Deception signal identification and filtering method of shipborne positioning terminal

Abstract

The invention discloses a deception signal filtering method for a shipborne positioning terminal, and the method comprises the steps: firstly obtaining original observation information, and obtaining the original observation information, such as position, speed, pseudo-range, pseudo-range rate, ephemeris, time, signal power and the like, from a GNSS baseband signal processing module and an INS resolving module; secondly, deception jamming is identified based on the original observation information and the deception jamming characteristic quantity; deception jamming information is filtered again, and Kalman filtering is carried out on pseudo-range and pseudo-range rate observed quantity information to obtain error correction information of INS position, speed and the like without deception jamming; and finally, correcting the position, speed and other information calculated by the INS according to the error correction information, and outputting the position, speed and other information fused by the integrated navigation system. According to the deception jamming signal filtering method provided by the invention, GNSS deception jamming attacks can be quickly and accurately identified, GNSS deception jamming information can be filtered, and ships on the sea can be ensured to correctly sail according to a planned route.

Description

Technical Field

[0001] The invention relates to the technical field of satellite navigation and positioning, and in particular to a method for identifying and filtering deceptive signals of a shipborne positioning terminal. Background Art

[0002] GNSS provides global positioning, navigation and timing services, which can greatly facilitate ships' navigation at sea. However, GNSS also has certain security risks. For example, GNSS signals are highly vulnerable and can be interfered with by malicious attacks from criminals, causing the receiver to produce incorrect positioning. For ships sailing at sea, if the navigation system is successfully deceived by criminals, it will pose a huge threat to the safety of personnel and property on board. Therefore, it is an urgent problem to study how to improve the deception interference detection capability of ship navigation systems. Common deception interference detection methods include single antenna receiver signal feature deception interference detection, signal spatial correlation deception interference detection, machine learning deception interference detection, and encryption authentication deception interference detection.

[0003] The deception interference detection based on the signal characteristics of a single antenna receiver identifies deception by detecting features such as signal power, noise power, carrier-to-noise ratio, pseudorange, and positioning results. The disadvantage is that it is sometimes difficult to identify interference for synchronous deceptions that are more concealed. The deception interference detection method based on signal spatial correlation has high requirements for the receiver antenna, and the antenna is large in size and high in cost. The deception interference detection method based on machine learning has a large amount of computation and can only detect deception interference in trained scenarios, while the actual scenarios are complex and changeable, resulting in unsatisfactory interference identification effects. The deception interference detection method based on encryption and authentication requires modifications to the existing satellite signal system, and this solution is not feasible.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0005] In view of the above problems, the present invention provides a deception signal identification and filtering method for a shipborne positioning terminal, which can quickly and accurately identify GNSS deception interference attacks and filter out GNSS deception interference information, ensuring that ships at sea navigate correctly according to the planned routes.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The object of the present invention is to provide a method for identifying and filtering fraudulent signals of a shipborne positioning terminal, comprising:

[0008] S1, original observation information acquisition: obtain the original position, velocity, pseudorange, pseudorange rate, carrier-to-noise ratio, ephemeris, time, signal power and other information from the GNSS baseband signal processing module and INS solution module respectively;

[0009] S2, deception interference identification: detect deception interference based on original observation information and deception interference feature identification;

[0010] S3, deception interference information filtering and combined navigation Kalman filtering: According to the deception interference identification results of S2, the pseudo-range and pseudo-distance information with deception interference risks and those affected by deception interference are filtered out, and the remaining pseudo-range and pseudo-distance information is selected for Kalman filtering to obtain the error correction information such as the position, speed, and attitude of the INS;

[0011] S4, output the position, speed, attitude and other information after error correction: correct the position, speed and other information solved by INS according to the error correction information provided by S3, and output the position, speed and other information after the integrated navigation system is integrated.

[0012] Further, step S1 is specifically as follows:

[0013] The GNSS RF front-end module receives satellite signals and down-converts them into baseband signals. The baseband signal processing module captures, tracks, and locates the signals. It extracts original observation information such as position, velocity, pseudorange, pseudorange rate, and signal power from the receiver.

