GNSS anti-deception jamming method and system based on multi-correlator cooperative REKF

Abstract

The invention relates to the field of GNSS anti-deception jamming, in particular to a GNSS anti-deception jamming method and a GNSS anti-deception jamming system based on multi-correlator collaborative REKF. According to the GNSS anti-deception jamming method and the GNSS anti-deception jamming system based on multi-correlator collaborative REKF, chip intervals are subdivided in a tracking loop, the number of correlators is increased, and weighting processing is carried out by utilizing output results of the correlators. The algorithm shows advantages in the aspects of detection probability, detection accuracy and detection range. Meanwhile, the detection result is transmitted to the positioning calculation module, and different positioning methods are selected according to the difference of the detection result, so that the PVT solution can still keep high precision in a deception environment, and the problem of low precision of the existing deception interference resisting method is solved.

Description

Technical Field

[0001] The present invention relates to the field of GNSS anti-spoofing interference, and particularly to a GNSS anti-spoofing interference method and system with multi-correlator collaborative REKF. Background Art

[0002] The Global Navigation Satellite System (GNSS) provides continuous positioning, velocity measurement, and time service for users by broadcasting information such as pseudorange and orbital parameters from satellites. GNSS plays an indispensable role, especially in the fields of aviation, navigation, and ground transportation. However, due to factors such as low signal power, public signal characteristics, and time sensitivity, the global navigation satellite system is vulnerable to various forms of interference. To ensure the security of GNSS spatio-temporal information, researching effective anti-spoofing technologies for navigation has become the core issue in the field of satellite navigation.

[0003] The SQM technology judges whether there is spoofing interference by evaluating the abnormal conditions of the correlation peak. The classic SQM algorithms are mainly the Delta and Ratio algorithms, and both of these algorithms construct detection indicators based on the output results of the in-phase branch. The Ratio algorithm judges the abnormality of the signal by detecting the sharpness of the correlation peak, and the Delta algorithm mainly realizes the detection by detecting the symmetry of the correlation peak. C. Sun et al. proposed a spoofing detection method based on the moving variance of the SQM index, which realizes the detection of spoofing interference by detecting the fluctuation in the interaction stage of the real signal and the spoofing signal. Pirsiavash et al. introduced a two-dimensional SQM detection algorithm, which combines the Doppler frequency and the code delay. Dehghanian et al. proposed a detection method based on power monitoring. Wang Wenyi et al. proposed a multi-star joint spoofing detection algorithm based on the slope of the zero-crossing point of the S curve, and distinguishes multipath interference and spoofing attacks by checking the consistency of satellites in different directions.

[0004] Most of the above SQM algorithms only utilize the information of three correlators, and there are certain limitations in the information utilization rate, which limits the detection accuracy. Summary of the Invention

[0005] The purpose of the present invention is to provide a GNSS anti-spoofing interference method and system with multi-correlator collaborative REKF, aiming to solve the problem of low accuracy of existing anti-spoofing interference methods.

[0006] To achieve the above purpose, in the first aspect, the present invention provides a GNSS anti-spoofing interference method with multi-correlator collaborative REKF, including the following steps:

[0007] Input the pre - captured Doppler and code phase offset values into the carrier NCO and code NCO;

[0008] Generate two branches of i and q, multiply them with the local pseudo - code, perform correlation operations, and accumulate the results to generate I p and Q p correlation values;

[0009] Configure a set of correlator groups with specific chip intervals, and then perform linear combination and weighted calculation on the output results of each channel to obtain the calculation result;

[0010] Process the calculation result to obtain the proposed algorithm and perform detection;

[0011] Derive the detection threshold under the condition of a preset false - alarm probability, and solve the corresponding detection probability value;

[0012] Further judge the detection probability, set a reasonable threshold. If the detection probability is lower than the threshold, it is determined that there is no spoofing interference currently, and the judgment result 0 is output; otherwise, it is determined that there is spoofing interference, and the judgment result 1 is output;

[0013] Send the judgment result to the positioning and solution module, and select different positioning methods according to the difference of the detection results;

[0014] If a spoofing signal is detected, enable the robust extended Kalman filter algorithm for positioning and solution. If no spoofing is detected, use the least - squares method to calculate the position.

