A method and system for detecting signal integrity spoofing in array antennas

Abstract

This invention relates to the field of array antenna signal integrity detection technology, and discloses a method and system for detecting signal integrity spoofing in array antennas. The method includes: acquiring received signals from the array antenna and analyzing the quality parameters of the received signals; identifying receiving units in the array antenna with inherent signal bias based on the quality parameters, and marking these receiving units as special processing units; for the special processing units, establishing and continuously updating a defect feature model characterizing their inherent signal bias under the condition of no spoofing signal; eliminating the inherent signal bias from the real-time received signals of the special processing units based on the defect feature model to obtain the bias-eliminated signal; detecting whether there is abnormal phase or frequency drift in the bias-eliminated signal; and determining that a spoofing attack exists when the detected abnormal phase or frequency drift exceeds a preset spoofing judgment threshold. This invention effectively solves the problem of difficulty in identifying spoofing attacks.

Description

Technical Field

[0001] This invention relates to the field of array antenna signal integrity detection technology, and more specifically, to an array antenna signal integrity spoofing detection method and system. Background Technology

[0002] In intelligent transportation systems, vehicle positioning terminals receive satellite navigation signals via onboard array antennas to achieve high-precision positioning. Array antennas typically consist of multiple independent dipole elements, each responsible for receiving signals from a specific direction and determining the signal's incident angle and source through precise phase comparison. However, during daily vehicle operation, the dipole elements of the array antenna may experience microscopic fatigue in their internal material structure due to prolonged mechanical stress and temperature cycling, leading to the accumulation of subtle stresses. This stress accumulation is a slow, evolving process, and its initial manifestations are often difficult to identify using conventional online monitoring methods.

[0003] Over time, the accumulation of internal stress can cause subtle but continuous changes in the geometry of the oscillator element, such as deformation of its size, shape, or connection points with the substrate. This geometric change directly affects its electromagnetic wave reception characteristics, particularly its input impedance. When an oscillator element experiences impedance mismatch, the received satellite navigation signal entering the RF front end will exhibit a weak, typically frequency-dependent phase shift and amplitude attenuation. However, this change in signal characteristics is often very subtle initially, with an amplitude below the system's preset deception threshold, preventing current signal integrity detection mechanisms from recognizing it as an anomaly.

[0004] It is against this backdrop that a highly skilled attacker might launch a slow-drag spoofing attack. This attack is characterized by extremely slow changes in the phase and frequency of the forged satellite navigation signal, with carefully controlled signal amplitude. This ensures that the introduced phase deviation on most normally functioning array channels remains below the phase deviation threshold used by the system to detect spoofing. In this way, the attacker attempts to slowly and imperceptibly drag the vehicle's reported position away from its true location without triggering an immediate alarm. This attack method aims to exploit the system's sensitivity to rapid, drastic changes while evading detection of slow, gradual changes.

[0005] When this slow-drag spoofing signal is applied to the entire array antenna, the system's spoofing detection algorithm should detect the anomaly by verifying the strict consistency of the received signal phase across all channels. However, because the weights of previously damaged channels have been systematically reduced, the phase information they provide is largely ignored in the multi-channel consistency check calculation. This weakens the algorithm's ability to perform multi-channel cross-comparison, significantly reducing its sensitivity to detecting minute and synchronous phase changes between channels. In this situation, even if the spoofing attack introduces a weak but consistent phase deviation, the system may fail to effectively identify this carefully planned spoofing attack due to the lack of a key, independently valuable channel data point. This puts the vehicle's location information at risk of being tampered with, potentially leading to misjudgments by the intelligent traffic management system, causing traffic chaos or safety hazards.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a method and system for detecting signal integrity spoofing in array antennas. This addresses the problem in existing technologies where the inherent signal bias of the array antenna vibrator unit due to long-term operation prevents the system from effectively identifying slow-drag spoofing attacks, thereby affecting vehicle positioning accuracy and safety.

[0008] In a first aspect, the present invention provides a method for detecting signal integrity spoofing in an array antenna, comprising:

[0009] Acquire the received signals from each receiving element in the array antenna and analyze the quality parameters of the received signals;

[0010] Based on the quality parameters, the receiving units in the array antenna that exhibit inherent signal bias are identified, and these receiving units are marked as special processing units.

[0011] For special processing units, under the condition of no deceptive signals, a defect feature model characterizing their inherent signal bias is established and continuously updated.

[0012] From the real-time received signal of the special processing unit, the inherent signal bias is eliminated based on the defect feature model to obtain the bias-free signal;

[0013] Detect whether there is abnormal phase or frequency drift in the signal after offset removal;

[0014] When the detected abnormal phase or frequency drift exceeds the preset deception judgment threshold, a deception attack is determined to exist.

[0015] Through this technical solution, the present invention can effectively identify and process receiving units with inherent signal bias in array antennas. By establishing a defect feature model and eliminating the bias, the true characteristics of the signal are restored, thereby improving the detection capability of slow drag-and-drop spoofing attacks and solving the problem in the prior art that spoofing attacks are difficult to identify due to defects in internal components.

[0016] Furthermore, in some implementations, the quality parameters include signal strength, carrier-to-noise ratio, and inter-channel signal phase difference between each receiving unit.

[0017] This technical solution enables the comprehensive evaluation of the health status of the receiving unit using multi-dimensional signal quality parameters, thereby improving the accuracy of identifying inherent signal bias.

[0018] More specifically, in some implementations, the step of identifying receiver elements in the array antenna that exhibit inherent signal bias based on quality parameters includes:

[0019] When the signal strength of the receiving unit is continuously lower than the average signal strength of all remaining receiving units within the preset monitoring time window, or the carrier-to-noise ratio of the receiving unit is continuously lower than the average carrier-to-noise ratio of all remaining receiving units within the preset monitoring time window, and the signal phase difference between its channels continuously exceeds the preset normal range within the preset monitoring time window, the receiving unit will be identified as a receiving unit with inherent signal bias.

[0020] Based on the above, the present invention further proposes a step for establishing and continuously updating a defect feature model characterizing the inherent signal bias of a special processing unit under the condition of no deceptive signal, including:

[0021] Under conditions without deceptive signals, the signal performance data of the special processing unit under different environmental conditions are collected, and the corresponding environmental parameters are obtained through environmental sensors.

