechanical connection, also can be to be electrically connected; Can be directly to link to each other, also can link to each other indirectly by intermediary, can be the connection of two element internals.For the ordinary skill in the art, can understand above-mentioned term concrete implication in the present invention as the case may be.

In the present invention, unless clear and definite regulation and restriction are arranged in addition, first feature second feature it " on " or D score can comprise that first and second features directly contact, can comprise that also first and second features are not directly contacts but by the contact of the additional features between them.And, first feature second feature " on ", " top " and " above " comprise first feature directly over second feature and oblique upper, or only represent that the first characteristic level height is higher than second feature.First feature second feature " under ", " below " and " below " comprise first feature under second feature and tiltedly, or only represent that the first characteristic level height is less than second feature.

Embodiment one

Any global position system, its essence and target all are to determine user's position and speed.The present invention just is being based on this common ground, proposes field, the location concept of position-based, speed aspect.Based on this concept, can be with INS and the super profound combination of GNSS: position, the speed supplementary of utilizing INS to provide, GNSS uses direct position to estimate (DPE) algorithm Estimated Position Error, velocity error, clocking error, with this closed circuit.Because system is sensor with MIMU, baseband correlators directly, in the top layer combinational algorithm, realize all navigation features, the sensor of field, location when namely this algorithm is empty as sensitivity with the GNSS baseband correlators, with the sensor of MIMU as responsive inertial field, realized the complete fusion of GNSS and MINS, down auxiliary at INS, GNSS associating multi-channel information carries out vector phase demodulation and vector positioning, therefore is called super deep integrated navigation algorithm.

For responsive satnav field, the design adopts correlator bank as sensing element, the correlator output valve is subjected to mainly that customer location, speed, clock float, clock influences partially, therefore, consideration is floated at user's three-dimensional position, three-dimensional velocity, clock, inclined to one side 8 dimensions of clock are carried out grid and divided, at every satellite, the corresponding correlator of each grid combination, correlator bank is exported one group of accumulated value behind pre-detection T integral time, and wherein maximum corresponding correct three-dimensional position, three-dimensional velocity, the clock of correlator accumulated value floats with clock inclined to one side.Utilize observed reading correction customer location, speed, clock partially and after clock floats, begin the next round search again.

The specific algorithm principle as shown in Figure 1.

T ₀Constantly customer location, speed, clock partially and clock float known, division grid position search lattice 101, speed search lattice 102, the inclined to one side search grid of clock, clock float search grid centered by this original state, can be mapped to each satellite carrier Doppler and code phase search lattice 103.Suppose to have n satellites in view, the correlator output valve of corresponding n the satellite of each grid is added up the corresponding right user state error of maximal value in all accumulated values.Can carry out next round search and renewal behind the error update.

Suppose three-dimensional position, three-dimensional velocity, clock partially, clock floats 8 dimensions and divides the quantity of search grid and be respectively n ₁～n ₈, then the grid total quantity of Hua Fening is:

N=n ₁×n ₂×n ₃×n ₄×n ₅×n ₆×n ₇×n ₈

For typical GNSS navigation application, consider each dimension division 5 grids, i.e. n ₁=n ₂=n ₃=n ₄=n ₅=n ₆=n ₇=n ₈=5, then this moment N=390625, namely need 390625 correlators just can finish the tracking of 1 satellite-signal, quantity so huge correlator bank can't realize, has limited the application of direct position algorithm for estimating in the GNSS navigation field based on the satnav field.

Can use some strategies to reduce correlator quantity, optimize algorithm complex.

Use the high precision crystal oscillator, clock partially and clock float 2 dimensions search grid quantity and can reduce, each dimension of typical case selects for use 3 to search for lattice.

Altitude channel is less to the influence of correlator accumulated value, therefore can introduce the pressure altimeter assisting navigation, thereby reduces the search lattice quantity of short transverse, and each dimension of typical case height and derivative thereof is selected 3 search lattice for use.

Down auxiliary at INS, can compress user's horizontal direction position, speed search lattice size, in typical case, location finding lattice size is 5m, scope 30m, the process interpolation can obtain the precision of 1m; Speed search lattice size is 2m/s, scope 12m/s, and the process interpolation can obtain the precision less than 0.5m/s.Be that horizontal level, 4 dimensions of speed respectively need 7 search lattice.

To sum up, grid adds up to: N=n ₁* ... * n ₈=3 ⁴* 9 ⁴=194481.Apparent number still is excessive, can't realize by hardware.

For the GNSS signal, as long as provide carrier frequency accurately, pseudo-code phase then can guarantee the tenacious tracking signal.Therefore, for each satellite, can with user's three-dimensional position, three-dimensional velocity, clock partially, clock floats 8 dimension variablees and is mapped on carrier frequency, the code phase 2 dimension variablees, thereby reduce the quantity of correlator.Be three-dimensional position, three-dimensional velocity, clock partially, clock be floated to carrier frequency, code phase be mapped as the many-one linear mapping.

As shown in Figure 2, be positioned at x ₁y ₁z ₁Grid in the zone all will be mapped to the ρ on the projection vector ₁The zone is positioned at x ₂y ₂z ₂Grid in the zone all will be mapped to the ρ on the projection vector ₂The zone, thus realize that 3 dimensions are to the mapping of 1 dimension.

Therefore, arrive under the known situation of satellite projection vector the user, three-dimensional velocity, clock can be floated, the inclined to one side 8 dimension variablees of three-dimensional position, clock project to carrier frequency, two dimensions of code phase, divide grid in carrier frequency, two dimensions of code phase then, after obtaining correlator output, again by the projection vector inverse operation be mapped to that three-dimensional velocity, clock are floated, in the three-dimensional position, the inclined to one side grid of clock, thereby 8 dimension grids are divided boil down to 2 dimension grids to be divided, reduce correlator quantity significantly, guarantee the hardware realizability.

The present embodiment method as shown in Figure 3, specifically according to following steps:

Step S01, the integrated navigation system initiation parameter arranges;

Step S02, GNSS catch satellite and obtain navigation message, and INS finishes big misalignment initial alignment under GNSS is auxiliary;

Step S03, float according to the position of INS output and the inclined to one side and initial clock of initial clock of speed and GNSS output, carrying out first grid divides, and finish from the position, speed, clock is floated to clock partially and carries the mapping of frequency and code phase mutually, realize the division of second grid, corresponding each second grid element center point is set up correlator;

Step S04 carries out the coherent integration of 1 bit text length, correlator output correlation;

Step S05 judges whether to reach T integral time, if do not reach, enters step S06, otherwise enters step S07;

Step S06, system carries out noncoherent accumulation and strap-down navigation resolves, and adjusts described second grid when resolving fructufy according to described strap-down navigation and divide the center, and inertial navigation resolves speed and equates with the navigation message code check or be multiple to concern.Return step S05;

Step S07 carries out second grid to the inverse mapping of first grid with the correlation of every satellite, carries out peaked search then, and the inclined to one side sum of errors clock of site error, velocity error, clock that obtains in the T time floats error;

Step S08 with the observed quantity as the integrated kalman filter device of each error of obtaining among the step S07, carries out INS error update and clock error update, then with the kalman filter state zero clearing;

Step S09, the search grid size adaptation is adjusted;

Step S10, strap-down navigation resolve and upgrade after customer location, the speed, enter next algorithm circulation.

