CN103645483A - Beidou signal capturing method in weak signal environment - Google Patents

Abstract

The invention relates to a Beidou signal capturing method in a weak signal environment. The method comprises the following steps: S1, arranging a down-conversion unit and carrying out down conversion processing on a received intermediate-frequency sampling signal; S2, carrying out NH code stripping on the received signal respectively, carrying out transformation into a frequency domain signal, carrying out multiplying with a multiplexing sum of a local code frequency domain value and then carrying out inverse transformation into a time domain; S3, carrying out conjugate multiplication on a coherent accumulation value at current time and a coherent accumulation value at previous time and carrying out summation; and S4, carrying out taylor series expansion on an amplitude difference value of two-side spectral lines of the peak value of the coherent integration result, deriving a quasi-linear relation of a frequency value and the amplitude difference value, and solving a frequency estimation value by using the linear relation. According to the invention, correlation calculation can be carried out on coherent values corresponding all code phases by one time; the signal to noise ratio is improved and the detection efficiency is improved; and the squaring loss of the non-coherent integration can be reduced. A problem of bit flipping caused by navigation data modulation can be solved, thereby improving the detection probability; the code phase and carrier wave frequency-offset estimation precision is enhanced; and the calculating speed and the calculating precision are guaranteed and the good stability and practicability are realized.

Description

Big Dipper signal acquisition methods under a kind of weak signal environment

Technical field

The invention belongs to navigation signal detection technique field, particularly Big Dipper signal acquisition methods, can be used for Big Dipper weak signal and catches.

Background technology

Global Positioning System (GPS) (Global Navigation Satellite System, GNSS) signal is the spread-spectrum signal through direct sequence spread spectrum modulation, to the catching of satellite navigation signals through Direct-Spread modulation, is the matter of utmost importance that GNSS system need to solve.And when signal conditioning is undesirable, for example, under indoor, forest and urban environment condition, block, the phenomenon such as multipath and interference is more serious, energy has more weakening and decline, received signal to noise ratio has deterioration greatly, and common catching method will defy capture and trace into navigation satellite signal.The subject matter that the acquisition algorithms such as current serial/parallel algorithm, quick segmentation algorithm, FFT/IFFT solve is to reduce calculated amount, raising acquisition speed.With the development of large scale integrated circuit, the bottleneck of calculating overcomes, and problem concentrates on catching of signal under low signal-to-noise ratio, by suitable algorithm, improves rear survey signal to noise ratio (S/N ratio), reaches catching Low SNR signal.GNSS is by adding long coherent integration and non-coherent integration time to improve signal to noise ratio (S/N ratio), thereby improve the sensitivity of input, but, the coherent integration gain that navigation signal bit reversal, frequency deviation cause declines and code is offset, Squared Error Loss and the restriction of Complex Channel to coherent integration length of non-coherent integration, all can have a huge impact the design of high sensitive receiver detection algorithm and performance.

In order to have improved signal to noise ratio (S/N ratio) and then to have improved detection probability, overcome the bit reversal problem that navigation data modulation brings, reduce the Squared Error Loss of non-coherent integration simultaneously, and improve frequency offset estimation accuracy, propose differential coherent accumulative in conjunction with the method for frequency domain frequency deviation algorithm for estimating.The method adopts and realizes under the environment of FPGA and DSP, has very strong practical significance.

Summary of the invention

In order to address the above problem, Big Dipper signal acquisition methods under a kind of weak signal environment of the present invention, it comprises,

S1 down-converter unit, carries out down-converted to the if sampling signal receiving;

S2 peels off NH code by the signal receiving respectively, and transforms to frequency domain, and after multiplying each other with the volume of returning to work of local code frequency domain value, another mistake transforms to time domain;

S3 is by the conjugate multiplication of the coherent accumulation value of the coherent accumulation value in the current moment and previous moment summation;

S4 makes Taylor series expansion to the amplitude difference of coherent integration result peak value both sides spectral line, has derived the almost linear relation of frequency values and amplitude difference, utilizes this linear relationship to solve and obtains frequency estimation.

