CN115236702B - Covert directional deception method based on exponential deception signal model - Google Patents

Abstract

The present application relates to a covert directional deception method based on an exponential deception signal model. The method comprises: using an exponential deception signal model to generate an exponential deception signal to perform GNSS deception interference on a target unmanned system from the initial moment of deception, and obtaining an objective function relationship between the combined navigation output results at different moments and the GNSS deception signal offset; recursively deducing the objective function relationship, adjusting the deception signal coefficient according to the obtained final function relationship, using the obtained position deception signal to perform directionally deception on the combined navigation system, constructing a concealed optimization design model for directional deception according to the obtained position deception offset, velocity error and attitude error, optimizing the deception signal coefficient through a traversal algorithm, and obtaining an optimal GNSS deception signal model; using the optimal GNSS deception signal model to generate an optimal GNSS deception signal to perform covert directional deception on the target unmanned system. The use of this method can improve the success rate of deception.

Description

Covert directional deception method based on exponential deception signal model

Technical Field

The present application relates to the field of unmanned system navigation technology, and in particular to a covert directional deception method based on an exponential deception signal model.

Background Art

In recent years, GNSS spoofing technology has been widely used in key security defense, public safety protection and key facility protection. For example, GNSS spoofing jammers are installed around key facilities such as airports, oil terminals and gas stations to achieve key security defense and key facility protection.

However, most of the current unmanned systems use a combined navigation mode that uses a satellite navigation system as an auxiliary means. There are deception detection modules in the navigation systems of many deception targets. The deception signals generated by conventional deception methods can be easily detected and eliminated by the deception detection module, resulting in deception failure and a low deception success rate.

Summary of the invention

Based on this, it is necessary to provide a covert directional deception method based on an exponential deception signal model that can improve the success rate of deception in response to the above technical problems.

A covert directional deception method based on an exponential deception signal model, the method comprising:

Obtain INS data, GNSS data and the INS/GNSS loose integrated navigation system model of the target unmanned system to be deceived;

Discretize the INS/GNSS loose integrated navigation system model to obtain a discretized INS/GNSS loose integrated navigation system model;

The INS data and GNSS data are fused by using discrete Kalman filter and discretized INS/GNSS loose integrated navigation system model to obtain integrated navigation output results; the integrated navigation output results include integrated navigation position output, integrated navigation velocity output and integrated navigation attitude output;

The exponential deception signal generated by the exponential deception signal model is used to perform GNSS deception interference on the target unmanned system from the initial deception moment, and the objective function relationship between the combined navigation output results and the GNSS deception signal offset at different moments when the target unmanned system is deceived is obtained;

The objective function relation is recursively deduced to obtain a final function relation between the combined navigation output result at the final moment when the target unmanned system is deceived and the GNSS deception signal offset; the final function relation includes the deception signal coefficient;

The deception signal coefficient is adjusted according to the final functional relationship to obtain a position deception signal, and the position deception signal is used to perform directional deception on the integrated navigation system to obtain the position offset of the directional deception and the speed error and attitude error caused by the directional deception to the integrated navigation system;

According to the position deception offset, velocity error and attitude error, a concealment optimization design model of directional deception is constructed. The deception signal coefficient of the concealment optimization design model is optimized through the traversal algorithm to obtain the optimal GNSS deception signal model.

The optimal GNSS spoofing signal model is used to generate the optimal GNSS spoofing signal to perform covert and directional deception on the target unmanned system.

In one embodiment, an exponential spoofing signal model is used to generate an exponential spoofing signal to perform GNSS spoofing interference on a target unmanned system from the initial moment of spoofing, and an objective function relationship between the combined navigation output results and the GNSS spoofing signal offset at different moments when the target unmanned system is spoofed is obtained, including:

The exponential deception signal model is used to generate an exponential deception signal to perform GNSS deception interference on the target unmanned system from the initial deception moment. The objective function relationship between the combined navigation position output at different moments and the GNSS deception signal offset is:

in, Indicates the combined navigation latitude position output, Indicates the combined navigation longitude position output, They are the latitude and longitude output after correct combined navigation correction when the GNSS signal is not deceptively interfered with. is the applied GNSS north velocity spoofing signal offset, is the applied GNSS east velocity spoofing signal offset, is the applied GNSS latitude spoofing signal offset, is the imposed GNSS longitude spoofing signal offset, K _∞ (3,1), K _∞ (3,2), K _∞ (3,3), K _∞ (3,4) represent the gain coefficients when calculating the latitude error estimate using the north velocity, east velocity, latitude and longitude observations, respectively; K _∞ (4,1), K _∞ (4,2), K _∞ (4,3), K _∞ (4,4) represent the gain coefficients when calculating the longitude error estimate using the north velocity, east velocity, latitude and longitude observations, respectively.

