GNSS deception / multipath interference detection method and device

Abstract

The invention discloses a GNSS (Global Navigation Satellite System) deception / multipath interference detection method and a GNSS deception / multipath interference detection device, which relate to the field of signal detection and are used for independently calculating a dynamic baseline of a signal quality metric (SAPTL) of each satellite in real time, namely a dynamic mean value and a standard deviation changing along with an environment. By performing normalization comparison on the real-time measurement and the dynamic baseline, the baseline drift influence caused by scene switching (such as entering a city canyon) can be eliminated, so that a standardized deviation sequence which is not interfered by an environmental background is obtained. And finally, analyzing the sequence by using a CUSUM change point detection algorithm which is extremely sensitive to tiny continuous change, and accurately capturing real signal abnormity caused by cheating or multipath instead of false fluctuation caused by environmental change. In this way, the fundamental problems that a fixed threshold value is poor in adaptability in a dynamic environment, and misjudgment and missed judgment are prone to being generated are solved, and the robustness and reliability of detection are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of signal detection, and more particularly, to a GNSS spoofing / multipath interference detection method and device. BACKGROUND

[0002] Global Navigation Satellite System (GNSS) has become an indispensable infrastructure in civil and military fields due to its all-weather and global coverage, providing positioning, velocity measurement and timing services. However, GNSS signals are extremely weak after long-distance transmission, and the signal structure is open, which is vulnerable to spoofing attacks and multipath interference. Spoofing attacks can maliciously manipulate the positioning results of receivers by broadcasting false signals, posing a serious security threat; multipath interference causes propagation errors due to signal reflection, reducing positioning accuracy. Since both will cause abnormal signal quality, how to effectively detect and accurately distinguish them has become a key challenge to ensure the security of GNSS space-time information.

[0003] To address this challenge, existing technologies have proposed some detection algorithms based on signal quality monitoring (SQM). For example, Delta and Ratio algorithms detect abnormalities by analyzing the distortion of correlation peaks, but their performance decreases when both in-phase and quadrature components fluctuate. The ELP algorithm uses the phase difference between the early and late branches as a detection quantity, but it will fail under certain phase conditions. More importantly, these traditional SQM methods usually rely on a fixed detection threshold (e.g., a threshold value for determining signal abnormalities) to determine whether the signal is abnormal. The inherent assumption of this method is that the normal statistical characteristics of the signal are stationary, but in actual applications, the working environment of GNSS receivers is dynamically changing. For example, when a vehicle drives from an open area into a city canyon with high-rise buildings, the multipath effect will be significantly enhanced, causing the baseline value of the signal quality monitoring quantity to fluctuate dramatically. At this time, a fixed threshold set for open areas will produce a large number of false alarms due to environmental changes; conversely, if a higher threshold is set to adapt to complex environments, hidden spoofing attacks may be missed. Therefore, the detection method using a fixed threshold in the existing technology has poor adaptability in dynamically changing environments, and it is difficult to balance the sensitivity and reliability of detection, which is a core pain point that needs to be addressed when moving towards large-scale practical applications such as autonomous driving and unmanned aerial logistics. The root of the problem lies in the fact that a fixed threshold cannot track and adapt to changes in the baseline of the normal state of the signal in real time.

[0004] In order to overcome this defect, there is an urgent need for a detection method that can dynamically adjust its judgment criteria, so that it can still accurately and robustly detect real abnormalities caused by spoofing or multipath in various complex and dynamically switching scenarios, thereby improving the practicality and reliability of the detection system in all scenarios. SUMMARY

[0005] To solve the existing problems, according to an aspect of the present application, a GNSS spoofing / multipath interference detection method is provided, which comprises: acquiring a SAPTL measurement time sequence of M satellites; performing split-channel dynamic baseline calculation on the SAPTL measurement time sequence of the M satellites to obtain dynamic mean and dynamic standard deviation of the M satellites; acquiring real-time SAPTL measurement of the i-th satellite; extracting dynamic mean and dynamic standard deviation of the i-th satellite from the dynamic mean and dynamic standard deviation of the M satellites; based on the dynamic mean and dynamic standard deviation of the i-th satellite, performing detection quantity normalization on the real-time SAPTL measurement of the i-th satellite to obtain normalized deviation of the i-th satellite; performing CUSUM change point detection on the normalized deviation of the i-th satellite to obtain an abnormal flag of the i-th satellite.

[0006] According to another aspect of the present application, a GNSS spoofing / multipath interference detection device is provided, which comprises: a SAPTL measurement time acquisition module for acquiring a SAPTL measurement time sequence of M satellites; a SAPTL measurement time dynamic calculation module for performing split-channel dynamic baseline calculation on the SAPTL measurement time sequence of the M satellites to obtain dynamic mean and dynamic standard deviation of the M satellites; a real-time SAPTL measurement acquisition module for acquiring real-time SAPTL measurement of the i-th satellite; an i-th satellite feature extraction module for extracting dynamic mean and dynamic standard deviation of the i-th satellite from the dynamic mean and dynamic standard deviation of the M satellites; a detection quantity normalization module for, based on the dynamic mean and dynamic standard deviation of the i-th satellite, performing detection quantity normalization on the real-time SAPTL measurement of the i-th satellite to obtain normalized deviation of the i-th satellite; and an i-th satellite generation module for performing CUSUM change point detection on the normalized deviation of the i-th satellite to obtain an abnormal flag of the i-th satellite.

[0007] Compared with the prior art, the GNSS spoofing / multipath interference detection method and device provided by the application can solve the technical problem that the existing GNSS interference detection method cannot adapt to a dynamic environment due to the use of a fixed threshold. An adaptive scheme based on dynamic baseline and change point detection is proposed. The scheme discards the static one-size-fits-all threshold and instead calculates the dynamic baseline of the signal quality metric (SAPTL) of each satellite in real time and independently, that is, the dynamic mean and standard deviation that change with the environment. By normalizing and comparing the real-time metric with the dynamic baseline, the influence of baseline drift caused by scene switching (such as entering a city canyon) can be eliminated, thereby obtaining a standardized deviation sequence that is not affected by environmental background interference. Finally, the CUSUM change point detection algorithm, which is extremely sensitive to small continuous changes, is used to analyze the sequence, which can accurately capture real signal abnormalities caused by spoofing or multipath, rather than false fluctuations caused by environmental changes. In this way, the fundamental problem of poor adaptability of the fixed threshold in a dynamic environment, easy misjudgment and missed judgment is solved, and the robustness and reliability of the detection are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0008] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description of embodiments of the present application taken in conjunction with the accompanying drawings. The accompanying drawings are provided to aid in the understanding of the present application and constitute a part of the specification, together with the written description. The accompanying drawings are not intended to limit the scope of the present application, but to explain the present application together with the written description. In the drawings, the same reference numerals refer to the same components or steps throughout.

[0009] Figure 1 A flowchart of the GNSS spoofing / multipath interference detection method according to the embodiments of the present application.

[0010] Figure 2 A data flow diagram of the GNSS spoofing / multipath interference detection method according to the embodiments of the present application.

