CN114609652A - Multi-frequency open-loop receiver tracking method and system under extreme ionosphere anomaly - Google Patents

Multi-frequency open-loop receiver tracking method and system under extreme ionosphere anomaly Download PDF

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CN114609652A
CN114609652A CN202210242415.4A CN202210242415A CN114609652A CN 114609652 A CN114609652 A CN 114609652A CN 202210242415 A CN202210242415 A CN 202210242415A CN 114609652 A CN114609652 A CN 114609652A
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erasing
determining
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CN114609652B (en
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王志鹏
方堃
尉一馨
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Beihang University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/24—Acquisition or tracking or demodulation of signals transmitted by the system
    • G01S19/29—Acquisition or tracking or demodulation of signals transmitted by the system carrier including Doppler, related
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/24—Acquisition or tracking or demodulation of signals transmitted by the system
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/24—Acquisition or tracking or demodulation of signals transmitted by the system
    • G01S19/30—Acquisition or tracking or demodulation of signals transmitted by the system code related
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
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Abstract

The invention provides a multi-frequency open-loop receiver tracking method and a multi-frequency open-loop receiver tracking system under extreme ionosphere anomaly, which belong to the field of navigation satellite positioning, and comprise the following steps: receiving navigation signals of each Beidou satellite in real time; determining the transmission time of the navigation signal from the satellite to the receiver at the current moment, determining the Doppler frequency of the navigation signal, and erasing the carrier wave of the navigation signal; adopting network enhancement system information to erase the navigation information of the carrier erasing signal; dividing the navigation erasing signal into a plurality of signal blocks according to the spread spectrum code period of the navigation signal, and performing coherent accumulation on the plurality of signal blocks to obtain an accumulated signal; erasing the spread spectrum codes of the accumulated signals to obtain complex signals; optimizing a state space formula of the Kalman filter based on a multi-frequency ionosphere scintillation model; and filtering the complex signal by adopting an optimized Kalman filter to obtain a tracking signal at the current moment. The positioning accuracy can be improved under the condition of the abnormality of the strong ionization layer.

Description

Multi-frequency open-loop receiver tracking method and system under extreme ionosphere anomaly
Technical Field
The invention relates to the field of navigation satellite positioning, in particular to a multi-frequency open-loop receiver tracking method and a multi-frequency open-loop receiver tracking system under extreme ionosphere anomaly.
Background
The ionosphere is one of the biggest sources of Global Navigation Satellite System (GNSS) positioning error. In ionospheric anomaly situations, the integrity, accuracy and availability of GNSS systems can be severely impacted. In particular, small-scale plasma irregularities in the ionosphere that vary over time can cause rapid, random fluctuations in the amplitude and phase of GNSS signals, a phenomenon known as ionospheric flicker. The occurrence of flicker is influenced by local time, season, latitude, and solar and geomagnetic activity. Research shows that the twinkling is concentrated in the equatorial extending zone of the geomagnetic equator of +/-20 degrees and high latitude areas of the geomagnetic north-south latitude of 65-90 degrees.
GNSS receivers need to track the satellite signals stably and continuously in order to calculate the propagation distance of the signals from the satellites to the receiver as accurately as possible. The receiver tracks the carrier of the received signal by a PLL (Phase Locked Loop), and strips the carrier component of the signal. Meanwhile, the code phase is tracked through a code ring, and the accurate code phase is obtained. When ionospheric scintillation occurs, the receiver carrier loop and code loop tracking performance is reduced, and phase and code tracking errors are increased, so that the GNSS carrier wave and pseudo-range observed quantity noise is increased. Under the anomaly of the strong ionosphere, the tracking loop of the GNSS receiver can generate frequent cycle slip and lock losing, and the GNSS receiver forms a great threat to the application of the GNSS. If satellite coverage is poor from the outset, the user's location availability may become unreliable in the presence of flicker. Even if the satellite coverage is good, strong flashes affecting multiple satellites can still disrupt the availability of location services. The distance measurement error due to these problems may be 3 to 10 times larger. With the great application of GNSS in projects with strict requirements on accuracy, precision and reliability of position estimation, the performance requirements on precise positioning are also continuously increasing, and therefore, it is necessary to suppress the influence of flicker on GNSS.
The strong ionosphere flicker usually causes the phenomena of deep fading of signal amplitude and rapid phase jitter to occur at the same time, so that frequent lock loss and cycle slip of a receiver occur, and serious threat is brought to the application of precise navigation positioning and the application with strict life safety requirements. The conventional GNSS tracking loop usually adopts a closed-loop tracking algorithm based on a proportional-integral filter or a Kalman Filter (KF), and the closed-loop tracking algorithm is not suitable for a strong-flicker situation, because deep fading causes a large error in estimation, thereby affecting the performance of the algorithm and the accuracy of positioning.
In addition, in the prior art, there is also a strategy for dealing with strong flicker by adopting an open loop architecture, and although the open loop tracking loop does not lose lock, the measurement accuracy is poor. Therefore, how to balance the requirements of both unlocking and ensuring the positioning accuracy becomes a problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a multi-frequency open-loop receiver tracking method and a multi-frequency open-loop receiver tracking system under extreme ionosphere anomaly, which can improve the positioning accuracy under the condition of strong ionosphere anomaly.