In one embodiment, an exponential spoofing signal model is used to generate an exponential spoofing signal to perform GNSS spoofing interference on the target unmanned system from the initial moment of spoofing, and the objective function relationship between the combined navigation speed output and the GNSS spoofing signal offset at different moments when the target unmanned system is spoofed is obtained as follows:

in, Indicates the north speed output of the combined navigation. Indicates the east speed output of the combined navigation. are the north velocity and east velocity output after correct combined navigation correction when the GNSS signal is not deceiving or interfered with. K _∞ (1,1), K _∞ (1,2), K _∞ (1,3), and K _∞ (1,4) represent the gain coefficients when the north velocity, east velocity, latitude, and longitude observations are used to calculate the north velocity error estimate. K _∞ (2,1), K _∞ (2,2), K _∞ (2,3), and K _∞ (2 ^, 4) represent the gain coefficients when the north velocity, east velocity, latitude, and longitude observations are used to calculate the east velocity error estimate.

In one embodiment, an exponential spoofing signal model is used to generate an exponential spoofing signal to perform GNSS spoofing interference on the target unmanned system from the initial moment of spoofing, and the objective function relationship between the combined navigation attitude output and the GNSS spoofing signal offset at different moments when the target unmanned system is deceived is obtained as follows:

in, They are the combined navigation roll angle, pitch angle and azimuth attitude outputs respectively. are the attitude results output after correct combined navigation correction when the GNSS signal is not deceiving or interfered with. K∞(5,1), _K∞ (5,2), _K∞ (5,3), and _K∞ (5,4) represent the gain coefficients for calculating the roll angle error estimate using the north velocity, east velocity, latitude, and longitude observations, respectively. _K∞ (6,1), _K∞ (6,2), _K∞ (6,3), and _K∞ (6,4) represent the gain coefficients for calculating the pitch angle error estimate using the north velocity, east velocity, latitude, and longitude observations, respectively. K∞(7,1), K∞(7,2), _K∞ (7,3), and _K∞ (7,4) represent the gain coefficients for calculating the azimuth error estimate using the north velocity, east velocity, latitude, and longitude observations, respectively.

In one embodiment, the integrated navigation system is directional-deceived using a position deceiving signal to obtain a position offset of the directional deceiving and a speed error and an attitude error caused by the directional deceiving to the integrated navigation system, including:

The position spoofing signal is used to spoof the integrated navigation system, and the offset of the position spoofing is obtained as follows:

Among them, k ₀ represents the initial moment of deception, k+n represents the final moment, k represents any moment between the initial moment of deception and the final moment, and n represents the total number of deception moments.

In one embodiment, the integrated navigation system is directional-deceived using the position deceiving signal, and the speed error caused by the directional deceiving to the integrated navigation system is obtained as follows:

In one embodiment, the integrated navigation system is directional-deceived using the position deceiving signal, and the attitude error caused by the directional deceiving to the integrated navigation system is obtained as follows:

In one embodiment, a concealment optimization design model of directional deception is constructed according to the position deception offset, velocity error and attitude error, and the deception signal coefficient of the concealment optimization design model is optimized by a traversal algorithm to obtain an optimal GNSS deception signal model, including:

According to the position deception offset, velocity error and attitude error, the concealment optimization design model of directional deception is constructed.

Among them, K _speed is the speed proportional factor, is the attitude error threshold in different directions, A _L , A _λ , B are the deception signal coefficients, is the deception distance in the same time, is the maximum value of the deception distance in the same time, They represent the true speed of the deceived target in the north and east directions at time k+n respectively.

In one of the embodiments, a velocity proportional factor and an attitude error threshold are set according to the spoofing target and the sensitivity of the navigation system to the spoofing signal detection, and the values of three spoofing signal coefficients of the GNSS spoofing signal that meet the concealment optimization design model are determined by a traversal method;

According to the determined spoofing signal coefficient value, an optimal GNSS spoofing signal model is obtained.

