5G-assisted high-robustness GNSS deception jamming detection method in GNSS-5G hybrid positioning system

Abstract

The invention discloses a 5G-assisted high-robustness GNSS deception jamming detection method in a GNSS-5G hybrid positioning system, and belongs to the field of satellite navigation signal processing, and the method comprises the following steps: building an improved information model through removing GNSS pseudo-range observed quantity in traditional information and replacing prior state estimation based on EKF with an SPP positioning result of GNSS; and performing normalized sum of squares on the improved innovation model to construct deception jamming detection statistics. The method has higher robustness in a deception maneuver mixed scene, and compared with a traditional method, the method has the advantage that high false alarm caused by user maneuver is remarkably inhibited.

Description

Technical Field

[0001] The present invention belongs to the technical field of satellite navigation signal processing, and in particular to a 5G-assisted highly robust GNSS spoofing interference detection method in a GNSS-5G hybrid positioning system. Background Art

[0002] The Global Navigation Satellite System (GNSS) is widely used to provide positioning services, but due to its open signal structure and weak signal strength, it is extremely vulnerable to attacks. Spoofing interference has become a major threat to GNSS users. Compared with suppression interference, spoofing interference is more difficult to detect, has high concealment and interference efficiency, and poses a serious threat to the safety of autonomous vehicles and other unmanned systems.

[0003] In order to deal with the threat of deception interference, researchers have proposed a variety of deception detection methods. Generally speaking, they can be divided into two categories: receiver autonomous detection methods and external information assisted detection methods. The former detects deception interference by the receiver itself. It uses different signal characteristics as indicators to identify deception interference, such as signal power, carrier-to-noise ratio (C / N0), signal quality indicators, and correlator output. However, since users do not know the specific deception mode in practical applications, such methods have difficulties in determining the optimal signal characteristics. In addition, environmental effects such as multipath can also cause these methods to have a high false alarm rate, reducing reliability. The latter uses external information to obtain additional protection against deception interference. External information can be obtained through array antennas, inertial navigation sensors (INS), or reference receivers.

[0004] In recent years, the hybrid positioning method of GNSS and 5G fusion has received more and more attention. Since 5G signals and satellite navigation signals are in different frequency bands, and 5G signals do not require periodic correction of satellite navigation observations, they can provide reliable external assistance for GNSS and effectively improve the GNSS's deception interference detection capability. The traditional anomaly detection technology based on Kalman filter innovation can be extended to the GNSS-5G fusion system to achieve 5G-assisted deception interference detection. However, the existing deception interference detection algorithm based on innovation often assumes that the user terminal moves at a uniform speed, and lacks sufficient consideration for the complex changes in terminal maneuvers. When the user accelerates, decelerates, yaws or turns left and right in a short period of time, the mismatch between the EKF system model and the actual trajectory of the receiver will cause significant model errors, resulting in abnormal filter innovation values ​​and an increase in false alarm rates. At present, the research on the highly robust deception interference detection method assisted by 5G in the above-mentioned complex dynamic scenarios is still in the exploratory stage and needs further in-depth research. Therefore, the present invention proposes a highly robust GNSS deception interference detection method assisted by 5G in a GNSS-5G hybrid positioning system. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a 5G-assisted highly robust GNSS spoofing interference detection method in a GNSS-5G hybrid positioning system to solve the problems existing in the above-mentioned prior art.

[0006] To achieve the above object, the present invention provides a 5G-assisted highly robust GNSS spoofing interference detection method in a GNSS-5G hybrid positioning system, comprising the following steps:

[0007] An improved innovation model is constructed by removing the GNSS pseudorange observations in the traditional innovation and replacing the EKF-based prior state estimation with the GNSS SPP positioning results.

[0008] The improved innovation model is normalized and squared to construct a deception interference detection statistic.

[0009] Optionally, the improved innovation model constructed is:

[0010]

[0011] In the formula, κ[n] represents the improved new information, z BS [n] represents the set of 5G and GNSS observations without GNSS pseudorange observations, It represents the predicted value of 5G observation obtained by mapping the positioning result of SPP, Indicates the SPP positioning result.