[0014] INS performs calculations based on the measurement data provided by the IMU. It extracts raw observation information such as position, velocity, pseudorange, and pseudorange rate from the inertial navigation system.

[0015] Further, step S2 is specifically as follows:

[0016] Deception interference is mainly divided into synchronous deception and asynchronous deception interference, wherein synchronous deception interference signal power is small and concealment is high; while asynchronous deception interference relies on high power to achieve strong correlation interference to capture the target receiver loop. Therefore, asynchronous deception interference can be identified according to signal power characteristics; and for synchronous deception interference, the present invention provides a method for identifying synchronous deception interference by combining multiple information quantities based on normalized position, velocity, pseudorange, and pseudorange rate deviation for sliding correlation accumulation, which has high anti-noise performance and deception interference identification success rate, and can quickly identify deception interference.

[0017] 1. Asynchronous deception interference identification:

[0018] Asynchronous deception jamming achieves strong jamming by transmitting high-power deception signals. The GNSS signals emitted by satellites in the sky have very low power after being transmitted over long distances to the ground. For example, the power of ground GPS signals is around -160dBW. Therefore, asynchronous deception jamming can be identified by detecting the power of GNSS signals.

[0019] 2. Synchronous deception interference identification:

[0020] In general, the signal power of synchronous deception interference is slightly higher than the real signal power when invading the GNSS receiver loop, and will not cause the GNSS receiver tracking loop to lose lock. It is highly concealed and difficult to identify synchronous deception interference through power characteristics. However, deception interference will cause the positioning results of the GNSS receiver to be inconsistent with the current real position, speed and other information, while the INS system relies on mechanical integration to solve the position and speed information and is not affected by deception interference. Therefore, consider using the inconsistency of the observation information provided by GNSS and INS to identify deception interference. The observation information selected to identify deception interference mainly includes position, speed, pseudorange and pseudorange rate. By performing sliding correlation accumulation on the observation information deviation, deception interference can be quickly identified. At the same time, due to the selection of a certain length of data for sliding correlation accumulation and the selection of multiple correlation detection quantities for deception identification estimation, it has high anti-noise performance and deception interference identification success rate.

[0021] (1) Normalized position-related detection quantity

[0022] The calculation formula of normalized position-related detection quantity is:

[0023]

[0024] Where L represents the number of sliding observation data points; σ p It indicates the maximum deviation between the position solved by the GNSS receiver and the position solved by the INS when the integrated navigation system is working normally without interference; p GNSS Position information provided by GNSS receiver; INS is the location information provided by INS; k represents the current system time.

[0025] (2) Normalized speed-related detection quantity

[0026]

[0027] In the formula, σ v It indicates the maximum deviation between the speed solved by the GNSS receiver and the speed solved by the INS when the integrated navigation system is working normally without interference; v GNSS Provides velocity information for GNSS receivers; INS Provides speed information to INS.

[0028] (3) Normalized pseudo-range correlation detection quantity

[0029]

[0030] Where N is the total number of signal processing channels of the GNSS receiver; σ ρ It represents the maximum deviation between the pseudorange calculated by the GNSS receiver and the pseudorange calculated by the INS when the integrated navigation system works normally without interference; ρ GNSS Pseudorange information provided by the GNSS receiver; ρ INS Pseudorange information provided by INS.

[0031] (4) Normalized pseudo-range rate related detection quantity

[0032] The calculation formula of normalized pseudo-range rate related detection quantity is:

[0033]

[0034] In the formula, It indicates the maximum deviation between the pseudo-range rate calculated by the GNSS receiver positioning and the pseudo-range rate calculated by the INS when the integrated navigation system is working normally without interference; Pseudorange rate information provided to GNSS receivers; Pseudorange rate information provided to INS.

[0035] Deception interference is judged based on the deception interference feature recognition quantity, and there are three judgment results as follows.