The above-mentioned covert directional deception method based on the exponential deception signal model, the present invention introduces the exponential deception signal model to generate an exponential deception signal, and performs GNSS deception interference on the target unmanned system from the initial moment of deception, and obtains the target function relationship between the combined navigation output results at different moments when the target unmanned system is deceived and the GNSS deception signal offset; then the target function relationship is recursively deduced to obtain the final function relationship between the combined navigation output result at the final moment when the target unmanned system is deceived and the GNSS deception signal offset, from which the influence of the GNSS deception signal on the INS/GNSS combined navigation output can be analyzed, and then the deception signal coefficient is adjusted according to the final function relationship to obtain a position deception signal, and the position deception signal is used to perform direction deception on the combined navigation system to obtain the position deception offset of the directional deception, as well as the speed error and attitude error caused by the directional deception to the combined navigation system, and the optimal GNSS deception signal model that satisfies the covert directional deception is constructed and determined by studying the constraints of the speed and attitude errors caused by the GNSS deception signal, and the optimal GNSS deception signal model is used to realize the covert directional deception of the target unmanned system in the INS/GNSS loose combined navigation mode, thereby improving the success rate of deception of the target unmanned system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic flow chart of a covert directional deception method based on an exponential deception signal model in one embodiment;

FIG2 is a schematic diagram of a deception scenario in one embodiment;

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

In one embodiment, as shown in FIG1 , a covert directional deception method based on an exponential deception signal model is provided, comprising the following steps:

Step 102, obtaining INS data, GNSS data and an INS/GNSS loosely integrated navigation system model of the target unmanned system to be deceived, discretizing the INS/GNSS loosely integrated navigation system model to obtain a discretized INS/GNSS loosely integrated navigation system model.

INS data and GNSS data are obtained by using INS system (inertial navigation system) and GNSS system (satellite navigation system) respectively. INS data includes north and east speed, latitude and longitude, roll angle, pitch angle and azimuth obtained by INS solution, and GNSS data includes north and east speed, latitude and longitude provided by GNSS. The INS/GNSS loosely combined navigation system model of the target unmanned system to be deceived is an existing model, which will not be elaborated in this patent.

Step 104, using a discrete Kalman filter and a discretized INS/GNSS loose integrated navigation system model to fuse the INS data and the GNSS data to obtain an integrated navigation output result; the integrated navigation output result includes an integrated navigation position output, an integrated navigation velocity output and an integrated navigation attitude output.

Step 106, using an exponential deception signal model to generate an exponential deception signal to perform GNSS deception interference on the target unmanned system starting from the initial deception moment, and obtaining an objective function relationship between the combined navigation output results at different moments when the target unmanned system is deceived and the GNSS deception signal offset.

Assuming that GNSS deception interference is implemented on the deception target from the initial deception time k ₀ , the position and velocity information received by the target receiver at any time k is:

in, is the applied GNSS velocity and position spoofing signal offset. This application adopts an exponential spoofing signal offset model including two coefficients A and B as

The objective function relationship between the combined navigation position output at time k and the GNSS spoofing signal offset after Kalman filter fusion and feedback correction is:

in, Indicates the combined navigation latitude position output, Indicates the combined navigation longitude position output, They are the latitude and longitude output after correct combined navigation correction when the GNSS signal is not deceptively interfered with. is the applied GNSS north velocity spoofing signal offset, is the applied GNSS east velocity spoofing signal offset, is the applied GNSS latitude spoofing signal offset, is the imposed GNSS longitude spoofing signal offset, K _∞ (3,1), K _∞ (3,2), K _∞ (3,3), K _∞ (3,4) represent the gain coefficients when calculating the latitude error estimate using the north velocity, east velocity, latitude and longitude observations, respectively; K _∞ (4,1), K _∞ (4,2), K _∞ (4,3), K _∞ (4,4) represent the gain coefficients when calculating the longitude error estimate using the north velocity, east velocity, latitude and longitude observations, respectively.

The objective function relationship between the combined navigation velocity output at any time k during the deception process and the GNSS deception signal offset is:

in, Indicates the north speed output of the combined navigation. Indicates the east speed output of the combined navigation. are the north velocity and east velocity output after correct combined navigation correction when the GNSS signal is not deceiving or interfered with. K _∞ (1,1), K _∞ (1,2), K _∞ (1,3), and K _∞ (1,4) represent the gain coefficients when the north velocity, east velocity, latitude, and longitude observations are used to calculate the north velocity error estimate. K _∞ (2,1), K _∞ (2,2), K _∞ (2,3), and K _∞ (2,4) represent the gain coefficients when the north velocity, east velocity, latitude, and longitude observations are used to calculate the east velocity error estimate.

