Beidou positioning power frequency harmonic interference suppression method and system and medium

Abstract

The invention discloses a Beidou positioning power frequency harmonic interference suppression method and system and a medium, and the method comprises the steps: receiving a mixed radio frequency signal, and carrying out the down-conversion of the mixed radio frequency signal to obtain an analog intermediate frequency signal; converting the analog intermediate frequency signal into a digital intermediate frequency signal sequence; performing multi-layer discrete wavelet transform on the digital intermediate frequency signal sequence to generate an approximate coefficient sub-band and a detail coefficient sub-band; calculating the energy of the wavelet coefficients in all the sub-bands, comparing the energy with an adaptive energy threshold, and identifying an interference dominant sub-band; a self-adaptive threshold function is used for wavelet coefficients in the interference dominant sub-band so as to suppress interference components in the wavelet coefficients, and a processed wavelet coefficient set is obtained; performing inverse discrete wavelet transform on the processed wavelet coefficient set to obtain a reconstructed digital intermediate frequency signal sequence; and performing baseband processing on the reconstructed digital intermediate frequency signal sequence to obtain a Beidou positioning radio frequency signal after interference suppression. According to the invention, continuity and accuracy of positioning in a complex electromagnetic environment can be guaranteed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of global navigation satellite system signal processing, and particularly relates to a Beidou positioning power frequency harmonic interference suppression method, system and medium. BACKGROUND

[0002] The Beidou satellite navigation system (hereinafter referred to as Beidou) is one of the global navigation satellite systems, which provides all-weather, global coverage positioning, navigation and timing services by broadcasting high-precision ranging signals to ground users. However, the Beidou signal is transmitted from the high-orbit satellite of tens of thousands of kilometers, and is very weak when it reaches the ground. This extremely low signal power makes it extremely susceptible to various radio interferences, resulting in a decline in receiver performance, and even unable to work normally.

[0003] In the power transmission channel environment, these power lines will produce strong electromagnetic radiation, and the spectral characteristics are strong periodic signals of the fundamental frequency (usually 50 Hz in China) and its harmonics (100 Hz, 150 Hz, 200 Hz, etc.). Although these interference frequencies (a few tens to a few hundred hertz) are far away from the carrier frequency (1561.098 MHz) of the Beidou signal, under the action of nonlinear devices (such as low-noise amplifiers, mixers) in the receiver RF front end, these strong low-frequency interference signals may occur frequency aliasing through intermodulation, mixing or in the analog-to-digital conversion sampling process, and finally enter the intermediate frequency processing bandwidth of the receiver. Once it enters the intermediate frequency, it appears as a narrow-band interference with extremely strong energy, which seriously pollutes the useful Beidou signal, and may cause the receiver signal acquisition failure, tracking loop lockout, and finally cause the positioning interruption.

[0004] In recent years, wavelet transform has also been applied to navigation signal interference suppression due to its excellent time-frequency localization characteristics. For example, a patent proposes a GPS interference filtering method based on wavelet packet decomposition. This method identifies the disturbed sub-band by calculating the "entropy" of each decomposition level, and filters the signal part in the sub-band that exceeds the noise threshold. First, it uses "entropy" as the criterion, and the entropy measures the "degree of disorder" or "uncertainty" of the signal, which is effective for identifying some structured interference signals. However, for the quasi-steady, strong periodic harmonic interference generated by high-voltage lines, its most prominent physical characteristic is not low entropy, but its "high concentration" of energy in a certain low-frequency sub-band. Secondly, this method aims to filter out interference higher than the known noise floor, which has strong universality, but does not specifically use the energy distribution characteristics of the power frequency harmonic interference for optimization.

[0005] In summary, the prior art in dealing with strong, harmonic, quasi-steady low-frequency interference generated by high-voltage lines, or there are problems of complex implementation and poor robustness (such as notch filter), or there are problems of insufficient direct criteria and optimization (such as general wavelet denoising method). Therefore, an innovative method is urgently needed to efficiently and robustly suppress such specific interference while maximizing the protection of useful Beidou signals. SUMMARY

[0006] The purpose of the present application is to overcome the shortcomings of the prior art and provide a Beidou positioning power frequency harmonic interference suppression method, system and medium, which can prevent the deterioration of receiver signal acquisition and tracking performance and ensure positioning continuity and accuracy in complex electromagnetic environments.

