CN104062667A - GPS weak signal tracking system based on I/Q branch correlation integral observation filtering - Google Patents

Abstract

The invention discloses a GPS weak signal tracking system based on I/Q branch correlation integral observation filtering. A receiver is used for receiving a satellite signal which is converted into an intermediate frequency signal through down conversion and transmitted to a frequency mixer. The frequency mixer is also used for receiving local sine and cosine reproduction carrier signals outputted by a local carrier digital-controlled oscillator, and outputting a I-branch output signal and a II-branch output signal to a correlation arithmetic unit. The correlation arithmetic unit also receives a local advanced C/A code, an instant reproduction C/A code and a lagged reproduction C/A code, wherein the local advanced C/A code, the instant reproduction C/A code and the lagged reproduction C/A code are generated by a code generator. Results are outputted to an integral eraser, and six-way relevant integral values are generated and outputted to a Kalman filter. The Kalman filter obtains an estimated carrier phase difference, an estimated carrier frequency difference and an estimated code phase difference, and the estimated carrier phase difference, the estimated carrier frequency difference and the estimated code phase difference are transmitted to the local carrier digital-controlled oscillator and the code generator. The system effectively reduces the noise intensity of the satellite signal, can trace the satellite signal better in a weak-signal environment, and improves the tracing precision.

Description

GPS weak signal tracker based on I/Q branch road correlation integral observation filtering

Technical field

The invention belongs to gps signal and follow the tracks of field, relate in particular to the GPS weak signal tracker based on I/Q branch road correlation integral observation filtering.

Background technology

GPS receiver from satellite reception to signal be the modulation signal of spread spectrum, by catching with tracking phase, to satellite-signal despreading, demodulation, just can obtain navigation message.Gps signal tracking phase, the guestimate value to current satellite signal carrier frequency and code phase that signalling channel obtains from acquisition phase, by the progressively meticulous estimation to these two signal parameters of track loop.Under faint condition, simply increase and can not meet tracer request integral time, utilize bit synchronization method can avoid the loop integral time to cross over navigation message data bit edge.Multipath effect while utilizing EKF (EKF) can erasure signal to follow the tracks of, Construction of A Model is simpler, but can not effectively follow the tracks of feeble signal.By the triangular wave peak dot in signal auto-correlation function characteristic, adopt the means of matching, can avoid the noncontinuity of Jacobi equation in EKF calculating process, but tracking error is larger.

Summary of the invention

The object of the invention is to provide a kind of GPS weak signal tracker based on I/Q branch road correlation integral observation filtering that can effectively follow the tracks of GPS feeble signal.

The present invention is achieved through the following technical solutions:

GPS weak signal tracker based on I/Q branch road correlation integral observation filtering, comprises receiver, local carrier digital controlled oscillator, frequency mixer, code generator, integration remover, correlation operator, Kalman filter,

Receiver, for receiving satellite signal, through being downconverted into intermediate-freuqncy signal, sends frequency mixer to;

Frequency mixer also receives this locality sine, the cosine reproduction carrier signal of local carrier digital controlled oscillator output, intermediate-freuqncy signal and local sinusoidal reproduction carrier signal are carried out to mixing computing, obtain I branch output signal, send correlation operator to, intermediate-freuqncy signal and local cosine reproduction carrier signal are carried out to mixing computing, obtain Q branch output signal, send correlation operator to;

Correlation operator this locality that also receiving code generator produces reappears in advance C/A code, immediately reappears C/A code and lag behind reproduction C/A code, by I branch output signal and Q branch output signal reappear in advance C/A code with this locality respectively, immediately reappear C/A code, the reproduction C/A code that lags behind carries out related calculation, result sends integration remover to;

Integration remover produces six tunnel correlation integral value I according to the information receiving _e, I _p, I _l, Q _e, Q _p, Q _l, send Kalman filter to; The carrier phase difference that Kalman filter obtains estimating, carrier frequency difference and code phase difference, send carrier phase difference and carrier frequency difference to local carrier digital controlled oscillator, sends code phase difference to code generator;

Local carrier digital controlled oscillator produces local sinusoidal, cosine reproduction carrier signal according to the information receiving, and flows to frequency mixer; Code generator produces local leading reproduction C/A code, immediately reappears C/A code and hysteresis reproduction C/A code according to the information of reception, sends correlation operator to.