The objective function relationship between the combined navigation attitude output at any time k in the deception process and the GNSS deception signal offset is:

in, They are the combined navigation roll angle, pitch angle and azimuth attitude outputs respectively. are the attitude results output after correct combined navigation correction when the GNSS signal is not deceiving or interfered with. K _∞ (5,1), K _∞ (5,2), K _∞ (5,3), and K _∞ (5,4) represent the gain coefficients for calculating the roll angle error estimate using the north velocity, east velocity, latitude, and longitude observations, respectively. K _∞ (6,1), K _∞ (6,2), K _∞ (6,3), and K _∞ (6,4) represent the gain coefficients for calculating the pitch angle error estimate using the north velocity, east velocity, latitude, and longitude observations, respectively. K _∞ (7,1), K _∞ (7,2), K _∞ (7,3), and K _∞ (7,4) represent the gain coefficients for calculating the azimuth error estimate using the north velocity, east velocity, latitude, and longitude observations, respectively.

It can be seen from the objective function relationship that the GNSS spoofing signal has an impact on the estimated values of the position output, velocity output and attitude output of the INS/GNSS integrated navigation, and the magnitude of this impact is related to the spoofing signal. Therefore, by designing the GNSS spoofing interference signal, it is possible to control the position offset, velocity offset and attitude offset output by the integrated navigation system.

Step 108, recursively extrapolate the objective function relationship to obtain a final function relationship between the combined navigation output result at the final moment when the target unmanned system is deceived and the GNSS deception signal offset; the final function relationship includes the deception signal coefficient.

The functional relationship between the integrated navigation output result after feedback correction at the final time k+n and the offset of the GNSS spoofing signal is obtained by recursion, and the stability of the integrated navigation output result by the GNSS spoofing signal is analyzed.

After the GNSS spoofing interference generates a position offset, although the INS will not be affected by the spoofing interference, the position and velocity offsets generated by the GNSS spoofing signal will be directly accumulated to the next moment, just like the constant drift of the inertial device. Assuming that the GNSS spoofing interference is implemented on the spoofing target from the moment k ₀ , the position output result after feedback correction at the final moment k+n when the expected directional spoofing target is reached can be obtained recursively. That is, the final functional relationship between the combined navigation position output and the GNSS spoofing signal offset is:

Similarly, the objective function relationship between the integrated navigation speed output and the GNSS spoofing signal offset is recursively deduced to obtain the final functional relationship between the integrated navigation speed output and the GNSS spoofing signal offset at the final moment. That is, GNSS spoofing interference is implemented on the spoofing target from the moment k _0. The recursive deduction shows that the integrated navigation speed output after feedback correction at the final moment k+n is:

The objective function relationship between the integrated navigation attitude output and the GNSS spoofing signal offset is recursively deduced to obtain the final functional relationship between the integrated navigation attitude output and the GNSS spoofing signal offset at the final moment. That is, GNSS spoofing interference is implemented on the spoofing target from the moment k _0. The recursive deduction can obtain the integrated navigation attitude output after feedback correction at the final moment k+n:

By analyzing the final functional relationship between the combined navigation position output and the GNSS spoofing signal offset, the coefficient function The convergence of is used to verify the stability of GNSS deception interference, so: Given 0<K _∞ (j,j)<1, 0<1-K _∞ (j,j)<1, then:

Therefore, the deception coefficient function converges. After the offset signal caused by deceptive interference is fused into the integrated navigation filter, the resulting position offset is stable. Taking the implementation of GNSS deceptive interference on the deceptive target from time k ₀ , the position output result after feedback correction at time k+n can be obtained recursively as an example. Since K _∞ (3,3)≈K _∞ (4,4), when the deceptive offsets applied to the longitude and latitude are the same, the longitude and latitude offsets output after the offsets are fused into the integrated navigation filter are the same.

Step 110, adjusting the deception signal coefficient according to the final functional relationship to obtain a position deception signal, using the position deception signal to perform directional deception on the integrated navigation system to obtain a position deception offset as well as a speed error and an attitude error caused by the directional deception.

The final functional relationship includes the deception signal coefficients _AL , _Aλ , and _BP . By adjusting the values of _AL , _Aλ , and _BP to control the position deception signal, the ratio of the deception offset in the latitude and longitude directions output by the integrated navigation system is made a stable constant, thereby achieving the purpose of combined navigation directional deception of the deception target.

Step 112, construct a concealed optimization design model for directional deception based on the position deception offset, velocity error and attitude error, optimize the deception signal coefficient of the concealed optimization design model through a traversal algorithm to obtain the optimal GNSS deception signal model; use the optimal GNSS deception signal model to generate the optimal GNSS deception signal to perform concealed directional deception on the target unmanned system.

The study constructs a covert optimization design model that satisfies the covert directional deception by using the constraints of speed and attitude errors caused by GNSS spoofing signals. The deception signal coefficients of the covert optimization design model are optimized through a traversal algorithm to obtain the optimal GNSS spoofing signal model. The optimal GNSS spoofing signal model is used to generate the optimal GNSS spoofing signal to achieve covert directional deception of the target unmanned system in the INS/GNSS loosely integrated navigation mode, thereby improving the success rate of deception of the target unmanned system.

