Positioning error suppression and compensation method in ionospheric scintillation environment

Abstract

The invention relates to a satellite navigation signal processing technology, and provides a positioning error suppression and compensation method in an ionospheric scintillation environment. The method comprises the following steps: simulating an ionized layer scintillation signal, and performing scalar tracking to obtain basic navigation information; then, constructing a state equation and a measurement equation of a delay locked loop (VDLL), and adding a pseudo-range error caused by ionospheric flicker to a state quantity to carry out real-time prediction; carrying out optimal state estimation through a navigation filter, and introducing a fuzzy logic controller to dynamically adjust the weight of a measurement noise covariance matrix; predicting a pseudo-range at the next moment according to the state quantity output by the navigation filter, and converting the pseudo-range into a code frequency; and finally, adjusting a local pseudo code to form a closed loop, and repeating the vector tracking loop processing step until a loop ending condition is reached. According to the method, the problem of pseudo-range error accumulation caused by ionospheric flicker is solved, and an anti-interference mechanism from error source suppression to closed-loop compensation is formed by dynamically adjusting and measuring the weight of a noise covariance matrix.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation. Specifically, it particularly relates to a method for suppressing and compensating positioning errors in an ionospheric scintillation environment. Background Art

[0002] When satellite signals pass through the ionosphere, affected by the uneven electron density in the ionosphere, the signal amplitude and phase will show random fluctuations, resulting in a decrease in the signal carrier-to-noise ratio and a decline in quality, thereby causing positioning errors. In severe cases, it may even cause the receiver to lose lock and communication interruption. How to improve the robustness of the receiver so that the receiver can still accurately position after receiving satellite signals interfered by ionospheric scintillation is an urgent problem to be solved currently.

[0003] The vector tracking technology integrates the tracking and position calculation parts of each channel through a navigation filter, making full use of the information of each channel. When the signal quality of a certain channel is poor or the signal is interrupted, the information of other channels can be used for assistance, thereby improving the tracking performance of the receiver and obtaining stronger stability and anti-interference ability. For the tracking of ionospheric scintillation signals, the vector tracking method combines the tracking information of each channel and the receiver calculation results. The code phase error and carrier frequency error output by the discriminator of each channel are converted through information conversion and then input into the navigation filter for calculation to correct the user's position and speed. The corrected information is converted to obtain the control quantities of the code NCO and carrier NCO, and the local code phase and carrier frequency are adjusted to close the tracking loop. However, when the scintillation intensity is large, the signal phase fluctuates randomly greatly, and the Vector Delay Lock Loop (VDLL) cannot accurately track the signal phase information, which limits the improvement of the receiver positioning accuracy and tracking performance, resulting in the stability of the tracking loop and the positioning accuracy being affected. Moreover, the rapid amplitude fading and phase mutation caused by strong ionospheric scintillation will increase the measurement error of the navigation filter, resulting in a large error in the state prediction result, and further causing positioning errors. Therefore, a method for suppressing and compensating positioning errors in an ionospheric scintillation environment is designed to form an anti-interference mechanism from error source suppression to closed-loop compensation, reducing the positioning error of the receiver in the ionospheric scintillation environment. Summary of the Invention

[0004] The present invention proposes a method for suppressing and compensating positioning errors in an ionospheric scintillation environment, adding the pseudorange error caused by ionospheric scintillation to the state quantity for real-time prediction to achieve real-time compensation of positioning errors. In addition, a fuzzy logic controller is added to the navigation filter to control the size of the measurement noise covariance matrix, overcoming the problems of decreased stability, large positioning errors, and easy loss of lock of the traditional tracking loop when interfered by ionospheric scintillation.