In order to achieve the purpose, the invention provides the following scheme:
a multi-frequency open-loop receiver tracking method under extreme ionospheric anomaly, comprising:
receiving navigation signals of each Beidou satellite in real time;
aiming at a navigation signal of any Beidou satellite, determining the transmission time of the navigation signal from the Beidou satellite to a receiver at the current moment according to the position of the Beidou satellite at the previous moment and the position of the receiver; the position of the Beidou satellite at the initial moment and the transmission time of the navigation signal are predetermined;
determining the Doppler frequency of the navigation signal according to the transmission time;
erasing the carrier of the navigation signal according to the Doppler frequency to obtain a carrier erasing signal;
erasing the navigation information of the carrier erasing signal by adopting network enhanced system information to obtain a navigation erasing signal;
dividing the navigation erasing signal into a plurality of signal blocks according to the spread spectrum code period of the navigation signal, and performing coherent accumulation on the plurality of signal blocks to obtain an accumulated signal;
erasing the spread spectrum code of the accumulated signal to obtain a complex signal;
optimizing a state space formula of the Kalman filter based on a multi-frequency ionosphere scintillation model to obtain an optimized Kalman filter; the multi-frequency ionosphere scintillation model is obtained by modeling the amplitude and the phase of an ionosphere scintillation component according to sample signals transmitted on the frequencies of Beidou B1C, B2a and B1I by adopting a multiple autoregressive model;
and filtering the complex signal by adopting the optimized Kalman filter to obtain a tracking signal at the current moment.
Optionally, the determining the doppler frequency of the navigation signal according to the transmission time specifically includes:
determining the running speed of the Beidou satellite at the current moment according to the transmission time;
and determining the Doppler frequency of the navigation signal according to the running speed of the Beidou satellite at the current moment.
Optionally, determining a doppler frequency of the beidou satellite navigation signal by using the following formula:
Figure BDA0003542999320000031
wherein,
Figure BDA0003542999320000032
is the Doppler frequency of the navigation signal, I is the unit direction vector from the receiver to the Beidou satellite, VSVThe running speed of the Beidou satellite at the current moment,
Figure BDA0003542999320000033
is a receiver clock frequency offset estimate.
Optionally, the erasing the carrier of the navigation signal according to the doppler frequency to obtain a carrier erasure signal specifically includes:
determining a Doppler frequency shift of the navigation signal according to the Doppler frequency;
determining a carrier of the navigation signal according to the Doppler frequency shift;
and erasing the carrier wave of the navigation signal to obtain a carrier wave erasing signal.
Optionally, the following formula is adopted to perform coherent accumulation on the multiple signal blocks to obtain an accumulated signal:
Figure BDA0003542999320000034
where w [ N ] is the accumulated signal, L is the number of spreading code periods, z [ N + kN ] is the kN-th signal block, N is the number of samples in each signal block, and N represents the discrete signal.
Optionally, the erasing the spreading code of the accumulated signal to obtain a complex signal specifically includes:
extracting the sign of the accumulated signal to obtain a binary sequence;
and multiplying the binary sequence and the accumulated signal to obtain a complex signal.
Optionally, the multi-frequency ionospheric scintillation model is:
Figure BDA0003542999320000041
Figure BDA0003542999320000042
where ρ iss,kIs the amplitude of the kth sample signal, θs,kIs the phase of the kth sample signal, q is the order of the multi-frequency ionospheric scintillation model, ΓiIs a set of gain coefficients of a multivariate autoregressive model, k is a constant, ρs,k-iIs the amplitude of the k-i sample signal, θs,k-iFor the phase of the k-i sample signal, eρ,kIs a zero mean value Gaussian random variable, ∑ρSum ΣθIs the noise covariance matrix of the multivariate autoregressive model.
In order to achieve the above purpose, the invention also provides the following scheme:
a multi-frequency open-loop receiver tracking system under extreme ionosphere anomaly comprises a capturing unit and a tracking unit;
the capturing unit is used for receiving navigation signals of all Beidou satellites in real time;
the tracking unit is connected with the capturing unit, and comprises:
the transmission time determining module is connected with the capturing unit and used for determining the transmission time of the navigation signal from the Beidou satellite to the receiver at the current moment by adopting a linear interpolation method according to the position of the Beidou satellite at the previous moment, the position of the receiver, the transmission time of the navigation signal at the previous moment and the time interval between the current moment and the previous moment aiming at the navigation signal of any Beidou satellite; the position of the Beidou satellite at the initial moment and the transmission time of the navigation signal are predetermined;
a Doppler frequency determining module, connected to the transmission time determining module, for determining the Doppler frequency of the navigation signal according to the transmission time;
the carrier erasing module is respectively connected with the Doppler frequency determining module and the capturing unit and is used for erasing the carrier of the navigation signal according to the Doppler frequency to obtain a carrier erasing signal;
the navigation erasing module is connected with the carrier erasing module and is used for erasing the navigation information of the carrier erasing signal by adopting network enhanced system information to obtain a navigation erasing signal;
the accumulation module is connected with the navigation erasing module and used for dividing the navigation erasing signal into a plurality of signal blocks according to the spread spectrum code period of the navigation signal and performing coherent accumulation on the plurality of signal blocks to obtain an accumulated signal;
the spread spectrum code erasing module is connected with the accumulation module and is used for erasing the spread spectrum code of the accumulated signal to obtain a complex signal;
the optimization module is used for optimizing a state space formula of the Kalman filter based on the multi-frequency ionosphere scintillation model to obtain an optimized Kalman filter; the multi-frequency ionosphere scintillation model is obtained by modeling the amplitude and the phase of an ionosphere scintillation component according to sample signals transmitted on the frequencies of Beidou B1C, B2a and B1I by adopting a multiple autoregressive model;
and the filtering module is respectively connected with the spread spectrum code erasing module and the optimizing module and is used for filtering the complex signal by adopting the optimizing Kalman filter to obtain a tracking signal at the current moment.