[0011] Figure 3 A schematic diagram of a multipath interference model in the GNSS spoofing / multipath interference detection method according to the embodiments of the present application.

[0012] Figure 4 A schematic diagram of the generation of a generative spoof in the GNSS spoofing / multipath interference detection method according to the embodiments of the present application.

[0013] Figure 5 A flowchart of step S1 in the GNSS spoofing / multipath interference detection method according to the embodiments of the present application.

[0014] Figure 6 A flowchart of step S6 in the GNSS spoofing / multipath interference detection method according to the embodiments of the present application.

[0015] Figure 7 A block diagram of a GNSS spoofing / multipath interference detection device according to an embodiment of the present application. DETAILED DESCRIPTION

[0016] Embodiments of the present disclosure will be described in more detail with reference to the drawings. While certain embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be interpreted as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly and completely understood.

[0017] To address the problems in the foregoing background art description, the present application proposes a GNSS spoofing / multipath interference detection method. Figure 1 A flowchart of a GNSS spoofing / multipath interference detection method according to an embodiment of the present application. Figure 2 A data flow diagram of a GNSS spoofing / multipath interference detection method according to an embodiment of the present application. As shown in Figure 1 and Figure 2 As shown in the foregoing, the GNSS spoofing / multipath interference detection method according to an embodiment of the present application includes: step S1, acquiring a SAPTL measurement time sequence of M satellites; step S2, performing split-channel dynamic baseline calculation on the SAPTL measurement time sequence of the M satellites to obtain dynamic mean and dynamic standard deviation of the M satellites; step S3, acquiring real-time SAPTL measurement of an i-th satellite; step S4, extracting dynamic mean and dynamic standard deviation of the i-th satellite from the dynamic mean and dynamic standard deviation of the M satellites; step S5, based on the dynamic mean and dynamic standard deviation of the i-th satellite, performing detection quantity normalization on the real-time SAPTL measurement of the i-th satellite to obtain normalized deviation of the i-th satellite; and step S6, performing CUSUM change point detection on the normalized deviation of the i-th satellite to obtain an abnormality flag of the i-th satellite.

[0018] It is worth mentioning that spoofing and multipath interference are two main interference forms affecting the reliability of a global navigation satellite system (GNSS). Specifically, multipath interference is a phenomenon caused by the signal propagation environment. Figure 3 A schematic diagram of a multipath interference model in a GNSS spoofing / multipath interference detection method according to an embodiment of the present application. As shown in Figure 3The multipath interference model shown, GNSS signals in the process of propagation, especially in complex environments such as urban canyons, are easily affected by obstacles such as buildings, mountains, etc. to reflect or diffract, thereby forming multiple reflection paths in addition to the direct path. Because the reflection path is longer, these multipath signals have time delay and attenuation relative to the direct signal. When these signals with different time delays and phases are superimposed at the receiver antenna, they will interfere with and distort the composite signal. In a multipath environment, the received signal at the receiving antenna can be represented as: ; where the superscripts ad, m represent the true direct signal and the multipath signal, respectively, n t is a Gaussian white noise with zero mean, which is one of the main sources of GNSS measurement error and positioning accuracy.

[0019] Unlike the natural formation of multipath interference, spoofing interference is a deliberate malicious attack. Figure 4 The schematic diagram of the principle of the generation of the generated spoofing in the GNSS spoofing / multipath interference detection method according to the embodiments of the application. As shown in Figure 4 , the attacker transmits a fake satellite signal with stronger power but containing false spatiotemporal information, intending to hijack the tracking loop of the receiver and make it output false positioning, speed and time information, which is extremely harmful. In the scenario considering only spoofing, the received signal can be represented as: ; where the superscript s represents the spoofing signal.

[0020] Therefore, in the dynamic and complex scenario where multipath and spoofing exist simultaneously, the received signal contains the true signal, the multipath signal, the spoofing signal and the noise, which can be represented as: .

[0021] From the formation mechanism of the signal, whether it is a multipath signal, a true direct signal or a spoofing signal, they all have similar signal structures, which are represented as: ; where, is the signal power, C is the pseudo-random spreading code, is the navigation data, is the code delay of C / A, is the Doppler shift, is the carrier phase. It is this high similarity in structure that makes it difficult for traditional detection methods that use fixed thresholds or single features to effectively distinguish between unintentional multipath interference and malicious spoofing attacks in dynamically changing environments, resulting in decreased detection performance, false positives and false negatives.

[0022] ​To cope with these challenges, the application applies the detection algorithm of SAPTL-MA-MSC. The core idea of this algorithm is a three-stage fine processing flow: first, it uses SAPTL (three-point autocorrelation power sum) as the basis for signal quality measurement, which is very sensitive to the symmetry changes of the correlation peak and can effectively reflect the signal distortion caused by spoofing or multipath; then, through the MA (moving average) technology, the dynamic baseline of the SAPTL time series of each satellite is estimated, and the mean and standard deviation that change with the environment are calculated, thus solving the problem of poor adaptability of the traditional fixed threshold method in dynamic scenarios; finally, the MSC (multi-satellite comparison) strategy is used to comprehensively analyze the detection results of all visible satellites, and according to the number and proportion of affected satellites, it is distinguished whether it is a multipath interference affecting a few satellites or a spoofing attack attempting to affect all satellites. Thus, the application first calculates the SAPTL measurement time series corresponding to SAPTL, then performs channel-by-channel dynamic baseline calculation to obtain the dynamic mean and standard deviation corresponding to MA, and finally aggregates the anomaly flags of all satellites to distinguish spoofing from multipath corresponding to MSC. In this way, the dynamic baseline overcomes the adaptability defect of the traditional fixed threshold method in complex environments, and through multi-satellite information fusion, it solves the problem that single signal quality monitoring cannot effectively distinguish between spoofing attacks and multipath interference, thus significantly improving the robustness and accuracy of the detection system in dynamic and real scenarios.

[0023] In step S1, the SAPTL measurement time series of M satellites is obtained. It should be understood that in GNSS signal processing, the shape of the correlation peak is a direct physical manifestation of signal quality. Ideally, the correlation peak is sharp and symmetrical, but under spoofing or multipath interference, the superposition of real signals and interference signals will cause the correlation peak to distort, such as broadening, skewing or appearing side lobes. This distortion is directly reflected in the output power of different branches of the correlator. Therefore, in order to build a detection foundation that can sensitively capture such distortion, a measure that can quantify the overall shape change of the correlation peak is needed. To this end, the application obtains the SAPTL measurement time series of M satellites to establish such a foundation, providing original and high-fidelity input data for subsequent dynamic baseline estimation and anomaly detection.