[0036] 1) When the normalized correlation detection quantity satisfies 0≤R p,k ≤1,0≤R v,k ≤1,0≤R ρ,k,j ≤1(j=1,2,...,N), When , it is determined that the system has not been attacked by deception interference;

[0037] 2) When the normalized correlation detection quantity satisfies 0≤R p,k ≤1,0≤R v,k ≤1,0≤R ρ,k ≤1(j=1,2,...,N), When the system is judged to be at risk of deception interference attack, the PRN number of the satellite with the risk of deception interference attack is recorded;

[0038] 3) When the normalized correlation detection quantity satisfies R ｐ,k >1, R ｖ,k >1, R ρ,k >1(j=1,2,...,N), When the system is judged to be under deception interference attack, the PRN number of the satellite causing the deception interference attack is recorded.

[0039] Further, step S3 is specifically as follows:

[0040] Select the system state quantity X k for:

[0041] X k =[Δλ ΔL Δh ΔV e ΔV n ΔV u φ e φ n φ u δt u δt ru ]

[0042] Where Δλ, ΔL, Δh are the errors of longitude, latitude and altitude respectively; ΔV e , ΔV n , ΔV u are the east, north and celestial errors respectively; φ e ,φ n ,φ u are the platform angle errors in the east, north and celestial directions respectively; δt u ,δt ru are the clock error and clock bias of the GNSS receiver clock respectively.

[0043] Then the system state equation is established as:

[0044] X k =Φ k,k-1 X(k-1)+W(k-1)

[0045] In the formula, Φ k,k-1 is the system state transfer matrix; W(k-1) is the system noise matrix.

[0046] Select system observation Z k for:

[0047]

[0048] Where Δρ is the pseudo-range difference between the GNSS receiver and the inertial navigation measurement; It is the pseudo-range rate difference between GNSS receiver and inertial navigation measurement.

[0049] The method of the present invention realizes anti-deception interference by filtering out pseudo-range and pseudo-range rate information that are subject to deception interference or have the risk of deception interference in the integrated navigation system loop. According to S2, if the recognition result is that it is not attacked by deception interference, all receiver channels participate in the filtering operation; if the recognition result is that it is attacked by deception interference or has the risk of deception interference, the system isolates these abnormal channels and selects normal channels to participate in the filtering operation to ensure that the Kalman filter outputs correct error correction information such as position and speed.

[0050] The system observation equation is further established as:

[0051] Z k =HX k +V k

[0052] Where V k is the observation noise, and H is the observation matrix.

[0053] It is generally assumed that the process noise W k and observation noise V k is Gaussian white noise and is uncorrelated, the noise covariance matrix Q k and the observation noise covariance matrix is ​​R k They are:

[0054]

[0055] Then the one-step state prediction matrix for:

[0056]

[0057] Covariance prediction matrix P k|k-1 for:

[0058] P k|k-1 =ΦP k-1|k-1 Φ T +Q k

[0059] Filter gain matrix K k for:

[0060]

[0061] State Estimator for:

[0062]

[0063] Prediction mean square error matrix P k for:

[0064] P k =(IK kH)P k|k-1

[0065] Further, step S4 is specifically as follows:

[0066] The system corrects the position, speed and other information calculated by INS according to the error correction information obtained by Kalman filter calculation, and outputs the position, speed and other information of the GNSS / INS combination after error correction.

[0067] By adopting the above technical solution, the present invention has the following beneficial effects:

[0068] 1) The present invention identifies a full range of deception interference types, including synchronous deception and asynchronous deception interference.

[0069] 2) The present invention performs interference identification based on correlation accumulation, which has the advantages of small amount of calculation, high interference identification rate and fast identification;