Optionally, the doppler frequency determination module comprises:
the satellite speed determining submodule is connected with the transmission time determining module and used for determining the operating speed of the Beidou satellite at the current moment according to the transmission time;
and the frequency determination submodule is connected with the satellite speed determination submodule and used for determining the Doppler frequency of the navigation signal according to the running speed of the Beidou satellite at the current moment.
Optionally, the carrier erasure module includes:
a Doppler frequency shift determining submodule connected with the Doppler frequency determining module and used for determining the Doppler frequency shift of the navigation signal according to the Doppler frequency;
the carrier determining submodule is connected with the Doppler frequency shift determining submodule and used for determining the carrier of the navigation signal according to the Doppler frequency shift;
and the erasing submodule is respectively connected with the carrier determining submodule and the capturing unit and is used for erasing the carrier of the navigation signal to obtain a carrier erasing signal.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the method comprises the steps of sequentially erasing navigation signal carriers, navigation information and spread spectrum codes to obtain complex signals, modeling the amplitude and the phase of an ionosphere scintillation component by using a multivariate autoregressive model, then including the complex signals into a state equation of an open-loop Kalman filter, and filtering the complex signals by using an optimized Kalman filter, so that the possibility that a traditional closed-loop tracking loop is unlocked due to strong scintillation is avoided, and the requirements of the receiver tracking loop on usability and precision under the influence of strong scintillation are considered.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a multi-frequency open-loop receiver tracking method under extreme ionospheric anomaly in accordance with the present invention;
FIG. 2 is a block diagram of an open loop receiver for processing strong flicker signals;
fig. 3 is a schematic block diagram of a multi-frequency open-loop receiver tracking system under extreme ionospheric anomaly according to the present invention.
Description of the symbols:
the device comprises a capturing unit-1, a tracking unit-2, a transmission time determining module-21, a Doppler frequency determining module-22, a carrier erasing module-23, a navigation erasing module-24, an accumulating module-25, a spread spectrum code erasing module-26, an optimizing module-27 and a filtering module-28.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The invention aims to provide a multi-frequency open-loop receiver tracking method and a multi-frequency open-loop receiver tracking system under extreme ionosphere anomaly.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.
The receiver working principle based on the PLL/DLL loop is as follows: after the signals are input, all visible satellites of a user are firstly identified by the acquisition module, if a certain satellite is visible, rough estimation of signal frequency and code phase needs to be given, then the tracking module refines the two satellites, the values are tracked when the signal characteristics change along with time, after the signals are correctly tracked, the C/A codes and the carrier waves can be removed from the signals, and navigation data can be demodulated and pseudo-range can be calculated after tracking.
As shown in fig. 1 and fig. 2, the multi-frequency open-loop receiver tracking method under extreme ionospheric anomaly of the present invention includes:
s1: and receiving the navigation signals of the Beidou satellites in real time. Specifically, the total navigation signal r [ n ] received by the receiver is:
Figure BDA0003542999320000071
wherein N represents a discrete signal, NsatIs the number of Beidou satellites, Pn]For the strength (power) of the received signal at the output of the receiver analog-to-digital converter, diIs the navigation information of the ith Beidou satellite, TsFor a sampling period, τiCode delay of the ith Beidou satellite, ciIs the spreading code of the ith Beidou satellite; f. ofi＝fIF+fDCarrier frequency, f, of navigation signals for the ith Beidou satelliteIFAt a nominal intermediate frequency, fDIs a Doppler shift;
Figure BDA0003542999320000072
is a general phase contribution, η n]For thermal noise, the converter of the receiver samples the frequency on the GNSS intermediate frequency signal
Figure BDA0003542999320000073
And (5) operating.
S2: aiming at the navigation signal of any Beidou satellite, determining the transmission time of the navigation signal from the Beidou satellite to the receiver at the current moment by adopting a linear interpolation method according to the position of the Beidou satellite at the previous moment, the position of the receiver, the transmission time of the navigation signal at the previous moment and the time interval between the current moment and the previous moment. The position of the Beidou satellite at the initial moment and the transmission time of the navigation signal are predetermined.
S3: and determining the Doppler frequency of the navigation signal according to the transmission time.
S4: and erasing the carrier of the navigation signal according to the Doppler frequency to obtain a carrier erasing signal. Specifically, the carrier erasure signal x [ n ] is:
Figure BDA0003542999320000074
s5: and erasing the navigation information of the carrier erasing signal by adopting network enhanced system information to obtain a navigation erasing signal. Specifically, an accurate Navigation information copy is generated by using a-GNSS (Assisted-Global Navigation Satellite System) information, and the Navigation message is erased.
The navigation wipe signal z [ n ] is:
Figure BDA0003542999320000081
s6: and dividing the navigation erasing signal into a plurality of signal blocks according to the spread spectrum code period of the navigation signal, and performing coherent accumulation on the plurality of signal blocks to obtain an accumulated signal. Specifically, a plurality of signal blocks are coherently accumulated to obtain an accumulated signal by using the following formula:
Figure BDA0003542999320000082
where w [ N ] is the accumulated signal, L is the number of spreading code periods, z [ N + kN ] is the kN-th signal block, N is the number of samples in each signal block, and N represents the discrete signal.