Because the search grid limited amount, grid is divided more big, and the satellite-signal tracking power is more strong, but tracking accuracy is more poor.Therefore to adjust the search grid size according to kalman filter state covariance matrix self-adaptation, when guaranteeing sane tracking satellite signal, improve tracking accuracy as far as possible.

When step S07 searches for, according to the influence size ordering of correlator output valve, be followed successively by that horizontal velocity, vertical speed, clock are floated, horizontal level, highly, clock is inclined to one side.Therefore, can according to original state fixedly three-dimensional position, vertical speed, clock partially and clock float, horizontal velocity search is obtained horizontal velocity error accurately, the horizontal velocity after fixed error is upgraded is then searched for vertical speed, and the like.Under the situation that computational resource allows, can carry out iteration according to top order, up to algorithm convergence.This search strategy is divided into the following search of a plurality of 2 dimensions with 8 dimension search, effectively reduces algorithm complex.

The vector loop is subject to the non-linear and phase detector working range of phase detector, and pre-detection T integral time is generally 20～200ms, and is limited to the weak signal tracking power, and when carrier-to-noise ratio reduced, phase detector error increased, and makes the loop losing lock easily.Compared to the vector loop, direct position is estimated not use phase detector in (DPE) algorithm loop, therefore be not subjected to the influence of the non-linear and working range of phase detector, pre-detection T integral time is chosen as 1～3 second, and under low carrier-to-noise ratio situation good performance is arranged also.

In the present embodiment, the foundation of system model comprises DPE algorithm and combinational algorithm.

The DPE algorithm uses maximum Likelihood, and the DPE method is equivalent to optimization:

Wherein, r (t) is GNSS receiving baseband signal plus noise, S _k(t) be k the satellite baseband signal that receiver produces, N is satellites in view quantity, T ₀Be the initial moment of pre-detection integration, T is pre-detection integral time, and t is that receiver generates the baseband signal time series.Suppose S _k(t) cross over D bit navigation message, in typical case D value 50～150(1～3 second).

In the n data bit, k the local baseband signal s of satellite _k(t) be:

s _k(t)=a _k,nm _k(t)exp[j(ω _kt+φ _k,n)](2)

A wherein _{K, n}Be signal amplitude, relevant with carrier-to-noise ratio; m _k(t) be the CA sign indicating number, determined partially by customer location, clock; ω _kBe the carrier wave circular frequency, have user velocity, clock to float decision; φ _{K, n}Be carrier phase.Because signal amplitude can be weighted by the carrier-to-noise ratio estimated value, carrier phase estimates to require grid to divide overstocked and can't realize, does not therefore consider a in the discussion below _{K, n}And φ _{K, n}Influence.

(1) formula is launched, is obtained:

Because R is only relevant with the reception signal, therefore minimizes [R-S], is equivalent to maximization [S], that is:

I wherein _{K, n}Being the time span of k satellite n data bit, is 20ms for the GPS size.(3) inner summation number be noncoherent accumulation in the formula, and outside summation number adds up for each satellite channel fusion.

CA sign indicating number sequence waveform m _k(t) finding the solution formula is:

$m_k\left(t\right)\&cong;C_k[t-b\left(T_0\right)-{\mathrm{\&tau;}}_k\left(T_0\right)-(t-T_0){\mathrm{\&tau;}}_k^{\&prime;}\left(T_0\right)]---\left(4\right)$

C wherein _k(t _GNSS) for the GNSS time being the satellite CA sign indicating number generation function of parameter, C _k(t _GNSS) known; τ _k(T ₀) be T ₀K the satellite-signal transmission delay that arrives; τ ' _k(T ₀) be rate of change time delay that is caused by Doppler frequency; B (T ₀) be that clock is inclined to one side, provided by navigation message.Several Parameters is calculated as follows:

${\mathrm{\&tau;}}_k\left(T_0\right)\&cong;\frac{|p_k\left(T_0\right)-p\left(T_0\right)|}{c+v_k\left(T_0\right)\&CenterDot;u_k\left(T_0\right)}\sec ---\left(5\right)$

${\mathrm{\&tau;}}_k^{\&prime;}\left(T_0\right)\&cong;\frac{1}{c}\left\{\right[v_k\left(T_0\right)-v\left(T_0\right)]\&CenterDot;u_k\left(T_0\right)\}\sec /\sec ---\left(6\right)$

P wherein _k(T ₀) be k satellite position; P (T ₀) be customer location; v _k(T ₀) be k satellite velocities; u _k(T ₀) be that the user is to the projection vector of k satellite; V (T ₀) be user velocity; C is the light velocity.As seen, under the inclined to one side situation of known navigation message, customer location, clock, can resolve CA sign indicating number sequence m according to (4)～(6) formula _k(t).

Base band Doppler frequency ω _kCalculating formula is:

${\mathrm{\&omega;}}_k=2\mathrm{\&pi;}\left\{\frac{1}{\mathrm{\&lambda;}}\right[v\left(T_0\right)-v_k\left(T_0\right)]\&CenterDot;u_k\left(T_0\right)-\stackrel{\&CenterDot;}{b}\left(T_0\right)f\}---\left(7\right)$

Wherein f is carrier frequency, and λ is carrier wavelength.As seen, under the situation that known navigation message, user velocity, clock float, can find the solution base band Doppler frequency ω _k

According to horizontal velocity, vertical speed, clock float, horizontal level, highly, the inclined to one side order of clock searches for, because searching method is similar, is that example describes with the horizontal velocity search below, principle of work as shown in Figure 4:

Suppose that the initial moment INS of n data bit outgoing position, speed are Clock partially and clock float for

Carry out 8 dimension grids and divide centered by this group parameter, the quantity that each dimension is divided grid is followed successively by n ₁～n ₈, and n ₁～n ₈Be odd number.

Because customer location, clock are biased to code phase, user velocity, clock are biased to the linear mapping that is mapped as of carrier doppler frequency, therefore can be according to n ₁～n ₈Grid is divided code phase offset and the carrier doppler frequency shift (FS) obtain each satellite maximum, and no matter bias size all is divided into 9 parts in code phase, two dimensions of carrier doppler, as shown in Figure 5:

