General aviation deception signal diagnosis method and system based on mixed period

Abstract

The invention discloses a general aviation deception signal diagnosis method and system based on a mixed period, and the method comprises the steps: obtaining the GNSS signals of a plurality of satellites and the acceleration information of an aircraft through a receiver and an inertial measurement instrument, and enabling the GNSS signals to comprise pseudo-range and absolute power; at two adjacent sampling time points, calculating the position coefficient of each GNSS signal based on the pseudo-range set, and calculating the position difference of the aircraft based on the acceleration information; calculating a real-time diagnosis coefficient of each GNSS signal based on the position coefficient and the position difference, comparing a real-time diagnosis threshold value, and marking the signal as a suspected deception signal or a real signal; and in the diagnosis period, calculating a period diagnosis coefficient based on the absolute power of the suspected deception signal, comparing the period diagnosis coefficient with a period diagnosis threshold value, and marking the suspected deception signal or the interfered GNSS signal. According to the invention, by combining a plurality of diagnosis features, the limitation of the existing deception signal diagnosis method is avoided, and the diagnosis efficiency and the diagnosis accuracy are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of satellite signal detection, and particularly relates to a general aviation spoofing signal diagnosis method and system based on a hybrid period. Background Art

[0002] General aviation refers to aviation activities other than military, police, customs anti-smuggling flights, and public air transportation flights. With the increasing maturity of aircraft technology and the expansion of social production and life, general aviation has gradually played an increasingly important role in many aspects such as medical and health, ocean monitoring, and disaster relief. Compared with traditional land and sea activity methods, general aviation broadens the activity scope, not only improves the activity efficiency, but also has extremely high economic potential.

[0003] Currently, the navigation and positioning of general aircraft mainly rely on GNSS signals. However, due to the limitations of long signal path and easy noise interference of GNSS signals, general aircraft are easily interfered by spoofing signals, posing potential accident safety hazards. For the spoofing signals of general aircraft, researchers have proposed various diagnostic methods. For example, the Doppler frequency shift method is used to detect spoofing signals. For example, a monitor is used to identify and track the output correlation peak, and then determine whether the GNSS signal is a real signal or a spoofing signal. Although the above methods can screen out some spoofing signals to a certain extent, on the one hand, due to the continuous change of spoofing signals, the diagnostic method based on a single feature has obvious deficiencies in accuracy. On the other hand, during the navigation process, in addition to real-time diagnosis of received signals, it is also necessary to make tracking judgments on target signals to avoid systematic failures caused by spoofing signal interference, which is not considered in the prior art.

[0004] In summary, there is a need in the industry to propose a new general aviation spoofing signal diagnosis scheme to overcome the above defects. Summary of the Invention

[0005] The present invention provides a general aviation spoofing signal diagnosis technology based on a hybrid period to solve the technical problems of single feature and low diagnostic accuracy in current aviation spoofing signal diagnosis methods.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: The present invention provides a general aviation spoofing signal diagnosis method based on a hybrid period, including: S1: Obtain GNSS signals of multiple satellites and acceleration information of the aircraft through a receiver and an inertial measurement unit carried on the aircraft. The GNSS signals include pseudorange and absolute power, and the acceleration information includes the linear acceleration and angular acceleration of the aircraft; S2: Construct a pseudorange set from the pseudoranges obtained at each sampling time point, calculate the position coefficients of each GNSS signal based on the pseudorange sets at two adjacent sampling time points, and calculate the position difference of the aircraft between the corresponding two sampling time points based on the linear acceleration and angular acceleration; S3: Initialize the real-time diagnosis threshold, calculate the real-time diagnosis coefficients of each GNSS signal based on the position coefficients of each GNSS signal and the position difference of the aircraft, and mark the GNSS signal as a suspected spoofing signal when the real-time diagnosis coefficient is less than the real-time diagnosis threshold, otherwise it is a genuine signal; S4: Initialize the periodic diagnosis threshold and diagnosis period. During the diagnosis period, calculate the periodic diagnosis coefficient based on the absolute power of the suspected spoofing signal, and mark the suspected spoofing signal as a spoofing signal when the periodic diagnosis coefficient is greater than the periodic diagnosis threshold, otherwise it is an interfered GNSS signal; S5: Output the signal diagnosis result.