Due to thermal noise n]Presence of pseudo-random noise code ciInvisible and therefore unable to estimate and remove the pseudorandom noise code. In principle, the pseudo-random noise code can be deleted by an erasure operation, but this requires sub-chip level time synchronization and fine assistance. This condition is difficult to satisfy using only appropriate a-GNSS information and knowledge of the receiver's position. Since thermal noise can be modeled as a zero-mean gaussian random variable, the accumulation process can increase the signal-to-noise ratio of the signal until the spreading code modulated in the navigation signal emerges from the noise floor.
In the present embodiment, the navigation signal z [ n ]]Is divided into lengths TcodeA plurality of signal blocks of (2), wherein TcodeIs the spreading code period. Assuming that a period consists of N samples, L periods are coherently added to obtain a signal w [ N ] of length N]。
Some residual distortion will remain due to other unmodeled effects that cannot be averaged to zero by the summation process, and due to some residual noise contribution. Thus, a fixed sampling frequency f is givensIncreasing the number L of spreading code periods, the noise contribution tends to be averaged out, but this requires many received signal segments, i.e. a large observation window. Due to the non-stationarity of the scintillation process, the statistical distribution of the signal amplitude varies with time, and if L is too large it will not be possible to capture significant variations in the statistical distribution, since they will all be contained in the same observation window, so L must balance this resolution with the ability to reduce the noise impact when analyzing.
In addition, not only the carrier frequency but also the code rate are changed due to the doppler effect, resulting in stretching and compressing thereof. Suppose RcAt a nominal chip rate, fL1For transmitting the frequency, the actual chip rate at time t can be found
Figure BDA0003542999320000091
Comprises the following steps:
Figure BDA0003542999320000092
wherein f isD(t) is the Doppler shift on the carrier at time t.
Given a fixed sampling frequency fsThe time varying code rate results in a period-varying number of samples per chip. Furthermore, fsAnd RcAre unlikely to be multiples of each other, and thus, in subsequent PRN code periods, the same chip may not be represented by the same number of samples, and the chips of subsequent samples may not be perfectly aligned, thereby threatening the effect of coherent accumulation of the code periods. If not from w n]With the transformed samples removed, the residual modulation remains in y [ n ] after the code erasure]In (1). For this reason, the present invention employs an algorithm that discards the transformed samples both before and after symbol conversion to remove the transformed samples from the output signal, preventing residual modulation from remaining in the signal after erasure of the spreading code.
S7: and erasing the spread spectrum code of the accumulated signal to obtain a complex signal. Specifically, the sign of the accumulated signal is extracted to obtain a binary sequence. And multiplying the binary sequence and the accumulated signal to obtain a complex signal. In this embodiment, the sign of the accumulated signal is extracted by the sign operator to obtain a binary sequence:
Figure BDA0003542999320000093
will binary sequence
Figure BDA0003542999320000094
Multiplying by the accumulated signal w [ n ]]Namely, the erasure of the spreading code from the accumulated signal is realized to obtain a complex signal:
Figure BDA0003542999320000095
and the tracking jitter of the loop is further reduced by adopting the self-adaptive bandwidth and prolonging the coherent integration time in the open loop.
S8: optimizing a state space formula of the Kalman filter based on a multi-frequency ionosphere scintillation model to obtain an optimized Kalman filter; the multi-frequency ionosphere scintillation model is obtained by modeling the amplitude and the phase of an ionosphere scintillation component according to sample signals transmitted on the frequencies of Beidou B1C, B2a and B1I by adopting a multiple autoregressive model.
S9: and filtering the complex signal by adopting the optimized Kalman filter to obtain a tracking signal at the current moment. And the tracking signal at the current moment is used for tracking the Beidou satellite. And calculating a pseudo range according to the tracking signal so as to realize navigation positioning.
GNSS open loop processing utilizes a statically known receiver position and a reference oscillator to generate an accurate local copy of a reference carrier. When the reference position and time of the receiver are known and a network connection providing the a-GNSS message is available, certain components of the signal can be correctly predicted. They are removed from the input signal leaving only random contributions due to thermal noise and other disturbances, including flicker effects. The contribution of the flicker may be separated from the GNSS signal.
Before a strong scintillation event begins, the receiver should be in steady state tracking and a valid receiver time solution has been obtained. Specifically, in step S2, assume that at time T, the received signal is at T- Δ T1Time of day transmitted from satellite, where at1Is the signal propagation time. XSV(T-Δt1) For signal transmission time T- Δ T1Satellite position of time, XRXIs the location of the receiver. Signals arriving at the receiver at T + Δ T are at T + Δ T- Δ T2Transmitted from a satellite, where at2Is the signal propagation time. Δ t2And Δ t1May not be equal because the satellite has already been from XSV(T-Δt1) Move to XSV(T+ΔT-Δt2). To predict the Doppler frequency at the receive time T + Δ T, Δ T needs to be determined2The value of (c). Since Δ T is very small, the change in pseudorange over this time period can be approximated as linearly dependent on Δ T, which can then be obtained by simple linear interpolation2。
TxFor reception at T- Δ T1Time of signal transmitted at + Δ T，txIs the corresponding propagation time. Thus calculating Δ t2The process comprises the following steps:
satellite determination at T-delta T using ephemeris parameters1Position X at + Δ TSV(T-Δt1+ΔT)。
Δtx＝||XSV(T-Δt1+ΔT)-XRX||/C；
Tx＝T-Δt1+ΔT+Δtx；
Δt2＝ΔT(Δtx-Δt1)/(Tx-T)+Δt1；
Wherein C is the speed of light, XRXFor the position of the receiver, Δ txAccording to Δ t1And XRXThe calculated signal transmission time.