The GPS weak signal tracker that the present invention is based on I/Q branch road correlation integral observation filtering can also comprise:

1, the intermediate-freuqncy signal of i satellite-signal of receiver output is:

Wherein, A is normalized signal amplitude, and D (k) is numeric data code, the C/A code that C (k) plays for satellite, f ₁for radiofrequency signal incoming frequency, f _dfor signal Doppler shift, k is moment epoch, it is carrier wave initial phase;

The sinusoidal reproduction in this locality carrier signal S that local carrier digital controlled oscillator produces _oSwith local cosine reproduction carrier signal S _oCfor:

Wherein, A _ofor amplitude, f _ofor frequency, for phase place,

Intermediate-freuqncy signal and local sinusoidal reproduction carrier signal S _oSthe result of doing mixing budget is:

Wherein,

$A_e=\frac{1}{2}A\·A_O$

f _e＝f ₁+f _d-f _O

Intermediate-freuqncy signal and local cosine reproduction carrier signal S _oCthe result of doing mixing budget is:

2, the related operation method in correlation operator is:

$R\left(k\right)=\frac{1}{N}\underset{n=0}{\overset{N-1}{\mathrm{\Σ}}}C\left(n\right)\·C_O(n-k)$

Wherein, the C/A code in the output signal that C (k) is frequency mixer, C _o(k) this locality producing for code generator reappears in advance C/A code or immediately reappears C/A code or the reproduction C/A code that lags behind, and N counts out for participating in the discrete data of related operation.

3, the quantity of state of Kalman filter is:

Wherein, A is normalized signal amplitude, for carrier phase difference, δ w is carrier frequency difference, and δ a is carrier frequency variation rate, and δ τ is code phase difference, λ _lfor satellite signal carrier wavelength, λ _cAfor C/A code wavelength, W _kfor process noise, also claim system noise, be designated as $W_k={\left[\begin{array}{ccccc}{\mathrm{\ω}}_1& {\mathrm{\ω}}_2& {\mathrm{\ω}}_3& {\mathrm{\ω}}_4& {\mathrm{\ω}}_5\end{array}\right]}^T,$ W _kfor zero-mean white noise sequence;

The output six tunnel correlation integral value I of integration remover _e, I _p, I _l, Q _e, Q _p, Q _lobserved quantity for Kalman filter:

Wherein, δ is the interval of local reproduction C/A code lead-lag, R (ε _i) be local reproduction C/A code autocorrelation function, V _kfor measurement noise.

4, in Kalman filter, adopt UT conversion to produce the weighting sequence w of Sigma point χ and symmetric sampling:

${\mathrm{\χ}}_0=\stackrel{\‾}{x}$

${\mathrm{\χ}}_i=\stackrel{\‾}{x}+{\left(\sqrt{(n+\mathrm{\λ})P}\right)}_i^T,i=\mathrm{1,2},\·\·\·,n$

${\mathrm{\χ}}_{i+n}=\stackrel{\‾}{x}-{\left(\sqrt{(n+\mathrm{\λ})P}\right)}_i^T,i=\mathrm{1,2},\·\·\·,n$

Wherein, state variable X _kinitial distribution average is square error matrix is P, λ=α ²(n+ κ)-n is a scalar, and α arrives the distance of average, 10 for controlling each point ^-4≤ α≤1, κ is a scalar, the effect of scalar β is the impact that reduces High Order Moment;

The time renewal process of Kalman filter is:

χ _i,k|k-1＝f(χ _i,k-1|k-1)i＝1,2,…,2n

${\widehat{\mathrm{\χ}}}_{k|k-1}=\underset{i=0}{\overset{2n}{\mathrm{\Σ}}}{w}_i^m{\mathrm{\χ}}_{i,k|k-1}$

$P_{k|k-1}=\underset{i=0}{\overset{2n}{\mathrm{\Σ}}}{w}_i^c({\mathrm{\χ}}_{i,k|k-1}-{\hat{X}}_{k|k-1}){({\mathrm{\χ}}_{i,k|k-1}-{\hat{X}}_{k|k-1})}^T+Q_k$