In the deception scenario of Figure 2, a GNSS deception signal model is used to generate a GNSS deception signal to perform covert and directional deception on the target unmanned system.

In the above-mentioned covert directional deception method based on the exponential deception signal model, the present invention introduces an exponential deception signal model to generate an exponential deception signal to perform GNSS deception interference on the target unmanned system from the initial moment of deception, and obtains the target function relationship between the combined navigation output results at different moments when the target unmanned system is deceived and the GNSS deception signal offset; the target function relationship is recursively deduced to obtain the final function relationship between the combined navigation output result at the final moment when the target unmanned system is deceived and the GNSS deception signal offset, from which the influence of the GNSS deception signal on the INS/GNSS combined navigation output can be analyzed, and then the deception signal coefficient is adjusted according to the final function relationship to obtain a position deception signal, and the position deception signal is used to perform direction deception on the combined navigation system to obtain the position deception offset, the speed error and the attitude error caused by the direction deception, and the constraint conditions of the speed and attitude errors caused by the GNSS deception signal are studied to construct and determine the optimal GNSS deception signal model that satisfies the covert directional deception, and the optimal GNSS deception signal model is used to realize the covert directional deception of the target unmanned system in the INS/GNSS loose combined navigation mode, thereby improving the success rate of deception of the target unmanned system.

In one embodiment, an exponential spoofing signal model is used to generate an exponential spoofing signal to perform GNSS spoofing interference on a target unmanned system from the initial moment of spoofing, and an objective function relationship between the combined navigation output results and the GNSS spoofing signal offset at different moments when the target unmanned system is spoofed is obtained, including:

The exponential deception signal model is used to generate an exponential deception signal to perform GNSS deception interference on the target unmanned system from the initial deception moment. The objective function relationship between the combined navigation position output of the target unmanned system at different moments of deception and the GNSS deception signal offset is obtained as follows:

in, Indicates the combined navigation latitude position output, Indicates the combined navigation longitude position output, They are the latitude and longitude output after correct combined navigation correction when the GNSS signal is not deceptively interfered with. is the applied GNSS north velocity spoofing signal offset, is the applied GNSS east velocity spoofing signal offset, is the applied GNSS latitude spoofing signal offset, is the imposed GNSS longitude spoofing signal offset, K _∞ (3,1), K _∞ (3,2), K _∞ (3,3), K _∞ (3,4) represent the gain coefficients when calculating the latitude error estimate using the north velocity, east velocity, latitude and longitude observations, respectively; K _∞ (4,1), K _∞ (4,2), K _∞ (4,3), K _∞ (4,4) represent the gain coefficients when calculating the longitude error estimate using the north velocity, east velocity, latitude and longitude observations, respectively.

In one embodiment, an exponential spoofing signal model is used to generate an exponential spoofing signal to perform GNSS spoofing interference on the target unmanned system from the initial moment of spoofing, and the objective function relationship between the combined navigation speed output and the GNSS spoofing signal offset at different moments when the target unmanned system is deceived is obtained as follows:

in, Indicates the north speed output of the combined navigation. Indicates the east speed output of the combined navigation. are the north velocity and east velocity output after correct combined navigation correction when the GNSS signal is not deceiving or interfered with. K _∞ (1,1), K _∞ (1,2), K _∞ (1,3), and K _∞ (1,4) represent the gain coefficients when the north velocity, east velocity, latitude, and longitude observations are used to calculate the north velocity error estimate. K _∞ (2,1), K _∞ (2,2), K _∞ (2,3), and K _∞ (2,4) represent the gain coefficients when the north velocity, east velocity, latitude, and longitude observations are used to calculate the east velocity error estimate.

In one embodiment, an exponential deception signal model is used to generate an exponential deception signal to perform GNSS deception interference on the target unmanned system from the initial deception moment, and the objective function relationship between the combined navigation attitude output and the GNSS deception signal offset at different moments when the target unmanned system is deceived is obtained as follows:

in, They are the combined navigation roll angle, pitch angle and azimuth attitude outputs respectively. are the attitude results output after correct combined navigation correction when the GNSS signal is not deceiving or interfered with. K _∞ (5,1), K _∞ (5,2), K _∞ (5,3), and K _∞ (5,4) represent the gain coefficients for calculating the roll angle error estimate using the north velocity, east velocity, latitude, and longitude observations, respectively. K _∞ (6,1), K _∞ (6,2), K _∞ (6,3), and K _∞ (6,4) represent the gain coefficients for calculating the pitch angle error estimate using the north velocity, east velocity, latitude, and longitude observations, respectively. K _∞ (7,1), K _∞ (7,2), K _∞ (7,3), and K _∞ (7,4) represent the gain coefficients for calculating the azimuth error estimate using the north velocity, east velocity, latitude, and longitude observations, respectively.