[0070] 3) The present invention provides a method for filtering out deception interference. When deception interference is detected, the system filters out the GNSS satellite information that is subject to deception interference, and uses the remaining GNSS receiver channels that are not subject to deception interference to perform combined navigation positioning with the INS, thereby ensuring that the positioning information output by the combined navigation system is not affected by deception interference. At the same time, the method can accurately identify which satellite signal is subject to deception interference attack, isolate the GNSS receiver channel subject to deception interference, retain the channel that is not subject to deception interference to perform combined navigation positioning with the INS, and greatly improve the stability and positioning accuracy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0072] Figure 1 The flowchart of the method for identifying and filtering spoofing signals of a shipborne positioning terminal according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0073] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0074] Figure 1A flow chart of a method for identifying and filtering deceptive signals of a shipborne positioning terminal according to an embodiment of the present invention is shown. In response to GNSS deceptive interference, this patent invention provides a deceptive interference filtering method under a GNSS / INS tightly integrated navigation system. For asynchronous deceptive interference, the present invention identifies deceptive interference through signal power characteristics; and for synchronous deceptive interference with higher concealment, the present invention provides a deceptive interference filtering method based on normalized correlation accumulation based on the auxiliary information provided by the inertial navigation system, including position, speed, pseudorange and pseudorange rate information, which can quickly and accurately identify and filter out deceptive interference.

[0075] A method for filtering out deception interference of a shipborne positioning terminal, the method comprising the following steps:

[0076] Step 1: Obtain original observation information.

[0077] The GNSS RF front-end module receives satellite signals and down-converts them into baseband signals. The baseband signal processing module captures, tracks, and locates the signals. It extracts original observation information such as position, velocity, pseudorange, pseudorange rate, and signal power from the receiver.

[0078] INS performs calculations based on the measurement data provided by the IMU. It extracts raw observation information such as position, velocity, pseudorange, and pseudorange rate from the inertial navigation system.

[0079] Step 2: Deception Interference Identification

[0080] Deception interference is mainly divided into synchronous deception and asynchronous deception interference, wherein synchronous deception interference signal power is small and concealment is high; while asynchronous deception interference relies on high power to achieve strong correlation interference to capture the target receiver loop. Therefore, asynchronous deception interference can be identified according to signal power characteristics; and for synchronous deception interference, the present invention provides a method for identifying synchronous deception interference by combining multiple information quantities based on normalized position, velocity, pseudorange, and pseudorange rate deviation for sliding correlation accumulation, which has high anti-noise performance and deception interference identification success rate, and can quickly identify deception interference.

[0081] 1. Asynchronous deception interference identification:

[0082] Asynchronous deception jamming achieves strong jamming by transmitting high-power deception signals. The GNSS signals emitted by satellites in the sky have very low power after being transmitted over long distances to the ground. For example, the power of ground GPS signals is around -160dBW. Therefore, asynchronous deception jamming can be identified by detecting the power of GNSS signals.

[0083] 2. Synchronous deception interference identification:

[0084] In general, the signal power of synchronous deception interference is slightly higher than the real signal power when invading the GNSS receiver loop, and will not cause the GNSS receiver tracking loop to lose lock. It is highly concealed and difficult to identify synchronous deception interference through power characteristics. However, deception interference will cause the positioning results of the GNSS receiver to be inconsistent with the current real position, speed and other information, while the INS system relies on mechanical integration to solve the position and speed information and is not affected by deception interference. Therefore, consider using the inconsistency of the observation information provided by GNSS and INS to identify deception interference. The observation information selected to identify deception interference mainly includes position, speed, pseudorange and pseudorange rate. By performing sliding correlation accumulation on the observation information deviation, deception interference can be quickly identified. At the same time, due to the selection of a certain length of data for sliding correlation accumulation and the selection of multiple correlation detection quantities for deception identification estimation, it has high anti-noise performance and accurate deception interference identification success rate.

[0085] (1) Normalized position-related detection quantity

[0086] The calculation formula of normalized position-related detection quantity is:

[0087]

[0088] Where L represents the number of sliding observation data points; σ p It indicates the maximum deviation between the position solved by the GNSS receiver and the position solved by the INS when the integrated navigation system is working normally without interference; p GNSS Position information provided by GNSS receiver; INS is the location information provided by INS; k represents the current system time.

[0089] (2) Normalized speed-related detection quantity

[0090]

[0091] In the formula, σ v It indicates the maximum deviation between the speed solved by the GNSS receiver and the speed solved by the INS when the integrated navigation system is working normally without interference; v GNSS Provides velocity information for GNSS receivers; INS Provides speed information to INS.

