GNSS receiver interference detection method based on smooth pseudo Wigner-Hough transformation

Abstract

The invention discloses a GNSS (Global Navigation Satellite System) receiver interference detection method based on smooth pseudo Wigner-Hough transformation. According to the method, down-conversion and analog-to-digital conversion (ADC) are carried out on GNSS received signals affected by electromagnetic interference to obtain digital intermediate frequency signals, the digital intermediate frequency signals are analyzed and expressed, time-frequency analysis is carried out on the digital intermediate frequency signals by using smooth pseudo-Wigner-Ville Distribution (SPWVD), smooth pseudo-Wigner-Hough Transform (SPWHT) is obtained by combining the time-frequency analysis with Hough Transform, the peak value of the SPWHT is detected in a parameter space, and the GNSS received signals subjected to the electromagnetic interference influence are analyzed and expressed. Therefore, the GNSS electromagnetic interference signal can be effectively detected. According to the method, the problem of time-frequency resolution compromise in the existing time-frequency analysis technology is solved, the interference influence of cross-term can be eliminated, and meanwhile, the aggregation characteristic of energy distribution in GNSS interference detection is remarkably enhanced, so that the electromagnetic interference detection performance of a GNSS receiver in a complex electromagnetic interference environment is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite navigation signal processing, and in particular to a GNSS receiver interference detection method based on smoothed pseudo Wigner-Hough transform. Background Art

[0002] The Global Navigation Satellite System (GNSS) has been widely used in various fields of civil and military, and the complex electromagnetic interference environment has had a serious impact on satellite navigation applications. Although the GNSS uses the Direct Sequence Spread Spectrum (DSSS) scheme in signal design to ensure that the GNSS signal has a certain anti-interference ability, the GNSS signal power received by the GNSS receiver antenna is very low. Unintentional or intentional electromagnetic interference will inevitably cause the GNSS receiver performance to deteriorate severely, and even cause the GNSS receiver to completely lose lock and be unable to locate. GNSS interference signals can usually be interpreted as a certain type of frequency or amplitude modulation. For example, Linear Frequency Modulation (LFM) interference is a typical broadband non-stationary interference with the potential to seriously affect the navigation and positioning performance of GNSS receivers. Time-frequency analysis technology can be used in GNSS interference detection. For example, the common spectrogram technology is limited by the uncertainty principle and inevitably has the problem of time-frequency resolution compromise, resulting in poor time-frequency resolution. Wigner-Ville Distribution (WVD) is usually used to solve the problem of limited time-frequency resolution of spectrogram technology. Although the WVD method can provide near-optimal time-frequency resolution for GNSS interference detection technology based on time-frequency analysis, the interaction between different components of the GNSS received signal subjected to electromagnetic interference leads to serious and unacceptable cross-interference terms. WVD converts the signal into an energy distribution on the time-frequency plane, and its distribution law depends on the frequency modulation method of the signal. For example, when there is swept frequency interference in the received GNSS signal, the WVD method will produce a linear time-frequency distribution with concentrated energy in the time-frequency domain. Therefore, swept frequency interference detection can be transformed into a problem of searching for specific linear patterns in the time-frequency domain. By combining WVD with Hough Transform (HT), Wigner-Hough Transform (WHT) can be obtained, which is a linear integral projection transform. WHT uses the line-point dual transformation method to more effectively search for linear patterns corresponding to GNSS swept frequency interference. Pseudo Wigner-Ville distribution (Pseudo WVD, PWVD) uses a low-pass filter window function to suppress the cross-interference terms in the quadratic time-frequency distribution. Therefore, PWVD can be combined with HT to establish Pseudo Wigner-Hough transform (PWHT), thereby improving the GNSS interference detection performance.Furthermore, the smoothed pseudo WVD (SPWVD) uses a two-dimensional low-pass filter function, which can effectively suppress the cross-interference terms between multiple signal components. Therefore, the present invention combines the SPWVD method with HT to create a GNSS interference detection method based on smoothed pseudo Wigner-Hough transform (SPWHT), which can significantly improve the interference detection performance of GNSS receivers. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a GNSS receiver interference detection method based on smoothed pseudo Wigner-Hough transform (Smoothed Pseudo Wigner-Hough transform, SPWHT).