[0024] In one specific embodiment of the application, Figure 5 The flow chart of step S1 in the GNSS spoofing / multipath interference detection method according to the embodiment of the application. As Figure 5As shown, in step S1, the SAPTL measurement time series of M satellites are acquired, including: step S11, capturing and tracking the original IQ sampling data output by the receiver radio frequency front end at time t to obtain the quadrature component and in-phase component of the early branch, the quadrature component and in-phase component of the prompt branch, and the quadrature component and in-phase component of the late branch; step S12, performing power conversion on the quadrature component and in-phase component of the early branch, the quadrature component and in-phase component of the prompt branch, and the quadrature component and in-phase component of the late branch to obtain the early branch power, the prompt branch power and the late branch power; step S13, calculating the sum of the early branch power, the prompt branch power and the late branch power, and normalizing it to obtain the SAPTL value at time t as the SAPTL measurement.

[0025] In the above embodiments, the specific process of step S1 is as follows: in order to elaborate the technical solutions of the present application, a specific application scenario is taken as an example for illustration. For example, a vehicle-mounted device equipped with a GNSS receiver is driving from an open suburban highway into a high-rise urban center area, and the receiver can currently stably track M=8 visible satellites. For any one of the satellites (for example, PRN15 satellite), the acquisition process of its SAPTL metric is as follows: first, step S11 is performed. In the front-end processing flow of the GNSS receiver, the weak radio frequency signal received by the antenna contains visible satellite signals, thermal noise and potential interference signals. First, it is amplified by a low-noise amplifier, and then it is mixed with the signal generated by the local oscillator to down-convert it from the high-frequency radio frequency band to a fixed intermediate frequency. Subsequently, the intermediate frequency analog signal is sampled and quantized by an analog-to-digital converter (ADC), thereby converting it into a digital signal. In order to completely preserve the amplitude and phase information of the signal, the quadrature sampling technique is used to decompose the digital intermediate frequency signal into two mutually orthogonal components: in-phase component (I) and quadrature component (Q). The sequence of digital values generated at each sampling time by this pair, i.e. the original IQ sampling data, constitutes the starting point of the signal processing flow. In implementation, the signal processing module first performs signal acquisition on the input original IQ sampling data for the satellite, such as PRN15 satellite. The essence of acquisition is to search for the target signal in a huge uncertainty space. This space is composed of two dimensions: the Doppler shift caused by the relative motion of the satellite and the receiver, and the code phase caused by the signal propagation delay. In order to search, the signal processing module generates a local copy of the pseudo-random code identical to PRN15 satellite. Subsequently, within a preset Doppler frequency search range, for example, with a step of 500 Hz, from -5 kHz to +5 kHz, the local code copy is correlated with the input original IQ data at each frequency point. This process is equivalent to trying all possible code phase alignment modes at each frequency. When the correlation peak energy calculated by a certain combination of frequency and code phase significantly exceeds the acquisition threshold set according to the background noise level, PRN15 satellite is successfully acquired. At this time, the frequency and code phase corresponding to the combination are the rough estimates of the Doppler frequency shift and code phase of the satellite signal. After successful acquisition, the processing flow switches to the tracking phase. The tracking loop, a precise closed-loop feedback control structure composed of a code loop (delay lock loop, DLL) and a carrier loop (Costas loop), takes over and locks the initial parameters obtained in the acquisition phase. The code loop is to achieve and maintain high-precision, dynamic synchronization between the locally generated pseudo-random code and the received satellite signal code phase. For this purpose, the code loop's numerical controlled oscillator (NCO) will generate three closely spaced local code copies according to the acquired code phase, corresponding to the three code phases of advance, instant and lag.The interval of the three code phases is a key parameter that is preset, for example, the early and late branches can be set to be 0.5 chip width ahead and delayed with respect to the prompt branch, respectively. At time t, the three local code copies are correlated with the input original IQ sample data stream in parallel, which is essentially to project the energy of the input signal to the three specific code phase points.

[0026] In order to theoretically illustrate the influence of the interference signal on the correlator of the receiver and provide a mathematical basis for the detection principle of the present application, a general signal receiving model is first established. The received signal of the model can contain a real direct signal, a multipath signal, a deception signal and noise at the same time. Under this theoretical model, the output of the correlator can represent the cross-correlation function R(t, τ) of the received signal and the local pseudo code, which is the superposition of the cross-correlation results of the real direct signal, the multipath signal, the deception signal and the noise with the local code as shown in the following formula: ; wherein τ is the delay between the local code and the received signal code. Since the deception signal, the multipath signal and the real direct signal have similar structures, their correlation functions can be respectively represented in the form of the correlation function of the real signal at different delays τ1 and τ2: .

[0027] Specifically, after the local prompt code copy is correlated with the input data, the in-phase component and the quadrature component of the prompt branch are output; at the same time, the local early code copy is correlated with the input data to obtain the in-phase component and the quadrature component of the early branch; and the local late code copy is correlated with the input data to obtain the in-phase component and the quadrature component of the late branch. These six values are the outputs at time t for the PRN15 satellite. Taking the prompt branch as an example, its in-phase component and quadrature component are the linear superposition of the correlation results of the signal components and are jointly affected by the signal power P, the code phase error (τ-τ') and the carrier phase error (φ-φ'): , ; wherein is the autocorrelation power of the spread spectrum code.

[0028] Meanwhile, the discriminator of the code loop uses the energy (or IQ components) of the early branch and the late branch to calculate the code phase error, and controls the numerically controlled oscillator (NCO) through a loop filter to dynamically adjust the generation phase of the local code, so that it is always locked to the code phase of the received signal. The carrier loop also works in a similar way, using the IQ components of the prompt branch to track the phase and frequency changes of the carrier. This closed-loop feedback mechanism enables the receiver to continuously and stably output accurate early, prompt and late IQ components, even when the vehicle is moving.

[0029] Next, step S12 is performed. The power is a measure of signal energy, which is obtained by calculating the sum of squares of the in-phase component and the quadrature component, i.e., the square of the amplitude. The specific calculation process of the amplitude is as follows: first, calculate the early branch amplitude E, which is the square root of the sum of the squares of the in-phase component and the quadrature component of the early branch; second, calculate the prompt branch amplitude P, which is the square root of the sum of the squares of the in-phase component and the quadrature component of the prompt branch; finally, calculate the late branch amplitude L, which is the square root of the sum of the squares of the in-phase component and the quadrature component of the late branch. The calculation process is as follows: ; wherein, E , P and L all obey the Rice distribution, and P is taken as an example, let W = P , the probability density function is ; wherein, is the mean of W when there is no deception, is the noise variance, is the 0th order modified Bessel function. When the signal-to-noise ratio is much greater than 1, E , P and L all approximately obey the normal distribution. Then, the squares of E , P and L are calculated to obtain the early branch power Ec, the prompt branch power Pc and the late branch power Lc.

[0030] Subsequently, step S13 is performed. This step first adds the three power values Ec, Pc, Lc to obtain a total power sum. Then, in order to make the metric value have a consistent dimension and facilitate subsequent processing, the total power sum needs to be normalized. Normalization is done by dividing by an adjustment factor. The adjustment factor can be pre-set, and a reasonable way to set it is to take the expected value or long-term average of the instantaneous branch power P when the receiver is stably tracking the signal in an open environment without interference. For example, if the stable average value of the instantaneous branch power is measured to be 10000 units in the calibration environment, the adjustment factor can be set to 10000. Finally, the SAPTL value at time t is calculated as (Ec+Pc+Lc) divided by the adjustment factor.

