Interference signal detection method and electronic device

Abstract

An interference signal detection method and an electronic device are applied to a baseband signal processing process of a navigation receiver, and the method comprises the following steps: determining a target branch according to a tracking state of a tracking loop to a satellite; acquiring a power value output by the target branch in the current detection period; obtaining the index number of the correlator with the maximum output power value of the target branch in the current detection period and the index number of the correlator with the maximum output power value of the target branch in the previous detection period; if the power value of at least one branch in the target branches exceeds a preset tracking power threshold and the absolute value of the difference between the index numbers in the current detection period and the previous detection period is not greater than 2, determining that a stable abnormal event occurs, and adding 1 to the numerical value of an update counter; otherwise, subtracting 1 from the numerical value of the updated counter; and after the numerical value of the counter exceeds a preset counter threshold, determining that the satellite has a deception interference signal in a tracking loop, and detecting an interference signal with a code phase close to that of a real signal.

Description

TECHNICAL FIELD

[0001] The present application relates to satellite navigation technology, in particular to a method for detecting interference signals and an electronic device. BACKGROUND

[0002] Global Navigation Satellite System (GNSS) has been widely used in military, civil field of positioning, navigation and timing service. However, since the GNSS signal is extremely weak when it reaches the ground from space, the receiver is vulnerable to attacks by malicious deception interference. Deception interference induces the receiver to produce false positioning and timing results by transmitting a fake signal similar in structure to the real satellite signal but carrying false information, which poses a serious threat to the key infrastructure relying on GNSS.

[0003] Among the many deception interference detection techniques, the detection method based on the Auto Gain Control (AGC) of the navigation receiver is concerned due to its good real-time performance and no need for additional hardware cost. The working principle is as follows: when the strong power deception signal and the real signal enter the receiver RF front end at the same time, the input total power will increase significantly, and the AGC module will automatically reduce its gain coefficient in order to maintain the stability of the output signal power. Therefore, by monitoring the abnormal decrease of the AGC gain coefficient, the detection of strong deception interference can be realized.

[0004] The detection scheme based on the front-end AGC is mainly aimed at strong power interference signals. When the deception signal and the real signal code phase are equivalent, this scheme may fail. SUMMARY

[0005] The embodiments of the present application provide a method for detecting interference signals, a storage medium and an electronic device.

[0006] A method for detecting interference signals is applied to the baseband signal processing process of a navigation receiver, and the method comprises the following steps. According to the tracking state of the satellite tracking loop, a target branch is determined; if the current real signal is locked, the target branch is the lag branch; if the current is locked to the deception interference signal, the target branch is the lead branch; The power value output by the target branch in the current detection period is obtained, and the index number of the correlator with the maximum output power value of the target branch in the current detection period and the last detection period is obtained; If the power value of at least one branch in the target branch exceeds the preset tracking power threshold, and the absolute value of the difference between the index numbers in the current detection period and the last detection period is not greater than 2, it is determined that it is a stable abnormal event, and the value of the counter is updated to 1; otherwise, the value of the counter is updated to 1. After the value of the counter exceeds a preset counter threshold, it is determined that the satellite has a spoofing jamming signal in the tracking loop.

[0007] A jamming signal detection method applied to a baseband signal processing procedure of a navigation receiver, the method comprising: obtaining first detection information, second detection information and third detection information; when the first condition and the second condition are both satisfied, determining that there is a jamming signal in a received signal in the baseband signal processing procedure; the first condition is that the first detection information indicates that there is a jamming signal in an intermediate frequency input signal, but the jamming signal is not a strong power jamming signal; the second condition is that a number of suspicious satellites is greater than a preset suspicious satellite threshold, wherein the number of suspicious satellites is determined according to the second detection information and the third detection information; wherein: the second detection information comprises second sub-information of a corresponding capture signal of each satellite in a signal capture stage, wherein each second sub-information records whether there is a jamming signal in a capture signal of a corresponding satellite, the jamming signal satisfying a preset power similarity condition with a real signal; the third detection information comprises third sub-information of a corresponding tracking signal of each satellite in a signal tracking stage, wherein each third sub-information records whether there is a jamming signal in a tracking signal of a corresponding satellite, the jamming signal satisfying a preset code phase similarity condition with a real signal; and each third sub-information is obtained by using the method described above; wherein, if one of the second sub-information and the third sub-information of the same satellite indicates that there is a jamming signal, and the other indicates that there is no jamming signal, the satellite is determined to be a suspicious satellite, and a total number of all the suspicious satellites is taken as the number of suspicious satellites.

[0008] An electronic device comprising a memory and a processor, the memory storing a computer program, and the processor being configured to run the computer program to execute the method described above.

[0009] The embodiments of the present application realize full coverage detection of the two scenarios of lagging spoofing and leading spoofing by adaptive target branch selection in a tracking state, solve the problem of missed detection of a single strategy, effectively distinguish between continuous spoofing and instantaneous interference by introducing double judgment conditions of power and correlation peak position stability and a counter mechanism, and significantly reduce the false alarm probability of the system; the inherent tracking loop correlator resources of the receiver are fully utilized, performance is improved by algorithm innovation, and no additional hardware cost is needed.

[0010] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the application. Other advantages of this application can be realized and obtained by means of the solutions described in the description and the accompanying drawings. Attached Figure Description

[0011] The accompanying drawings are used to provide an understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0012] Figure 1 This is a flowchart illustrating the interference signal detection method provided in Embodiment 1 of this application; Figure 2 This is a flowchart illustrating the interference signal detection method provided in Embodiment 2 of this application; Figure 3 This is a flowchart illustrating the interference signal detection method provided in Embodiment 3 of this application; Figure 4 This is a flowchart illustrating the interference signal detection method provided in Embodiment 4 of this application. Detailed Implementation

[0013] This application describes several embodiments, but these descriptions are exemplary and not restrictive, and it will be apparent to those skilled in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with, or may replace, any feature or element of any other embodiment.

[0014] This application includes and contemplates combinations of features and elements known to those skilled in the art. The embodiments, features, and elements disclosed in this application can also be combined with any conventional features or elements to form unique inventive solutions. Any feature or element of any embodiment can also be combined with features or elements from other inventive solutions to form another unique inventive solution. Therefore, it should be understood that any feature shown and / or discussed in this application can be implemented individually or in any suitable combination. Therefore, the embodiments are not limited except by the limitations imposed by the appended claims and their equivalents. Furthermore, various modifications and changes can be made within the scope of the appended claims.

[0015] Furthermore, in describing representative embodiments, the specification may have presented methods and / or processes as a specific sequence of steps. However, the method or process should not be limited to the specific order of steps described herein, to the extent that it does not depend on such a specific order. As will be understood by those skilled in the art, other sequences of steps are also possible. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation of the claims. Moreover, the claims concerning the method and / or process should not be limited to the steps performed in the written order, and those skilled in the art will readily understand that these orders can be varied and still remain within the spirit and scope of the embodiments of this application.

[0016] The solution proposed in this application is applied to the baseband processing of a navigation receiver. This baseband processing is divided into three sequentially connected stages according to the order of signal processing: signal preprocessing, signal acquisition, and signal tracking. Specifically, the signal preprocessing stage is responsible for the initial conditioning and monitoring of the intermediate frequency signal after analog-to-digital conversion; the signal acquisition stage is responsible for two-dimensional search and initial locking of the satellite signal; and the signal tracking stage is responsible for precise and continuous tracking of the acquired signal to obtain observations.

[0017] For the three processing stages mentioned above, this application provides corresponding deception and interference detection schemes: Option 1: Use the method described in Example 1 below to detect whether there is strong power interference in the intermediate frequency signal during the signal preprocessing stage; Option 2: Using the method described in Example 2 below, detect whether there are interference signals with power close to the actual signal in the acquired signals of each satellite during the signal acquisition phase; Option 3: Using the method described in Example 3 below, during the signal tracking phase, detect whether there are interference signals in the tracking signals of each satellite that are close to the phase of the true signal code.

[0018] The three solutions mentioned above can be used independently or combined flexibly to meet different interference detection requirements in the baseband processing process.

[0019] Independent use means that each solution can independently complete the detection and output results at its corresponding stage, and the triggering conditions are as follows: Option 1 can be used independently: Once the navigation receiver is started and initialized, this option will run automatically and continuously. Option 2 can be used independently: It will be executed automatically after the receiver completes the signal acquisition operation for each satellite; Option 3 can be used independently: It will be automatically activated after the receiver enters a stable tracking state of the satellite signal.

[0020] Combined use refers to the simultaneous application of two or more methods in the same navigation receiver, as detailed below: Combining Scheme 1 and Scheme 2: In addition to conducting independent detection at each stage, the detection results of Scheme 1 (such as strong interference intensity indication) can be used as prior information to dynamically adjust the sensitivity parameters of the acquisition module in Scheme 2 (such as the number of incoherent integrations) to improve the probability of capturing weak real signals under strong interference background.

[0021] Combining Scheme 1 and Scheme 3: In addition to conducting independent detection at each stage, the detection results of Scheme 1 can provide prior information for Scheme 3, assisting the tracking loop in identifying and prioritizing the locking of the relevant peaks of the real signal during initialization, thereby reducing the probability of falsely locking onto the deceptive signal.

[0022] Combining Scheme 2 and Scheme 3: In addition to conducting independent detection at each stage, the detection results of Scheme 2 (such as the multiple signal sources captured and their characteristics) can provide prior information for Scheme 3, assisting the tracking loop in identifying and prioritizing the locking of the correlation peak of the real signal during initialization, thereby reducing the probability of mistakenly locking onto the deceptive signal.