At this time according to Δ t2And ephemeris parameters, which are used for calculating the time T + delta T-delta T of the satellite at the signal transmitting time2Running speed V ofSV。
Further, step S3 specifically includes:
s31: and determining the running speed of the Beidou satellite at the current moment according to the transmission time.
S32: and determining the Doppler frequency of the navigation signal according to the running speed of the Beidou satellite at the current moment. Specifically, the following formula is adopted to determine the doppler frequency of the Beidou satellite navigation signal:
Figure BDA0003542999320000111
wherein,
Figure BDA0003542999320000112
is the Doppler frequency of the navigation signal, I is the unit direction vector from the receiver to the Beidou satellite, VSVThe running speed of the Beidou satellite at the current moment,
Figure BDA0003542999320000113
for receiver clock frequency offset estimation, the method can be used for flicker estimationThe average determination of the doppler residuals of the quiet period before the start.
Further, the Doppler residual is the difference l.V between the frequency estimated based on the closed-loop algorithm and the frequency calculated based on the receiver-satellite relative motionSVλ, the receiver clock frequency offset estimate is determined in this embodiment according to the following equation
Figure BDA0003542999320000114
Figure BDA0003542999320000115
Wherein,
Figure BDA0003542999320000116
for frequencies estimated based on a closed loop algorithm (CLT algorithm),<>time-averaged.
The Doppler frequency of a navigation signal is obtained by utilizing known network enhancement system information in an open loop, the transmission time of the navigation signal is obtained by utilizing linear interpolation according to the position of a satellite, the speed of the satellite and the position and the reference time of a receiver, the Doppler frequency of the navigation signal is further obtained, and an accurate local reference carrier is generated so as to ideally erase a radio frequency carrier.
The present invention does not include a DLL (delay locked loop) or FLL (frequency locked loop) by removing the frequency fiThe residual carrier at (a) demodulates the signal to baseband. The receiver position is treated as a known quantity and the range equation for each satellite signal is solved to obtain the clock bias. The average bias from all available satellite signals is used to predict the doppler frequency and then the carrier is erased to demodulate the signal to baseband.
Further, step S4 specifically includes:
s41: and determining the Doppler frequency shift of the navigation signal according to the Doppler frequency.
S42: and determining the carrier wave of the navigation signal according to the Doppler frequency shift.
S43: and erasing the carrier wave of the navigation signal to obtain a carrier wave erasing signal.
Since ionospheric flicker is frequency dependent and different satellite links are subject to different propagation conditions. Although phase perturbations may be correlated for different frequency signal components of the same satellite, deep amplitude attenuation tends to occur at different times for different frequency signals, so that inter-frequency assistance may be performed using a multi-frequency architecture. Ionospheric flicker can have a severe impact on carrier phase measurements and pseudorange observations, and to be able to extract the required phase component for positioning from the flicker component, the magnitude and phase of ionospheric flicker can be modeled using a multivariate autoregressive model formula and then embedded into the state space formula of a kalman filter.
In a multi-frequency architecture, the receiver may make multiple measurements on different frequencies of a single satellite. For example, using BDS (BeiDou Navigation Satellite System) sample signals transmitted at B1C, B2a, and B1I frequencies, the measurements are:
Figure BDA0003542999320000121
wherein,
Figure BDA0003542999320000122
for the kth sample signal at the frequency B1C,
Figure BDA0003542999320000123
for the kth sample signal at the frequency B2a,
Figure BDA0003542999320000124
for the kth sample signal at the frequency B1I,
Figure BDA0003542999320000125
for the signal amplitude of the kth sample signal at the frequency B1C,
Figure BDA0003542999320000126
for the signal amplitude of the kth sample signal at the frequency B2a,
Figure BDA0003542999320000127
for the signal amplitude of the kth sample signal at the frequency B1I,
Figure BDA0003542999320000128
for the phase of the kth sample signal at the frequency of B1C,
Figure BDA0003542999320000129
for the phase of the kth sample signal at the frequency B2a,
Figure BDA00035429993200001210
is the phase of the k-th sample signal at the frequency B1I, d represents the locally generated carrier signal, s represents the received signal, j is the imaginary unit,
Figure BDA00035429993200001211
is the thermal noise of the kth sample signal at the frequency B1C,
Figure BDA00035429993200001212
is the thermal noise of the kth sample signal at the frequency B2a,
Figure BDA00035429993200001213
is the thermal noise of the kth sample signal at the frequency of B1I.
In this embodiment, the multi-frequency ionospheric scintillation model is:
Figure BDA0003542999320000131
Figure BDA0003542999320000132
where ρ iss,kIs the amplitude of the kth sample signal, θs,kThe phase of the kth sample signal is defined, q is the order of a multi-frequency ionosphere scintillation model and is determined by the condition that the model approaches to the actual amplitude and phase, q is more than or equal to 3 and more than or equal to 1, and gamma isiIs a set of gain coefficients of a multivariate autoregressive model, k is a constant, ρs,k-iIs the amplitude of the k-i sample signal, θs,k-iFor the phase of the k-i sample signal, eρ,kIs a zero mean value Gaussian random variable, ∑ρSum ΣθIs a noise covariance matrix of a multivariate autoregressive model. The amplitude and phase of the first sample signal are initial values.
Figure BDA0003542999320000133
Each element in the matrix has a dimension M x M, given by the number of bands, BMThe Beidou signals B1C, B2a and B1I are shown, and have M frequency bands,
the multi-frequency ionosphere scintillation model recursion algorithm aims to deduce the optimal value of a gain coefficient matrix according to estimation errors, minimize mean square errors and gradually correct initial estimation values.
