Improved frequency domain narrowband interference suppression double-threshold determination method and system

Abstract

The invention relates to the technical field of satellite navigation signal processing, and discloses an improved frequency domain narrowband interference suppression double-threshold determination method and system. The method comprises the following steps: carrying out fast Fourier transform on a zero intermediate frequency time domain signal to obtain a spectrum data sequence, carrying out modulus calculation on the spectrum data sequence to calculate a spectrum mean value and generate a high threshold value and a low threshold value, counting the number of points exceeding the high threshold value and marking interference positions exceeding the low threshold value to generate a deletion sequence, whether interference position zero setting processing is carried out on the frequency spectrum data sequence or not is judged according to the number of the points to obtain a processed frequency spectrum sequence, and fast Fourier inversion is carried out on the processed frequency spectrum sequence to obtain time domain data after interference suppression. According to the method, the problems of missing detection and false detection in interference detection of a traditional single-threshold method are solved by introducing a double-threshold mechanism, and the accuracy and reliability of narrowband interference suppression are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of satellite navigation signal processing, and in particular to an improved frequency domain narrowband interference suppression double threshold determination method and system. BACKGROUND

[0002] Direct sequence spread spectrum technology is widely used in satellite navigation and other fields due to its excellent concealment and anti-multipath, multi-address interference capability, but in actual operation, the system will be affected by natural environment or man-made narrowband interference, and when the interference strength exceeds the spread spectrum gain of the system, additional interference suppression means needs to be involved to ensure normal working performance. The frequency domain narrowband interference suppression technology is one of the commonly used anti-interference methods in spread spectrum systems, and its basic principle is to detect the interference position by using the difference in the frequency spectrum characteristics of the spread spectrum signal and the interference signal after the received signal is subjected to fast Fourier transform, and then removing or attenuating the interference spectrum component and restoring it to the time domain signal through inverse transform. This technology is widely used due to its simple and direct design, fast suppression speed and other advantages. The traditional method usually uses the amplitude spectrum mean multiplied by a preset threshold parameter as the threshold value for judging interference, and the frequency points greater than the threshold are judged as frequency points with interference. Although this single threshold decision method is simple, it requires very accurate threshold setting, and it is difficult to adapt to complex and variable interference environment in actual application.

[0003] The method of using a single threshold parameter to judge the existence of interference in the prior art has obvious defects. If the threshold is set too high, part of the interference information may be missed, resulting in incomplete identification and suppression of the interference frequency component, and the residual interference component continues to affect the subsequent despreading and demodulation performance, which reduces the working reliability of the receiver in a strong interference environment. If the threshold is set too low, false detection may occur, which may misjudge the normal spread spectrum signal spectrum fluctuation as interference, and perform incorrect zero processing on the useful signal spectrum component, resulting in signal energy loss and spectrum distortion, and reducing the signal quality and demodulation performance. The single threshold parameter cannot meet the dual requirements of accurately judging the existence of interference and accurately determining the interference range, and there is an inherent contradiction between interference detection sensitivity and false suppression capability, which limits the application effect of the frequency domain anti-interference technology in complex electromagnetic environment. SUMMARY

[0004] The present application provides an improved frequency domain narrowband interference suppression double threshold determination method and system, which solves the problems of missed detection and false detection in the traditional single threshold method by introducing a double threshold mechanism, and improves the accuracy and reliability of narrowband interference suppression.

[0005] In a first aspect, the present application provides an improved frequency domain narrowband interference suppression double threshold determination method, which comprises: Step S1, converting a zero intermediate frequency time domain signal into a frequency spectrum data sequence through fast Fourier transform; Step S2, performing a modulus operation on the frequency spectrum data sequence to obtain a modulus value sequence, calculating a frequency spectrum mean value according to the modulus value sequence, and multiplying the frequency spectrum mean value by a high threshold factor and a low threshold factor to obtain a high threshold value and a low threshold value; Step S3, counting the number of points in the modulus value sequence that exceed the high threshold value, marking positions in the modulus value sequence that exceed the low threshold value as interference positions, and generating a deletion sequence; Step S4, when the number of points is zero, retaining the frequency spectrum data sequence, and when the number of points is greater than zero, setting the values of the frequency spectrum data sequence corresponding to the interference positions to zero according to the deletion sequence to obtain a processed frequency spectrum sequence; Step S5, performing inverse fast Fourier transform on the processed frequency spectrum sequence to obtain time domain data after interference suppression.

[0006] In a second aspect, the present application provides an improved frequency domain narrowband interference suppression double threshold determination system, which comprises: A conversion module configured to convert a zero intermediate frequency time domain signal into a frequency spectrum data sequence through fast Fourier transform; A calculation module configured to perform a modulus operation on the frequency spectrum data sequence to obtain a modulus value sequence, calculate a frequency spectrum mean value according to the modulus value sequence, and multiply the frequency spectrum mean value by a high threshold factor and a low threshold factor to obtain a high threshold value and a low threshold value; A marking module configured to count the number of points in the modulus value sequence that exceed the high threshold value, mark positions in the modulus value sequence that exceed the low threshold value as interference positions, and generate a deletion sequence; A deletion module configured to, when the number of points is zero, retain the frequency spectrum data sequence, and when the number of points is greater than zero, set the values of the frequency spectrum data sequence corresponding to the interference positions to zero according to the deletion sequence to obtain a processed frequency spectrum sequence; An inhibition module configured to perform inverse fast Fourier transform on the processed frequency spectrum sequence to obtain time domain data after interference suppression.

[0007] In a third aspect, an improved frequency domain narrowband interference suppression double threshold determination device is provided, which comprises a memory and at least one processor, the memory having instructions stored therein; the at least one processor invokes the instructions in the memory to enable the improved frequency domain narrowband interference suppression double threshold determination device to perform the improved frequency domain narrowband interference suppression double threshold determination method described above.

[0008] In a fourth aspect, a computer readable storage medium is provided, in which instructions are stored, when running on a computer, cause the computer to execute the improved frequency domain narrowband interference suppression double-threshold determination method described above.