[0023] A comprehensive combination of Scheme 1, Scheme 2 and Scheme 3: In addition to conducting independent testing at each stage, the method described in Example 4 below can be used to perform joint logical judgment on the test results of the three stages, and comprehensively determine whether there are deceptive interference signals in the baseband processing process, thereby constructing a joint detection system with full coverage and cross-verification, which greatly improves the reliability and robustness of the detection.

[0024] The three solutions presented in this application constitute a customizable and scalable interference detection system. The appropriate solution can be selected based on the complexity and performance requirements of the application scenario.

[0025] Example 1 Traditional AGC detection methods have significant limitations in practical applications, specifically in the following aspects: In scenarios with strong power interference signals, existing methods can only make judgments through single or short-term observations. However, instantaneous strong signals in the actual electromagnetic environment (such as impulse noise, accidental interference, etc.) can also cause temporary anomalies in the gain coefficient, resulting in an increase in the false alarm rate of the system.

[0026] Furthermore, existing methods fail to establish an effective benchmark reference system and lack a continuous tracking and verification process for changes in the gain coefficient, making it difficult for the system to distinguish between one-off interference events and persistent deception attacks, and thus unable to make reliable decisions in complex and ever-changing real-world application environments.

[0027] In view of this, this embodiment provides an interference signal detection method that can reliably detect interference in deception scenarios with different power intensities, and also has the ability to identify transient interference, thereby reducing the probability of false alarms in the system.

[0028] The method described in this embodiment is applicable to various satellite navigation receivers, especially civilian navigation devices with a single frequency and single antenna architecture. In this scenario, the receiver front-end typically includes an automatic gain control module to stabilize the intermediate frequency signal power, and this method is based on this existing hardware module without adding any additional hardware costs.

[0029] The core of this method lies in proposing a two-stage detection mechanism based on benchmark comparison. This mechanism first establishes a stable gain benchmark under interference-free conditions, and then compares the difference between the current gain state and the benchmark in real-time monitoring, thereby achieving reliable identification of strong power spoofing interference and improving the ability to resist transient interference.

[0030] See Figure 1 The above interference detection method includes steps A11 to A13.

[0031] Step A11: Obtain the average power value of the intermediate frequency signal within a preset window after processing by the digital AGC module.

[0032] The intermediate frequency (IF) signal originates from the down-converted output signal of the navigation receiver's RF front-end. This signal has been sampled into a digital IF signal by an analog-to-digital converter. The preset window length is set to cover multiple pseudo-random code periods, typically an integer multiple of 1023 chips, to ensure statistical significance. Each window contains multiple sampling points, and the average power value of the window is calculated by averaging the squared amplitudes of all sampling points within the window.

[0033] The formula for calculating the average power value is as follows: ; Where P represents the average power value, N represents the total number of sampling points in the window length, and K is the gain coefficient; in, The intermediate frequency signal at time t is expressed as: ; in, Indicates the signal amplitude; This represents the data information at time t. This represents the code sequence at time t. This represents the frequency of the radio frequency signal at time t. Represents the noise at time t; Where t = 0, 1, 2, 3, ..., N; N is an integer greater than or equal to 2, and K is a number greater than 0.

[0034] Step A12: Determine the target gain coefficient of the digital AGC module based on the average power value of each window, so that the average power value meets the preset power stability condition in multiple consecutive windows.

[0035] This power stability condition indicates that the digital AGC module's closed-loop control system has reached a dynamic equilibrium state. Specifically, when a change in the input signal power is detected, causing the system to deviate from its stable state, the AGC initiates an iterative adjustment process. By dynamically adjusting the gain coefficient, the difference between the signal power value after gain control and the preset gain power threshold gradually converges and tends to stabilize.

[0036] When the average power value fails to meet the preset power stability condition, it indicates a significant change in the input signal environment, possibly due to the introduction of deceptive interference signals or other interference factors. At this point, the digital AGC module enters a dynamic adjustment phase, iteratively updating the gain coefficient window by window to transition the system from its current unstable state to a new stable state. The criteria for determining the power stability condition include: within multiple consecutive window periods, the gain coefficient exhibits slight fluctuations around a fixed value, and the fluctuation amplitude does not exceed a preset tolerance range; simultaneously, the deviation between the signal power value after gain control and the gain power threshold remains within the allowable error range.

[0037] When the system meets the above power stability conditions again, it indicates that the AGC loop has completed the convergence process. At this time, the current stable gain coefficient is determined as the target gain coefficient. This target gain coefficient can accurately reflect the gain configuration required to maintain normal operation under the new signal environment, and provides a reliable basis for judgment of strong power interference detection.

[0038] Step A13: Determine whether there is a strong power interference signal in the intermediate frequency signal based on the preset reference gain coefficient and the target gain coefficient.

[0039] The reference gain coefficient is the gain coefficient of the digital AGC module during the period when no deception interference is confirmed.

[0040] During cold or warm starts, the digital AGC module of the navigation receiver quickly enters the working state. Actual testing shows that the AGC loop reaches a stable operating state within tens of milliseconds after the receiver's power-on initialization. This rapid stabilization characteristic ensures that the gain coefficient within a preset time after startup accurately reflects the normal gain level under a non-spoofing interference environment. Therefore, the reference gain coefficient can be determined based on the gain coefficient within a preset time after the navigation receiver's startup, avoiding a complex reference establishment process and minimizing implementation complexity while ensuring performance.

[0041] In the detection of high-power interference signals, compared with methods that directly rely on the total input power, this embodiment uses the gain coefficient as the detection criterion, which has a significant accuracy advantage, mainly reflected in the following aspects: First, the gain coefficient is the output result of the dynamic adjustment of the AGC closed-loop control system, which comprehensively reflects the long-term trend of the input signal power. Direct power measurements are susceptible to short-term interference such as transient noise and multipath effects, resulting in significant fluctuations. The gain coefficient, through loop filtering, effectively smooths out these random fluctuations, providing a more stable detection benchmark.

[0042] Secondly, the change in gain coefficient has a clear cause of signal interference. When strong power spoofing interference is present, the total input power of the receiver front end increases, and the AGC will systematically reduce the gain coefficient to maintain output stability. This change is directional and continuous, while a simple increase in power measurement may stem from multiple factors and lacks this signal characteristic.

[0043] Furthermore, the gain coefficient comparison offers better environmental adaptability. While the nominal power level of the receiver may vary under different operating environments, the stable operating mechanism of the AGC system ensures the relative stability of the gain coefficient. By establishing a comparison mechanism with a reference gain coefficient, the impact of environmental differences can be effectively eliminated.

[0044] Using the above-described interference detection method, this embodiment determines the target gain coefficient based on power stability conditions, and determines whether there is a strong power interference signal in the intermediate frequency signal based on the target gain coefficient and the reference gain coefficient, which significantly reduces the false alarm probability and improves the accuracy of interference detection; it fully utilizes the receiver's inherent digital AGC module and achieves performance improvement through algorithm innovation without adding any additional hardware resources.

[0045] In one specific embodiment, during the signal preprocessing stage, regarding step A13: determining whether a strong power interference signal exists in the intermediate frequency signal based on a preset reference gain coefficient and the target gain coefficient, the specific judgment mechanism is explained as follows: The core of this embodiment lies in effectively distinguishing between real deceptive interference and transient interference or normal system fluctuations by quantitatively evaluating the changing trend and magnitude of the gain coefficient.

[0046] Case 1: The target gain coefficient is less than the reference gain coefficient, and the absolute value of the difference is greater than the coefficient difference threshold.

[0047] In Case 1, a decrease in the gain coefficient indicates that the system needs to suppress abnormally enhanced input power. A difference greater than the threshold ensures the statistical significance of the change, excluding normal system fluctuations. Therefore, this case directly indicates the presence of strong power spoofing interference.

[0048] Case 2: The target gain coefficient is less than the reference gain coefficient, but the absolute value of the difference is less than or equal to the coefficient difference threshold.

[0049] In scenario two, although the gain coefficient shows a decreasing trend, the change is not significant and may originate from system transients or minor interference. Therefore, this indicates the presence of interference, but it is not a high-power interference signal. The above judgment method effectively avoids misjudgments caused by minor system fluctuations.

[0050] In the above embodiments, the method for determining the coefficient difference threshold includes: conducting extensive simulation tests on the dynamic response of the AGC module under different signal-to-noise ratios and different interference intensities, statistically analyzing the fluctuation range of the gain coefficient of the system under normal operation and interference conditions, thereby determining an optimal threshold value that can both ensure detection sensitivity and effectively suppress false alarms.

[0051] In step A13, by comparing the relative magnitude and absolute difference between the target gain coefficient and the reference gain coefficient, and introducing a coefficient difference threshold as a significance criterion, misjudgments caused by minor system fluctuations or transient processes are effectively avoided. Furthermore, this determination mechanism has low computational complexity, is easy to implement in embedded systems, and is highly suitable for deployment in resource-constrained navigation receivers.

[0052] In one specific embodiment, during the signal preprocessing stage, for step A12: determining the target gain coefficient of the digital AGC module based on the average power value of each window, the specific implementation includes steps A121 to A123.

[0053] This embodiment introduces a window-based iterative adjustment and adaptive step size control mechanism to ensure the rapid convergence and stable tracking of the gain coefficient of the digital AGC module, providing a reliable basis for subsequent deception and interference determination.

[0054] Step A121: Obtain the comparison result between the average power value of the i-th window and the preset gain power threshold, i=1,2,3,...,N, where N is an integer greater than or equal to 2.

