A General Aviation Deception Signal Diagnosis Method and System Based on Hybrid Periods

Abstract

The present invention discloses a general aviation spoofing signal diagnosis method and system based on a hybrid period, including: acquiring GNSS signals of multiple satellites and acceleration information of an aircraft through a receiver and an inertial measurement unit respectively, where the GNSS signals include pseudorange and absolute power; at two adjacent sampling time points, calculating the position coefficient of each GNSS signal based on the pseudorange set, and calculating the position difference of the aircraft based on the acceleration information; calculating the real-time diagnosis coefficient of each GNSS signal based on the position coefficient and the position difference, comparing with the real-time diagnosis threshold, and marking as a suspected spoofing signal or a real signal; within the diagnosis period, calculating the period diagnosis coefficient based on the absolute power of the suspected spoofing signal, comparing with the period diagnosis threshold, and marking as a spoofing signal or an interfered GNSS signal. By combining multiple diagnostic features, the present invention avoids the limitations of existing spoofing signal diagnosis methods and improves the diagnosis efficiency and accuracy.

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 multiple aspects such as medical and health, ocean monitoring, and disaster relief. Compared with traditional land and sea activity methods, general aviation broadens the activity range, not only improving the activity efficiency but also having 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 paths and susceptibility to noise interference of GNSS signals, general aircraft are easily interfered by spoofing signals, posing potential accident safety hazards. For spoofing signals of general aircraft, researchers have proposed various diagnostic methods. For example, the Doppler frequency shift method is used to detect spoofing signals, such as using a monitor to identify and track the output correlation peak, and then determining whether the GNSS signal is a true 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 existing technology.

[0004] In summary, the industry needs 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:

[0007] The present invention provides a general aviation spoofing signal diagnosis method based on a hybrid period, including:

[0008] 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 linear acceleration and angular acceleration of the aircraft;

[0009] S2: Form a set of pseudorange values obtained at each sampling time point. Calculate the position coefficients of each GNSS signal based on the sets of pseudorange values 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.

[0010] 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. When the real-time diagnosis coefficient is less than the real-time diagnosis threshold, mark the GNSS signal as a suspected spoofing signal; otherwise, mark it as a genuine signal.

[0011] 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, mark it as an interfered GNSS signal.

[0012] S5: Output the signal diagnosis result.

[0013] Further, the step S1 includes:

[0014] 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.

[0015] The receiver acquires GNSS signals of multiple satellites and performs preprocessing. The GNSS signals include pseudorange and absolute power. The preprocessing includes performing Gaussian filtering on the GNSS signals to eliminate signal noise.

[0016] The inertial measurement unit acquires the acceleration information of the aircraft. The acceleration information includes the linear acceleration and angular acceleration of the aircraft.

[0017] Further, the step S2 includes:

[0018] Divide every two adjacent sampling time points into a group, and start 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.

[0019] At each sampling time point, the receiver receives GNSS signals from the same n satellites and obtains the corresponding set of pseudorange values Q . Specifically, at the sampling time point , the set of pseudorange values 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;

[0020] Based on pseudorange set and , calculate the position coefficients of each GNSS signal;

[0021] Based on the linear acceleration and angular acceleration, calculate the aircraft's acceleration at the sampling time. and sampling time points Position difference .

[0022] Furthermore, in step S2, the calculation process of the position coefficient of each GNSS signal includes:

[0023] (1) In the pseudorange set In the extraction Other pseudoranges , calculate the position coordinates of the aircraft 、 and , specifically:

[0024] ;

[0025] in, m Represents the satellite number and is an integer greater than zero. m ≠ j , Indicates the m Satellites x Axis coordinates, Indicates the m Satellites y Axis coordinates, Indicates the m Satellites z Axis coordinates, Indicates that in the pseudorange set Targeting the j Satellite pseudorange calculation for aircraft x Axis coordinates, Indicates that in the pseudorange set Targeting the j Satellite pseudorange calculation for aircrafty The axis coordinate, which represents the aircraft's axis coordinate calculated for the pseudorange of the j th satellite in the pseudorange set; z The axis coordinate, which represents the clock bias coefficient, which represents the clock bias of the th m satellite at the sampling time point;

[0026] (2) In the pseudorange set , extract the other pseudoranges except , and calculate the position coordinates , , and of the aircraft, specifically:

[0027] ;

[0028] Among them, represents the aircraft's axis coordinate calculated for the pseudorange of the j th satellite in the pseudorange set, x the axis coordinate, represents the aircraft's axis coordinate calculated for the pseudorange of the j th satellite in the pseudorange set, y the axis coordinate, represents the aircraft's axis coordinate calculated for the pseudorange of the j th satellite in the pseudorange set, z the axis coordinate, which represents the clock bias of the th m satellite at the sampling time point;

[0029] (3) Calculate the position coefficient j of the GNSS signal of the th satellite, specifically:

[0030] ;

[0031] (4) Repeat the above steps to obtain the position coefficients of each GNSS signal.