[0007] To achieve the above-mentioned purpose, the present application is realized by using the following technical scheme:

[0008] In a first aspect, the present application provides a Beidou positioning power frequency harmonic interference suppression method, comprising:

[0009] Receiving a mixed radio frequency signal including a Beidou navigation signal and an interference signal, down-converting it to an intermediate frequency to obtain an analog intermediate frequency signal;

[0010] Sampling the analog intermediate frequency signal to convert it into a digital intermediate frequency signal sequence;

[0011] Using multi-layer discrete wavelet transform on the digital intermediate frequency signal sequence to perform signal decomposition, generating an approximate coefficient sub-band and multiple detail coefficient sub-bands;

[0012] Calculating the energy of all wavelet coefficients in the approximate coefficient sub-band and each detail coefficient sub-band, and comparing it with the corresponding adaptive energy threshold, and determining the interference dominant sub-band according to the comparison result;

[0013] Using an adaptive threshold function to process the wavelet coefficients in the interference dominant sub-band to suppress or eliminate the interference components therein, obtaining a set of processed wavelet coefficients;

[0014] Performing inverse discrete wavelet transform on the set of processed wavelet coefficients to obtain a reconstructed digital intermediate frequency signal sequence;

[0015] Performing baseband processing on the reconstructed digital intermediate frequency signal sequence to obtain a Beidou positioning radio frequency signal after interference suppression.

[0016] Optionally, the multi-layer discrete wavelet transform uses Daubechies wavelet with order not less than 20 as the mother wavelet.

[0017] Optionally, using multi-layer discrete wavelet transform on the digital intermediate frequency signal sequence to perform signal decomposition comprises:

[0018] Determination of the decomposition level of the signal ;

[0019] Performing signal decomposition of the first level: inputting the digital intermediate frequency signal sequence into a low-pass decomposition filter and a high-pass decomposition filter Performing convolution operation and downsampling the output of the two decomposition filters respectively to calculate the approximation coefficients and the detail coefficients after downsampling;

[0020] Performing signal decomposition of any one of the second to When performing signal decomposition of any one of the second to , inputting the approximation coefficients obtained by signal decomposition of the previous level as the input of the low-pass decomposition filter and the high-pass decomposition filter of the next level, performing convolution operation, downsampling processing, and calculating the approximation coefficients and the detail coefficients;

[0021] After decomposition of the level, the digital intermediate frequency signal sequence is decomposed into one approximation coefficient subband and detail coefficient subbands .

[0022] Optional, the decomposition level when using multi-layer discrete wavelet transform to perform signal decomposition According to the sampling frequency and the harmonic frequency to be suppressed, the condition is as follows:

[0023] ,

[0024] Wherein, is the sampling frequency, is the highest harmonic frequency to be suppressed;

[0025] The frequency range corresponding to the approximation coefficient is , and the frequency range corresponding to the detail coefficient is .

[0026] Optionally, the acquisition of the adaptive capability threshold corresponding to the approximation coefficient subband and each detail coefficient subband comprises:

[0027] The highest frequency detail coefficient subband is estimated using the median absolute deviation to obtain the background noise variance , and the calculation formula is:

[0028] ,

[0029] Wherein, represents taking the absolute value of all detail coefficients in the subband , and calculating the median value thereof;

[0030] According to the background noise variance The adaptive energy thresholds for the corresponding approximation coefficient subbands and each detail coefficient subband are calculated using the following formula:

[0031] ,

[0032] in, Indicates the corresponding sub-band Adaptive energy threshold, It is a sub-belt Number of coefficients, sub-bands This represents any one of the approximation coefficient subband and the detail coefficient subband; It is a constant factor greater than 1.

[0033] Optionally, the adaptive threshold function may be a hard threshold function;

[0034] The wavelet coefficients within the interference-dominant subband are processed using an adaptive threshold function, as expressed by the formula:

[0035] ,

[0036] in, These are the original wavelet coefficients. These are the processed wavelet coefficients. It is a preset threshold.

[0037] Optionally, performing inverse discrete wavelet transform on the processed wavelet coefficient set includes:

[0038] The processed wavelet coefficient set is taken as the first... The input to the hierarchical inverse discrete wavelet transform is the approximation coefficients obtained from performing the inverse discrete wavelet transform at each level, along with the detail coefficients of the previous level. This is used as the input to the inverse discrete wavelet transform of the next level. The inverse discrete wavelet transform of each level is performed sequentially from the first level until the corresponding approximate coefficients are output after the first level inverse discrete wavelet transform, which are used as the reconstructed digital intermediate frequency signal sequence.

[0039] The inverse discrete wavelet transform process at each level includes: upsampling the input approximation coefficients and detail coefficients, then performing convolution calculations through low-pass reconstruction filters and high-pass reconstruction filters respectively, and adding the outputs of the low-pass reconstruction filters and high-pass reconstruction filters to obtain the approximation coefficients of the next level.

[0040] Optionally, the upsampling of the input approximation coefficients and detail coefficients uses an upsampling factor of 2.