Wherein, χ _{i, k-1|k-1}be i Sigma sampled point k-1 estimated value constantly, f () is nonlinear function, for k-1 predicts k quantity of state predicted value constantly, P constantly _k|k-1for k-1 predicts the k square error battle array of quantity of state constantly, Q constantly _kcovariance matrix for process noise;

The measurement renewal process of Kalman filter is:

$P_{\mathrm{ZZ}}=\underset{i=0}{\overset{2n}{\mathrm{\Σ}}}{w}_i^c(Z_{i,k|k-1}-{\hat{Z}}_{k|k-1}){(Z_{i,k|k-1}-{\hat{Z}}_{k|k-1})}^T+R_k$

$P_{\mathrm{XZ}}=\underset{i=0}{\overset{2n}{\mathrm{\Σ}}}{w}_i^c{({\mathrm{\χ}}_{i,k|k-1}-{\hat{X}}_{k|k-1})(Z_{i,k|k-1}-{\hat{Z}}_{k|k-1})}^T$

P _zZfor the measurement amount square error battle array after measuring value upgrades, P _xZfor the simple crosscorrelation square error battle array of quantity of state and measurement amount after measuring value upgrades, R _kfor measurement noise covariance matrix.

Beneficial effect of the present invention is:

The error that the present invention brings in the time of can overcoming the output of traditional Discr., can accurate modeling to nonlinear system, effectively reduces satellite-signal noise intensity, and better tracking satellite signal under weak signal environment, has improved tracking accuracy.Receiver I/Q branch road correlation integral value, as the most original data of track loop, overcomes the error of bringing when traditional Discr. is exported; Reduce the impact of receiver internal thermal noise and dynamic stress error, reduce track loop losing lock phenomenon.Application UKF filtering algorithm is processed filtering to loop, has overcome the nonlinear relationship between traditional receiver Discr. output and quantity of state.Process nonlinear properties Fast Convergent, to dynamic parameter, can accurately estimate.

Accompanying drawing explanation

Fig. 1 is the track loop structural drawing based on I/Q branch road correlation integral observation filtering;

Fig. 2 is Kalman filter track loop structural drawing.

Embodiment

Below in conjunction with accompanying drawing, the present invention is described in further details.

The core technology that gps signal based on I/Q branch road correlation integral observation filtering is followed the tracks of is to utilize I/Q branch road correlation integral to build centralized Filtering Model and application UKF filtering algorithm, its basic thought is to using receiver tracking loop circuit I/Q branch road correlation integral value as observed quantity, adopt centralized wave filter to replace Discr. in traditional GPS track loop, application UKF filtering algorithm is combined estimation to the characteristic quantity in carrier wave ring and code ring, reaches the isoparametric accurate tracking of carrier frequency, carrier phase and code phase.Because the quantity of state in receiver I/Q branch road correlation integral value and centralized wave filter exists nonlinear relationship, therefore adopt UKF filtering algorithm to can be good at overcoming this difficult problem.

System modelling accuracy is as the gps signal of observed quantity, to follow the tracks of the guardian technique problem facing based on I/Q branch road correlation integral value, due to systematic perspective measure and quantity of state to be estimated between be nonlinear relationship, so the choosing for aobvious particularly important of system modelling accuracy of quantity of state.For a nonlinear system, the information that the increase of quantity of state dimension more can accurately each state of reactive system, calculated amount also can increase accordingly simultaneously.For simplied system structure and reduce system-computed amount, while setting up model, conventionally choose the parameters such as carrier phase, carrier frequency and code phase as the quantity of state of system.

As shown in Figure 1, the satellite-signal that receiver arrives by antenna reception, through being downconverted into intermediate-freuqncy signal, do mixing computing with the local carrier signal that local carrier digital controlled oscillator (NCO) produces, this locality reproduction C/A Ma Zuo six road related operations that mixing results and code generator produce, after integration-remover, as the input signal participation UKF filtering of centralized wave filter, by filtering result feedback, give local NCO, thereby realize the closure of loop.