In one embodiment, the integrated navigation system is directional-deceived using a position deceiving signal to obtain a position deceiving offset and a speed error and an attitude error caused by the directional deceiving, including:

The position spoofing signal is used to spoof the integrated navigation system, and the position spoofing offset is obtained as follows:

Among them, k ₀ represents the initial moment of deception, k+n represents the final moment, k represents any moment between the initial moment of deception and the final moment, and n represents the total number of deception moments.

In one embodiment, the position spoofing signal is used to spoof the integrated navigation system, and the speed error caused by the spoofing is:

In one embodiment, the position spoofing signal is used to perform orientation spoofing on the integrated navigation system, and the attitude error caused by the orientation spoofing is obtained as follows:

In a specific embodiment, the position spoofing offset of the integrated navigation system during the spoofing process is:

Among them, A _L , A _λ and B are non-zero GNSS spoofing signal coefficients. When the spoofing offsets in the longitude and latitude directions are not 0, the azimuth of the directional spoofing is:

Therefore, the value of K _S can be changed by designing the deception signal coefficients A _L , A _λ , that is, different directional deception effects can be achieved. When the deception offset in the longitude direction is 0, the azimuth of the directional deception can be obtained by adjusting the positive and negative of the deception signal in the latitude direction: ψ ₂ = ±90°; when the deception offset in the latitude direction is 0, the azimuth of the directional deception can be obtained by adjusting the positive and negative of the deception signal in the longitude direction: ψ ₃ = ±180°.

In summary, the azimuth of directional deception is:

The velocity error caused by the GNSS position spoofing signal after feedback correction at time k+n is:

From the relationship of _K∞ value, we can know that _K∞ (1,1) _≈K∞ (2,2); _K∞ (1,3)> _K∞ (2,4); _K∞ (3,3) _≈K∞ (4,4), then [1- _K∞ (1,1) _-K∞ (3,3)]≈[1- _K∞ (2,2) _-K∞ (4,4)], so even if the position deception offset applied on the longitude and latitude is the same, the impact of the GNSS position deception signal on the northbound speed is always greater than the impact on the eastbound speed. When designing the concealment optimization design model, the present application designs the deception signal to reduce the impact of the deception signal on the speed output, avoids the speed error caused by the deception from being detected by the deception detection algorithm, and thus improves the concealment of the deception algorithm.

The attitude error caused by the GNSS position spoofing signal after feedback correction at time k+n is:

in, They are the attitude results after correct correction when the GNSS signal is not spoofed.

Since _K∞ ( _{3,3)≈K∞(4,4),K∞(6,3)>K∞ ( 5,4} ), _K∞ (7,3)> _K∞ (6,3), it can be seen that GNSS deception interference will affect the attitude of the integrated navigation output, especially the azimuth. And this influence is related to the deception interference signal. The present application designs the deception signal to reduce the influence of the deception signal on the attitude output, thereby making the deception detector of the target unmanned system invalid, thereby achieving the purpose of covert deception.

In one embodiment, a concealment optimization design model of directional deception is constructed according to the position deception offset, velocity error and attitude error, and the deception signal coefficient of the concealment optimization design model is optimized by a traversal algorithm to obtain an optimal GNSS deception signal model, including:

The concealment optimization design model of directional deception is constructed based on the position deception offset, velocity error and attitude error:

Among them, K _speed is the speed proportional factor, is the attitude error threshold in different directions, A _L , A _λ , B are the deception signal coefficients, is the deception distance in the same time, is the maximum value of the deception distance in the same time, They represent the true speed of the deceived target in the north and east directions at time k+n respectively.

In one of the embodiments, a velocity proportional factor and an attitude error threshold are set according to the spoofing target and the sensitivity of the navigation system to the spoofing signal detection, and the values of three spoofing signal coefficients of the GNSS spoofing signal that meet the constraint conditions in the concealment optimization design model are determined by a traversal method;

According to the determined spoofing signal coefficient value, an optimal GNSS spoofing signal model is obtained.