[0012] Optionally, the process of constructing the spoofing interference detection statistic further includes calculating a 5G observation quantity prediction value based on the improved innovation model, and determining that the improved innovation will not cause a false alarm by comparing and analyzing the 5G observation quantity prediction value with a true value;

[0013] When there is deception interference, there is a difference between the predicted value of the 5G observation quantity obtained by mapping the positioning result of the SPP and the true value;

[0014] When there is no deception interference but user maneuvering, the predicted value of the 5G observation quantity obtained by mapping the SPP positioning result is consistent with the true value.

[0015] Optionally, the deception interference detection statistic constructed based on the improved innovation model is:

[0016] ζ[n]=κ T [n]υ -1 [n]κ[n]

[0017] Where ζ[n] represents the deception interference detection statistic, κ[n] represents the improved innovation, and υ[n] represents the noise covariance of the improved innovation.

[0018] Optionally, the noise covariance of the improved new information includes the covariance of the 5G observation quantity and the projection of the covariance of the SPP positioning result in the 5G observation domain;

[0019] Wherein, the expression of the noise covariance of the improved innovation is:

[0020]

[0021] In the formula, R BS [n] represents the covariance of 5G observations, R SPP [n] represents the projection of SPP covariance to 5G observations, H BS [n] is the Jacobian of the 5G observation equation.

[0022] Optionally, calculating a detection threshold based on a spoofing interference detection statistic;

[0023] Among them, the expression for calculating the detection threshold is:

[0024] T ξ =F -1 [(1-P fa )3L]={x:F(x|3L)=1-P fa}

[0025] Where, T ξ Represents the detection threshold, P fa is the false alarm probability, and F(x|·) is the cumulative probability density function.

[0026] Optionally, the update rate of the observation quantity in the GNSS-5G hybrid positioning system is changed by improving the value of the new information and the detection threshold.

[0027] The present invention also discloses a computer terminal device, comprising:

[0028] one or more processors;

[0029] A memory, coupled to the processor, for storing one or more programs;

[0030] When the one or more programs are executed by the one or more processors, the one or more processors implement a 5G-assisted highly robust GNSS spoofing interference detection method in a GNSS-5G hybrid positioning system.

[0031] The present invention also discloses a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a highly robust GNSS spoofing interference detection method assisted by 5G in a GNSS-5G hybrid positioning system is implemented.

[0032] Compared with the prior art, the present invention has the following advantages and technical effects:

[0033] The present invention constructs an improved innovation model by removing the GNSS pseudorange observation in the traditional innovation and replacing the EKF-based prior state estimation with the SPP positioning result of the GNSS, and then constructs a deception interference detection statistic by normalizing the square sum of the improved innovation model.

[0034] The present invention effectively avoids the impact of EKF prediction errors in maneuvering scenarios by constructing improved new information, thereby improving the robustness of deception interference detection; compared with traditional methods, the present invention can improve the average deception detection rate and improve the security and stability of GNSS applications; the present invention is combined with 5G technology, has a wide range of applications, and has certain strategic significance for national security. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0036] Figure 1 This is a principle comparison diagram of the traditional innovation and the proposed improved innovation in the GNSS-5G fusion positioning system according to an embodiment of the present invention;

[0037] Figure 2 1 is a diagram showing the changes in acceleration and speed of a user in a deception and maneuvering hybrid simulation scenario according to an embodiment of the present invention, wherein (a) represents the real trajectory acceleration, (b) represents the deception trajectory acceleration, (c) represents the real trajectory speed, and (d) represents the deception trajectory speed;

[0038] Figure 3 A schematic diagram of a simulation of a deception jamming implementation process according to an embodiment of the present invention;

[0039] Figure 4 is a deception detection rate curve at different deception separation speeds in a pure deception interference scenario according to an embodiment of the present invention;

[0040] Figure 5 : ROC curves of two methods in a pure deception interference scenario according to an embodiment of the present invention;

[0041] Figure 6 The trigger rate changes over time in the deception and maneuvering mixed scenario of the embodiment of the present invention;

[0042] Figure 7 is a curve showing a change in the actual false alarm rate versus the theoretical false alarm rate in a mixed scenario of deception and maneuvering according to an embodiment of the present invention;

[0043] Figure 8is a curve showing a change in the deception detection rate versus the actual false alarm rate in an embodiment of the present invention;