[0032] It should be noted that the position coefficient is a parameter calculated using the pseudorange and is related to its signal source, and its purpose is to cooperate with the next step of detecting spoofing signals; by calculating the position coefficients of each GNSS signal, spoofing signals can be more prominently highlighted.

[0033] Furthermore, the step S3 includes:

[0034] Initialize the real-time diagnostic threshold ;

[0035] Calculate the real-time diagnostic coefficient for each GNSS signal based on the position coefficient of each GNSS signal and the position difference of the aircraft, and perform consistency diagnosis, specifically:

[0036] (1) Calculate the cumulative position deviation of all GNSS signals R , specifically:

[0037] ;

[0038] where represents the absolute value calculation;

[0039] (2) Calculate the position deviation coefficient of each GNSS signal , specifically:

[0040] ;

[0041] where represents the position deviation coefficient of the j th GNSS signal;

[0042] (3) Calculate the real-time diagnostic coefficient of each GNSS signal , specifically:

[0043] ;

[0044] where represents the real-time diagnostic coefficient of the j th GNSS signal;

[0045] (4) Compare the magnitude relationship between the real-time diagnostic coefficient and the real-time diagnostic threshold, specifically:

[0046] ;

[0047] where represents the flag bit of the j th GNSS signal. When is 1, it means the j th GNSS signal is a suspected spoofing signal. When is 0, it means the j th GNSS signal is a real signal.

[0048] Furthermore, the step S4 includes:

[0049] Initialize the periodic diagnostic threshold and the diagnostic period. Within the diagnostic period, every intervalv One diagnostic subset is set for each sampling time point, and the number of diagnostic subsets is h , and each diagnostic subset contains e sampling time points, e where is an integer greater than 2;

[0050] For the j pseudorange corresponding to which the GNSS signal is marked as a suspected spoofing signal, within the diagnostic period, calculate the periodic diagnostic coefficient based on the absolute power of the suspected spoofing signal and perform consistency diagnosis, specifically:

[0051] (1) Calculate the mean value of the absolute power of the suspected spoofing signal in each diagnostic subset , specifically:

[0052] ;

[0053] Wherein, f represents the diagnostic subset number, P represents the absolute power, represents the absolute power of the suspected spoofing signal at the d th sampling time point within the diagnostic period;

[0054] (2) Calculate the mean square value of the absolute power of the suspected spoofing signal in each diagnostic subset , specifically:

[0055] ;

[0056] (3) Calculate the periodic diagnostic coefficient of the suspected spoofing signal, specifically:

[0057] ;

[0058] (4) Compare the magnitude relationship between the periodic diagnostic coefficient and the periodic diagnostic threshold, specifically:

[0059] ;

[0060] Wherein, when is 1, it indicates that the suspected spoofing signal is a spoofing signal, and when is 0, it indicates that the suspected spoofing signal is an interfered GNSS signal.

[0061] It should be noted that in step S4, the interval parameter v refers to the number of sampling time points between the first sampling time points of two adjacent diagnostic subsets. For example, when v = 3, eWhen = 3, the first, second, and third diagnostic subsets respectively include the {1, 2, 3}, {4, 5, 6}, and {7, 8, 9} sampling time points in the diagnostic cycle. When v = 4, e When = 3, the first, second, and third diagnostic subsets respectively include the {1, 2, 3}, {5, 6, 7}, and {9, 10, 11} sampling time points in the diagnostic cycle. When v = 2, e When = 4, the first, second, and third diagnostic subsets respectively include the {1, 2, 3, 4}, {3, 4, 5, 6}, and {5, 6, 7, 8} sampling time points in the diagnostic cycle; by setting the lengths of the diagnostic cycle, sampling interval, and diagnostic subset, long-term monitoring and flexible sampling of suspected spoofing signals are achieved; and when v ≧ e is satisfied, duplicate calculations can be avoided, while ensuring diagnostic accuracy, improving calculation efficiency, and thus reducing hardware costs.