[0092] (3) Normalized pseudo-range correlation detection quantity

[0093]

[0094] Where N is the total number of signal processing channels of the GNSS receiver; σ ρIt represents the maximum deviation between the pseudorange calculated by the GNSS receiver and the pseudorange calculated by the INS when the integrated navigation system works normally without interference; ρ GNSS Pseudorange information provided by the GNSS receiver; ρ INS Pseudorange information provided by INS.

[0095] (4) Normalized pseudo-range rate related detection quantity

[0096] The calculation formula of normalized pseudo-range rate related detection quantity is:

[0097]

[0098] In the formula, It indicates the maximum deviation between the pseudo-range rate calculated by the GNSS receiver positioning and the pseudo-range rate calculated by the INS when the integrated navigation system is working normally without interference; Pseudorange rate information provided to GNSS receivers; Pseudorange rate information provided to INS.

[0099] Deception interference is judged based on the deception interference feature recognition quantity, and there are three judgment results as follows.

[0100] 1) When the normalized correlation detection quantity satisfies 0≤R p,k ≤1,0≤R v,k ≤1,0≤R ρ,k,j ≤1(j=1,2,...,N), When , it is determined that the system has not been attacked by deception interference;

[0101] 2) When the normalized correlation detection quantity satisfies 0≤R p,k ≤1,0≤R v,k ≤1,0≤R ρ,k ≤1(j=1,2,...,N), When the system is judged to be at risk of deception interference attack, the PRN number of the satellite with the risk of deception interference attack is recorded;

[0102] 3) When the normalized correlation detection quantity satisfies R p,k >1, R v,k >1, R ρ,k >1(j=1,2,...,N), When the system is judged to be under deception interference attack, the PRN number of the satellite causing the deception interference attack is recorded.

[0103] Step 3: Deception interference information filtering and combined navigation Kalman filtering

[0104] Select the system state quantity X k for:

[0105] X k =[Δλ ΔL Δh ΔV e ΔV n ΔV u φ e φ n φ u δt u δt ru ]

[0106] Where Δλ, ΔL, Δh are the errors of longitude, latitude and altitude respectively; ΔV e , ΔV n , ΔV u are the east, north and celestial errors respectively; φ e ,φ n ,φ u are the platform angle errors in the east, north and celestial directions respectively; δt u ,δt ru are the clock error and clock bias of the GNSS receiver clock respectively.

[0107] Then the system state equation is established as:

[0108] X k =Φ k,k-1 X(k-1)+W(k-1)

[0109] In the formula, Φ k,k-1 is the system state transfer matrix; W(k-1) is the system noise matrix.

[0110] Select system observation Z k for:

[0111]

[0112] Where Δρ is the pseudo-range difference between the GNSS receiver and the inertial navigation measurement; It is the pseudo-range rate difference between GNSS receiver and inertial navigation measurement.

[0113] The method of the present invention realizes anti-deception interference by filtering out pseudo-range and pseudo-range rate information that are subject to deception interference or have the risk of deception interference in the loop of the integrated navigation system. According to step 2, if the recognition result is that it is not attacked by deception interference, all receiver channels participate in the filtering operation; if the recognition result is that it is attacked by deception interference or has the risk of deception interference, the system isolates these abnormal channels and selects normal channels to participate in the filtering operation to ensure that the Kalman filter outputs correct error correction information such as position and speed.

[0114] The system observation equation is further established as:

[0115] Z k =HXk +V k

[0116] Where V k is the observation noise, and H is the observation matrix.

[0117] It is generally assumed that the process noise W k and observation noise V k is Gaussian white noise and is uncorrelated, the noise covariance matrix Q k and the observation noise covariance matrix is ​​R k They are:

[0118]

[0119] Then the one-step state prediction matrix for:

[0120]

[0121] Covariance prediction matrix P k|k-1 for:

[0122] P k|k-1 =ΦP k-1|k-1 Φ T +Q k

[0123] Filter gain matrix K k for:

[0124]

[0125] State Estimator for:

[0126]

[0127] Prediction mean square error matrix P k for:

[0128] P k =(IK k H)P k|k-1 .