On the basis of technique scheme, described step S1 comprises: the if sampling signal r (n) receiving is carried out to down-converted:

$\begin{array}{c}s\left(n\right)=r\left(n\right)*e^{-\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000432595600000031.png"\; state="\{\{state\}\}">j</figure-callout>}2\mathrm{\&pi;}(f_{\mathrm{IF}}+i*\mathrm{\&Delta;f})}\\ =\{h\left(l\right)*c\left(n\right)*e^{j*2\mathrm{\&pi;}{f}_1}+P\left(n\right)\}*e^{-\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000432595600000031.png"\; state="\{\{state\}\}">j</figure-callout>}2\mathrm{\&pi;}(f_{\mathrm{IF}}+i*\mathrm{\&Delta;f})}\end{array}$

Wherein s (n) is baseband signal, and P (n) is the noise of intermediate-freuqncy signal r (n), f ₁for receiving the intermediate frequency carrier frequency of signal, f _iFfor setting IF-FRE, △ f is step-size in search, and i is searching times.

On the basis of technique scheme, described step S2 comprises: the corresponding addition of value after L 1ms is relevant, complete the coherent integration of L ms data,

Then complete repeatedly the coherent integration of Lms,

$\left\{\begin{array}{c}{R}_1\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_1\left(n\right)*{\mathrm{NH}}_k\left(1\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\\ R_2\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_2\left(n\right)*{\mathrm{NH}}_k\left(2\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\\ \&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\\ R_L\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_L\left(n\right)*{\mathrm{NH}}_k\left(L\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\end{array}\right.$

$Y_i\left(n\right)=\underset{i=1}{\overset{L}{\mathrm{\&Sigma;}}}{R}_i\left(n\right)$

Wherein FFT () represents the number of winning the confidence Fast Fourier Transform (FFT), and IFFT () represents to ask the inverse fast Fourier transform of signal, and conj () represents to ask the complex conjugate of signal, s _i(n) represent according to num _sKIPmaxbase band after delay adopts signal, NH _k(i) represent definite NH code sequence, c (n) represents the local ranging code producing.R _i(n) represent certain 1ms coherent integration result, Y _i(n) represent i Lms coherent integration result.

On the basis of technique scheme, described in peel off NH code and comprise,

$\left\{\begin{array}{c}{R}_1\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_1(n+{\mathrm{num}}_{\mathrm{SKIP}})*{\mathrm{NH}}_k\left(i\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\\ R_2\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_2(n+{\mathrm{num}}_{\mathrm{SKIP}})*{\mathrm{NH}}_k\left(i\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\\ \&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\\ R_{20}\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_L(n+{\mathrm{num}}_{\mathrm{SKIP}})*{\mathrm{NH}}_k\left(i\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\end{array}\right.$

$Y_{\left({\mathrm{num}}_{\mathrm{SIKP},}k\right)}\left(n\right)=\underset{i=1}{\overset{20}{\mathrm{\&Sigma;}}}{R}_i\left(n\right)$

Wherein, wherein FFT () represents the number of winning the confidence Fast Fourier Transform (FFT), and IFFT () represents to ask the inverse fast Fourier transform of signal, and conj () represents to ask the complex conjugate of signal, s _i(n) represent that base band adopts signal, c (n) represents the local ranging code producing, num _sKIPexpression starts delay sampling from sampled data counts, its scope control in 1ms sampling number, NH _k(i) represent the ranking results of a ring shift right k position of NH code, R _i(n) represent certain 1ms coherent integration result,

represent num _sKIPthe 20ms coherent integration result of the ranking results of individual corresponding NH ring shift right k position,

$[{\mathrm{num}}_{\mathrm{SKIP}\max },k_{\max }]=\max \left(\right|Y_{\left({\mathrm{num}}_{\mathrm{SKIP},}k\right)}\left|\right)$

Wherein

the reference position that represents 1ms data, k _maxrepresent that corresponding NH code sequence indicates.

Estimate the product of the value that navigation data bits value corresponding to current adjacent two coherent accumulation values is ± 1, after then multiplying each other with difference accumulation, carry out coherent accumulation, formula is as follows:

$Z\left(n\right)=|\underset{i=2}{\overset{M}{\mathrm{\&Sigma;}}}{a}_{i-1}*{Y^{*}}_{i-1}\left(n\right)Y_i\left(n\right)|$

Wherein, Y ^* _i-1(n) represent the conjugation of the coherent accumulation value of previous moment, Y _i(n) be expressed as the coherent accumulation value of current time, a _i-1the product that represents the value (value is ± 1) of the navigation data bits that current adjacent two coherent accumulation values are corresponding, Z (n) represents differential coherent accumulative result.