[0005] The implementation steps of the present invention are as follows:

[0006] Step 1: Generation of ionospheric scintillation signals. A baseband signal is generated by a software satellite signal simulator, and combined with the amplitude and phase scintillation sequences generated by the Cornell model. Multiplication and phase superposition are respectively performed on the baseband signal, and finally, an ionospheric scintillation signal s(t) with characteristics of random fluctuations in amplitude and phase is output;

[0007] Step 2: Initialization of the vector tracking loop. Using the scalar tracking output results, provide the initial values of state prediction for the navigation filter;

[0008] Step 3: Calculate the ionospheric amplitude scintillation index S4 for each channel according to the carrier-to-noise ratio results of each channel output by scalar tracking;

[0009] Step 4: Select the error quantities of the receiver's position, velocity, clock error, and clock drift as state variables, and add the pseudorange errors caused by ionospheric scintillation in each channel to the state variables to establish the state equation of the VDLL;

[0010] Step 5: Input the ionospheric scintillation signal into the tracking loop, perform correlation with the local carrier and local code, and obtain six correlation values after integration and accumulation processing: I E 、I P 、I L and Q E 、Q P 、Q L ;

[0011] Step 6: Input the correlation values into the code loop and carrier loop discriminators, and calculate the code phase error δτ and carrier phase error

[0012] Step 7: Convert the code phase error δτ into a pseudorange error δρ through information conversion;

[0013] Step 8: Input the carrier phase error into the carrier tracking loop for tracking, and the carrier tracking loop adopts a Costas loop;

[0014] Step 9: Select the pseudorange error δρ and pseudorange rate error of each channel as measurement quantities, construct the measurement equation of the VDLL, and the pseudorange error δρ and pseudorange rate error together form the measurement matrix Z;

[0015] Step 10: Introduce a fuzzy logic controller into the navigation filter. Its inputs are the ionospheric amplitude scintillation index S4 of each channel and the innovation |γ k |. Calculate the weight coefficient β of the measurement noise covariance matrix through preset fuzzy rules, and input the measurement matrix Z and the state prediction value into the navigation filter together to achieve optimal state estimation;

[0016] Step Eleven: Use the state variables calculated by the navigation filter to predict the pseudorange value at the next moment

[0017] Step Twelve: Based on the pseudorange value Predict the code frequency at the next moment

[0018] Step Thirteen: Adjust the local pseudocode and carrier wave according to the code frequency and carrier frequency to form a closed loop, and repeat Steps Five to Thirteen until the loop end condition is reached, so as to achieve continuous positioning.

[0019] Preferably, the specific content of Step Three is as follows:

[0020] The satellite signal strength can be obtained by calculating the carrier-to-noise ratio of each channel according to the scalar tracking output The amplitude scintillation index of each channel can be obtained from the signal strength I

[0021] Preferably, the specific content of Step Four is as follows:

[0022] Taking the receiver position error, velocity error, species difference, clock drift and pseudorange error caused by ionospheric scintillation as the state vector, construct the VDLL state equation, and its expression is: δX k = FδX k-1 + ω k-1 , where F is the state transition matrix, ω k-1 is the system noise, dynamically predict and compensate the pseudorange error caused by ionospheric scintillation of each channel, and form a full-link anti-jamming mechanism from error source suppression to closed-loop compensation.

[0023] Preferably, the specific content of Step Ten is as follows:

[0024] Input the ionospheric amplitude scintillation index S4 of each channel and the innovation |γ k | into the fuzzy logic controller, and the output is the weight β of the measurement noise covariance matrix. Dynamically adjust the measurement noise covariance matrix through the preset fuzzy rules. When both the ionospheric amplitude scintillation index S4 and the innovation |γ k | are relatively large, increase the weight β of the measurement noise covariance matrix to make the measurement noise covariance matrix increase.

[0025] Preferably, the specific content of Step Eleven is as follows:

[0026] Using the state variables calculated by the navigation filter, combined with the pseudorange error caused by the troposphere and ionosphere, the formula for predicting the pseudorange value at the next moment is: where and respectively represent the satellite position at the k+1 moment and the predicted receiver position and respectively represent the pseudorange errors caused by satellite clock error, ionospheric delay, tropospheric delay, and ionospheric scintillation, represents the receiver clock error, S k takes a value of 0 or 1, indicating whether to compensate for the pseudorange error caused by scintillation. When S4 > 0.3, S k = 1; when S4 < 0.3, S k = 0.