[0044] Fig. 9 1 is an actual false alarm rate curve when the user is maneuvering at different system update rates according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0046] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0047] Embodiment 1

[0048] In order to solve the problem of high false alarm of deception detection caused by user maneuvers, the present invention proposes a 5G-assisted highly robust GNSS deception interference detection method. First, by establishing a mathematical model of new information under deception interference and user maneuvering scenarios, the intrinsic mechanism of abnormal new information and high false alarm caused by user maneuvers is explored. Secondly, by mapping the single-point positioning results of GNSS to the 5G observation domain to replace the observations predicted by EKF, and calculating the difference between the observations and the actual observations, an improved deception detection statistic is innovatively designed. Finally, through simulation and sports car tests, the detection performance of the proposed method in deception interference scenarios is evaluated, and it is verified that it has higher robustness in the mixed scenario of deception maneuvers, and can significantly suppress the high false alarm problem caused by user maneuvers.

[0049] This embodiment provides a 5G-assisted highly robust GNSS spoofing interference detection method in a GNSS-5G hybrid positioning system, including the following steps:

[0050] An improved innovation model is constructed by removing the GNSS pseudorange observations in the traditional innovation and replacing the EKF-based prior state estimation with the GNSS SPP positioning result; as a specific implementation of this embodiment, as shown in step 1:

[0051] Step 1: Build improved innovation based on traditional innovation

[0052] Based on the traditional new information calculation formula, the GNSS pseudorange observation in the first term is deleted, and the second term based on the EKF prior state estimation is replaced by the SPP positioning result of GNSS. The predicted value of 5G observation is obtained by mapping it to the 5G observation domain, which effectively avoids the influence of EKF prediction error in maneuvering scenarios, thereby improving the robustness of deception interference detection.

[0053] Innovation is a process variable generated during the EKF iteration, which represents the difference between the observation and its prior estimate. Let J represent the total number of all observations in the GNSS-5G fusion positioning system, including TOA, A-AOD and Z-AOD observations of 5G base stations and GNSS pseudorange observations, so J = 3K + M, where K represents the number of base stations and M represents the number of visible satellites. The dimension of the innovation γ[n] is the same as that of the observation, which is J. The innovation corresponding to all J observations is defined as

[0054]

[0055] Where z[n] represents the set of 5G and GNSS observations, represents the previous state vector containing position, velocity, and clock error, and h is an observation matrix composed of Definition, and By Definition, where represents the distance from the UE to the satellite, and ω[n] represents the parameter defining the UE clock error.

[0056] When there is no deceptive interference, γ[n] has a mean of zero and a covariance of The Gaussian distribution of It can be expressed as

[0057]

[0058] Among them, R[n] represents the covariance of 5G observations, H[n] is the 5G observation equation matrix, and Y - [n] is the matrix of UE positions in the previous state.

[0059] The deception detection statistic q[n] can be constructed using the normalized sum of squares of all J observations, that is,

[0060]

[0061] When there is no deception interference, q[n] follows a chi-square distribution with degrees of freedom (DOF) of J.

[0062] q[n]~χ(J)

[0063] Then, the detection threshold can be solved by the following formula:

[0064] T q =F -1 [(1-P fa )J] = {x:F(x|J) = 1-P fa}

[0065] Among them, P fa is the false alarm probability, and F(x|J) is the cumulative probability density function, defined as

[0066]

[0067] Where Γ(·) represents the gamma function. If the deception detection statistic q[n] is greater than T d , then there is deception interference; otherwise, there is no deception signal.

[0068] The traditional method uses the difference between the observed value and its prior estimate to construct a deception detection statistic. In non-stationary scenarios such as user maneuvers, this method has a serious false alarm problem. In order to solve the above problem, the present invention proposes an improved new information that can effectively detect deception interference while avoiding the false alarm problem in user maneuvering scenarios.

[0069] Figure 1 The proposed innovation is compared with the traditional innovation construction method. The proposed innovation κ[n] is defined as follows

[0070]

[0071] Compared with the traditional new information γ[n], κ[n] has two significant characteristics. First, its first term only contains the three observations of TOA, A-AOD and Z-AOD of the 5G base station, but does not contain the GNSS pseudorange observations. In addition, the second term does not use the predicted value of the observation based on EKF, but uses the SPP positioning result of GNSS. The predicted value of 5G observation is obtained by mapping it to the 5G observation domain. In this process, the SPP positioning result of GNSS can be realized by the WLS algorithm. Replaced with SPP positioning results The proposed method can effectively avoid the EKF prediction error in maneuvering scenarios. pre [n ma ], thereby improving the robustness of deception interference detection.