[0055] The gain power threshold serves as a reference for AGC loop adjustment. It can be preset based on the fixed gain configuration of the receiver front-end RF and the ADC quantization characteristics, representing the ideal output power level that the system expects to maintain. When the average power value exceeds this threshold, it indicates that the current gain setting is too high and the gain needs to be reduced; conversely, the gain needs to be increased.

[0056] The average power value of the i-th window is calculated using the current gain coefficient of the digital AGC module in the i-th window. This current gain coefficient accurately reflects the power level of the output signal after gain adjustment, thus ensuring a closed-loop relationship between power detection and gain control, enabling the system to accurately perceive the actual power state under the current gain setting.

[0057] Step A122: If the average power value of the i-th window is greater than the gain power threshold, it indicates that the input signal is too strong and the gain needs to be reduced to avoid saturation. Therefore, the value of the current gain coefficient is reduced by a preset first step length value. Otherwise, it indicates that the input signal is too weak and the gain needs to be increased to maintain the signal-to-noise ratio. Therefore, the value of the current gain coefficient is increased by a preset second step length value. The updated current gain coefficient is used as the corresponding current gain coefficient of the (i+1)-th window. Both the first step length value and the second step length value are greater than 0.

[0058] The first and second step lengths can be adjusted based on the degree of drastic change in signal power. This degree of change refers to the fluctuation range of the average power value between adjacent windows, which can be quantified by calculating the power change rate or standard deviation.

[0059] Specifically, the principle for adjusting the step size is as follows: when a rapid change in power is detected, a larger step size is used to accelerate the convergence speed; when the power change tends to level off, a smaller step size is used to improve stability accuracy. This adaptive mechanism ensures the system's rapid response and stable tracking in dynamic environments.

[0060] Step A123: If the current gain coefficients corresponding to at least two consecutive windows are within the range of the fixed value, then the fixed value is determined as the target gain coefficient; otherwise, the value of i is updated to i+1, and then step A121 is executed.

[0061] Based on the stability principle of the AGC loop, when the system enters a steady state, the gain coefficient will fluctuate slightly around the optimal value. This slight fluctuation range is considered to be the "range of values ​​corresponding to the fixed value".

[0062] In step A12 above, through the target gain coefficient determination mechanism, the system performs precise power monitoring and gain adjustment based on window iteration. This not only ensures that the output signal power is maintained within the ideal range and the accuracy of the target gain coefficient, but also ensures smooth changes in the gain coefficient through the adaptive step size adjustment mechanism, effectively avoiding over-adjustment and oscillation, thereby comprehensively improving the system stability.

[0063] In one specific embodiment, during the signal preprocessing stage, a triggering mechanism for determining the target gain coefficient in step A14 is defined to ensure that the time-consuming target gain coefficient determination process is initiated only when specific conditions are met, thereby optimizing system resource utilization while ensuring detection accuracy.

[0064] Specifically, if the average power value is greater than the preset gain power threshold and the current gain coefficient is less than the reference gain coefficient, the operation of determining the target gain coefficient is triggered.

[0065] Specifically, the gain power threshold is determined based on statistical results of the system noise floor and normal signal power. By collecting long-term operating data of the receiver under interference-free conditions, the statistical distribution characteristics of the input signal power are calculated, and the gain power threshold is set as the upper limit of the normal fluctuation range. This ensures that the system can effectively distinguish between abnormal power increases caused by deceptive interference and normal fluctuations in background noise, providing a reliable criterion for the activation of the target gain coefficient.

[0066] In this embodiment, the triggering conditions include the gain power threshold determination condition (average power value is greater than preset gain power threshold) and the gain coefficient determination condition (current gain coefficient is less than reference gain coefficient).

[0067] Regarding the gain power threshold determination condition: In the operating environment of a navigation receiver, the input signal always contains Gaussian white noise. Under normal conditions without deceptive interference, the total input power of the receiver mainly consists of the real signal and background noise, and its power value fluctuates randomly within a certain range. The preset gain power threshold is set based on the system noise floor and the statistical characteristics of normal signal power, and its value is usually set at the upper limit of the normal fluctuation range. When deceptive interference signals are mixed in, the total input power will exceed this normal fluctuation range, showing a statistically significant increase. Therefore, using a "greater than" criterion can effectively capture this power anomaly caused by deceptive signals, while avoiding misjudging normal noise fluctuations as interference.

[0068] Regarding the gain coefficient determination criteria: In a clean signal environment, the AGC system will stabilize around an optimal gain value. When a deceptive interference signal is introduced, the increase in total input power comes not only from the deceptive signal itself but also from the additional power generated by the interaction between the deceptive signal and noise. This compound power increase drives the AGC system to make a more significant response than simple signal enhancement, resulting in a greater reduction in the gain coefficient to maintain output stability. Therefore, a significant decrease in the gain coefficient becomes an important characteristic distinguishing genuine deceptive interference from ordinary noise fluctuations.

[0069] Setting dual conditions is based on a balance between system reliability and efficiency. Single-gain power threshold judgment is susceptible to false alarms caused by transient strong signals, while single-gain threshold judgment may lead to misjudgments due to factors such as system temperature drift. Therefore, the combined effect of dual conditions ensures that detection is only triggered when power anomalies and system responses occur simultaneously, significantly improving the accuracy of the judgment.

[0070] In step A12 above, the coordinated judgment of dual conditions effectively eliminates false triggering caused by instantaneous interference and system fluctuations, and avoids unnecessary calculation of the target gain coefficient.

[0071] Similarly, in the above embodiments, if the average power value is less than the gain power threshold, or if the current gain coefficient is greater than or equal to the reference gain coefficient, it is determined that there is no strong power interference signal.

[0072] An average power value less than the gain power threshold indicates that the total power of the input signal has not shown a statistically significant abnormal increase. Spoofing interference, especially strong power spoofing, will inevitably lead to an increase in the total power at the receiver input. If the power does not exceed the gain power threshold, the possibility of strong interference can be preliminarily ruled out from a power perspective.

[0073] If the current gain coefficient is greater than or equal to the reference gain coefficient, it indicates that the automatic gain control system has not responded with a significant reduction in gain, meaning that it has not detected an increase in input power that requires substantial compensation. Therefore, the possibility of strong interference can be preliminarily ruled out from the perspective of the gain coefficient.

[0074] If either of the above two conditions is met, it can be determined that there is no high-power interference signal.

[0075] The detection process is terminated in advance when the power is not abnormal or the gain does not drop significantly, effectively filtering out instantaneous interference and normal system fluctuations, and avoiding unnecessary subsequent calculations.

[0076] In one specific embodiment, a phased early warning mechanism is introduced during the signal preprocessing stage. This mechanism triggers a pre-alarm notification in the initial stage of detecting abnormal signal power, before definitively confirming it as deceptive interference; subsequently, based on the final determination of the target gain coefficient, it performs either a confirmation alarm or discards the pre-alarm. The specific implementation includes steps A14 and A15.

[0077] Step A14: Simultaneously with triggering the determination of the target gain coefficient, issue a pre-warning message.

[0078] This early warning mechanism aims to address the issue of false alarms that may be caused by transient power fluctuations. Instantaneous power fluctuations or brief strong signal interference may cause the system to temporarily deviate from its stable state. Issuing a formal alarm solely based on this would result in an excessively high false alarm rate. Therefore, this early warning phase is necessary for buffering and further confirmation.

[0079] The early warning function mainly realizes the functions of preliminary screening and early warning. It indicates that the system has identified potential interference threats and started a more in-depth detection process (i.e., iterative determination of the target gain coefficient). At the same time, it reminds the system or users to pay attention to this risk and allows preparation time for subsequent operations.

[0080] Step A15: After confirming the presence of a strong power interference signal, confirm the pre-alarm message and output an alarm message; and after confirming the absence of a strong power interference signal, discard the pre-alarm message.

[0081] If a strong power spoofing interference signal is confirmed, the previously issued pre-alarm message is verified and a formal alarm message is output. This ensures that only verified and persistent interference will trigger the final alarm, greatly improving the reliability of the system and reducing the probability of false alarms.

[0082] If it is confirmed that there are no strong power interference signals, this discarding mechanism can automatically filter and clear these invalid warnings caused by brief anomalies, preventing them from causing unnecessary interference to users or causing the system to perform erroneous operations.

[0083] This embodiment, through the aforementioned alarm mechanism, not only achieves early detection of potential risks using pre-warning, but also significantly improves the reliability and credibility of the final output results through confirmation or rejection operations based on precise judgment. This effectively reduces the interference of false alarms to users and ensures that the receiver can provide stable and reliable safety status indications in complex electromagnetic environments.

[0084] In one specific embodiment, during the signal preprocessing stage, the method further includes step A16, which is used to achieve intelligent linkage between the preprocessing stage and the acquisition stage.

[0085] In practical applications, when the power of the deceptive interference signal is greater than 10dB, most of the real signal power is suppressed to less than -143dBm, necessitating an increase in the sensitivity of capturing the real signal. Therefore, this embodiment achieves optimal allocation of detection resources by introducing an adaptive adjustment mechanism for capture parameters based on preprocessing results, specifically including: Step A16: Output parameter adjustment information based on the absolute difference between the reference gain coefficient and the target gain coefficient, wherein the parameter adjustment information is used to adjust the value of the sensitivity parameter in the signal acquisition operation.

[0086] The absolute difference between the reference gain coefficient and the target gain coefficient can reflect abnormal conditions in the signal environment.