[0041] After the first Hierarchical inverse discrete wavelet transform yields the input for the first... The reconstruction expression of the approximation coefficients of the level is:

[0042]

[0043] wherein, represents the input approximation coefficients of the first level, represents the approximation coefficients of the inverse discrete wavelet transform of the level, and are the approximation coefficients and the detail coefficients of the input to the first level, respectively; is a discrete time index, representing the th sampling point in the signal; is an index of the decomposed coefficients; represents a reconstruction function of a low-pass reconstruction filter, represents a reconstruction function of a high-pass reconstruction filter, represents a convolution index after up-sampling. In a second aspect, the present application provides a system for suppressing power frequency harmonic interference in Beidou positioning, comprising:

[0044] a radio frequency front end, configured to receive a mixed radio frequency signal comprising a Beidou navigation signal and an interference signal, down-convert the mixed radio frequency signal to an intermediate frequency to obtain an analog intermediate frequency signal;

[0045] an analog-to-digital converter, configured to sample the analog intermediate frequency signal and convert it into a digital intermediate frequency signal sequence;

[0046] a wavelet decomposition module, configured to use a multi-level discrete wavelet transform to perform signal decomposition on the digital intermediate frequency signal sequence, to generate an approximation coefficient subband and a plurality of detail coefficient subbands;

[0047] an interference subband identification module, configured to calculate the energy of all wavelet coefficients in the approximation coefficient subband and each detail coefficient subband, and compare the energy with a corresponding adaptive energy threshold, to determine a dominant interference subband according to the comparison result;

[0048] an interference suppression module, configured to use an adaptive threshold function to process the wavelet coefficients in the dominant interference subband, to suppress or eliminate interference components therein, to obtain a set of processed wavelet coefficients;

[0049] a signal reconstruction module, configured to perform an inverse discrete wavelet transform on the set of processed wavelet coefficients, to obtain a reconstructed digital intermediate frequency signal sequence;

[0050] a baseband processing unit, configured to perform baseband processing on the reconstructed digital intermediate frequency signal sequence, to obtain a Beidou positioning radio frequency signal after interference suppression.

[0051] a baseband processing unit, configured to perform baseband processing on the reconstructed digital intermediate frequency signal sequence, to obtain a Beidou positioning radio frequency signal after interference suppression.

[0052] ​​In a third aspect, the application provides a computer readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the BeiDou positioning power frequency harmonic interference suppression method of the first aspect.

[0053] Compared with the prior art, the application has the following beneficial effects:

[0054] The BeiDou positioning power frequency harmonic interference suppression method provided by the application can separate the BeiDou signal and the power frequency harmonic interference according to the corresponding characteristics through the decomposition of the BeiDou positioning radio frequency signal by the multi-layer discrete wavelet transform, can successfully suppress or eliminate the interference components in the identified dominant subband by using the adaptive threshold function, and can guarantee the continuity and accuracy of the BeiDou positioning in the complex electromagnetic environment through the inverse discrete wavelet transform for signal reconstruction, so as to realize the interference suppression of the BeiDou positioning power frequency harmonic.

[0055] The application can separate the power frequency interference and its multiple harmonics into different low-frequency subbands at the same time through one wavelet decomposition, does not need to design an independent filter for each harmonic, greatly simplifies the processing flow and has high multiple harmonic suppression capacity.

[0056] The application is not sensitive to the slight fluctuation of the power grid frequency based on the energy detection, can be effectively identified and suppressed as long as the interference energy is still concentrated in the corresponding subband, has better environmental adaptability and stronger robustness.

[0057] The application has excellent signal fidelity, can accurately process only a few subband coefficients that are seriously polluted by the interference, does not affect the useful signal components in other subbands, and thus maximally retains the integrity of the BeiDou signal.

[0058] The application significantly improves the signal-to-interference-and-noise ratio of the input signal of the receiver by effectively filtering the strong interference, creates good working conditions for the subsequent signal capture, tracking loop and navigation text demodulation, improves the comprehensive performance of the receiver, and finally realizes lower data error rate, more stable carrier and symbol tracking, and more accurate and continuous position, speed and time solution results. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 Fig. 1 is a flowchart of the BeiDou positioning power frequency harmonic interference suppression method provided by the application;

[0060] Figure 2 Fig. 2 is a structural schematic diagram of the BeiDou positioning power frequency harmonic interference suppression system provided by the application;

[0061] Figure 3 Fig. 3 is a flowchart of the example of the BeiDou positioning power frequency harmonic interference suppression method provided by the embodiment 2 of the application.