[0009] In the technical solutions provided in the present application, the double-threshold decision mechanism is introduced to realize accurate identification and effective suppression of narrowband interference, overcoming the inherent defects of the traditional single threshold method in interference detection. By converting the zero intermediate frequency time domain signal into a frequency spectrum data sequence through N-point fast Fourier transform, the time domain aliasing signal components are separated into independent frequency channels using the frequency domain processing method, which creates favorable conditions for subsequent interference detection. The modulus operation is performed on the frequency spectrum data sequence to obtain a modulus value sequence and calculate the frequency spectrum mean value, which truly reflects the average energy level of the spread spectrum signal and noise, providing an objective reference benchmark for threshold setting and avoiding the poor adaptability problem caused by manually setting a fixed threshold value. The frequency spectrum mean value is multiplied by a high threshold factor and a low threshold factor to obtain a high threshold value and a low threshold value, and the threshold value is adaptively adjusted according to the multiple relationship, ensuring the rationality of the decision standard under different receiving conditions. The number of points exceeding the high threshold value in the modulus value sequence is counted, and this statistical result is used as the overall decision basis for judging whether there is interference. The setting of the high threshold ensures that only the frequency points that are significantly higher than the normal energy level trigger interference judgment, effectively avoiding the misjudgment of normal fluctuations of the signal as interference and reducing the false detection rate. The positions of the modulus value sequence exceeding the low threshold value are marked as interference positions to generate a deletion sequence, and the moderate relaxation of the low threshold compared to the high threshold enables the main lobe and side lobe frequency components of the interference signal to be accurately identified, expanding the coverage of interference detection and preventing the influence of residual interference on subsequent processing, thereby reducing the missed detection rate. When the number of points is zero, the frequency spectrum data sequence is retained without performing the zeroing operation, avoiding unnecessary spectral damage to the useful signal in the absence of interference and maintaining the integrity and original characteristics of the signal. When the number of points is greater than zero, the values of the frequency spectrum data sequence corresponding to the interference positions are set to zero according to the deletion sequence. This selective spectral zeroing method accurately acts on the frequency positions affected by interference, while suppressing interference and maximizing the preservation of the spectral components of the spread spectrum signal, resulting in less signal loss compared to full-band filtering and other methods.

[0010] The core advantage of the double threshold mechanism is to separate the interference existence judgment and the interference range determination, the high threshold is responsible for the overall decision to avoid false rejection, the low threshold is responsible for accurate marking to prevent missed detection, and the two work together to balance the detection sensitivity and false rejection suppression capability, so that the algorithm shows stronger robustness in the complex electromagnetic interference environment in satellite navigation and other applications. The N-point inverse fast Fourier transform is performed on the processed spectrum sequence to obtain the time domain data after interference suppression, and the complete frequency domain processing and time domain reconstruction process ensures that the signal after interference suppression can be directly used for the despreading module, the inverse transform process converts the interference suppression effect in the frequency domain into the improvement of the time domain signal quality, so that the subsequent spread spectrum code capture and tracking process can be carried out under the condition of higher signal-to-noise ratio, the requirement of the receiver on the signal power is reduced, the working ability of the system in the environment where weak signal and strong interference coexist is improved, the available working range of the receiver is expanded, and the overall scheme effectively improves the anti-interference performance and working reliability of the direct sequence spread spectrum system through algorithm level improvement. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor based on these drawings.

[0012] Figure 1 An embodiment schematic diagram of the improved frequency domain narrowband interference suppression double threshold determination method in the embodiments of the present application; Figure 2 An embodiment schematic diagram of the improved frequency domain narrowband interference suppression double threshold determination system in the embodiments of the present application; Figure 3 The structure schematic block diagram of the improved frequency domain narrowband interference suppression double threshold determination device in the embodiments of the present application. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide an improved frequency domain narrowband interference suppression double threshold determination method and system. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not have to be used to describe a particular order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "comprising" or "having" and any variation thereof is intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0014] For ease of understanding, the specific flow of the embodiments of the present application is described below, please refer to Figure 1 One embodiment of the improved frequency domain narrowband interference suppression double threshold determination method in the embodiments of the present application includes: Step S1, converting the zero intermediate frequency time domain signal into a frequency spectrum data sequence through fast Fourier transform; Step S2, performing a modulus operation on the frequency spectrum data sequence to obtain a modulus value sequence, calculating a frequency spectrum mean value according to the modulus value sequence, and multiplying the frequency spectrum mean value by a high threshold factor and a low threshold factor to obtain a high threshold value and a low threshold value; Step S3, counting the number of points exceeding the high threshold value in the modulus value sequence, marking the positions exceeding the low threshold value in the modulus value sequence as interference positions, and generating a deletion sequence; Step S4, when the number of points is zero, the frequency spectrum data sequence is reserved, and when the number of points is greater than zero, the values corresponding to the interference positions in the frequency spectrum data sequence are set to zero according to the deletion sequence, to obtain a processed frequency spectrum sequence; Step S5, performing inverse fast Fourier transform on the processed frequency spectrum sequence to obtain time domain data after interference suppression.

[0015] It can be understood that the execution subject of the present application can be an improved frequency domain narrowband interference suppression double threshold determination system, and can also be a terminal or a server, which is not limited here. The embodiments of the present application take the server as the execution subject for example.

[0016] Specifically, the zero intermediate frequency time domain signal obtained by the receiving end needs to be converted to the frequency domain for analysis, and the conversion process is specifically implemented by fast Fourier transform. Fast Fourier transform is an algorithm for converting a discrete time domain signal into a discrete frequency domain signal, and its basic principle is to decompose the N-point discrete Fourier transform into a combination of discrete Fourier transforms of multiple shorter sequences, thereby reducing the computational complexity. In the present scheme, the input zero intermediate frequency time domain signal sequence contains 1024 complex symbols, each symbol consists of a real part and an imaginary part, and the quantization bit width is 16 bits. After 1024-point fast Fourier transform, the time domain signal is converted into a frequency spectrum data sequence with the same number of points, and each element in the sequence represents the amplitude and phase information of the corresponding frequency component. During the transformation process, the periodic characteristics of the time domain signal are mapped to the discrete frequency points in the frequency domain, and the spread spectrum signal presents a relatively flat spectrum distribution in the frequency domain, while the narrowband interference signal produces obvious energy peaks at specific frequency positions. The difference in spectral characteristics lays the foundation for subsequent interference detection.