[0015] Among them, in "Generate two branches of i and q, multiply them with the local pseudo - code, perform correlation operations, and accumulate the results to generate I p and Q p correlation values", the generation of two branches of i and q is that the carrier NCO generates a in - phase carrier and multiplies it with the input intermediate - frequency signal to obtain the i branch, and the quadrature - phase carrier multiplies it with the input intermediate - frequency signal to obtain the q branch. After sequentially performing carrier stripping and pseudo - code stripping on the input intermediate - frequency signal, the i and q branches perform correlation accumulation to obtain I p 、Q p two branches.

[0016] Among them, in "If a spoofing signal is detected, enable the robust extended Kalman filter algorithm for positioning and solution. If no spoofing is detected, use the least - squares method to calculate the position", the least - squares method for calculating the position needs to extract the original code pseudo - range observables from the observation file, calculate the satellite coordinates at the receiving moment according to the ephemeris file, solve the pseudo - range observable equation, and perform least - squares iterative solution of the receiver coordinates.

[0017] Among them, in the statement "If a spoofing signal is detected, the robust extended Kalman filter algorithm is enabled for positioning solution. If no spoofing is detected, the least squares method is used to calculate the position", the robust extended Kalman filter algorithm uses the least squares iterative solution result as the initial value, sets the initial state covariance matrix, predicts the state of the receiver at the next moment using the motion model, corrects the predicted state using the observation data at the current moment, fuses the predicted state and the observation data to obtain the optimal state estimate at the current moment, and enhances the positioning reliability through robust adjustment, and finally obtains the positioning result.

[0018] In the second aspect, a GNSS anti-spoofing interference system with multi-correlator collaborative REKF is used for the multi-correlator collaborative REKF GNSS anti-spoofing interference method described in the first aspect, and includes a signal reception and processing module, a signal interception module, a carrier loop module, a code loop module, a PRN code generation module, an LS positioning solution module, an LS velocity measurement module, and a REKF algorithm solution module;

[0019] The signal reception and processing module multiplies the in-phase and quadrature local carrier signals generated by the carrier NCO with the digital intermediate frequency signal respectively to obtain the i and q baseband signals, and then multiplies them with the local pseudo-code and accumulates them to obtain the I p and Q p correlation values;

[0020] The signal interception module is used to intercept the signal after carrier stripping. After frame synchronization, a signal with a cycle length is taken each time for subsequent signal tracking;

[0021] The carrier loop module is used to adjust the local carrier phase to make it consistent with the input signal frequency in real time;

[0022] The code loop module is used to adjust the local code phase to make it consistent with the input signal code phase in real time;

[0023] The PRN code generation module is used to generate a PRN code consistent with the received signal;

[0024] The LS positioning solution module is used to load the satellite ephemeris, obtain the position information and construct the pseudo-range observation equation, and use the least squares method for solution to finally obtain the receiver position and clock offset;

[0025] The LS velocity measurement module is used to load the satellite ephemeris, obtain the velocity information and construct the Doppler observation equation, and use the least squares method for solution to finally obtain the receiver velocity and clock drift; The REKF algorithm solution module is used to maintain high-precision PVT solution under spoofing interference.