On the basis of technique scheme, original frequency is compensated, then repeat iterative estimate J time, finally obtain high precision carrier frequency and code phase.

$\hat{x}=\frac{D}{{\mathrm{AQ\&alpha;}}_1}=\frac{\sin c(\mathrm{Qx}/Q_I)}{{\mathrm{G\&alpha;}}_1}D$

$\hat{f}=f_{\mathrm{ini}}+\underset{i=0}{\overset{J-1}{\mathrm{\&Sigma;}}}\hat{x}/N_c{Q}_I$

Wherein, represent intermediate variable estimated value, D represents peak value left and right sides spectral line amplitude difference, and Q represents the hop count of coherent accumulation, Q _irepresent that FFT counts, have Q Lms coherent integration, count as Q _ifFT conversion, G represents the peak value of FFT, α ₁get 4/ π.F _inirepresent the resulting frequency estimation of differential coherence, N _cfor spreading code Cycle Length,

represent high precision carrier estimation value.

Compared with prior art, the present invention is owing to adopting spectrum correlation algorithm to make a correlation corresponding to all code phases of correlation computations of this method energy.And use coherent accumulation and differential coherent accumulative combination algorithm, improve signal to noise ratio (S/N ratio) and then improved detection probability, reduce the Squared Error Loss of non-coherent integration;

The present invention simultaneously adopts differential coherent accumulative, has estimated navigation data bits saltus step, overcomes the bit reversal problem that navigation data modulation brings, and has improved detection probability; The linear frequency deviation estimation model that employing launches based on Tayor carries out secondary capturing under low complex degree, has effectively improved code phase and Nonlinear Transformation in Frequency Offset Estimation precision.Adopt the module of FPGA+DSP to realize, arithmetic speed and precision have all obtained guarantee, have very strong stability and practicality.

Accompanying drawing explanation

Fig. 1 is system architecture diagram of the present invention;

Fig. 2 is the coherent integration Elementary Function block diagram in system of the present invention;

Fig. 3 is the differential coherence Elementary Function block diagram in system of the present invention;

Fig. 4 is the secondary fine capturing unit functional block diagram in system of the present invention;

Fig. 5 is the experimental situation Elementary Function block diagram in system of the present invention.

Embodiment

Please refer to Fig. 1 to Fig. 5 elaborates to the present invention.

Please refer to Fig. 1, under a kind of weak signal environment of the present invention Big Dipper signal acquisition methods comprise with

Lower step:

S1 down-converter unit, carries out down-converted to the if sampling signal receiving;

Step S1 comprises: the if sampling signal r (n) receiving is carried out to down-converted:

$\begin{array}{c}s\left(n\right)=r\left(n\right)*e^{-\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000432595600000031.png"\; state="\{\{state\}\}">j</figure-callout>}2\mathrm{\&pi;}(f_{\mathrm{IF}}+i*\mathrm{\&Delta;f})}\\ =\{h\left(l\right)*c\left(n\right)*e^{j*2\mathrm{\&pi;}{f}_1}+P\left(n\right)\}*e^{-\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000432595600000031.png"\; state="\{\{state\}\}">j</figure-callout>}2\mathrm{\&pi;}(f_{\mathrm{IF}}+i*\mathrm{\&Delta;f})}\end{array}$

Wherein s (n) is baseband signal, and P (n) is the noise of intermediate-freuqncy signal r (n), f ₁for receiving the intermediate frequency carrier frequency of signal, f _iFfor setting IF-FRE, △ f is step-size in search, and i is searching times.