[0017] After obtaining the frequency spectrum data sequence, the amplitude information of each frequency point needs to be extracted and the interference decision threshold needs to be determined. Modulus operation is a calculation process for the modulus value of a complex number. For complex frequency spectrum data, the modulus value is calculated by summing the squares of the real part and the imaginary part and then taking the square root. The 1024 complex elements in the frequency spectrum data sequence are sequentially subjected to modulus operation, and 1024 real modulus values are obtained, which reflect the energy of each frequency point. Then, all the modulus values are subjected to arithmetic average calculation, and the 1024 modulus values are added and divided by 1024 to obtain the spectrum mean value. The mean value represents the average energy level in the entire frequency band, and mainly reflects the energy of the spread spectrum signal and noise in the absence of strong interference. The high threshold factor and the low threshold factor are pre-set multiple coefficients, and the high threshold factor is usually 3 and the low threshold factor is usually 2. The spectrum mean value is multiplied by the two factors to obtain the high threshold value and the low threshold value. The high threshold value is set relatively high to ensure that only the frequency points that are significantly higher than the normal energy level are identified as interference, avoiding misjudgment of normal signal fluctuations as interference; the low threshold value is set relatively low to expand the detection range of the interference frequency points after confirming the existence of interference, ensuring that the interference signal is fully suppressed.

[0018] After the double threshold values are determined, the modulus value sequence needs to be compared and analyzed point by point to identify the interference position. Each modulus value in the modulus value sequence is compared with the high threshold value, and the number of points exceeding the high threshold value is counted, which is denoted as m. Meanwhile, each modulus value in the modulus value sequence is compared with the low threshold value, and for the positions exceeding the low threshold value, 1 is marked in the deletion sequence, and for the positions not exceeding the low threshold value, 0 is marked. The length of the deletion sequence is the same as that of the spectrum data sequence, and its function is to record which frequency positions need to be subjected to interference suppression processing. The decision result m of the high threshold value is used to judge whether there is interference, and if m is equal to 0, it indicates that the energy of all frequency points does not exceed the high threshold value, which means that there is no strong interference signal to be suppressed at present; if m is greater than 0, it indicates that at least one frequency point has energy significantly higher than the normal level, and it is confirmed that there is interference. The decision result of the low threshold value forms the deletion sequence, which accurately identifies which frequency positions are affected by interference, and the spectrum components corresponding to these positions need to be processed by zeroing.

[0019] According to the interference decision result, the spectrum data is subjected to corresponding processing. When the point number m is zero, it indicates that there is no strong interference exceeding the system spread spectrum gain in the received signal, at this time all data in the spectrum data sequence are useful signals and noise components, which should be completely retained, and the original spectrum data sequence is directly output as the processed spectrum sequence. When the point number m is greater than zero, it is confirmed that there is interference signal to be suppressed, at this time the spectrum data sequence is subjected to selective zeroing operation according to the deletion sequence. The specific operation is to compare the spectrum data sequence with the deletion sequence bit by bit, at the positions marked as 1 in the deletion sequence, the complex value at the corresponding position of the spectrum data sequence is set to 0, that is, the amplitude and phase information of the frequency point is completely removed; at the positions marked as 0 in the deletion sequence, the original value at the corresponding position of the spectrum data sequence is retained unchanged. After this selective processing, the spectrum components of the interference frequency points are eliminated, while the spectrum components of the frequency points where the useful signals are located are retained, forming the processed spectrum sequence.

[0020] The processed spectrum sequence needs to be converted back to the time domain for subsequent despreading and demodulation processing. The inverse fast Fourier transform is the inverse process of the fast Fourier transform, which converts the discrete frequency components in the frequency domain back to the time domain discrete signal. The processed spectrum sequence is subjected to N-point inverse fast Fourier transform, and the discrete frequency components in the frequency domain are recombined into the time domain signal sequence. The time domain data obtained after the inverse transform has the same sampling point number and data format as the original zero intermediate frequency time domain signal, but the narrowband interference components therein have been suppressed, and the signal quality is improved. The time domain data after interference suppression can be input to the subsequent despreading module for spread spectrum code synchronization and despreading processing.

[0021] In a specific embodiment, step S1 comprises: obtaining a zero intermediate frequency time domain signal after frequency down-conversion at a receiving end; The zero intermediate frequency time domain signal is N-point sampled to obtain a time domain signal sequence; The time domain signal sequence is input into a fast Fourier transform operation unit to perform N-point fast Fourier transform; A frequency spectrum data sequence containing N frequency points is output, and each frequency point in the frequency spectrum data sequence contains a real part and an imaginary part.

[0022] Specifically, the down-conversion processing of the receiving end converts the radio frequency signal into a zero intermediate frequency signal. The zero intermediate frequency refers to that the carrier frequency of the signal is completely down-converted to a zero frequency position, at which the spectrum center of the signal is located at the zero frequency point, and the signal exists in the form of a baseband complex number. The zero intermediate frequency time domain signal contains data of two channels of in-phase components and quadrature components, which correspond to the real part and the imaginary part of the complex number signal, respectively. The zero intermediate frequency time domain signal after down-conversion is a continuous analog signal, which needs to be sampled and quantized by an analog-to-digital converter to be converted into a digital signal for subsequent digital signal processing. The zero intermediate frequency time domain signal obtained by the receiving end retains all the information content of the original spread spectrum signal, and also contains various interference signals and noise components superimposed in the transmission process, which are mixed together in the time domain and are difficult to distinguish and suppress directly.