[0007] Further, the step S1 includes: Install a receiver and an inertial measurement unit at the geometric center position of the aircraft, and use the position coordinates calculated by the receiver and the inertial measurement unit as the position of the aircraft; The receiver acquires GNSS signals of multiple satellites and performs preprocessing. The GNSS signals include pseudoranges and absolute power, and the preprocessing includes performing Gaussian filtering on the GNSS signals to eliminate signal noise; The inertial measurement unit acquires the acceleration information of the aircraft, and the acceleration information includes the linear acceleration and angular acceleration of the aircraft.

[0008] Further, the step S2 includes: Divide every two adjacent sampling time points into a group, start the diagnosis from one of the groups, the first sampling time point is and the second sampling time point is , where represents the group number and takes an integer greater than zero; At each sampling time point, the receiver receives GNSS signals from the same n satellites and acquires the corresponding pseudorange set Q , specifically: at the sampling time point , the pseudorange set is , , at the sampling time point , the pseudorange set is , , where j represents the satellite number and takes an integer greater than zero, represents the pseudorange of the satellite, represents at the sampling time point The pseudorange of the j th satellite obtained, represents the pseudorange of the th satellite obtained at the sampling time point; j Based on the pseudorange set and , calculate the position coefficients of each GNSS signal; Based on the linear acceleration and angular acceleration, calculate the position difference of the aircraft between the sampling time point and the sampling time point . .

[0009] Furthermore, in the step S2, the calculation process of the position coefficients of each GNSS signal includes: (1) In the pseudorange set , extract the other pseudoranges except , and calculate the position coordinates , and of the aircraft, specifically: ; where m represents the satellite number and takes an integer greater than zero, m ≠ j , represents the m th satellite's x axis coordinate, represents the m th satellite's y axis coordinate, represents the m th satellite's z axis coordinate, represents the aircraft's axis coordinate calculated for the pseudorange of the j th satellite in the pseudorange set x , represents the aircraft's axis coordinate calculated for the pseudorange of the j th satellite in the pseudorange set y , represents the aircraft's axis coordinate calculated for the pseudorange of the j th satellite in the pseudorange set z , represents the clock error coefficient, represents the clock error of the th satellite at the sampling time point m ; (2) In the pseudorange set extract other pseudoranges except to calculate the position coordinates of the aircraft, , and specifically: ; wherein, represents the axis coordinate of the aircraft calculated from the pseudorange for the j rd satellite in the pseudorange set, x represents the axis coordinate of the aircraft calculated from the pseudorange for the j th satellite in the pseudorange set, y represents the axis coordinate of the aircraft calculated from the pseudorange for the j th satellite in the pseudorange set, z represents the clock error of the th satellite at the sampling time point; m (3) Calculate the position coefficient j of the GNSS signal of the th satellite, specifically: ; (4) Repeat the above steps to obtain the position coefficients of each GNSS signal.

[0010] It should be noted that the position coefficient is a parameter calculated using pseudoranges and is related to its signal source. Its purpose is to cooperate with the next step of spoofing signal detection; by calculating the position coefficients of each GNSS signal, spoofing signals can be more prominently highlighted.

[0011] Furthermore, step S3 includes: Initialize the real-time diagnostic threshold ; Based on the position coefficients of each GNSS signal and the position difference of the aircraft, calculate the real-time diagnostic coefficients of each GNSS signal and perform consistency diagnosis, specifically: (1) Calculate the cumulative position deviation R of all GNSS signals, specifically: ; wherein, represents the absolute value calculation; (2) Calculate the position deviation coefficient ​​​​, specifically as follows: ; Among them, represents the position deviation coefficient of the j th GNSS signal; (3) Calculate the real-time diagnosis coefficient of each GNSS signal , specifically as follows: ; Among them, represents the real-time diagnosis coefficient of the j th GNSS signal; (4) Compare the magnitude relationship between the real-time diagnosis coefficient and the real-time diagnosis threshold, specifically as follows: ; Among them, represents the flag bit of the j th GNSS signal. When is 1, it means that the j th GNSS signal is a suspected spoofing signal. When is 0, it means that the j th GNSS signal is a real signal.