$\left[\begin{array}{ccccc}{\tilde{p}}_{n,x}-\frac{n_1-1}{2}{\mathrm{\&Delta;p}}_x& ...& {\tilde{p}}_{n,x}& ...& {\tilde{p}}_{n,x}+\frac{n_1-1}{2}{\mathrm{\&Delta;p}}_x\\ {\tilde{p}}_{n,y}-\frac{n_2-1}{2}{\mathrm{\&Delta;p}}_y& ...& {\tilde{p}}_{n,y}& ...& {\tilde{p}}_{n,y}+\frac{n_2-1}{2}{\mathrm{\&Delta;p}}_y\\ {\tilde{p}}_{n,z}-\frac{n_3-1}{2}{\mathrm{\&Delta;p}}_z& ...& {\tilde{p}}_{n,z}& ...& {\tilde{p}}_{n,z}+\frac{n_3-1}{2}{\mathrm{\&Delta;p}}_z\\ {\tilde{b}}_n-\frac{n_7-1}{2}\mathrm{\&Delta;b}& ...& {\tilde{b}}_n& ...& {\tilde{b}}_n+\frac{n_7-1}{2}\mathrm{\&Delta;b}\end{array}\right]\&DoubleRightArrow;\left[\begin{array}{ccccc}{m}_1-{4\mathrm{\&Delta;<figure-callout\; id="1"\; label="m"\; filenames="HDA00003018026600032.png"\; state="\{\{state\}\}">m</figure-callout>}}_1& ...& {\mathrm{<figure-callout\; id="1"\; label="m"\; filenames="HDA00003018026600032.png"\; state="\{\{state\}\}">m</figure-callout>}}_1& ...& {\mathrm{<figure-callout\; id="1"\; label="m"\; filenames="HDA00003018026600032.png"\; state="\{\{state\}\}">m</figure-callout>}}_1+4{\mathrm{\&Delta;<figure-callout\; id="1"\; label="m"\; filenames="HDA00003018026600032.png"\; state="\{\{state\}\}">m</figure-callout>}}_1\\ .& & .& & .\\ .& ...& .& ...& .\\ .& & .& & .\\ m_k-4{\mathrm{\&Delta;m}}_k& ...& m_k& ...& m_k+4{\mathrm{\&Delta;m}}_k\\ .& & .& & .\\ .& ...& .& ...& .\\ .& & .& & .\\ m_N-4{\mathrm{\&Delta;m}}_N& ...& m_N& ...& m_N+4{\mathrm{\&Delta;m}}_N\end{array}\right]$

$\left[\begin{array}{ccccc}{\tilde{v}}_{n,x}-\frac{n_4-1}{2}{\mathrm{\&Delta;v}}_x& ...& {\tilde{v}}_{n,x}& ...& {\tilde{v}}_{n,x}+\frac{n_4-1}{2}{\mathrm{\&Delta;v}}_x\\ {\tilde{v}}_{n,y}-\frac{n_5-1}{2}{\mathrm{\&Delta;v}}_y& ...& {\tilde{v}}_{n,y}& ...& {\tilde{v}}_{n,y}+\frac{n_5-1}{2}{\mathrm{\&Delta;v}}_y\\ {\tilde{v}}_{n,z}-\frac{n_6-1}{2}{\mathrm{\&Delta;v}}_z& ...& {\tilde{v}}_{n,z}& ...& {\tilde{v}}_{n,z}+\frac{n_6-1}{2}{\mathrm{\&Delta;v}}_z\\ {\widetilde{\stackrel{\&CenterDot;}{b}}}_n-\frac{n_8-1}{2}\mathrm{\&Delta;}\stackrel{\&CenterDot;}{b}& ...& {\widetilde{\stackrel{\&CenterDot;}{b}}}_n& ...& {\widetilde{\stackrel{\&CenterDot;}{b}}}_n+\frac{n_8-1}{2}\mathrm{\&Delta;}\stackrel{\&CenterDot;}{b}\end{array}\right]\&DoubleRightArrow;\left[\begin{array}{ccccc}{\mathrm{\&omega;}}_1-{4\mathrm{\&Delta;\&omega;}}_1& ...& {\mathrm{\&omega;}}_1& ...& {\mathrm{\&omega;}}_1+4{\mathrm{\&Delta;\&omega;}}_1\\ .& & .& & .\\ .& ...& .& ...& .\\ .& & .& & .\\ {\mathrm{\&omega;}}_k-4{\mathrm{\&Delta;\&omega;}}_k& ...& {\mathrm{\&omega;}}_k& ...& {\mathrm{\&omega;}}_k+4{\mathrm{\&Delta;\&omega;}}_k\\ .& & .& & .\\ .& ...& .& ...& .\\ .& & .& & .\\ {\mathrm{\&omega;}}_N-4{\mathrm{\&Delta;\&omega;}}_N& ...& {\mathrm{\&omega;}}_N& ...& {\mathrm{\&omega;}}_N+4{\mathrm{\&Delta;\&omega;}}_N\end{array}\right]$

For satellite k, with center stain correspondence code phase place, carrier doppler frequency correlator is set, then every satellite need arrange 81 correlators, behind pre-detection T integral time, obtains 81 correlations, enters the search phase.At first according to search center fixed position, vertical speed, clock partially and clock float, with 81 correlation inverse mappings of satellite k in horizontal velocity search two dimensional surface.

Successively the correlation inverse mapping of other satellites is searched in the two dimensional surface to horizontal velocity, according to each satellite carrier-to-noise ratio weight addition, maximum of points is corresponding horizontal velocity error, can be used as the observed quantity of integrated kalman filter device afterwards.

After the search of horizontal velocity error finishes, the fixing horizontal velocity error, the search vertical speed, and the like.All search for end, obtain velocity error, site error, the inclined to one side error of clock, clock and float 8 observed quantities of error, feed back to the integrated kalman filter device system state is upgraded.

In the present embodiment, geographic coordinate system elect as east northeast ground coordinate system (subscript N, E, D represent north orientation respectively, east orientation, ground to), the error model state vector is:

Wherein,

Be the inertial navigation attitude error, Be the inertial navigation velocity error,

Be the inertial navigation gyroscope constant value drift,

Be the inertial navigation accelerometer constant value drift, δ L, δ λ and δ h are respectively inertial navigation latitude error, longitude error and height error, and b is clock jitter,

Be clock drift.

Kalman filtering state equation is:

Wherein:

$F=\left[\begin{array}{cccccc}{F}_{11}& F_{12}& F_{13}& F_{14}& 0_{3\&times;3}& 0_{3\&times;2}\\ F_{21}& F_{22}& F_{23}& 0_{3\&times;3}& F_{25}& 0_{3\&times;2}\\ 0_{3\&times;3}& F_{32}& F_{33}& 0_{3\&times;3}& 0_{3\&times;3}& 0_{3\&times;2}\\ 0_{6\&times;3}& 0_{6\&times;3}& 0_{6\&times;3}& 0_{6\&times;3}& 0_{6\&times;3}& 0_{3\&times;2}\\ 0_{2\&times;3}& 0_{2\&times;3}& 0_{2\&times;3}& 0_{2\&times;3}& 0_{2\&times;3}& F_{46}\end{array}\right],$ $W=\left[\begin{array}{c}-C_{b_s}^n{\mathrm{\&epsiv;}}_w^{b_s}\\ C_{b_s}^n{\&dtri;}_w^{b_s}\\ 0_{11\&times;1}\end{array}\right]$

$F_{11}=(-{\mathrm{\&omega;}}_{\mathrm{in}}^n\&times;)=\left[\begin{array}{ccc}0& -({\mathrm{\&omega;}}_{\mathrm{ie}}\sin L+\frac{V_E}{R_N+h}\tan L)& \frac{V_N}{R_M+h}\\ {\mathrm{\&omega;}}_{\mathrm{ie}}\sin L+\frac{V_E}{R_N+h}\tan L& 0& {\mathrm{\&omega;}}_{\mathrm{ie}}\cos L+\frac{V_E}{R_N+h}\\ -\frac{V_N}{R_M+h}& -({\mathrm{\&omega;}}_{\mathrm{ie}}\cos L+\frac{V_E}{R_N+h})& 0\end{array}\right]$

$F_{12}=\left[\begin{array}{ccc}0& \frac{1}{R_N+h}& 0\\ -\frac{1}{R_M+h}& 0& 0\\ 0& -\frac{\tan L}{R_N+h}& 0\end{array}\right]$