[0023] N-point sampling of the zero intermediate frequency time domain signal is a process of converting a continuous time domain signal into a discrete time domain signal. The sampling process periodically takes values of the continuous signal according to a preset sampling frequency, and the sampling frequency needs to satisfy the Nyquist sampling theorem to avoid spectrum aliasing. N-point sampling means that N discrete time signal amplitude values are extracted in one processing period, and each sampling point contains the instantaneous values of the in-phase component and the quadrature component. The time domain signal sequence obtained after sampling is a discrete sequence composed of N complex elements, and each element in the sequence represents the signal state at a certain sampling time. In the present scheme, N is 1024, that is, 1024 complex sampling points are collected in each processing period, and each sampling point is quantized to a 16-bit digital quantity by an analog-to-digital converter, with 16 bits for the real part and 16 bits for the imaginary part. The time interval between the sampling points in the time domain signal sequence is determined by the sampling frequency, and the sampling points are distributed with equal time intervals. This uniform sampling ensures that the time domain characteristics of the signal can be accurately captured and characterized.

[0024] The time-domain signal sequence is input to a dedicated operation unit for frequency-domain transform processing. The fast Fourier transform operation unit is a hardware or software module for implementing time-frequency transform, and a butterfly operation structure of the fast Fourier transform algorithm is implemented inside the unit. An N-point fast Fourier transform converts N time-domain sampling points into N frequency-domain frequency points, and the transform process is based on complex number operation, each time-domain sampling point is subjected to complex multiplication and addition operation with a corresponding rotation factor. The operation unit decomposes the entire transform process into multiple stages of butterfly operation according to a base-2 or base-4 decomposition algorithm of the fast Fourier transform, each stage of butterfly operation processes the calculation of part of the frequency points, and the operation results of each stage are transmitted step by step until the calculation of all frequency points is completed. In a 1024-point transform, the base-2 algorithm requires 10 stages of butterfly operation, each stage processes 512 butterfly units, and the operation complexity is greatly reduced through this hierarchical processing mode. The fast Fourier transform operation unit needs to call a pre-stored rotation factor table during processing, and the rotation factor is a complex number weight coefficient with an amplitude of 1 but a phase varying with the frequency point position. These rotation factors determine how time-domain signal components are allocated to each frequency point.

[0025] The fast Fourier transform operation unit outputs a frequency spectrum data sequence after completing the calculation. The sequence also contains N elements, but each element represents frequency domain information rather than time domain information, and the element number corresponds to the frequency point position on the frequency axis. The N frequency points in the frequency spectrum data sequence are uniformly distributed in the frequency range from zero frequency to the sampling frequency, and the frequency interval between the frequency points is equal to the sampling frequency divided by N. Since the input signal is a complex signal, the frequency spectrum data output by the fast Fourier transform is also a complex number, and each frequency point contains a real part and an imaginary part. The real part represents the amplitude of the cosine component of the frequency component, and the imaginary part represents the amplitude of the sine component of the frequency component, which together describe the complete information of the frequency component. The real and imaginary values of each frequency point in the frequency spectrum data sequence reflect the energy intensity of the corresponding frequency component in the signal. The spread spectrum signal has a uniform energy distribution in the frequency domain due to its wideband characteristic, while the narrowband interference signal is concentrated on a few frequency points to form a significant energy protrusion. This difference in frequency domain characteristics makes it possible to identify and suppress interference in the subsequent process.

[0026] In a specific embodiment, step S2 comprises: The real part and the imaginary part of each frequency point in the frequency spectrum data sequence are respectively squared, the squared real part and the squared imaginary part are added, and the square root of the sum is calculated to obtain a modulus value sequence; All N modulus values in the modulus value sequence are accumulated and summed, and the sum is divided by N to obtain a spectral mean value; The spectral mean value is multiplied by a high threshold factor to obtain a high threshold value; The spectral mean value is multiplied by a low threshold factor to obtain a low threshold value.

[0027] Specifically, each frequency point in the spectrum data sequence is stored in the form of complex number, containing two values of real part and imaginary part. The modulus operation is a mathematical process to extract the amplitude information of complex number, and for any complex number, its modulus is equal to the square root of the sum of the square of the real part and the square of the imaginary part. When processing the spectrum data sequence, modulus calculation needs to be performed on each of the N frequency points. The specific calculation process is to square the real part of a certain frequency point, and at the same time, square the imaginary part of the frequency point. The squaring operation eliminates the positive and negative signs of the values, so that the result is always non-negative. Then, the squared real part and the squared imaginary part are added to obtain the sum of the two squared values. Finally, the square root of the sum is calculated, i.e. the square root, to obtain the modulus of the frequency point. The modulus is a real number, and its size directly reflects the energy intensity of the signal component at the frequency point. The larger the modulus, the stronger the signal energy at the frequency position. According to the same operation steps, all N frequency points in the spectrum data sequence are processed in turn, and each frequency point undergoes three operations of squaring, adding and square root to generate a corresponding modulus. The N moduli are arranged in frequency point order to form a modulus sequence. The sequence has the same length and frequency point correspondence as the spectrum data sequence, but the data form is converted from complex number to real number, which is convenient for subsequent threshold comparison operation.

[0028] The modulus sequence reflects the energy distribution at each frequency position. In order to determine a reasonable interference decision threshold, the average energy level in the entire frequency band needs to be calculated. The calculation of the spectrum mean is realized by statistical operation on all elements in the modulus sequence. First, the N moduli in the modulus sequence are added to obtain the sum, i.e. the first modulus is added to the subsequent moduli one by one, and after N addition operations, the sum of all moduli is obtained. The sum value represents the energy accumulation value of N frequency points, reflecting the total energy in the entire processing bandwidth. Then, the cumulative sum result is divided by N, and the division operation realizes the energy averaging processing, and the quotient obtained is the spectrum mean. The spectrum mean represents the average energy level of a single frequency point. In an ideal case, when the signal only contains spread spectrum signal and noise, the modulus of each frequency point should fluctuate around the mean value, and the fluctuation amplitude is determined by the random characteristics of the signal. When there is narrowband interference in the signal, the modulus of the frequency point affected by the interference will be significantly higher than the spectrum mean, and this energy difference provides a basis for interference detection.