S2 peels off NH code by the signal receiving respectively, and transforms to frequency domain, and after multiplying each other with the volume of returning to work of local code frequency domain value, another mistake transforms to time domain;

Wherein step S2 comprises: the corresponding addition of value after L 1ms is relevant, complete the coherent integration of Lms data,

Then complete repeatedly the coherent integration of Lms,

$\left\{\begin{array}{c}{R}_1\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_1\left(n\right)*{\mathrm{NH}}_k\left(1\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\\ R_2\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_2\left(n\right)*{\mathrm{NH}}_k\left(2\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\\ \&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\\ R_L\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_L\left(n\right)*{\mathrm{NH}}_k\left(L\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\end{array}\right.$

$Y_i\left(n\right)=\underset{i=1}{\overset{L}{\mathrm{\&Sigma;}}}{R}_i\left(n\right)$

Wherein FFT () represents the number of winning the confidence Fast Fourier Transform (FFT), and IFFT () represents to ask the inverse fast Fourier transform of signal, and conj () represents to ask the complex conjugate of signal, s _i(n) represent according to num _sKIPmaxbase band after delay adopts signal, NH _k(i) represent definite NH code sequence, c (n) represents the local ranging code producing.R _i(n) represent certain 1ms coherent integration result, Y _i(n) represent i Lms coherent integration result.

The described NH of peeling off code comprises,

$\left\{\begin{array}{c}{R}_1\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_1(n+{\mathrm{num}}_{\mathrm{SKIP}})*{\mathrm{NH}}_k\left(i\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\\ R_2\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_2(n+{\mathrm{num}}_{\mathrm{SKIP}})*{\mathrm{NH}}_k\left(i\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\\ \&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\\ R_{20}\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_L(n+{\mathrm{num}}_{\mathrm{SKIP}})*{\mathrm{NH}}_k\left(i\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\end{array}\right.$

$Y_{\left({\mathrm{num}}_{\mathrm{SIKP},}k\right)}\left(n\right)=\underset{i=1}{\overset{20}{\mathrm{\&Sigma;}}}{R}_i\left(n\right)$

Wherein, wherein FFT () represents the number of winning the confidence Fast Fourier Transform (FFT), and IFFT () represents to ask the inverse fast Fourier transform of signal, and conj () represents to ask the complex conjugate of signal, s _i(n) represent that base band adopts signal, c (n) represents the local ranging code producing, num _sKIPexpression starts delay sampling from sampled data counts, its scope control in 1ms sampling number, NH _k(i) represent the ranking results of a ring shift right k position of NH code, R _i(n) represent certain 1ms coherent integration result,

represent num _sKIPthe 20ms coherent integration result of the ranking results of individual corresponding NH ring shift right k position,

$[{\mathrm{num}}_{\mathrm{SKIP}\max },k_{\max }]=\max \left(\right|Y_{\left({\mathrm{num}}_{\mathrm{SKIP},}k\right)}\left|\right)$

Num wherein _sKIPmaxthe reference position that represents 1ms data, k _maxrepresent that corresponding NH code sequence indicates.

S3 is by the conjugate multiplication of the coherent accumulation value of the coherent accumulation value in the current moment and previous moment summation;

It comprises, estimates the product of the value that navigation data bits value corresponding to current adjacent two coherent accumulation values is ± 1, after then multiplying each other with difference accumulation, carries out coherent accumulation, and formula is as follows:

$Z\left(n\right)=|\underset{i=2}{\overset{M}{\mathrm{\&Sigma;}}}{a}_{i-1}*{Y^{*}}_{i-1}\left(n\right)Y_i\left(n\right)|$

Wherein, Y ^* _i-1(n) represent the conjugation of the coherent accumulation value of previous moment, Y _i(n) be expressed as the coherent accumulation value of current time, a _i-1the product that represents the value (value is ± 1) of the navigation data bits that current adjacent two coherent accumulation values are corresponding, Z (n) represents differential coherent accumulative result.

S4 makes Taylor series expansion to the amplitude difference of coherent integration result peak value both sides spectral line, has derived the almost linear relation of frequency values and amplitude difference, utilizes this linear relationship to solve and obtains frequency estimation.

It comprises original frequency is compensated, then repeats iterative estimate J time, finally obtains high precision carrier frequency and code phase.