[0072] The improved innovation model is normalized and squared to construct a deception interference detection statistic, as a specific implementation of this embodiment, as shown in step 2-3:

[0073] Step 2: Obtain the predicted value of the observation and compare it with the actual value

[0074] Map the SPP positioning results to the 5G observation domain to obtain the predicted value of the 5G observation quantity, and compare the predicted value with the true value. Substitute the data into the improved new information constructed in step 1.

[0075] By mapping the SPP positioning results to the 5G observation domain, this embodiment can obtain the predicted value of the 5G observation quantity. By comparing the predicted value with the true value, it can be found that when there is deception interference, the SPP positioning results of GNSS will be manipulated by the interference source, causing the positioning results to deviate from the true position. At this time, the predicted value of the 5G observation quantity obtained by mapping the SPP positioning results is The difference between the actual observation value and the proposed innovation value will increase significantly. Therefore, the proposed innovation value will gradually increase and eventually exceed the preset threshold. However, when there is no deception interference but there is user maneuvering, the GNSS SPP positioning result remains accurate. In this case, the 5G observation value prediction value obtained by mapping the positioning result based on SPP is It is consistent with the actual 5G observation. Therefore, at this time, the improved new information still appears as zero-mean Gaussian noise, which will not cause false alarms. In view of the fact that this embodiment maps the SPP positioning results to the 5G observation domain and constructs the improved new information accordingly, the dimension of the new information is the same as the dimension of the 5G observation, both of which are 3L.

[0076] Step 3: Construct spoofing jammer detection statistics

[0077] Using the data obtained in step 2, the deception interference detection statistic is constructed by calculating the improved normalized sum of squares of new information, and the detection threshold of the proposed method is obtained.

[0078] The deception jamming detection statistic ζ[n] is constructed by calculating the improved normalized square sum of innovations as follows:

[0079] ζ[n]=κ T [n]υ -1 [n]κ[n]

[0080] Among them, κ[n] represents the improved innovation. The noise covariance υ[n] of the improved innovation consists of two parts. The first part is the covariance of the 5G observations, and the second part is the covariance R of the SPP positioning results. SPP [n] Projection in the 5G observation domain, i.e.

[0081]

[0082] Among them, R BS [n] represents the covariance of 5G observations, and R SPP [n] is to project the SPP covariance onto the 5G observation, H BS [n] is the Jacobian of the 5G observation equation.

[0083] When there is no deception interference, the deception interference detection statistic ζ[n] follows the chi-square distribution

[0084] ζ[n]~χ(3L)

[0085] The DOF of the spoofing jammer detection statistic ζ[n] is equal to the dimension 3L of the 5G observation. Therefore, the detection threshold T of the proposed method is ξ It can be obtained by the following formula

[0086] T ξ =F -1 [(1-P fa )3L]={x:F(x|3L)=1-P fa}

[0087] Where, T ξ Represents the detection threshold, P fa is the false alarm probability, and F(x|·) is the cumulative probability density function.

[0088] Step 4: Evaluate the model based on update rate

[0089] The update rate of the observation is one of the important factors affecting the false alarm rate of deception detection in user dynamic scenarios. By calculating and analyzing the update rate data, the model obtained in step 3 can be evaluated and optimized.

[0090] Standard EKF usually requires GNSS and 5G observations to have the same observation update rate. Considering that the standard update rate of pseudorange observations of commercial GNSS receivers is 1Hz, the update rate of 5G observations should also be uniformly set to 1Hz.

[0091] Through calculation and analysis, it can be seen that the impact of increasing the system update rate is reflected in three aspects: first, the value of new information becomes smaller, second, the detection threshold becomes higher, and third, the number of detections in the same time period increases. Among them, the smaller the value of new information and the higher the threshold, the more false alarms caused by user maneuvers, while the more detections exacerbate the false alarm problem in user maneuvers.