[0062] Figure 4 Fig. 2 shows a flow diagram of signal decomposition based on multi-layer discrete wavelet transform in the embodiment 2 of the present application;

[0063] Figure 5 Fig. 3 shows a flow diagram of identifying interference dominant subband in the embodiment 2 of the present application. DETAILED DESCRIPTION

[0064] The technical solutions of the present application will be described in detail below with the aid of the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solutions of the present application, but not limitations of the technical solutions of the present application. In the case of no conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0065] The term "and / or", only describes the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " generally represents that the associated objects before and after it are in an "or" relationship.

[0066] Embodiment 1

[0067] This embodiment introduces a method for suppressing power frequency harmonic interference of Beidou positioning, as shown in Figure 1 The method comprises the following steps:

[0068] Receiving a mixed radio frequency signal comprising a Beidou navigation signal and an interference signal, and down-converting it to an intermediate frequency to obtain an analog intermediate frequency signal;

[0069] Sampling the analog intermediate frequency signal to convert it into a digital intermediate frequency signal sequence;

[0070] Using multi-layer discrete wavelet transform to decompose the digital intermediate frequency signal sequence to generate an approximate coefficient subband and a plurality of detail coefficient subbands;

[0071] Calculating the energy of all wavelet coefficients in the approximate coefficient subband and each detail coefficient subband, and comparing it with the corresponding adaptive energy threshold value to determine the interference dominant subband according to the comparison result;

[0072] Using an adaptive threshold function to process the wavelet coefficients in the interference dominant subband to suppress or eliminate the interference components therein, to obtain a set of processed wavelet coefficients;

[0073] Performing inverse discrete wavelet transform on the set of processed wavelet coefficients to obtain a reconstructed digital intermediate frequency signal sequence;

[0074] The reconstructed digital intermediate frequency signal sequence is subjected to baseband processing to obtain a BeiDou positioning radio frequency signal after interference suppression.

[0075] The embodiment decomposes and reconstructs the BeiDou positioning radio frequency signal based on wavelet transform, effectively suppresses power grid frequency and harmonic interference, and guarantees continuity and accuracy of BeiDou positioning in a complex electromagnetic environment.

[0076] Embodiment 2

[0077] Reference Figure 3 On the basis of embodiment 1, the embodiment specifically introduces the technical solutions.

[0078] I. Signal acquisition and down-conversion

[0079] In the high-voltage transmission line environment, the embodiment receives a mixed radio frequency signal including a BeiDou navigation signal , a strong periodic interference signal , and a Gaussian noise signal , which is represented as:

[0080]

[0081] The BeiDou navigation signal includes a carrier, a pseudo-random noise code, and navigation messages, and is represented as:

[0082]

[0083] wherein, represents an amplitude of the signal, represents a carrier frequency, represents an initial phase; is a navigation message, which is a low-speed data stream transmitting satellite position, clock error, and ephemeris information, and has a form of a binary sequence with a value range of {+1, -1}; represents a pseudo-random noise code, which has a form of a binary sequence with a value range of {+1, -1}.

[0084] The strong periodic interference signal is a power grid frequency and harmonic interference signal in a continuous time domain, and is represented in a Fourier series form, with a formula as follows:

[0085]

[0086] wherein, is a fundamental frequency, which is in the embodiment; is an amplitude of a fundamental component; is an initial phase angle of the fundamental component, with a unit of radian.​​​​ is an integer greater than 1; is the amplitude of the harmonic component; is the frequency of the harmonic component, ; is the initial phase angle of the harmonic component; denotes time.

[0087] Since the BeiDou navigation signal has a frequency of GHz level, the strong periodic interference signal has a frequency of KHz level, and the two are too different in the frequency domain, a local oscillator is used to produce a pure signal = , where is the frequency generated by the local oscillator, which depends on the architecture design of the receiver and is not a fixed value; the signal is multiplied by the mixed radio frequency signal , so that the high-frequency mixed radio frequency signal is converted into an analog intermediate frequency signal.

[0088] II. Signal discretization

[0089] The analog intermediate frequency signal is sampled to convert it into a digital intermediate frequency signal sequence, which is represented as follows:

[0090] ,

[0091] wherein, denotes the discrete time index, denotes the signal component in the digital intermediate frequency signal sequence, is the BeiDou navigation signal component, is the strong periodic interference signal component from high-voltage lines and the like, is the Gaussian noise signal component.

[0092] When sampling, the sampling frequency is a key design parameter, and according to the conventional practice of BeiDou receiver design, the frequency is usually set in the range of tens of megahertz, and in the present embodiment, .