[0031] At the latest time t, by performing the above steps S11 to S13, the SAPTL value of PRN15 satellite at this moment can be calculated, that is, the SAPTL metric. Then, the metric at this latest time is appended to the end of the existing historical data sequence of the satellite, thereby updating and constructing the SAPTL metric time sequence containing the information of the current time. This process is applied in parallel to all 8 visible satellites, and finally the set of SAPTL metric time sequences of M satellites equal to 8 is obtained.

[0032] In step S2, the SAPTL metric time sequences of the M satellites are subjected to split-channel dynamic baseline calculation to obtain the dynamic mean and dynamic standard deviation of the M satellites. Correspondingly, in the actual application of GNSS, the electromagnetic environment in which the receiver is located is not constant. When a vehicle drives from an open area into a city valley with high-rise buildings, or passes through a tunnel or an overpass, the shielding and reflection of signals will change dramatically, resulting in significant enhancement or weakening of multipath effects. The dynamic changes in such an environment will cause the normal level (i.e. baseline) of the SAPTL metric value as an indicator of signal quality to drift and fluctuate. If a fixed threshold is used for anomaly detection, a large number of false alarms or missed reports will occur when the environment changes. Therefore, in order to make the detection method discard the limitations of the static threshold, make it able to perceive and adapt to environmental changes in real time, and provide a normal reference standard for subsequent normalization processing and anomaly detection that is adjusted adaptively according to the environment, it is necessary to perform split-channel dynamic baseline calculation on the SAPTL metric time sequence.

[0033] In one embodiment of the present application, the step S2 is implemented as follows: a sliding window is established and maintained for each satellite (i.e. each channel) to track the recent signal statistics dynamically. The sliding window moves along the time axis and only the data within the window is counted. First, the length of the sliding window W is set. The length is chosen to balance the response speed and the estimation stability. A shorter window can capture the transient change of the environment more sensitively, but the result is not stable enough due to the random noise; on the contrary, a longer window can provide a smoother and more reliable estimation, but the response to the sudden change of the environment is relatively slow. In this embodiment, the receiver's SAPTL metric is updated at a rate of 10 Hz, and a sliding window with a length of W equal to 1000 epochs is set, which corresponds to the data of the past 100 seconds, and can effectively respond to the minute-level environmental changes while ensuring the stability of the statistics.

[0034] At each new time epoch t, when the latest SAPTL value of a satellite, for example PRN15, is generated, the corresponding sliding window moves forward by one epoch. At this time, the window contains 1000 latest SAPTL values of the satellite from time t-W+1 to time t. Then, the standard statistical calculation is performed on the 1000 data points: first, all the SAPTL values in the window are summed, and then divided by the window length W, i.e. 1000, to obtain the dynamic mean value of the satellite at time t; then, the difference between each SAPTL value in the window and the dynamic mean value is calculated, and the square of the difference is calculated, then the sum of all squared differences is summed and divided by W, and finally the square root of the result is taken to obtain the dynamic standard deviation of the satellite at time t.

[0035] This calculation process is applied to all M satellites being tracked in parallel. That is, an independent sliding window with a length of 1000 is maintained for each of the 8 satellites, and at each epoch t, the dynamic mean value and the dynamic standard deviation of each satellite are calculated respectively. In this way, a set of dynamic baselines that can reflect the normal fluctuation range of each satellite under the current signal environment is obtained, i.e. M sets of dynamic mean time series and M sets of dynamic standard deviation time series are obtained.

[0036] In step S3, the real-time SAPTL metric of the i-th satellite is obtained. It can be understood that after the dynamic baseline calculation of the historical data is completed, the focus of the detection process naturally shifts to the current time. The historical data analysis provides a dynamic, environment-adaptive definition of the normal signal state, but this definition itself cannot directly determine whether the current state is abnormal. The essence of anomaly detection is to accurately compare the current observation value with the dynamic definition of the normal range. Therefore, in order to apply the dynamically established normal baseline to real-time anomaly judgment, it is necessary to obtain the latest signal quality state of each satellite for subsequent detailed analysis and processing.

[0037] In a specific embodiment of the present application, the specific process of step S3 is as follows: here, the index i represents any satellite to which the current processing process is directed, for example, it can be the aforementioned PRN15 satellite, or any other one of the M visible satellites. The process is realized through an accurate data extraction operation. Specifically, from the set of SAPTL metric time series generated in parallel for all M satellites in step S1, according to the index i of the current target satellite, the latest time t element value of the SAPTL metric time series corresponding to the satellite is directly located and extracted, and this value is the real-time SAPTL metric of the i-th satellite.

[0038] In step S4, the dynamic mean and dynamic standard deviation of the i-th satellite are extracted from the dynamic mean and dynamic standard deviation of the M satellites. It can be understood that in the detection process, the dynamic baseline of all M visible satellites has been calculated and maintained in parallel, forming a set containing the dynamic mean and standard deviation of each satellite. At the same time, the real-time SAPTL metric of the i-th satellite at the current time is also obtained. At this time, in order to evaluate the real-time metric of this particular satellite meaningfully, a general baseline shared by all satellites cannot be used, because the signal strength, elevation angle and multipath environment experienced by each satellite are different, resulting in unique normal baseline for each satellite. Therefore, in order to ensure that the subsequent normalization processing is carried out for the signal characteristics of the satellite itself, so as to obtain a standardized deviation that accurately reflects the degree of deviation from its own normal state, it is necessary to perform the step of extracting the dynamic mean and dynamic standard deviation of the i-th satellite from the dynamic baseline set of the M satellites.

[0039] In a specific embodiment of the present application, the specific process of step S4 is as follows: The corresponding value for the i-th satellite is extracted from the dynamic means and dynamic standard deviations of the M satellites in a similar manner. First, the dynamic mean time series set generated for all satellites in step S2 is located. By using the same target satellite index i, the element value of the dynamic mean time series for that satellite at the latest time t is directly extracted. This value is the dynamic mean of the i-th satellite at the current time. Subsequently, the dynamic standard deviation time series set is accessed in the same manner, and the element value of the corresponding sequence at the latest time t is extracted based on index i. This value is the dynamic standard deviation of the satellite at the current time.

[0040] In step S5, the real-time SAPTL measurement of the i-th satellite is normalized based on the dynamic mean and dynamic standard deviation of the i-th satellite to obtain the normalized deviation of the i-th satellite. Accordingly, during the signal detection process, although the real-time SAPTL measurement value and its unique dynamic baseline (mean and standard deviation) for a specific satellite have been obtained, these raw values ​​themselves are not suitable for direct anomaly determination. The magnitude of the real-time SAPTL value varies dramatically with factors such as the signal environment and satellite elevation angle, and its absolute value itself is not universally meaningful. A high value considered abnormal in an open area may be completely normal in an urban canyon. Therefore, to eliminate the baseline drift effect caused by environmental changes and ensure that the measurement has a unified scale and statistical significance, thereby enabling the application of a standardized, environmentally invariant judgment criterion, the real-time SAPTL measurement needs to be normalized.

