Full channel and partial channel identification method and system for single antenna repeater deception

Abstract

This invention discloses a method and system for identifying full-channel and partial-channel deception using a single-antenna forwarding system. It directly utilizes the existing pseudorange, Doppler observations, and PVT calculation results from the receiver, requiring no additional hardware support or complex signal preprocessing. The algorithm has a clear structure, low computational load, and is easy to run in real-time on resource-constrained embedded receivers, demonstrating good engineering feasibility. Based on the inherent consistency between pseudorange deviation and Doppler integral deviation, deception detection is performed without relying on the user's specific motion state. It maintains high detection sensitivity and stability in real-world scenarios with low speed, static conditions, or complex motion patterns, significantly improving applicability and reliability in various deception environments.

Description

Technical Field

[0001] This invention relates to navigation system spoofing detection technology, and in particular to a method and system for identifying full-channel and partial-channel spoofing by a single antenna relay. Background Technology

[0002] GNSS provides continuous, high-precision PNT (Programmable Noise, Telemetry, and Navigation) services to various users, but its civilian signal power is low and its structure is open, making it highly vulnerable to malicious spoofing attacks. Relay-based spoofing attacks, due to their low implementation cost and relatively low technical threshold, have become one of the main risks threatening the reliability of navigation and positioning. Doppler shift reflects the relative velocity between the satellite and the user receiver. Under normal circumstances, due to the high predictability of satellite orbits and the consistency of signal propagation paths, the Doppler shift of GNSS signals will be consistent with the actual motion of the receiver. However, in spoofing scenarios, attackers find it difficult to accurately estimate the user's true state in real time. Therefore, the forged signals they generate are affected by the relative motion between the spoofing device and the user, leading to unavoidable deviations in the observed Doppler shift. For this reason, Doppler deviation modeling and analysis has become an effective method for detecting spoofing signals. Many studies focus on extracting and analyzing this deviation from raw observations.

[0003] One approach to detection involves directly extracting Doppler bias from the raw measurements. This can be achieved by examining the correlation of Doppler frequency shifts between different channels, extracting implicit biases using interpolation, or calculating the first-order difference of Doppler between channels. However, these methods typically assume the user is stationary or exhibits a significant specific motion pattern (such as nonlinear motion or vertical reciprocating motion) and require additional complex processing to extract Doppler bias, limiting their applicability in real-world environments. Another approach avoids directly addressing biases in the raw observations. Instead, it indirectly achieves spoofing detection by analyzing the impact of spoofing attacks on the PVT solution. Examples include monitoring anomalies in receiver clock drift estimates, analyzing Doppler positioning residuals, comparing pseudorange change rates with Doppler velocity measurements, or comparing inconsistencies between direct and indirect velocity determination results. While these methods avoid the complex extraction of raw biases, their detection performance is often highly dependent on the relative motion velocity between the user and the spoofing source. The detection probability decreases significantly in scenarios with weak relative motion, and they struggle to effectively handle both full-channel and partial-channel spoofing scenarios simultaneously.

[0004] Existing spoofing detection technologies focus on detecting the presence of spoofing attacks, failing to accurately distinguish between full-channel and partial-channel spoofing. This limitation of "detecting but not distinguishing" leads to serious problems. It prevents receivers from initiating optimal defense strategies based on attack type: under full-channel spoofing, misjudgment may delay switching to non-GNSS backup navigation sources (such as inertial navigation or visual positioning) to prevent decisions based on incorrect locations; under partial-channel spoofing, overreaction may occur, leading to unnecessary service interruptions, loss of the opportunity to maintain partial navigation capabilities using remaining healthy signals, and reduced system availability and resilience.

[0005] In summary, existing technologies have the following limitations: First, existing methods either rely on complex extraction of Doppler bias from raw observations or on indirect verification based on PVT calculation results. The former requires complex additional processing and often assumes specific user motion states, while the latter is highly sensitive to the relative velocity between the user and the spoofer, resulting in a significant decrease in detection performance in scenarios with weak relative motion, and it is difficult to handle both full-channel and partial-channel scenarios simultaneously. Second, existing methods focus on binary detection of whether or not spoofing is present, and both lack the ability to distinguish between full-channel and partial-channel spoofing attack modes, which prevents the receiver from initiating precise defense based on the attack type.