Fig. 2 is centralized filter tracks loop structure figure, as seen from the figure, the intermediate-freuqncy signal of n tracking channel through carrier wave peel off and code related operation after, pass through respectively integration-remover, eliminate high-frequency signal composition and noise in I/Q tributary signal, to improve carrier-to-noise ratio, enter centralized wave filter, each passage I/Q branch road correlation integral is as the observed quantity of UKF filtering.

The present invention specifically comprises following step:

Step 1, carrier wave is peeled off and code related operation;

Receiver utilizes the satellite-signal that connection of antenna is subject to deliver to the processing of radio frequency leading portion, by multistage mixing, convert radiofrequency signal to intermediate-freuqncy signal, carry out mixing computing with this locality sine and cosine reproduction carrier signal that local carrier digital controlled oscillator produces, making to comprise Doppler shift in input signal is thoroughly peeled off in interior intermediate frequency carrier, leading, the instant and six road C/A codes that lag behind that intermediate-freuqncy signal after processing and C/A code generator produce carry out related operation, form six tunnel correlation integral value after integration-remover.

I the satellite-signal of being exported by receiver radio frequency front end can be write as:

A is normalized signal amplitude, and D (k) is numeric data code, the C/A code that C (k) plays for satellite, f ₁for radiofrequency signal incoming frequency, f _dfor signal Doppler shift, k is moment epoch, it is carrier wave initial phase.

This locality sine that local carrier NCO produces and cosine reproduction signal are:

In formula, A _ofor this locality reproduction signal amplitude, f _ofor this locality reproduction signal frequency, for this locality reproduction signal phase.

By intermediate-freuqncy signal S and local sinusoidal reproduction signal S _oSthe Na Tiao loop branch of mixing becomes I branch road, by intermediate-freuqncy signal S and local cosine reproduction signal S _oCthe Na Tiao loop branch of mixing becomes Q branch road, when intermediate-freuqncy signal S reappears signal S with local sine on I branch road _oSwhile doing multiplicative mixing computing in frequency mixer, the product i obtaining _p(k) be

Wherein,

$A_e=\frac{1}{2}A\·A_O$

f _e＝f ₁+f _d-f _O（4）

In formula (3), first, equal sign the right is low-frequency component, and second is radio-frequency component, f _ewith be respectively intermediate-freuqncy signal S and local sinusoidal reproduction signal S _oSbetween carrier frequency difference and carrier phase difference.

Mixing results i _p(k) through low-pass filter, filter out radio-frequency component, obtain following filtering result

The quantity of state control information feeding back by Kalman filter, delivers to carrier wave NCO and upgrades the local reproduction of output signal, thereby the phase place between local reproduction signal and the intermediate-freuqncy signal of input is consistent constantly.When input and output signal phase place is consistent substantially, the f in formula (5) _ewith be tending towards null value, i _p(k) only remaining signal amplitude, numeric data code D (k) and pseudo-code C (k), thus thoroughly reach the effect that carrier wave is peeled off.

In like manner, can derive intermediate-freuqncy signal S and reappear signal S with local cosine on Q branch road _oCmixing results, mixing results obtains after low-pass filter filters out radio-frequency component

The C/A code receiving in signal is C (k), with this locality reproduction C/A code C _o(k) carry out related operation and obtain following correlated results:

$R\left(k\right)=\frac{1}{N}\underset{n=0}{\overset{N-1}{\mathrm{\Σ}}}C\left(n\right)\·C_O(n-k)---\left(7\right)$

Wherein, N counts out for participating in the discrete data of related operation, the long image data amount of common corresponding 1ms.

Mixing results i on I branch road _p(k) and lag behind reproduction C/A code leading, instant with this locality carries out related calculation simultaneously respectively, produces i _e, i _pand i _lsignal, expresses for simplifying, and omits the moment epoch k in signal.Mixing results q on Q branch road _p(k) also carry out related calculation with these three parts of C/A coded signals respectively, generate q simultaneously _e, q _pand q _lsignal.Now the C/A code in intermediate-freuqncy signal is thoroughly peeled off, the i after despreading _e, i _p, i _l, q _e, q _pand q _lonly contain us and want the numeric data code information D (k) obtaining, passed through after integration-remover, form six tunnel correlation integral value I _e, I _p, I _l, Q _e, Q _pand Q _l, as the observed quantity of Kalman filter, as shown in fig. 1.