[0129] Step 4: The system outputs the position, speed and other information after error correction

[0130] The system corrects the position, velocity, attitude and other information calculated by INS according to the error correction information obtained by Kalman filter calculation, and outputs the position, velocity, attitude and other information after error correction.

[0131] Compared with the prior art, the present invention mainly innovates in the following aspects:

[0132] 1) In the signal domain, asynchronous deceptive interference is identified based on signal power.

[0133] 2) In the information domain, synchronous deception interference is detected based on the normalized position deviation related accumulation, velocity deviation related accumulation, pseudo-range deviation related accumulation, and pseudo-range rate deviation related accumulation. At the same time, it can identify which satellite is attacked by deception interference.

[0134] 3) The normalized correlation accumulation formula provided by the present invention is created according to the actual deception interference scenario. The deception interference identification method based on correlation accumulation has the advantages of strong anti-noise ability, small amount of calculation, high interference recognition rate and fast recognition speed.

[0135] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0136] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A method for identifying and filtering fraudulent signals of a shipborne positioning terminal, characterized in that: include: S1, original observation information acquisition: obtain the original position, velocity, pseudorange, pseudorange rate, carrier-to-noise ratio, ephemeris, time, and signal power information from the GNSS baseband signal processing module and the INS solution module respectively; S2, deception interference identification: detect deception interference based on original observation information and deception interference feature identification; S3, deception interference information filtering and combined navigation Kalman filtering: According to the deception interference identification results of S2, the pseudo-range and pseudo-distance information with deception interference risks and deception interference are filtered out, and the remaining pseudo-range and pseudo-distance information is selected for Kalman filtering combination to obtain the position and velocity error correction information of INS; S4, output the position, velocity and attitude information after error correction: correct the position, velocity and attitude information solved by INS according to the error correction information provided by S3, and output the position, velocity and attitude information after the fusion of the integrated navigation system.

2. The method for identifying and filtering spoofing signals of a shipborne positioning terminal according to claim 1, characterized in that: Step S1 is specifically as follows: The GNSS RF front-end module receives satellite signals and down-converts them into baseband signals. The baseband signal processing module captures, tracks, and locates the signals. It extracts the original observation information from the receiver: position, velocity, pseudorange, pseudorange rate, and signal power. INS performs calculations based on the measurement data provided by the IMU; Extract raw observation information from the inertial navigation system: position, velocity, pseudorange, pseudorange rate.

3. The method for identifying and filtering spoofing signals of a shipborne positioning terminal according to claim 1, characterized in that: Step S2 is specifically as follows: Deception interference is divided into synchronous deception interference and asynchronous deception interference; Asynchronous deception interference identification: Asynchronous deception jamming achieves strong interference by transmitting high-power deception signals. The power of GNSS signals transmitted by satellites in the sky is very low after being transmitted over a long distance to the ground. Therefore, asynchronous deception jamming can be identified by detecting the power of GNSS signals. Synchronous deception interference identification: Identify spoofing interference using inconsistencies in observation information provided by GNSS and INS; The observation information selected for identifying deception interference includes position, velocity, pseudorange and pseudorange rate. The deception interference is quickly identified by sliding correlation accumulation of the observation information deviation. At the same time, due to the selection of a certain length of data for sliding correlation accumulation and the selection of multiple correlation detection quantities for deception identification estimation, it has higher anti-noise performance and deception interference identification success rate. Among them, (1) The normalized position-related detection quantity is as follows: The calculation formula of normalized position-related detection quantity is: Where L represents the number of sliding observation data points; σ p It indicates the maximum deviation between the position solved by the GNSS receiver and the position solved by the INS when the integrated navigation system is working normally without interference; p GNSS Position information provided by GNSS receiver; INS is the location information provided by INS; k represents the current system time; (2) The normalized speed-related detection quantities are as follows: In the formula, σ v It indicates the maximum deviation between the speed solved by the GNSS receiver and the speed solved by the INS when the integrated navigation system is working normally without interference; v GNSS Provides velocity information for GNSS receivers; INS Speed ​​information provided to INS; (3) The normalized pseudo-range related detection quantity is as follows: Where N is the total number of signal processing channels of the GNSS receiver; σ ρ It represents the maximum deviation between the pseudorange calculated by the GNSS receiver and the pseudorange calculated by the INS when the integrated navigation system works normally without interference; ρ GNSS Pseudorange information provided by the GNSS receiver; ρ INS Pseudorange information provided to INS; (4) The normalized pseudo-range rate related detection quantity is as follows: The calculation formula of normalized pseudo-range rate related detection quantity is: In the formula, It indicates the maximum deviation between the pseudo-range rate calculated by the GNSS receiver positioning and the pseudo-range rate calculated by the INS when the integrated navigation system is working normally without interference; Pseudorange rate information provided to GNSS receivers; Pseudorange rate information provided to INS; Deception interference is judged based on the deception interference feature recognition quantity, and there are three judgment results: 1) When the normalized correlation detection quantity satisfies 0≤R p,k ≤1,0≤R v,k ≤1,0≤R ρ,k,j ≤1(j=1,2,...,N), (j=1,2,...,N), it is determined that the system has not been attacked by deception interference; 2) When the normalized correlation detection quantity satisfies 0≤R p,k ≤1,0≤R v,k ≤1,0≤R ρ,k ≤1(j=1,2,...,N), When (j=1,2,...,N), the system is judged to be at risk of deception interference attack, and the PRN number of the satellite with the risk of deception interference attack is recorded; When the normalized correlation detection quantity satisfies R p,k >1, R v,k >1, R ρ,k >1(j=1,2,...,N), When (j=1,2,...,N), it is determined that the system is under a deception interference attack, and the PRN number of the satellite causing the deception interference attack is recorded.