[0006] The terms used in this invention are explained as follows:

[0007] GNSS: Abbreviation for Global Navigation Satellite System. Currently, there are four major global navigation systems: the US GPS, Russia's GLONASS, Europe's Gailieo, and China's BeiDou.

[0008] Receiver: In this article, it refers to a satellite navigation receiver. It receives satellite signals, acquires, tracks and demodulates them, calculates the satellite's position, and obtains the receiver's position, velocity and time through positioning calculation.

[0009] PNT stands for Positioning, Navigation, and Timing. This represents the complete service capabilities of GNSS as a spatiotemporal information infrastructure.

[0010] PVT is an abbreviation for Position, Velocity, and Time. It refers to the navigation results, namely position, velocity, and time, directly calculated by a GNSS receiver through processing satellite signals. Summary of the Invention

[0011] The technical problem to be solved by the present invention is to provide a method and system for identifying full-channel and partial-channel spoofing of a single antenna, which, without relying on the user's specific motion state and complex signal processing, can simultaneously achieve efficient spoofing detection and accurately distinguish between full-channel and partial-channel attack modes.

[0012] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for identifying full-channel and partial-channel spoofing by single-antenna forwarding, comprising the following steps:

[0013] S1. For the i-th satellite channel, based on the satellite ephemeris and the approximate location of the receiver, calculate the distance between the receiver and the i-th satellite channel. Geometric distance between satellites and relative velocity ;

[0014] S2. Using the geometric distance of the i-th satellite at the k-th epoch of the receiver. and the pseudorange of the i-th satellite at the k-th epoch. Calculate pseudorange deviation: Using the relative velocity of the i-th satellite at the k-th epoch. And the pseudorange rate of the i-th satellite at the k-th epoch obtained by Doppler frequency shift. Calculate pseudorange rate deviation: ,in, It's the wavelength. It is the Doppler frequency shift between the i-th satellite and the receiver at the k-th epoch;

[0015] S3. Integrate the pseudorange rate deviation over time to obtain the equivalent pseudorange deviation estimate. : ;in, Represents the epoch interval;

[0016] S4. Construct the core deception detection quantity for the i-th satellite using the following formula. : ;

[0017] S5, based on Based on the statistical properties under the no-deception assumption, calculate the corresponding detection decision threshold (Threshold):

[0018] ;in, This represents the variance of the detection volume under the no-deception assumption. It is the inverse function of the Gaussian error function, and Pfa is the preset false alarm rate;

[0019] S6. Compare the core deception detection quantity of the i-th satellite with the detection decision threshold Threshold. If the core deception detection quantity exceeds the threshold, it is determined that there is a deception signal in the channel of the i-th satellite; otherwise, it is determined that the signal of the channel is reliable and there is no deception.

[0020] The method of the present invention further includes:

[0021] Repeat steps S2 to S6 until all satellite channels have been processed.

[0022] If all channels are determined to be "not deceptive", the system is in a normal state; if all channels are determined to be "deceptive", and the core deceptive detection deviation is consistent, the system is determined to be under full-channel deceptive attack; if all channels are determined to be "deceptive", and the core deceptive detection deviation is different, the system is determined to be under partial-channel deceptive attack.

[0023] This invention uses the inconsistency of deviations introduced by pseudorange and Doppler in the forwarding spoofing source as the basis for verification. It can get rid of the dependence on the user's specific motion state and still maintain excellent detection robustness in scenarios where the performance of existing methods is significantly reduced, such as low speed and static conditions. This invention can not only achieve efficient detection of spoofing attacks, but more importantly, it can actively achieve accurate identification of both full-channel spoofing and partial-channel spoofing attack modes without additional hardware and complex signal processing.