[0012] Furthermore, the step S4 includes: Initialize the periodic diagnosis threshold and the diagnosis period. During the diagnosis period, set a diagnosis subset every v sampling time points. The number of diagnosis subsets is h , and each diagnosis subset contains e sampling time points, e taking an integer greater than 2; For the GNSS signal corresponding to the j th pseudorange marked as a suspected spoofing signal, during the diagnosis period, calculate the periodic diagnosis coefficient based on the absolute power of the suspected spoofing signal and perform consistency diagnosis, specifically as follows: (1) Calculate the mean value of the absolute power of the suspected spoofing signal in each diagnosis subset , specifically as follows: ; Among them, f represents the diagnosis subset serial number, P represents the absolute power, represents the absolute power of the suspected spoofing signal at the d th sampling time point during the diagnosis period; (2) Calculate the mean square value of the absolute power of the suspected spoofing signal in each diagnosis subset , specifically as follows: ; (3) Calculate the period diagnosis coefficient of the suspected spoofing signal , specifically as follows: ; (4) Compare the magnitude relationship between the period diagnosis coefficient and the period diagnosis threshold, specifically as follows: ; Among them, when is 1, it indicates that the suspected spoofing signal is a spoofing signal. When is 0, it indicates that the suspected spoofing signal is a GNSS signal interfered.

[0013] It should be noted that in step S4, the interval parameter v refers to the number of sampling time points separated between the first sampling time points of two adjacent diagnostic subsets. For example, when v = 3, e = 3, the first, second, and third diagnostic subsets respectively include the {1, 2, 3}, {4, 5, 6}, {7, 8, 9} sampling time points in the diagnostic period. When v = 4, e = 3, the first, second, and third diagnostic subsets respectively include the {1, 2, 3}, {5, 6, 7}, {9, 10, 11} sampling time points in the diagnostic period. When v = 2, e = 4, the first, second, and third diagnostic subsets respectively include the {1, 2, 3, 4}, {3, 4, 5, 6}, {5, 6, 7, 8} sampling time points in the diagnostic period; by setting the diagnostic period length, sampling interval length, and diagnostic subset length, long-term monitoring and flexible sampling of the suspected spoofing signal are realized; and when v ≧ e , duplicate calculations can be avoided, while ensuring the diagnostic accuracy, improving the calculation efficiency, and thus reducing the hardware cost.

[0014] The period diagnosis coefficient calculated based on the absolute power of the sampling is essentially a statistical result of the discrete degree of the suspected spoofing signal within the diagnostic period. Since the discrete fluctuation of the spoofing signal is large, while the discrete fluctuation of the non-spoofing signal is small, a reasonable threshold can be set through experience for signal diagnosis.

[0015] The present invention also provides a general aviation spoofing signal diagnosis system based on a hybrid period, including: Signal receiving module: Obtain GNSS signals of multiple satellites and acceleration information of the aircraft through a receiver and an inertial measurement unit carried on the aircraft. The GNSS signals include pseudorange and absolute power, and the acceleration information includes linear acceleration and angular acceleration of the aircraft; Real-time diagnosis module: Construct a pseudorange set with the pseudoranges obtained at each sampling time point, calculate the position coefficients of each GNSS signal based on the pseudorange sets at two adjacent sampling time points, and calculate the position difference of the aircraft between the corresponding two sampling time points based on the linear acceleration and angular acceleration; Initialize the real-time diagnosis threshold, calculate the real-time diagnosis coefficients of each GNSS signal based on the position coefficients of each GNSS signal and the position difference of the aircraft, and mark the GNSS signal as a suspected spoofing signal when the real-time diagnosis coefficient is less than the real-time diagnosis threshold, otherwise it is a real signal; Periodic diagnosis module: Initialize the periodic diagnosis threshold and diagnosis period. During the diagnosis period, calculate the periodic diagnosis coefficient based on the absolute power of the suspected spoofing signal, and mark the suspected spoofing signal as a spoofing signal when the periodic diagnosis coefficient is greater than the periodic diagnosis threshold, otherwise it is an interfered GNSS signal; Result output module: Output the signal diagnosis result.