[0062] The periodic diagnostic coefficient calculated based on the absolute power of the sampling is essentially a statistical result of the dispersion degree of the suspected spoofing signal within the diagnostic cycle. Since the dispersion of the spoofing signal fluctuates greatly, while that of the non-spoofing signal fluctuates little, a reasonable threshold can be set empirically for signal diagnosis.

[0063] The present invention also provides a general aviation spoofing signal diagnostic system based on a hybrid cycle, including:

[0064] Signal receiving module: respectively 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;

[0065] Real-time diagnostic module: form a pseudorange set with the pseudorange obtained at each sampling time point, calculate the position coefficient of each GNSS signal based on the pseudorange sets of 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 diagnostic threshold, calculate the real-time diagnostic 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 diagnostic coefficient is less than the real-time diagnostic threshold, mark the GNSS signal as a suspected spoofing signal, otherwise as a real signal;

[0066] Periodic diagnostic module: initialize the periodic diagnostic threshold and diagnostic cycle, and within the diagnostic cycle, calculate the periodic diagnostic coefficient based on the absolute power of the suspected spoofing signal. When the periodic diagnostic coefficient is greater than the periodic diagnostic threshold, mark the suspected spoofing signal as a spoofing signal, otherwise as an interfered GNSS signal;

[0067] Result output module: Output the diagnostic result of the signal.

[0068] The beneficial effects brought by the technical solution provided by the present invention at least include:

[0069] 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 using the set of pseudorange corresponding to two adjacent sampling time points and the position difference of the aircraft between the two sampling time points, it screens out suspected spoofing signals to achieve the purpose of real-time diagnosis. Then, it conducts long-term tracking and monitoring diagnosis on the suspected spoofing signals. Through the absolute power corresponding to each sampling time point within the diagnosis period and flexible sampling, it further determines 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 accuracy.

[0070] 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, it first calculates the position coefficients of the signals of each satellite based on the pseudorange, then calculates the position difference through the inertial measurement unit of the aircraft, and finally makes a 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 the calculation of the position coefficient and the consistency judgment, a good signal diagnosis effect can also be achieved. Therefore, the solution of the present invention has high applicability.

[0071] 3. When the present invention conducts long-period diagnosis, it only further diagnoses the suspected spoofing signals. Through long-term tracking and the diagnostic results of historical spoofing signals, it reasonably sets the length of the diagnostic subset and the number of diagnostic intervals to obtain the periodic diagnosis coefficient. Although the present invention uses multiple features for the diagnosis of spoofing signals, it does not simply perform brute-force parallel computing in terms of computing power, and can reduce the computing power cost on the premise of ensuring the diagnosis accuracy. Brief Description of the Drawings

[0072] Figure 1 It is a schematic flow chart of a method for diagnosing general aviation spoofing signals based on a hybrid period provided by Embodiment 1 of the present invention. Detailed Embodiment

[0073] The following further describes the present invention with reference to the drawings, but does not limit the present invention in any way. Any transformation or replacement made based on the teachings of the present invention belongs to the protection scope of the present invention.

[0074] Embodiment 1

[0075] As shown Figure 1 in the figure, this embodiment provides a general aviation spoofing signal diagnosis method based on a hybrid period, including the following steps:

[0076] S1: Obtain the GNSS signals of multiple satellites and the acceleration information of the aircraft through the receiver and inertial measurement unit carried on the aircraft, specifically:

[0077] Carry 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;

[0078] The receiver obtains the GNSS signals of multiple satellites and performs preprocessing. The GNSS signals include pseudorange and absolute power, and the preprocessing includes Gaussian filtering of the GNSS signals to eliminate signal noise;

[0079] 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.