[0022] Based on signal performance data, the stable phase offset and amplitude attenuation of the special processing unit relative to the normal receiving unit are determined;

[0023] By combining stable phase offset, amplitude attenuation, and environmental parameters, a parameterized function relating environmental parameters to stable phase offset and amplitude attenuation is established as a defect feature model. Adaptive filtering techniques are then used to adjust and update the parameters of the defect feature model.

[0024] Preferably, the step of eliminating the inherent signal bias from the real-time received signal from the special processing unit based on the defect feature model to obtain the bias-free signal includes:

[0025] Collect current environmental parameters, and determine the corresponding stable phase offset and amplitude attenuation based on the defect feature model inputting the current environmental parameters;

[0026] Based on a determined stable phase offset and amplitude attenuation, the real-time received signal of the special processing unit is eliminated or compensated to obtain the signal after offset elimination.

[0027] In some preferred embodiments, the step of detecting whether there is abnormal phase or frequency drift in the signal after bias removal includes:

[0028] Calculate the instantaneous frequency of the signal after offset removal;

[0029] Acquire vehicle motion status and satellite orbit information, and predict the actual satellite signal frequency based on vehicle motion status and satellite orbit information;

[0030] If there is a deviation between the instantaneous frequency and the actual satellite signal frequency, it is determined that there is an abnormal frequency drift in the signal after offset elimination.

[0031] The global reference phase is obtained by weighted averaging the signal phases of the receiving units in the array antenna, excluding the special processing unit.

[0032] If the phase of the signal after offset removal deviates from the global reference phase, it is determined that there is an abnormal phase drift in the signal after offset removal.

[0033] Furthermore, when the detected abnormal phase or frequency drift exceeds a preset deception detection threshold, the steps to determine the existence of a deception attack include:

[0034] Calculate the instantaneous frequency deviation between the instantaneous frequency and the actual satellite signal frequency;

[0035] Calculate the instantaneous phase deviation between the offset-removed signal phase and the global reference phase;

[0036] When the instantaneous frequency deviation exceeds the preset frequency deception judgment threshold or the instantaneous phase deviation exceeds the preset phase deception judgment threshold, a deception attack is determined to exist.

[0037] As a technological improvement, the method also includes:

[0038] In response to the confirmation of a deception attack, an early warning message is sent to the cloud-based dispatch platform, and the vehicle location calculation strategy is adjusted.

[0039] As a further improvement, the steps to adjust the vehicle localization solution strategy include:

[0040] The system prioritizes using the received signals from receivers that are not susceptible to spoofing attacks for positioning calculations, or, based on a preset security strategy, switches to a robust positioning mode and prioritizes using the sensor data from the vehicle's own motion sensors for positioning calculations.

[0041] Secondly, the present invention also discloses an array antenna signal integrity spoofing detection system for performing the method described above, the system comprising:

[0042] The signal acquisition and analysis module is used to acquire the received signals from each receiving unit in the array antenna and analyze the quality parameters of the received signals.

[0043] The unit identification and marking module is used to identify the receiving units in the array antenna that have inherent signal bias based on quality parameters, and to mark the receiving units with inherent signal bias as special processing units;

[0044] The model building and updating module is used to build and continuously update a defect feature model that characterizes the inherent signal bias of a special processing unit under the condition of no deceptive signal.

[0045] The bias elimination module is used to eliminate the inherent signal bias from the real-time received signal from the special processing unit based on the defect feature model, so as to obtain the bias-eliminated signal.

[0046] The drift detection module is used to detect whether there is abnormal phase or frequency drift in the signal after offset elimination;

[0047] The deception attack detection module is used to determine the existence of a deception attack when the detected abnormal phase or frequency drift exceeds a preset deception detection threshold.

[0048] This technical solution provides an integrated hardware or software system that can realize the functions of the above methods, offering a complete solution for detecting signal integrity spoofing in array antennas, with good practicality and operability.

[0049] In summary, this invention provides a method and system for detecting signal integrity spoofing in an array antenna. The method acquires the received signals from each receiving unit in the array antenna and analyzes their quality parameters, identifying receiving units with inherent signal bias and marking them as special processing units. For these special processing units, this invention establishes and continuously updates a defect feature model characterizing their inherent signal bias under conditions without spoofing signals. This model can accurately capture weak and slowly changing phase shifts and amplitude attenuations caused by internal component degradation (such as impedance mismatch), overcoming the problem in existing technologies of difficulty in distinguishing between internal defects and external interference. Subsequently, based on this defect feature model, this invention eliminates the inherent signal bias from the real-time received signals of the special processing units, restoring the true characteristics of the signal, thereby avoiding the problem of the system ignoring damaged channel data due to downweighting. Finally, by detecting whether there is abnormal phase or frequency drift in the signal after bias elimination and comparing it with a preset spoofing judgment threshold, this invention can effectively identify slow-drag spoofing attacks. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a method for detecting signal integrity deception in an array antenna, as provided in an embodiment of the present invention.

[0051] Figure 2 This is a schematic diagram of the structure of an array antenna signal integrity spoofing detection system provided in an embodiment of the present invention.

[0052] Labeling Explanation: 210 Signal Acquisition and Analysis Module; 220 Unit Identification and Labeling Module; 230 Model Building and Update Module; 240 Bias Elimination Module; 250 Drift Detection Module; 260 Spoofing Attack Detection Module. Detailed Implementation

[0053] The technical solutions of this invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some, not all, of the embodiments of this invention. The components of this invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0054] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0055] In traditional intelligent transportation systems, the built-in channel health monitoring and fault-tolerance logic continuously evaluates the performance of each array oscillator unit during long-term operation. When the signal quality of a damaged channel consistently falls below that of other normal channels—for example, manifested as a persistently low carrier-to-noise ratio, high phase noise, or unstable signal strength—the system automatically marks it as a low-confidence channel according to its internal rules. To ensure the stability and continuity of the overall positioning solution and prevent a single abnormal channel from having an excessive impact on the final positioning result, the system algorithm proactively reduces the data weight from that channel. This weight reduction is an adaptive strategy adopted by the system to improve its robustness, aiming to prevent local hardware degradation from affecting global performance. It is in this context that a highly skilled attacker might launch a slow-drag spoofing attack. This attack is characterized by extremely slow phase and frequency changes in the forged satellite navigation signal, and the signal amplitude is carefully controlled so that the phase deviation introduced on most normally functioning array channels remains below the phase deviation threshold used by the system to determine spoofing. In this way, the attacker attempts to slowly and imperceptibly drag the vehicle's reported position away from its true position without triggering an immediate alarm. In this scenario, even if a spoofing attack introduces a weak but consistent phase deviation, the system may still fail to effectively identify such a meticulously planned attack due to the lack of a crucial, independently referenced channel data point. This exposes the vehicle's location information to the risk of tampering, potentially leading to misjudgments by the intelligent traffic management system and causing traffic chaos or safety hazards.