[0016] The beneficial effects brought by the technical solution provided by the present invention at least include: 1. The present invention uses a hybrid period to realize the diagnosis of general aviation spoofing signals. First, it monitors the GNSS signals of multiple satellites of the aircraft in real time. By the pseudorange sets corresponding to two adjacent sampling time points and the position difference of the aircraft between the two sampling time points, suspected spoofing signals are screened out to achieve the purpose of real-time diagnosis. Then, long-term tracking and monitoring diagnosis are carried out on the suspected spoofing signals. Through the absolute power corresponding to each sampling time point within the diagnosis period and flexible sampling, it is further determined whether the signal is a real spoofing signal or an interfered signal; Compared with the traditional diagnosis method that relies on a single feature, the present invention combines real-time diagnosis and long-period diagnosis, combines the pseudorange feature and the absolute power feature, increases the information richness of spoofing signal detection, thereby reducing errors and improving the diagnosis efficiency and diagnosis accuracy.

[0017] 2. When the present invention conducts real-time diagnosis, considering that it is difficult to directly distinguish the signals at a single sampling time point, first calculate the position coefficients of each satellite signal based on the pseudorange, then calculate the position difference through the inertial measurement unit of the aircraft, and finally perform consistency judgment based on the position coefficients and the position difference; The technical solution of the present invention not only has a good signal diagnosis effect for moving aircraft, but also for stationary aircraft, when a spoofing signal is generated and interferes with the position of the aircraft, through position coefficient calculation and consistency judgment, a good signal diagnosis effect can also be achieved. Therefore, the solution of the present invention has high applicability.

[0018] 3. When the present invention performs long - cycle diagnosis, it only further diagnoses suspected deception signals. By long - term tracking and the diagnostic results of historical deception signals, the length of the diagnostic subset and the number of diagnostic intervals are reasonably set to obtain the cycle diagnosis coefficient. Although the present invention uses multiple features for diagnosing deception signals, it does not perform simple and crude parallel computing in terms of computing power, and can reduce the computing power cost on the premise of ensuring diagnostic accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic flow chart of a general aviation deception signal diagnosis method based on a hybrid cycle provided by Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The present invention will be further described below with reference to the drawings, but the present invention is not limited in any way. Any transformation or replacement based on the teachings of the present invention falls within the protection scope of the present invention.

[0021] Embodiment 1

[0022] As Figure 1 shown, this embodiment provides a general aviation deception signal diagnosis method based on a hybrid cycle, including the following steps: S1: The GNSS signals of multiple satellites and the acceleration information of the aircraft are respectively obtained through a receiver and an inertial measurement unit carried on the aircraft. Specifically: The receiver and the inertial measurement unit are carried at the geometric center position of the aircraft, and the position coordinates calculated by the receiver and the inertial measurement unit are used as the position of the aircraft; The receiver obtains the GNSS signals of multiple satellites and performs pre - processing. The GNSS signals include pseudorange and absolute power, and the pre - processing includes Gaussian filtering of the GNSS signals to eliminate signal noise; The inertial measurement unit obtains the acceleration information of the aircraft, and the acceleration information includes the linear acceleration and angular acceleration of the aircraft.

