Multipath signal detection method based on correlator signal output and RGB color space combination

Abstract

The invention discloses a multipath signal detection method based on correlator signal output and RGB color space combination, and the method comprises the steps: obtaining a plurality of normalized correlation value distribution characteristics and corresponding correlation value vectors according to a normalized correlation value function of a single branch through constructing a correlator output model containing a plurality of correlators and a plurality of signals; and constructing a three-dimensional Gaussian distribution model to generate Gaussian distribution function characteristics of RGB colors, comparing signal correlation values with the Gaussian distribution function characteristics of corresponding channels, and judging whether correlation value distribution is abnormal or not based on a detection threshold value so as to realize detection of multipath signals. According to the invention, through combination of feature distribution of output signals of different types of signal correlators and RGB color space, visual and rapid detection and discrimination of multipath signals are realized on a correlation domain level.

Description

Technical Field

[0001] The present invention relates to a technology in the field of GNSS navigation, specifically a multipath signal detection method based on the combination of correlator signal output and RGB color space. Background Art

[0002] The multipath effect is the most extensive and important factor affecting the accuracy of GNSS navigation and positioning currently, which will cause a sharp decline in positioning accuracy. Most current multipath detection algorithms are based on signal backend parameters and are extremely vulnerable to the complexity of the signal-to-positioning processing process, resulting in a long detection time and a decrease in accuracy. Therefore, it is of great significance to realize accurate and fast multipath detection based on signal correlation domain parameters. Summary of the Invention

[0003] Aiming at the problem that it is difficult to extract the characteristics of existing navigation signals in the correlation domain and it is impossible to directly detect multipath signals in the front-end correlation domain, the present invention proposes a multipath signal detection method based on the combination of correlator signal output and RGB color space, and realizes the intuitive and fast detection and discrimination of multipath signals at the correlation domain level through the combination of the characteristic distribution of the correlator output signals of different types of signals and the RGB color space.

[0004] The present invention is realized through the following technical solutions:

[0005] The present invention relates to a multipath signal detection method based on the combination of correlator signal output and RGB color space. By constructing a correlator output model containing multiple correlators and various signals, multiple normalized correlation value distribution characteristics and their corresponding correlation value vectors are obtained according to the normalized correlation value function of a single branch, and then a three-dimensional Gaussian distribution model is constructed to generate the Gaussian distribution function characteristics of RGB colors. By comparing the signal correlation value with the Gaussian distribution function characteristics of the corresponding channel and judging whether there is an abnormality in the correlation value distribution based on the detection threshold, the detection of multipath signals is realized. Technical Effects

[0006] The present invention processes signal parameters in the front-end correlation domain, can retain the original information of the signal, judge whether the multipath effect occurs during the signal propagation process from the root, and avoid the influence of backend data distortion on the accuracy of multipath detection; starting from the joint distribution characteristics of the correlation values of different correlators, the normalized correlation value distribution is deduced, and the distribution characteristics are mapped to the RGB color space, so as to realize the extraction of correlation value characteristics and detect multipath signals through the correlation values; taking the signal correlation value situation on the IQ branch as the entry point and applying the correlation value parameters, it has a solid theoretical support and wide applicability, and can well meet the needs of users.

[0007] Compared with the prior art, the present invention can deduce the correlation of different correlator noise distributions among different branches, and perform Taylor expansion linearization on the noise distribution on this basis to obtain a three-dimensional Gaussian distribution of correlation values that is more in line with theoretical reality. Then, the correlation value distribution is mapped to the RGB color space to intuitively display the correlation value distribution characteristics between different signals, realizing the detection of multipath signals in the GNSS correlation domain at a single frequency band, and achieving more accurate and rapid detection. It does not require a large amount of data analysis, the results are more real-time, the efficiency is higher, and the requirements for equipment are lower. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 Schematic diagram of the system of the present invention;

[0009] Figure 2 Flow chart of the present invention;

[0010] Figure 3 Distribution diagram of the theoretical direct signal / simulated direct signal of the embodiment;

[0011] Figure 4 Distribution diagram of the theoretical direct signal / simulated multipath signal of the embodiment;