In a specific embodiment, in order to optimize the covert directional deception of the target unmanned system, it is necessary to maximize the deception distance within a fixed time period while ensuring the concealment of the directional deception, that is, the target unmanned system is deceived at the farthest distance. From the influence of the GNSS position deception signal on the INS/GNSS combined navigation attitude and velocity estimation values, it can be seen that in order to achieve covert directional deception, it is necessary to reduce the influence of the GNSS position deception signal on the attitude and velocity. The key point is to reduce the influence of the GNSS position deception signal on the north velocity and heading angle, thereby avoiding the GNSS deception signal from being detected by the deception detection algorithm of the target unmanned platform. Through the analysis results of the position deception offset, the velocity error and the attitude error caused by the directional deception, reasonable constraints are set to enhance the concealment of the directional deception, and the concealment optimization design of the directional deception is converted into the following model:

in, is the deception distance in the same time, is the maximum value of the deception distance in the same time; They represent the true speed of the deceptive target in the north and east directions at time k+n respectively. If the deceptive device and the deceptive target are located on the same carrier, then is the speed of the vehicle; if the spoofer and the target are not on the same vehicle, it means that the north and east speeds of the spoofer vehicle are approximately equal when the speeds of the spoofer and the target are approximately equal, and K _speed is the speed proportional factor; are the attitude error thresholds respectively.

The values of the three coefficients _AL , _Aλ , and B of the GNSS spoofing signal that meet the conditions are determined by the traversal method. The required optimal GNSS spoofing signal model can be obtained through the values of _AL , _Aλ , and B, thereby realizing the covert directional spoofing of the target unmanned system in the INS/GNSS loosely integrated navigation mode.

When the speed proportional factor K _speed and attitude error threshold are set Afterwards, the values of the three coefficients _AL , _Aλ , and B of the GNSS spoofing signal that meets the conditions can be determined by the traversal method, and the required GNSS spoofing signal model can be obtained through the values of _AL , _Aλ , and B. Among them, It can be set to different values according to the spoofing target and the sensitivity of the navigation system to spoofing signal detection.

It should be understood that, although the various steps in the flowchart of FIG. 1 are shown in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in FIG. 1 may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims (9)