[0087] The acquisition processing operation includes at least carrier stripping, pseudocode stripping, and coherent and incoherent integration processing. Parameters characterizing sensitivity in these processing operations include the number of incoherent integration iterations, the acquisition detection threshold, and the code phase search step. When a strong power interference signal is determined to exist in the intermediate frequency signal, the detection sensitivity is improved by extending the incoherent integration time, lowering the detection threshold, and increasing the encryption search step by at least one of these methods.

[0088] When the absolute difference is large, the acquisition sensitivity needs to be significantly increased to cope with the real signal suppressed by strong power interference; when the absolute difference is small, the parameters can be slightly increased or maintained to ensure processing efficiency.

[0089] Through step A16, the system can dynamically optimize the acquisition parameters according to the actual interference intensity, improve the probability of acquiring the real signal, and thus achieve a precise match between the acquisition sensitivity and the actual operating environment.

[0090] Example 2 The detection scheme based on front-end AGC in Example 1 above is mainly aimed at high-power interference signals. This scheme may fail when the power of the spoofed signal is comparable to that of the real signal.

[0091] Therefore, this embodiment provides a method for detecting interference signals during the signal acquisition stage, which can reliably identify deceptive interference signals with power equivalent to the real signal without increasing hardware costs.

[0092] The method described in this embodiment is applicable to various types of satellite navigation receivers, especially civilian navigation devices with a single frequency and single antenna architecture. In this scenario, the receiver is based on a standard baseband processing architecture, eliminating the need for additional hardware costs.

[0093] The core of this method lies in the closed-loop detection mechanism of "detection-clustering grouping-recapture verification".

[0094] Specifically, when the power of the deceptive signal is comparable to that of the real signal, the two will generate significant correlation peaks simultaneously during the acquisition phase. Based on the above characteristics, clustering is used to group peaks with similar code phase / Doppler into different signal source groups. Then, reacquisition verification is performed to eliminate random noise. Based on the finally existing stable signal source, it is determined whether there is an interference signal with similar power to the real signal in the satellite's acquired signal.

[0095] See Figure 2 The interference signal detection method described in this embodiment mainly includes steps B11 to B16.

[0096] Step B11: Acquire the received intermediate frequency signal to obtain multiple candidate acquisition results for the same satellite number, where each candidate acquisition result includes the correlation peak, code phase and Doppler frequency.

[0097] By performing carrier stripping, pseudocode stripping, and coherent and incoherent integration on the intermediate frequency signal, the baseband signal was obtained, and the signal search in the two-dimensional search space of code phase and Doppler frequency was completed.

[0098] Select multiple candidate capture results (e.g., 8) in descending order of the correlation peak values ​​in all code phases.

[0099] Step B12: Perform clustering processing on the multiple candidate capture results to obtain K first signal source groups, and record the correlation peak, code phase and Doppler frequency represented by each group; wherein, the clustering processing is used to group the capture results that both the code phase difference and the Doppler frequency difference satisfy the preset first similarity condition into the same group; wherein, K is an integer greater than or equal to 2.

[0100] The purpose of this step is to aggregate spatially adjacent peak points into different signal source groups, thereby distinguishing peak clusters from different signal sources.

[0101] The reason for clustering based on code phase difference and Doppler frequency difference is that peak values ​​generated by the same signal source are highly consistent in both code phase and Doppler dimensions, while peak values ​​from different signal sources show significant separation in these two dimensions. By setting a first similarity condition, real signals and deceptive signals can be effectively distinguished.

[0102] Preferably, the code phase difference in the first similarity condition is set to be less than half a chip.

[0103] In GNSS signal structures, the main lobe width of the autocorrelation function of a pseudo-random code is approximately one chip, with peak energy concentrated within ±0.5 chips. Therefore, when two correlation peaks are located within half a chip, they are highly likely to originate from the same signal source (e.g., different sampling points or the same signal slightly affected by multipath propagation). Thus, setting the code phase difference in the first similarity condition to less than half a chip effectively distinguishes different signal sources: when the code phase difference between the deception and the real signal is greater than half a chip, they can be correctly identified as different groups, avoiding signal source confusion. This threshold simultaneously considers the typical spacing of the receiver correlator (usually 0.5 chips) and the signal search step, ensuring complete capture of the real signal source and avoiding unnecessary segmentation of the same signal source. Furthermore, this threshold has a certain tolerance for code phase jitter caused by multipath effects, reducing the probability of false alarms due to environmental factors while maintaining detection sensitivity.

[0104] Preferably, K is 3.

[0105] Under normal conditions without deception interference, the correlation peak distribution of a single satellite signal after acquisition and processing typically includes one main peak and one to two significant sidelobe peaks. Setting K to 3 can cover all significant peaks under normal signal conditions, while reserving detection space for potential deception signals. This value has been verified by a large amount of experimental data, achieving an optimal balance between detection capability and computational complexity.

[0106] Step B13: Based on the code phase and Doppler frequency of each of the first signal source groups, perform re-acquisition verification processing to obtain multiple sets of verification acquisition results.

[0107] Reacquisition is performed within a smaller search range, centered on the code phase and Doppler frequency represented by each signal source group. This step verifies the existence and stability of the signal source obtained in the initial acquisition through independent secondary observations, effectively eliminating the influence of accidental noise or spurious peaks.

[0108] The reason for centering the search on the code phase and Doppler frequency represented by each signal source group is that a real signal source will stably produce correlation peaks at its true parameter locations, while accidental noise peaks are unlikely to reappear at the same locations. This local verification strategy centered on the initial detection result can both confirm the stability of the signal source and ensure the efficiency of the verification process.

[0109] Step B14: Perform the clustering process on each group of verification capture results to obtain K second signal source groups, and record the correlation peak, code phase and Doppler frequency represented by each group.

[0110] The clustering process in step B12 is performed again on the multiple sets of verification results obtained from the recapture, resulting in K verified signal source groups. This repeated clustering process ensures the accuracy of signal source identification and eliminates random errors that may exist in the initial capture.

[0111] Step B15: Determine the number of groups that are successfully matched between the K first signal source groups and the K second signal source groups based on the relevant peak values, code phase, and Doppler frequency.

[0112] The first and second signal source groups are matched for consistency, and the code phase difference and Doppler frequency difference between each group are calculated. If the differences are all less than a preset tolerance, the signal source is considered to have successfully passed the verification. This dual verification mechanism significantly improves the reliability of the detection.

[0113] Step B16: Based on the number of successfully matched groups, determine whether there is an interference signal in the satellite's acquired signal that has a power similarity to the real signal that meets a preset power condition; If the number of successfully matched stable signal sources is greater than one, it indicates the presence of multiple signals from different sources, meaning there are interference signals with power comparable to the actual signal but different code phases. Therefore, it is determined that the satellite's acquired signal contains interference signals that meet the preset power similarity condition.

[0114] This embodiment effectively identifies power-equivalent spoofing signals that simultaneously generate significant correlation peaks during the capture phase through a "clustering-recapture verification" mechanism. This method compensates for the blind spots of traditional AGC detection, improves detection accuracy, and is entirely based on existing hardware architecture, achieving performance improvements through algorithmic innovation without requiring any additional hardware resources.

[0115] In one specific embodiment, during the signal acquisition stage, step B14, which determines the number of successfully matched groups between the K first signal source groups and the K second signal source groups based on the relevant peak value, code phase, and Doppler frequency, is specifically implemented through steps B141 and B142.

[0116] This embodiment establishes a reliable signal source matching criterion through a dual verification mechanism, ensuring that only stable signal sources are recognized as valid detection results.

[0117] Step B141: For each first signal source group, if the code phase difference and Doppler frequency difference between the first signal source group and any second signal source group satisfy the preset second similarity condition, the first signal source group is determined to be successfully matched. The reason for using both code phase difference and Doppler frequency difference for joint determination is that: code phase difference reflects the positional relationship of the signal in the pseudo-code dimension, and the real signal source should maintain code phase consistency during reacquisition; Doppler frequency difference reflects the dynamic characteristics of the signal, and a stable signal source should have continuity in the Doppler dimension. Meeting both conditions simultaneously can effectively eliminate random noise interference and ensure the reliability of the matching results.

[0118] The comparison of each first signal source group with all second signal source groups is based on the following reasons: First, measurement errors may occur during the reacquisition process, and global comparison ensures that the best match is found and avoids omissions; second, even if the signal source parameters change slightly, global comparison can correctly identify their correspondence; finally, this ensures that all possible matching relationships are fully considered, improving the completeness of the detection.

[0119] Step B142: Determine the number of existing signal sources based on the number of successfully matched first signal source groups.

[0120] Each successfully matched first signal source group has been confirmed through a recapture verification process, indicating that the signal source is a stable and repeatable real signal source, rather than transient noise or spurious peaks. Therefore, the number of successful matches directly reflects the number of independently existing stable signal sources in the current environment.

[0121] Furthermore, for step B141, a method for determining the second similarity condition is provided. This method achieves adaptive optimization of the matching condition by establishing a dynamic parameter determination mechanism based on the characteristics of the actual signal environment. The specific implementation includes: When it is determined that there is no deception interference signal on the satellite, the code phases corresponding to the first two or three relevant peaks in the satellite's acquisition results are merged according to the order of the relevant peaks from high to low to obtain the merged code phases. Step B1411: After performing a false alarm verification operation based on the preset Doppler frequency and the combined code phase, obtain the Doppler frequency and code phase in the false alarm verification result.

[0122] To overcome the problem of poor environmental adaptability of fixed matching thresholds, this method uses the normal signal characteristics under no deception interference as a benchmark and obtains the typical code phase distribution to provide a reliable reference for setting subsequent matching conditions.

[0123] Step B1412: Determine the second similarity condition based on the Doppler frequency and code phase in the false alarm verification result and the Doppler frequency and code phase used in the false alarm verification operation.