[0056] In this regard, firstly, referring to Figure 1 This invention proposes a method for detecting signal integrity spoofing in array antennas, comprising:

[0057] S1. Obtain the received signals of each receiving unit in the array antenna and analyze the quality parameters of the received signals;

[0058] S2. Based on the quality parameters, identify the receiving units in the array antenna that have inherent signal bias, and mark the receiving units with inherent signal bias as special processing units.

[0059] S3. For special processing units, under the condition of no deceptive signals, establish and continuously update the defect feature model that characterizes their inherent signal bias.

[0060] S4. From the real-time received signal of the special processing unit, the inherent signal bias is eliminated based on the defect feature model to obtain the signal after bias elimination.

[0061] S5. Detect whether there is abnormal phase or frequency drift in the signal after offset elimination;

[0062] S6. When the detected abnormal phase or frequency drift exceeds the preset deception judgment threshold, a deception attack is determined to exist.

[0063] An array antenna typically refers to a system composed of multiple independent receiving units (e.g., vibrating elements) used to receive satellite navigation signals. Each receiving unit is an independent signal receiving channel within the array antenna. The quality parameters of the received signal refer to indicators that measure the performance of the received signal, such as signal strength, carrier-to-noise ratio, and phase stability. Inherent signal bias refers to a systematic deviation in the signal phase or amplitude caused by defects in the receiving unit itself (such as impedance mismatch, material fatigue, etc.). This bias is not caused by the external environment or spoofing signals. A special processing unit refers to a receiving unit identified as having inherent signal bias, requiring specialized correction processing. A defect characteristic model is a mathematical model used to characterize the inherent signal bias of a special processing unit. This model describes the characteristics of the bias and its relationship with environmental conditions. The signal after bias elimination refers to the signal after removing the inherent signal bias from the real-time received signal of the special processing unit, making it closer to the ideal unbiased signal. Abnormal phase or frequency drift refers to an irregular change in the signal phase or frequency compared to the expected value, which is often a characteristic of spoofing attacks. The deception detection threshold is a preset critical value used to determine whether a deception attack exists. When the detected abnormal drift exceeds this threshold, it is determined to be a deception attack.

[0064] This invention, by establishing and continuously updating a defect feature model, can accurately eliminate inherent signal bias from the real-time received signals of a specialized processing unit. This means that even if a receiving unit has a weak, long-accumulated inherent bias, its signal can be restored to a near-"pure" state after correction, thus regaining its reference value in deception detection. This processing method enables the system to more accurately distinguish between signal anomalies caused by defects in the receiving unit itself and anomalies caused by external deception attacks.

[0065] For example, in existing technologies, the signal of a receiver unit with an inherent phase bias may be ignored during multi-channel consistency verification due to reduced weight, making it difficult for the system to detect even weak but consistent phase deviations introduced by a spoofing attack. However, the method of this invention, after eliminating the inherent bias, allows the signal of this specially processed unit to once again serve as a reliable reference, participating in spoofing detection along with signals from other normal receiver units. When a spoofing attack occurs, even slow, weak phase or frequency drifts become more pronounced in the signal after the inherent bias has been eliminated, making them easier to detect and compare with a preset spoofing threshold.

[0066] Therefore, the method of this invention effectively solves the problem of decreased detection sensitivity in existing systems when facing slow drag-and-drop spoofing attacks due to the superposition of internal component defects and multipath effects. By accurately modeling and eliminating inherent biases, this invention not only improves the accuracy of spoofing detection but also enhances the overall robustness and security of the vehicle positioning system, providing more reliable navigation assurance for intelligent transportation systems.

[0067] Specifically, in some implementations of the above-mentioned array antenna signal integrity spoofing detection method, the quality parameters can be specifically defined.

[0068] According to the above-mentioned array antenna signal integrity spoofing detection method, the quality parameters include signal strength, carrier-to-noise ratio, and inter-channel signal phase difference of each receiving unit.

[0069] Specifically, signal strength refers to the power of the signal received by the receiving unit, usually expressed in dBm or dBW. Signal strength is a fundamental indicator of signal quality; low signal strength may indicate a faulty receiving unit, obstruction, or signal attenuation. Carrier-to-noise ratio (CNR) is the ratio of the carrier power to the noise power of the received signal, usually expressed in dB. CNR is a key parameter for measuring the intelligibility of a signal against a noisy background; a higher CNR generally means better signal quality and a lower bit error rate. The phase difference between channels of the receiving units refers to the phase difference between the output signals of different receiving units in the array antenna when receiving the same signal. Ideally, when the array antenna receives a plane wave signal, the phase difference between the receiving units should conform to a specific geometric relationship; any phase difference deviating from this expected relationship may indicate an inherent bias or external interference in the receiving unit.

[0070] The present invention uses signal strength, carrier-to-noise ratio (CNR), and inter-channel signal phase difference as quality parameters to perform a multi-dimensional and comprehensive evaluation of the signal reception status of each receiving unit in an array antenna. Signal strength and CNR reflect the physical quality of the signal from the perspectives of energy and signal-to-noise ratio, while the inter-channel signal phase difference reflects the consistency of the receiving units from the perspectives of spatial and temporal synchronization. By comprehensively analyzing these parameters, receiving units that exhibit abnormal signal performance due to inherent defects (such as hardware aging, calibration errors, or localized obstruction) can be more accurately identified, i.e., receiving units with inherent signal bias. This multi-parameter comprehensive evaluation mechanism helps distinguish between general signal attenuation caused by environmental changes and localized signal bias caused by specific receiving unit malfunctions, thereby improving the accuracy of identification.

[0071] In some embodiments of the present invention described above, a method is proposed to identify receiver units with inherent signal bias in an array antenna based on quality parameters. However, in practical applications, accurately and robustly identifying these receiver units with inherent signal bias is crucial to ensuring the effectiveness of subsequent deception detection. Inaccurate identification may lead to unnecessary special processing of normal units or the omission of truly defective units.