[0012] Figure 5 Performance comparison chart of the detection success rate of the simulated multipath signal under different multipath parameters of the embodiment and the traditional method. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] As Figure 1As shown in the figure, this embodiment relates to a multipath signal detection and recognition system based on the RGB color space, including: a correlator noise model, a correlator output model, a normalization unit, a three-dimensional Gaussian distribution unit, and a color space mapping unit, where: while the correlator noise model obtains the simplified noise variance based on the noise variance under the ideal front-end filtering assumption and the carrier-to-noise ratio information of the navigation signal, by performing Cholesky decomposition on the noise distributions of multiple correlators and the relative delays between the correlators, the correlation of the noises of multiple correlators and the correlation coefficient matrix between each noise term are obtained; the correlator output model processes the mathematical models of LOS signals and NLOS signals according to the code delay, energy attenuation, phase rotation parameters, and the ideal mathematical models of the early, prompt, and late correlators, in combination with the correlator noise model, to obtain the corresponding correlation values of the LOS signals and NLOS signals on the I branch and Q branch of the early (E), prompt (P), and late (L) correlators; the normalization unit performs normalization processing on the correlation values of different branches of the early, prompt, and late correlators with the modulus value of the correlation value output by the prompt branch correlator as the normalization basis to obtain the normalized correlator output; the three-dimensional Gaussian distribution unit performs a first-order Taylor expansion on the noise distributions of different branches of different correlators according to the principal component terms of the normalized correlation value function noise to obtain a linearized correlation value output model approximately conforming to the Gaussian distribution, and then derives the three-dimensional Gaussian joint distribution of the correlation values on the Q branch of the early, prompt, and late correlators through three-dimensional Gaussian joint distribution; the color space mapping unit performs mapping of the amplification coefficients of different distribution values of different channels and processes the quantiles of the set three-dimensional Gaussian joint distribution of correlation values according to the R, G, and B color channels in the RGB color space to detect whether there are multipath signals.

[0014] As Figure 2 shown in the figure, this embodiment is based on the above system and realizes an intuitive multipath signal detection method with a certain error through the combined use of eigenvalue comparison of the correlator output signals under different signals and the RGB color space, including:

[0015] Step 1: Construct a correlator output model including multiple correlators and multiple signals, specifically including:

[0016] Step 1.1, model the correlator output through an analytical model, specifically: Where: C is the signal strength, π is the pi, Δf d is the residual Doppler frequency, T c is the coherent integration time, R(·) is the autocorrelation function, Δτ d is the code delay, e refers to the natural constant, j refers to the imaginary part, is the phase rotation, η cis noise, with independent real and imaginary parts;

[0017] Step 1.2, in the case of an ideal filter, the noise η c has a mean of 0, so the noise variance based on the ideal front-end filtering assumption is: where: N0 is the noise power spectral density, B IF is the receiver front-end bandwidth, N is the number of samples, f s is the sampling frequency; by normalizing the correlator amplitude, the simplified noise distribution expression is obtained, that is, the magnitude of the correlation value is which can be normalized to 1, and the simplified noise variance at this time is: where: C / N0 is the carrier-to-noise ratio; introducing multiple correlators, the correlation of the noises of multiple correlators is obtained through the noise distribution of a single correlator and the relative delay between each correlator, and the correlation coefficient matrix of each noise term where E n , P n , L n are the noise terms of the E, P, and L correlators respectively, E is the identity matrix, is the variance of the noise term distribution, d s is the interval between the E and L correlators. This correlation coefficient matrix C η is obtained through Cholesky decomposition: The noise on the correlator output is finally where: E i , P i and L i are three complex independent Gaussian random variables with independent real and imaginary parts;

[0018] Step 1.3, by constructing and superimposing the LOS signal modeling result and the NLOS signal modeling, the correlator output model of the multipath signal is obtained, specifically including:

[0019] i) Under the LOS signal, model the correlation values of the E, P, and L correlators on the I and Q branches respectively, specifically including: where: A is the normalization coefficient, E I , E Q are the output signals of the leading correlator on the real and imaginary parts respectively, represents leading by 0.5 chip, E Qi , E Ii are the noises on the Q and I branches respectively, and the outputs of the P and L correlators are the same, and the corresponding code delays are Δτ d ,

[0020] ii) Under NLOS signals, introduce code delay, energy attenuation, and phase rotation parameters, and model the correlation values of the three correlators E, P, and L on the I and Q branches respectively, specifically including: where: α is the energy attenuation coefficient, Δf = Δf d + Δf N , Δτ = Δτ d + Δτ N and are the total code delay, total Doppler frequency residual, and total carrier phase error respectively, which are composed of measurement errors and additional code delay, Doppler frequency residual, and carrier phase rotation caused by non-line-of-sight signals.