1. A covert directional deception method based on an exponential deception signal model, characterized in that the method comprises: Obtain INS data, GNSS data and the INS/GNSS loose integrated navigation system model of the target unmanned system to be deceived; Discretizing the INS/GNSS loosely integrated navigation system model to obtain a discretized INS/GNSS loosely integrated navigation system model; The INS data and the GNSS data are fused by using a discrete Kalman filter and a discretized INS/GNSS loose integrated navigation system model to obtain an integrated navigation output result; the integrated navigation output result includes an integrated navigation position output, an integrated navigation speed output and an integrated navigation attitude output; Using an exponential spoofing signal generated by an exponential spoofing signal model to perform GNSS spoofing interference on the target unmanned system from the initial moment of spoofing, and obtaining an objective function relationship between the combined navigation output results and the GNSS spoofing signal offset at different moments when the target unmanned system is deceived; Recursively deducing the objective function relational expression, obtaining a final function relational expression of the combined navigation output result at the final moment when the target unmanned system is deceived and the GNSS deception signal offset; the final function relational expression includes a deception signal coefficient; The deception signal coefficient is adjusted according to the final functional relationship to obtain a position deception signal, and the position deception signal is used to perform directional deception on the integrated navigation system to obtain a position deception offset, and a speed error and an attitude error caused by the directional deception to the integrated navigation system; A concealment optimization design model of directional deception is constructed according to the position deception offset, velocity error and attitude error, and a deception signal coefficient of the concealment optimization design model is optimized by a traversal algorithm to obtain an optimal GNSS deception signal model; The optimal GNSS spoofing signal model is used to generate an optimal GNSS spoofing signal to perform covert directional spoofing on the target unmanned system. 2. The method according to claim 1 is characterized in that the exponential deception signal generated by the exponential deception signal model is used to perform GNSS deception interference on the target unmanned system from the initial deception moment, and the objective function relationship between the combined navigation output result and the GNSS deception signal offset at different moments when the target unmanned system is deceived is obtained, comprising: The exponential deception signal generated by the exponential deception signal model is used to perform GNSS deception interference on the target unmanned system from the initial deception moment, and the objective function relationship between the combined navigation position output of the target unmanned system at different moments of being deceived and the GNSS deception signal offset is obtained as follows: in, Indicates the combined navigation latitude position output, Indicates the combined navigation longitude position output, They are the latitude and longitude output after correct combined navigation correction when the GNSS signal is not deceptively interfered with. is the applied GNSS north velocity spoofing signal offset, is the applied GNSS east velocity spoofing signal offset, is the applied GNSS latitude spoofing signal offset, is the imposed GNSS longitude spoofing signal offset, K _∞ (3,1), K _∞ (3,2), K _∞ (3,3), K _∞ (3,4) represent the gain coefficients when calculating the latitude error estimate using the north velocity, east velocity, latitude and longitude observations, respectively; K _∞ (4,1), K _∞ (4,2), K _∞ (4,3), K _∞ (4,4) represent the gain coefficients when calculating the longitude error estimate using the north velocity, east velocity, latitude and longitude observations, respectively. 3. The method according to claim 2, characterized in that the method further comprises: The exponential deception signal generated by the exponential deception signal model is used to perform GNSS deception interference on the target unmanned system from the initial deception moment, and the objective function relationship between the combined navigation speed output and the GNSS deception signal offset at different moments when the target unmanned system is deceived is obtained as follows: in, Indicates the north speed output of the combined navigation. Indicates the east speed output of the combined navigation. are the north velocity and east velocity output after correct combined navigation correction when the GNSS signal is not deceiving or interfered with. K _∞ (1,1), K _∞ (1,2), K _∞ (1,3), and K _∞ (1,4) represent the gain coefficients when the north velocity, east velocity, latitude, and longitude observations are used to calculate the north velocity error estimate. K _∞ (2,1), K _∞ (2,2), K _∞ (2,3), and K _∞ (2,4) represent the gain coefficients when the north velocity, east velocity, latitude, and longitude observations are used to calculate the east velocity error estimate. 4. The method according to claim 3, characterized in that the method further comprises: The exponential deception signal generated by the exponential deception signal model is used to perform GNSS deception interference on the target unmanned system from the initial deception moment, and the objective function relationship between the combined navigation attitude output and the GNSS deception signal offset of the target unmanned system at different moments of being deceived is obtained as follows: in, They are the combined navigation roll angle, pitch angle and azimuth attitude outputs respectively. are the attitude results output after correct combined navigation correction when the GNSS signal is not deceiving or interfered with. K _∞ (5,1), K _∞ (5,2), K _∞ (5,3), and K _∞ (5,4) represent the gain coefficients for calculating the roll angle error estimate using the north velocity, east velocity, latitude, and longitude observations, respectively. K _∞ (6,1), K _∞ (6,2), K _∞ (6,3), and K _∞ (6,4) represent the gain coefficients for calculating the pitch angle error estimate using the north velocity, east velocity, latitude, and longitude observations, respectively. K _∞ (7,1), K _∞ (7,2), K _∞ (7,3), and K _∞ (7,4) represent the gain coefficients for calculating the azimuth error estimate using the north velocity, east velocity, latitude, and longitude observations, respectively. 5. The method according to claim 4 is characterized in that the integrated navigation system is directional-deceived by using the position deceiving signal to obtain the position offset of the directional deceiving and the speed error and attitude error caused by the directional deceiving to the integrated navigation system, comprising: The position deception signal is used to perform directional deception on the integrated navigation system, and the position offset of the directional deception is obtained as follows: Among them, k ₀ represents the initial moment of deception, k+n represents the final moment, k represents any moment between the initial moment of deception and the final moment, and n represents the total number of deception moments. 6. The method according to claim 5, characterized in that the method further comprises: The position deception signal is used to perform directional deception on the integrated navigation system, and the speed error caused by the directional deception on the integrated navigation system is obtained as follows: 7. The method according to claim 6, characterized in that the method further comprises: The position deception signal is used to perform directional deception on the integrated navigation system, and the attitude error caused by the directional deception on the integrated navigation system is obtained as follows: 8. The method according to claim 7 is characterized in that a concealment optimization design model of directional deception is constructed according to the position deception offset, velocity error and attitude error, and the deception signal coefficient of the concealment optimization design model is optimized by a traversal algorithm to obtain an optimal GNSS deception signal model, comprising: According to the position deception offset, speed error and attitude error, the concealment optimization design model of directional deception is constructed as follows: Among them, K _speed is the speed proportional factor, is the attitude error threshold in different directions, A _L , A _λ , B are the deception signal coefficients, is the deception distance in the same time, is the maximum value of the deception distance in the same time, They represent the true speed of the deceived target in the north and east directions at time k+n respectively. 9. The method according to claim 8, characterized in that the method further comprises: The velocity scale factor and attitude error threshold are set according to the spoofing target and the sensitivity of the navigation system to the spoofing signal detection, and the values of the three spoofing signal coefficients of the GNSS spoofing signal that meet the concealment optimization design model are determined by the traversal method; According to the determined spoofing signal coefficient value, an optimal GNSS spoofing signal model is obtained.