[0024] Step S6 further includes: performing a binary hypothesis test on the comparison result between the core deception detection quantity of the i-th satellite and the detection decision threshold Threshold, wherein the expression for the binary hypothesis test is: ;in, This represents the mean of the number of tests conducted under the assumption of no deception.

[0025] As an inventive concept, the present invention also provides a full-channel and partial-channel identification system for single-antenna forwarding spoofing, comprising:

[0026] The first calculation unit is used to calculate the distance between the receiver and the i-th satellite channel, based on the satellite ephemeris and the approximate location of the receiver. Geometric distance between satellites and relative velocity ;

[0027] The second calculation unit is used to utilize the geometric distance of the i-th satellite at the k-th epoch. and the pseudorange of the i-th satellite at the k-th epoch. Calculate pseudorange deviation: Using the relative velocity of the i-th satellite at the k-th epoch. And the pseudorange rate of the i-th satellite at the k-th epoch obtained by Doppler frequency shift. Calculate pseudorange rate deviation: ,in, It's the wavelength. It is the Doppler frequency shift between the i-th satellite and the receiver at the k-th epoch;

[0028] The integrator unit is used to integrate the pseudorange rate deviation over time to obtain an equivalent pseudorange deviation estimate. : ;in, Represents the epoch interval;

[0029] The third calculation unit is used to construct the core deception detection quantity of the i-th satellite using the following formula. : ;

[0030] The fourth computing unit is used for... Based on the statistical properties under the no-deception assumption, calculate the corresponding detection decision threshold (Threshold): ;in, This represents the variance of the detection volume under the no-deception assumption. It is the inverse function of the Gaussian error function, and Pfa is the preset false alarm rate;

[0031] The comparison unit is used to compare the core deception detection quantity of the i-th satellite with the detection decision threshold Threshold. If the core deception detection quantity exceeds the threshold, it is determined that there is a deception signal in the channel of the i-th satellite; otherwise, it is determined that the signal of the channel is reliable and there is no deception.

[0032] The system of the present invention also includes:

[0033] The judgment unit is used to make the following judgments after all satellite channels have been processed: if all channels are judged to be "not deceptive", the system is in a normal state; if all channels are judged to be "deceptive", and the core deceptive detection deviation is consistent, the system is judged to have suffered a full-channel deceptive attack; if all channels are judged to be "deceptive", and the core deceptive detection deviation is not the same, the system is judged to have suffered a partial-channel deceptive attack.

[0034] As an inventive concept, the present invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the steps of the above method.

[0035] As an inventive concept, the present invention also provides a computer-readable storage medium having a computer program / instructions stored thereon; when the computer program / instructions are executed by a processor, they implement the steps of the above-described method.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] 1. This invention directly utilizes the existing pseudorange and Doppler observations and PVT calculation results of the receiver, without the need for additional hardware support or complex signal preprocessing. The algorithm has a clear structure, low computational load, and is easy to run in real time in resource-constrained embedded receivers, and has good engineering feasibility.

[0038] 2. This invention performs deception detection based on the inherent consistency between pseudorange bias and Doppler integral bias. It does not depend on the user's specific motion state and can maintain high detection sensitivity and stability in real-world scenarios with low speed, static conditions, or complex motion patterns, significantly improving its applicability and reliability in various deception environments.

[0039] 3. This invention possesses attack pattern recognition capabilities, supporting differentiated defense: Unlike traditional methods that can only determine "whether deception exists," this invention's method, by analyzing the consistency patterns of detection quantities across various satellite channels, can accurately distinguish between "full-channel deception" and "partial-channel deception" attack types. This allows the receiver to intelligently activate targeted defense strategies based on the attack type. Attached Figure Description

[0040] Figure 1 This is a system architecture diagram of an embodiment of the present invention;

[0041] Figure 2 This is a flowchart of a method according to an embodiment of the present invention;

[0042] Figure 3 For the detection volume in some channel scenarios, cases 1 to 8 ;

[0043] Figure 4 For the detection volume of 8 satellite channels in case 1 of the partial channel scenario ;