[0027] Preferably, the membership function of the fuzzy logic controller in step ten adopts a triangular function. The domain of S4 is [0,1], which is divided into three fuzzy sets: weak ionospheric scintillation, medium ionospheric scintillation, and strong ionospheric scintillation; the domain of |γ k | is [0,200], which is divided into three fuzzy sets: mild anomaly, moderate anomaly, and severe anomaly.

[0028] Preferably, the input innovation of the fuzzy logic controller in step ten is expressed as:

[0029] As described above, a method for suppressing and compensating positioning errors in an ionospheric scintillation environment according to the present invention has the following beneficial effects:

[0030] (1) In terms of the loop structure, the noise covariance matrix is dynamically adjusted by the fuzzy logic controller. When the scintillation intensity is large, the noise covariance matrix is increased, thereby reducing the Kalman gain, decreasing the weight of the measurement value of this channel in the posterior estimation, and reducing the influence of measurement errors on the navigation filter, so as to achieve stable tracking of satellite signals in an ionospheric scintillation environment;

[0031] (2) In terms of the algorithm, the pseudorange error caused by ionospheric scintillation is introduced into the state quantity and is predicted in real time by the navigation filter, so as to achieve real-time compensation for the positioning error caused by ionospheric scintillation during the optimal state estimation process, improving the positioning accuracy and robustness of the receiver in an ionospheric scintillation environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to further illustrate the content described in the present invention, the following further detailed description of the specific embodiments of the present invention will be made in conjunction with the accompanying drawings. It should be understood that these drawings are only typical examples and should not be regarded as limiting the scope of the present invention.

[0033] Figure 1 is the overall block diagram of the method for suppressing and compensating positioning errors in an ionospheric scintillation environment provided by the embodiment of the present invention;

[0034] Figure 2 is the flowchart of the method for suppressing and compensating positioning errors in an ionospheric scintillation environment provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] The following specific examples illustrate the embodiments of the present invention. Those skilled in the art can easily understand the other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0036] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0037] The present invention proposes a method for suppressing and compensating positioning errors in the ionospheric scintillation environment. The specific idea is to predict the pseudorange error caused by ionospheric scintillation as a state quantity to achieve real-time compensation of positioning errors. In addition, a fuzzy logic controller is used to dynamically adjust the noise covariance matrix in the navigation filter to achieve stable tracking of satellite signals in the ionospheric scintillation environment. The overall block diagram of the method for suppressing and compensating positioning errors in the ionospheric scintillation environment is as Figure 1 shown, and specifically includes the following steps:

[0038] Step 1: Generation of ionospheric scintillation signals. First, a software simulator is used to simulate satellite signals, and then the Cornell model is used to generate scintillation sequences, including amplitude scintillation sequences and phase scintillation sequences. The simulated amplitude scintillation sequence is multiplied by the simulated satellite signal, and the phase scintillation sequence is added to the phase of the simulated satellite signal, thereby generating the satellite signal s(t) interfered by ionospheric scintillation.

[0039]

[0040] Among them, A0 is the signal amplitude; f IF is the intermediate frequency; f d is the Doppler frequency; is the initial carrier phase; C(t) is the spreading code; D(t) is the navigation message; n(t) is the additive Gaussian white noise; δA is the amplitude attenuation index caused by ionospheric scintillation; is the phase offset error caused by ionospheric scintillation.

[0041] Step 2: Since the navigation filter in vector tracking needs to set initial values, at least one scalar tracking is required before vector tracking to obtain basic navigation information, including receiver position, velocity, clock error, clock drift, and carrier-to-noise ratio, etc.

[0042] Step 3: Calculate the amplitude scintillation index S4 from the carrier-to-noise ratio data of each channel. The formula is as follows:

[0043]

[0044] where I = (A0δA) 2 is the signal strength, which can be represented by the carrier-to-noise ratio:

[0045]

[0046] Step 4: Establish the state equation of the vector delay lock loop (VDLL). In the present invention, the error quantities of the receiver's position, velocity, clock error, and clock drift are selected as state variables. In addition, the pseudorange error caused by ionospheric scintillation of each channel is added to the state variables to predict the pseudorange error caused by scintillation in real time and achieve real-time compensation of the positioning error. The state variables can be expressed as:

[0047]