[0021] iii) Superimpose the LOS signal modeling results and the NLOS signal modeling to obtain the correlator output model of the multipath signal, specifically including: where: where E IM , E QM are the output signals of the E correlator on the I and Q branches in the multipath case. The P and L outputs are the same.

[0022] Step 2: Calculate multiple normalized correlation value distribution characteristics through the normalized correlation value function of a single branch, specifically including:

[0023] Step 2.1: Normalize the correlation values of the E Q , P Q , L Q branches obtained in Step 1 by the amplitude of the P-branch correlation value, and obtain its relationship with multiple noise terms, specifically including:

[0024]

[0025]

[0026] where: a = sinc(πΔf d T c ), b2 = R(Δτ d ),

[0027] Step 2.2, simplify the original noise terms through the correlation of multiple noise terms in Step 1.2, and obtain the relationship between the correlation values of each branch after normalization and each independent noise term, that is, the independent noise vector. By performing a first-order Taylor expansion on the independent noise vector, obtain the linear relationship between the correlation values of each branch after normalization and the independent noise vector, specifically including:

[0028] a) E Q Branch: E Qi = N Q1 , P Qi = 0.5N Q1 + 0.866N Q2 , N Q1 and N Q2 are independent of each other, that is, obtain the independent noise vector N = [N Q1 , P Ii , N Q2 . For , perform a first-order Taylor expansion:

[0029] b) P Q Branch: P Qi and P Ii are independent of each other and P Ii = 0.5N I1 + 0.866N I2 , P Qi = 0.5N Q1 + 0.866N Q2 , N I1 and N I2 between, N Q1 and N Q2 are independent of each other. N = [P Ii , P Qi For , perform a first-order Taylor expansion:

[0030] c) L Q Branch: L Qi = 0.5774N Q2 + 0.8165N Q3 , P Qi = 0.5N Q1 + 0.866N Q2 and N Q1 , N Q2 , N Q3 are independent of each other, that is, N = [N Q1 , P Ii , N Q2 , N Q3 . For Perform the first-order Taylor expansion:

[0031] where: N is the linearly independent noise vector of different correlators on the Q branch, N Q1 , N Q2 , N Q3 is C η obtained by Cholesky decomposition to get the basis vectors corresponding to the A c space.

[0032] Step 2.3, through the linear calculation expressed by the noise distribution in Step 1.2, obtain the distribution characteristics of the correlation values of each branch after normalization, specifically including:

[0033]

[0034]

[0035]

[0036] Step 3, according to the correlation value vector, construct a three-dimensional Gaussian distribution model about , specifically: the probability density where: |Σ| is the determinant of Σ, and are the normalized correlation values of the P Q and L Q branches respectively, μ1, μ2, μ3 are the means of the correlation value distributions of the E Q , P Q , L Q branches respectively, are the variances of the correlation value distributions of the E Q , P Q , L Q branches respectively, are the covariances of the noise of the E Q and P Q , E Q and L Q , L Q and P Q branches respectively.

[0037] The covariance mentioned above is preferably obtained by numerical simulation of actual signal data or simulation signal data.

[0038] For the probability density mentioned above, in the LOS signal scenario, the specific values of each parameter are:

[0039] Step 4: Compare using the characteristics of the Gaussian distribution function of the RGB color space and related values. Determine the color threshold by setting different quantiles for the three-dimensional Gaussian distribution function, and detect whether there is a multipath signal. Specifically: B c = 0, where: R c , G c , B c are the RGB three-color channels corresponding to the RGB color space respectively, ε is the amplification coefficient corresponding to different quantiles, t thr is the distribution function value corresponding to different quantiles.

[0040] The detection mentioned above is specifically: when the distribution function value corresponding to a set of related values is less than or equal to the distribution function value corresponding to the set quantile, that is, C(x) = 0, R c , G c , B c = (0, 255, 0). At this time, this set of related values appears green in the RGB color space, and it is judged that there is no multipath signal; when the distribution function value corresponding to a set of related values is greater than the distribution function value corresponding to the set quantile, that is, C(x) > 0, R c , G c , B c = (εC(x), 255 - εC(x), 0). At this time, this set of related values appears non-green in the RGB color space, and it is judged that there is a multipath signal.