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is a GNSS receiver interference detection method based on smoothed pseudo Wigner-Hough transform, comprising the following steps:

[0005] Step 1: Receive the GNSS space signal using the GNSS receiver antenna in an electromagnetic interference environment, and down-convert it using the receiver RF front end to obtain the GNSS intermediate frequency signal;

[0006] Step 2: Analyze and represent the GNSS intermediate frequency signal;

[0007] Step 3: Use the smoothed pseudo-Wigner-Ville distribution to perform time-frequency transformation analysis on the GNSS intermediate frequency signal in analytical form;

[0008] Step 4: Combine the smoothed pseudo Wigner-Ville distribution with the Hough transform to obtain the smoothed pseudo Wigner-Hough transform of the GNSS intermediate frequency signal in analytical form;

[0009] Step 5: Search the energy distribution peak of the smoothed pseudo-Wigner-Hough transform in the ρ-θ parameter space to effectively detect GNSS electromagnetic interference;

[0010] Step 6: Perform performance analysis and evaluation on the GNSS electromagnetic interference detection results.

[0011] Preferably, the method for analyzing and representing the GNSS intermediate frequency signal in step 2 is as follows:

[0012]

[0013] Where y(t) represents the intermediate frequency signal obtained by the original GNSS receiver after down-conversion by the RF front end. represents the Hilbert Transform of the GNSS intermediate frequency signal y(t), j is the imaginary root unit, y a (t) represents the analytical form of the GNSS intermediate frequency signal y(t).

[0014] Preferably, in step 3, the time-frequency transform analysis is performed using smoothed pseudo Wigner-Ville distribution (SPWVD) for the GNSS intermediate frequency signal in analytical form, and further includes the following steps:

[0015]

[0016] Where t represents time; ω represents angular frequency; u is the integral variable; τ represents time lag; y a (t) represents the analytical form of the GNSS intermediate frequency signal y(t); h(·) is the time window function, g(·) represents the frequency window function; j is the imaginary root unit.

[0017] Preferably, performing Hough transform on the smoothed pseudo Wigner-Ville distribution of the GNSS signal in step 4 to obtain a smoothed pseudo Wigner-Hough transform result further includes the following steps:

[0018] Step 4.1: For the line l in the time-frequency surface, the standard parameterization form of the Hough transform is as follows:

[0019] ρ=xcosθ+ysinθ (3)

[0020] Where θ is the distance from the origin O of the coordinate system to the line l; θ represents the angle between the x-axis and the normal of the line l passing through the origin O.

[0021] Step 4.2: Combine the smooth pseudo Wigner-Ville distribution with the Hough transform to obtain the smooth pseudo Wigner-Hough transform, which is expressed as follows:

[0022]

[0023] Preferably, in step 5, the peak value of the energy distribution of the smoothed pseudo Wigner-Hough transform is searched in the ρ-θ parameter space, so as to effectively detect the GNSS electromagnetic interference, and the following steps are also included:

[0024] When the smoothed pseudo-Wigner-Hough transform method is used to detect electromagnetic interference in GNSS received signals, a sharp energy peak will inevitably appear in a specific point domain of the ρ-θ parameter space, presenting an energy distribution characteristic that matches the GNSS electromagnetic interference (for example, swept frequency interference, continuous wave interference, etc.); GNSS electromagnetic interference can be effectively detected by locally searching for the peak point of the smoothed pseudo-Wigner-Hough transform in a smaller neighborhood in the ρ-θ parameter space.

[0025] Preferably, the performance analysis and evaluation of the GNSS electromagnetic interference detection results in step 6 also includes the following steps:

[0026] In order to describe the Hough transform energy distribution of GNSS electromagnetic interference signals in the ρ-θ parameter space, the present invention proposes a new evaluation index: zero Hough transform energy percentage; its mathematical expression is as follows:

[0027]

[0028] Where N represents the total number of grid cells in the entire parameter space S, excluding the local neighborhood P where the Hough transform energy peak appears (SP), where P is a small local neighborhood where the Hough transform energy distribution peak appears in the parameter space S; N zero It represents the number of grid cells whose Hough transform energy distribution value is zero in the (SP) region.