Noise covariance matrix sigma of multi-frequency ionospheric scintillationρSum ΣθThe inter-frequency correlation of the time series is well embodied. Found from the estimated noise covariance, albeit ΣρAlmost diagonal, i.e. ionospheric flicker amplitude has no correlation, but a significant value in the main diagonal appears at ΣθI.e., ionospheric scintillation phase, has a high inter-frequency correlation.
Because each frequency does not simultaneously generate deep fading, a multifrequency ionosphere scintillation model and a multivariate autoregressive model are used for modeling the amplitude and the phase of an ionosphere scintillation component, and parameters of the multivariate autoregressive model are selected from scintillation data to independently simulate a scintillation gain component of a signal.
Specifically, the state space formula of the optimized kalman filter is:
Figure BDA0003542999320000141
Figure BDA0003542999320000142
wherein,
Figure BDA0003542999320000143
and
Figure BDA0003542999320000144
k | k-1 represents the predicted estimate of the measured value used to time k-1 at time k,
Figure BDA0003542999320000145
and
Figure BDA0003542999320000146
from thetas,kThe compound is obtained by the method in (1),
Figure BDA0003542999320000147
and
Figure BDA0003542999320000148
from rhos,kIs obtained in (1).
In order to further reduce the tracking jitter in the open loop, the invention adopts a method of self-adaptive bandwidth and prolonging coherent integration time, and the noise bandwidth B is usednControlling the amount of noise allowed into the loop filter, a wider bandwidth helps to improve the dynamic performance of the tracking loop, and a narrower bandwidth helps to ensure more accurate tracking; coherent integration refers to the time length of integration accumulation, the long coherent integration time can ensure that a tracking loop guarantees high sensitivity to weak signals, the short coherent integration time can ensure robustness to high dynamic signals, the longer the integration time or the smaller the correlation interval, the better the tracking precision, and the coherent integration time is limited by a navigation data period. And adjusting the values of the noise bandwidth and the coherent integration time according to the flicker condition. The invention adopts the self-adaptive bandwidth and prolongs the coherence time, which is beneficial to improving the tracking precision of the open loop.
In addition, the present invention uses a skewness index R4 to determine the severity of amplitude flicker.
Specifically, the S4 index represents the severity of amplitude flicker, which is defined as the normalized variance of the received signal strength:
Figure BDA0003542999320000151
wherein I is signal strength calculated from the receiver output, and I ═ R _ Y2R is the amplitude envelope of the received signal;<>represents a time average.
Due to slow oscillation caused by antenna directional diagram, tropospheric scattering and multipath reflection from ground objects, it is necessary to eliminate the signal amplitude trend by filtering, and obtain < P > and < N > after low-pass filtering using a 6-order low-pass butterworth filter with a cutoff frequency of 0.1Hz, where P is the signal power of the tracking signal y [ N ] and N is the noise power of the tracking signal y [ N ].
The actual signal intensity variance over 1 minute is then estimated using the P and < P > sequences:
Figure BDA0003542999320000152
where M is the total number of samples in 1 minute. Average signal power of the same period
Figure BDA0003542999320000153
Comprises the following steps:
Figure BDA0003542999320000154
thus, the S4 index is:
Figure BDA0003542999320000155
in addition, R is the assumed normalized signal amplitude:
Figure BDA0003542999320000156
absence of computation S in open-loop architecture4Similar to the correlator output, although the rate obtained is very different, these samples still contain information about the fluctuations due to flicker, which can be detected by defining a new indicator based on the statistical characteristics of the histogram.
Skewness R using a histogram based on the processed samples (normalized signal amplitude at each sample point)4The flicker amplitude is statistically measured to characterize the severity of amplitude flicker in an open loop, skewness being a measure of symmetry that represents the degree to which the probability distribution of a real-valued random variable lacks symmetry about its mean.
For univariate X1,X2,X3Skewness of R4Estimated as:
Figure BDA0003542999320000161
wherein, XiRepresenting the tracking signal y [ n ] at a certain sampling point]The strength of (a) is high,
Figure BDA0003542999320000162
is X1To Xiσ is the standard deviation of the tracking signal strength, and M is the number of samples. The shape of the variable distribution is changed due to the flickering activity, thereby changing its symmetry. When flicker is present, the histogram of the sample deviates significantly from the Gaussian distribution, and therefore, a skewness-based metric can be used to estimate flicker activity.
The amplitude flicker index is used for measuring the flicker intensity on the signal, and the calculated amplitude flicker index should be smaller as the previous measure for suppressing flicker by multi-frequency open loop is better.
The invention provides a loop tracking method of a multi-frequency open loop. The open loop process is to use a static known receiver position and reference oscillator to generate accurate reference carrier and navigation information copies, erase the carrier and navigation information, and erase the pseudo-random code using coherent accumulation. By utilizing the statistical relationship among flashes of different frequency bands, the ionospheric scintillation amplitude and phase component are modeled as an MAR (Multiple auto-regression) process, an enhanced state space formula considering a plurality of frequencies is researched and included in a state to be tracked by an open loop filter, and the problem of carrier tracking precision is solved.
As shown in fig. 3, the multi-frequency open-loop receiver tracking system under extreme ionospheric anomaly of the present invention includes an acquisition unit 1 and a tracking unit 2.
The capturing unit 1 is used for receiving navigation signals of all Beidou satellites in real time.
The tracking unit 2 is connected with the capturing unit 1, and the tracking unit 2 comprises: a transmission time determination module 21, a doppler frequency determination module 22, a carrier erasure module 23, a navigation erasure module 24, an accumulation module 25, a spreading code erasure module 26, an optimization module 27, and a filtering module 28.