[0072] In response, the present invention further proposes the following steps for identifying receiving units with inherent signal bias in an array antenna based on quality parameters:

[0073] When the signal strength of the receiving unit is continuously lower than the average signal strength of all remaining receiving units within the preset monitoring time window, or the carrier-to-noise ratio of the receiving unit is continuously lower than the average carrier-to-noise ratio of all remaining receiving units within the preset monitoring time window, and the signal phase difference between its channels continuously exceeds the preset normal range within the preset monitoring time window, the receiving unit will be identified as a receiving unit with inherent signal bias.

[0074] Specifically, the aforementioned preset monitoring time window refers to a pre-defined period of time, such as several seconds, tens of seconds, or longer. Its purpose is to ensure that the detected signal anomalies are continuous, rather than momentary interference or brief fluctuations. Continuous monitoring within this time window effectively distinguishes between intermittent anomalies and inherent defects. Signal strength and carrier-to-noise ratio (CNR) are crucial parameters for measuring the quality of the received signal. When the signal strength of a receiving unit is consistently lower than the average of other normal receiving units, or its CNR is consistently lower than the average, this usually indicates that the receiving unit has problems such as decreased reception capability, gain attenuation, or increased noise. In practical applications, the aforementioned inter-channel signal phase difference refers to the difference between the phase of the signal received by the receiving unit and the phase of the signal received by other receiving units in the array antenna. When this phase difference consistently exceeds the preset normal range within the preset monitoring time window, it may indicate structural or physical defects in the internal circuitry, antenna elements, or signal path of the receiving unit, causing an abnormal shift in signal phase.

[0075] Traditional methods for detecting signal integrity spoofing in array antennas may face challenges such as insufficient model accuracy and poor environmental adaptability when establishing and continuously updating defect feature models representing the inherent signal bias of specific processing units. Simply building a model may fail to fully capture the complexity and dynamic changes of inherent signal bias, especially under varying environmental conditions. This could lead to incomplete elimination of inherent signal bias, thus affecting the accuracy of subsequent spoofing detection. Failure to address these issues may increase the risk of false positives or false negatives in spoofing attacks. To address this, this invention proposes a more refined and adaptive method for establishing and updating defect feature models. By comprehensively considering environmental factors and signal performance data, this method improves the model's ability to represent inherent signal bias and its environmental adaptability.

[0076] To address this, the present invention further proposes the following steps for establishing and continuously updating a defect feature model characterizing the inherent signal bias of a special processing unit under conditions without deceptive signals:

[0077] Under conditions without deceptive signals, the signal performance data of the special processing unit under different environmental conditions are collected, and the corresponding environmental parameters are obtained through environmental sensors.

[0078] Based on signal performance data, the stable phase offset and amplitude attenuation of the special processing unit relative to the normal receiving unit are determined;

[0079] By combining stable phase offset, amplitude attenuation, and environmental parameters, a parameterized function relating environmental parameters to stable phase offset and amplitude attenuation is established as a defect feature model. Adaptive filtering techniques are then used to adjust and update the parameters of the defect feature model.

[0080] Specifically, collecting signal performance data of the special processing unit under different environmental conditions, without spoofing signals, refers to long-term, multi-scenario monitoring and recording of the received signal identified as the special processing unit while the array antenna is operating normally and is not subjected to spoofing attacks. This signal performance data can include instantaneous phase, instantaneous frequency, signal strength, carrier-to-noise ratio, etc., with the aim of comprehensively understanding the inherent bias characteristics of the special processing unit under different operating states. Simultaneously, acquiring corresponding environmental parameters through environmental sensors refers to using devices such as temperature sensors, humidity sensors, barometric pressure sensors, and vibration sensors to record environmental information of the special processing unit in real time or periodically, such as ambient temperature, humidity, and mechanical vibration, with the aim of establishing the correlation between environmental factors and signal bias.

[0081] Specifically, determining the stable phase offset and amplitude attenuation of the special processing unit relative to normal receiving units, based on signal performance data, can be understood as quantifying its inherent, non-random phase deviation and signal amplitude attenuation by comparing the signal performance of the special processing unit with the average signal performance of other normal receiving units in the array antenna or the performance of a known standard signal. Stable phase offset refers to the fixed or slowly changing deviation between the phase of the output signal of the special processing unit and the ideal or reference phase when receiving a signal. Amplitude attenuation refers to the continuous reduction in the amplitude of the signal received by the special processing unit relative to the ideal or reference amplitude. The purpose is to accurately quantify the inherent defects of the special processing unit, providing an accurate benchmark for subsequent offset elimination.

[0082] In practical applications, a parameterized function relating the stable phase bias, amplitude attenuation, and environmental parameters is established as a defect feature model. This function can be based on the relationship between environmental parameters and the stable phase bias and amplitude attenuation. For example, a multinomial regression model, support vector machine model, or neural network model can be used. The environmental parameters are taken as input, and the predicted stable phase bias and amplitude attenuation are output. The aim is to construct a model that can dynamically predict the inherent signal bias based on real-time environmental changes, thereby improving the accuracy and adaptability of bias elimination.

[0083] Furthermore, adaptive filtering techniques are used to adjust and update the parameters of the defect feature model. For example, Kalman filtering, the least mean square (LMS) algorithm, or the recursive least squares (RLS) algorithm can be employed. Based on new signal performance data and environmental parameters, the internal parameters of the defect feature model are adjusted in real time or periodically. The purpose is to enable the defect feature model to continuously learn and adapt to the long-term drift or short-term fluctuations of the inherent bias of the special processing unit, ensuring that the model always maintains high accuracy.

[0084] In some preferred embodiments, it is assumed that an array antenna is deployed on a vehicle, and one of the receiving units exhibits inherent signal phase bias and amplitude attenuation under different temperature and humidity conditions due to manufacturing defects or long-term use. To establish and update its defect characteristic model, firstly, under normal vehicle operation and without spoofing attacks, received signal data from the receiving unit 100, such as its instantaneous phase and signal strength, are continuously acquired. Simultaneously, ambient temperature and humidity parameters around the receiving unit 100 are acquired in real time using temperature and humidity sensors installed on the vehicle.

[0085] Specifically, over a period of time, as the ambient temperature changes from -20°C to 50°C and the humidity changes from 20% to 90%, the signal performance data of the receiving unit 100 is recorded. By comparing the signal phase of the receiving unit 100 with the average phase of other normal receiving units in the array antenna, the stable phase offset of the receiving unit 100 under different environmental conditions can be calculated. Similarly, by comparing the signal strength, its amplitude attenuation can be determined.

[0086] Subsequently, these collected environmental parameters (temperature, humidity) and the calculated stable phase offset and amplitude decay are used as training data to establish a parameterized function. For example, a polynomial regression model can be used, where the phase offset and amplitude decay are modeled as polynomial functions of temperature and humidity. This function is the defect feature model.