[0029] This indicator is used to analyze the performance of Hough transform energy distribution, such as aggregation and cross-term suppression, so as to comprehensively evaluate the GNSS electromagnetic interference detection capability. When SPWHT is used to detect electromagnetic interference in GNSS receiving signals, sharp energy distribution peaks will inevitably appear in specific areas of the ρ-θ parameter space, showing energy distribution characteristics that match the GNSS electromagnetic interference. By locally searching for the peak points of the SPWHT energy distribution in the smaller point areas of the ρ-θ parameter space, GNSS electromagnetic interference can be effectively detected.

[0030] The beneficial effects of the present invention are:

[0031] The present invention analyzes and represents the GNSS intermediate frequency signal that will be affected by electromagnetic interference, then uses the smooth pseudo Wigner-Ville distribution for time-frequency transformation, and further combines it with the Hough transform to obtain the smooth pseudo Wigner-Hough transform (SPWHT). By searching for the SPWHT peak in the parameter space, the GNSS electromagnetic interference signal can be effectively detected. The present invention solves the time-frequency resolution tradeoff problem existing in the existing time-frequency analysis technology, eliminates the influence of cross-term interference, and enhances the aggregation characteristics of energy distribution in GNSS interference detection, thereby ensuring that the electromagnetic interference detection performance of the GNSS receiver in a complex electromagnetic interference environment can be significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0033] Figure 1 This is a smoothed pseudo Wigner-Ville distribution of the GPS L1-C / A signal affected by swept frequency interference according to an embodiment of the present invention.

[0034] Figure 2 Schematic diagram of Hough transform principle according to an embodiment of the present invention.

[0035] Figure 3 This is a smoothed pseudo Wigner-Hough transform of the GPS L1-C / A signal affected by swept frequency interference according to an embodiment of the present invention.

[0036] Figure 4 It is a schematic diagram of evaluating and solving the zero Hough transform energy percentage in the Hough transform-based GNSS interference detection according to an embodiment of the present invention.

[0037] Figure 5 The figure is a comparative analysis result of the zero Hough transform energy percentage in GNSS interference detection between the embodiment of the present invention and the prior art. DETAILED DESCRIPTION

[0038] The GNSS receiver interference detection method based on smoothed pseudo Wigner-Hough transform includes the following steps:

[0039] Step 1: Receive the GNSS space signal using the GNSS receiver antenna in an electromagnetic interference environment, and down-convert it using the receiver RF front end to obtain the GNSS intermediate frequency signal;

[0040] Step 2: The method for analyzing and representing the GNSS intermediate frequency signal is as follows:

[0041]

[0042] Where y(t) represents the intermediate frequency signal obtained by the original GNSS receiver after down-conversion by the RF front end. represents the Hilbert Transform of the GNSS intermediate frequency signal y(t), j is the imaginary root unit, y a (t) represents the analytical form of the GNSS intermediate frequency signal y(t).

[0043] Step 3: Use smoothed pseudo-Wigner-Ville distribution (SPWVD) to perform time-frequency transformation analysis on the GNSS intermediate frequency signal affected by electromagnetic interference in analytical form, as shown below:

[0044]

[0045] Where t represents time; ω represents angular frequency; u is the integral variable; τ represents time lag; y a (t) represents the analytical form of the GNSS intermediate frequency signal y(t); h(·) is the time window function, g(·) represents the frequency window function; j is the imaginary root unit.

[0046] Figure 1 is the smoothed pseudo Wigner-Ville distribution (SPWVD) of the GPSL1-C / A signal under the conditions of carrier-to-noise ratio C / N0 of 46dB-Hz and interference-to-noise ratio JNR of -6dB and subjected to swept frequency interference. Figure 1 It can be seen from a that the peak of the energy distribution is clearly shown in a linear region in the time-frequency surface, and the corresponding SPWVD contour lines are as follows: Figure 1 As shown in Figure b, it represents the energy distribution contribution of the swept frequency interference in the GPS L1-C / A received signal; the straight line determined by the SPWVD energy peak of the GPS L1-C / A signal affected by the swept frequency interference can reveal the frequency modulation law of the swept frequency interference signal. Figure 1 It can be seen that the SPWVD method can better suppress the cross terms on the time-frequency plane by using a two-dimensional smoothing function, but this is at the expense of the time-frequency aggregation of the analyzed signal; the time-frequency resolution that the SPWVD method can provide when used in GNSS electromagnetic interference detection is not ideal, which brings great difficulties to accurately grasp the time-varying characteristics of GNSS electromagnetic interference.