$F_{13}=\left[\begin{array}{ccc}-{\mathrm{\&omega;}}_{\mathrm{ie}}\sin L& 0& 0\\ 0& 0& 0\\ -{\mathrm{\&omega;}}_{\mathrm{ie}}\cos L-\frac{V_E}{R_N+h}{\sec }^{2}L& 0& 0\end{array}\right]$

$F_{14}=-\left[\begin{array}{ccc}\cos \mathrm{\&theta;}\cos \mathrm{\&psi;}& -\cos \mathrm{\&gamma;}\sin \mathrm{\&psi;}+\sin \mathrm{\&gamma;}\sin \mathrm{\&theta;}\cos \mathrm{\&psi;}& \sin \mathrm{\&gamma;}\sin \mathrm{\&psi;}+\cos \mathrm{\&gamma;}\sin \mathrm{\&theta;}\cos \mathrm{\&psi;}\\ \cos \mathrm{\&theta;}\sin \mathrm{\&psi;}& \cos \mathrm{\&gamma;}\cos \mathrm{\&psi;}+\sin \mathrm{\&gamma;}\sin \mathrm{\&theta;}\sin \mathrm{\&psi;}& -\sin \mathrm{\&gamma;}\cos \mathrm{\&psi;}+\cos \mathrm{\&gamma;}\sin \mathrm{\&theta;}\sin \mathrm{\&psi;}\\ -\sin \mathrm{\&theta;}& \sin \mathrm{\&gamma;}\cos \mathrm{\&theta;}& \cos \mathrm{\&gamma;}\cos \mathrm{\&theta;}\end{array}\right]$

$F_{21}=(f^n\&times;)=\left[\begin{array}{ccc}0& {-f}_z^n& f_y^n\\ f_z^n& 0& {-f}_x^n\\ {-f}_y^n& f_x^n& 0\end{array}\right]$

$F_{22}=\left[\begin{array}{ccc}\frac{V_D}{R_M+h}& -2({\mathrm{\&omega;}}_{\mathrm{ie}}\sin L+\frac{V_E}{R_N+h}\tan L)& \frac{V_N}{R_M+\mathrm{<figure-callout\; id="2"\; label="h"\; filenames="HDA00003018026600012.png"\; state="\{\{state\}\}">h</figure-callout>}}\\ {2\mathrm{\&omega;}}_{\mathrm{ie}}\sin L+\frac{V_E}{R_N+h}\tan L& \frac{V_D}{R_N+h}+\frac{V_N}{R_N+h}\mathrm{<figure-callout\; id="2"\; label="tan\; L"\; filenames="HDA00003018026600012.png"\; state="\{\{state\}\}">tan</figure-callout>}L& 2{\mathrm{\&omega;}}_{\mathrm{ie}}\cos L+\frac{V_E}{R_N+h}\\ -\frac{2V_N}{R_M+h}& -2({\mathrm{\&omega;}}_{\mathrm{ie}}\cos L+\frac{V_E}{R_N+h})& 0\end{array}\right]$

$F_{23}=\left[\begin{array}{ccc}-({2V}_E{\mathrm{\&omega;}}_{\mathrm{ie}}\cos L+\frac{V_E^2}{R_N+h}{\sec }^{2}L)& 0& 0\\ 2V_N{\mathrm{\&omega;}}_{\mathrm{ie}}\cos L-{2V}_E{\mathrm{\&omega;}}_{\mathrm{ie}}\sin L+\frac{V_N{V}_E}{R_N+h}{\sec }^{2}L& 0& 0\\ {2V}_E{\mathrm{\&omega;}}_{\mathrm{ie}}\sin L& 0& -\frac{2g}{R_M}\end{array}\right]$

$F_{25}=\left[\begin{array}{ccc}\cos \mathrm{\&theta;}\cos \mathrm{\&psi;}& -\cos \mathrm{\&gamma;}\sin \mathrm{\&psi;}+\sin \mathrm{\&gamma;}\sin \mathrm{\&theta;}\cos \mathrm{\&psi;}& \sin \mathrm{\&gamma;}\sin \mathrm{\&psi;}+\cos \mathrm{\&gamma;}\sin \mathrm{\&theta;}\cos \mathrm{\&psi;}\\ \cos \mathrm{\&theta;}\sin \mathrm{\&psi;}& \cos \mathrm{\&gamma;}\cos \mathrm{\&psi;}+\sin \mathrm{\&gamma;}\sin \mathrm{\&theta;}\sin \mathrm{\&psi;}& -\sin \mathrm{\&gamma;}\cos \mathrm{\&psi;}+\cos \mathrm{\&gamma;}\sin \mathrm{\&theta;}\sin \mathrm{\&psi;}\\ -\sin \mathrm{\&theta;}& \sin \mathrm{\&gamma;}\cos \mathrm{\&theta;}& \cos \mathrm{\&gamma;}\cos \mathrm{\&theta;}\end{array}\right]$

$F_{32}=\left[\begin{array}{ccc}\frac{1}{R_M+h}& 0& 0\\ 0& \frac{1}{(R_N+h)\cos L}& 0\\ 0& 0& -1\end{array}\right]F_{33}=\left[\begin{array}{ccc}0& 0& -\frac{V_N}{{(R_M+h)}^2}\\ \frac{V_E\tan L}{(R_N+h)\cos L}& 0& -\frac{V_E}{{(R_N+h)}^2\cos L}\\ 0& 0& 0\end{array}\right]F_{46}=\left[\begin{array}{cc}0& 1\\ 0& 0\end{array}\right]$

Wherein, V _N, V _EAnd V _DBe respectively north orientation, east orientation and the ground of carrier to speed, L is the geographic latitude of carrier positions, and h is the carrier sea level elevation, R _MBe earth radius of curvature of meridian, R _NBe earth radius of curvature in prime vertical, ω _IeBe the spin velocity of the earth,

With Be the specific force of inertial navigation,

Be the noise of gyro, Noise for accelerometer.

The Kalman filter observation vector is:

Obtained by the DPE maximal possibility estimation, for the velocity error observed quantity, need be transformed into navigation coordinate system by the ECEF coordinate system.

The Kalman filter observation equation is: Z=HX+V

Wherein, $H=\left[\begin{array}{cccc}{0}_{3\&times;3}& 0_{3\&times;3}& I_{3\&times;3}& 0_{3\&times;2}\\ 0_{3\&times;3}& I_{3\&times;3}& 0_{3\&times;3}& 0_{3\&times;2}\\ 0_{2\&times;3}& 0_{2\&times;3}& 0_{2\&times;3}& I_{2\&times;2}\end{array}\right]$ Be observing matrix, V is observation noise.

Position, the speed supplementary of utilizing INS to provide, GNSS uses direct position to estimate (DPE) algorithm Estimated Position Error, velocity error, clocking error, with this closed circuit.Because system is sensor with MIMU, baseband correlators directly, in the top layer combinational algorithm, realize all navigation features, namely this algorithm has been realized the complete fusion of GNSS and MINS with the sensor of GNSS baseband correlators as field, sensitivity volume location.

Present embodiment can compatible all global position systems, comprise GPS, big-dipper satellite positioning system, glonass system and Galileo navigational system.