[0080] S2: 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 of 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, specifically:

[0081] 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;

[0082] At each sampling time point, the receiver receives the GNSS signals of the same n satellites and obtains 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 the pseudorange of the th satellite obtained at the sampling time point j , represents the pseudorange of the th satellite obtained at the sampling time point jPseudoranges of satellites;

[0083] In this embodiment, the diagnosis starts from the 6th group, i.e. =6, the first sampling time point is The second sampling time point is , set the receiver to receive signals from 6 satellites, that is =6, at the sampling time point , the pseudorange set is , , at the sampling time point , the pseudorange set is , ;

[0084] Based on pseudorange set and , calculate the position coefficients of each GNSS signal, specifically:

[0085] (1) In the pseudorange set Extract Other pseudoranges , calculate the position coordinates of the aircraft 、 and , specifically:

[0086] ;

[0087] in, j ∈[1, 6], m Represents the satellite number and is an integer greater than zero. m ≠ j , for example when j =5, m You can choose 1, 2, 3, 4, and 6 respectively. Indicates the m Satellites x Axis coordinates, Indicates the m Satellites y Axis coordinates, Indicates the m Satellites z Axis coordinates, Indicates that in the pseudorange set Targeting the j Satellite pseudorange calculation for aircraft x Axis coordinates, Indicates that in the pseudorange set Targeting the j Satellite pseudorange calculation for aircraft y Axis coordinates, Indicates that in the pseudorange set The aircraft's j axial coordinates for the pseudorange calculation of the z th satellite, denotes the clock error coefficient, denotes the clock error of the th m satellite at the sampling time point;

[0088] (2) In the pseudorange set Extract the other pseudoranges except to calculate the position coordinates of the aircraft, , and , specifically:

[0089] ;

[0090] where denotes the aircraft's axial coordinates for the pseudorange calculation of the j th satellite in the pseudorange set x , denotes the aircraft's axial coordinates for the pseudorange calculation of the j th satellite in the pseudorange set y , denotes the aircraft's axial coordinates for the pseudorange calculation of the j th satellite in the pseudorange set z , denotes the clock error of the th m satellite at the sampling time point;

[0091] (3) Calculate the position coefficient j of the GNSS signal of the th satellite, specifically:

[0092] ;

[0093] (4) Repeat the above steps to obtain the position coefficients of each GNSS signal;

[0094] Calculate the position difference between the sampling time points and of the aircraft based on the linear acceleration and angular acceleration. The position calculation method here is a conventional technical means in the art and will not be elaborated.

[0095] S3: Initialize the real-time diagnosis threshold , calculate the real-time diagnostic coefficients of each GNSS signal based on the position coefficients of each GNSS signal and the position difference of the aircraft, and perform consistency diagnosis. The process of consistency diagnosis is specifically as follows:

[0096] (1) Calculate the cumulative position deviation of all GNSS signals R , specifically:

[0097] ;

[0098] Among them, represents absolute value calculation;

[0099] (2) Calculate the position deviation coefficient of each GNSS signal , specifically:

[0100] ;

[0101] Among them, represents the j th position deviation coefficient of the GNSS signal;

[0102] (3) Calculate the real-time diagnostic coefficient of each GNSS signal , specifically:

[0103] ;

[0104] Among them, represents the j th real-time diagnostic coefficient of the GNSS signal;

[0105] (4) Compare the magnitude relationship between the real-time diagnostic coefficient and the real-time diagnostic threshold, specifically:

[0106] ;

[0107] Among them, represents the j th flag bit of the GNSS signal. When is 1, it means the j th GNSS signal is a suspected spoofing signal. When is 0, it means the j th GNSS signal is a real signal;

[0108] 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.

[0109] S4: Initialize the periodic diagnostic threshold And the diagnostic cycle. During the diagnostic cycle, the periodic diagnostic coefficient is calculated based on the absolute power of the suspected deception signal, and consistency diagnosis is performed. The specific process of consistency diagnosis is as follows:

[0110] During the diagnostic cycle, one diagnostic subset is set at every v sampling time points. The number of diagnostic subsets is h , and each diagnostic subset contains e sampling time points, e where

[0111] is an integer greater than 2; v = 3, e = 3, h = 5;

[0112] (1) Calculate the mean value of the absolute power of the suspected deception signal in each diagnostic subset , specifically:

[0113] ;

[0114] where f represents the diagnostic subset serial number, f ∈[1, 5], P represents the absolute power, represents the absolute power of the suspected deception signal at the d th sampling time point within the diagnostic cycle;

[0115] That is:

[0116] ;

[0117] (2) Calculate the mean square value of the absolute power of the suspected deception signal in each diagnostic subset , specifically:

[0118] ;

[0119] That is:

[0120] ;

[0121] (3) Calculate the periodic diagnostic coefficient of the suspected deception signal, specifically:

[0122] ;

[0123] (4) Compare the magnitude relationship between the periodic diagnostic coefficient and the periodic diagnostic threshold, specifically:

[0124] ;

[0125] 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 jammed GNSS signal;

[0126] In this embodiment, , that is, the suspected spoofing signal is indeed a spoofing signal.