[0047] Step 4: In order to solve the problem of poor time-frequency resolution of the smoothed pseudo Wigner-Ville distribution in step 3, it is further combined with the Hough transform to obtain the smoothed pseudo Wigner-Hough transform (SPWHT) of the GNSS intermediate frequency signal containing electromagnetic interference, thereby improving its energy concentration in GNSS interference detection, as shown below:

[0048] Step 4.1: Figure 2 As shown, for the straight line l in the time-frequency surface, the standard parameterized form of the Hough transform is expressed as follows:

[0049] ρ=xcosθ+ysinθ (3)

[0050] Where ρ is the distance from the origin O of the coordinate system to the line l; θ represents the angle between the x-axis and the normal of the line l passing through the origin O.

[0051] Step 4.2: Combine the smoothed pseudo Wigner-Ville distribution with the Hough transform to obtain the smoothed pseudo Wigner-Hough transform (SPWHT), which is expressed as follows:

[0052]

[0053]

[0054] Step 5: Search for the energy distribution peak in the ρ-θ parameter space for the smoothed pseudo-Wigner-Hough transform result in step 4. When the smoothed pseudo-Wigner-Hough transform method is used to detect electromagnetic interference in the GNSS receiving signal, a sharp energy peak will appear in a specific point domain of the ρ-θ parameter space, showing an energy distribution characteristic that matches the GNSS electromagnetic interference (for example, swept frequency interference, continuous wave interference, etc.); by locally searching for the energy distribution peak point of the smoothed pseudo-Wigner-Hough transform in a smaller neighborhood of the ρ-θ parameter space, the GNSS electromagnetic interference can be effectively detected.

[0055] Figure 3 It is the SPWHT used for GNSS interference detection. Figure 3 It can be seen from the ρ-θ parameter space that the energy peak of the swept frequency interference in the GPS L1-C / A receiving signal can be accumulated and enhanced at the intersection of different sinusoidal curves that appear simultaneously and become clearly visible. These sinusoidal curves correspond to different collinear points on the frequency modulation law straight line of the GNSS swept frequency interference obtained by the SPWVD method on the time-frequency plane. Figure 3As shown in FIG. 1 , the SPWHT energy distribution of the swept frequency interference is effectively enhanced at the intersection of different sinusoidal curves in the parameter space, forming a sharp energy peak; however, in other areas outside the intersection of the ρ-θ parameter space, the energy distribution from the Gaussian white noise or the useful GPS L1-C / A signal is significantly weakened to a lower level and is not effectively accumulated. Therefore, the SPWHT method proposed in this embodiment can clearly distinguish the swept frequency interference from the GPS L1-C / A signal, and it is easy to extract the characteristics of the swept frequency interference. Compared with the use of the SPWVD technology to globally detect collinear points on a straight line in the time-frequency space, the SPWHT method proposed in this embodiment is very suitable for efficiently processing the GNSS electromagnetic interference detection problem, because this method only needs to observe the local energy peak at a specific intersection in the ρ-θ parameter space; when the intersection position of different sinusoidal curves is determined in the ρ-θ parameter space, the characteristic parameters related to the frequency modulation law of the GNSS swept frequency interference in the time-frequency plane can be estimated.

[0056] Step 6: The mechanism for performing performance evaluation on the interference detection results of the GNSS signal SPWHT obtained in step 5 is described as follows:

[0057] In order to understand the Hough transform energy distribution of GNSS interference signals in the ρ-θ parameter space, this embodiment proposes an evaluation criterion of zero Hough transform energy percentage to evaluate the aggregation and cross-term suppression of Hough transform energy distribution in GNSS interference detection.

[0058] The evaluation mechanism of zero Hough transform energy percentage is as follows: Figure 4 As shown. The parameter space S is a grid for evaluating the Hough transform energy distribution, forming a two-dimensional space of ρ and θ; where Δρ is the grid cell size along the ρ direction, and Δθ is the grid cell size along the θ direction. If the peak of the Hough transform energy distribution is located at a specific grid cell C in the parameter space S, the rectangular local neighborhood P can be constructed using cell C as the geometric center, where the main part of the Hough transform energy distribution is concentrated in the neighborhood P. δ ρ represents the size of the rectangular local neighborhood P along the ρ direction, δ θ represents the size of the rectangular local neighborhood P along the θ direction. Therefore, the entire parameter space S is divided into two parts: the local neighborhood P where the Hough transform energy is mainly concentrated and distributed, and the other area (SP) where the remaining Hough transform energy is distributed.