Embodiment two

Be illustrated in figure 6 as the system architecture diagram of present embodiment, the super deep integrated navigation system of this GNSS/MINS mainly is made of GNSS system, MINS system and correlator bank 606.

Wherein, the GNSS system through radio-frequency front-end 602, with described satellite-signal down coversion sampling back output intermediate-freuqncy signal, and receives satellite navigation message by antenna 601 receiving satellite signals; The MINS system obtains measurement data by MIMU603, obtains carrier positions, speed and attitude after strapdown inertial navitation system (SINS) 604 is resolved.

This system also comprises:

Carrier frequency and code phase computing unit 605 are used for GNSS signal(-) carrier frequency and code phase are calculated in conjunction with described satellite navigation message in speed and the position of the output of described MINS system, to produce local signal;

Integrated kalman filter device 609 feeds back to the MINS system with state error and carries out error correction;

Correlator bank 606 is used for carrying out relevant with intermediate-freuqncy signal described local signal;

Projection mapping unit 607 carries out projection mapping with the output result of described correlator bank 606;

Search unit 608 is searched for the output result of described projection mapping unit 607, obtains maximal value, and namely position error, velocity error, the inclined to one side sum of errors clock of clock float error, as the observed quantity of described integrated kalman filter device.

Described search unit 608 according to horizontal velocity, vertical speed, clock float, horizontal level, highly, the inclined to one side order of clock carries out peaked search, the horizontal velocity error, vertical speed error, the clock that obtain in the corresponding T time float error, horizontal position error, height error, the inclined to one side error of clock.

Present embodiment can compatible all global position systems, comprise GPS, big-dipper satellite positioning system, glonass system and Galileo navigational system.

Present embodiment uses the high precision crystal oscillator, makes clock partially and clock floats 2 dimensions and searches for the grid quantity and can reduce.Altitude channel is less to the influence of correlator accumulated value, therefore can introduce the pressure altimeter assisting navigation, thereby reduces the search lattice quantity of short transverse.

Embodiment three

Present embodiment proposes a kind of device that is applied to the method for embodiment one, as shown in Figure 7.

This GNSS/MINS combined navigation device comprises: antenna 701 and radio-frequency module 702, and the interface by FPGA706 is with GNSS intermediate frequency data drawing-in system; Pressure altimeter 703, the interface by FPGA706 is with barometer altitude data drawing-in system; MIMU704, the interface by FPGA706 is with MIMU704) the measurement data drawing-in system; And power module 705, DSP708 and host computer 707.

Wherein, described combined navigation device also comprises correlator bank 709, described DSP708 will calculate code phase, carrier frequency and projection vector, and write FPGA706 inside by the EMIF bus, FPGA706 divides grid automatically and is mapped to described correlator bank 709 according to the search grid size, behind pre-detection T integral time, FPGA706 handles output valve and the maximizing of correlator bank 709, it is the site error in the corresponding T time, velocity error, clock partially and clock float error, described DSP708 reads in described site error, velocity error, clock partially and clock float the error observed reading and the User Status amount upgraded.

Present embodiment can compatible all global position systems, comprise GPS, big-dipper satellite positioning system, glonass system and Galileo navigational system.

Present embodiment uses the high precision crystal oscillator, makes clock partially and clock floats 2 dimensions and searches for the grid quantity and can reduce.Altitude channel is less to the influence of correlator accumulated value, therefore can introduce the pressure altimeter assisting navigation, thereby reduces the search lattice quantity of short transverse.

This hardware structure takes full advantage of the FPGA parallel processing capability, uses FPGA to finish a large amount of related operations and search arithmetic, and certain search strategy of arranging in pairs or groups can guarantee the embedded realization of super deep integrated navigation system.

Need to prove, describe and to be understood that in the process flow diagram or in this any process of otherwise describing or method, expression comprises the module of code of the executable instruction of the step that one or more is used to realize specific logical function or process, fragment or part, and the scope of preferred implementation of the present invention comprises other realization, wherein can be not according to order shown or that discuss, comprise according to related function by the mode of basic while or by opposite order, carry out function, this should be understood by the embodiments of the invention person of ordinary skill in the field.

In the description of this instructions, concrete feature, structure, material or characteristics that the description of reference term " embodiment ", " some embodiment ", " example ", " concrete example " or " some examples " etc. means in conjunction with this embodiment or example description are contained at least one embodiment of the present invention or the example.In this manual, the schematic statement to above-mentioned term not necessarily refers to identical embodiment or example.And concrete feature, structure, material or the characteristics of description can be with the suitable manner combination in any one or more embodiment or example.

Although illustrated and described embodiments of the invention above, be understandable that, above-described embodiment is exemplary, can not be interpreted as limitation of the present invention, those of ordinary skill in the art can change above-described embodiment under the situation that does not break away from principle of the present invention and aim within the scope of the invention, modification, replacement and modification.

Claims (20)

1. the super deep integrated navigation method of GNSS/MINS is sensor with MIMU and GNSS baseband correlators, carries out integrated navigation and calculates, and comprising:

Step S01, the integrated navigation system initiation parameter arranges;

Step S02, GNSS catch satellite and obtain navigation message, and INS finishes big misalignment initial alignment under GNSS is auxiliary;

Step S03, float according to the position of INS output and the inclined to one side and initial clock of initial clock of speed and GNSS output, carrying out first grid divides, and finish from the position, speed, clock is floated to clock partially and carries the mapping of frequency and code phase mutually, realize the division of second grid, corresponding each second grid element center point is set up correlator;

Step S04 carries out the coherent integration of 1 bit text length, correlator output correlation;

Step S05 judges whether to reach T integral time, if do not reach, enters step S06, otherwise enters step S07;

Step S06, system carries out noncoherent accumulation and strap-down navigation resolves, and adjusts described second grid when resolving fructufy according to described strap-down navigation and divide the center, returns step S05;

Step S07 carries out second grid to the inverse mapping of first grid with the noncoherent accumulation correlation of every satellite, carries out peaked search then, and the inclined to one side sum of errors clock of site error, velocity error, clock that obtains in the T time floats error;

Step S08 with the observed quantity as the integrated kalman filter device of each error of obtaining among the step S07, carries out INS error update and clock error update, then with the kalman filter state zero clearing;

Step S09, the search grid size adaptation is adjusted;

Step S10, strap-down navigation resolve and upgrade after customer location, the speed, enter next algorithm circulation.

2. Combinated navigation method according to claim 1, it is characterized in that, described first grid be divided at three-dimensional position, three-dimensional velocity, clock float, clock octuple grid on the upper side divides, the two-dimensional grid that second grid is divided on GNSS carrier frequency, pseudo-code phase is divided.

3. Combinated navigation method according to claim 2 is characterized in that, described super deep integrated navigation method utilizes pressure altimeter to introduce barometer altitude data assisting navigation.

4. Combinated navigation method according to claim 2 is characterized in that, described super deep integrated navigation method is used the high precision crystal oscillator.