[0029] The determination of the interference decision threshold needs to introduce a multiple coefficient on the basis of the spectrum mean. The high threshold factor is a value greater than 1, which is usually set to 3 in the present scheme. The role of the factor is to establish a higher decision standard on the basis of the spectrum mean. The high threshold value is obtained by multiplying the spectrum mean by the high threshold factor. The high threshold value is set to 3 times the spectrum mean, which means that only when the modulus value of a certain frequency point exceeds 3 times the average level, the frequency point is preliminarily determined as a frequency point where interference may exist. This higher threshold setting avoids misjudging the normal signal energy fluctuation as interference, because the energy fluctuation of the spread spectrum signal and the noise usually does not exceed 3 times the mean value. Only the energy of the narrowband interference signal which is highly concentrated in a certain frequency point can produce such a significant energy peak. The high threshold value is mainly used for the overall decision of whether there is interference. When there is a frequency point in the modulus value sequence that exceeds the high threshold value, it is confirmed that the signal contains strong interference components that need to be suppressed.

[0030] The low threshold factor is also a value greater than 1, which is usually set to 2 in the present scheme. The factor is used to establish a relatively loose decision standard. The low threshold value is obtained by multiplying the spectrum mean by the low threshold factor. The low threshold value is set to 2 times the spectrum mean, which is lower than the high threshold value but still higher than the fluctuation range of the normal signal. The role of the low threshold value is to accurately identify all the frequency positions affected by the interference after it is confirmed that there is interference. Since the interference signal may have a certain spectral spread in the frequency domain, its energy is not only concentrated in the center frequency point, but also has a high energy distribution in the adjacent frequency points. The modulus values of these adjacent frequency points may not reach the high threshold value but will exceed the low threshold value. Using the low threshold value to mark the interference position can identify all the main lobe and side lobe frequency components of the interference, ensuring the integrity of the interference suppression. The double threshold mechanism judges the existence of interference through the high threshold, and determines the coverage range of the interference through the low threshold. The two are used together to avoid misjudgment and prevent missed judgment, achieving accurate identification and complete marking of the narrowband interference.

[0031] In a specific embodiment, step S3 comprises: The modulus values in the modulus value sequence are compared with the high threshold value point by point, the number of modulus values greater than the high threshold value is counted, and the number of points is recorded; The modulus values in the modulus value sequence are compared with the low threshold value point by point; The frequency points in the modulus value sequence greater than the low threshold value are marked as interference positions, and the corresponding positions are assigned a value of 1; The frequency points in the modulus value sequence less than or equal to the low threshold value are marked as non-interference positions, and the corresponding positions are assigned a value of 0, generating a deletion sequence with a length of N.

[0032] Specifically, each modulus value in the modulus value sequence needs to be compared with the high threshold value to determine whether there is a significant energy anomaly. Point-by-point numerical comparison means that the first modulus value in the modulus value sequence is compared with the high threshold value to determine whether the modulus value is greater than the high threshold value, then the second modulus value is compared in the same way, and so on until the comparison of all N modulus values is completed. In each comparison, if the modulus value is greater than the high threshold value, the counter value is increased by 1, and if the modulus value is less than or equal to the high threshold value, the counter value remains unchanged. After N comparison operations, the accumulated value in the counter is the number of modulus values greater than the high threshold value, which is recorded as the point number m. The point number m reflects the total number of frequency points with strong energy peaks in the frequency spectrum. When m is equal to 0, it means that the energy of all frequency points does not exceed the high threshold value, indicating that there is no strong interference in the signal that needs to be suppressed, and the received signal is mainly composed of spread spectrum signals and background noise. When m is greater than 0, it means that at least one or more frequency points have energy significantly higher than the normal level, and these high-energy frequency points correspond to the frequency position of the narrowband interference signal, indicating that there is interference in the signal that needs to be suppressed. The statistical result of the point number m is used as the basis for the subsequent processing decision, which determines whether to directly output the original frequency spectrum data or to perform interference suppression operation.

[0033] After completing the high threshold decision, a low threshold value is used to analyze the modulus value sequence in more detail to determine the complete distribution range of the interference. Each modulus value in the modulus value sequence is compared with the low threshold value point by point. The comparison process is also from the first modulus value to the low threshold value, and then sequentially to the low threshold value until the comparison of all N modulus values is completed. The low threshold value is less than the high threshold value, so more frequency point modulus values will exceed this threshold value. These frequency points not only include the center frequency position of the interference, but also include the adjacent frequency positions affected by the spectrum expansion of the interference signal. For the frequency points in the modulus value sequence that are greater than the low threshold value, their positions are marked as interference positions, and the corresponding positions in the created mark sequence are assigned a value of 1. The value 1 indicates that the frequency point is affected by interference and needs to be suppressed. For the frequency points in the modulus value sequence that are less than or equal to the low threshold value, their positions are marked as non-interference positions, and the corresponding positions in the mark sequence are assigned a value of 0. The value 0 indicates that the frequency point is a normal signal component and does not need to be suppressed. After processing the N frequency points according to this assignment rule, a deletion sequence of length N is generated. The sequence has the same length and position correspondence relationship as the modulus value sequence and the frequency spectrum data sequence. The deletion sequence is a binary sequence composed of 0 and 1, where 1 corresponds to the position of all frequency points that need to be zeroed, and 0 corresponds to the position of the frequency points that need to be retained. The generation of the deletion sequence completes the conversion from energy detection to position marking, providing accurate position indexes for the subsequent selective processing of the frequency spectrum data sequence. Through the sequence, each frequency point can be accurately located to determine whether it should be retained or zeroed, achieving accurate identification and marking of the interference spectrum components.