[0059] The calculation formula of zero Hough transform energy percentage is as follows:

[0060]

[0061] Where N represents the total number of grid cells in the entire parameter space S, excluding the local neighborhood P where the Hough transform energy peak appears (SP), where P is a small local neighborhood where the Hough transform energy distribution peak appears in the parameter space S; N zero It represents the number of grid cells whose Hough transform energy distribution value is zero in the (SP) region.

[0062] Figure 5 The zero Hough transform energy percentage analysis results of different GNSS interference detection techniques such as WHT, PWHT and SPWHT for GPS L1-C / A signals affected by swept frequency interference are provided, where the carrier-to-noise ratio C / N0 of GPS L1-C / A signals is 46dB-Hz. Figure 5 It can be seen that as the interference-to-noise ratio (JNR) of the swept frequency interference increases, the zero Hough transform energy percentage index of different Hough transform-based GNSS interference detection technologies will increase monotonically, which indicates that when using different types of Hough transforms for GNSS interference detection, as the interference intensity increases, most of the Hough transform energy is more concentrated in the specific intersection area where the energy peak appears in the parameter space, while the residual Hough transform energy distribution in other areas is almost suppressed to zero. Figure 5 It can be seen that the zero Hough transform energy percentage result obtained by using the SPWHT method proposed in this embodiment for GNSS interference detection is much better than other technologies such as WHT and PWHT; in particular, the zero Hough transform energy percentage result of the SPWHT method is at least twice that of the existing WHT technology. The SPWHT method proposed in this embodiment shows excellent energy aggregation characteristics and cross-term suppression capabilities in GNSS sweep frequency interference detection. Compared with other technologies such as WHT and PWHT, the SPWHT method has significantly improved GNSS interference detection performance, thereby proving its rationality and effectiveness in GNSS receiver electromagnetic interference detection.

[0063] This embodiment provides a GNSS receiver interference detection method based on smoothed pseudo Wigner-Hough transform (SPWHT), which uses Hilbert transform to analytically represent the GNSS intermediate frequency signal affected by electromagnetic interference, and then uses smoothed pseudo Wigner-Ville distribution to perform time-frequency transform, and then combines it with Hough transform to obtain the SPWHT of the satellite navigation signal affected by electromagnetic interference. By searching for the peak of the SPWHT energy distribution in the ρ-θ parameter space, GNSS electromagnetic interference can be effectively detected. This embodiment solves the time-frequency resolution trade-off problem existing in the existing GNSS interference detection based on time-frequency analysis, and can also effectively eliminate cross-term interference, enhance the energy distribution aggregation characteristics of the electromagnetic interference component in the GNSS received signal, and ensure that the GNSS interference detection performance is significantly improved.

[0064] In summary, the GNSS receiver interference detection method based on smoothed pseudo-Wigner-Hough transform proposed in this embodiment can provide superior GNSS interference detection performance, has good development prospects in the anti-interference design of GNSS receivers, and lays a solid foundation for ensuring the realization of the anti-interference function of GNSS receivers in complex electromagnetic interference environments.

[0065] The above-described embodiments of the present invention do not constitute a limitation on the protection scope of the present invention. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A GNSS receiver interference detection method based on smoothed pseudo-Wigner-Hough transform, comprising the following steps: Step 1: Receive the GNSS space signal using the GNSS receiver antenna in an electromagnetic interference environment, and down-convert it using the receiver RF front end to obtain the GNSS intermediate frequency signal; Step 2: Analyze and represent the GNSS intermediate frequency signal; Step 3: Use the smoothed pseudo-Wigner-Ville distribution to perform time-frequency transformation analysis on the GNSS intermediate frequency signal in analytical form; Step 4: Combine the smoothed pseudo Wigner-Ville distribution with the Hough transform to obtain the smoothed pseudo Wigner-Hough transform of the GNSS intermediate frequency signal in analytical form; Step 5: Search the energy distribution peak of the smoothed pseudo-Wigner-Hough transform in the ρ-θ parameter space to effectively detect GNSS electromagnetic interference; Step 6: Perform performance analysis and evaluation on the GNSS electromagnetic interference detection results.