5. according to claim 3 or 4 described Combinated navigation methods, it is characterized in that described super deep integrated navigation method is used maximum Likelihood, the maximal possibility estimation formula is:

Wherein, r (t) is GNSS receiving baseband signal plus noise, S _k(t) be k the satellite baseband signal that receiver produces, N is satellites in view quantity, T ₀Be the initial moment of pre-detection integration, T is pre-detection integral time, and t is that receiver generates the baseband signal time series.Suppose S _k(t) cross over D bit navigation message, the D value 50～150 in typical case;

In the n data bit, k the local baseband signal s of satellite _k(t) be:

s _k(t)=a _k,nm _k(t)exp[j(ω _kt+φ _k,n)]

A wherein _{K, n}Be signal amplitude, relevant with carrier-to-noise ratio; m _k(t) be the CA sign indicating number, determined partially by customer location, clock; ω _kBe the carrier wave circular frequency, float decision by user velocity, clock; φ _{K, n}Be carrier phase;

The maximal possibility estimation formula is launched, is obtained:

Because R is only relevant with the reception signal, therefore minimizes [R-S], is equivalent to maximization [S], that is:

I wherein _{K, n}Be the time span of k satellite n data bit, inner summation number be noncoherent accumulation, and outside summation number adds up for each satellite channel fusion;

CA sign indicating number sequence waveform m _k(t) finding the solution formula is:

$m_k\left(t\right)\&cong;C_k[t-b\left(T_0\right)-{\mathrm{\&tau;}}_k\left(T_0\right)-(t-T_0){\mathrm{\&tau;}}_k^{\&prime;}\left(T_0\right)]$

C wherein _k(t _GNSS) for the GNSS time being the satellite CA sign indicating number generation function of parameter, C _k(t _GNSS) known; τ _k(T ₀) be T ₀K the satellite-signal transmission delay that arrives; τ ' _k(T ₀) be rate of change time delay that is caused by Doppler frequency; B (T ₀) be that clock is inclined to one side, provided by navigation message, Several Parameters is calculated as follows:

${\mathrm{\&tau;}}_k\left(T_0\right)\&cong;\frac{|p_k\left(T_0\right)-p\left(T_0\right)|}{c+v_k\left(T_0\right)\&CenterDot;u_k\left(T_0\right)}\sec$

${\mathrm{\&tau;}}_k^{\&prime;}\left(T_0\right)\&cong;\frac{1}{c}\left\{\right[v_k\left(T_0\right)-v\left(T_0\right)]\&CenterDot;u_k\left(T_0\right)\}\sec /\sec$

P wherein _k(T ₀) be k satellite position; P (T ₀) be customer location; v _k(T ₀) be k satellite velocities; u _k(T ₀) be that the user is to the projection vector of k satellite; V (T ₀) be user velocity; C is the light velocity;

Base band Doppler frequency ω _kCalculating formula is:

${\mathrm{\&omega;}}_k=2\mathrm{\&pi;}\left\{\frac{1}{\mathrm{\&lambda;}}\right[v\left(T_0\right)-v_k\left(T_0\right)]\&CenterDot;u_k\left(T_0\right)-\stackrel{\&CenterDot;}{b}\left(T_0\right)f\}$

Wherein f is carrier frequency, and λ is carrier wavelength.As seen, under the situation that known navigation message, user velocity, clock float, can find the solution base band Doppler frequency ω _k

6. Combinated navigation method according to claim 5, it is characterized in that, in step S07, according to horizontal velocity, vertical speed, clock float, horizontal level, highly, the inclined to one side order of clock carries out peaked search, the horizontal velocity error, vertical speed error, the clock that obtain in the corresponding T time float error, horizontal position error, height error, the inclined to one side error of clock.

7. Combinated navigation method according to claim 6 is characterized in that, described horizontal velocity error searching method is:

Suppose that the initial moment INS of n data bit outgoing position, speed are

Clock partially and clock float for

Carry out 8 dimension grids and divide centered by this group parameter, the quantity that each dimension is divided grid is followed successively by n ₁～n ₈, and n ₁～n ₈Be odd number, at every satellite, be divided into 9 parts in code phase, two dimensions of carrier doppler:

$\left[\begin{array}{ccccc}{\tilde{p}}_{n,x}-\frac{n_1-1}{2}{\mathrm{\&Delta;p}}_x& ...& {\tilde{p}}_{n,x}& ...& {\tilde{p}}_{n,x}+\frac{n_1-1}{2}{\mathrm{\&Delta;p}}_x\\ {\tilde{p}}_{n,y}-\frac{n_2-1}{2}{\mathrm{\&Delta;p}}_y& ...& {\tilde{p}}_{n,y}& ...& {\tilde{p}}_{n,y}+\frac{n_2-1}{2}{\mathrm{\&Delta;p}}_y\\ {\tilde{p}}_{n,z}-\frac{n_3-1}{2}{\mathrm{\&Delta;p}}_z& ...& {\tilde{p}}_{n,z}& ...& {\tilde{p}}_{n,z}+\frac{n_3-1}{2}{\mathrm{\&Delta;p}}_z\\ {\tilde{b}}_n-\frac{n_7-1}{2}\mathrm{\&Delta;b}& ...& {\tilde{b}}_n& ...& {\tilde{b}}_n+\frac{n_7-1}{2}\mathrm{\&Delta;b}\end{array}\right]\&DoubleRightArrow;\left[\begin{array}{ccccc}{m}_1-{4\mathrm{\&Delta;m}}_1& ...& m_1& ...& m_1+4{\mathrm{\&Delta;m}}_1\\ .& & .& & .\\ .& ...& .& ...& .\\ .& & .& & .\\ m_k-4{\mathrm{\&Delta;m}}_k& ...& m_k& ...& m_k+4{\mathrm{\&Delta;m}}_k\\ .& & .& & .\\ .& ...& .& ...& .\\ .& & .& & .\\ m_N-4{\mathrm{\&Delta;m}}_N& ...& m_N& ...& m_N+4{\mathrm{\&Delta;m}}_N\end{array}\right]$

$\left[\begin{array}{ccccc}{\tilde{v}}_{n,x}-\frac{n_4-1}{2}{\mathrm{\&Delta;v}}_x& ...& {\tilde{v}}_{n,x}& ...& {\tilde{v}}_{n,x}+\frac{n_4-1}{2}{\mathrm{\&Delta;v}}_x\\ {\tilde{v}}_{n,y}-\frac{n_5-1}{2}{\mathrm{\&Delta;v}}_y& ...& {\tilde{v}}_{n,y}& ...& {\tilde{v}}_{n,y}+\frac{n_5-1}{2}{\mathrm{\&Delta;v}}_y\\ {\tilde{v}}_{n,z}-\frac{n_6-1}{2}{\mathrm{\&Delta;v}}_z& ...& {\tilde{v}}_{n,z}& ...& {\tilde{v}}_{n,z}+\frac{n_6-1}{2}{\mathrm{\&Delta;v}}_z\\ {\widetilde{\stackrel{\&CenterDot;}{b}}}_n-\frac{n_8-1}{2}\mathrm{\&Delta;}\stackrel{\&CenterDot;}{b}& ...& {\widetilde{\stackrel{\&CenterDot;}{b}}}_n& ...& {\widetilde{\stackrel{\&CenterDot;}{b}}}_n+\frac{n_8-1}{2}\mathrm{\&Delta;}\stackrel{\&CenterDot;}{b}\end{array}\right]\&DoubleRightArrow;\left[\begin{array}{ccccc}{\mathrm{\&omega;}}_1-{4\mathrm{\&Delta;\&omega;}}_1& ...& {\mathrm{\&omega;}}_1& ...& {\mathrm{\&omega;}}_1+4{\mathrm{\&Delta;\&omega;}}_1\\ .& & .& & .\\ .& ...& .& ...& .\\ .& & .& & .\\ {\mathrm{\&omega;}}_k-4{\mathrm{\&Delta;\&omega;}}_k& ...& {\mathrm{\&omega;}}_k& ...& {\mathrm{\&omega;}}_k+4{\mathrm{\&Delta;\&omega;}}_k\\ .& & .& & .\\ .& ...& .& ...& .\\ .& & .& & .\\ {\mathrm{\&omega;}}_N-4{\mathrm{\&Delta;\&omega;}}_N& ...& {\mathrm{\&omega;}}_N& ...& {\mathrm{\&omega;}}_N+4{\mathrm{\&Delta;\&omega;}}_N\end{array}\right]$