[0026] A GNSS anti-spoofing interference method using multiple correlators to cooperate with REKF includes the following steps: Input the values of Doppler and code phase offset obtained in advance during acquisition into the carrier NCO and code NCO; Generate two branches, i and q, multiply them with the local pseudo-code, perform correlation operations, and accumulate the results to generate I p and Q p correlation values; Configure a set of correlator groups with specific chip intervals, then perform linear combination and weighted calculation on the output results of each channel to obtain the calculation results; Process the calculation results to obtain the proposed algorithm and perform detection; Derive the detection threshold under the condition of a preset false alarm probability and solve the corresponding detection probability value; Further judge the detection probability, set a reasonable threshold. If the detection probability is lower than the threshold, it is determined that there is no spoofing interference currently, and the judgment result 0 is output; Otherwise, it is determined that there is spoofing interference, and the judgment result 1 is output; Send the judgment result to the positioning and solution module, and select different positioning methods according to the difference in detection results; If a spoofing signal is detected, enable the robust extended Kalman filter algorithm for positioning and solution. If no spoofing is detected, use the least squares method to calculate the position. By subdividing the chip interval and increasing the number of correlators in the tracking loop in the present invention, and performing weighted processing on the output results of multiple correlators, compared with the traditional SQM spoofing detection algorithm, the proposed algorithm shows advantages in terms of detection probability, detection accuracy, and detection range. At the same time, the detection result is transmitted to the positioning and solution module, and different positioning methods are selected according to the difference in the detection result, so that the PVT solution can still maintain high accuracy in a spoofing environment, thus solving the problem of low accuracy of existing anti-spoofing interference methods. Brief Description of the Drawings

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0028] Figure 1 is a flowchart of a GNSS anti-spoofing interference method using multiple correlators to cooperate with REKF provided by the present invention.

[0029] Figure 2 is a flowchart of obtaining the receiver speed information using the least squares method.

[0030] Figure 3 is a flowchart of the REKF algorithm.

[0031] Figure 4It is a schematic diagram of a GNSS anti-spoofing interference system with multiple correlators collaborating with REKF provided by the present invention.

[0032] In the figure: 1 - signal receiving and processing module, 2 - signal interception module, 3 - carrier loop module, 4 - code loop module, 5 - PRN code generation module, 6 - LS positioning and solution module, 7 - LS speed measurement module, 8 - REKF algorithm solution module. Specific implementation mode

[0033] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, in which the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present invention, but should not be construed as a limitation to the present invention.

[0034] Please refer to Figures 1 to 3 , on the first aspect, the present invention provides a GNSS anti-spoofing interference method with multiple correlators collaborating with REKF, including the following steps:

[0035] S1 Input the values of Doppler and code phase offset obtained in advance during acquisition into the carrier NCO and code NCO;

[0036] Specifically, the prerequisite for the parameters that must be obtained before tracking is the Doppler frequency shift and code phase offset generated during the satellite signal process. Inputting them into the carrier NCO and code NCO can ensure that the subsequent tracking signal can be basically aligned with the local pseudo-code phase, reducing the loss of signal-to-noise ratio.

[0037] Taking the GPS L1 C / A signal as an example, the performance of a single-antenna receiver when affected by an induced spoofing signal interference was studied. At this time, the intermediate-frequency signal contains the real signal, spoofing signal, and noise part, and can be expressed as:

[0038] x(t) = x r (t) + x s (t) + n(t)

[0039] In the above expression, represents the real signal, is the spoofing signal, and n(t) is additive white Gaussian noise with a mean of 0. Given that the two have the same signal structure, the real signal and spoofing signal can be expressed in the following form:

[0040]

[0041] Among them, the subscripts r and s are the real signal and spoofing signal respectively. p l (t) is the signal power; D lis signal navigation data; C is the signal pseudo-random spreading code; τ l is the code delay of the signal; represents the Doppler frequency shift caused by relative motion; φ l is the initial carrier phase.

[0042] S2 generates two branches of i and q, multiplies them with the local pseudo-code, performs correlation operations, and accumulates the results to generate I p and Q p correlation values;

[0043] The generation of the two branches of i and q is that the carrier NCO generates an in-phase carrier multiplied by the input intermediate-frequency signal to obtain the i branch, and an orthogonal-phase carrier multiplied by the input intermediate-frequency signal to obtain the q branch. After the input intermediate-frequency signal is successively subjected to carrier stripping and pseudo-code stripping, the i and q branches are subjected to correlation accumulation to obtain the I p and Q p two branches.

[0044] Specifically, the carrier NCO generates two carriers of COS and SIN with Doppler shift, which are multiplied by the intermediate-frequency input signal to obtain the i branch containing all signal energy and the q branch containing only noise respectively. It is used to adjust the carrier tracking loop and the code tracking loop so that the signal power output by the i branch is maximized during the entire tracking process, and at the same time, the q branch output contains only noise as much as possible, thereby reducing the loss of signal intensity. At this time, the outputs of I P and Q P are respectively:

[0045]

[0046] P represents the signal power; τ c is the local spreading code phase; is the replica signal carrier phase; R(.) is the cross-correlation function.