Step 2, the signal trace model based on coherent integration observation filtering;

Choose normalized signal amplitude A, carrier phase difference carrier frequency difference δ w, carrier frequency variation rate δ a, code phase difference state δ τ is that quantity of state row are write state equation.

I/Q branch road is leading, instant, the six tunnel correlation integral value that lag behind are as the observed quantity of UKF filtering, and row are write measurement equation.

GPS track loop is in the nature digital phase-locked loop, from the angle analysis of control system, it is a classical Phase Tracking control system, Kalman filtering algorithm is a kind of optimal estimation method of utilizing minimum variance criterion, can accurately estimate system state parameter, and can more efficiently system be controlled, in order to follow the tracks of accurately frequency and the phase place of input signal, select k moment filter status amount to be:

In formula, A is normalized signal amplitude, for carrier phase difference, δ w is carrier frequency difference, and δ a is carrier frequency variation rate, and δ τ is code phase difference.The state equation of Kalman filter is as follows:

In formula, λ _lfor satellite-signal L ₁carrier wavelength, λ _cAfor C/A code wavelength, its value is about 293m, W _kfor process noise, also claim system noise, be designated as: $W_k={\left[\begin{array}{ccccc}{\mathrm{\ω}}_1& {\mathrm{\ω}}_2& {\mathrm{\ω}}_3& {\mathrm{\ω}}_4& {\mathrm{\ω}}_5\end{array}\right]}^T,$ W _kfor zero-mean white noise sequence.

Utilize I/Q branch road six tunnel correlation integral output valves in step 1 as the observed quantity of Kalman filter, set up the nonlinear equation relevant to quantity of state and be:

In formula, δ is the interval of local C/A code lead-lag, R (ε _i) be C/A code autocorrelation function.V _kfor measurement noise, the variance battle array of noise is:

$\begin{array}{c}{R}_k=E\left(V_k{V_k}^T\right)\\ ={\mathrm{\σ}}_{N}N\left[\begin{array}{cccccc}1& 0& 1-\mathrm{\δ}& 0& 1-2\mathrm{\δ}& 0\\ 0& 1& 0& 1-\mathrm{\δ}& 0& 1-2\mathrm{\δ}\\ 1-\mathrm{\δ}& 0& 1& 0& 1-\mathrm{\δ}& 0\\ 0& 1-\mathrm{\δ}& 0& 1& 0& 1-\mathrm{\δ}\\ 1-2\mathrm{\δ}& 0& 1-\mathrm{\δ}& 0& 1& 0\\ 0& 1-2\mathrm{\δ}& 0& 1-\mathrm{\δ}& 0& 1\end{array}\right]\end{array}---\left(11\right)$

In formula, σ _nit is the noise intensity after coherent signal I, Q process.

Because observation equation and quantity of state exist nonlinear relationship, apply traditional Kalman filtering algorithm and can cause estimated accuracy poor, therefore adopt UKF filtering algorithm to carry out loop processed filtering, finally obtain the carrier phase difference estimating carrier frequency difference δ w and code phase difference δ τ.

Step 3, the Kalman filter based on UKF

Adopt UKF filtering algorithm to replace loop filter, eliminate the contradiction of traditional tracking between dynamic and filtering accuracy.Utilize the strong nonlinearity tracking power of UKF, replace Discr., phase place, frequency and the code phase of hyperchannel carrier wave under weak signal environment are carried out to accurate tracking.

UKF filtering algorithm is the effective combination that adopts UT conversion and legacy card Kalman Filtering framework, and by the estimation of nonlinear function probability density function is obtained to state estimation, UT conversion is core and the basis of UKF algorithm.The thought of UT conversion is: guaranteeing to sample, average and covariance are under the prerequisite of P, select 2n+1 point set (Sigma point set), n is state variable dimension, nonlinear transformation is applied to each Sigma point of sampling, obtains the point set after non-linear conversion and p _yit is the statistic of Sigma point set after conversion.