For satellite k, with each grid element center correspondence code phase place, carrier doppler frequency correlator is set, then every satellite need arrange 81 correlators, behind pre-detection T integral time, obtains 81 correlations, enters the search phase; At first according to search center fixed position, vertical speed, clock partially and clock float, with 81 correlation inverse mappings of satellite k in horizontal velocity search two dimensional surface; Successively the correlation inverse mapping of other satellites is searched in the two dimensional surface to horizontal velocity, according to each satellite carrier-to-noise ratio weight addition, maximum of points is corresponding horizontal velocity error afterwards.

8. Combinated navigation method according to claim 7 is characterized in that, after horizontal velocity search finishes, the fixing horizontal velocity error, according to identical method carry out successively that vertical speed, clock are floated, horizontal level, highly, the inclined to one side error search of clock.

9. according to claim 3 or 4 described Combinated navigation methods, it is characterized in that Kalman filtering error model vector is:

Wherein, geographic coordinate system is elected east northeast ground coordinate system as, and subscript N, E, D represent north orientation respectively, east orientation, ground to,

Be the inertial navigation attitude error,

Be the inertial navigation velocity error, Be the inertial navigation gyroscope constant value drift,

Be the inertial navigation accelerometer constant value drift, δ L, δ λ and δ h are respectively inertial navigation latitude error, longitude error and height error, and b is clock jitter,

Be clock drift;

Kalman filtering state equation is:

Wherein:

$F=\left[\begin{array}{cccccc}{F}_{11}& F_{12}& F_{13}& F_{14}& 0_{3\&times;3}& 0_{3\&times;2}\\ F_{21}& F_{22}& F_{23}& 0_{3\&times;3}& F_{25}& 0_{3\&times;2}\\ 0_{3\&times;3}& F_{32}& F_{33}& 0_{3\&times;3}& 0_{3\&times;3}& 0_{3\&times;2}\\ 0_{6\&times;3}& 0_{6\&times;3}& 0_{6\&times;3}& 0_{6\&times;3}& 0_{6\&times;3}& 0_{3\&times;2}\\ 0_{2\&times;3}& 0_{2\&times;3}& 0_{2\&times;3}& 0_{2\&times;3}& 0_{2\&times;3}& F_{46}\end{array}\right],$ $W=\left[\begin{array}{c}-C_{b_s}^n{\mathrm{\&epsiv;}}_w^{b_s}\\ C_{b_s}^n{\&dtri;}_w^{b_s}\\ 0_{11\&times;1}\end{array}\right]$

$F_{11}=(-{\mathrm{\&omega;}}_{\mathrm{in}}^n\&times;)=\left[\begin{array}{ccc}0& -({\mathrm{\&omega;}}_{\mathrm{ie}}\sin L+\frac{V_E}{R_N+h}\tan L)& \frac{V_N}{R_M+h}\\ {\mathrm{\&omega;}}_{\mathrm{ie}}\sin L+\frac{V_E}{R_N+h}\tan L& 0& {\mathrm{\&omega;}}_{\mathrm{ie}}\cos L+\frac{V_E}{R_N+h}\\ -\frac{V_N}{R_M+h}& -({\mathrm{\&omega;}}_{\mathrm{ie}}\cos L+\frac{V_E}{R_N+h})& 0\end{array}\right]$

$F_{12}=\left[\begin{array}{ccc}0& \frac{1}{R_N+h}& 0\\ -\frac{1}{R_M+h}& 0& 0\\ 0& -\frac{\tan L}{R_N+h}& 0\end{array}\right]$

$F_{13}=\left[\begin{array}{ccc}-{\mathrm{\&omega;}}_{\mathrm{ie}}\sin L& 0& 0\\ 0& 0& 0\\ -{\mathrm{\&omega;}}_{\mathrm{ie}}\cos L-\frac{V_E}{R_N+h}{\sec }^{2}L& 0& 0\end{array}\right]$

$F_{14}=-\left[\begin{array}{ccc}\cos \mathrm{\&theta;}\cos \mathrm{\&psi;}& -\cos \mathrm{\&gamma;}\sin \mathrm{\&psi;}+\sin \mathrm{\&gamma;}\sin \mathrm{\&theta;}\cos \mathrm{\&psi;}& \sin \mathrm{\&gamma;}\sin \mathrm{\&psi;}+\cos \mathrm{\&gamma;}\sin \mathrm{\&theta;}\cos \mathrm{\&psi;}\\ \cos \mathrm{\&theta;}\sin \mathrm{\&psi;}& \cos \mathrm{\&gamma;}\cos \mathrm{\&psi;}+\sin \mathrm{\&gamma;}\sin \mathrm{\&theta;}\sin \mathrm{\&psi;}& -\sin \mathrm{\&gamma;}\cos \mathrm{\&psi;}+\cos \mathrm{\&gamma;}\sin \mathrm{\&theta;}\sin \mathrm{\&psi;}\\ -\sin \mathrm{\&theta;}& \sin \mathrm{\&gamma;}\cos \mathrm{\&theta;}& \cos \mathrm{\&gamma;}\cos \mathrm{\&theta;}\end{array}\right]$

$F_{21}=(f^n\&times;)=\left[\begin{array}{ccc}0& {-f}_z^n& f_y^n\\ f_z^n& 0& {-f}_x^n\\ {-f}_y^n& f_x^n& 0\end{array}\right]$

$F_{22}=\left[\begin{array}{ccc}\frac{V_D}{R_M+h}& -2({\mathrm{\&omega;}}_{\mathrm{ie}}\sin L+\frac{V_E}{R_N+h}\tan L)& \frac{V_N}{R_M+h}\\ {2\mathrm{\&omega;}}_{\mathrm{ie}}\sin L+\frac{V_E}{R_N+h}\tan L& \frac{V_D}{R_N+h}+\frac{V_N}{R_N+h}\tan L& 2{\mathrm{\&omega;}}_{\mathrm{ie}}\cos L+\frac{V_E}{R_N+h}\\ -\frac{2V_N}{R_M+h}& -2({\mathrm{\&omega;}}_{\mathrm{ie}}\cos L+\frac{V_E}{R_N+h})& 0\end{array}\right]$