[0041] After specific actual experiments, the satellite frequency band is selected as the GPS satellite L1 frequency band, the signal carrier-to-noise ratio is set to 40 dB-Hz respectively, the correlator interval is 1 chip, the initial code delay, frequency and phase error of the simulation data are all set to 0, the sampling frequency is set to 2*10 6 Hz, the correlation integration time is 0.01 s, the atomic clock reference frequency is 10.23*10 6 Hz, the loop orders of the delay locked loop (DLL) and the phase locked loop (PLL) are 2 and 3 respectively, the bandwidths are 0.5 and 5 H respectively, and the crystal oscillator parameters of the temperature compensated crystal oscillator and the oven controlled crystal oscillator are 3.565*10 -26 and 5.261*10 -26 respectively, and the total simulation duration is 500 s; the multipath attenuation coefficients are set to 0.708 / 0.5 / 0.355 respectively, the multipath code delay takes 0 - 1.5 chips, the interval is 0.01 chip, and the multipath phase rotation takes 0 - pi, the interval is 0.01pi;

[0042] According to this method, the E in the theoretical distribution is calculatedQ , L Q has a covariance of -1 * 10 -5 , E Q , P Q and L Q , P Q has a covariance of 0.0025; Based on the above-mentioned angle-only relative orbit determination method and the calculated conditions and technical parameters set above, Matlab software is used for simulation verification, and the simulation time is 500 s.

[0043] As Figure 3 shown in Figure 4 , they are respectively the distribution of the theoretical direct signal / simulated direct signal and the theoretical direct signal / simulated multipath signal mapped to the color space deduced by the present invention on the Q branch; As Figure 5 shown in, the left figure is the method of the present invention, and the right figure is the traditional method using pseudorange residual parameters. It is a comprehensive evaluation of the multipath signal detection rate under different multipath parameters. It can be seen from the color distribution in the figure that the detection success rate of the multipath signal by this method is higher and the detection range is wider than that of the traditional method. When the code delay in the multipath parameters is 0.1 - 1.4 chips and the phase rotation is 20° - 170°, the detection success rate is relatively high, and there are certain detection blind spots in other cases.

[0044] In summary, this method starts from the joint distribution characteristics of the correlation values of different correlators, deduces the distribution of the normalized correlation values, and maps the distribution characteristics to the RGB color space, thus solving the problem that it is difficult to extract the characteristics of the navigation signal in the correlation domain and it is impossible to directly detect the multipath signal in the front-end correlation domain. Only relying on the correlation value outputs E Q , P Q , L Q of the three groups of correlators on the Q branch can achieve real-time, fast, and accurate detection of the multipath signal in the correlation domain. Since this method processes the signal parameters in the front-end correlation domain, it can retain the original information of the signal and judge whether multipath effects occur during the signal propagation process from the root, avoiding the influence of backend data distortion on the accuracy of multipath detection. Compared with the prior art that detects multipath signals based on parameters such as pseudorange residuals and carrier-to-noise ratios in the measurement domain, it is faster, more accurate, and the detection characteristics are more obvious.

[0045] The above specific implementation can be locally adjusted by those skilled in the art in different ways without departing from the principles and purposes of the present invention. The protection scope of the present invention is subject to the claims and is not limited by the above specific implementation. All implementation solutions within its scope are subject to the constraints of the present invention.

Claims

1. A multipath signal detection method based on the combination of correlator signal output and RGB color space, characterized in that, By constructing a correlator output model that includes multiple correlators and various signals, multiple normalized correlation value distribution features and their corresponding correlation value vectors are obtained according to the normalized correlation value function of a single branch. Furthermore, a three-dimensional Gaussian distribution model is constructed to generate the Gaussian distribution function features of RGB colors. By comparing the signal correlation value with the Gaussian distribution function features of the corresponding channel and judging whether there is an abnormality in the correlation value distribution based on the detection threshold, the detection of multipath signals is realized.