[0087] After the model is established, adaptive filtering techniques, such as Kalman filters, can be used to continuously update it. When new signal performance data and environmental parameters are collected, the Kalman filter adjusts and optimizes the parameters of the defect feature model based on these new observations and the model's predictions. For example, if a parameter of the receiving unit 100 (such as its aging degree) changes slowly, causing its inherent bias characteristics to drift, the adaptive filtering technique can promptly capture this change and update the model parameters, ensuring that the model always accurately reflects the current inherent bias state of the receiving unit 100. In this way, even if the inherent defect characteristics of the receiving unit 100 change over time or in the environment, the defect feature model can maintain its high accuracy, thus effectively supporting subsequent spoofing signal detection.

[0088] Specifically, the steps described above, which involve eliminating the inherent signal bias from the real-time received signal of the special processing unit based on the defect feature model to obtain the bias-free signal, can be further refined into the following operations.

[0089] The steps described above for eliminating the inherent signal bias from the real-time received signal from the special processing unit, based on the defect feature model, to obtain the bias-free signal include:

[0090] Collect current environmental parameters, and determine the corresponding stable phase offset and amplitude attenuation based on the defect feature model inputting the current environmental parameters;

[0091] Based on a determined stable phase offset and amplitude attenuation, the real-time received signal of the special processing unit is eliminated or compensated to obtain the signal after offset elimination.

[0092] Specifically, when performing inherent signal bias cancellation, the current environmental parameters must first be collected. These environmental parameters may include, but are not limited to, temperature, humidity, air pressure, and electromagnetic field strength, and can be acquired using environmental sensors integrated into the array antenna system. Subsequently, these current environmental parameters are input into a pre-established and continuously updated defect feature model. This defect feature model is a parameterized function characterizing the relationship between environmental parameters and stable phase bias and amplitude attenuation. By inputting the current environmental parameters, the model can output stable phase bias and amplitude attenuation corresponding to the current environmental conditions. These biases and attenuations represent the inherent signal defect manifestations of the special processing unit relative to the normal receiving unit in the current environment.

[0093] Furthermore, based on the determined stable phase offset and amplitude attenuation, the real-time received signal of the special processing unit is eliminated or compensated. The elimination or compensation operation aims to counteract the inherent phase offset and amplitude attenuation present in the signal of the special processing unit. For example, the stable phase offset can be counteracted by inverting the phase of the real-time received signal, and the signal strength lost due to amplitude attenuation can be recovered by multiplying by a compensation factor. Thus, the inherent signal offset of the signal after elimination or compensation is effectively removed, resulting in an offset-eliminated signal. This offset-eliminated signal is closer to the signal performance of a normal receiving unit, providing a cleaner input for subsequent spoofing detection.

[0094] In some embodiments of the present invention, a method for detecting signal integrity spoofing in an array antenna is proposed, which includes a step of detecting whether there is abnormal phase or frequency drift in the signal after offset removal. However, in actual implementation, if the specific implementation method of this detection step is not clearly defined, it may lead to insufficient detection accuracy or limited ability to identify different types of spoofing attacks, thereby affecting the overall reliability of spoofing detection.

[0095] In response, this invention further proposes a step for detecting whether there is abnormal phase or frequency drift in the signal after bias elimination, including:

[0096] Calculate the instantaneous frequency of the signal after offset removal;

[0097] Acquire vehicle motion status and satellite orbit information, and predict the actual satellite signal frequency based on vehicle motion status and satellite orbit information;

[0098] If there is a deviation between the instantaneous frequency and the actual satellite signal frequency, it is determined that there is an abnormal frequency drift in the signal after offset elimination.

[0099] The global reference phase is obtained by weighted averaging the signal phases of the receiving units in the array antenna, excluding the special processing unit.

[0100] If the phase of the signal after offset removal deviates from the global reference phase, it is determined that there is an abnormal phase drift in the signal after offset removal.

[0101] Specifically, calculating the instantaneous frequency of the signal after bias elimination can be achieved using various signal processing techniques, such as by differentiating the signal's phase over time. The instantaneous frequency reflects the signal's frequency characteristics at a given moment and is a key indicator for identifying frequency anomalies.

[0102] The acquisition of vehicle motion status and satellite orbit information aims to provide the necessary data foundation for predicting the frequency of real satellite signals. Vehicle motion status can be provided by the vehicle's own inertial measurement unit (IMU), wheel speed sensors, or onboard navigation system. Satellite orbit information is typically obtained through broadcast ephemeris or precise ephemeris. Based on this information, the Doppler shift of the received real satellite signal under the current vehicle position and motion state can be accurately calculated, thereby predicting the frequency of the real satellite signal.

[0103] In practical applications, when the calculated instantaneous frequency deviates from the predicted actual satellite signal frequency beyond a preset tolerance range, it can be determined that there is an abnormal frequency drift in the signal after offset removal. This deviation may be caused by frequency shifts introduced by deception signals.

[0104] Furthermore, to perform phase anomaly detection, a global reference phase can be obtained by weighted averaging the signal phases of the receiving units in the array antenna, excluding the special processing unit. This is because, in an array antenna, typically only some receiving units may be susceptible to spoofing attacks or have inherent biases, while the signal phases of most normally functioning receiving units can serve as a reliable reference. Weighted averaging effectively suppresses random noise from individual normal receiving units, resulting in a more stable global reference phase.

[0105] Furthermore, when there is a deviation between the phase of the signal after offset removal and the global reference phase that exceeds a preset tolerance range, it can be determined that there is an abnormal phase drift in the signal after offset removal. This phase drift is also one of the typical characteristics of spoofing attacks.

[0106] However, in practical applications, simply determining the presence of deviation may not be sufficient to accurately and robustly identify spoofing attacks. This is because, in complex electromagnetic environments, even without spoofing attacks, signals may be affected by noise, multipath effects, or environmental changes, resulting in minute phase or frequency fluctuations. If these fluctuations are simply interpreted as abnormal, they may lead to false alarms, thereby affecting the reliability of the system.

[0107] To address this, the present invention further proposes a step for determining the existence of a deception attack when the detected abnormal phase or frequency drift exceeds a preset deception determination threshold, including:

[0108] Calculate the instantaneous frequency deviation between the instantaneous frequency and the actual satellite signal frequency;

[0109] Calculate the instantaneous phase deviation between the offset-removed signal phase and the global reference phase;

[0110] When the instantaneous frequency deviation exceeds the preset frequency deception judgment threshold or the instantaneous phase deviation exceeds the preset phase deception judgment threshold, a deception attack is determined to exist.