[0127] S5: Output the signal diagnosis result.

[0128] Embodiment 2

[0129] This embodiment also provides a method for diagnosing general aviation spoofing signals based on a hybrid period. The order of the steps adopted is the same as that in Embodiment 1. The difference lies in that in step S4, the diagnosis period includes 19 sampling time points, v =5, e =4, h =4; then there is:

[0130] (1) Calculate the mean value of the absolute power of the suspected spoofing signal in each diagnostic subset :

[0131] ;

[0132] (2) Calculate the mean value of the square of the absolute power of the suspected spoofing signal in each diagnostic subset :

[0133] ;

[0134] Based on the above data, further calculation gives: , that is, the suspected spoofing signal is indeed a spoofing signal, which is the same as the signal diagnosis result in Embodiment 1.

[0135] Embodiment 3

[0136] This embodiment provides a general aviation spoofing signal diagnosis system based on a hybrid period, including the following modules:

[0137] 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 the linear acceleration and angular acceleration of the aircraft;

[0138] Real-time diagnosis module: The pseudorange obtained at each sampling time point forms a pseudorange set. Based on the pseudorange sets of two adjacent sampling time points, the position coefficients of each GNSS signal are calculated. Based on the linear acceleration and angular acceleration, the position difference of the aircraft between the corresponding two sampling time points is calculated; 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. 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 genuine signal;

[0139] 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. When the periodic diagnosis coefficient is greater than the periodic diagnosis threshold, mark the suspected spoofing signal as a spoofing signal, otherwise it is an interfered GNSS signal;

[0140] Result output module: Output the signal diagnosis result.

[0141] Implement the general aviation spoofing signal diagnosis method based on the hybrid period in Embodiments 1 and 2 through the above system.

[0142] As used herein, the term "preferred" means used as an example, illustration, or exemplification. Any aspect or design described as "preferred" herein should not necessarily be construed as more advantageous than other aspects or designs. Instead, the use of the term "preferred" is intended to present concepts in a specific 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" means that any one of the permutations is naturally included. 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 examples.

[0143] 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 that performs the specified function of the component (e.g., it is functionally equivalent), unless otherwise indicated, even if it is not structurally equivalent to the disclosed structure that performs the function in the exemplary implementations of the present disclosure shown herein. In addition, although a particular feature of the present disclosure has been disclosed with respect to only one of several implementations, such a 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 include in a manner similar to the term "comprising."

[0144] Each functional unit in the embodiments of the present invention may be integrated into a processing module, may exist physically alone for each unit, or may be integrated into one module with two or more units. The above integrated module may be implemented in the form of hardware or in the form of a software functional module. When the above 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 devices or systems may execute the storage method in the corresponding method embodiment.

[0145] In summary, the above embodiments are one implementation of the present invention, but the implementation of the present invention is 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 methods and are all included in the protection scope of the present invention.

Claims

1. A general aviation deception signal diagnosis method based on a hybrid period, characterized in that It includes the following steps: S1: Obtain the GNSS signals of multiple satellites and the acceleration information of the aircraft through the receiver and 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: 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; 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. When the real-time diagnosis coefficient is less than the real-time diagnosis threshold, mark this GNSS signal as a suspected spoofing signal, otherwise it is a real 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. When the periodic diagnosis coefficient is greater than the periodic diagnosis threshold, mark this suspected spoofing signal as a spoofing signal, otherwise it is an interfered GNSS signal; S5: Output the signal diagnosis result.

2. The general aviation deception signal diagnosis method based on a hybrid period according to claim 1, wherein The step S1 includes: Carry the receiver and inertial measurement unit at the geometric center position of the aircraft, and use the position coordinates calculated by the receiver and inertial measurement unit as the position of the aircraft; The receiver obtains the GNSS signals of multiple satellites and performs preprocessing. The GNSS signals include pseudorange and absolute power, and the preprocessing includes performing Gaussian filtering on the GNSS signals to eliminate signal noise; The inertial measurement unit obtains the acceleration information of the aircraft. The acceleration information includes the linear acceleration and angular acceleration of the aircraft.