[0023] S2: The pseudoranges obtained at each sampling time point are formed into a pseudorange set. The position coefficients of each GNSS signal are calculated based on the pseudorange sets of two adjacent sampling time points, and the position difference of the aircraft between the corresponding two sampling time points is calculated based on the linear acceleration and angular acceleration. Specifically: Every two adjacent sampling time points are divided into a group, and diagnosis starts from one of the groups. The first sampling time point is and the second sampling time point is , where represents the group number and takes an integer greater than zero; At each sampling time point, the receiver receives from the samen the GNSS signals of satellites and obtain the corresponding set of pseudorange Q , specifically: at the sampling time point , the set of pseudorange is , , at the sampling time point , the set of pseudorange is , , where j represents the satellite number and takes an integer greater than zero, represents the pseudorange of the satellite, represents the -th j satellite's pseudorange obtained at the sampling time point , and represents the j -th satellite's pseudorange; In this embodiment, the diagnosis starts from the 6th group, that is, = 6, the first sampling time point is , the second sampling time point is , it is set that the receiver receives signals from 6 satellites, that is, = 6, at the sampling time point , the set of pseudorange is , , at the sampling time point , the set of pseudorange is ; Based on the set of pseudorange and , calculate the position coefficients of each GNSS signal, specifically: (1) In the set of pseudorange , extract the other pseudorange except to calculate the position coordinates , and of the aircraft, specifically: ; where j ∈[1, 6], m represents the satellite number and takes an integer greater than zero, m ≠ j , for example, when j = 5, m can be taken as 1, 2, 3, 4, 6 respectively, represents the m -th x axis coordinate of the satellite, represents the m -thy Axis coordinate, indicating the m th satellite's z axis coordinate, indicating the aircraft's axis coordinate calculated from the pseudorange for the j th satellite in the pseudorange set x ; indicating the aircraft's axis coordinate calculated from the pseudorange for the j th satellite in the pseudorange set y ; indicating the aircraft's axis coordinate calculated from the pseudorange for the j th satellite in the pseudorange set z ; indicating the clock error coefficient, indicating the clock error of the th satellite at the sampling time point m ; (2) In the pseudorange set , extract the other pseudoranges except , and calculate the position coordinates , , and of the aircraft, specifically: ; where indicates the aircraft's axis coordinate calculated from the pseudorange for the j th satellite in the pseudorange set x ; indicates the aircraft's axis coordinate calculated from the pseudorange for the j th satellite in the pseudorange set y ; indicates the aircraft's axis coordinate calculated from the pseudorange for the j th satellite in the pseudorange set z ; indicates the clock error of the th satellite at the sampling time point m ; (3) Calculate the position coefficient j of the GNSS signal of the th satellite, specifically: ; (4) Repeat the above steps to obtain the position coefficients of each GNSS signal; Calculate the position difference of the aircraft at the sampling time point and the sampling time point based on the linear acceleration and angular acceleration. The position calculation method is a conventional technical means in the art and will not be elaborated here.

[0024] S3: Initialize the real-time diagnosis threshold and calculate the real-time diagnosis coefficient of each GNSS signal based on the position coefficient of each GNSS signal and the position difference of the aircraft, and perform consistency diagnosis. The process of consistency diagnosis is specifically as follows: (1) Calculate the cumulative position deviation of all GNSS signals R , specifically: ; where represents the absolute value calculation; (2) Calculate the position deviation coefficient of each GNSS signal , specifically: ; where represents the position deviation coefficient of the j th GNSS signal; (3) Calculate the real-time diagnosis coefficient of each GNSS signal , specifically: ; where represents the real-time diagnosis coefficient of the j th GNSS signal; (4) Compare the magnitude relationship between the real-time diagnosis coefficient and the real-time diagnosis threshold, specifically: ; where represents the flag bit of the j th GNSS signal. When is 1, it means that the j th GNSS signal is a suspected spoofing signal. When is 0, it means that the j th GNSS signal is a real signal; In this embodiment, the 5th GNSS signal is marked as a suspected spoofing signal. Next, it is necessary to perform periodic tracking and further diagnosis on this signal. After this signal is confirmed as a suspected spoofing signal, the navigation and positioning calculation of the aircraft does not adopt any information of this satellite signal first.

[0025] S4: Initialize the periodic diagnosis threshold ​And the diagnostic cycle. During the diagnostic cycle, calculate the cycle diagnostic coefficient based on the absolute power of the suspected spoofing signal and perform consistency diagnosis. The specific process of consistency diagnosis is as follows: During the diagnostic cycle, at intervals of v Set a diagnostic subset at each sampling time point. The number of diagnostic subsets is h Each diagnostic subset contains e Sampling time points, e Take an integer greater than 2; In this embodiment, the diagnostic cycle contains 15 sampling time points, v = 3, e = 3, h = 5; (1) Calculate the mean value of the absolute power of the suspected spoofing signal in each diagnostic subset , specifically: ; Among them, f Represents the diagnostic subset number, f ∈[1, 5], P Represents the absolute power, Represents the absolute power of the suspected spoofing signal at the d th sampling time point within the diagnostic cycle; That is: ; (2) Calculate the mean square value of the absolute power of the suspected spoofing signal in each diagnostic subset , specifically: ; That is: ; (3) Calculate the cycle diagnostic coefficient of the suspected spoofing signal , specifically: ; (4) Compare the magnitude relationship between the cycle diagnostic coefficient and the cycle diagnostic threshold, specifically: ; Among them, when is 1, it indicates that the suspected spoofing signal is a spoofing signal. When is 0, it indicates that the suspected spoofing signal is an interfered GNSS signal; In this embodiment, , that is, the suspected spoofing signal is indeed a spoofing signal.