2. The GNSS receiver interference detection method based on smoothed pseudo-Wigner-Hough transform according to claim 1, characterized in that: The method for analyzing and representing the GNSS intermediate frequency signal in step 2 is as follows: Where y(t) represents the intermediate frequency signal obtained by the original GNSS receiver after down-conversion by the RF front end. represents the Hilbert transform of the GNSS intermediate frequency signal y(t), j is the imaginary root unit, y a (t) represents the analytical form of the GNSS intermediate frequency signal y(t).

3. The GNSS receiver interference detection method based on smoothed pseudo Wi gner-Hough transform according to claim 2, characterized in that: In step 3, the time-frequency transform analysis is performed on the GNSS intermediate frequency signal in analytical form by using the smoothed pseudo Wigner-Ville distribution, and the following steps are also included: Where t is time; ω is angular frequency; u is the integral variable; τ is time lag (1ag); y a (t) represents the analytical form of the GNSS intermediate frequency signal y(t); h(·) is the time window function, g(·) represents the frequency window function; j is the imaginary root unit.

4. The GNSS receiver interference detection method based on smoothed pseudo Wi gner-Hough transform according to claim 3, characterized in that: Performing Hough transform on the smoothed pseudo Wigner-Ville distribution of the GNSS signal in step 4 to obtain a smoothed pseudo Wigner-Hough transform result also includes the following steps: Step 4.1: For the line l in the time-frequency surface, the standard parameterization form of the Hough transform is as follows: p=x cosθ+y sinθ (3) Where ρ is the distance from the origin 0 of the coordinate system to the line l; θ represents the angle between the x-axis and the normal of the line l passing through the origin 0; Step 4.2: Combine the smoothed pseudo Wigner-Ville distribution with the Hough transform to obtain the smoothed pseudo Wigner-Hough transform, and the method is expressed as follows.

5. The GNSS receiver interference detection method based on smoothed pseudo Wigner-Hough transform (SPWHT) according to claim 4, characterized in that: In step 5, the peak value of the energy distribution of the smoothed pseudo Wigner-Hough transform is searched in the ρ-θ parameter space, so as to effectively detect the GNSS electromagnetic interference, and the following steps are also included: When the smoothed pseudo-Wigner-Hough transform method is used to detect electromagnetic interference in GNSS received signals, a sharp energy peak will inevitably appear in the specific point domain of the ρ-θ parameter space, presenting an energy distribution characteristic that matches the GNSS electromagnetic interference; by searching for the local peak point of the smoothed pseudo-Wigner-Hough transform in the ρ-θ parameter space, the GNSS electromagnetic interference can be effectively detected.

6. The GNSS receiver interference detection method based on smoothed pseudo Wigner-Hough transform (SPWHT) according to claim 5, characterized in that: The performance analysis and evaluation of the GNSS electromagnetic interference detection results in step 6 also includes the following steps: In order to describe the Hough transform energy distribution of GNSS electromagnetic interference signals in the ρ-θ parameter space, a new evaluation index "zero Hough transform energy percentage" is introduced here; its mathematical expression is as follows: Where N represents the total number of grid cells in the entire parameter space S, excluding the local neighborhood P where the Hough transform energy peak appears (SP), where P is a small local neighborhood where the Hough transform energy distribution peak appears in the parameter space S; N zero It represents the number of grid cells whose Hough transform energy distribution value is zero in the (SP) region; This indicator is used to analyze the performance of Hough transform energy distribution, such as aggregation and cross-term suppression, so as to comprehensively evaluate the GNSS electromagnetic interference detection capability. When SPWHT is used to detect electromagnetic interference in GNSS receiving signals, sharp energy distribution peaks will inevitably appear in specific areas of the ρ-θ parameter space, showing energy distribution characteristics that match the GNSS electromagnetic interference. By locally searching for the peak points of the SPWHT energy distribution in the smaller point areas of the ρ-θ parameter space, GNSS electromagnetic interference can be effectively detected.

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