[0124] By combining theoretical benchmarks with actual measurement errors, a dynamic matching standard based on actual system performance is established, generating a second similarity condition that adapts to the specific working environment. This ensures that the second similarity condition can effectively identify the real signal source while tolerating the inherent measurement errors of the system.

[0125] In step B141 above, the dynamic condition determination mechanism effectively solves the problem of rigid parameter settings in traditional methods, providing a more intelligent and reliable matching judgment basis for deception interference detection.

[0126] Furthermore, for step B142, a code phase separation degree verification mechanism is introduced to reliably confirm the existence of real multiple signal sources. The specific implementation methods include steps B1421 and B1422.

[0127] Step B1421: If the number of successfully matched groups is greater than 1, calculate the difference in the representative code phase between any two groups.

[0128] Traditional methods only focus on the number of peaks and cannot distinguish between the sidelobe peaks of multiple real signal sources and the sidelobe peaks of the same signal source.

[0129] To overcome the limitations of relying solely on peak counts, this method leverages the inherent separation in code phase between deceptive and genuine signals. Based on the number of successfully matched groups, the difference representing the code phase between any two groups is calculated. This difference, serving as a spatial separation feature of code phase, quantifies the actual separation degree of different signal sources, thus providing a more reliable basis for judgment.

[0130] Step B1422: If at least one code phase difference is greater than the preset code phase separation threshold, then the number of stable signal sources confirmed to exist is greater than 1.

[0131] The aforementioned code phase separation threshold can effectively distinguish between multiple real signal sources and small offsets caused by measurement errors, avoiding misjudgments caused by system measurement errors or signal fluctuations, and ensuring that only signal sources with significant code phase separation are identified as independent.

[0132] The phase separation threshold of this code is usually set to be greater than one chip.

[0133] In step B142 above, the innovative code phase separation verification mechanism provides a reliable technical solution for confirming the existence of multiple signal sources, effectively solving the problem of misjudgment in deception interference detection by traditional methods.

[0134] In summary, in step B14, the signal source matching relationship is accurately identified by the dual joint determination of code phase and Doppler frequency, thus improving the matching accuracy. The global comparison strategy adopted effectively overcomes the measurement error in the reacquisition process and maintains stable matching performance.

[0135] In one specific embodiment, during the signal acquisition phase, the specific determination mechanism for step B15—determining whether there is a deception interference signal in the satellite's acquired signal based on the number of successfully matched groups—is explained.

[0136] The core of this embodiment lies in improving the reliability of detecting deception interference of comparable power by introducing a joint judgment condition of deception suspect count and preset duration. Specific implementation methods include: If the number of existing signal sources is greater than 1, then update the satellite's suspected spoofing count by 1; If the count of suspected deception exceeds a preset count threshold within a preset time period, it is determined that the satellite's captured signal contains an interference signal whose power meets a preset power condition similar to that of the real signal.

[0137] The update mechanism for counting suspected deception is as follows: In complex electromagnetic environments, the appearance of multiple signal sources in a single instance may be caused by accidental factors. By accumulating the count, only when the same satellite is repeatedly confirmed to have multiple signal source characteristics in consecutive or multiple detection cycles is it considered reliable evidence of a continuous deception attack, thus effectively filtering out false alarms caused by transient interference.

[0138] The final decision mechanism, based on duration and threshold, ensures real-time detection by pre-setting a duration, avoiding decision delays caused by indefinite accumulation. Simultaneously, setting a counting threshold provides a configurable stringency standard for the final decision. This design enables the system to effectively distinguish between continuous spoofing attacks and occasional signal anomalies. Only when anomalous features repeat with a sufficiently high frequency within a specific time window is an interference signal with power close to the real signal identified.

[0139] In step B15 above, the statistical decision mechanism transforms the uncertainty of a single detection into a highly reliable decision conclusion through statistical regularities over time, thereby enabling the detection system to effectively resist transient interference in complex and ever-changing application environments.

[0140] In one specific embodiment, during the signal acquisition phase, the method further includes steps B17 and B18, which are used to explain the triggering conditions for grouping multiple candidate acquisition signals in step B12.

[0141] This embodiment introduces a pre-judgment mechanism based on the number of peak values ​​to achieve intelligent start and stop of the detection process, effectively saving computing resources.

[0142] Step B17: Obtain the number of candidate capture signals whose peak values ​​are greater than a preset peak threshold, and obtain the first number.

[0143] After the capture and processing is completed and before the cluster analysis begins, the system can quickly screen out abnormal scenarios that may have multiple signal sources by using the key indicator of peak count, thereby significantly improving system efficiency.

[0144] Step B18: If the first quantity does not exceed a preset quantity threshold, grouping of the multiple candidate capture signals is not allowed.

[0145] The preset quantity threshold is usually set based on the statistical characteristics of the peak quantity under normal signal conditions to ensure that the triggering conditions can effectively identify anomalies without missing potential deceptive interference.

[0146] This mechanism, based on the comparison between a first quantity and a preset quantity threshold, ensures that computationally intensive clustering analysis is only initiated when multi-peak anomalies are present, thereby avoiding unnecessary overhead.

[0147] In addition, the method for determining the peak threshold in step B17 is explained: By establishing a dynamic threshold determination mechanism based on actual signal characteristics, adaptive optimization of the peak threshold was achieved.

[0148] Specifically, the peak threshold is determined based on the satellite acquisition results during periods without deception interference. The top two or three peaks from the highest to lowest time-related peaks are selected, and their values ​​are used as the benchmark for setting the threshold.

[0149] This method establishes a threshold benchmark using actual capture results during periods free from deceptive interference, ensuring the threshold matches actual operating conditions and improving environmental adaptability. The selection rule is to sort relevant peak values ​​from highest to lowest, taking the first two or three peaks. Selecting the first two peaks accurately reflects the main peak characteristics, while selecting the first three further covers the main sidelobe information. These peaks represent typical levels under normal signal conditions, providing a reliable basis for setting reasonable peak thresholds.

[0150] The dynamic threshold determination mechanism described above effectively solves the limitations of traditional fixed threshold methods, providing a more accurate and reliable threshold benchmark for deception interference detection.

[0151] In steps B17 and B18 above, the intelligent triggering mechanism avoids unnecessary clustering analysis operations, saves computing resources, and improves system operating efficiency.

[0152] In one specific embodiment, during the signal acquisition stage, the method further includes steps B19 and B20, which are used to achieve intelligent linkage between the preprocessing stage and the acquisition stage.

[0153] This embodiment achieves optimal configuration of detection resources by introducing an adaptive adjustment mechanism for capture parameters based on preprocessing results, specifically including: Step B19: Obtain the intensity of the strong power interference present in the current intermediate frequency signal.

[0154] The purpose of this step is to enable the acquisition module in the receiver to detect abnormalities in the signal environment in advance.

[0155] Optionally, the intensity of the high-power interference can be determined by the absolute difference between the target gain coefficient and the reference gain coefficient in the method of Embodiment 1 above.

[0156] For example, two thresholds can be set, namely the difference between the first coefficient and the difference between the second coefficient, where the difference between the first coefficient and the difference between the second coefficient are greater than the difference between the second coefficient.

[0157] When the absolute difference is greater than or equal to the first coefficient difference, it indicates that the intensity of the strong power interference is high interference; when the first coefficient difference is greater than or equal to the absolute difference, it indicates that the intensity of the strong power interference is medium interference; when the absolute difference is less than the second coefficient difference, it indicates that the intensity of the strong power interference is low interference.

[0158] Step B20: Adjust the parameters of the capture process according to the intensity of the high-power interference.

[0159] Acquisition processing involves operations such as carrier stripping, pseudocode stripping, and coherent and incoherent integration. Key sensitivity parameters include the number of incoherent integration iterations, the acquisition detection threshold, and the code phase search step. When strong power interference is present, adjusting these parameters—for example, increasing the incoherent integration time, decreasing the detection threshold, or increasing the search step—can effectively improve acquisition sensitivity.

[0160] Differentiated acquisition strategies are adopted for interference environments of varying intensities. Specifically, when the intensity of high-power interference is large, the acquisition sensitivity needs to be significantly increased to cope with the real signal suppressed by the high-power interference; when the intensity of high-power interference is low, the parameters can be slightly increased or maintained to ensure processing efficiency.

[0161] Through steps B19 and B20, the system can dynamically optimize the acquisition parameters according to the actual interference intensity, improve the probability of acquiring the real signal, and thus achieve a precise match between the acquisition sensitivity and the actual operating environment.

[0162] Example 3 During the tracking phase of a navigation receiver, when the pseudo-code phase of the spoofing interference signal is close to the phase of the real signal code (e.g., within a few chip range), existing technologies struggle to quickly and accurately identify spoofing interference. For example, in the tracking loop, if the receiver has locked onto the correlation peak of the real signal, but the spoofing signal lags behind by a certain number of chips, or the receiver mistakenly locks onto the correlation peak of the spoofing signal, existing methods lack an effective multi-correlator detection mechanism, making real-time spoofing identification impossible with low computational complexity. Furthermore, existing spoofing detection schemes in the tracking phase often rely on a single parameter (such as power variation or carrier-to-noise ratio). When the power of the spoofing signal is comparable to or slightly higher than the power of the real signal, the detection sensitivity is insufficient, leading to a high false negative rate. Simultaneously, existing technologies lack an effective mechanism for handling correlator peak jumps during tracking, failing to guarantee tracking stability and accuracy when the phase of the spoofing signal and the real signal code is close.

