CN109639303B - Interference detection and suppression method based on windowing processing - Google Patents

Interference detection and suppression method based on windowing processing Download PDF

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CN109639303B
CN109639303B CN201811638158.6A CN201811638158A CN109639303B CN 109639303 B CN109639303 B CN 109639303B CN 201811638158 A CN201811638158 A CN 201811638158A CN 109639303 B CN109639303 B CN 109639303B
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李志强
孙健俊
殷君
聂晟昱
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Space E Star Communication Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B1/06—Receivers
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    • H04B1/1027—Means associated with receiver for limiting or suppressing noise or interference assessing signal quality or detecting noise/interference for the received signal
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
    • H04B1/06—Receivers
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    • H04B1/1027—Means associated with receiver for limiting or suppressing noise or interference assessing signal quality or detecting noise/interference for the received signal
    • H04B1/1036—Means associated with receiver for limiting or suppressing noise or interference assessing signal quality or detecting noise/interference for the received signal with automatic suppression of narrow band noise or interference, e.g. by using tuneable notch filters
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
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Abstract

The invention discloses an interference detection and suppression method based on windowing processing. The method comprises the steps of frequency domain transformation, interference detection, interference suppression and time domain transformation, wherein overlapping windowing is carried out after the frequency domain transformation, overlap removing processing is carried out after the time domain transformation, in addition, background noise estimation and self-adaption determination of an interference detection threshold can be carried out on a transformed frequency domain digital signal in the interference detection, the accuracy of interference signal detection is improved, and the signal-to-noise ratio loss brought by the windowing processing is reduced. The method has strong universality and is particularly suitable for detecting the interference signals in satellite communication and microwave communication.