[0044] Figure 5 The detection volume for all channels in cases 1 to 4 ;

[0045] Figure 6 For the detection volume of 8 satellite channels in the full-channel scenario case 1 . Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Example 1

[0048] This invention constructs a novel deviation detection statistic by establishing and monitoring the consistency relationship between the receiver pseudorange deviation and the equivalent pseudorange deviation obtained by integrating the pseudorange rate deviation corresponding to Doppler. Under normal, deception-free conditions, the two are highly consistent. However, when subjected to a repeater-based deception attack, this inherent consistency is disrupted because the deception signal systematically destroys the geometric and kinematic models followed by the pseudorange and Doppler observations. This disruption is reflected in the PVT results, leading to a significant anomaly in the deviation statistic. This invention not only utilizes this anomaly to achieve rapid and highly sensitive deception detection but also, further, achieves accurate differentiation between full-channel deception and partial-channel deception by analyzing the patterns of consistency disruption between satellite channels.

[0049] like Figure 1 and Figure 2 As shown, the method of Embodiment 1 of the present invention includes the following steps:

[0050] Step 1: Satellite signal acquisition, tracking, and positioning calculation

[0051] For the i-th satellite channel, the GNSS receiver sequentially acquires and tracks the signal, and uses the observations output from the tracking loop to perform positioning calculations. This process obtains the pseudorange and Doppler observations of each satellite, and estimates the receiver's position and velocity state. Simultaneously, based on the satellite ephemeris and the receiver's approximate position, the distance between the receiver and the i-th satellite channel is calculated. Geometric distance between satellites and relative velocity .

[0052] Step 2: Use the pseudorange observations obtained in Step 1 and the corresponding geometric distance between the satellite and the receiver. Calculate pseudorange deviation:

[0053] Where k represents the kth epoch.

[0054] Step 3: Use the pseudorange rate corresponding to the Doppler frequency shift observations obtained in Step 1 and the corresponding relative velocity between the satellite and the receiver. Calculate pseudorange rate deviation:

[0055] in, It's the wavelength. It is the Doppler frequency shift between the i-th satellite and the receiver.

[0056] Step 4: Integrate the pseudorange rate deviation calculated in Step 2 over time to obtain the equivalent pseudorange deviation estimate, which is expressed as:

[0057] in, Represents the epoch interval.

[0058] Step 5: For the i-th satellite, calculate the pseudorange deviation in Step 2. The equivalent pseudorange deviation obtained in step 4 To improve performance, build a core deception detection volume. Its expression is:

[0059] In an ideal situation without deception It follows a Gaussian distribution with zero mean. When repeater spoofing exists, the spoofing signal disrupts the inherent dynamic consistency between pseudorange and pseudorange rate, introducing additional bias. This causes a significant shift in the statistical distribution of the detection volume. The statistical distribution is as follows:

[0060]

[0061] Step 6: Based on the system's preset false alarm rate Pfa, Based on the statistical properties under the no-deception assumption, calculate the corresponding detection decision threshold (Threshold):

[0062] in, It is the inverse function of the Gaussian error function.

[0063] Step 7: Compare the detection count of the i-th satellite with the threshold calculated in Step 6 to perform a binary hypothesis test:

[0064] If the detection quantity exceeds the threshold, it is determined that there is a spoofing signal in the channel of the i-th satellite; otherwise, it is determined that the signal of the channel is reliable and there is no spoofing.

[0065] Step 8: Repeat steps 2 through 6 until all satellite channels have been processed. Make a comprehensive judgment based on the independent detection results of each channel:

[0066] If all channels are determined to be "not fraudulent", then the system is in a normal state.

[0067] If all channels are judged to be "spoofing", and the detection deviations are basically consistent, then the system is judged to have suffered a full-channel spoofing attack.

[0068] If all channels are judged to be "spoofing", and the detection deviations are not the same, then the system is judged to have suffered a partial channel spoofing attack.