2. The multipath signal detection method based on the combination of correlator signal output and RGB color space according to claim 1, characterized in that, The described correlator output model is constructed in the following way: Step 1.1, model the correlator output through an analysis model, specifically: Where: C is the signal strength, π is the pi, Δf d is the residual Doppler frequency, T c is the coherent integration time, R(·) is the autocorrelation function, Δτ d is the code delay, e refers to the natural constant, j refers to the imaginary part, is the phase rotation, η c is the noise, having independent real and imaginary parts; Step 1.2, in the case of an ideal filter, the noise η c has a mean of 0, so the noise variance based on the ideal front-end filtering assumption is: where: N0 is the noise power spectral density, B IF is the receiver front-end bandwidth, N is the number of samples, f s is the sampling frequency; by normalizing the amplitude of the correlator, a simplified expression for the noise distribution is obtained, that is, the amplitude of the correlation value is which can be normalized to 1, and the simplified noise variance at this time is: Where: C / N0 is the carrier-to-noise ratio; multiple correlators are introduced, and the correlation of the noises of multiple correlators is obtained from the noise distribution of a single correlator and the relative delay between correlators, and the correlation coefficient matrix between each noise term Where E n , P n , L n are the noise terms of the E, P, and L correlators respectively, E is the identity matrix, is the variance of the noise term distribution, d s is the interval between the E and L correlators. This correlation coefficient matrix C η is obtained through Cholesky decomposition processing: The noise on the correlator output is finally Where: E i , P i and L i are three complex independent Gaussian random variables with independent real and imaginary parts; Step 1.3, obtain the correlator output model of multipath signals by constructing and superimposing the modeling results of LOS signals and NLOS signals, specifically including: i) Under LOS signals, model the correlation values of the E, P, and L correlators on the I and Q branches respectively, specifically including: Where: A is a normalization coefficient, E I , E Q are the output signals of the early correlator on the real and imaginary parts respectively, represents an early 0.5 chip, E Qi , E Ii are the noises on the Q and I branches respectively. Similarly for the outputs of the P and L correlators, and the corresponding code delays are Δτ d , ii) Under NLOS signals, introduce code delay, energy attenuation, and phase rotation parameters, and model the correlation values of the E, P, and L correlators on the I and Q branches respectively, specifically including: where: α is the energy attenuation coefficient, Δf = Δf d + Δf N , Δτ = Δτ d + Δτ N and are the total code delay, the total Doppler frequency residual, and the total carrier phase error, respectively, which are composed of measurement errors and additional code delays, Doppler frequency residuals, and carrier phase rotations caused by non-line-of-sight signals; iii) Superimpose the LOS signal modeling results and the NLOS signal modeling to obtain the correlator output model of the multipath signal, specifically including: Wherein: where E IM , E QM are the output signals on the I and Q branches of the E correlator in the multipath case respectively; the same applies to the P and L outputs.

3. The multipath signal detection method based on the combination of correlator signal output and RGB color space according to claim 1, characterized in that, The described normalized correlation value distribution features are obtained in the following way: Step 2.1: Normalize the relevant values of the P branch for E obtained in Step 1 Q , P Q , L Q branch-related values, and obtain their relationships with multiple noise terms, specifically including: Wherein: Step 2.2, simplify the original noise terms through the correlation of multiple noise terms in Step 1.2, and obtain the relationship between the correlation values of each branch after normalization and each independent noise term, that is, the independent noise vector. By performing a first-order Taylor expansion on the independent noise vector, obtain the linear relationship between the correlation values of each branch after normalization and the independent noise vector, specifically including: a) E Q Branch: E Qi = N Q1 , P Qi = 0.5N Q1 + 0.866N Q2 , N Q1 is independent of N Q2 , that is, the independent noise vector N = [N Q1 , P Ii , N Q2 is obtained. Perform a first-order Taylor expansion on : b) P Q Branch: P Qi With P Ii Independent of each other and P Ii = 0.5N I1 + 0.866N I2 P Qi = 0.5N Q1 + 0.866N Q2 N I1 Between N and N I2 Independent of each other, N = [P Q1 Between N and N Q2 Independent of each other, N = [P Ii , P Qi pair Perform the first-order Taylor expansion on c) L Q Branch: L Qi = 0.5774N Q2 + 0.8165N Q3 , P Qi = 0.5N Q1 + 0.866N Q2 and N Q1 , N Q2 , N Q3 are independent of each other, i.e., N = [N Q1 , P Ii , N Q2 , N Q3 , perform the first-order Taylor expansion on : where: N is the linearly independent noise vector of different correlators on the Q branch, N Q1 , N Q2 , N Q3 is C η obtained by Cholesky decomposition to get A c the basis vectors corresponding to the space; Step 2.3, obtain the distribution features of the correlation values of each branch after normalization through linear calculations expressed by noise distribution, specifically including:

4. The multipath signal detection method based on the combination of correlator signal output and RGB color space according to claim 1, characterized in that, The specific three-dimensional Gaussian distribution model is as follows: the probability density where: |Σ| is the determinant of Σ, and are the correlation values after normalization of the P Q and L Q branches respectively. μ1, μ2, μ3 are the means of the correlation value distributions of the E Q , P Q , L Q branches respectively, are the variances of the correlation value distributions of the E Q , P Q , L Q branches respectively, are the covariances of the noise of the E Q and P Q , the E Q and L Q , the L Q and P Q branches respectively.

5. The multipath signal detection method based on the combination of correlator signal output and RGB color space according to claim 4, characterized in that, The described covariance is obtained through numerical simulation of actual signal data or simulation signal data.

6. The multipath signal detection method based on the combination of correlator signal output and RGB color space according to claim 4, characterized in that, In the LOS signal scenario, the specific values of each parameter of the described probability density are:

7. The multipath signal detection method based on the combination of correlator signal output and RGB color space according to claim 4, characterized in that, The threshold is obtained by comparing the Gaussian distribution function characteristics of the RGB color space with relevant values. The color threshold is determined by setting different quantiles for the three-dimensional Gaussian distribution function to detect whether there is a multipath signal. Specifically: B c = 0, where: R c , G c , B c are the RGB color channels corresponding to the RGB color space respectively, ε is the amplification coefficient corresponding to different quantiles, t thr is the distribution function value corresponding to different quantiles.

8. The multipath signal detection method based on the combination of correlator signal output and RGB color space according to claim 4 or 7, characterized in that, The detection mentioned above is specifically as follows: when the distribution function value corresponding to a set of correlation values is less than or equal to the distribution function value corresponding to the set quantile, that is, when C(x) = 0, R c , G c , B c = (0, 255, 0). At this time, this set of correlation values appears as green in the RGB color space, and it is determined that there is no multipath signal; when the distribution function value corresponding to a set of correlation values is greater than the distribution function value corresponding to the set quantile, that is, when C(x) > 0, R c , G c , B c = (εC(x), 255 - εC(x), 0). At this time, this set of correlation values appears as non - green in the RGB color space, and it is determined that there is a multipath signal.

9. A multipath signal detection and identification system based on RGB color space for implementing any one of the methods described in claims 1-8, characterized in that, Including: Correlator noise model, correlator output model, normalization unit, three-dimensional Gaussian distribution unit, and color space mapping unit, where: while the correlator noise model obtains the simplified noise variance based on the noise variance under the ideal front-end filtering assumption and the carrier-to-noise ratio information of the navigation signal, by performing Cholesky decomposition on the noise distributions of multiple correlators and the relative delays between the correlators, the correlation of the noises of multiple correlators and the correlation coefficient matrix between each noise term are obtained; the correlator output model processes the mathematical models of LOS signals and NLOS signals according to the code delay, energy attenuation, phase rotation parameters, and the ideal mathematical models of the early, prompt, and late correlators, in combination with the correlator noise model, to obtain the corresponding correlation values of the LOS signals and NLOS signals on the I branch and Q branch of the early (E), prompt (P), and late (L) correlators; the normalization unit normalizes the correlation values of different branches of the early, prompt, and late correlators with the modulus value of the correlation value output by the prompt branch correlator as the normalization basis to obtain the normalized correlator output; the three-dimensional Gaussian distribution unit performs a first-order Taylor expansion on the noise distributions of different branches of different correlators according to the principal component terms of the normalized correlation value function noise to obtain a linearized correlation value output model approximately conforming to the Gaussian distribution, and then derives the three-dimensional Gaussian joint distribution of the correlation values on the Q branch of the early, prompt, and late correlators through three-dimensional Gaussian joint distribution; the color space mapping unit performs mapping of the amplification coefficients of different distribution values in different channels and processes the set three-dimensional Gaussian joint distribution quantiles of the correlation values according to the R, G, B color channels in the RGB color space and a set of three-dimensional Gaussian joint distribution value information of the correlation values to detect whether there are multipath signals.

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