[0041] In a specific embodiment of the present application, the specific process of step S5 is as follows: Step S5, based on the dynamic mean and dynamic standard deviation of the i-th satellite, normalize the real-time SAPTL measurement of the i-th satellite to obtain a normalized deviation of the i-th satellite, including: based on the dynamic mean and dynamic standard deviation of the i-th satellite, normalize the real-time SAPTL measurement of the i-th satellite according to the following formula, wherein the formula is: ;in, is the real-time SAPTL measurement of the i-th satellite, is the dynamic mean of the i-th satellite, is the dynamic standard deviation of the i-th satellite, is the normalized deviation of the ith satellite. Firstly, the numerator calculates the difference between the real-time SAPTL metric value and its dynamic mean value, which represents the deviation of the current observation value from its recent normal center. If there is no anomaly, the difference should fluctuate in a small range around zero. Secondly, the difference is divided by the dynamic standard deviation. The standard deviation is a measure of how much the data fluctuates around its mean value, representing the amplitude of normal fluctuations in this environment. By dividing the deviation by the standard deviation, the original deviation is actually converted into a relative deviation in units of standard deviations, which is a standard Z-score transformation. For example, at the latest time t, the 5th satellite, if the real-time SAPTL metric value of the satellite is 1.6, and its dynamic mean value calculated by the sliding window is 1.2, and the dynamic standard deviation is 0.2. Then, the normalized deviation of the satellite is calculated as (1.6-1.2) / 0.2=2. The result 2 means that the SAPTL value at the current time is higher than its recent normal mean value by 2 standard deviations. This normalized deviation value eliminates the influence of the dimension and absolute size of the original metric.

[0042] In step S6, the normalized deviation of the ith satellite is subjected to CUSUM change point detection to obtain the anomaly flag of the ith satellite. It can be understood that after the normalization processing of the real-time observation value is completed, the standardized deviation value is obtained. However, the spoofing or multipath interference event is often not an isolated instantaneous pulse, but a persistent deviation of the signal statistical characteristics in a period of time. If only a simple instantaneous threshold judgment is made on each normalized deviation value, it is easy to be affected by random noise of a single data point, resulting in insufficient sensitivity and reliability of detection, and it is difficult to effectively identify weak but persistent micro anomalies. Therefore, in order to accumulate weak but persistent abnormal signal evidence, amplify the small change of the signal mean value, and at the same time maintain robustness to transient random noise, so as to realize rapid and reliable detection of the starting point of the abnormal event, it is necessary to perform CUSUM change point detection on the normalized deviation.

[0043] In one specific embodiment of the present application, Figure 6 is a flowchart of step S6 in the GNSS spoofing / multipath interference detection method according to the embodiment of the present application. As shown in the figure, Figure 6 Step S6, the normalized deviation of the ith satellite is subjected to CUSUM change point detection to obtain the anomaly flag of the ith satellite, includes: step S61, based on the normalized deviation of the ith satellite, the previous epoch cumulative value of the ith satellite is iteratively updated to obtain the current epoch cumulative value of the ith satellite; step S62, based on the comparison between the decision threshold and the current epoch cumulative value of the ith satellite, the anomaly flag of the ith satellite is generated.

[0044] In the above embodiments, the specific procedure of step S1 is as follows: first, step S61 is performed. In one specific embodiment of the present application, step S61, based on the normalized deviation of the ith satellite, iteratively updates the previous epoch accumulation value of the ith satellite to obtain the current epoch accumulation value of the ith satellite, comprising: step S611, adding the normalized deviation of the ith satellite to the previous epoch accumulation value of the ith satellite and then subtracting a relaxation amount to obtain an intermediate sum; and step S612, taking the maximum of the intermediate sum and zero to obtain the current epoch accumulation value of the ith satellite.

[0045] Specifically, this process maintains an independent accumulation variable for each satellite, denoted as S_i. Before the process starts, this accumulation S_i(0) is initialized to zero. At each new time epoch t, the accumulation is updated according to the following iterative formula: This update process is specifically divided into two sub-steps. The first step is step S611, which calculates an intermediate sum. The normalized deviation at the current time is added to the accumulation value at the previous time , and then a pre-set relaxation amount is subtracted. The relaxation amount is a key adjustment parameter, which is set according to the minimum deviation to be detected, and is generally set to half of the size of the mean deviation to be detected, in order to balance between detection sensitivity and robustness to noise. It represents the range within which the normalized deviation is allowed to normally fluctuate without triggering accumulation, and is set to a small positive number, for example 0.5. The role of this value is that only when the normalized deviation is significantly greater than for a long time, the accumulation value will increase; if the deviation is less than , the accumulation value will decrease, thereby filtering out small, meaningless random fluctuations. The second step is step S612, which takes the maximum of the intermediate sum and zero to obtain the current epoch accumulation value . This operation ensures that the accumulation value will never be negative. If the intermediate sum becomes negative due to a series of small deviation values, it will be reset to zero. This means that the CUSUM algorithm has the feature of memory reset: once the signal returns to normal, the accumulated abnormal evidence will be quickly cleared, and the algorithm is ready to detect the next abnormality.

[0046] Next, step S62 is performed. In one embodiment of the present application, step S62, based on the comparison between the decision threshold and the current epoch accumulation value of the ith satellite, generates the anomaly flag of the ith satellite, including: step S621, in response to the current epoch accumulation value of the ith satellite being greater than the decision threshold, determining the anomaly flag of the ith satellite as true; step S622, in response to the current epoch accumulation value of the ith satellite being less than or equal to the decision threshold, determining the anomaly flag of the ith satellite as false.

[0047] Specifically, a decision threshold H is first set. This threshold H represents the strength of the accumulated evidence required for determining the occurrence of anomaly. The setting of H needs to balance between the sensitivity of detection and the false alarm rate. A lower H value can detect anomaly faster, but can increase the false alarm due to noise accumulation; a higher H value can provide more reliable detection, but at the expense of certain detection speed. In this embodiment, the decision threshold H can be set to 5. The comparison process is specifically divided into two mutually exclusive conditions. First, step S621, it is determined whether the current epoch accumulation value is greater than the decision threshold H. If , it means that the normalized deviation is persistently and significantly deviated from the normal level, and the accumulated evidence is sufficient, at this time, the anomaly flag Flag_i(t) of the ith satellite is determined as true, indicating that a spoofing or multipath interference event is detected. Secondly, step S622, if is less than or equal to the decision threshold h, in response to this condition, the anomaly flag Flag_i(t) of the ith satellite is determined as false. This indicates that so far, the accumulated evidence is not sufficient to determine the occurrence of anomaly, and the signal state is considered to be normal. As a specific example, for the 5th satellite, if the satellite is PRN15, its accumulation value is 3.8, the relaxation amount is set to 0.5, and the decision threshold h is set to 5. At the current time t, its normalized deviation is 2.0. First, update the accumulation value: . Then, make a decision comparison: because 5.3 is greater than the threshold 5, the anomaly flag Flag_5(t) of the satellite is set to true. This flag can be directly used to trigger the alarm or data discard of the receiver and other response measures.