$F_{23}=\left[\begin{array}{ccc}-({2V}_E{\mathrm{\&omega;}}_{\mathrm{ie}}\cos L+\frac{V_E^2}{R_N+h}{\sec }^{2}L)& 0& 0\\ 2V_N{\mathrm{\&omega;}}_{\mathrm{ie}}\cos L-{2V}_E{\mathrm{\&omega;}}_{\mathrm{ie}}\sin L+\frac{V_N{V}_E}{R_N+h}{\sec }^{2}L& 0& 0\\ {2V}_E{\mathrm{\&omega;}}_{\mathrm{ie}}\sin L& 0& -\frac{2g}{R_M}\end{array}\right]$

$F_{25}=\left[\begin{array}{ccc}\cos \mathrm{\&theta;}\cos \mathrm{\&psi;}& -\cos \mathrm{\&gamma;}\sin \mathrm{\&psi;}+\sin \mathrm{\&gamma;}\sin \mathrm{\&theta;}\cos \mathrm{\&psi;}& \sin \mathrm{\&gamma;}\sin \mathrm{\&psi;}+\cos \mathrm{\&gamma;}\sin \mathrm{\&theta;}\cos \mathrm{\&psi;}\\ \cos \mathrm{\&theta;}\sin \mathrm{\&psi;}& \cos \mathrm{\&gamma;}\cos \mathrm{\&psi;}+\sin \mathrm{\&gamma;}\sin \mathrm{\&theta;}\sin \mathrm{\&psi;}& -\sin \mathrm{\&gamma;}\cos \mathrm{\&psi;}+\cos \mathrm{\&gamma;}\sin \mathrm{\&theta;}\sin \mathrm{\&psi;}\\ -\sin \mathrm{\&theta;}& \sin \mathrm{\&gamma;}\cos \mathrm{\&theta;}& \cos \mathrm{\&gamma;}\cos \mathrm{\&theta;}\end{array}\right]$

$F_{32}=\left[\begin{array}{ccc}\frac{1}{R_M+h}& 0& 0\\ 0& \frac{1}{(R_N+h)\cos L}& 0\\ 0& 0& -1\end{array}\right]F_{33}=\left[\begin{array}{ccc}0& 0& -\frac{V_N}{{(R_M+h)}^2}\\ \frac{V_E\tan L}{(R_N+h)\cos L}& 0& -\frac{V_E}{{(R_N+h)}^2\cos L}\\ 0& 0& 0\end{array}\right]F_{46}=\left[\begin{array}{cc}0& 1\\ 0& 0\end{array}\right]$

Wherein, V _N, V _EAnd V _DBe respectively north orientation, east orientation and the ground of carrier to speed, L is the geographic latitude of carrier positions, and h is the carrier sea level elevation, R _MBe earth radius of curvature of meridian, R _NBe earth radius of curvature in prime vertical, ω _IeBe the spin velocity of the earth,

With

Be the specific force of inertial navigation,

Be the noise of gyro,

Noise for accelerometer;

The Kalman filter observation vector is: $Z=[\mathrm{\&Delta;x},\mathrm{\&Delta;y},\mathrm{\&Delta;z},{\mathrm{\&Delta;v}}_N,{\mathrm{\&Delta;v}}_E,{\mathrm{\&Delta;v}}_D,\mathrm{\&Delta;b},\mathrm{\&Delta;}\stackrel{\&CenterDot;}{b}];$

The Kalman filter observation equation is: Z=HX+V;

Wherein,

$H=\left[\begin{array}{cccc}{0}_{3\&times;3}& 0_{3\&times;3}& I_{3\&times;3}& 0_{3\&times;2}\\ 0_{3\&times;3}& I_{3\&times;3}& 0_{3\&times;3}& 0_{3\&times;2}\\ 0_{2\&times;3}& 0_{2\&times;3}& 0_{2\&times;3}& I_{2\&times;2}\end{array}\right]$ Be observing matrix, V is observation noise.

10. Combinated navigation method according to claim 1 is characterized in that, in step S06, inertial navigation resolves speed and equates or be the multiple relation with the navigation message code check.

11. Combinated navigation method according to claim 1 is characterized in that, in step 07, adjusts the search grid size according to kalman filter state covariance matrix self-adaptation.

12. Combinated navigation method according to claim 1 is characterized in that, described GNSS system is GPS, big-dipper satellite positioning system, glonass system or Galileo navigational system.

13. the super deep integrated navigation system of GNSS/MINS comprises:

The GNSS system by antenna (601) receiving satellite signal, through radio-frequency front-end (602), with described satellite-signal down coversion sampling back output intermediate-freuqncy signal, and receives satellite navigation message;

The MINS system passes through MIMU(603) obtain measurement data, after resolving, strapdown inertial navitation system (SINS) (604) obtains carrier positions, speed and attitude;

Carrier frequency and code phase computing unit (605) are used for GNSS signal(-) carrier frequency and code phase are calculated in conjunction with described satellite navigation message in speed and the position of the output of described MINS system, to produce local signal; And

Integrated kalman filter device (609) feeds back to the MINS system with state error and carries out error correction;

It is characterized in that, also comprise:

Correlator bank (606) is used for carrying out relevant with intermediate-freuqncy signal described local signal;

Projection mapping from second grid to first grid is finished with the output result of described correlator bank (606) in projection mapping unit (607); And

Search unit (608) is searched for the output result of described projection mapping unit (607), obtains maximal value, and namely position error, velocity error, the inclined to one side sum of errors clock of clock float error, as the observed quantity of described integrated kalman filter device.

14. integrated navigation system according to claim 13, it is characterized in that, described search unit (608) according to horizontal velocity, vertical speed, clock float, horizontal level, highly, the inclined to one side order of clock carries out peaked search, the horizontal velocity error, vertical speed error, the clock that obtain in the corresponding T time float error, horizontal position error, height error, the inclined to one side error of clock.

15. integrated navigation system according to claim 13 is characterized in that, described GNSS system is GPS, big-dipper satellite positioning system, glonass system or Galileo navigational system.

16. integrated navigation system according to claim 13 is characterized in that, described system also comprises pressure altimeter, for system introduces the barometer altitude data.

17. Combinated navigation method according to claim 13 is characterized in that, described system also comprises the high precision crystal oscillator.

18. one kind is applied to each described Combinated navigation method of claim 1-12, perhaps is applied to the combined navigation device in each described integrated navigation system of claim 13-17, comprising:

Antenna (701) and radio-frequency module (702) pass through FPGA(706) interface is GNSS intermediate frequency data drawing-in system; MIMU(704), pass through FPGA(706) interface is MIMU(704) the measurement data drawing-in system; And power module (705), DSP(708) and host computer (707); It is characterized in that, described combined navigation device also comprises correlator bank (709), described DSP(708) will calculate code phase, carrier frequency and projection vector, and write FPGA(706 by the EMIF bus) inside, FPGA(706) divide grid automatically and be mapped to described correlator bank (709) according to the search grid size, behind pre-detection T integral time, FPGA(706) output valve and the maximizing of processing correlator bank (709), it is the site error in the corresponding T time, velocity error, clock partially and clock float error, described DSP(708) read in described site error, velocity error, clock partially and clock float the error observed reading and the User Status amount upgraded.

19. combined navigation device according to claim 18 is characterized in that, also comprises pressure altimeter (703), passes through FPGA(706) interface is barometer altitude data drawing-in system.

20. combined navigation device according to claim 18 is characterized in that, described GNSS system is GPS, big-dipper satellite positioning system, glonass system or Galileo navigational system.

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