4. The method for identifying and filtering fraudulent signals of a shipborne positioning terminal according to claim 1, characterized in that:Step S3 specifically includes: selecting the system state quantity X k for: X k =[Δλ ΔL Δh ΔV e ΔV n ΔV u f e f n f u δt u δt ru ] Where Δλ, ΔL, Δh are the errors of longitude, latitude and altitude respectively; ΔV e , ΔV n , ΔV u are the east, north and celestial errors respectively; φ e ,φ n ,φ u are the platform angle errors in the east, north and celestial directions respectively; δt u ,δt ru are the clock error and clock bias of the GNSS receiver clock respectively; Then the system state equation is established as: X k =Φ k,k-1 X(k-1)+W(k-1) In the formula, Φ k,k-1 is the system state transfer matrix; W(k-1) is the system noise matrix; Select system observation Z k for: Where Δρ is the pseudo-range difference between the GNSS receiver and the inertial navigation measurement; It is the pseudo-range rate difference between GNSS receiver and inertial navigation measurement; Anti-deception interference is achieved by filtering out pseudorange and pseudorange rate information that is subject to deception interference or has the risk of deception interference on the integrated navigation system loop; according to S2, if the recognition result is that it is not attacked by deception interference, all receiver channels participate in the filtering operation; if the recognition result is that it is attacked by deception interference or has the risk of deception interference, the system isolates these abnormal channels and selects normal channels to participate in the filtering operation to ensure that the Kalman filter outputs the correct position, velocity, and attitude error correction information; The system observation equation is further established as: Z k =HX k +V k Where V k is the observation noise, H is the observation matrix Assume that the process noise W k and observation noise V k is Gaussian white noise and is uncorrelated, the noise covariance matrix Q k and the observation noise covariance matrix is ​​R k They are: Then the one-step state prediction matrix for: Covariance prediction matrix P k|k-1 for: P k|k-1 =ΦP k-1|k-1 Φ T +Q k Filter gain matrix K k for: State Estimator for: Prediction mean square error matrix P k for: P k =(I-K k H)P k|k-1 。 5. The method for identifying and filtering fraudulent signals of a shipborne positioning terminal according to claim 1, characterized in that: Step S4 specifically includes: the system corrects the position, velocity, and attitude information calculated by the INS according to the error correction information obtained by the Kalman filter calculation, and outputs the position, velocity, and attitude information of the GNSS / INS combination after error correction.

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