The transmission time determining module 21 is connected to the capturing unit 1, and the transmission time determining module 21 is configured to determine, for a navigation signal of any one of the Beidou satellites, transmission time of the navigation signal at a current time from the Beidou satellite to the receiver by using a linear interpolation method according to a position of the Beidou satellite at a previous time, a position of the receiver, transmission time of the navigation signal at the previous time, and a time interval between the current time and the previous time. The position of the Beidou satellite at the initial moment and the transmission time of the navigation signal are predetermined.
The doppler frequency determination module 22 is connected to the transmission time determination module 21, and the doppler frequency determination module 22 is configured to determine the doppler frequency of the navigation signal according to the transmission time.
Specifically, the doppler frequency determination module 22 includes: satellite velocity determination submodule frequency determination submodule.
The satellite speed determining submodule is connected with the transmission time determining module 21, and is used for determining the operating speed of the Beidou satellite at the current moment according to the transmission time.
The frequency determination submodule is connected with the satellite speed determination submodule and used for determining the Doppler frequency of the navigation signal according to the running speed of the Beidou satellite at the current moment.
The carrier erasing module 23 is connected to the doppler frequency determining module 22 and the capturing unit 1, respectively, and the carrier erasing module 23 is configured to erase the carrier of the navigation signal according to the doppler frequency to obtain a carrier erasing signal.
Specifically, the carrier erasing module 23 includes: a Doppler frequency shift determining sub-module, a carrier wave determining sub-module and an erasing sub-module.
The doppler shift determining submodule is connected to the doppler frequency determining module 22, and the doppler shift determining submodule is configured to determine a doppler shift of the navigation signal according to the doppler frequency.
The carrier determining submodule is connected with the Doppler frequency shift determining submodule and is used for determining the carrier of the navigation signal according to the Doppler frequency shift.
The erasing submodule is respectively connected with the carrier determining submodule and the capturing unit 1, and is used for erasing the carrier of the navigation signal to obtain a carrier erasing signal.
The navigation erasing module 24 is connected with the carrier erasing module 23, and the navigation erasing module 24 is configured to erase the navigation information of the carrier erasing signal by using network enhanced system information to obtain a navigation erasing signal.
The accumulation module 25 is connected to the navigation erasure module 24, and the accumulation module 25 is configured to divide the navigation erasure signal into a plurality of signal blocks according to the spreading code period of the navigation signal, and perform coherent accumulation on the plurality of signal blocks to obtain an accumulated signal.
The spreading code erasing module 26 is connected to the accumulating module 25, and the spreading code erasing module 26 is configured to erase the spreading code of the accumulated signal to obtain a complex signal.
The optimization module 27 is configured to optimize a state space formula of the kalman filter based on the multi-frequency ionosphere scintillation model to obtain an optimized kalman filter; the multi-frequency ionosphere scintillation model is obtained by modeling the amplitude and the phase of an ionosphere scintillation component according to sample signals transmitted on the frequencies of Beidou B1C, B2a and B1I by adopting a multiple autoregressive model.
The filtering module 28 is connected to the spreading code erasing module 26 and the optimizing module 27, respectively, and the filtering module 28 is configured to filter the complex signal by using the optimized kalman filter to obtain a tracking signal at the current time.
In addition, developers need to consider not only software functions but also execution performance when constructing a software receiver. The high computational throughput required during signal acquisition and signal tracking prevents a standard software receiver that employs a Single Instruction Single Data (SISD) algorithm on a CPU from operating in real time. Working in conjunction with the CPU is a GPU (graphics processing unit). The GPU consists of a large number of parallel processors, with high floating point performance and high memory bandwidth. It can be used to accelerate the acquisition and tracking process in a software receiver.
Compared with the prior art, the multi-frequency open-loop receiver tracking system under the extreme ionosphere anomaly has the same beneficial effects as the multi-frequency open-loop receiver tracking method under the extreme ionosphere anomaly, and the description is omitted here.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principle and the embodiment of the present invention are explained by applying specific examples, and the above description of the embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (10)

1. A multi-frequency open-loop receiver tracking method under extreme ionospheric anomaly, characterized by comprising:
receiving navigation signals of each Beidou satellite in real time;
aiming at the navigation signal of any Beidou satellite, determining the transmission time of the navigation signal from the Beidou satellite to the receiver at the current moment by adopting a linear interpolation method according to the position of the Beidou satellite at the previous moment, the position of the receiver, the transmission time of the navigation signal at the previous moment and the time interval between the current moment and the previous moment; the position of the Beidou satellite at the initial moment and the transmission time of the navigation signal are predetermined;
determining the Doppler frequency of the navigation signal according to the transmission time;
erasing the carrier of the navigation signal according to the Doppler frequency to obtain a carrier erasing signal;
erasing the navigation information of the carrier erasing signal by adopting network enhanced system information to obtain a navigation erasing signal;
dividing the navigation erasing signal into a plurality of signal blocks according to the spread spectrum code period of the navigation signal, and performing coherent accumulation on the plurality of signal blocks to obtain an accumulated signal;
erasing the spread spectrum code of the accumulated signal to obtain a complex signal;
optimizing a state space formula of the Kalman filter based on a multi-frequency ionosphere scintillation model to obtain an optimized Kalman filter; the multi-frequency ionospheric scintillation model is obtained by modeling the amplitude and phase of an ionospheric scintillation component according to sample signals transmitted at frequencies of Beidou B1C, B2a and B1I by adopting a multiple autoregressive model;
and filtering the complex signal by adopting the optimized Kalman filter to obtain a tracking signal at the current moment.