[0092] The present invention considers two scenarios, pure deception interference scenario and deception maneuver hybrid scenario, respectively during simulation to evaluate the deception interference detection performance of the proposed method and its robustness under maneuvering conditions. In the pure deception interference scenario, the user maintains a constant speed of 10m / s to drive north. From the 90th second, the deception source starts to transmit deception signals and attempts to manipulate the satellite navigation positioning results to make them shift eastward. In the deception maneuver hybrid scenario, the user also drives northward at an initial speed of 10m / s and performs acceleration and deceleration maneuvers in the north direction from the 50th second. From the 90th second, the deception source transmits deception signals and attempts to inject an eastward position offset into the user at a deception separation speed of 1m / s. Here, the deception separation speed V spIt is used to characterize the intensity of deception interference, which indicates the speed at which the deception source attempts to control the navigation positioning solution to deviate eastward. In order to verify the robustness of the proposed method in the maneuvering scenario, this embodiment uses a polynomial model to simulate the acceleration and deceleration process of the vehicle. In this process, the vehicle first decelerates from an initial speed of 10m / s to a minimum speed v min = 0m / s, then accelerate to the maximum speed v max = 25m / s, and the acceleration and deceleration are carried out according to this rule, thus forming Figure 2 (a) shows the S-shaped acceleration change and Figure 2 (c) The speed change shown. Figure 2 (b) and Figure 2 (d) shows the acceleration and velocity changes of the deceptive trajectory respectively.

[0093] In the simulation, the user receiver uses the navigation signal of GPS L1 band for positioning. Pseudorange noise mainly comes from satellite orbit determination error, satellite clock error, atmospheric disturbance, internal noise of the receiver and multipath effect. The above errors are independent of each other, and the sum of their errors approximately obeys a Gaussian distribution with a mean of zero, while their standard deviation is related to the satellite elevation angle. In the simulation, the standard deviation of pseudorange noise of different satellites is uniformly set to 2m. For other simulation parameters related to GNSS satellites, see Table 1.

[0094] In terms of 5G base station simulation, this embodiment mainly considers the microcell scenario. The distance between base stations dBS-BS is set to 50m, and the distance from the base station to the roadside dBS-road is set to 30m. All 5G base stations are evenly distributed on one side of the road in a collinear manner. Figure 3 As shown. In order to evaluate the basic performance of the proposed method, this embodiment mainly considers the LOS propagation condition, which is widely adopted in related work. Under this condition, the user always accesses the nearest LOS base station. For the TOA, A-AOD and Z-AOD observations of 5G, their noise variance is set according to the Cramer-Rao lower bound. The simulation parameters of the 5G base station are shown in Table 2. The process noise standard deviation σ in the EKF v Set to 3.5 m / s, the standard deviation of the clock drift model noise in the EKF is σ ε Set to 10-4.

[0095] Table 1

[0096]

[0097] Table 2

[0098]

[0099]

[0100] Figure 4The proposed method and the traditional method are compared in terms of the variation of the deception detection rate over time in pure deception interference scenarios. sp The results show that V sp =4m / s when the deception detection rate is significantly greater than V sp =0.5m / s deception detection rate, which means that with the deception separation speed V sp The detection rate is significantly improved, so a faster deception separation speed is more conducive to the detector to identify deception interference. Figure 4 The results show that the detection performance of the proposed method is significantly better than that of the traditional method in the first few seconds of the spoofing attack. sp =0.5m / s, the proposed method improves the detection rate from 55% of the traditional method to 84% at the 102nd second, an increase of 29%. Considering that the detection of slow-changing deception attacks is more difficult, the performance improvement of the proposed method in this scenario is more practical.

[0101] Figure 5 The receiver operating characteristic (ROC) curves of the proposed method and the traditional method are compared. The ROC curve is an important graphical tool for measuring the discrimination ability of a detector. Its vertical axis represents the detection rate and the horizontal axis represents a set of theoretical false alarm rates. This embodiment reflects the overall ROC performance in the deception jamming scenario by calculating the average deception detection rate in the signal segment from the 90th second to the 120th second. The results show that when V sp =4m / s and the theoretical false alarm rate is higher than 10-4, the average deception detection rates of the proposed method and the traditional method are both over 92%. sp =0.5m / s and the theoretical false alarm rate is 10-4, the proposed method improves the average detection rate from 56% of the traditional method to 68%. The above results further verify the advantages of the proposed method in detecting slowly varying deception attacks.