Nonlinear system model after Kalman filter discretize is:

$\begin{array}{c}{X}_{K+1}=f\left(X_K\right)+W_K\\ Z_{K+1}=h\left(X_K\right)+V_K\end{array}---\left(12\right)$

State variable X _kinitial distribution average is square error matrix is P.UT conversion produces the weighting sequence w of Sigma point χ and symmetric sampling, is expressed as:

${\mathrm{\χ}}_0=\stackrel{\‾}{x}---\left(13\right)$

${\mathrm{\χ}}_i=\stackrel{\‾}{x}+{\left(\sqrt{(n+\mathrm{\λ})P}\right)}_i^T,i=\mathrm{1,2},\·\·\·,n---\left(14\right)$

${\mathrm{\χ}}_{i+n}=\stackrel{\‾}{x}-{\left(\sqrt{(n+\mathrm{\λ})P}\right)}_i^T,i=\mathrm{1,2},\·\·\·,n---\left(15\right)$

In formula, by Cholesky, to decompose the i capable (or row) of the root mean square matrix obtaining; λ=α ²(n+ κ)-n is a scalar, and α arrives the distance of average for controlling each point, and its span is generally (10 ^-4≤ α≤1); κ is also a scalar, conventionally gets 0; The effect of scalar β is the impact that reduces High Order Moment, and when quantity of state is Gaussian distribution, its optimal value gets 2.

$\begin{array}{cc}{w}_0^m=\mathrm{\λ}/(n+\mathrm{\λ})& w_0^c=w_0^m+1-{\mathrm{\α}}^2+\mathrm{\β}---\left(16\right)\end{array}$

$w_i^m=w_i^c=1/2(n+\mathrm{\λ}),i=\mathrm{1,2},\·\·\·2n---\left(17\right)$

Time renewal process:

χ _i,k|k-1＝f(χ _i,k-1|k-1)i＝1,2,…,2n (18)

In formula, the estimated value χ in i the Sigma sampled point k-1 moment _{i, k-1|k-1}after nonlinear function f () conversion, obtain k-1 and constantly predict k estimated value χ constantly _{i, k|k-1}, χ _{i, k|k-1}weighted value with i Sigma sampled point after computing, obtain k-1 and constantly predict k quantity of state predicted value constantly

${\widehat{\mathrm{\χ}}}_{k|k-1}=\underset{i=0}{\overset{2n}{\mathrm{\Σ}}}{w}_i^m{\mathrm{\χ}}_{i,k|k-1}---\left(19\right)$

$P_{k|k-1}=\underset{i=0}{\overset{2n}{\mathrm{\Σ}}}{w}_i^c({\mathrm{\χ}}_{i,k|k-1}-{\hat{X}}_{k|k-1}){({\mathrm{\χ}}_{i,k|k-1}-{\hat{X}}_{k|k-1})}^T+Q_k---\left(20\right)$

In formula, P _k|k-1for k-1 predicts the k square error battle array of quantity of state constantly, Q constantly _kcovariance matrix for process noise.

Measure renewal process:

$P_{\mathrm{ZZ}}=\underset{i=0}{\overset{2n}{\mathrm{\Σ}}}{w}_i^c(Z_{i,k|k-1}-{\hat{Z}}_{k|k-1}){(Z_{i,k|k-1}-{\hat{Z}}_{k|k-1})}^T+R_k---\left(21\right)$

$P_{\mathrm{XZ}}=\underset{i=0}{\overset{2n}{\mathrm{\Σ}}}{w}_i^c{({\mathrm{\χ}}_{i,k|k-1}-{\hat{X}}_{k|k-1})(Z_{i,k|k-1}-{\hat{Z}}_{k|k-1})}^T---\left(22\right)$

In formula, P _zZfor the measurement amount square error battle array after measuring value upgrades, P _xZfor the simple crosscorrelation square error battle array of quantity of state and measurement amount after measuring value upgrades, R _kfor measurement noise covariance matrix.