$\hat{x}=\frac{D}{{\mathrm{AQ\&alpha;}}_1}=\frac{\sin c(\mathrm{Qx}/Q_I)}{{\mathrm{G\&alpha;}}_1}D$

$\hat{f}=f_{\mathrm{ini}}+\underset{i=0}{\overset{J-1}{\mathrm{\&Sigma;}}}\hat{x}/N_c{Q}_I$

Wherein, represent intermediate variable estimated value, D represents peak value left and right sides spectral line amplitude difference, and Q represents the hop count of coherent accumulation, Q _irepresent that FFT counts, have Q Lms coherent integration, count as Q _ifFT conversion, G represents the peak value of FFT, α ₁get 4/ π.F _inirepresent the resulting frequency estimation of differential coherence, N _cfor spreading code Cycle Length,

represent high precision carrier estimation value.

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

1) realize environment

At indoor collection Big Dipper signal, as original signal, process.

2), with reference to Fig. 2, through receiver, obtain the sampled data of intermediate frequency, to set intermediate frequency f _iFas benchmark, take △ f as step-size in search, hunting zone is with f _iFas benchmark ± f, carry out altogether 2f/ △ f secondary frequencies search, according to formula

$s\left(n\right)=r\left(n\right)*e^{-\mathrm{<figure-callout\; id="2"\; label="j"\; filenames="HDA0000432595600000031.png"\; state="\{\{state\}\}">j</figure-callout>}2\mathrm{\&pi;}(f_{\mathrm{IF}}+i*\mathrm{\&Delta;f})}=r\left(n\right)*(\cos \left(2\mathrm{\&pi;}(f_{\mathrm{IF}}+i*\mathrm{\&Delta;f})\right)+j*\sin \left(2\mathrm{\&pi;}(f_{\mathrm{IF}}+i*\mathrm{\&Delta;f})\right))$

Intermediate-freuqncy signal is dropped to fundamental frequency and carry out coherent integration processing.First the reception signal that completes fast every 1ms according to the method for FFT-IFFT in FPGA is relevant to local signal, gets 20ms data and completes NH code and peel off, and is then added the correlated results of Lms is corresponding, and obtaining length is the coherent integration result of Lms.Acquired results is passed to DSP by FIFO, in DSP, carry out following processing.

3) with reference to Fig. 3, the data that obtain from a upper functional unit are carried out to differential coherence processing, formula is as follows

$Z\left(n\right)=|\underset{i=2}{\overset{M}{\mathrm{\&Sigma;}}}{a}_{i-1}*{Y^{*}}_{i-1}\left(n\right)Y_i\left(n\right)|$

Wherein, Y ^* _i-1(n) represent the conjugation of the coherent accumulation value of previous moment, Y _i(n) be expressed as the coherent accumulation value of current time, a _ithe product that represents the value (value is ± 1) of the navigation data bits that current adjacent two coherent accumulation values are corresponding, Z (n) represents differential coherent accumulative result.According to useful signal, always there is the principle in peak value, by the peak-to-peak value of adjacent coherent integration is differed from and mould compare, the phase relation before and after judging between adjacent coherent integration, estimates a _ivalue, to avoid causing the phenomenon of cancelling out each other by the saltus step of navigation data bits, the detection probability of the signal of increase.Adopt difference accumulative to reduce Square loss simultaneously.Search the first peak value and the second peak value of Z (n), and obtain peakedness ratio, if this ratio is greater than thresholding λ, showing to capture satellite then carries out processing below, otherwise, when having traveled through all frequencies, all do not reach threshold value and show not capture, change a satellite and catch.

4) with reference to Fig. 4, through method above, can estimate code phase and the carrier frequency of gps signal, wherein the precision of code phase is set according to actual computation amount and computation complexity, the estimated accuracy of carrier frequency is △ f, pass through acquired results, if sampling signal is carried out to the compensation of code phase and frequency, then carry out the coherent integration of Lms, Q Lms coherent integration result carried out to Q _ithe FFT conversion of point, passes through formula

$\hat{x}=\frac{D}{{\mathrm{AQ\&alpha;}}_1}=\frac{\sin c(\mathrm{Qx}/Q_I)}{{\mathrm{G\&alpha;}}_1}D$

$\hat{f}=f_{\mathrm{ini}}+\underset{i=0}{\overset{J-1}{\mathrm{\&Sigma;}}}\hat{x}/N_c{Q}_I$

Wherein, represent intermediate variable estimated value, D represents peak value left and right sides spectral line amplitude difference, and Q represents the hop count of coherent accumulation, Q _irepresent that FFT counts, have Q Lms coherent integration, count as Q _ifFT conversion, G represents the peak value of FFT, α ₁get 4/ π.F _inirepresent the resulting frequency estimation of differential coherence, N _cfor spreading code Cycle Length,

represent high precision carrier estimation value.Carry out frequency deviation estimation, then the data after first compensation are compensated for the second time, again according to the method with reference to Fig. 4, carry out after J iteration, final capture-data is transferred to weak signal and follows the tracks of link, follow the tracks of processing.