3. The general aviation spoofing signal diagnosis method based on a hybrid period according to claim 1, wherein The step S2 includes: Every two adjacent sampling time points are divided into a group. Starting from one of the groups for diagnosis, the first sampling time point is , and the second sampling time point is , where represents the group serial number and takes an integer greater than zero; At each sampling time point, the receiver receives GNSS signals from the same n number of satellites and obtains the corresponding pseudorange set Q , specifically: at sampling time point , the pseudorange set is , , at 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 the pseudorange of the th satellite obtained at sampling time point j , represents the pseudorange of the th satellite obtained at sampling time point j ; Based on the pseudorange set and , calculate the position coefficients of each GNSS signal; Calculate the position difference of the aircraft between the sampling time point and the sampling time point based on the linear acceleration and angular acceleration. .

4. The general aviation spoofing signal diagnosis method based on a hybrid period according to claim 3, wherein In the step S2, the calculation process of the position coefficients of each GNSS signal includes: (1) In the pseudorange set extract other pseudoranges except and calculate the position coordinates , and of the aircraft, specifically: ； Among them, 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 axis coordinate calculated for the j th satellite in the pseudorange set x , represents the th satellite's j axis coordinate calculated for the pseudorange of the aircraft in the pseudorange set y , represents the th satellite's j axis coordinate calculated for the pseudorange of the aircraft 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, specifically: , and as follows: ； Among them, represents the aircraft axis coordinate calculated for the j th satellite in the pseudorange set x ; represents the aircraft axis coordinate calculated for the j th satellite in the pseudorange set y ; represents the aircraft axis coordinate calculated for the j th satellite in the pseudorange set z ; represents the clock offset of the th m satellite at the sampling time point; (3) Calculate the position coefficient of the GNSS signal of the j th satellite, specifically: ​ ； (4) Repeat the above steps to obtain the position coefficients of each GNSS signal.

5. The general aviation spoofing signal diagnosis method based on a hybrid period according to claim 1, wherein The step S3 includes: Initialize real-time diagnostic thresholds ; Based on the position coefficients of each GNSS signal and the position difference of the aircraft, calculate the real-time diagnosis coefficients of each GNSS signal and perform consistency diagnosis, specifically: (1)Calculate the position cumulative deviation of all GNSS signals R , specifically as follows: ； Among them, represents absolute value calculation; (2) Calculate the position deviation coefficients of each GNSS signal , specifically: ； Among them, represents the position deviation coefficient of the j th GNSS signal; (3) Calculate the real-time diagnostic coefficients of each GNSS signal , specifically: ； Among them, represents j the real-time diagnostic coefficient of the (4) Compare the magnitude relationship between the real-time diagnosis coefficient and the real-time diagnosis threshold, specifically: ； 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 genuine signal.

6. The general aviation spoofing signal diagnosis method based on a hybrid period according to claim 1, wherein The step S4 includes: Initialize the diagnostic threshold for the cycle and the diagnostic cycle. During the diagnostic cycle, set a diagnostic subset every v sampling time points. The number of diagnostic subsets is h , and each diagnostic subset contains e sampling time points, e where is an integer greater than 2. For the j pseudorange corresponding to which the GNSS signal is marked as a suspected spoofing signal, within 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 of the absolute power of the suspected deception signals within each diagnostic subset , specifically: ； Among them, f represents the diagnostic subset serial number, P represents the absolute power, represents the absolute power of the suspected deception signal at the d th sampling time point within the diagnostic cycle; (2)Calculate the mean square of the absolute power of the suspected deception signals within each diagnostic subset , specifically as follows: ； (3) Calculate the periodic diagnosis coefficient of the suspected deception signal , specifically: ； (4) Compare the magnitude relationship between the periodic diagnosis coefficient and the periodic diagnosis threshold, specifically: ； Wherein, when is 1, it indicates that the suspected spoofing signal is a spoofing signal, and when is 0, it indicates that the suspected spoofing signal is an interfered GNSS signal.

7. A general aviation deception signal diagnosis system based on a hybrid period, characterized in that, It includes: Signal reception module: Obtain the GNSS signals of multiple satellites and the acceleration information of the aircraft through the receiver and 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, 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. 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: 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 GNSS signal interfered; Result output module: Output the signal diagnosis result; To implement the general aviation spoofing signal diagnosis method based on the hybrid period as described in any one of claims 1-6.

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