[0048] In particular, in the application of CUSUM change point detection algorithm, the slack and the decision threshold are two core parameters that determine its detection performance. If these two parameters are set to fixed values, although they can work effectively when the signal environment (such as signal-to-noise ratio, multipath intensity) is stable, their performance will deteriorate significantly in real dynamic scenarios. For example, when the receiver enters an area with severe multipath effect, the baseline fluctuation of the signal quality metric will naturally increase. At this time, a fixed, lower slack will mistakenly accumulate these normal baseline fluctuations, causing the cumulative sum to easily exceed the decision threshold, thereby generating a large number of false alarms. Conversely, if the slack and the decision threshold are set too high to avoid false alarms, then when the signal environment returns to clean, the detector will become too sluggish and will not be able to effectively detect a concealed spoofing attack with normal noise energy, resulting in a missed detection. Therefore, in order to enable the detection device to maintain a balance between high sensitivity and low false alarm rate in various complex signal environments, the present application adopts a dual adaptive CUSUM threshold mechanism based on noise perception to realize intelligent and dynamic adjustment of the detection parameters.

[0049] Based on this, in one preferred embodiment of the present application, the slack and the decision threshold are determined by a dual-loop adaptive CUSUM threshold mechanism based on noise perception, which includes constructing an approximate formula between the slack and the decision threshold, i.e. ; wherein, is the slack, is the decision threshold, is the average anomaly-free running length. It should be understood that, in order to achieve high consistency and predictability of the detector performance in different environments, a quantitative and explicitly physically meaningful performance constraint model needs to be established first. In the statistical theory of CUSUM, the slack and the decision threshold jointly determine two key performance indicators: the average running length when there is no anomaly (representing the false alarm rate) and the average running length when there is an anomaly (representing the detection speed). If only is adjusted without adjusting , and correspondingly when adapting to environmental changes, it will be out of control, resulting in unpredictable detection performance. For example, increases, the accumulation speed slows down, and if remains unchanged, will become extremely large (the false alarm rate is extremely low), but will also become extremely large (the detection is extremely insensitive). Therefore, this step mathematically strongly correlates with by introducing an approximate formula based on . In specific implementation, first set a desired false alarm performance target, for example, set the false alarm probability , For one ten-thousandth, the corresponding is one ten-thousand. Subsequently, based on the approximate formula of , a function relationship is constructed with as a constant target, as an independent variable, as a dependent variable, which lays a solid theoretical foundation for subsequent adaptive adjustment, ensuring that how to calculate a matching according to environmental changes, so that the false alarm probability of the system is always locked at the expected level.

[0050] The state standard deviation and the basic relaxation amount are subjected to noise-sensitive Sigmoid relaxation mapping to obtain the relaxation amount, that is, ; wherein, and are the adaptive relaxation amount and the dynamic standard deviation at moment, is the basic relaxation amount, representing the minimum tolerance set in the ideal environment (for example ), for example, it can be set to 0.5 to ensure that the system has basic anti-noise ability even in the ideal environment. is used to set the adjustment range, that is, the magnitude of the upward floating value, to prevent from being set to an unreasonable large value in extreme environments to ensure the robustness of the algorithm. is the reference fluctuation threshold, used as the inflection point of the function, representing the critical point between normal and noisy environments, for example, it can be set to 1.0, representing the standard deviation level of a good signal environment. And is used to control the steepness of the function curve near to adjust the sensitivity, that is, the larger, the faster the transition, meaning that the system reacts to environmental changes dramatically, and the smaller, the smoother the transition, the stronger the adaptability, for example, it can be set to 2.0. Further, it is necessary to convert the abstract environmental noise level into a specific relaxation amount that can be directly used by the CUSUM algorithm. The physical meaning of the relaxation amount is the normal fluctuation amplitude that the system can tolerate and does not count towards the exception accumulation, and this amplitude is most directly represented by the dynamic standard deviation of the signal quality metric time series However, the simple linear correlation between the two has its drawbacks, namely, it is too sensitive to small changes in the standard deviation, and in extreme noise environments, it may cause the k value to increase without limit, making the detector completely ineffective. Therefore, this step uses a Sigmoid-like function to provide a smooth, bounded and nonlinear mapping. In specific implementation, the above formula is used for calculation, that is, through this nonlinear mapping, the relaxation amount is realized. Intelligent perception of environmental noise: In a low-noise environment, Smoothly approaching , so that the detector remains highly alert; in a high noise environment, Smoothly approaching the upper limit makes the detector more tolerant, thus avoiding sudden parameter changes and loss of control, and greatly enhancing the robustness of the algorithm. In particular, the preset parameter values ​​in the above formula are ultimately determined based on extensive prior experimental data, simulation analysis, and a trade-off and optimization of the performance requirements (such as the desired false alarm rate and detection sensitivity) for target application scenarios (e.g., automotive and aviation).

[0051] The approximate formula and the relaxation amount are reversely solved to obtain the decision threshold. That is to say, after obtaining the adaptive relaxation amount that can reflect the environmental changes in real time, the decision threshold that matches it must be calculated immediately to complete the double adaptive closed loop to ensure the key execution link of the detector performance constancy. In specific implementation, the real-time relaxation amount calculated in the previous step is used to calculate the decision threshold. , that is, in Relaxation at any moment , and the performance goals set in advance in the first step For example, 10,000 is substituted into the approximate formula. and are all known numbers, the formula becomes a The transcendental equation is a finite element model. By reversing this equation using numerical methods (such as the Newton iteration method or the bisection method), the unique decision threshold corresponding to the current moment is obtained. This ensures precise nonlinear compensation of the decision threshold for the slack. As ambient noise increases, the slack increases accordingly, and the calculated decision threshold also increases nonlinearly, ensuring that the cumulative sum's decision boundary and tolerance adjust synchronously. Ultimately, the detector maintains a preset false alarm probability of 1 in 10,000 in any signal environment, from clean to noisy, achieving highly consistent detection performance.

[0052] By performing steps 3-6 for each satellite, the anomaly flags of M satellites are obtained. However, after generating the anomaly flags for each satellite independently, although the signal quality of a single satellite can be judged and the corresponding countermeasures can be triggered, this only completes the detection of anomalies, and the identification of anomalies is not achieved. In complex GNSS application scenarios, the impact patterns of different types of interference sources (such as multipath and spoofing) on the satellite constellation are essentially different: multipath interference has locality and only affects a few specific path satellites; while spoofing attacks often have globality and aim to hijack the entire receiver, which will simultaneously affect most or all satellites in the field of view. Therefore, in order to understand the nature of the interference event from a higher dimension, distinguish whether it is a local multipath interference or a global spoofing attack, and thus enable the start of more targeted and higher-level countermeasures, comprehensive analysis of the anomaly flags of all M satellites is required.