[0034] In a specific embodiment, step S4 comprises: determining whether the number of points is equal to zero; when the number of points is equal to zero, directly taking the spectrum data sequence as the processed spectrum sequence; when the number of points is greater than zero, performing point-by-point position correspondence between the spectrum data sequence and the deletion sequence; according to the positions assigned as 1 in the deletion sequence, setting the frequency point values at the corresponding positions of the spectrum data sequence to zero, and according to the positions assigned as 0 in the deletion sequence, retaining the frequency point values at the corresponding positions of the spectrum data sequence, to obtain the processed spectrum sequence.

[0035] Specifically, the value of the number of points m determines the branch direction of the subsequent processing flow. The determination of whether the number of points is equal to zero is achieved by comparing m with the value 0. This determination operation distinguishes two different signal states. When the number of points is equal to zero, it means that the modulus values of all frequency points in the modulus sequence do not exceed the high threshold value, and there is no frequency point with significantly abnormal energy in the entire frequency band. At this time, there is no strong interference component exceeding the system spread spectrum gain in the received signal, and the signal is mainly composed of the useful signal modulated by the spread spectrum code and random noise. In this case, all frequency points in the spectrum data sequence belong to normal signal components, and no spectrum zeroing operation is needed. The original spectrum data sequence should be kept intact. Directly taking the spectrum data sequence as the processed spectrum sequence means that no modification is made to the spectrum data. The real and imaginary parts of the N frequency points remain their original values from the output of the fast Fourier transform. These spectrum data will be directly converted into time domain signals by the subsequent inverse fast Fourier transform. This direct output processing method avoids unnecessary spectrum zeroing operation in the absence of interference, prevents additional spectrum damage to the useful signal, and maintains the integrity of the signal.

[0036] When the number of points is greater than zero, it means that at least one frequency point in the modulus sequence exceeds the high threshold value, confirming that the received signal contains narrowband interference that needs to be suppressed. At this time, the spectral data sequence must be selectively processed to eliminate the interference components. The processing process is realized by using the spectral data sequence in cooperation with the deletion sequence. Both the spectral data sequence and the deletion sequence contain N elements, and the elements of the same sequence number in the two sequences correspond to the same frequency point position. Point-by-point position correspondence between the two sequences means that the first frequency point of the spectral data sequence is corresponded to the first element of the deletion sequence, the second frequency point is corresponded to the second element, and so on to establish N one-to-one correspondence. The element value in the deletion sequence is 0 or 1, which indicates the processing method to be taken for the corresponding frequency point. According to the position with a value of 1 in the deletion sequence, a zero operation is performed on the corresponding position of the spectral data sequence, that is, the real and imaginary parts of the frequency point are set to 0, and the spectral information of the frequency point is completely cleared and no longer contains any amplitude and phase information. According to the position with a value of 0 in the deletion sequence, the frequency point value of the corresponding position of the spectral data sequence is retained, that is, the real and imaginary parts of the frequency point maintain their original values unchanged, and the spectral information of the frequency point is completely saved. According to this rule, the N frequency points are processed one by one, the spectral components corresponding to the positions marked as 1 in the deletion sequence are set to zero, and the spectral components corresponding to the positions marked as 0 are retained. After this selective processing, a new spectral sequence is formed, which is the processed spectral sequence. The frequency components affected by interference in the processed spectral sequence have been eliminated, while the frequency components corresponding to the spread spectrum signal and noise are retained, realizing the suppression of narrowband interference in the frequency domain. The sequence will be sent to the inverse fast Fourier transform unit to convert the time domain signal for subsequent processing.

[0037] In a specific embodiment, step S5 comprises: inputting the processed spectral sequence into the inverse fast Fourier transform operation unit; performing N-point inverse fast Fourier transform operation on the processed spectral sequence; converting the inverse-transformed frequency domain data into time domain complex data, and outputting the interference-suppressed time domain data containing real and imaginary parts.

[0038] Specifically, the processed spectrum sequence has completed the interference suppression operation in the frequency domain, and needs to be converted back to the time domain form to be used by the subsequent despreading and demodulation modules of the receiver. The processed spectrum sequence is input to an inverse fast Fourier transform operation unit, which implements the reverse transformation function from the frequency domain to the time domain. The internal structure of the inverse fast Fourier transform operation unit is similar to that of the fast Fourier transform operation unit, and also adopts a hierarchical processing architecture of butterfly operation, but the rotation factor used in the operation process is the conjugate complex of the fast Fourier transform rotation factor, and the result needs to be normalized when finally output. The processed spectrum sequence contains N frequency points, each frequency point is stored in complex form, containing real and imaginary parts, and these spectrum data are input to the inverse transform operation unit in frequency point order. The N-point inverse fast Fourier transform operation is performed on the processed spectrum sequence, and the operation unit processes the input N frequency domain complex numbers according to the inverse transform algorithm. The inverse transform process recombines each frequency component in the frequency domain into a time domain signal, and the value of each time domain sampling point is determined by the weighted superposition result of all frequency domain frequency points, and the weight coefficient is provided by the inverse transform rotation factor. In a 1024-point inverse transform, the operation unit also needs to perform 10 levels of butterfly operation, each level of operation maps part of the frequency domain information to a time domain sampling point, and the results of each level of operation are finally accumulated and passed to form a complete time domain output sequence.

[0039] The output of the inverse fast Fourier transform is time domain complex data, which restores the time sequence characteristics of the signal. The process of inverse transform operation converts the frequency domain data into time domain complex data, which is essentially a vector synthesis of discrete frequency components according to their phase relationship, and reconstructs the instantaneous amplitude variation of the signal on the time axis. The output time domain data contains N time domain sampling points, each sampling point is a complex number composed of real and imaginary parts, corresponding to the in-phase component and quadrature component of the zero intermediate frequency signal respectively. The N time domain sampling points are arranged in chronological order, and the time interval between adjacent sampling points is the same as the original sampling time interval, maintaining the time domain continuity of the signal. Since the interference frequency components are zeroed in the frequency domain processing stage, these frequency components no longer contribute to the time domain signal in the inverse transform process, so the energy of the narrowband interference signal in the output time domain data is greatly weakened. The output time domain data is the time domain data after interference suppression, which retains the spread spectrum signal and noise components, effectively suppresses the narrowband interference components, and improves the interference noise ratio of the signal, creating more favorable signal conditions for subsequent spread spectrum code acquisition, tracking and data demodulation, enabling the receiver to maintain normal working performance in the presence of strong interference.