2. The method according to claim 1, wherein the determining the doppler frequency of the navigation signal according to the transmission time specifically comprises:
determining the running speed of the Beidou satellite at the current moment according to the transmission time;
and determining the Doppler frequency of the navigation signal according to the running speed of the Beidou satellite at the current moment.
3. The method of claim 2, wherein the following formula is used to determine the doppler frequency of the beidou satellite navigation signal:
Figure FDA0003542999310000021
wherein,
Figure FDA0003542999310000022
is the Doppler frequency of the navigation signal, I is the unit direction vector from the receiver to the Beidou satellite, VSVThe running speed of the Beidou satellite at the current moment,
Figure FDA0003542999310000023
is a receiver clock frequency offset estimate.
4. The method according to claim 1, wherein the erasing the carrier of the navigation signal according to the doppler frequency to obtain a carrier erasure signal comprises:
determining a Doppler frequency shift of the navigation signal according to the Doppler frequency;
determining a carrier of the navigation signal according to the Doppler frequency shift;
and erasing the carrier of the navigation signal to obtain a carrier erasing signal.
5. The method of claim 1, wherein the coherent accumulation of the signal blocks is performed by using the following formula to obtain an accumulated signal:
Figure FDA0003542999310000024
where w [ N ] is the accumulated signal, L is the number of spreading code periods, z [ N + kN ] is the kN-th signal block, N is the number of samples in each signal block, and N represents the discrete signal.
6. The method according to claim 1, wherein said erasing the spreading codes of the accumulated signals to obtain complex signals comprises:
extracting the sign of the accumulated signal to obtain a binary sequence;
and multiplying the binary sequence and the accumulated signal to obtain a complex signal.
7. The method of claim 1, wherein the multi-frequency ionospheric scintillation model is:
Figure FDA0003542999310000031
Figure FDA0003542999310000032
where ρ iss,kIs the amplitude of the kth sample signal, θs,kIs the phase of the kth sample signal, q is the order of the multi-frequency ionospheric scintillation model, ΓiIs a set of gain coefficients of a multivariate autoregressive model, k is a constant, ρs,k-iIs the amplitude of the k-i sample signal, θs,k-iFor the phase of the k-i sample signal, eρ,kIs a zero mean value Gaussian random variable, ∑ρSum ΣθIs a noise covariance matrix of a multivariate autoregressive model.
8. A multi-frequency open-loop receiver tracking system under extreme ionosphere anomaly is characterized by comprising a capturing unit and a tracking unit;
the capturing unit is used for receiving navigation signals of all Beidou satellites in real time;
the tracking unit is connected with the capturing unit, and comprises:
the transmission time determining module is connected with the capturing unit and is used for determining the transmission time of the navigation signal from the Beidou satellite to the receiver at the current moment by adopting a linear interpolation method according to the position of the Beidou satellite at the previous moment, the position of the receiver, the transmission time of the navigation signal at the previous moment and the time interval between the current moment and the previous moment aiming at the navigation signal of any Beidou satellite; the position of the Beidou satellite at the initial moment and the transmission time of the navigation signal are predetermined;
a doppler frequency determining module, connected to the transmission time determining module, for determining a doppler frequency of the navigation signal according to the transmission time;
the carrier erasing module is respectively connected with the Doppler frequency determining module and the capturing unit and is used for erasing the carrier of the navigation signal according to the Doppler frequency to obtain a carrier erasing signal;
the navigation erasing module is connected with the carrier erasing module and is used for erasing the navigation information of the carrier erasing signal by adopting network enhanced system information to obtain a navigation erasing signal;
the accumulation module is connected with the navigation erasing module and used for dividing the navigation erasing signal into a plurality of signal blocks according to the spread spectrum code period of the navigation signal and performing coherent accumulation on the plurality of signal blocks to obtain an accumulated signal;
the spread spectrum code erasing module is connected with the accumulation module and is used for erasing the spread spectrum code of the accumulated signal to obtain a complex signal;
the optimization module is used for optimizing a state space formula of the Kalman filter based on the multi-frequency ionosphere scintillation model to obtain an optimized Kalman filter; the multi-frequency ionosphere scintillation model is obtained by modeling the amplitude and the phase of an ionosphere scintillation component according to sample signals transmitted on the frequencies of Beidou B1C, B2a and B1I by adopting a multiple autoregressive model;
and the filtering module is respectively connected with the spread spectrum code erasing module and the optimizing module and is used for filtering the complex signal by adopting the optimized Kalman filter to obtain a tracking signal at the current moment.
9. The system of claim 8, wherein the doppler frequency determination module comprises:
the satellite speed determining submodule is connected with the transmission time determining module and used for determining the operating speed of the Beidou satellite at the current moment according to the transmission time;
and the frequency determination submodule is connected with the satellite speed determination submodule and used for determining the Doppler frequency of the navigation signal according to the running speed of the Beidou satellite at the current moment.
10. The system of claim 8, wherein the carrier erasure module comprises:
a Doppler frequency shift determining submodule connected with the Doppler frequency determining module and used for determining the Doppler frequency shift of the navigation signal according to the Doppler frequency;
the carrier determining submodule is connected with the Doppler frequency shift determining submodule and used for determining the carrier of the navigation signal according to the Doppler frequency shift;
and the erasing submodule is respectively connected with the carrier determining submodule and the capturing unit and is used for erasing the carrier of the navigation signal to obtain a carrier erasing signal.
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