[0163] In view of this, this embodiment provides a method for detecting deception interference during the signal tracking stage. It can achieve rapid and accurate identification of deception interference in scenarios where the phase of the deception signal and the real signal code are close. At the same time, it has the ability to automatically switch to the real signal when the deception signal is mistakenly locked, so as to reduce the false detection rate and false locking risk of the system.

[0164] The method described in this embodiment is applied to the baseband tracking module of various satellite navigation receivers. In this scenario, the receiver tracking loop is usually configured with multiple correlators (such as lead, instant, and lag branches), and this method is based on this existing hardware architecture to achieve deception interference detection and suppression without adding any additional hardware costs.

[0165] The core of this method lies in constructing a joint detection and error correction mechanism based on multi-correlator power monitoring and state machine decision-making. This mechanism achieves reliable identification of deceptive interference with close code phase by real-time monitoring of power anomalies in leading and lagging branches and continuous verification by integrating historical states.

[0166] See Figure 3 The above-mentioned deception interference detection method includes steps C11 to C14.

[0167] Step C11: Determine the target branch based on the tracking status of the tracking loop on the satellite; wherein, if the real signal has been locked, the target branch is a lagging branch; if the target branch has been mistakenly locked to a deceptive interference signal, the target branch is a leading branch.

[0168] The purpose of this step is to clarify the targets of subsequent anomaly monitoring and ensure that the detection strategy matches the current actual signal locking situation. Specifically: In the navigation receiver delay-locked loop, an instantaneous correlator is used to track the peak value of the currently locked signal. Wherein: When the receiver has locked onto the real signal, and a spoofing interference signal exists with its pseudocode phase lagging behind the real signal, the energy of the spoofing signal will mainly appear in the hysteresis correlator branch. Since the peak value of the cross-correlation function between the spoofing signal and the local pseudocode has a positive time delay relative to the instantaneous correlation position of the real signal, monitoring the hysteresis branch as a target allows direct capture of the energy characteristics of the spoofing interference signal located behind the real signal.

[0169] When a receiver mistakenly locks onto a spoofing signal, the immediate correlator actually tracks the correlation peak of the spoofing signal. Because the pseudo-code phase of the real signal leads the spoofing signal, the cross-correlation peak between the real signal and the local pseudo-code has a negative time delay relative to the current lock position; therefore, the energy will mainly appear in the leading branch. Thus, by targeting the leading branch, the energy of the real signal located ahead of the currently locked signal can be effectively detected, revealing the abnormal state where the receiver has mistakenly locked onto a lagging spoofing signal.

[0170] This dynamic target branch selection mechanism ensures that, regardless of the false locking scenario, the system can find evidence of deception interference in the correct signal direction, fundamentally solving the problem of incomplete scenario coverage by a single detection strategy.

[0171] Step C12: Obtain the power value output by the target branch in the current detection period; and obtain the index number of the correlator with the largest output power value of the target branch in the current detection period and the previous detection period.

[0172] This step collects two key types of data for deception and interference decision-making: the first is the power value, used to assess the signal strength of the target branch; the second is the index number of the maximum power correlator, used to identify the location of energy focusing within the target branch. Together, these two data quantify the intensity and stability of the correlation peak, providing crucial input for subsequent decision-making.

[0173] After carrier and pseudocode stripping, during coherent integration time The expressions for the power output values ​​of the leading correlator branches E0 to E7, the instantaneous correlator branch P, and the lagging correlator branches L0 to L7 are as follows: ; ...; ; ; ; ...; .

[0174] in: n represents the nth coherent integration time interval; 'a' represents the amplitude of the received signal; R(τ) represents the autocorrelation function of the pseudo-random code, where the independent variable τ represents the phase difference between the locally reproduced pseudo-code and the input signal pseudo-code; This is the loss term caused by imperfect carrier stripping, where This represents the frequency difference between the local carrier and the signal carrier; to These represent the power values ​​of the lead correlator branches from the 0th to the 6th in the nth cycle; P(n) represents the power value of the instantaneous correlator branch in the nth cycle. to These represent the power values ​​output by the 0th to 7th lag correlator branches in the nth cycle.

[0175] In practical applications, the power values ​​of all branches in the target branch within the current detection period can be obtained.

[0176] Step C13: If the power value of at least one branch in the target branch exceeds the preset tracking power threshold, and the absolute value of the difference between the index number in the current detection period and the index number in the previous detection period is not greater than 2, it is determined to be a stable abnormal event, and the value of the counter is incremented by 1; otherwise, the value of the counter is decremented by 1.

[0177] This step aims to establish a state accumulation mechanism based on persistence and stability. This mechanism determines events based on the dual conditions of power anomalies and positional stability. Only when the power exceeds the limit and the position of the relevant peak is basically stable (absolute index difference ≤ 2) is it considered a valid stable anomaly. This mechanism effectively filters out power spikes and relevant peak fluctuations caused by noise and transient interference, effectively distinguishing between occasional anomalies and persistent deception, and significantly reducing false alarms.

[0178] Furthermore, the counter's increment / decrement mechanism utilizes time-domain persistence to enhance decision reliability. When a stable anomaly is detected, the counter increments by 1 to achieve cumulative confirmation. If either of the above conditions is not met, the counter decrements by 1. This indicates that either there is no energy anomaly in the current period, or the anomaly location is unstable (possibly due to noise). This design effectively prevents false alarms triggered by accidental interference (such as random noise spikes or transient multipath propagation), thereby significantly reducing the system's false alarm probability.

[0179] Step C14: After the value of the counter exceeds the preset counter threshold, it is determined that there is an interference signal in the satellite tracking signal whose code phase meets the preset code phase similarity condition with the real signal.

[0180] Based on high-quality evidence of abnormal states accumulated over a long period of time, a final judgment was made that deception and interference existed.

[0181] By setting a counter threshold instead of relying on a single event decision, the alarm output is ensured to be based on continuously verified and highly credible evidence, ultimately achieving highly reliable, low-false-alarm detection of deception interference.

[0182] Through the above-described deception interference signal processing method, this embodiment achieves full coverage detection of both delayed and advanced deception scenarios by selecting the target branch adaptively based on the tracking state, thus solving the problem of missed detection by a single strategy. By introducing dual judgment conditions of power and correlation peak position stability and a counter mechanism, it effectively distinguishes between continuous deception and transient interference, significantly reducing the false alarm probability of the system. It fully utilizes the inherent tracking loop correlator resources of the receiver and achieves performance improvement through algorithm innovation without adding any additional hardware costs.

[0183] In one specific embodiment, during the signal tracking phase, the mechanism for determining the tracking power threshold in step C13 is described in detail: The core of this embodiment lies in a dual tracking power threshold determination mechanism. This mechanism clearly distinguishes the tracking power threshold into a signal power threshold and a noise power threshold, and independently and adaptively determines their values, thereby achieving refined identification of deception interference signals in the tracking loop.

[0184] The tracking power threshold includes a signal power threshold and a noise power threshold. When the power value output by at least one branch in the target branch is greater than both the signal power threshold and the noise power threshold, it is determined that the power value is greater than the tracking power threshold.

[0185] For noise power threshold: The noise power threshold is determined based on the noise floor of the satellite in the current detection period.

[0186] In practical applications, receiver noise is typically Gaussian white noise, whose instantaneous power fluctuates randomly around the noise floor. However, at certain moments, the instantaneous power of the noise may be much higher than the noise floor. If the noise power threshold is directly set to the noise floor, these noise peaks will frequently exceed the threshold, leading to an excessively high probability of false alarms. Therefore, a safety margin (such as 10dB) is added to the noise floor to ensure that random noise fluctuations do not trigger detection, greatly suppressing false alarms.

[0187] The noise power threshold establishes an absolute minimum power benchmark for identifying valid signals. Any related branch with a power value below this threshold is considered meaningless noise and is directly excluded, not participating in subsequent deception interference judgment. This effectively utilizes the real-time noise information of the tracking loop, dynamically tracking changes in the environmental noise floor, thereby ensuring that noise peaks are not misjudged as deception signals in low signal-to-noise ratio environments, greatly reducing the system's false alarm probability.

[0188] For signal power threshold: Once the true correlation peak of the received signal from the satellite has been locked, the signal power threshold is determined based on the satellite's instantaneous correlator power value and carrier-to-noise ratio (CNR) within the current detection period. Specific details are as follows: Using the instantaneous branch power P(n) of the currently locked real signal as a dynamic benchmark, an adjustment amount is set according to the signal quality (i.e., the CNR). When the signal quality is good (high CNR), a lower relative threshold (i.e., P(n) - a larger adjustment amount) is allowed to capture slightly weaker spoofing signals; when the signal quality is poor (low CNR), a higher relative threshold (i.e., P(n) - a smaller adjustment amount) needs to be set to prevent false alarms caused by noise fluctuations. This allows the signal power threshold to adapt to changes in channel conditions, rather than using a fixed value, thereby significantly improving the robustness of the system while ensuring detection sensitivity.

[0189] Preferably, based on the value of CNR, a corresponding target adjustment value is determined from the preset mapping relationship between CNR intervals and adjustment values, and the signal power threshold is determined by subtracting the target adjustment value from the instantaneous correlator power value; wherein, the higher the interval in which the CNR is located, the larger the mapped adjustment value.

[0190] This approach divides continuous CNR values ​​into multiple discrete intervals and assigns a specific adjustment value to each interval to characterize its detection sensitivity. This enables the system to identify and adapt to different signal quality scenarios, laying the foundation for achieving optimal detection performance.