[0026] S5: Output the signal diagnostic result.

[0027] Embodiment 2 This embodiment also provides a method for diagnosing general aviation spoofing signals based on a hybrid period. The order of steps adopted is the same as that in Embodiment 1, except that in step S4, the diagnostic period includes 19 sampling time points. v =5, e =4, h =4; then there is: (1) Calculate the mean of the absolute power of the suspected spoofing signals in each diagnostic subset : ; (2) Calculate the mean of the squares of the absolute power of the suspected spoofing signals in each diagnostic subset : ; Further calculate based on the above data to obtain: , that is, this suspected spoofing signal is indeed a spoofing signal, which is the same as the signal diagnosis result in Embodiment 1.

[0028] Embodiment 3 This embodiment provides a general aviation spoofing signal diagnosis system based on a hybrid period, including the following modules: Signal reception module: Obtain GNSS signals of multiple satellites and the acceleration information of the aircraft through a receiver and an inertial measurement unit carried on the aircraft. The GNSS signals include pseudorange and absolute power, and the acceleration information includes the linear acceleration and angular acceleration of the aircraft; Real-time diagnosis module: Form a pseudorange set with the pseudoranges obtained at each sampling time point. Calculate the position coefficients of each GNSS signal based on the pseudorange sets of adjacent two sampling time points, and calculate the position difference of the aircraft between the corresponding two sampling time points based on the linear acceleration and angular acceleration; Initialize the real-time diagnosis threshold, and calculate the real-time diagnosis coefficient of each GNSS signal based on the position coefficients of each GNSS signal and the position difference of the aircraft. When the real-time diagnosis coefficient is less than the real-time diagnosis threshold, mark this GNSS signal as a suspected spoofing signal, otherwise as a real signal; Periodic diagnosis module: Initialize the periodic diagnosis threshold and the diagnostic period. During the diagnostic period, calculate the periodic diagnosis coefficient based on the absolute power of the suspected spoofing signals. When the periodic diagnosis coefficient is greater than the periodic diagnosis threshold, mark this suspected spoofing signal as a spoofing signal, otherwise as an interfered GNSS signal; Result output module: Output the signal diagnosis result.

[0029] Implement the method for diagnosing general aviation spoofing signals based on a hybrid period in Embodiments 1 and 2 through the above system.

[0030] As used herein, the term "preferred" is intended to be used as an example, illustration, or exemplification. Any aspect or design described herein as "preferred" should not necessarily be construed as more advantageous than other aspects or designs. On the contrary, the use of the term "preferred" is intended to present concepts in a concrete manner. As used in this application, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from the context, "X uses A or B" is intended to naturally include any one of the recited permutations. That is, if X uses A; X uses B; or X uses both A and B, then "X uses A or B" is satisfied in any of the foregoing instances.

[0031] Moreover, although the present disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art based on a reading and understanding of this specification and the drawings. The present disclosure includes all such modifications and variations and is limited only by the scope of the appended claims. In particular with respect to the various functions performed by the above-described components (e.g., elements, etc.), the terms used to describe such components are intended to correspond to any component (unless otherwise indicated) that performs the specified function of the described component (e.g., that is functionally equivalent), even if not structurally equivalent to the disclosed structure that performs the function in the exemplary implementations of the present disclosure shown herein. Additionally, although a particular feature of the present disclosure has been disclosed with respect to only one of several implementations, such feature may be combined with one or other features of other implementations as may be desired and advantageous for a given or particular application. Moreover, insofar as the terms "comprises," "has," "contains," or any variation thereof are used in a particular embodiment or claim, such terms are intended to be inclusive in a manner similar to the term "includes."

[0032] Each functional unit in the embodiments of the present invention may be integrated into one processing module, may exist separately physically as individual units, or may be integrated into one module with two or more units. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disk, or the like. Each of the above-mentioned devices or systems may execute the storage method in the corresponding method embodiment.

[0033] In summary, the above embodiments are one implementation manner of the present invention, but the implementation manners of the present invention are not limited by the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement manners and are all included in the protection scope of the present invention.