[0111] Specifically, instantaneous frequency deviation refers to the difference between the instantaneous frequency of the signal after offset removal and the predicted frequency of the actual satellite signal based on vehicle motion and satellite orbit information. This deviation quantifies the degree of deviation between the received signal frequency and the expected true frequency. The instantaneous frequency can be understood as the frequency value of the signal at a specific moment, while the actual satellite signal frequency refers to the theoretical frequency calculated under ideal, interference-free conditions based on the satellite's orbital parameters and the motion state of the receiving equipment.

[0112] Furthermore, instantaneous phase deviation refers to the difference obtained by comparing the signal phase after offset removal with the global reference phase. This deviation quantifies the degree of deviation between the received signal phase and the average phase of other normal receiving elements in the array antenna. The global reference phase is usually obtained by weighted averaging of the signal phases of the receiving elements in the array antenna, excluding special processing elements, with the aim of providing a relatively stable phase reference that is not affected by a single abnormal element.

[0113] As a preferred implementation, the preset frequency spoofing detection threshold and the preset phase spoofing detection threshold are determined comprehensively based on factors such as the actual application scenario, system performance requirements, and historical data analysis. These thresholds aim to distinguish between signal deviations caused by normal environmental fluctuations and significant deviations caused by malicious spoofing attacks. When any deviation exceeds its corresponding preset threshold, a spoofing attack is considered to have occurred.

[0114] In some preferred embodiments, it is assumed that during the operation of an array antenna system, the signal after offset removal by its special processing unit is continuously monitored. At a certain moment, the instantaneous frequency deviation between the instantaneous frequency and the actual satellite signal frequency is calculated to be 10 Hz, and simultaneously, the instantaneous phase deviation between the offset-removed signal phase and the global reference phase is 5 degrees. If the system's preset frequency spoofing threshold is 8 Hz and the phase spoofing threshold is 10 degrees, then because the instantaneous frequency deviation of 10 Hz exceeds the frequency spoofing threshold of 8 Hz, even if the instantaneous phase deviation of 5 degrees does not exceed the phase spoofing threshold of 10 degrees, the system will still determine that a spoofing attack exists. Conversely, if the instantaneous frequency deviation is 5 Hz and the instantaneous phase deviation is 3 degrees, then neither exceeds their respective preset thresholds, and the system will determine that no spoofing attack exists. This threshold-based judgment mechanism makes the determination of spoofing attacks more quantitative and objective, avoiding the uncertainty brought about by subjective judgment.

[0115] The aforementioned array antenna signal integrity spoofing detection method can effectively identify spoofing attacks. However, in practical applications, simply detecting spoofing attacks may not be sufficient to address complex threat scenarios. Further measures are needed to respond to and handle confirmed spoofing attacks to ensure system security and reliability. Failure to respond promptly can lead to vehicle positioning errors, thereby posing security risks. Therefore, this invention further proposes a scheme for taking response measures after a spoofing attack is confirmed.

[0116] In some embodiments of the present invention, the method further includes:

[0117] In response to the confirmation of a deception attack, an early warning message is sent to the cloud-based dispatch platform, and the vehicle location calculation strategy is adjusted.

[0118] Specifically, responding to the confirmation of a deception attack means that once the aforementioned methods detect abnormal phase or frequency drift exceeding a preset deception threshold, thus confirming the existence of a deception attack, the system will immediately trigger subsequent response mechanisms. Sending an early warning to the cloud-based dispatch platform involves transmitting the detected deception attack event and its related information, such as attack type, affected receiving units, and attack intensity, via network communication to a remote, centralized cloud-based dispatch platform. This platform can be a central system responsible for managing and monitoring the operational status of a large number of vehicles, aiming to promptly notify operators or relevant management departments to take macro-level countermeasures, such as adjusting vehicle dispatch in affected areas or initiating higher-level security protocols. In practical applications, "adjusting the vehicle's positioning solution strategy" means that after confirming a deception attack, the vehicle's autonomous positioning system no longer relies entirely on potentially deceitful GNSS signals for positioning. For example, a more robust positioning mode can be switched to prioritize the use of data from other sensors on the vehicle itself, such as inertial measurement units (IMU), wheel speed sensors, lidar (LiDAR), or vision sensors, for positioning calculations, or high-precision map information can be combined for auxiliary positioning to reduce the impact of spoofing attacks on the vehicle's positioning accuracy and ensure the safe operation of the vehicle.

[0119] In practical applications, how to specifically adjust the positioning solution strategy to maximize the continuity and accuracy of vehicle positioning, especially when some receiving units are affected by spoofing attacks, is a technical problem that requires further refinement and optimization. Without targeted strategies, the positioning accuracy and reliability of the vehicle may be severely affected, even leading to positioning failure. To address this, this invention proposes a more refined and robust positioning solution adjustment scheme, aiming to ensure that the vehicle maintains high-integrity positioning capabilities even when spoofing attacks occur.

[0120] The steps for adjusting the vehicle's positioning solution strategy include: prioritizing the use of received signals from receivers that are not susceptible to spoofing attacks for positioning solution, or switching to a robust positioning mode based on a preset security strategy, prioritizing the use of sensor data from the vehicle's own motion sensors for positioning solution.

[0121] Specifically, when a spoofing attack is detected, the system does not simply stop positioning or use signals from all receiving units for calculation. Instead, it first evaluates each receiving unit in the array antenna. Prioritizing the use of signals from receiving units not subject to spoofing attacks for positioning calculation means that after identifying some receiving units as being under spoofing attacks, the system actively excludes the signals from these attacked units, using only the signal data from those receiving units determined to be unaffected and with good signal integrity for positioning calculation. The purpose is to ensure the reliability of the input data for positioning calculation by eliminating contaminated data sources, thereby maintaining the accuracy and reliability of positioning.

[0122] In addition, as an alternative or supplementary strategy, the system can switch to a robust positioning mode based on a preset security policy. In this mode, the system will prioritize using the sensor data from the vehicle's own motion sensors for positioning calculations. These motion sensors may include, but are not limited to, inertial measurement units (IMUs), wheel speed sensors, and high-precision map matching systems. This sensor data is generally not easily affected by external radio signal spoofing, and therefore can serve as an independent and reliable positioning data source when GNSS signals are attacked. The purpose is to provide a backup, highly intact positioning solution in cases where GNSS signals are completely unreliable or severely compromised, to prevent positioning interruptions or erroneous positioning results.