[0191] Specifically, in high CNR (high-quality channel) environments, the signal is clear and noise has little impact. The system can use a lower relative threshold (i.e., subtract a larger adjustment value) to capture deceptive signals with power close to the real signal and stronger concealment, thereby improving detection sensitivity. Conversely, in low CNR (poor-quality channel) environments, the signal is easily overwhelmed by noise. To avoid false alarms, a higher relative threshold (i.e., subtract a smaller adjustment value) is needed to prioritize system stability. This negative correlation between "high CNR - large adjustment - low threshold" and "low CNR - small adjustment - high threshold" is key to achieving dynamic optimization of detection performance.

[0192] Through the aforementioned signal power threshold determination mechanism, the signal power threshold is intelligently followed by the receiver's actual working environment. When the channel conditions are good, it captures weak deception traces; when the channel conditions are poor, it avoids noise interference. Thus, it achieves the best balance between deception interference detection sensitivity and system robustness across the entire operating range, significantly improving the practicality and reliability of the deception interference detection method during the tracking phase.

[0193] As an example, this embodiment employs a stepped adaptive threshold adjustment mechanism, which is achieved by finely dividing the CNR value range and configuring an optimized specific adjustment value for each range. The specific implementation includes: Fixed adjustment strategy for low CNR region: When the CNR is less than 30dB, the adjustment value is set to a fixed value.

[0194] In low signal-to-noise ratio environments, the signal itself fluctuates significantly. An excessively large adjustment value (i.e., setting the threshold too low) will significantly amplify the impact of noise fluctuations, leading to a sharp increase in the false alarm rate. Therefore, using a small fixed value (such as 3dB) can ensure the stability of the system while maintaining the ability to detect strong deceptive interference, thus preventing frequent false alarms due to environmental noise.

[0195] Multi-interval dynamic adjustment strategy for high CNR regions: When the CNR is greater than or equal to 30dB, the range of the CNR value is divided into at least two intervals, each of which is set with its own adjustment value.

[0196] One exemplary setup method is as follows: When the CNR is greater than or equal to 30dB and less than 35dB, the adjustment value is 8dB; When the CNR is greater than or equal to 35dB and less than 40dB, the adjustment value is 10dB; When the CNR is greater than or equal to 40 dB, the adjustment value is 15 dB.

[0197] Within the range of excellent to extremely high signal quality, different quality levels correspond to varying degrees of deceptive signal concealment. As the CNR (Cost Reduction Ratio) increases, the system's noise suppression capability becomes stronger, allowing for progressively larger adjustment values ​​to set lower detection thresholds. This enables the system to detect extremely weak deceptive signals with power only 15dB lower than the real signal under optimal channel conditions (e.g., CNR ≥ 40dB), significantly enhancing its ability to detect covert deception in clean signal environments.

[0198] Through the meticulously configured mapping relationship described above, a precise match is achieved between the detection sensitivity for spoofing interference and the signal environment quality. It transforms continuous signal quality changes into discrete, optimized detection strategies, enabling the receiver to operate robustly in harsh environments and detect subtle changes in favorable conditions. This tiered design ensures that the adaptive threshold adjustment method consistently delivers optimal detection performance in real-world, complex wireless channel environments.

[0199] When the true correlation peak of the deceptive interference signal has been identified, the methods for determining the signal power threshold include: If the tracking state is a reliable state, the signal power threshold is determined based on the instantaneous correlation power value of the current detection period; otherwise, the signal power threshold is determined based on the instantaneous correlation power value minus a preset power value.

[0200] When a loop has stably locked onto a signal (even a spoofed signal), all its parameters (such as carrier loop and code loop errors) indicate that it is in a stable tracking state. At this point, using a more lenient threshold can avoid misjudging the real signal (which may be located in the leading branch) as an invalid signal, thereby ensuring the detection sensitivity of potential real signals in the leading branch and creating conditions for subsequent correct peak jumping.

[0201] When the tracking state is not a reliable state, it indicates that the loop is in a dynamic adjustment or incomplete convergence phase. In this case, the reference value of the branch power P(n) itself decreases, and the signal fluctuates significantly. By subtracting a large fixed value (e.g., 13dB), the detection threshold is raised, effectively filtering out power fluctuations and unreliable peaks caused by loop transient processes. This significantly reduces the probability of false alarms in this phase and prevents the system from making incorrect peak-jumping decisions due to instantaneous fluctuations.

[0202] The preset power value is 13dB. As an empirical value, it can ensure that the real signal on the leading branch can be captured while effectively suppressing most of the power jitter caused by loop instability, achieving the best balance between detection sensitivity and robustness against transient interference.

[0203] Through the aforementioned signal power threshold determination mechanism linked to the tracking status, this system achieves intelligent coordination between the deception interference detection strategy and the inherent stability of the tracking loop. The system can automatically identify the stable state of the loop and switch to the most suitable detection criteria under different states. This state-based processing strategy greatly improves the accuracy and overall success rate of identifying the true signal and triggering peak jumps in complex scenarios such as false locking of deception signals.

[0204] In summary, this embodiment achieves accurate identification of abnormal correlation peaks on lagging or leading branches through the synergistic effect of signal power threshold and noise power threshold.

[0205] In one specific embodiment, during the signal tracking phase, the method further includes step C15, for performing a correction operation after determining that the satellite has a spoofing interference signal.

[0206] Step C15: Perform peak skipping processing to switch the instantaneous correlator code phase of the tracking loop to the code phase corresponding to the identified leading correlator that matches the characteristics of the real signal.

[0207] Once an interference signal with a code phase similar to the real signal is detected in the tracking signal, peak skipping processing is immediately triggered. This processing switches the instantaneous correlator code phase of the tracking loop from the correlation peak of the currently mis-locked spoofing signal to the correlation peak that matches the characteristics of the real signal, identified by the aforementioned detection process (such as monitoring the lead branch). This allows the receiver to quickly escape the spoofing signal without having to repeat the time-consuming acquisition process, greatly shortening the system recovery time, which is crucial for high-dynamic, high-real-time applications.

[0208] The "lead correlator that conforms to the characteristics of the real signal" referred to in the peak-skipping operation is an abnormal peak identified by monitoring the power of the leading branch and applying corresponding threshold judgments when a spoofing signal is falsely locked. Its basic principle is that spoofing signals usually have a code delay (i.e., hysteresis) relative to the real signal. Therefore, the energy of the real signal will be reflected in the leading branch of the tracking loop. The peak-skipping operation utilizes this physical characteristic to directly switch the loop center from the hysteretic spoofing peak to the leading real peak.

[0209] Through the above-mentioned peak-jumping processing mechanism, when subjected to deception attacks, it can not only detect them in time, but also quickly and automatically restore the correct tracking state. This realizes the integrated closed-loop processing of deception interference detection and correction, which significantly enhances the active defense capability and system resilience of the navigation receiver.

[0210] Optionally, in the above method for processing deceptive interference signals, after performing step C16, steps C17 and C18 are also included.

[0211] Step C17: If the peak-jumping operation fails, the phase of the instantaneous correlation code is switched back to the position before the peak-jumping. If the counter continues to exceed the counter threshold, the peak-jumping process is repeated.

[0212] Step C18: If the peak jumping operation is successful, continue tracking the real signal.

[0213] In step C17, if the peak-hopping operation fails (e.g., the carrier-to-noise ratio (CNR) falls below a preset threshold after the peak-hopping), the immediate correlator code phase is switched back to its pre-peak-hopping position. This safety backoff mechanism aims to prevent the loop from continuing to deteriorate until it loses lock after a correction failure, providing the system with a stable and safe state. If the counter value subsequently exceeds the threshold (indicating that spoofing interference still exists), the peak-hopping process is repeated. This design constitutes an intelligent retry loop: as long as the spoofing threat is not eliminated, the system will repeatedly attempt correction while ensuring safety, thereby significantly improving the overall recovery success rate.

[0214] In step C18, if the peak-jumping operation is successful (for example, the CNR recovers and stabilizes above the normal threshold after the peak-jumping), the system continues to track the real signal and seamlessly transitions to normal operating mode.

[0215] Through the above-mentioned peak-jumping post-processing mechanism, even if the first correction attempt fails, the system will not crash. Instead, it can autonomously backtrack, evaluate, and try again, which greatly improves the fault tolerance and robustness of the entire anti-spoofing system. This ensures that the receiver can remain resilient in the face of continuous or dynamically changing spoofing attacks, maximizing the probability of it recovering and maintaining the correct tracking state.

[0216] Furthermore, at the outset of this method, the current lock state of the receiver tracking loop is used as a known input condition or determined through other auxiliary means. The initial determination of this state can be based on the detection results of the interference signal in Embodiment 2.

[0217] Example 4 Existing single-stage spoofing interference detection methods have limitations when facing complex or highly concealed spoofing attacks. For example, spoofing signals with comparable power may not be detected by the front-end AGC, and spoofing signals with similar code phases are difficult to identify in the early stages of tracking. Furthermore, false alarms may occur at different detection stages due to environmental noise or transient interference. Therefore, there is an urgent need in the field for a method that can integrate detection information from multiple processing stages, perform cross-validation, and make joint decisions.

[0218] In view of this, this embodiment provides a method for detecting interference signals. This method establishes a joint decision mechanism based on multi-stage information fusion, and performs correlation analysis on the detection results of the three stages of signal preprocessing, acquisition, and tracking. By utilizing their information complementarity, it achieves accurate identification of complex deception attacks that are not high-power and involve multiple satellites working together.

[0219] See Figure 4 The method includes steps D11 and D12.

[0220] Step D11: Obtain the first detection information, the second detection information, and the third detection information.