[0053] In one specific embodiment of the present application, the specific process is as follows: the previous stage generates a set of anomaly flags {Flag_1(t), Flag_2(t),..., Flag_8(t)} for all M equal to 8 satellites in parallel at the latest time t. The implementation process mainly includes three stages of anomaly information aggregation, anomaly satellite counting and application of classification rules for judgment.

[0054] First, at each latest time t, anomaly satellite information aggregation is performed. This stage collects the anomaly flags of all M equal to 8 satellites to form a Boolean type anomaly flag array. For example, at time t, if the anomaly flags of the 2nd, 3rd, 5th, 6th, and 7th satellites are true and the rest are false, the aggregated anomaly flag array is {false, true, true, false, true, true, true, false}. This array comprehensively reflects the affected situation of the entire visible satellite constellation at the current time.

[0055] Next, anomaly satellite counting is performed. In order to increase the stability of the decision and avoid false judgments caused by single-point noise, the anomaly flags in a short decision window (for example, the last 10 epochs) can be smoothed. One method is to count the number of times each satellite anomaly flag is true in the window, and then perform statistics. The present embodiment uses the instantaneous counting method, that is, directly counting the number of elements with a value of true in the anomaly flag array at the current epoch t. In the above example, the anomaly flag array {false, true, true, false, true, true, true, false} has a total of 5 true, so the anomaly satellite count at the current time is 5.

[0056] Finally, the current state is decided by applying a pre-defined classification rule. This rule is based on the comparison between the number of abnormal satellites and the total number of visible satellites, and different state intervals are divided by setting thresholds. Two key thresholds are needed to be pre-set: a low count threshold T_low and a high proportion threshold P_high. T_low is used to distinguish between no interference and interference state, and can be set to 1, meaning that as long as there is at least 1 satellite abnormal, it is considered to exist interference. P_high is used to distinguish between multipath interference and spoofing attack, which represents the proportion of affected satellites in the total number. Considering the global feature of spoofing attack, P_high can be set to 0.5, that is, when the affected satellites reach or exceed half of the total number, it is determined to be spoofing attack.

[0057] Based on these pre-set values, the classification rule is as follows: first, it is judged whether the number of abnormal satellites is less than the low count threshold T_low. If the number of abnormal satellites is 0, that is, less than 1, the final decision result is no interference. Second, if the above condition is not met, it is judged whether the ratio of the number of abnormal satellites to the total number of visible satellites is greater than or equal to the high proportion threshold P_high. In this embodiment, the total number of visible satellites is 8, and P_high is 0.5, so the judgment condition is abnormal satellite count / 8>=0.5, that is, whether the number of abnormal satellites is greater than or equal to 4. If this condition is met, the final decision result is spoofing attack. Third, if the above two conditions are not met, that is, the number of abnormal satellites is between 1, 2 or 3, that is, greater than or equal to T_low but less than M*P_high, the final decision result is multipath interference.

[0058] Taking the above example, the number of abnormal satellites is 5. This value is not less than 1, but greater than or equal to 4, that is, 8*0.5, so the second judgment condition is met, and the final decision output is spoofing attack. This final decision result provides the receiver with a clear insight into the nature of the current interference environment, enabling it to initiate more complex coping strategies such as switching to a backup positioning technology or issuing a high-level security alert to the user.

[0059] Particularly, the implementation scenario of the present application is illustrated by a vehicle-mounted device equipped with a GNSS receiver, which is currently able to stably track M = 8 visible satellites, as the vehicle is driving from an open suburban highway into a downtown area with high-rise buildings: in the initial suburban driving stage, the signal quality is excellent. The SAPTL metric of all satellites stably fluctuates around 1.0 with a small variation. Therefore, the dynamic mean and dynamic standard deviation calculated by the long sliding window also stably fluctuate around 1.0 and 0.1, respectively. Based on this dynamic baseline, the normalized deviation of each satellite randomly fluctuates around zero with a small amplitude, which is far from enough to make the CUSUM cumulative value grow to the decision threshold 5, so the anomaly flag of all satellites remains false. At this time, the abnormal satellite count is zero, and the final decision result correctly determines that there is no interference. When the vehicle enters the urban area, the low-elevation PRN4 and PRN7 satellites begin to be significantly affected by multipath interference, and their SAPTL values fluctuate dramatically and rise to about 1.5 as a whole. The dynamic baseline adjustment mechanism of the present application begins to work, and the long sliding window captures this change, so that the dynamic mean and standard deviation of these two satellites gradually adaptively climb to reflect the new and more complex signal environment. During this baseline adjustment process, the normalized deviation may temporarily be slightly high, but since multipath interference usually only affects a few satellites, the abnormal satellite count may temporarily become 1 or 2. This count value is greater than or equal to the low threshold 1, but less than the product of the total number of satellites 8 and the high proportion threshold 0.5 (i.e. 4), so the final decision result is updated to multipath interference, accurately identifying the current environmental characteristics. Subsequently, in the downtown area, a spoofing source suddenly starts broadcasting spoofing signals to all 8 satellites. At the moment of attack, the SAPTL values of all satellites jump sharply from their respective current baselines (partly raised due to multipath). However, due to the inertia of the long sliding window, the dynamic mean and standard deviation cannot immediately respond to this sudden change and still maintain around the values before the attack. This causes a huge positive spike in the normalized deviation of all satellites. For example, the normalized deviation of PRN1 satellite may be as high as 9.33. These huge deviation values make the CUSUM cumulative value of all satellites quickly accumulate and break through the decision threshold 5 in a very short time (a few epochs), causing the anomaly flag of all 8 satellites to be set to true almost simultaneously. Therefore, the abnormal satellite count instantaneously becomes 8. The ratio of this count value to the total number of satellites is 1.0, which is much greater than the high proportion threshold 0.5, and the final decision result is immediately and decisively updated to spoofing attack, achieving rapid and accurate identification of this global threat.

[0060] In summary, the GNSS spoofing / multipath interference detection method described in the embodiments of this application addresses the technical issue that existing GNSS interference detection methods use fixed thresholds and are unable to adapt to dynamic environments. Instead, an adaptive solution based on dynamic baselines and change point detection is proposed. This solution abandons static, one-size-fits-all thresholds and instead independently calculates a dynamic baseline for each satellite's signal quality metric (SAPTL) in real time. This baseline consists of a dynamic mean and standard deviation that changes with the environment. By normalizing and comparing the real-time metric with this dynamic baseline, the effects of baseline drift caused by scene transitions (such as entering an urban canyon) are eliminated, resulting in a standardized deviation sequence that is unaffected by environmental background interference. Finally, this sequence is analyzed using the CUSUM change point detection algorithm, which is extremely sensitive to small, sustained changes. This algorithm accurately captures true signal anomalies caused by spoofing or multipath, rather than spurious fluctuations caused by environmental changes. This solves the fundamental problem of fixed thresholds' poor adaptability in dynamic environments, which can easily lead to false and missed detections, significantly improving the robustness and reliability of detection.