[0047] S3 configures a set of correlator groups with specific chip intervals, and then performs linear combination and weighted calculation on the output results of each channel to obtain the calculation result;

[0048] Specifically, a set of correlator groups with specific chip intervals is configured, and then linear combination and weighted calculation are performed on the output results of each channel. Three groups of correlators with chip intervals of 0.4, 0.5, and 0.6 chips are selected. The correlator with a chip interval closer to the instant code interval can be regarded as a narrowband correlator, and vice versa as a wideband correlator. Since the output of the wideband correlator is more vulnerable to noise, an inverse proportional relationship is used to set the weights. By reducing the weights of the wideband correlators and increasing the weights of the narrowband correlators, the error can be effectively reduced and the detection accuracy can be improved.

[0049] S4 processes the calculation results to obtain the proposed algorithm and conducts detection;

[0050] Specifically, the results obtained from the weighted calculation in S3 are processed to obtain the proposed algorithm and conduct detection.

[0051] Its algorithm expression is:

[0052]

[0053] Where: I E (n), I L (n) and I P (n) are respectively:

[0054]

[0055] The experiment selects the medium-level spoofing interference data in TEXBAT as the experimental data set for result analysis and performance comparison. DS7 is used for medium-level spoofing detection. DS7 is an I / Q type spoofing data set with a traction strategy of carrier synchronization and a data length of 468 s. Carrier synchronization requires that the spoofing signal and the target receiver be completely consistent in frequency and phase. This synchronization state improves the effectiveness of the spoofing attack. By running the data set through the software receiver, the output results are obtained.

[0056] S5 derives the detection threshold under the condition of a preset false alarm probability and solves the corresponding detection probability value;

[0057] Specifically, in the field of satellite navigation spoofing detection, spoofing detection can be regarded as a binary hypothesis testing problem. H0 represents no spoofing interference effect, and H1 represents being affected by spoofing interference. Under the condition of a preset false alarm probability, first the detection threshold is derived, and then the corresponding detection probability value is solved. By comparing the statistic with the preset threshold value, if the statistical value is within the threshold interval, it is determined that there is no spoofing interference effect; if the value exceeds this interval, it is determined that there is spoofing interference. Theoretically, the false alarm probability P f and the detection probability P d can be derived from the following formula.

[0058]

[0059] Among them, Th1 and Th u respectively represent the lower and upper limits of spoofing detection. Given that the spoofing signal parameters are unknown and change with time, statistical methods are used to calculate the detection probability. The formulas for calculating the false alarm probability and the detection probability using statistical methods are

[0060]

[0061] In the above formula, sqmmc(t) is the detected output value, num(.) represents the number of samples that meet the conditions, M represents the total number of samples without spoofing interference, and N corresponds to the total number of samples when spoofing interference occurs. Under the condition of a preset false alarm probability, first obtain the detection threshold through formula derivation, and then solve the corresponding detection probability value. To accurately calculate the detection probability, first divide the original data sequence into k equal-length sub-windows, and each window contains ω sample points. Within each sub-window, compare all the sampling points within the interval with a preset threshold one by one, and count the number of sample points exceeding the threshold. By calculating the ratio of this number to the total number of sampling points within the current sub-window, obtain the detection probability under this sub-window. Subsequently, the window slides forward to ensure that the sample points in the new window do not overlap with the previous window. Finally, output the detection probability corresponding to each window.

[0062] S6 further judges the detection probability, sets a reasonable threshold. If the detection probability is lower than the threshold, it is determined that there is no spoofing interference currently, and the judgment result 0 is output; otherwise, it is determined that there is spoofing interference, and the judgment result 1 is output.

[0063] Specifically, further judge the detection probability obtained in S5, set a reasonable threshold. If the detection probability is lower than the threshold, it is determined that there is no spoofing interference currently, and the judgment result 0 is output; otherwise, it is determined that there is spoofing interference, and the judgment result 1 is output.