[0040] In a specific embodiment, the frequency domain data after inverse transform is converted into time domain complex data, and the time domain data after interference suppression containing real and imaginary parts is output, including: extracting the complex result after N-point inverse fast Fourier transform operation; separating real part and imaginary part of each time domain sampling point in the complex result; arranging the separated real part and imaginary part in time sequence to form time domain complex data; outputting the time domain complex data as time domain data after interference suppression.

[0041] Specifically, after the inverse fast Fourier transform operation unit completes N-point inverse transform calculation, the output result is stored in the output buffer of the operation unit. Extracting the complex result after N-point inverse fast Fourier transform operation refers to reading out the data in the operation unit output buffer, which is the direct operation output of inverse transform and contains the reconstructed time domain signal information after frequency domain interference suppression. The complex result contains N elements, each element corresponding to the signal instantaneous value at a time domain sampling time, and the arrangement order of the elements is consistent with the sampling order on the time axis. During the inverse transform operation process, the result will be normalized, usually by dividing the accumulated result by N to ensure that the amplitude of the inverse transform output maintains the same dimension relationship as the original time domain signal. Each element in the extracted complex result is stored in complex format, and in a digital signal processing system, two independent data channels or storage areas are usually used to store the real part and the imaginary part, or the real part and the imaginary part are arranged continuously by using interleaved storage. Separating real part and imaginary part of each time domain sampling point in the complex result is to separate the complex format data into two independent real data streams, the real part representing the in-phase channel data of the zero intermediate frequency signal, and the imaginary part representing the quadrature channel data.

[0042] The separation process starts from the first time domain sampling point, extracts the real part and the imaginary part of the sampling point, and then performs the same separation operation on the second sampling point, and sequentially processes until the real part and the imaginary part of N sampling points are completed. The separated real part and imaginary part are arranged in time sequence, and the time sequence is determined by the serial number of the sampling point. The sampling point with smaller serial number corresponds to earlier sampling time, and the sampling point with larger serial number corresponds to later sampling time. According to this time sequence, the real part and the imaginary part of N sampling points are reorganized to form an ordered data sequence. The composition of the time domain complex data is that the separated real part and imaginary part are associated in pairs. The real part and the imaginary part of each time domain sampling point form two components of a complex number, and N pairs of real part and imaginary part data together constitute a complete time domain complex data sequence. The time domain complex data maintains the double-channel characteristics of the zero intermediate frequency signal, and can completely represent the amplitude and phase changes of the signal in the time domain. The output time domain complex data is the time domain data after interference suppression. The output operation transmits the processed time domain data from the current processing unit to the next processing module of the receiver, which is usually a spread spectrum code correlator or a despreading module. The narrowband interference signal in the output time domain data has been suppressed by frequency domain zeroing, and the signal quality has been significantly improved compared to before suppression. The data can be directly used for subsequent spread spectrum code synchronization capture, code tracking and data symbol demodulation processing, and completes the interference suppression conversion process from the received signal to the available baseband signal.

[0043] The improved frequency domain narrowband interference suppression double threshold determination method in the embodiments of the present application is described above, and the improved frequency domain narrowband interference suppression double threshold determination system in the embodiments of the present application is described below. Please refer to Figure 2 An embodiment of the improved frequency domain narrowband interference suppression double threshold determination system in the embodiments of the present application includes: A conversion module is configured to convert the zero intermediate frequency time domain signal into a frequency spectrum data sequence through fast Fourier transform; A calculation module is configured to perform a modulus operation on the frequency spectrum data sequence to obtain a modulus value sequence, calculate a frequency spectrum mean value according to the modulus value sequence, and multiply the frequency spectrum mean value by a high threshold factor and a low threshold factor to obtain a high threshold value and a low threshold value; A marking module is configured to count the number of points in the modulus value sequence that exceed the high threshold value, mark the positions in the modulus value sequence that exceed the low threshold value as interference positions, and generate a deletion sequence; A deletion module is configured to retain the frequency spectrum data sequence when the number of points is zero, and set the values of the frequency spectrum data sequence corresponding to the interference positions to zero according to the deletion sequence when the number of points is greater than zero to obtain a processed frequency spectrum sequence; An interference suppression module is configured to perform inverse fast Fourier transform on the processed frequency spectrum sequence to obtain time domain data after interference suppression.

[0044] The above Figure 2 The improved frequency domain narrowband interference suppression double threshold determination system in the embodiment of the application is described in detail from the perspective of a modular functional entity, and the improved frequency domain narrowband interference suppression double threshold determination device in the embodiment of the application is described in detail from the perspective of hardware processing.

[0045] Referring to Figure 3 The embodiment of the application also provides an improved frequency domain narrowband interference suppression double threshold determination device. The improved frequency domain narrowband interference suppression double threshold determination device can be a server, and the internal structure of the improved frequency domain narrowband interference suppression double threshold determination device can be as shown in Figure 3 The improved frequency domain narrowband interference suppression double threshold determination device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. The processor of the computer is used to provide computing and control capabilities. The memory of the improved frequency domain narrowband interference suppression double threshold determination device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the improved frequency domain narrowband interference suppression double threshold determination device is used to store the corresponding data in the embodiment. The network interface of the improved frequency domain narrowband interference suppression double threshold determination device is used to communicate with the external terminal through the network connection. The computer program is executed by the processor to implement the above method.

[0046] Those skilled in the art can understand Figure 3 The structure shown in the above

[0047] The application also provides a computer readable storage medium. The computer readable storage medium can be a non-volatile computer readable storage medium, or can be a volatile computer readable storage medium. The computer readable storage medium stores instructions. When the instructions are run on a computer, the computer executes the steps of the improved frequency domain narrowband interference suppression double threshold determination method.

[0048] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, system and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein.