[0061] Figure 7 FIG is a block diagram of a GNSS spoofing / multipath interference detection device according to an embodiment of the present application. Figure 7 As shown, the GNSS spoofing / multipath interference detection device 100 according to an embodiment of the present application includes: a SAPTL measurement time acquisition module 110, used to obtain the SAPTL measurement time series of M satellites; a SAPTL measurement time dynamic calculation module 120, used to perform channel-by-channel dynamic baseline calculation on the SAPTL measurement time series of the M satellites to obtain the dynamic mean and dynamic standard deviation of the M satellites; a real-time SAPTL measurement acquisition module 130, used to obtain the real-time SAPTL measurement of the i-th satellite; The satellite feature extraction module 140 is used to extract the dynamic mean and dynamic standard deviation of the i-th satellite from the dynamic mean and dynamic standard deviation of the M satellites; the detection quantity normalization module 150 is used to perform detection quantity normalization on the real-time SAPTL measurement of the i-th satellite based on the dynamic mean and dynamic standard deviation of the i-th satellite to obtain the normalized deviation of the i-th satellite; the i-th satellite generation module 160 is used to perform CUSUM change point detection on the normalized deviation of the i-th satellite to obtain an abnormality mark of the i-th satellite.

[0062] As described above, the GNSS spoofing / multipath interference detection apparatus 100 according to an embodiment of the present application can be implemented in various wireless terminals, such as a server equipped with a GNSS spoofing / multipath interference detection algorithm. In one possible implementation, the GNSS spoofing / multipath interference detection apparatus 100 according to an embodiment of the present application can be integrated into a wireless terminal as a software module and / or a hardware module. For example, the GNSS spoofing / multipath interference detection apparatus 100 can be a software module in the operating system of the wireless terminal, or can be an application developed for the wireless terminal; of course, the GNSS spoofing / multipath interference detection apparatus 100 can also be one of the many hardware modules of the wireless terminal.

[0063] Alternatively, in another example, the GNSS spoofing / multipath interference detection apparatus 100 and the wireless terminal may also be separate devices, and the GNSS spoofing / multipath interference detection apparatus 100 may be connected to the wireless terminal via a wired and / or wireless network and transmit interaction information in accordance with an agreed data format.

[0064] Here, those skilled in the art will appreciate that the specific operations of each step in the above-mentioned GNSS spoofing / multipath interference detection device have been described in detail above. Figures 1 to 6 The GNSS spoofing / multipath interference detection method has been described in detail, and therefore, its repeated description will be omitted.

Claims

1. A GNSS spoofing / multipath interference detection method, characterized in that: include: Get the SAPTL measurement time series of M satellites; Performing channel-by-channel dynamic baseline calculation on the SAPTL measurement time series of the M satellites to obtain a dynamic mean and a dynamic standard deviation of the M satellites; obtaining a real-time SAPTL measurement of the i-th satellite; The dynamic mean and dynamic standard deviation of the i-th satellite are extracted from the dynamic mean and dynamic standard deviation of the M satellites; based on the dynamic mean and dynamic standard deviation of the i-th satellite, the real-time SAPTL measurement of the i-th satellite is normalized to obtain the normalized deviation of the i-th satellite; and the normalized deviation of the i-th satellite is subjected to CUSUM change point detection to obtain an abnormal sign of the i-th satellite.

2. The GNSS spoofing / multipath interference detection method according to claim 1, characterized in that: The method obtains SAPTL measurement time series of M satellites, including: capturing and tracking original IQ sampling data output by a radio frequency front end of a receiver at time t to obtain the quadrature component and the in-phase component of the leading branch, the quadrature component and the in-phase component of the instantaneous branch, and the quadrature component and the in-phase component of the lagging branch; performing power conversion on the quadrature component and the in-phase component of the leading branch, the quadrature component and the in-phase component of the instantaneous branch, and the quadrature component and the in-phase component of the lagging branch to obtain the leading branch power, the instantaneous branch power, and the lagging branch power; and calculating the sum of the leading branch power, the instantaneous branch power, and the lagging branch power, and normalizing the sum to obtain the SAPTL value at time t as the SAPTL measurement.

3. The GNSS spoofing / multipath interference detection method according to claim 2, characterized in that: Based on the dynamic mean and the dynamic standard deviation of the i-th satellite, normalizing the real-time SAPTL measurement of the i-th satellite to obtain a normalized deviation of the i-th satellite includes: based on the dynamic mean and the dynamic standard deviation of the i-th satellite, normalizing the real-time SAPTL measurement of the i-th satellite according to the following formula, wherein the formula is: ;in, is the real-time SAPTL measurement of the i-th satellite, is the dynamic mean of the i-th satellite, is the dynamic standard deviation of the i-th satellite, is the normalized deviation of the i-th satellite.

4. The GNSS spoofing / multipath interference detection method according to claim 1, wherein: Performing CUSUM change point detection on the normalized deviation of the i-th satellite to obtain an abnormality flag of the i-th satellite, including: iteratively updating a previous epoch cumulative value of the i-th satellite based on the normalized deviation of the i-th satellite to obtain a current epoch cumulative value of the i-th satellite; and generating the abnormality flag of the i-th satellite based on a comparison between a decision threshold and the current epoch cumulative value of the i-th satellite.

5. The GNSS spoofing / multipath interference detection method according to claim 4, characterized in that: Based on the normalized bias of the i-th satellite, iteratively updating the previous epoch cumulative value of the i-th satellite to obtain the current epoch cumulative value of the i-th satellite, including: adding the normalized bias of the i-th satellite to the previous epoch cumulative value of the i-th satellite and then subtracting a relaxation amount to obtain an intermediate sum; and taking the larger value of the intermediate sum and zero to obtain the current epoch cumulative value of the i-th satellite.

6. The GNSS spoofing / multipath interference detection method according to claim 5, characterized in that: Based on a comparison between a decision threshold and a current epoch cumulative value of the i-th satellite, generating an abnormality flag of the i-th satellite, including: in response to the current epoch cumulative value of the i-th satellite being greater than the decision threshold, determining that the abnormality flag of the i-th satellite is true; in response to the current epoch cumulative value of the i-th satellite being less than or equal to the decision threshold, determining that the abnormality flag of the i-th satellite is false.

7. A GNSS spoofing / multipath interference detection device, characterized in that: include: A SAPTL measurement time acquisition module is used to obtain the SAPTL measurement time series of M satellites; a SAPTL measurement time dynamic calculation module is used to perform channel-by-channel dynamic baseline calculation on the SAPTL measurement time series of the M satellites to obtain the dynamic mean and dynamic standard deviation of the M satellites; A real-time SAPTL measurement acquisition module is used to obtain the real-time SAPTL measurement of the i-th satellite; An i-th satellite feature extraction module is configured to extract the dynamic mean and dynamic standard deviation of the i-th satellite from the dynamic mean and dynamic standard deviation of the M satellites; A detection quantity normalization module is used to normalize the real-time SAPTL measurement of the i-th satellite based on the dynamic mean and dynamic standard deviation of the i-th satellite to obtain the normalized deviation of the i-th satellite; an i-th satellite generation module is used to perform CUSUM change point detection on the normalized deviation of the i-th satellite to obtain an abnormal sign of the i-th satellite.

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