[0064] S7 sends the judgment result to the positioning and solution module, and selects different positioning methods according to the difference in detection results;

[0065] Specifically, the judgment result obtained in S6 is sent to the positioning and solution module, and different positioning methods are selected according to the difference in detection results.

[0066] S8 If a spoofing signal is detected, the robust extended Kalman filter algorithm is used for positioning and solution. If no spoofing is detected, the least squares method is used to calculate the position.

[0067] The least squares method for calculating the position needs to extract the raw code pseudorange observables from the observation file, calculate the satellite coordinates at the receiving time according to the ephemeris file, solve the pseudorange observable equation, and perform least squares iterative solution to calculate the receiver coordinates.

[0068] For the robust extended Kalman filter algorithm, use the least squares iterative solution result as the initial value, set the initial state covariance matrix, predict the state of the receiver at the next moment using the motion model, correct the predicted state using the observation data at the current moment, fuse the predicted state and the observation data to obtain the optimal state estimate at the current moment, and enhance the positioning reliability through robust adjustment, and finally obtain the positioning result.

[0069] Specifically, if a spoofing signal is detected, the Robust Extended Kalman Filter (REKF) algorithm is enabled for positioning solution. If no spoofing is detected, the least squares method is used to calculate the position.

[0070] When calculating the position using the least squares method, the position of the satellite in the ECEF (Earth-Centered, Earth-Fixed) coordinate system [x s , y s , z s needs to be calculated based on the reception time of the observation file and the orbital parameters in the ephemeris. Since signal propagation takes time, the reception time needs to be corrected for the propagation delay. After deducting errors such as ionospheric, tropospheric, and Earth rotation errors, the pseudorange observation equation is solved, and finally the position of the receiver [x i , y i , z i and clock offset and other information are obtained.

[0071] The implementation of REKF includes steps such as state prediction, measurement update, and adaptive adjustment. The following are the detailed descriptions of the state vector, state transition model, measurement model, and REKF algorithm:

[0072] The REKF algorithm needs to use the least squares iterative solution result as the initial value and set the initial state covariance matrix. The state vector of the initial value can be defined as

[0073] X = [x, y, z, δt u , v x , v y , v z , δf u T

[0074] Its initial state vector not only requires the position information of the receiver but also the velocity information of the receiver. For example, Figure 2 , the specific flowchart for obtaining the receiver velocity information using the least squares method.

[0075] The state transition model is used to predict the state of the next epoch:

[0076] X k+1 = Φ k+1,k X k + W k

[0077] Φ k+1,k is the state transition matrix, which describes the change of the state over time. W k is the process noise.

[0078] For the dynamic models of position and velocity, the state transition matrix can be expressed as:

[0079] ​

[0080] The measurement model relates the pseudorange measurement to the state vector.

[0081] L k = H k ΔX k + E k

[0082] The expressions of each parameter are as follows:

[0083] is the difference between the measured pseudorange and the predicted pseudorange.

[0084] H k is the geometric matrix, representing the linearized relationship between the measurement and the state, and ΔX k is the state deviation, and E k is the measurement noise.

[0085] Figure 3 Figure

[0086] Step 1: Initialize the state vector and the covariance matrix

[0087] Step 2: Predict the state and covariance at the next moment.

[0088]

[0089] Step 3: Calculate the Kalman gain matrix.

[0090]

[0091] where is the equivalent covariance matrix of the measurement noise, which is equal to initially and will be adjusted according to the adaptive factor in subsequent steps.

[0092] Step 4: Update the state vector and the covariance matrix.

[0093]

[0094] Step 5: Calculate the innovation vector to evaluate the estimation quality.

[0095]

[0096] If (λ is the preset threshold), then accept the current estimate and go to the prediction at the next moment; otherwise, enter the adaptive adjustment.

[0097] Step 6: When there are outliers in the innovation vector, calculate the adaptive factor Subsequently, by adjusting the diagonal elements of the measurement noise covariance matrix: Then return to Step 3 to recalculate the Kalman gain and state update.