[0093] III. Signal decomposition based on multi-layer discrete wavelet transform

[0094] The present embodiment processes the digital intermediate frequency signal sequence through multi-layer discrete wavelet transform. Discrete wavelet transform mainly discretizes the scale parameter and the translation parameter of continuous wavelet transform, and adopts a "binary" discretization strategy. Here, the definition of continuous wavelet transform is as follows:

[0095] ,

[0096] in, For , A continuous wavelet transform function with parameters; It is a continuous-time signal to be analyzed; It is a scale parameter, and >0 is used to control the scaling of the wavelet, and is inversely proportional to the frequency; These are translation parameters used to control the position of the wavelet on the time axis; It is the mother wavelet function .

[0097] In this embodiment, the discretized scale parameter Pick , An integer indicates that the frequency band is divided in powers of 2; the discretized translation parameter Pick , The integer is used; the multi-level discrete wavelet transform uses Daubechies wavelets of order not less than 20 as the mother wavelet.

[0098] Furthermore, the Discrete Wavelet Transform is implemented using a decomposition filter bank that includes a low-pass decomposition filter and a high-pass decomposition filter. The decomposition filter bank decomposes the signal into wavelet coefficients, which include approximation coefficients and detail coefficients. The approximation coefficients represent the low-frequency part of the signal and are the result of the original signal after low-pass filtering and downsampling. They contain the macroscopic trend and slowly changing characteristics of the signal and are a smoothed version of the signal at a coarser scale. The detail coefficients represent the high-frequency part of the signal and are the result of the original signal after high-pass filtering and downsampling. They capture the local changes, edges, abrupt changes and other detailed information of the signal.

[0099] Specifically, such as Figure 4 As shown, the process of signal decomposition based on multi-level discrete wavelet transform is as follows:

[0100] (1) In order to ensure that the power frequency and its main harmonics can be effectively separated into specific low-frequency sub-bands, it is necessary to determine the number of signal decomposition layers. The number of decomposition layers The sampling frequency and the harmonic frequencies to be suppressed are determined based on the following conditions:

[0101] ,

[0102] in, Sampling frequency, The highest harmonic frequency that needs to be suppressed;

[0103] The frequency range corresponding to the approximation coefficients is The frequency range corresponding to the detail coefficients is .

[0104] In the present embodiment, in order to effectively isolate the 50Hz fundamental wave and its first 10 harmonics of 500Hz, a sufficient decomposition level is required so that the lowest frequency approximation coefficient can cover this frequency range.

[0105] When the sampling frequency = 62MHz, the cut-off frequency of the approximation coefficient is approximately 500Hz, which is calculated as follows:

[0106] ,

[0107] It can be concluded that the decomposition level = 16 is reasonable.

[0108] This calculation shows that in order to implement the present application in practical applications, the appropriate decomposition level must be determined according to the selected sampling frequency, which is an important detail to ensure the implementability of the present application.

[0109] (2) Perform signal decomposition at the first level: input the digital intermediate frequency signal sequence into the low-pass decomposition filter and the high-pass decomposition filter respectively, and perform convolution operation, and perform down-sampling by a factor of 2 on the output results of the two decomposition filters respectively, calculate the approximation coefficient and the detail coefficient after down-sampling, and the calculation formula is as follows:

[0110] ,

[0111] ,

[0112] wherein, is the discrete time index, indicating the th sampling point in the signal; is the index of the decomposition coefficient, the value range of is half; represents the input original signal; represents the decomposition function of the low-pass decomposition filter, represents the decomposition function of the high-pass decomposition filter, represents the down-sampling convolution index;

[0113] (3) Perform signal decomposition at the second to When decomposing a signal at any level, the approximation coefficients obtained from the previous level are used as inputs to the low-pass and high-pass decomposition filters of the next level for convolution, downsampling, and calculation of approximation and detail coefficients.

[0114] (4) After After layer decomposition, the digital intermediate frequency signal sequence is decomposed into an approximate coefficient subband. and Sub-band of detail coefficients ,in It is the highest frequency detail coefficient subband.

[0115] IV. Identifying the dominant interference subband

[0116] Because BeiDou signals are broadband spread spectrum signals, their energy is relatively evenly distributed across all subbands after wavelet transform; while strong periodic power transmission frequency and harmonic interference are narrowband signals, their energy is highly concentrated on the wavelet coefficients of the corresponding low-frequency subbands. Therefore, interference identification is performed on all subbands to determine the dominant interference subband. Figure 5 As shown, the process of identifying the interference-dominant subband is as follows:

[0117] (1) Calculate the energy of all wavelet coefficients in the approximation coefficient subband and each detail coefficient subband. The calculation formula is as follows:

[0118] ,

[0119] in It is a sub-belt The first in Wavelet coefficients, subband Represents the approximate coefficient subband and detail coefficient subband Any one of the sub-bands.