K _k＝P _XZ(P _ZZ) ^-1(23)

${\hat{X}}_{k|k}={\hat{X}}_{k|k-1}+K_k(Z_k-{\hat{Z}}_{k|k-1})---\left(24\right)$

P _k|k＝P _k|k-1-K _kP _ZZK _k ^T(25)

In formula, for measuring value upgrades the k later optimal estimation value of state variable constantly, K _kfor filter gain matrix, P _k|ksquare error battle array for k moment quantity of state.

Kalman filter adopts UKF filtering algorithm, in time renewal process, choose a series of Sigma sampled point, after state equation and measurement equation, sampled point by these after nonlinear transformation comprehensively goes out the estimated value to current time system state amount according to weight separately.Measure in renewal process, utilize six tunnel correlation integral value to carry out the estimated value of correction card Thalmann filter quantity of state, by proofreading and correct later carrier frequency, carrier phase and code phase difference, deliver to local carrier and code NCO, the carrier wave of the local reproduction of real-time update and code output signal, thus complete closed-loop path formed.

Claims (5)

1. based on I/Q branch road correlation integral, observe the GPS weak signal tracker of filtering, it is characterized in that: comprise receiver, local carrier digital controlled oscillator, frequency mixer, code generator, integration remover, correlation operator, Kalman filter, receiver is for receiving satellite signal, through being downconverted into intermediate-freuqncy signal, send frequency mixer to;

Frequency mixer also receives this locality sine, the cosine reproduction carrier signal of local carrier digital controlled oscillator output, intermediate-freuqncy signal and local sinusoidal reproduction carrier signal are carried out to mixing computing, obtain I branch output signal, send correlation operator to, intermediate-freuqncy signal and local cosine reproduction carrier signal are carried out to mixing computing, obtain Q branch output signal, send correlation operator to;

Correlation operator this locality that also receiving code generator produces reappears in advance C/A code, immediately reappears C/A code and lag behind reproduction C/A code, by I branch output signal and Q branch output signal reappear in advance C/A code with this locality respectively, immediately reappear C/A code, the reproduction C/A code that lags behind carries out related calculation, result sends integration remover to;

Integration remover produces six tunnel correlation integral value I according to the information receiving _e, I _p, I _l, Q _e, Q _p, Q _l, send Kalman filter to; The carrier phase difference that Kalman filter obtains estimating, carrier frequency difference and code phase difference, send carrier phase difference and carrier frequency difference to local carrier digital controlled oscillator, sends code phase difference to code generator;

Local carrier digital controlled oscillator produces local sinusoidal, cosine reproduction carrier signal according to the information receiving, and flows to frequency mixer;

Code generator produces local leading reproduction C/A code, immediately reappears C/A code and hysteresis reproduction C/A code according to the information of reception, sends correlation operator to.

2. the GPS weak signal tracker based on I/Q branch road correlation integral observation filtering according to claim 1, is characterized in that: the intermediate-freuqncy signal of i satellite-signal of receiver output is:

Wherein, A is normalized signal amplitude, and D (k) is numeric data code, the C/A code that C (k) plays for satellite, f ₁for radiofrequency signal incoming frequency, f _dfor signal Doppler shift, k is moment epoch, it is carrier wave initial phase;

The sinusoidal reproduction in this locality carrier signal S that local carrier digital controlled oscillator produces _oSwith local cosine reproduction carrier signal S _oCfor:

Wherein, A _ofor amplitude, f _ofor frequency, for phase place,

Intermediate-freuqncy signal and local sinusoidal reproduction carrier signal S _oSthe result of doing mixing budget is:

Wherein,

$A_e=\frac{1}{2}A\·A_O$

f _e＝f ₁+f _d-f _O

Intermediate-freuqncy signal and local cosine reproduction carrier signal S _oCthe result of doing mixing budget is:

3. the GPS weak signal tracker based on I/Q branch road correlation integral observation filtering according to claim 2, is characterized in that: the related operation method in described correlation operator is:

$R\left(k\right)=\frac{1}{N}\underset{n=0}{\overset{N-1}{\mathrm{\Σ}}}C\left(n\right)\·C_O(n-k)$

Wherein, the C/A code in the output signal that C (k) is frequency mixer, C _o(k) this locality producing for code generator reappears in advance C/A code or immediately reappears C/A code or the reproduction C/A code that lags behind, and N counts out for participating in the discrete data of related operation.