FPGA used in the present invention adds the flush bonding processor that DSP builds, specific design scheme is with reference to Fig. 5, by antenna reception " two generations of the Big Dipper " navigation signal, through down conversion module, radiofrequency signal is down-converted to intermediate-freuqncy signal, then adopt the sampling channel on ten tunnels to sample to signal, intermediate-freuqncy signal after sampling is transferred to FPGA module, in this module, complete from intermediate frequency and drop to fundamental frequency, NH code is peeled off and is carried out related realization coherent integration with local signal, and wherein USB, RS232 and Internet are for communicating with host computer.The coherent integration data that FPGA is handled are imported in DSP, have been completed task and the high precision Frequency Estimation of differential coherence by DSP, adopt two DSP greatly to improve arithmetic speed and have reduced the execution time.DSP module also can be used other processor with similar functions to realize, as ARM; This area researchist can select suitable device according to physical condition.

In sum, only, for the present invention's preferred embodiment, with this, do not limit protection scope of the present invention, all equivalences of doing according to the scope of the claims of the present invention and description change and modify, within being all the scope that patent of the present invention contains.

Claims (6)

1. a Big Dipper signal acquisition methods under weak signal environment, is characterized in that: it comprises,

S1 down-converter unit, carries out down-converted to the if sampling signal receiving;

S2 peels off NH code by the signal receiving respectively, and transforms to frequency-region signal, and after multiplying each other with the volume of returning to work of local code frequency domain value, another mistake transforms to time domain;

S3 is by the conjugate multiplication of the coherent accumulation value of the coherent accumulation value in the current moment and previous moment summation;

S4 makes Taylor series expansion to the amplitude difference of coherent integration result peak value both sides spectral line, has derived the almost linear relation of frequency values and amplitude difference, utilizes this linear relationship to solve and obtains frequency estimation.

2. Big Dipper signal acquisition methods under a kind of weak signal environment as claimed in claim 1, is characterized in that: described step S1 comprises: the if sampling signal r (n) receiving is carried out to down-converted:

$\begin{array}{c}s\left(n\right)=r\left(n\right)*e^{-j2\mathrm{\&pi;}(f_{\mathrm{IF}}+i*\mathrm{\&Delta;f})}\\ =\{h\left(l\right)*c\left(n\right)*e^{j*2\mathrm{\&pi;}{f}_1}+P\left(n\right)\}*e^{-j2\mathrm{\&pi;}(f_{\mathrm{IF}}+i*\mathrm{\&Delta;f})}\end{array}$

Wherein s (n) is baseband signal, and P (n) is the noise of intermediate-freuqncy signal r (n), f ₁for receiving the intermediate frequency carrier frequency of signal, f _iFfor setting IF-FRE, △ f is step-size in search, and i is searching times.

3. Big Dipper signal acquisition methods under a kind of weak signal environment as claimed in claim 1, is characterized in that: described step S2 comprises: the value correspondence after L 1ms is relevant is added, and completes the coherent integration of L ms data,

Then complete repeatedly the coherent integration of Lms,

$\left\{\begin{array}{c}{R}_1\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_1\left(n\right)*{\mathrm{NH}}_k\left(1\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\\ R_2\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_2\left(n\right)*{\mathrm{NH}}_k\left(2\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\\ \&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\\ R_L\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_L\left(n\right)*{\mathrm{NH}}_k\left(L\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\end{array}\right.$

$Y_i\left(n\right)=\underset{i=1}{\overset{L}{\mathrm{\&Sigma;}}}{R}_i\left(n\right)$

Wherein FFT () represents the number of winning the confidence Fast Fourier Transform (FFT), and IFFT () represents to ask the inverse fast Fourier transform of signal, and conj () represents to ask the complex conjugate of signal, s _i(n) represent according to num _sKIPmaxbase band after delay adopts signal, NH _k(i) represent definite NH code sequence, c (n) represents the local ranging code producing.R _i(n) represent certain 1ms coherent integration result, Y _i(n) represent i Lms coherent integration result.