[0120] (2) For the highest frequency detail coefficient subband The variance of the background noise was estimated using the median absolute deviation. The calculation formula is:

[0121] ,

[0122] in, Indicates a pair of sub-bands Take the absolute value of all the detail coefficients and calculate their median;

[0123] (3) Based on the background noise variance The adaptive energy thresholds for the corresponding approximation coefficient subbands and each detail coefficient subband are calculated using the following formula:

[0124] ,

[0125] in, Indicates the corresponding sub-band Adaptive energy threshold, It is a sub-belt Number of coefficients, sub-bands Represents the approximate coefficient subband and detail coefficient subband Any sub-band in; It is a constant factor greater than 1.

[0126] (4) When calculating the subband energy Exceeding the adaptive energy threshold corresponding to this sub-band At that time, the subband was determined to be the dominant interference subband.

[0127] V. Processing wavelet coefficients

[0128] For subbands that are not identified as the dominant interference subbands, their wavelet coefficients remain unchanged in order to fully preserve the BeiDou signal information;

[0129] For subbands identified as the dominant interference subbands, an adaptive thresholding function is used to process their wavelet coefficients; the adaptive thresholding function is a hard thresholding function, which can preserve the amplitude of large coefficients, and the calculation formula is as follows:

[0130] ,

[0131] in, These are the original wavelet coefficients; These are the processed wavelet coefficients; It is a preset threshold value, which is the same for different interference-dominant components. The specific value can be selected based on experience.

[0132] VI. Signal Reconstruction Based on Inverse Discrete Wavelet Transform

[0133] The processed wavelet coefficient set is used as the first... The input to the hierarchical inverse discrete wavelet transform is the approximation coefficients obtained from performing the inverse discrete wavelet transform at each level, along with the detail coefficients of the previous level. This is used as the input to the inverse discrete wavelet transform of the next level. The inverse discrete wavelet transform of each level is performed sequentially from the first level until the corresponding approximate coefficients are output after the first level inverse discrete wavelet transform, which are used as the reconstructed digital intermediate frequency signal sequence.

[0134] The inverse discrete wavelet transform process of each level includes: performing factor-2 up-sampling on the input approximation coefficients and detail coefficients, and then performing convolution calculation through a low-pass reconstruction filter and a high-pass reconstruction filter respectively, adding the output results of the low-pass reconstruction filter and the high-pass reconstruction filter, and obtaining the approximation coefficients of the previous level.

[0135] The inverse discrete wavelet transform of the first level is performed on the reconstructed digital intermediate frequency signal sequence to obtain the approximation coefficients of the input first level. The inverse discrete wavelet transform of the first level is performed on the reconstructed digital intermediate frequency signal sequence to obtain the approximation coefficients of the input first level. The reconstruction expression of the approximation coefficients of the input first level is:

[0136] ,

[0137] wherein, indicates the approximation coefficients of the input first level inverse discrete wavelet transform, and are the approximation coefficients and the detail coefficients input to the first level and kept unchanged or processed by an adaptive threshold function; is a discrete time index, indicating the first sampling point in the signal; is an index of the decomposed coefficients; indicates a reconstruction function of the low-pass reconstruction filter, indicates a reconstruction function of the high-pass reconstruction filter, indicates a convolution index after up-sampling. In the embodiment, the low-pass reconstruction filter is a time-domain inversion of the low-pass decomposition filter, and the high-pass reconstruction filter is a time-domain inversion of the high-pass decomposition filter, which is expressed as:

[0138] ,

[0139] ,

[0140] ,

[0141] wherein, indicates the low-pass reconstruction filter, indicates the high-pass reconstruction filter; indicates the low-pass decomposition filter, indicates the high-pass decomposition filter.

[0142] Seven, output the interference suppressed Beidou positioning radio frequency signal

[0143] The reconstructed digital intermediate frequency signal sequence is subjected to baseband processing to obtain the interference suppressed Beidou positioning radio frequency signal, wherein the baseband processing includes signal acquisition, tracking, navigation text demodulation, and position, speed and time solution, etc. The processing process is prior art, and the embodiment will not be described in detail. ​​

[0144] In summary, the BeiDou positioning power frequency harmonic interference suppression method provided in this embodiment decomposes the BeiDou positioning radio frequency signal through multi-layer discrete wavelet transform, separating the signal from its interference based on corresponding characteristics; then, an adaptive threshold function is used to process the identified interference-dominant subband, which can successfully suppress or eliminate the interference components; finally, the signal is reconstructed through inverse discrete wavelet transform, ensuring the continuity and accuracy of BeiDou positioning in complex electromagnetic environments, thereby achieving BeiDou positioning power frequency harmonic interference suppression.