Claims

1. A general aviation deception signal diagnosis method based on hybrid cycle, characterized in that: The following steps are involved: S1: acquiring GNSS signals of multiple satellites and acceleration information of the aircraft respectively through a receiver and an inertial measurement unit carried on the aircraft, wherein the GNSS signal includes a pseudorange and an absolute power, and the acceleration information includes a linear acceleration and an angular acceleration of the aircraft; S2: The pseudoranges obtained at each sampling time point are combined into a pseudorange set, the position coefficients of each GNSS signal are calculated based on the pseudorange sets of two adjacent sampling time points, and the position difference of the aircraft between the two corresponding sampling time points is calculated based on the linear acceleration and angular acceleration; S3: Initialize the real-time diagnosis threshold, calculate the real-time diagnosis coefficient of each GNSS signal based on the position coefficient of each GNSS signal and the position difference of the aircraft, and when the real-time diagnosis coefficient is less than the real-time diagnosis threshold, mark the GNSS signal as a suspected spoofing signal, otherwise it is a real signal; S4: Initialize the periodic diagnosis threshold and the diagnosis period. During the diagnosis period, calculate the periodic diagnosis coefficient based on the absolute power of the suspected spoofing signal. When the periodic diagnosis coefficient is greater than the periodic diagnosis threshold, mark the suspected spoofing signal as a spoofing signal, otherwise it is a interfered GNSS signal. S5: Output signal diagnosis result.

2. The hybrid cycle-based general aviation deception signal diagnosis method according to claim 1, characterized in that: The step S1 comprises: A receiver and an inertial measurement unit are mounted at the geometric center of the aircraft, and the position coordinates calculated by the receiver and the inertial measurement unit are used as the position of the aircraft; The receiver acquires GNSS signals of multiple satellites and performs preprocessing, wherein the GNSS signals include pseudoranges and absolute powers, and the preprocessing includes performing Gaussian filtering on the GNSS signals to eliminate signal noise; The inertial measurement unit obtains acceleration information of the aircraft, where the acceleration information includes the linear acceleration and angular acceleration of the aircraft.

3. The hybrid cycle-based general aviation deception signal diagnosis method according to claim 1, characterized in that: The step S2 comprises: Every two adjacent sampling time points are divided into a group, and the diagnosis starts from one of the groups. The first sampling time point is The second sampling time point is ,in Represents the group number and takes an integer greater than zero; At each sampling time point, the receiver receives n GNSS signals of satellites and obtain the corresponding pseudorange set Q , specifically: at the sampling time point , the pseudorange set is , , at the sampling time point , the pseudorange set is , ,in, j represents the satellite number and is an integer greater than zero. represents the pseudorange of the satellite, Indicates the sampling time point Obtained j The pseudoranges of satellites, Indicates the sampling time point Obtained j Pseudoranges of satellites; Based on pseudorange set and , calculate the position coefficients of each GNSS signal; Based on the linear acceleration and angular acceleration, calculate the aircraft at the sampling time point and sampling time point The position difference .

4. The hybrid cycle-based general aviation deception signal diagnosis method according to claim 3, characterized in that: In step S2, the calculation process of the position coefficient of each GNSS signal includes: (1) In the pseudorange set In the extraction Other pseudoranges , calculate the position coordinates of the aircraft , and , specifically: ； in, m represents the satellite number and is an integer greater than zero. m ≠ j , Indicates m Satellite x Axis coordinates, Indicates m Satellite y Axis coordinates, Indicates m Satellite z Axis coordinates, Indicates that in the pseudorange set In the first j Satellite pseudorange calculation for aircraft x Axis coordinates, Indicates that in the pseudorange set In the first j Satellite pseudorange calculation for aircraft y Axis coordinates, Indicates that in the pseudorange set In the first j Satellite pseudorange calculation for aircraft z Axis coordinates, represents the clock error coefficient, Indicates the sampling time point No. m The clock error of the satellites; (2) In the pseudorange set In the extraction Other pseudoranges , calculate the position coordinates of the aircraft , and , specifically: ； in, Indicates that in the pseudorange set In the first j Satellite pseudorange calculation for aircraft x Axis coordinates, Indicates that in the pseudorange set In the first j Satellite pseudorange calculation for aircraft y Axis coordinates, Indicates that in the pseudorange set In the first j Satellite pseudorange calculation for aircraft z Axis coordinates, Indicates the sampling time point No. m The clock error of the satellites; (3) Calculate the j Position coefficients of GNSS signals from satellites , specifically: ； (4) Repeat the above steps to obtain the position coefficient of each GNSS signal.