[0048] The coordinate system of the state variables is selected as the Earth Centered Earth Fixed (ECEF) coordinate system. Where [δx k , δy k , δz k T is the position error in the xyz directions at time k, [δv x,k , δv y,k , δv z,k T is the velocity error in the xyz directions at time k, δt b,k , are the receiver clock error and clock drift at time k respectively, is the pseudorange error caused by ionospheric scintillation of each channel at time k. There are the following relationships between the state variables: δP k = δP k-1 + δV k-1 T, δV k = δV k-1 , where T is the update time of the navigation filter. According to the relationships between the state variables, the state equation can be written as:

[0049]

[0050] In the formula, [ω x,k-1 , ω​​y,k-1 , ω z,k-1 T is the position error noise at time k - 1, is the velocity error noise at time k - 1, are respectively the clock error noise, clock drift error noise and positioning error noise caused by scintillation at time k - 1. The above formula can be abbreviated as: δX k = FδX k-1 + ω k-1 , where F is the state transition matrix and ω k-1 is the system noise.

[0051] Step Five: Input the ionospheric scintillation signal into the vector tracking loop, mix it with the local carrier, and then correlate it with the locally generated early code, prompt code, and late code to obtain six correlation values: I E , I P , I L and Q E , Q P , Q L , where the expressions of I p , Q P are:

[0052]

[0053] where, ω e is the carrier frequency difference, T coh is the coherent integration time, φ e is the phase difference and θ e is the initial phase difference. n I and n Q are the noises on the I and Q branches respectively.

[0054] Step Six: Input the correlation values into the code loop and carrier loop discriminators, and calculate the code phase error δτ and carrier phase error In the present invention, the code loop uses a non - coherent early - minus - late amplitude discriminator, and the carrier loop discriminator uses a two - quadrant arctangent function discriminator. The specific formulas can be expressed as:

[0055]

[0056] where, the autocorrelation amplitudes E, L can be expressed as:

[0057]

[0058] Step Seven: Calculate the corresponding pseudorange error δρ through the code phase error δτ output by the discriminators of each channel in the loop. The specific calculation formula is:

[0059] ​

[0060] where f code represents the pseudocode rate.

[0061] Step 8: The carrier tracking loop adopts a traditional Costas phase-locked loop. The carrier phase error is input into the loop filter and fed back to the local carrier NCO after filtering.

[0062] Step 9: Select the pseudorange error and pseudorange rate error of each channel as the measurement quantities to establish the measurement equation of the VDLL. Since ionospheric scintillation causes a decrease in the quality of satellite signals and an increase in positioning errors, the pseudorange error caused by ionospheric scintillation is added to the pseudorange equation to compensate for the positioning errors caused by scintillation in real time. The pseudoranges of different satellites can be obtained from the following formula:

[0063]

[0064] In the formula, the subscript s represents the satellite, the subscript u represents the receiver, the subscript k represents the k-th moment, the superscript n represents different satellites, c is the speed of light, represents the distance between the n-th satellite and the receiver at the k-th moment, takes a value of 0 or 1, indicating whether the n-th satellite at the k-th moment needs to compensate for the pseudorange error caused by scintillation. When S4 > 0.3, When S4 < 0.3, Performing a first-order Taylor expansion of the above formula at (x i , y i , z i ) gives:

[0065]

[0066] In the formula, represents the unit vector on the line of sight between the n-th satellite and the receiver at the k-th moment, which can be expressed as:

[0067]

[0068] Therefore, the formula after Taylor expansion can be further written as:

[0069]

[0070] The above formula is the pseudorange expression. The pseudorange rate error can be obtained by taking the difference between the measured pseudorange rate and the predicted pseudorange rate. The measured pseudorange rate is calculated from the Doppler frequency, and the predicted pseudorange rate is calculated from the receiver velocity, satellite velocity, receiver clock drift, and satellite clock drift. The specific formula can be expressed as:

[0071]

[0072] In the formula, f d is the Doppler frequency, fL is the carrier frequency, v represents the velocity vector, represents the satellite clock drift of the nth satellite at time k.