[0145] Example 3

[0146] Based on the same inventive concept as Embodiment 1, this embodiment introduces a BeiDou positioning power frequency harmonic interference suppression system, such as... Figure 2 As shown, it includes an RF front-end, an analog-to-digital converter, a wavelet interference suppression processor, and a baseband processing unit; the wavelet interference suppression processor is located between the analog-to-digital converter and the baseband processing unit, and includes a wavelet decomposition module, an interference subband identification module, an interference suppression module, and a signal reconstruction module;

[0147] The radio frequency front end is used to receive a mixed radio frequency signal including Beidou navigation signals and interference signals, and downconvert it to an intermediate frequency to obtain an analog intermediate frequency signal;

[0148] The analog-to-digital converter is used to sample the analog intermediate frequency signal and convert it into a digital intermediate frequency signal sequence;

[0149] The wavelet decomposition module is used to decompose the digital intermediate frequency signal sequence using multi-level discrete wavelet transform to generate an approximate coefficient sub-band and multiple detail coefficient sub-bands.

[0150] The interference sub-band identification module is used to calculate the energy of all wavelet coefficients in the approximation coefficient sub-band and each detail coefficient sub-band, and compare it with the corresponding adaptive energy threshold, and determine the interference-dominant sub-band based on the comparison result.

[0151] The interference suppression module is used to process the wavelet coefficients in the interference-dominant subband using an adaptive threshold function to suppress or eliminate the interference components and obtain a processed set of wavelet coefficients.

[0152] The signal reconstruction module is used to perform inverse discrete wavelet transform on the processed wavelet coefficient set to obtain the reconstructed digital intermediate frequency signal sequence.

[0153] The baseband processing unit is used to perform baseband processing on the reconstructed digital intermediate frequency signal sequence to obtain the BeiDou positioning radio frequency signal after interference suppression.

[0154] Example 4

[0155] The embodiment provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize steps of the Beidou positioning power frequency harmonic interference suppression method in the embodiment 1.

[0156] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0157] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows and / or blocks.

[0158] These computer program instructions can also be stored in a computer readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce a product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows and / or blocks.

[0159] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows and / or blocks.

[0160] The embodiments of the present application are described above with reference to the accompanying drawings, but the present application is not limited to the above-described specific embodiments, and the above-described specific embodiments are merely illustrative, but not restrictive, and a person of ordinary skill in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims, and these all belong to the protection of the present application.

Claims

1. A method for suppressing power frequency harmonic interference in BeiDou positioning, characterized in that, include: It receives a mixed radio frequency signal, including BeiDou navigation signals and interference signals, and downconverts it to an intermediate frequency to obtain an analog intermediate frequency signal; The analog intermediate frequency signal is sampled and converted into a digital intermediate frequency signal sequence; The digital intermediate frequency signal sequence is decomposed using multi-level discrete wavelet transform to generate an approximate coefficient sub-band and multiple detail coefficient sub-bands; Calculate the energy of all wavelet coefficients in the approximation coefficient subband and each detail coefficient subband, and compare it with the corresponding adaptive energy threshold. Determine the interference-dominant subband based on the comparison results. An adaptive threshold function is used to process the wavelet coefficients within the interference-dominant subband to suppress or eliminate the interference components, resulting in a processed set of wavelet coefficients. The processed wavelet coefficient set is subjected to inverse discrete wavelet transform to obtain the reconstructed digital intermediate frequency signal sequence. The reconstructed digital intermediate frequency signal sequence is processed by baseband to obtain the BeiDou positioning radio frequency signal after interference suppression.

2. The method for suppressing power frequency harmonic interference in BeiDou positioning according to claim 1, characterized in that, The multi-layer discrete wavelet transform uses Daubechies wavelets of order no less than 20 as the mother wavelet.

3. The method for suppressing power frequency harmonic interference in BeiDou positioning according to claim 1, characterized in that, The digital intermediate frequency signal sequence is decomposed using multi-level discrete wavelet transform, including: Determine the number of decomposition layers of the signal ; Perform the first level of signal decomposition: input the digital intermediate frequency signal sequence into the low-pass decomposition filter respectively. and high-pass decomposition filter Perform convolution operations and downsample the outputs of the two decomposition filters respectively, and calculate the approximation coefficients and detail coefficients after downsampling; Perform the 2nd to When decomposing a signal at any level, the approximation coefficients obtained from the previous level are used as inputs to the low-pass and high-pass decomposition filters of the next level for convolution, downsampling, and calculation of approximation and detail coefficients. go through After layer decomposition, the digital intermediate frequency signal sequence is decomposed into an approximate coefficient subband. and Sub-band of detail coefficients .