5. The hybrid cycle-based general aviation deception signal diagnosis method according to claim 1, characterized in that: The step S3 comprises: Initialize real-time diagnostic thresholds ; Based on the position coefficients of each GNSS signal and the position difference of the aircraft, the real-time diagnostic coefficients of each GNSS signal are calculated, and consistency diagnosis is performed, specifically: (1) Calculate the cumulative position deviation of all GNSS signals R , specifically: ； in, Indicates absolute value calculation; (2) Calculate the position deviation coefficient of each GNSS signal , specifically: ； in, Indicates j Position deviation coefficient of each GNSS signal; (3) Calculate the real-time diagnostic coefficient of each GNSS signal , specifically: ； in, Indicates j Real-time diagnostic coefficients of GNSS signals; (4) Compare the relationship between the real-time diagnosis coefficient and the real-time diagnosis threshold, specifically: ； in, Indicates j The flag bit of a GNSS signal, when When it is 1, it means j GNSS signals are suspected of being spoofed signals. When it is 0, it means j The GNSS signals are real signals.

6. The hybrid cycle-based general aviation deception signal diagnosis method according to claim 1, characterized in that: The step S4 comprises: Initialization cycle diagnostic threshold and diagnostic cycle. During the diagnostic cycle, each interval v A diagnostic subset is set for each sampling time point. The number of diagnostic subsets is h , each diagnostic subset contains e Sampling time points, e Take an integer greater than 2; For j Pseudorange If the corresponding GNSS signal is marked as a suspected spoofing signal, the periodic diagnosis coefficient is calculated based on the absolute power of the suspected spoofing signal during the diagnosis period, and consistency diagnosis is performed, specifically: (1) Calculate the mean absolute power of the suspected deception signal in each diagnostic subset , specifically: ； in, f Indicates the diagnostic subset number, P represents the absolute power, Indicates that the suspected fraud signal is the first d The absolute power at each sampling time point; (2) Calculate the square mean of the absolute power of the suspected deception signal in each diagnostic subset , specifically: ； (3) Calculate the periodic diagnostic coefficient of suspected fraudulent signals , specifically: ； (4) Compare the relationship between the cycle diagnosis coefficient and the cycle diagnosis threshold, specifically: ； Among them, when When it is 1, it means that the suspected spoofing signal is a spoofing signal. When it is 0, it indicates that the suspected spoofing signal is a interfered GNSS signal.

7. A general aviation deception signal diagnosis system based on a hybrid cycle, characterized in that: include: Signal receiving module: obtains GNSS signals of multiple satellites and acceleration information of the aircraft through the receiver and inertial measurement unit carried on the aircraft, wherein the GNSS signal includes pseudorange and absolute power, and the acceleration information includes linear acceleration and angular acceleration of the aircraft; Real-time diagnosis module: The pseudoranges obtained at each sampling time point are combined into a pseudorange set, the position coefficients of each GNSS signal are calculated based on the pseudorange sets of two adjacent sampling time points, and the position difference of the aircraft between the two corresponding sampling time points is calculated based on the linear acceleration and angular acceleration; Initialize the real-time diagnosis threshold, calculate the real-time diagnosis coefficient of each GNSS signal based on the position coefficient of each GNSS signal and the position difference of the aircraft, and when the real-time diagnosis coefficient is less than the real-time diagnosis threshold, mark the GNSS signal as a suspected spoofing signal, otherwise it is a real signal; Periodic diagnosis module: Initializes the periodic diagnosis threshold and diagnosis period. During the diagnosis period, the periodic diagnosis coefficient is calculated based on the absolute power of the suspected spoofing signal. When the periodic diagnosis coefficient is greater than the periodic diagnosis threshold, the suspected spoofing signal is marked as a spoofing signal, otherwise it is a interfered GNSS signal. Result output module: output signal diagnosis results; To realize the general aviation deception signal diagnosis method based on hybrid cycle as described in any one of claims 1-6.

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