[0049] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing an improved frequency domain narrowband interference suppression double threshold determination device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0050] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An improved method of determining dual thresholds for narrowband interference suppression in the frequency domain, characterized by, The method comprises: Step S1, converting a zero intermediate frequency time domain signal into a frequency spectrum data sequence through fast Fourier transform; Step S2, performing a modulus operation on the frequency spectrum data sequence to obtain a modulus value sequence, calculating a frequency spectrum mean value according to the modulus value sequence, and multiplying the frequency spectrum mean value by a high threshold factor and a low threshold factor to obtain a high threshold value and a low threshold value; Step S3, counting the number of points in the modulus value sequence that exceed the high threshold value, marking positions in the modulus value sequence that exceed the low threshold value as interference positions, and generating a deletion sequence; Step S4, when the number of points is zero, retaining the frequency spectrum data sequence, and when the number of points is greater than zero, setting the values of the frequency spectrum data sequence at the interference positions to zero according to the deletion sequence to obtain a processed frequency spectrum sequence; Step S5, performing inverse fast Fourier transform on the processed frequency spectrum sequence to obtain time domain data after interference suppression.

2. The improved frequency domain narrowband interference mitigation double threshold determination method according to claim 1, characterized in that, The step S1 comprises: obtaining a zero intermediate frequency time domain signal after frequency down-conversion at a receiving end; sampling the zero intermediate frequency time domain signal to obtain a time domain signal sequence; inputting the time domain signal sequence into a fast Fourier transform operation unit to perform N-point fast Fourier transform; outputting the frequency spectrum data sequence containing N frequency points, each frequency point in the frequency spectrum data sequence containing a real part and an imaginary part.

3. The improved method for determining dual thresholds for narrow band interference rejection in frequency domain as claimed in claim 1 wherein, The step S2 comprises: performing square operation on the real part and the imaginary part of each frequency point in the frequency spectrum data sequence, adding the squared real part and the squared imaginary part, and performing square root operation to obtain the modulus value sequence; performing accumulation sum operation on all N modulus values in the modulus value sequence, dividing the accumulation sum result by N to obtain the frequency spectrum mean value; multiplying the frequency spectrum mean value by the high threshold factor to obtain the high threshold value; multiplying the frequency spectrum mean value by the low threshold factor to obtain the low threshold value.

4. The improved method for determining dual thresholds for narrow band interference rejection in frequency domain as claimed in claim 1 wherein, The step S3 comprises: comparing each modulus value in the modulus value sequence with the high threshold value point by point, counting the number of modulus values greater than the high threshold value, and recording the number of points; comparing each modulus value in the modulus value sequence with the low threshold value point by point; marking the frequency point positions greater than the low threshold value in the modulus value sequence as the interference positions and assigning values of 1 to the corresponding positions; marking the frequency point positions less than or equal to the low threshold value in the modulus value sequence as non-interference positions and assigning values of 0 to the corresponding positions to generate the deletion sequence with a length of N.

5. The improved method for determining dual thresholds for narrow band interference rejection in frequency domain as claimed in claim 1 wherein, The step S4 comprises: determining whether the number of points is equal to zero; when the number of points is equal to zero, directly taking the frequency spectrum data sequence as the processed frequency spectrum sequence; when the number of points is greater than zero, corresponding the frequency spectrum data sequence with the deletion sequence point by point; according to the positions with values of 1 in the deletion sequence, setting the frequency point values of the corresponding positions of the frequency spectrum data sequence to zero, and according to the positions with values of 0 in the deletion sequence, retaining the frequency point values of the corresponding positions of the frequency spectrum data sequence to obtain the processed frequency spectrum sequence.

6. The improved method for determining dual thresholds for narrow-band interference suppression in frequency domain as claimed in claim 1 wherein, The step S5 comprises: inputting the processed frequency spectrum sequence into an inverse fast Fourier transform operation unit; performing N-point inverse fast Fourier transform operation on the processed spectrum sequence; converting the inverse-transformed frequency domain data into time domain complex data, and outputting the interference-suppressed time domain data containing real and imaginary parts.

7. The improved frequency domain narrowband interference mitigation double threshold determination method of claim 6, wherein, The converting the inverse-transformed frequency domain data into time domain complex data, and outputting the interference-suppressed time domain data containing real and imaginary parts comprises: extracting the complex result after the N-point inverse fast Fourier transform operation; separating real and imaginary parts for each time domain sampling point in the complex result; arranging the separated real and imaginary parts in time sequence to form the time domain complex data; outputting the time domain complex data as the interference-suppressed time domain data.

8. An improved frequency domain narrowband interference mitigation dual threshold determination system, characterized by, The improved frequency domain narrowband interference suppression double-threshold determination system for implementing the improved frequency domain narrowband interference suppression double-threshold determination method according to any one of claims 1-7 comprises: a conversion module configured to convert a zero intermediate frequency time domain signal into a spectrum data sequence through fast Fourier transform; a calculation module configured to perform modulo operation on the spectrum data sequence to obtain a modulus value sequence, calculate a spectrum mean value according to the modulus value sequence, and multiply the spectrum mean value by a high threshold factor and a low threshold factor to obtain a high threshold value and a low threshold value; a marking module configured to count the number of points in the modulus value sequence that exceed the high threshold value, mark positions in the modulus value sequence that exceed the low threshold value as interference positions, and generate a deletion sequence; a deletion module configured to retain the spectrum data sequence when the number of points is zero, and set values corresponding to the interference positions in the spectrum data sequence to zero according to the deletion sequence when the number of points is greater than zero to obtain a processed spectrum sequence; an interference suppression module configured to perform inverse fast Fourier transform on the processed spectrum sequence to obtain interference-suppressed time domain data.

9. An improved frequency domain narrow band interference suppression dual threshold determination device, characterized by A computer program product comprising a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor implements the improved frequency domain narrowband interference suppression double-threshold determination method according to any one of claims 1-7 when executing the computer program.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when running on the processor, causes the processor to perform the improved frequency domain narrowband interference suppression double-threshold determination method according to any one of claims 1-7.

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