[0102] This example evaluates the performance of the proposed method in a mixed scenario of deception and maneuvering. In the simulation, the user starts maneuvering from the 50th second, while the deception interference starts from the 90th second. The specific changes in the real speed and the deception speed are shown in the figure below. Figure 2 shown. Figure 6The results show that the detection rate changes over time when the theoretical false alarm rate is 10-4. The results show that during the period from 50 to 90 seconds, there is only user maneuvering but no deception interference. At this time, the traditional method still frequently triggers the threshold, resulting in serious false alarm problems. In particular, the false alarm rate of the traditional method exceeds 70% at the 56th second, the 69th second, and the 80th to 84th second. The root cause of this phenomenon is Figure 2 The user acceleration shown in (a) reaches its maximum value at the above moment. As mentioned above, the alarm caused by the user's maneuver is a false alarm. Such a high false alarm rate seriously affects the effectiveness of the traditional method. In contrast, during the maneuver from 50 to 90 seconds, the false alarm rate of the proposed method remains below 0.2%. In addition, Figure 6 It also shows that after the 90th second of deception interference, the triggering rate of both methods increases significantly. In particular, within the first 10 seconds of deception, the deception detection rate of the proposed method shows a significant advantage over the traditional method. Taking the 96th second as an example, the proposed method increases the deception detection rate from 47% of the traditional method to 78%, which greatly improves the deception detection performance.

[0103] By analyzing the data from the 50th to the 90th second, Figure 7 The variation of the actual false alarm rate with the theoretical false alarm rate is further analyzed. During this period, there is only user maneuvering but no deception interference. When the theoretical false alarm rate is set to 10-4 and 10-3, the actual false alarm rate of the traditional method is as high as 39% and 49%, respectively, which seriously weakens the effectiveness of the method in practical applications. In contrast, the proposed method reduces the actual false alarm rate to 0.2% and 0.7%, respectively, significantly enhancing the robustness of the detection algorithm in maneuvering scenarios.

[0104] Figure 8 The relationship between the deception detection rate and the actual false alarm rate in the mixed deception and maneuvering scenario is analyzed. The results show that the deception detection rate of the proposed method is significantly better than that of the traditional method. In particular, when the actual false alarm rate is 10-1, the proposed method increases the average deception detection rate from 75% of the traditional method to 90%, and the detection performance advantage is obvious.

[0105] The system update rate will also affect the performance of the proposed method. In the previous simulation, this embodiment sets the system update rate to 1 Hz. To further explore the impact of the system update rate on performance, Fig. 9The actual false alarm rate curves caused by user maneuvers at different update rates in the mixed scenario of deception and maneuvering are compared. For the convenience of comparison, the CFAR criterion is adopted, and the data from the 50th to the 90th second are analyzed in detail. For the traditional method, when the theoretical false alarm rate is set to 0.01 and the system update rate increases from 1Hz to 4Hz, the actual false alarm rate decreases from 59% to 45%. Even so, the false alarm rate of 45% still significantly affects the practical application effect of the traditional method. Therefore, for the traditional method, simply increasing the update rate can only reduce the actual false alarm rate to a certain extent, and cannot fundamentally solve the false alarm problem. Compared with the traditional method, the proposed method greatly reduces the actual false alarm rate caused by user maneuvers. Under the conditions of the theoretical false alarm rate of 0.01 and the system update rate of 1Hz, the proposed method greatly reduces the actual false alarm rate to 3%. At the same time, the performance of the proposed method under different system update rates is generally consistent, and the actual false alarm rate is only slightly higher than the theoretical false alarm rate, indicating that the false alarm of the proposed method is mainly caused by system noise, and the influence of user maneuvers on it is relatively limited.

[0106] Aiming at the problem of high false alarm of deception detection caused by user maneuvers, the present invention proposes a 5G-assisted highly robust GNSS deception interference detection method. First, by establishing a mathematical model of new information under deception interference and user maneuvering scenarios, the inherent mechanism of abnormal new information and high false alarm caused by user maneuvers is explored. Secondly, by mapping the GNSS single-point positioning results to the 5G observation domain and calculating the difference between it and the actual observation, an improved new information is constructed. On this basis, a highly robust deception interference detection statistic is innovatively designed. The performance of the proposed method is verified by simulation and sports car tests. The results show that the average deception detection rate of the invented method is higher, which significantly enhances the robustness of deception interference detection in maneuvering scenarios.