[0123] Furthermore, when the spoofing attack has a large impact range, or when higher positioning robustness is desired, the solution of this invention can switch to a robust positioning mode according to a preset security strategy. In this mode, the system prioritizes using the sensor data from the vehicle's own motion sensors for positioning calculation. This strategy allows the vehicle's positioning system to no longer rely entirely on spoofable GNSS signals, but instead utilize internal, independent sensor data for positioning, thus providing a reliable backup positioning method when the external signal environment is harsh or under attack. It is precisely this multi-source fusion and strategy switching that enables the vehicle to maintain the continuity and high integrity of its positioning even when facing complex spoofing attacks.

[0124] However, its implementation requires a specific system architecture to support and execute the method, ensuring its efficient and stable operation. Without a clearly defined system implementation, the deployment and operation of this method may face challenges related to inter-module coordination, data flow management, and real-time processing capabilities.

[0125] Regarding this, secondly, refer to Figure 2 The present invention further proposes an array antenna signal integrity spoofing detection system for performing the above-mentioned array antenna signal integrity spoofing detection method, comprising:

[0126] The signal acquisition and analysis module 210 is used to acquire the received signals of each receiving unit in the array antenna and analyze the quality parameters of the received signals.

[0127] The unit identification and marking module 220 is used to identify the receiving units in the array antenna that have inherent signal bias according to the quality parameters, and mark the receiving units with inherent signal bias as special processing units;

[0128] The model building and updating module 230 is used to build and continuously update a defect feature model that characterizes the inherent signal bias of a special processing unit under the condition of no deceptive signal.

[0129] The bias elimination module 240 is used to eliminate the inherent signal bias from the real-time received signal from the special processing unit based on the defect feature model, so as to obtain the bias-eliminated signal.

[0130] The drift detection module 250 is used to detect whether there is abnormal phase or frequency drift in the signal after offset elimination;

[0131] The deception attack determination module 260 is used to determine the existence of a deception attack when the detected abnormal phase or frequency drift exceeds a preset deception determination threshold.

[0132] Specifically, the signal acquisition and analysis module 210 can be understood as the front-end processing unit of the system. Its main function is to receive the raw signals from each receiving unit in the array antenna in real time and perform preliminary quality assessments on these signals. For example, this module can calculate quality parameters such as the signal strength, carrier-to-noise ratio, and inter-channel signal phase difference between each receiving unit, with the aim of providing basic data for subsequent signal processing and anomaly detection.

[0133] The unit identification and marking module 220 is configured to receive the quality parameters output by the signal acquisition and analysis module 210 and perform intelligent analysis based on these parameters. Specifically, this module identifies receiving units that exhibit continuous abnormalities or deviate from the normal range by comparing parameters such as signal strength, carrier-to-noise ratio, and inter-channel signal phase difference of each receiving unit. For example, when the signal strength or carrier-to-noise ratio of a receiving unit is continuously below the average level, and its inter-channel signal phase difference continuously exceeds the preset normal range, the receiving unit will be identified as a receiving unit with inherent signal bias and marked as a special processing unit. The purpose is to isolate defective units for targeted processing.

[0134] In practical applications, the model building and updating module 230 is responsible for building and maintaining a defect feature model for the marked special processing unit. Under ideal conditions without spoofing signals, this module continuously collects signal performance data and corresponding environmental parameters, such as temperature and humidity, from the special processing unit. Based on this data, the module can determine the stable phase offset and amplitude attenuation of the special processing unit relative to the normal receiving unit. Furthermore, the module constructs a parameterized function representing the relationship between environmental parameters and stable phase offset and amplitude attenuation as the defect feature model. Through adaptive filtering techniques, such as Kalman filtering or the Least Mean Square (LMS) algorithm, the module continuously adjusts and updates the model's parameters to ensure that the model accurately reflects the inherent signal offset of the special processing unit under different environments. This aims to provide a precise correction basis for subsequent offset elimination.

[0135] The bias cancellation module 240 is designed to receive the real-time received signal from the special processing unit and the defect feature model provided by the model building and updating module 230. Specifically, this module first collects the current real-time environmental parameters and inputs them into the defect feature model to determine the stable phase bias and amplitude attenuation corresponding to the special processing unit under the current environment. Subsequently, based on these determined biases and attenuations, the module performs precise cancellation or compensation processing on the real-time received signal from the special processing unit to obtain a signal after eliminating the inherent signal bias. Its purpose is to restore the true state of the signal and provide a clean signal source for subsequent deception detection.

[0136] The drift detection module 250 receives the offset-reduced signal output by the offset cancellation module 240 and detects abnormal phase or frequency drift. Specifically, this module can calculate the instantaneous frequency of the offset-reduced signal and compare it with the predicted frequency of the real satellite signal based on the vehicle's motion state and satellite orbit information to detect whether abnormal frequency drift exists. Simultaneously, this module can also perform a weighted average of the signal phases of the receiving units in the array antenna (excluding the special processing unit) to obtain a global reference phase, and compare the offset-reduced signal phase with this global reference phase to detect whether abnormal phase drift exists. The purpose is to identify frequency or phase anomalies that may be caused by deceptive signals.

[0137] The spoofing attack determination module 260 is the final decision-making unit of the system. Its function is to determine whether a spoofing attack exists based on the output of the drift detection module 250. Specifically, this module calculates the instantaneous frequency deviation between the instantaneous frequency and the actual satellite signal frequency, as well as the instantaneous phase deviation between the signal phase after offset removal and the global reference phase. When either deviation exceeds a preset spoofing determination threshold, such as a frequency spoofing determination threshold or a phase spoofing determination threshold, the module determines that a spoofing attack exists, with the aim of timely detection and response to potential spoofing threats.

[0138] The present invention modularizes the array antenna signal integrity deception detection method, making the responsibilities of each functional unit clear and enabling them to work collaboratively. The signal acquisition and analysis module 210 serves as the data entry point, ensuring comprehensive acquisition of the original signal and preliminary quality assessment. The unit identification and marking module 220 effectively identifies and isolates receiving units with inherent signal biases, preventing their defects from interfering with the overall detection. The model building and updating module 230, through continuous learning and adaptation, provides accurate defect feature models for these special processing units, enabling the bias elimination module 240 to accurately remove inherent biases from the real-time signal and restore the true state of the signal. Based on this, the drift detection module 250 can more accurately identify abnormal phase or frequency drifts caused by deception attacks, without misjudging them as inherent defects in the receiving unit itself. Finally, the deception attack determination module 260, based on accurate drift detection results, can reliably determine whether a deception attack exists. This modular system design ensures high-precision deception detection even in the presence of inherent defects in the receiving unit, effectively solving the problem of false alarms or missed alarms caused by unit defects in traditional methods.