[0073] If N represents the number of visible satellites, the measurement matrix at time k can be expressed as:

[0074]

[0075] The measurement equation can be written as:

[0076]

[0077] That is, Z k = H k X k + υ k , where H k is the measurement matrix, and υ k is the measurement noise.

[0078] Step Ten: The navigation filter is usually implemented by a Kalman filter, which is divided into two processes: prediction and correction. The specific implementation process is as follows:

[0079] According to the system state equation, the state quantity at the next moment is:

[0080]

[0081] The corresponding covariance matrix prediction formula is:

[0082]

[0083] Introduce the measurement value to correct the predicted value. First, calculate the Kalman gain:

[0084]

[0085] Use the measurement value and the Kalman gain to update the state estimate value:

[0086]

[0087] Finally, update the covariance matrix:

[0088]

[0089] In the above five formulas, the size of the process noise covariance matrix Q reflects the dependence degree of the navigation filter on the system state model, and the size of the measurement noise covariance matrix R reflects the dependence degree of the Kalman filter on the measurement model. Both affect the performance of the navigation filter.

[0090] Introduce a fuzzy logic controller into the navigation filter, with the input being the ionospheric amplitude scintillation index S4 of each channel and the innovation |γ k |, and calculate the weight coefficient β of the measurement noise covariance matrix through preset fuzzy rules.

[0091] The innovation represents the deviation between the measured value and the predicted value. When the innovation is small, it indicates that the system prediction model is relatively accurate; when the innovation is large, it indicates that there is a large deviation between the measured value and the predicted value, which may be caused by model errors or measurement noise. Its expression is as follows:

[0092]

[0093] The fuzzy rule table is shown as follows, where "S" represents "small", "M" represents "medium", and "B" represents "large".

[0094]

[0095] The result of the fuzzy control output is a fuzzy set, which needs to be converted deterministically to achieve control applications. The present invention adopts the centroid defuzzification strategy, and its expression is:

[0096]

[0097] where n is the number of output membership functions, α k is the k-th output value of the membership function, and u(·) represents the elements in the universe of discourse.

[0098] When the ionospheric amplitude scintillation index and the innovation of a certain channel are large, the measured value of this channel becomes untrustworthy. At this time, the weight of the measured value of this channel in the posterior estimation of the navigation filter can be reduced by increasing the noise covariance matrix, so that the navigation filter trusts the predicted value more and further reduces the influence of measurement errors on the system. The specific control formula is as follows:

[0099]

[0100] Input the measurement matrix Z k and the state predicted value into the navigation filter together to achieve the optimal state estimation.

[0101] Step Eleven: Use the state quantity calculated by the navigation filter to predict the pseudorange value at the next moment The specific calculation formula is:

[0102]

[0103] where, and represent the satellite position and the predicted receiver position at the k+1 moment respectively, and respectively represent the pseudorange errors caused by satellite clock error, ionospheric delay, tropospheric delay and ionospheric scintillation, represents the receiver clock error, S k takes a value of 0 or 1, indicating whether it is necessary to compensate for the pseudorange error caused by scintillation.

[0104] Step Twelve: According to the pseudorange value predict the code frequency at the next moment The prediction formula is:

[0105]

[0106] Step Thirteen: Adjust the local pseudocode according to the predicted code frequency value, continue to correlate with the received signal, and perform the prediction at the next moment until the loop end condition is reached.

[0107] The flowchart of the positioning error suppression and compensation method in the ionospheric scintillation environment is as Figure 2 shown.