[0069] Table 1 Simulation parameters for some channel spoofing scenarios

[0070]

[0071] Figure 3 This demonstrates the detection quantity when the receiver is stationary or in low-speed motion, under conditions of partial channel spoofing (only PRN 2, 3, 6, and 9 are spoofed signals). The transient response was measured. Experimental results show that before the deception signal was introduced for the first 14 seconds, the detected values ​​fluctuated slightly around zero, conforming to the Gaussian distribution characteristics under the no-deception assumption, indicating that the system was in a normal state. Within 2 seconds of the deception being introduced, the detected values ​​rapidly deviated from zero, with a significant increase in amplitude, the magnitude of which varied depending on the scenario. This verifies the sensitivity of the method in this embodiment to deception in some channels; even when the receiver is stationary or moving at low speed, the detected values ​​can still effectively respond to the deception signal.

[0072] Figure 4 This demonstrates the detection capacity of 8 satellite channels in the case of partial channel deception (Case 1). The transient response is described. In partial channel spoofing scenarios, some signals come from spoofing satellites, while others come from real satellites. The PVT solution minimizes the sum of squared residuals of the observation equations for the eight satellites. Therefore, the result does not correspond to either the real or spoof state, but converges to an intermediate solution. This is because the positional inconsistencies between the real and spoofed signals are irreconcilable, forcing the estimator to compromise in the PVT solution. Consequently, the pseudorange bias and equivalent pseudorange bias of each satellite deviate from their consistency relationship, and the degree of deviation varies across different channels.

[0073] Table 2 Simulation parameters for the full-channel deception scenario

[0074]

[0075] Figure 5 The detection rate is shown in scenarios 1 through 4, where all signals are deceptive (all channels are deceptive signals). The transient response was analyzed. The results showed that after the addition of the spoofing signal, the detection volume of all channels increased synchronously with the increase of delay, and the offset amplitude was positively correlated with the delay. This is because in the full-channel scenario, the PVT solution converges to the spoofed state, and the increased replay delay is directly reflected in the pseudorange deviation of all satellite signals but not in the equivalent pseudorange deviation, causing the detection volume to increase linearly with the increase of delay.

[0076] Figure 6 This demonstrates the detection volume of 8 satellite channels in the case of full-channel deception (Case 1). The transient response. Experimental results show that under full-channel deception, the detection deviation of each channel is... The results exhibit high consistency, displaying a uniform, systematic shift, which contrasts sharply with the results under partial channel deception. This demonstrates that the method in this embodiment can effectively identify full-channel deception attacks through the consistent shift pattern of the detection quantity, and verifies its robustness and identification capabilities in both partial and full-channel deception scenarios.

[0077] Example 2

[0078] Embodiment 2 of the present invention provides a terminal device corresponding to Embodiment 1 above. The terminal device can be a processing device for a client, such as a mobile phone, a laptop, a tablet computer, a desktop computer, etc., to execute the method of the above embodiments.

[0079] The terminal device in this embodiment includes a memory, a processor, and a computer program stored in the memory; the processor executes the computer program in the memory to implement the steps of the method in Embodiment 1 described above.

[0080] In some implementations, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0081] In other implementations, the processor can be any type of general-purpose processor, such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation here.

[0082] Example 3

[0083] Embodiment 3 of the present invention provides a computer-readable storage medium corresponding to Embodiment 1 above, on which a computer program / instructions are stored. When the computer program / instructions are executed by a processor, they implement the steps of the method of Embodiment 1 above.