4. The method for suppressing power frequency harmonic interference in BeiDou positioning according to claim 3, characterized in that, Number of decomposition levels when using multi-level discrete wavelet transform for signal decomposition The sampling frequency and the harmonic frequencies to be suppressed are determined based on the following conditions: ， in, Sampling frequency, The highest harmonic frequency that needs to be suppressed; The frequency range corresponding to the approximation coefficient is The frequency range corresponding to the detail coefficients is .

5. The method for suppressing power frequency harmonic interference in BeiDou positioning according to claim 3, characterized in that, The acquisition of the adaptive capability thresholds corresponding to the approximation coefficient subbands and each detail coefficient subband includes: For the highest frequency detail coefficient subband The variance of the background noise was estimated using the median absolute deviation. The calculation formula is: ， in, Indicates a pair of sub-bands Take the absolute value of all the detail coefficients and calculate their median; Based on background noise variance The adaptive energy thresholds for the corresponding approximation coefficient subbands and each detail coefficient subband are calculated using the following formula: ， in, Indicates the corresponding sub-band Adaptive energy threshold, It is a sub-belt Number of coefficients, sub-bands This represents any one of the approximation coefficient subband and the detail coefficient subband; It is a constant factor greater than 1.

6. The method for suppressing power frequency harmonic interference in BeiDou positioning according to claim 1, characterized in that, The adaptive threshold function adopts a hard threshold function; The wavelet coefficients within the interference-dominant subband are processed using an adaptive threshold function, as expressed by the formula: ， in, These are the original wavelet coefficients. These are the processed wavelet coefficients. It is a preset threshold.

7. The method for suppressing power frequency harmonic interference in BeiDou positioning according to claim 3, characterized in that, The inverse discrete wavelet transform of the processed wavelet coefficient set includes: The processed wavelet coefficient set is taken as the first... The input to the hierarchical inverse discrete wavelet transform is the approximation coefficients obtained from performing the inverse discrete wavelet transform at each level, along with the detail coefficients of the previous level. This is used as the input to the inverse discrete wavelet transform of the next level. The inverse discrete wavelet transform of each level is performed sequentially from the first level until the corresponding approximate coefficients are output after the first level inverse discrete wavelet transform, which are used as the reconstructed digital intermediate frequency signal sequence. The inverse discrete wavelet transform process at each level includes: upsampling the input approximation coefficients and detail coefficients, then performing convolution calculations through low-pass reconstruction filters and high-pass reconstruction filters respectively, and adding the outputs of the low-pass reconstruction filters and high-pass reconstruction filters to obtain the approximation coefficients of the next level.

8. The method for suppressing power frequency harmonic interference in BeiDou positioning according to claim 7, characterized in that, The approximation coefficients and detail coefficients of the input are upsampled using an upsampling factor of 2. After the first Hierarchical inverse discrete wavelet transform yields the input for the first... The reconstruction expression for the approximation coefficients of the hierarchy is: ， in, Indicates the input for the first Approximation coefficients of hierarchical inverse discrete wavelet transform. and These are the inputs up to the number Approximation coefficients and detail coefficients at each level; It is a discrete-time index, representing the first element in the signal. One sampling point; It is the index of the coefficients after decomposition; This represents the reconstruction function of the low-pass reconstruction filter. This represents the reconstruction function of the high-pass reconstruction filter. This represents the convolution index after upsampling.

9. A BeiDou positioning power frequency harmonic interference suppression system, characterized in that, include: The radio frequency front end is used to receive mixed radio frequency signals, including BeiDou navigation signals and interference signals, and down-convert them to intermediate frequency to obtain analog intermediate frequency signals; An analog-to-digital converter is used to sample analog intermediate frequency signals and convert them into a digital intermediate frequency signal sequence. The wavelet decomposition module is used to decompose the digital intermediate frequency signal sequence using multi-level discrete wavelet transform to generate an approximate coefficient sub-band and multiple detail coefficient sub-bands. The interference sub-band identification module is used to calculate the energy of all wavelet coefficients in the approximate coefficient sub-band and each detail coefficient sub-band, and compare it with the corresponding adaptive energy threshold. Based on the comparison result, the interference-dominant sub-band is determined. The interference suppression module is used to process the wavelet coefficients in the interference-dominant subband using an adaptive threshold function to suppress or eliminate the interference components and obtain the processed wavelet coefficient set. The signal reconstruction module is used to perform inverse discrete wavelet transform on the processed wavelet coefficient set to obtain the reconstructed digital intermediate frequency signal sequence. The baseband processing unit is used to perform baseband processing on the reconstructed digital intermediate frequency signal sequence to obtain the BeiDou positioning radio frequency signal after interference suppression.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the Beidou positioning power frequency harmonic interference suppression method as described in any one of claims 1 to 8.

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