Claims

1. A method for suppressing and compensating positioning errors in an ionospheric scintillation environment, characterized in that The method comprises the following steps: Step 1: Generate a baseband signal through a software simulator, generate an amplitude and phase scintillation sequence using the Cornell model, perform multiplication and phase superposition on the basis of the baseband signal, and finally output an ionospheric scintillation signal; Step 2: Perform scalar tracking to obtain basic navigation information, including receiver position, speed, clock error, and clock drift information; Step 3: Calculate the ionospheric amplitude scintillation index S4 of each channel according to the carrier-to-noise ratio results of each channel output by the scalar tracking; Step 4: Select the error amounts of the receiver's position, speed, clock error, and clock drift as state variables, add the pseudorange errors caused by ionospheric scintillation of each channel to the state variables, and establish a state equation of the VDLL; Step 5: Input the ionospheric scintillation signal into a tracking loop, perform correlation with a local carrier and a local code, and obtain six correlation values after integral accumulation processing; Step 6: Input the relevant values into the discriminator to obtain the phase error δτ of each channel code and the carrier phase error Step 7: Convert the code phase error δτ into a pseudorange error δρ through information conversion; Step 8: Input the carrier phase error into the carrier tracking loop for tracking. The carrier tracking loop adopts a Costas loop; Step 9: Select the pseudorange error δρ and pseudorange rate error of each channel as the measurement quantity and construct the VDLL measurement equation; Step ten: Introduce a fuzzy logic controller into the navigation filter, with the input being the ionospheric amplitude scintillation index S4 of each channel and the innovation |γ k |, and dynamically adjust the weight coefficient of the measurement noise covariance matrix through preset fuzzy rules; Step Eleven: Predict the pseudorange value at the next moment using the state quantity calculated by the navigation filter Step Twelve: According to the pseudorange value Predict the code frequency at the next moment Step 13: Adjust the local pseudocode and carrier according to the code frequency and carrier frequency to form a closed loop, and repeat Steps 5 to 13 until the loop end condition is reached.

2. The method for suppressing and compensating positioning errors in an ionospheric scintillation environment according to claim 1, wherein The specific content of Step 3 is as follows: The carrier-to-noise ratio of each channel can be tracked according to the scalar, and the satellite signal strength can be obtained. The amplitude scintillation index of each channel can be obtained from the signal strength I.

3. The method for suppressing and compensating positioning errors in the ionospheric scintillation environment according to claim 1, wherein The specific content of Step 4 is as follows: Taking the receiver position error, velocity error, clock offset, clock drift, and pseudorange error caused by ionospheric scintillation as the state vector, a VDLL state equation is constructed, and its expression is: δX k = FδX k-1 + ω k-1 , where F is the state transition matrix, and ω k-1 is the system noise. The pseudorange error caused by ionospheric scintillation in each channel is dynamically predicted and compensated to form a full-link anti-jamming mechanism from error source suppression to closed-loop compensation.

4. The method for suppressing and compensating positioning errors in the ionospheric scintillation environment according to claim 1, wherein The specific content of Step 10 is as follows: The ionospheric amplitude scintillation index S4 of each channel and the innovation |γ k | are input into a fuzzy logic controller, and the output is the weight β of the measurement noise covariance matrix. The measurement noise covariance matrix is dynamically adjusted through preset fuzzy rules. When both the ionospheric amplitude scintillation index S4 and the innovation |γ k | are relatively large, the weight β of the measurement noise covariance matrix is increased to enlarge the measurement noise covariance matrix.

5. The method for suppressing and compensating positioning errors in the ionospheric scintillation environment according to claim 1, wherein The specific content of Step 11 is as follows: The formula for predicting the pseudorange value at the next moment by using the state quantity calculated by the navigation filter and combining the pseudorange errors caused by the troposphere and ionosphere is as follows: Wherein, and respectively represent the satellite position and the predicted receiver position at the (k + 1)-th moment, and respectively represent the pseudorange errors caused by satellite clock error, ionospheric delay, tropospheric delay and ionospheric scintillation, represents the receiver clock error, S k takes a value of 0 or 1, indicating whether it is necessary to compensate for the pseudorange error caused by scintillation. When S4 > 0.3, S k = 1; when S4 < 0.3, S k = 0.

6. The method for suppressing and compensating positioning errors in the ionospheric scintillation environment according to claim 4, characterized in that, The membership function of the fuzzy logic controller adopts a triangular function. The universe of discourse of S4 is [0, 1], which is divided into three fuzzy sets: weak ionospheric scintillation, medium ionospheric scintillation, and strong ionospheric scintillation; |γ k | The universe of discourse of is [0, 200], which is divided into three fuzzy sets: mild anomaly, moderate anomaly, and severe anomaly.

7. The method for suppressing and compensating positioning errors in the ionospheric scintillation environment according to claim 4, wherein The input innovation of the fuzzy logic controller is expressed as:

Images