[0084] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0085] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0086] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0087] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0088] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0089] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for identifying full-channel and partial-channel spoofing using a single-antenna forwarding system, characterized in that, Includes the following steps: S1, for the i-th satellite channel, compute the geometric distance between the receiver and the i-th satellite, and the relative velocity, according to the satellite ephemeris and the receiver approximate position ;​​ S2. Using the geometric distance of the i-th satellite at the k-th epoch of the receiver. and the pseudorange of the i-th satellite at the k-th epoch. Calculate pseudorange deviation: Using the relative velocity of the i-th satellite in the k-th epoch. And the pseudorange rate of the i-th satellite at the k-th epoch obtained by Doppler frequency shift Calculate pseudorange rate deviation: ,in, It's the wavelength. It is the Doppler frequency shift between the i-th satellite and the receiver at the k-th epoch; S3. Integrate the pseudorange rate deviation over time to obtain the equivalent pseudorange deviation estimate. : ;in, Represents the epoch interval; S4, constructing a core deception detection quantity of the i-th satellite using the following formula : ; S5, based on Based on the statistical properties under the no-deception assumption, calculate the corresponding detection decision threshold (Threshold): ;in, This represents the variance of the detection volume under the no-deception assumption. It is the inverse function of the Gaussian error function, and Pfa is the preset false alarm rate; S6. Compare the core deception detection quantity of the i-th satellite with the detection decision threshold Threshold. If the core deception detection quantity exceeds the threshold, it is determined that there is a deception signal in the channel of the i-th satellite; otherwise, it is determined that the signal of the channel is reliable and there is no deception.

2. The method of full and partial path identification of single antenna repeating deception according to claim 1, wherein, Also includes: Repeat steps S2 to S6 until all satellite channels have been processed. If all channels are determined to be "not deceptive", the system is in a normal state; if all channels are determined to be "deceptive", and the core deceptive detection deviation is consistent, the system is determined to be under full-channel deceptive attack; if all channels are determined to be "deceptive", and the core deceptive detection deviation is different, the system is determined to be under partial-channel deceptive attack.

3. The method of full and partial path identification of single antenna repeating spoofing of claim 1, wherein, Step S6 further includes: performing a binary hypothesis test on the comparison result between the core deception detection quantity of the i-th satellite and the detection decision threshold Threshold, wherein the expression for the binary hypothesis test is: ;in, This represents the mean of the number of tests conducted under the assumption of no deception.

4. A full and partial path identification system for single antenna repeater deception, characterized by, include: The first calculation unit is used to calculate the distance between the receiver and the i-th satellite channel, based on the satellite ephemeris and the approximate location of the receiver. Geometric distance between satellites and relative velocity ; The second calculation unit utilizes the geometric distance of the i-th satellite at the k-th epoch of the receiver. and the pseudorange of the i-th satellite at the k-th epoch. Calculate pseudorange deviation: Using the relative velocity of the i-th satellite in the k-th epoch. And the pseudorange rate of the i-th satellite at the k-th epoch obtained by Doppler frequency shift Calculate pseudorange rate deviation: ,in, It's the wavelength. It is the Doppler frequency shift between the i-th satellite and the receiver at the k-th epoch; The integrator unit is used to integrate the pseudorange rate deviation over time to obtain an equivalent pseudorange deviation estimate. : ;in, Represents the epoch interval; A third calculation unit is configured to construct a core spoofing detection quantity of the i-th satellite by using the following formula : ; The fourth computing unit is used for... Based on the statistical properties under the no-deception assumption, calculate the corresponding detection decision threshold (Threshold): ;in, This represents the variance of the detection volume under the no-deception assumption. It is the inverse function of the Gaussian error function, and Pfa is the preset false alarm rate; The comparison unit is used to compare the core deception detection quantity of the i-th satellite with the detection decision threshold Threshold. If the core deception detection quantity exceeds the threshold, it is determined that there is a deception signal in the channel of the i-th satellite; otherwise, it is determined that the signal of the channel is reliable and there is no deception.

5. The single antenna transponder spoofing full and partial path identification system of claim 4 wherein, Also includes: The judgment unit is used to make the following judgments after all satellite channels have been processed: if all channels are judged to be "not deceptive", the system is in a normal state; if all channels are judged to be "deceptive", and the core deceptive detection deviation is consistent, the system is judged to have suffered a full-channel deceptive attack; if all channels are judged to be "deceptive", and the core deceptive detection deviation is different, the system is judged to have suffered a partial-channel deceptive attack.

6. A terminal device comprising a memory, a processor, and a computer program stored on the memory; characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 3.

7. A computer readable storage medium having stored thereon computer programs / instructions; characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 3.

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