[0221] The first detection information records whether a strong power interference signal exists in the intermediate frequency (IF) signal, reflecting the macroscopic power anomaly of the IF signal. Preferably, the first detection information is obtained using the method described in Embodiment 1.

[0222] The second detection information includes second sub-information of the captured signal corresponding to each satellite during the signal acquisition phase. Each second sub-information records whether there is an interference signal in the captured signal of its corresponding satellite that has a power similar to the real signal, meeting a preset power condition. Each second sub-information reveals, from the acquisition level, whether there are multiple signal sources (real signal and one or more deceptive signals) with similar power targeting a specific satellite. Preferably, the second sub-information is obtained using the method described in Embodiment 2.

[0223] The third detection information includes third sub-information of the tracking signal corresponding to each satellite during the signal tracking phase. Each third sub-information records whether there is an interference signal in the tracking signal of its corresponding satellite whose code phase meets a preset code phase similarity condition with the real signal. Each third sub-information reveals, from the tracking level, whether there is deceptive interference with a similar code phase targeting a specific satellite. Preferably, each third sub-information is obtained using the method described in Embodiment 3.

[0224] Step D12: When the first condition and the second condition are met simultaneously, determine that there is an interference signal in the received signal during the baseband signal processing. The first condition is: the first detection information indicates that there is an interference signal in the intermediate frequency signal, but the interference signal is not a high-power interference signal.

[0225] Regarding the first condition: The scenario of strong power suppression spoofing was ruled out (this type of spoofing has been handled separately by Scheme 1). At the same time, it was confirmed that the system as a whole was in an abnormal state of interference, and that the interference had the characteristics of multiple signal sources superimposed (because a single spoofing signal is unlikely to cause a significant change in the total power), rather than a single strong interference source.

[0226] Furthermore, when the first detection information is determined using the method described in Embodiment 1, if the current target gain coefficient of the digital AGC module is less than the reference gain coefficient, and the absolute value of the difference between the two is less than the preset coefficient difference threshold, it indicates that the total input power has experienced a slight but continuous abnormal increase. This increase is sufficient to trigger an AGC response (manifested as a decrease in gain coefficient), but its degree has not yet reached the severe level of strong power deception. Therefore, it is determined that the first condition is met.

[0227] The second condition is: the number of suspicious satellites is greater than a preset suspicious satellite threshold, wherein the number of suspicious satellites is determined based on the second detection information and the third detection information; wherein, if one of the second sub-information and the third sub-information of the same satellite indicates the presence of an interference signal and the other indicates the absence of an interference signal, then the satellite is determined to be a suspicious satellite, wherein the total number of all the suspicious satellites is taken as the number of suspicious satellites.

[0228] Regarding the second condition: By defining "suspicious satellites" and counting their numbers, a key characteristic of spoofing attacks was captured—inconsistency. A spoofing attack may perfectly mimic the characteristics of a real signal in some processing stages (such as acquisition) and go undetected, or it may be detected in other stages (such as tracking) due to the dynamic characteristics of the loop. This inconsistency in detection results across stages is precisely the key characteristic that makes it difficult for spoofing signals to be perfectly hidden in all processing stages.

[0229] Preferably, the suspicious satellite threshold can be set to any value between 2 and 4. This value is set based on the geometric principle that effective deception requires affecting at least multiple satellites to cause incorrect positioning, ensuring that only systematic, multi-satellite deception attacks will trigger alarms, effectively filtering out accidental misjudgments of single satellites.

[0230] Simultaneous fulfillment of both conditions means that, based on the first condition, the system detects weak power anomalies caused by multiple signal sources; and based on the second condition, inconsistent detection results across multiple satellites are observed. These two sources of evidence are different and independent, but they both point to the same conclusion: the existence of a distributed, sophisticated, coordinated deception attack with power levels comparable to the real signal, designed to pass consistency checks. This joint determination significantly enhances the confidence level of the detection.

[0231] This embodiment constructs a multi-layered, cross-validated joint detection system by integrating detection information from the preprocessing, acquisition, and tracking stages. This method effectively identifies highly concealed spoofing interference that is difficult to detect with a single detection method (especially coordinated spoofing with comparable power and similar code phase), significantly reducing the system's false negative rate. Simultaneously, by setting a number of suspicious satellites, it fully utilizes the inconsistencies exhibited by spoofing attacks across multiple satellites and stages, effectively suppressing false alarms caused by environmental noise or transient interference. Thus, even in highly complex electromagnetic environments, it achieves highly reliable and robust detection of spoofing interference.

[0232] This application also provides a storage medium storing a computer program, wherein the computer program is configured to execute the methods described above when running.

[0233] An electronic device includes a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the methods described above.

[0234] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term "computer storage medium" includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

Claims

1. A method of interference signal detection, the method comprising: A baseband signal processing procedure applied to a navigation receiver, the method comprising: determining a target branch according to a tracking state of a satellite in a tracking loop; wherein, if a real signal is currently locked, the target branch is a lag branch; if a spoofing jamming signal is currently mislocked, the target branch is an advance branch; acquiring a power value output by the target branch in a current detection period; and acquiring index numbers of correlators outputting maximum power values by the target branch in the current detection period and in a previous detection period, respectively; if the power value of at least one branch in the target branch exceeds a preset tracking power threshold, and an absolute value of a difference between the index numbers in the current detection period and in the previous detection period is not greater than 2, determining that an abnormal event is stable once, and updating a value of a counter by 1; otherwise, updating the value of the counter by -1; after the value of the counter exceeds a preset counter threshold, determining that the satellite in the tracking loop has a spoofing jamming signal.

2. The method of claim 1, wherein: the tracking power threshold comprises a signal power threshold value and a noise power threshold value; wherein, in a case that a real correlation peak of a received signal of the satellite is locked, the noise power threshold value is determined according to a noise floor of the satellite in a current detection period; in a case that the real correlation peak of the received signal of the satellite is locked, the signal power threshold value is determined according to an instantaneous correlator power value and a carrier-to-noise ratio (CNR) of the satellite in the current detection period; in a case that a real correlation peak of a spoofing jamming signal is locked, if a tracking state is a trusted state, the signal power threshold is determined according to an instantaneous correlation power value in the current detection period; otherwise, the signal power threshold is determined according to a result of subtracting a preset power value from the instantaneous correlation power value.

3. The method of claim 2, wherein, in the case that the real correlation peak of the received signal of the satellite is locked, the signal power threshold value is determined in a manner comprising: determining a corresponding target adjustment value from a preset mapping relationship between CNR intervals and adjustment values according to a value of the CNR, and determining the signal power threshold by subtracting the target adjustment value from the instantaneous correlator power value; wherein, the higher the interval of the CNR, the greater the mapped adjustment value. the mapping relationship is configured in a manner comprising:

4. The method of claim 3, wherein, when the CNR is less than 30 dB, the adjustment value is a fixed value; when the CNR is greater than or equal to 30 dB, a value range of the CNR is divided into at least two intervals, wherein each interval is provided with a respective adjustment value.

5. The method of claim 4, wherein: when the CNR is less than 30 dB, the adjustment value is 3 dB; when the CNR is greater than or equal to 30 dB and less than 35 dB, the adjustment value is 8 dB; when the CNR is greater than or equal to 35 dB and less than 40 dB, the adjustment value is 10 dB; when the CNR is greater than or equal to 40 dB, the adjustment value is 15 dB. the preset power value is 13 dB.

6. The method of claim 2, wherein, after the satellite in the tracking loop is determined to have a spoofing jamming signal in the case that the real correlation peak of the spoofing jamming signal is locked, the method further comprises:

7. The method of claim 1, wherein, ​ The peak skipping is performed to switch the instant correlator code phase of the tracking loop to the code phase corresponding to the identified early correlator pair that matches the real signal characteristics.

8. The method of claim 7, wherein, After the peak skipping is performed, the method further comprises: If the peak skipping fails, the instant correlator code phase is switched back to the position before the peak skipping, and if the counter continuously exceeds the counter threshold, the peak skipping is repeatedly performed; If the peak skipping succeeds, the real signal tracking is continuously performed.

9. A method of detecting an interference signal, characterized by, The method is applied to a baseband signal processing procedure of a navigation receiver, and the method comprises: obtaining first detection information, second detection information and third detection information; when a first condition and a second condition are both satisfied, determining that there is an interference signal in a received signal in the baseband signal processing procedure; the first condition is that the first detection information indicates that there is an interference signal in an intermediate frequency input signal, but the interference signal is not a strong power interference signal; the second condition is that a number of suspicious satellites is greater than a preset suspicious satellite threshold, wherein the number of suspicious satellites is determined according to the second detection information and the third detection information; wherein: the second detection information comprises second sub-information of a corresponding acquisition signal of each satellite in a signal acquisition stage, wherein each second sub-information records whether there is an interference signal in the acquisition signal of the corresponding satellite, the interference signal having a power similar to that of a real signal and satisfying a preset power similarity condition; the third detection information comprises third sub-information of a corresponding tracking signal of each satellite in a signal tracking stage, wherein each third sub-information records whether there is an interference signal in the tracking signal of the corresponding satellite, the interference signal having a code phase similar to that of a real signal and satisfying a preset code phase similarity condition; and each third sub-information is obtained by using the method of any one of claims 1 to 8; wherein, if one of the second sub-information and the third sub-information of the same satellite indicates that there is an interference signal and the other indicates that there is no interference signal, the satellite is determined to be a suspicious satellite, and the total number of all the suspicious satellites is taken as the number of suspicious satellites. 10.An electronic device comprising a memory and a processor, the electronic device characterized by, The memory stores a computer program, and the processor is configured to run the computer program to execute the method in any one of claims 1 to 9.

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