[0098] Please refer to Figure 4 , in a second aspect, a GNSS anti-spoofing interference system with multiple correlators collaborating REKF is used for the GNSS anti-spoofing interference method with multiple correlators collaborating REKF described in the first aspect, and includes a signal reception and processing module 1, a signal interception module 2, a carrier loop module 3, a code loop module 4, a PRN code generation module 5, an LS positioning and solution module 6, an LS velocity measurement module 7, and a REKF algorithm solution module 8;

[0099] The signal reception and processing module 1 multiplies the in-phase and quadrature local carrier signals generated by the carrier NCO with the digital intermediate frequency signal respectively to obtain the i and q baseband signals, and then multiplies them with the local pseudo-code and accumulates to obtain I P and Q P correlation values;

[0100] The signal interception module 2 is used to intercept the signal after carrier stripping. After frame synchronization, a signal with a cycle length is taken each time for subsequent signal tracking;

[0101] The carrier loop module 3 is used to adjust the local carrier phase to make it consistent with the input signal frequency in real time;

[0102] The code loop module 4 is used to adjust the local code phase to make it consistent with the input signal code phase in real time;

[0103] The PRN code generation module 5 is used to generate a PRN code consistent with the received signal;

[0104] The LS positioning and solution module 6 is used to load the satellite ephemeris, obtain the position information and construct the pseudo-range observation equation, and use the least squares method for solution to finally obtain the receiver position and clock offset;

[0105] The LS velocity measurement module 7 is used to load the satellite ephemeris, obtain the velocity information and construct the Doppler observation equation, and use the least squares method for solution to finally obtain the receiver velocity and clock drift; the REKF algorithm solution module 8 is used to maintain high precision of the PVT solution under spoofing interference.

[0106] In this embodiment, the signal reception and processing module 1 is used to obtain the discrete digital intermediate frequency signal, multiply the digital intermediate frequency signal with the local cosine carrier to obtain the i-channel signal; similarly, multiply it with the local sine carrier to obtain the q-channel signal. Then multiply it with the local pseudo-code and accumulate to obtain I Pand Q P For the relevant values, the signal model is expressed as follows:

[0107]

[0108] The signal intercepting module 2 is used to intercept the signal after carrier stripping. After frame synchronization, a signal with a cycle length of 1 ms is taken each time for subsequent signal tracking. The carrier loop module 3 includes a carrier loop discriminator, a filter, and a carrier NCO, and is used to adjust the local carrier phase to make it consistent with the input signal frequency in real time. The code loop module 4 includes a code loop discriminator, a filter, and a carrier NCO, and is used to adjust the local code phase to make it consistent with the input signal code phase in real time. The PRN code generation module 5 is used to generate a PRN code consistent with the received signal. The LS positioning and solution module 6 loads the satellite ephemeris, obtains the position information and constructs a pseudorange observation equation, and uses the least squares method for solution to finally obtain the receiver position and clock error. The LS velocity measurement module 7 loads the satellite ephemeris, obtains the velocity information and constructs a Doppler observation equation, and uses the least squares method for solution to finally obtain the receiver velocity and clock drift. The REKF algorithm solution module 8 includes steps such as state prediction, measurement update, and adaptive adjustment. Its robustness ensures stability when spoofing signals exist, and may reduce the impact of abnormal measurements by adaptively adjusting the filtering parameters, so as to achieve high-precision PVT solution under spoofing interference.

[0109] The above-disclosed is only a preferred embodiment of the GNSS anti-spoofing interference method and system of a multi-correlator collaborative REKF of the present invention. Of course, the scope of the rights of the present invention cannot be limited thereby. Those of ordinary skill in the art can understand all or part of the processes of implementing the above embodiments, and the equivalent changes made according to the claims of the present invention still fall within the scope covered by the invention.