4. Big Dipper signal acquisition methods under a kind of weak signal environment as claimed in claim 3, is characterized in that: described in peel off NH code and comprise,

$\left\{\begin{array}{c}{R}_1\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_1(n+{\mathrm{num}}_{\mathrm{SKIP}})*{\mathrm{NH}}_k\left(i\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\\ R_2\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_2(n+{\mathrm{num}}_{\mathrm{SKIP}})*{\mathrm{NH}}_k\left(i\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\\ \&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\&CenterDot;\\ R_{20}\left(n\right)=\mathrm{IFFT}[\mathrm{FFT}(s_L(n+{\mathrm{num}}_{\mathrm{SKIP}})*{\mathrm{NH}}_k\left(i\right))*\mathrm{conj}\left(\mathrm{FFT}\left(c\left(n\right)\right)\right)]\end{array}\right.$

$Y_{\left({\mathrm{num}}_{\mathrm{SIKP},}k\right)}\left(n\right)=\underset{i=1}{\overset{20}{\mathrm{\&Sigma;}}}{R}_i\left(n\right)$

Wherein, wherein FFT () represents the number of winning the confidence Fast Fourier Transform (FFT), and IFFT () represents to ask the inverse fast Fourier transform of signal, and conj () represents to ask the complex conjugate of signal, s _i(n) represent that base band adopts signal, c (n) represents the local ranging code producing, num _sKIPexpression starts delay sampling from sampled data counts, its scope control in 1ms sampling number, NH _k(i) represent the ranking results of a ring shift right k position of NH code, R _i(n) represent certain 1ms coherent integration result,

represent num _sKIPthe 20ms coherent integration result of the ranking results of individual corresponding NH ring shift right k position,

$[{\mathrm{num}}_{\mathrm{SKIP}\max },k_{\max }]=\max \left(\right|Y_{\left({\mathrm{num}}_{\mathrm{SKIP},}k\right)}\left|\right)$

Num wherein _sKIPmaxthe reference position that represents 1ms data, k _maxrepresent that corresponding NH code sequence indicates.

5. Big Dipper signal acquisition methods under a kind of weak signal environment as claimed in claim 1, it is characterized in that: described step S3 comprises: the product of estimating the value that navigation data bits value corresponding to current adjacent two coherent accumulation values is ± 1, then after multiplying each other with difference accumulation, carry out coherent accumulation, formula is as follows:

$Z\left(n\right)=|\underset{i=2}{\overset{M}{\mathrm{\&Sigma;}}}{a}_{i-1}*{Y^{*}}_{i-1}\left(n\right)Y_i\left(n\right)|$

Wherein, Y ^* _i-1(n) represent the conjugation of the coherent accumulation value of previous moment, Y _i(n) be expressed as the coherent accumulation value of current time, a _i-1the product that represents the value (value is ± 1) of the navigation data bits that current adjacent two coherent accumulation values are corresponding, Z (n) represents differential coherent accumulative result.

6. Big Dipper signal acquisition methods under a kind of weak signal environment as claimed in claim 1, is characterized in that: described step S4 also comprises: original frequency compensated, then repeats iterative estimate J time, finally obtain high precision carrier frequency and code phase,

$\hat{x}=\frac{D}{{\mathrm{AQ\&alpha;}}_1}=\frac{\sin c(\mathrm{Qx}/Q_I)}{{\mathrm{G\&alpha;}}_1}D$

$\hat{f}=f_{\mathrm{ini}}+\underset{i=0}{\overset{J-1}{\mathrm{\&Sigma;}}}\hat{x}/N_c{Q}_I$

Wherein,

represent intermediate variable estimated value, D represents peak value left and right sides spectral line amplitude difference, and Q represents the hop count of coherent accumulation, Q _irepresent that FFT counts, have Q Lms coherent integration, count as Q _ifFT conversion, G represents the peak value of FFT, α ₁get 4/ π, f _inirepresent the resulting frequency estimation of differential coherence, N _cfor spreading code Cycle Length,

represent high precision carrier estimation value.

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