[0139] The above are merely embodiments of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of array antenna signal integrity spoofing detection, the method comprising: The method comprises the following steps: acquiring the received signals of each receiving unit in the array antenna, and analyzing the quality parameters of the received signals; identifying the receiving unit with inherent signal bias in the array antenna according to the quality parameters, and marking the receiving unit with inherent signal bias as a special processing unit; establishing and continuously updating a defect characteristic model representing the inherent signal bias of the special processing unit under the condition of no spoofing signal; eliminating the inherent signal bias based on the defect characteristic model from the real-time received signals of the special processing unit to obtain the signals after eliminating the bias; detecting whether there is abnormal phase or frequency drift in the signals after eliminating the bias; when the detected abnormal phase or frequency drift exceeds the preset spoofing judgment threshold, it is determined that there is a spoofing attack; the step of detecting whether there is abnormal phase or frequency drift in the signals after eliminating the bias comprises: calculating the instantaneous frequency of the signals after eliminating the bias; acquiring the vehicle motion state and satellite orbit information, and predicting the real satellite signal frequency based on the vehicle motion state and the satellite orbit information; when the instantaneous frequency deviates from the real satellite signal frequency, it is determined that there is abnormal frequency drift in the signals after eliminating the bias; performing weighted average on the signal phases of the receiving units in the array antenna except the special processing unit to obtain a global reference phase; when the phase of the signals after eliminating the bias deviates from the global reference phase, it is determined that there is abnormal phase drift in the signals after eliminating the bias.

2. The method of claim 1, wherein, The quality parameters include signal strength, carrier-to-noise ratio, and inter-channel signal phase difference of each receiving unit.

3. The method of claim 2, wherein, The step of identifying the receiving unit with inherent signal bias in the array antenna according to the quality parameters comprises: when the signal strength of the receiving unit continuously falls below the average of the signal strengths of all the remaining receiving units or the carrier-to-noise ratio of the receiving unit continuously falls below the average of the carrier-to-noise ratios of all the remaining receiving units in a preset monitoring time window, and the inter-channel signal phase difference of the receiving unit continuously exceeds the preset normal range in the preset monitoring time window, the receiving unit is identified as the receiving unit with inherent signal bias.

4. The method of claim 1, wherein, The step of establishing and continuously updating a defect characteristic model representing the inherent signal bias of the special processing unit under the condition of no spoofing signal comprises: under the condition of no spoofing signal, collecting signal performance data of the special processing unit under different environmental conditions, and obtaining corresponding environmental parameters through environmental sensors; determining the stable phase bias and amplitude attenuation of the special processing unit relative to the normal receiving unit based on the signal performance data; establishing a parameterized function representing the relationship between the environmental parameters and the stable phase bias and the amplitude attenuation as the defect characteristic model in combination with the stable phase bias, the amplitude attenuation, and the environmental parameters, and adjusting and updating the parameters of the defect characteristic model using adaptive filtering technology.

5. The method of claim 4, wherein, The step of eliminating inherent signal bias based on the defect feature model from the real-time receiving signal of the special processing unit to obtain a signal after eliminating bias includes: Collecting current environmental parameters, and inputting the current environmental parameters based on the defect feature model to determine corresponding stable phase bias and amplitude attenuation; Based on the determined stable phase bias and amplitude attenuation, the real-time receiving signal of the special processing unit is eliminated or compensated to obtain the signal after eliminating bias.

6. The method of claim 1, wherein, The step of determining that there is a spoofing attack when the detected abnormal phase or frequency drift exceeds the preset spoofing judgment threshold includes: Calculate the instantaneous frequency deviation between the instantaneous frequency and the real satellite signal frequency; Calculate the instantaneous phase deviation between the signal phase after eliminating bias and the global reference phase; When the instantaneous frequency deviation exceeds the preset frequency spoofing judgment threshold or the instantaneous phase deviation exceeds the preset phase spoofing judgment threshold, it is determined that there is a spoofing attack.

7. The method of claim 6, wherein, The method further includes: In response to the determination of the spoofing attack, an early warning information is sent to a cloud scheduling platform, and a positioning solution strategy of the vehicle is adjusted.

8. The method of claim 7, wherein, The step of adjusting the positioning solution strategy of the vehicle includes: Preferentially using the receiving signal of the receiving unit without spoofing attack for positioning solution, or switching to a robust positioning mode according to a preset safety strategy, and preferentially using the sensing data of the vehicle's own motion sensor for positioning solution.

9. An array antenna signal integrity spoofing detection system configured to perform the array antenna signal integrity spoofing detection method according to any one of claims 1 to 8, wherein, The system includes: A signal acquisition and analysis module for acquiring receiving signals of each receiving unit in an array antenna and analyzing quality parameters of the receiving signals; A unit identification and marking module for identifying receiving units with inherent signal bias in the array antenna according to the quality parameters, and marking the receiving units with inherent signal bias as special processing units; A model establishment and update module for establishing and continuously updating a defect feature model representing the inherent signal bias of the special processing units under the condition of no spoofing signal; A bias elimination module for eliminating inherent signal bias from the real-time receiving signal of the special processing unit based on the defect feature model to obtain a signal after eliminating bias; A drift detection module for detecting whether there is abnormal phase or frequency drift in the signal after eliminating bias; A spoofing attack judgment module for determining that there is a spoofing attack when the detected abnormal phase or frequency drift exceeds the preset spoofing judgment threshold. The step of detecting whether there is abnormal phase or frequency drift in the signal after eliminating bias includes: Calculate the instantaneous frequency of the signal after eliminating bias; Obtain vehicle motion state and satellite orbit information, and predict the real satellite signal frequency based on the vehicle motion state and the satellite orbit information; When the instantaneous frequency deviates from the real satellite signal frequency, it is determined that there is abnormal frequency drift in the signal after eliminating bias; The global reference phase is obtained by weighted average based on the signal phases of the receiving units in the array antenna except the special processing units; When the signal phase after the bias elimination deviates from the global reference phase, it is determined that there is an abnormal phase drift in the signal after the bias elimination.

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