Claims

1. A GNSS anti-spoofing jamming method using multiple correlators and REKF, characterized in that: The following steps are involved: Input the Doppler and code phase offset values obtained in advance in the capture into the carrier NCO and code NCO; Generate two branches, i and q, and multiply them with the local pseudo code, perform related operations, and accumulate the results to generate I p and Q p Related values; A set of correlators with a specific chip interval is configured, and then the output results of each channel are linearly combined and weighted to obtain the calculation result; Process the calculation results to obtain the proposed algorithm and perform testing; Under the condition of preset false alarm probability, the detection threshold value is derived and the corresponding detection probability value is solved; Further judge the detection probability and set a reasonable threshold. If the detection probability is lower than the threshold, it is determined that there is no deception interference and the judgment result is 0; otherwise, it is determined that there is deception interference and the judgment result is 1. The judgment results are sent to the positioning solution module, and different positioning methods are selected according to the differences in the detection results; If a deception signal is detected, the robust extended Kalman filter algorithm is enabled for positioning solution. If no deception is detected, the least squares method is used to calculate the position.

2. The GNSS anti-spoofing jamming method of multiple correlators and REKF according to claim 1, characterized in that: In "generate two branches i and q, multiply them with local pseudo code, perform relevant operations, and accumulate the results to generate I p and Q p In the correlation value, the two branches i and q are generated as carriers. The NCO generates an in-phase carrier and multiplies the input intermediate frequency signal to obtain the i branch, and the quadrature phase carrier and the input intermediate frequency signal are multiplied to obtain the q branch. After the input intermediate frequency signal is sequentially stripped of the carrier and the pseudo code, the i and q branches are correlated and accumulated to obtain I p , Q p Two branches.

3. The GNSS anti-spoofing jamming method of multiple correlators and REKF according to claim 1, characterized in that: In the section "If a spoofing signal is detected, the robust extended Kalman filter algorithm is enabled for positioning. If no spoofing is detected, the least squares method is used to calculate the position," the least squares method for calculating position requires extracting the raw pseudorange observations from the observation file, calculating the satellite coordinates at the reception time based on the ephemeris file, solving the pseudorange observation equation, and using the least squares iterative method to calculate the receiver coordinates.

4. The GNSS anti-spoofing jamming method of multiple correlators and REKF according to claim 1, wherein: In the section "If a spoofing signal is detected, the robust extended Kalman filter algorithm is enabled for positioning solution; if no spoofing is detected, the least squares method is used to calculate the position," the robust extended Kalman filter algorithm uses the least squares iterative solution result as the initial value, sets the initial state covariance matrix, uses the motion model to predict the state of the receiver at the next moment, uses the observation data at the current moment to correct the predicted state, fuses the predicted state and the observation data to obtain the optimal state estimate at the current moment, and enhances positioning reliability through robust adjustment to finally obtain a positioning result.

5. A GNSS anti-spoofing jamming system with multiple correlators and REKF, used in the GNSS anti-spoofing jamming method with multiple correlators and REKF according to any one of claims 1 to 4, characterized in that: It includes signal receiving and processing module, signal interception module, carrier loop module, code loop module, PRN code generation module, LS positioning solution module, LS speed measurement module and REKF algorithm solution module; The signal receiving and processing module is used to obtain a discrete digital intermediate frequency signal. The carrier NCO generates an in-phase and orthogonal local carrier signal, which is multiplied by the digital intermediate frequency signal to obtain the i and q baseband signals, which are then multiplied by the local pseudo code and accumulated to obtain I p and Q p Related values; The signal interception module is used to intercept the signal after stripping the carrier, and after frame synchronization, take a signal of a cycle length each time for subsequent signal tracking; The carrier loop module is used to adjust the local carrier phase so that it is consistent with the input signal frequency in real time; The code ring module is used to adjust the local code phase so that it is consistent with the input signal code phase in real time; The PRN code generation module is used to generate a PRN code consistent with the received signal; The LS positioning solution module is used to load the satellite's ephemeris, obtain position information and construct a pseudorange observation equation, and solve it using the least squares method to ultimately obtain the receiver position and clock error; The LS velocity measurement module is used to load the satellite's ephemeris, obtain velocity information and construct the Doppler observation equation, solve it using the least squares method, and ultimately obtain the receiver velocity and clock drift; The REKF algorithm solution module is used to ensure that the PVT solution can maintain high accuracy under deception interference.

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