[0107] Embodiment 2

[0108] This embodiment also discloses a computer terminal device, including:

[0109] one or more processors;

[0110] A memory, coupled to the processor, for storing one or more programs;

[0111] When the one or more programs are executed by the one or more processors, the one or more processors implement a 5G-assisted highly robust GNSS spoofing interference detection method in a GNSS-5G hybrid positioning system.

[0112] Embodiment 3

[0113] This embodiment also discloses a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a highly robust GNSS spoofing interference detection method assisted by 5G in a GNSS-5G hybrid positioning system is implemented.

[0114] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A 5G-assisted highly robust GNSS spoofing interference detection method in a GNSS-5G hybrid positioning system, characterized in that: The following steps are involved: An improved innovation model is constructed by removing the GNSS pseudorange observations in the traditional innovation and replacing the EKF-based prior state estimation with the GNSS SPP positioning results. The improved innovation model is normalized and squared to construct a deception interference detection statistic.

2. The 5G-assisted highly robust GNSS spoofing interference detection method in the GNSS-5G hybrid positioning system according to claim 1 is characterized in that: The improved innovation model constructed is: In the formula, κ[n] represents the improved new information, z BS [n] represents the set of 5G and GNSS observations without GNSS pseudorange observations, It represents the predicted value of 5G observation obtained by mapping the positioning result of SPP, Indicates the SPP positioning result.

3. The 5G-assisted highly robust GNSS spoofing interference detection method in the GNSS-5G hybrid positioning system according to claim 2 is characterized in that: The process of constructing the deception interference detection statistic also includes calculating the predicted value of the 5G observation quantity based on the improved innovation model, and determining that the improved innovation will not cause a false alarm by comparing and analyzing the predicted value of the 5G observation quantity with the true value; When there is deception interference, there is a difference between the predicted value of the 5G observation quantity obtained by mapping the positioning result of the SPP and the true value; When there is no deception interference but user maneuvering, the predicted value of the 5G observation quantity obtained by mapping the SPP positioning result is consistent with the true value.

4. The 5G-assisted highly robust GNSS spoofing interference detection method in the GNSS-5G hybrid positioning system according to claim 3 is characterized in that: The deception interference detection statistic constructed based on the improved innovation model is: ζ[n]=κ T [n]y -1 [n]k[n] Where ζ[n] represents the deception interference detection statistic, κ[n] represents the improved innovation, and υ[n] represents the noise covariance of the improved innovation.

5. The 5G-assisted highly robust GNSS spoofing interference detection method in the GNSS-5G hybrid positioning system according to claim 4 is characterized in that: The noise covariance of the improved new information includes the covariance of the 5G observation and the covariance of the SPP positioning result projected in the 5G observation domain; Wherein, the expression of the noise covariance of the improved innovation is: In the formula, R BS [n] represents the covariance of 5G observations, R SPP [n] represents the projection of SPP covariance to 5G observations, H BS [n] is the Jacobian of the 5G observation equation.

6. The 5G-assisted highly robust GNSS spoofing interference detection method in the GNSS-5G hybrid positioning system according to claim 5, characterized in that: Calculating a detection threshold based on deception interference detection statistics; Among them, the expression for calculating the detection threshold is: T ξ =F -1 [(1-P fa )3L]={x:F(x|3L)=1-P fa } Where, T ξ Represents the detection threshold, P fa is the false alarm probability, and F(x|·) is the cumulative probability density function.

7. The 5G-assisted highly robust GNSS spoofing interference detection method in the GNSS-5G hybrid positioning system according to claim 6, characterized in that: The update rate of the observation quantity in the GNSS-5G hybrid positioning system is changed by improving the value of the new information and the detection threshold.

8. A computer terminal device, characterized in that: include: one or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the 5G-assisted highly robust GNSS spoofing interference detection method in the GNSS-5G hybrid positioning system as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the 5G-assisted highly robust GNSS spoofing interference detection method in the GNSS-5G hybrid positioning system as described in any one of claims 1-7.

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