4. the GPS weak signal tracker based on I/Q branch road correlation integral observation filtering according to claim 3, is characterized in that: the quantity of state of Kalman filter is:

Wherein, A is normalized signal amplitude, for carrier phase difference, δ w is carrier frequency difference, and δ a is carrier frequency variation rate, and δ τ is code phase difference, λ _lfor satellite signal carrier wavelength, λ _cAfor C/A code wavelength, W _kfor process noise, also claim system noise, be designated as $W_k={\left[\begin{array}{ccccc}{\mathrm{\ω}}_1& {\mathrm{\ω}}_2& {\mathrm{\ω}}_3& {\mathrm{\ω}}_4& {\mathrm{\ω}}_5\end{array}\right]}^T,$ W _kfor zero-mean white noise sequence;

The output six tunnel correlation integral value I of integration remover _e, I _p, I _l, Q _e, Q _p, Q _lobserved quantity for Kalman filter:

Wherein, δ is the interval of local reproduction C/A code lead-lag, R (ε _i) be local reproduction C/A code autocorrelation function, V _kfor measurement noise.

5. the GPS weak signal tracker based on I/Q branch road correlation integral observation filtering according to claim 4, is characterized in that: in Kalman filter, adopt UT conversion to produce the weighting sequence w of Sigma point χ and symmetric sampling:

${\mathrm{\χ}}_0=\stackrel{\‾}{x}$

${\mathrm{\χ}}_i=\stackrel{\‾}{x}+{\left(\sqrt{(n+\mathrm{\λ})P}\right)}_i^T,i=\mathrm{1,2},\·\·\·,n$

${\mathrm{\χ}}_{i+n}=\stackrel{\‾}{x}-{\left(\sqrt{(n+\mathrm{\λ})P}\right)}_i^T,i=\mathrm{1,2},\·\·\·,n$

Wherein, state variable X _kinitial distribution average is square error matrix is P, λ=α ²(n+ κ)-n is a scalar, and α arrives the distance of average, 10 for controlling each point ^-4≤ α≤1, κ is a scalar, the effect of scalar β is the impact that reduces High Order Moment;

The time renewal process of Kalman filter is:

χ _i,k|k-1＝f(χ _i,k-1|k-1)i＝1,2,…,2n

${\widehat{\mathrm{\χ}}}_{k|k-1}=\underset{i=0}{\overset{2n}{\mathrm{\Σ}}}{w}_i^m{\mathrm{\χ}}_{i,k|k-1}$

$P_{k|k-1}=\underset{i=0}{\overset{2n}{\mathrm{\Σ}}}{w}_i^c({\mathrm{\χ}}_{i,k|k-1}-{\hat{X}}_{k|k-1}){({\mathrm{\χ}}_{i,k|k-1}-{\hat{X}}_{k|k-1})}^T+Q_k$

Wherein, χ _{i, k-1|k-1}be i Sigma sampled point k-1 estimated value constantly, f () is nonlinear function, constantly predict k quantity of state predicted value constantly, P _k|k-1for k-1 predicts the k square error battle array of quantity of state constantly, Q constantly _kcovariance matrix for process noise;

The measurement renewal process of Kalman filter is:

$P_{\mathrm{ZZ}}=\underset{i=0}{\overset{2n}{\mathrm{\Σ}}}{w}_i^c(Z_{i,k|k-1}-{\hat{Z}}_{k|k-1}){(Z_{i,k|k-1}-{\hat{Z}}_{k|k-1})}^T+R_k$

$P_{\mathrm{XZ}}=\underset{i=0}{\overset{2n}{\mathrm{\Σ}}}{w}_i^c{({\mathrm{\χ}}_{i,k|k-1}-{\hat{X}}_{k|k-1})(Z_{i,k|k-1}-{\hat{Z}}_{k|k-1})}^T$

P _zZfor the measurement amount square error battle array after measuring value upgrades, P _xZfor the simple crosscorrelation square error battle array of quantity of state and measurement amount after measuring value upgrades, R _kfor measurement noise covariance matrix.