Description

Interference detection and suppression method based on windowing processing
Technical Field
The present invention relates to the field of communication signal processing technologies, and in particular, to an interference detection and suppression method based on windowing for an interference signal.
Background
In radio communications such as satellite communications and mobile communications, various interference signals are often mixed into a communication signal when the communication signal is received by a receiver, and detection and cancellation of these interference signals are advantageous for correct reception of the communication signal.
In the prior art, the signal-to-noise ratio loss of communication signals occurs in the process of detecting and eliminating interference signals, and the threshold setting of interference detection is difficult to achieve optimization due to the fact that background noise cannot be accurately estimated.
Disclosure of Invention
The invention mainly solves the technical problem of providing an interference detection and suppression method based on windowing processing, and solves the problems of inaccurate background noise detection, signal to noise ratio reduction and the like existing in the detection and elimination of interference signals in the prior art.
In order to solve the above technical problem, one technical solution adopted by the present invention is to provide an interference detection and suppression method based on windowing processing, including the following steps: the method comprises the steps of frequency domain transformation, namely transforming a communication signal mixed with an interference signal into an input time domain digital signal through an AD sampler, windowing the input time domain digital signal, and then transforming the input time domain digital signal into a frequency domain digital signal through FFT; interference detection, namely performing background noise estimation on the frequency domain digital signal and then determining an interference detection threshold; interference suppression, wherein the component of the frequency domain digital signal which is greater than the interference detection threshold is regarded as an interference signal component, and the interference signal component is suppressed; and time domain transformation, namely, after the interference signal component in the frequency domain digital signal is suppressed, carrying out IFFT transformation on the frequency domain digital signal to restore the frequency domain digital signal into an output time domain digital signal.
In another embodiment of the interference detection and suppression method based on windowing processing, the windowing processing uses a window function including windowing processing of the input time-domain digital signal using a buttlet window, a hanning window, a Hamming window, a Blackman window, or a Blackman-Karris window.
In another embodiment of the interference detection and suppression method based on windowing, the input time-domain digital signal is a sequence x (k):
x(k)＝Ap(k)+n(k)
wherein, p (k) is a PN sequence with equal probability value of +/-l, and the length of the PN sequence is N; n (k) is zero mean and variance
Figure BDA0001930549070000021
A is the amplitude of the communication signal;
performing windowing on x (k), wherein the window function is w (k), and the sequence after windowing is as follows:
xw(k)＝Ap(k)w(k)+n(k)w(k)。
in another embodiment of the interference detection and suppression method based on windowing processing, the windowing processing is overlap windowing processing, and the time domain digital signal output by IFFT is subjected to overlap removing processing in the time domain transformation.
In another embodiment of the interference detection and suppression method based on windowing processing, the data sequence of the input time domain digital signal is equally segmented, the length of each data segment is N, the overlapping length of the front and rear adjacent data segments is Nr during overlapping windowing processing, and r (0 ≦ r < 1) is an overlapping factor.
In another embodiment of the interference detection and suppression method based on windowing, the overlap factor r is 1/2, and the input time-domain digital signal is a sequence x (k) ═ ap (k) + N (k), where p (k) is a PN sequence with equal probability value ± l, and its length is N; n (k) is zero mean and variance
Figure BDA0001930549070000022
A is the amplitude of the communication signal;
the output sequence after the overlapping windowing is
Figure BDA0001930549070000023
Figure BDA0001930549070000024
Wherein the content of the first and second substances,
Figure BDA0001930549070000031
w (k) is a window function.
In another embodiment of the interference detection and suppression method based on windowing, the overlap factor r is 1/2, and the input time-domain digital signal is a sequence x (k) ═ ap (k) + N (k), where p (k) is a PN sequence with equal probability value ± l, and its length is N; n (k) is zero mean and variance
Figure BDA0001930549070000032
A is the amplitude of the communication signal;
the output sequence after the overlapping windowing is
Figure BDA0001930549070000033
Figure BDA0001930549070000034
Wherein the content of the first and second substances,
Figure BDA0001930549070000035
w (k) is a window function.
In another embodiment of the interference detection and suppression method based on windowing, the input time domain digital signal is divided into two input sequences, the first input sequence is subjected to windowing, and then subjected to FFT, interference detection, interference suppression and IFFT to obtain a first output sequence, the second input sequence is subjected to N/2 delay and then windowing, and then subjected to FFT, interference detection, interference suppression and IFFT to obtain a second output sequence, and the first output sequence and the second output sequence are added to obtain the output time domain digital signal.
The invention has the beneficial effects that: the invention discloses an interference detection and suppression method based on windowing processing. The method comprises the steps of frequency domain transformation, interference detection, interference suppression and time domain transformation, wherein overlapping windowing is carried out after the frequency domain transformation, overlap removing processing is carried out after the time domain transformation, in addition, background noise estimation and self-adaption determination of an interference detection threshold can be carried out on a transformed frequency domain digital signal in the interference detection, the accuracy of interference signal detection is improved, and the signal-to-noise ratio loss brought by the windowing processing is reduced. The method has strong universality and is particularly suitable for detecting the interference signals in satellite communication and microwave communication.
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FIG. 1 is a schematic block diagram illustrating an embodiment of a method for interference detection and suppression based on windowing in accordance with the present invention;
fig. 2 is a flow diagram of another embodiment of an interference detection and suppression method based on windowing in accordance with the present invention;
FIG. 3 is a block diagram of an overlay windowing process in accordance with another embodiment of the interference detection and suppression method based on windowing;
FIG. 4 is an illustration of an overlap-windowing process in accordance with another embodiment of the interference detection and suppression method based on windowing in accordance with the present invention;
FIG. 5 is a schematic diagram of an overlap-windowing process according to another embodiment of the interference detection and suppression method based on windowing of the present invention;
fig. 6 is a flow diagram of another embodiment of an interference detection and suppression method based on windowing in accordance with the present invention;
FIG. 7 is a graph of interference rejection threshold versus bit error rate for another embodiment of a method for interference detection and rejection based on windowing in accordance with the present invention;
FIG. 8 is a graph of trap magnitude versus bit error rate for another embodiment of a method for interference detection and suppression based on windowing in accordance with the present invention;
FIG. 9 is a graph of windowing overlap versus bit error rate according to another embodiment of the interference detection and suppression method based on windowing in accordance with the present invention;
fig. 10 is a schematic block diagram of another embodiment of an interference detection and suppression method based on windowing according to the present invention.
Detailed Description
In order to facilitate an understanding of the invention, the invention is described in more detail below with reference to the accompanying drawings and specific examples. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It is to be noted that, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
With reference to the schematic diagram of fig. 1 and the flowchart of fig. 2, a flowchart of an embodiment of an interference detection and suppression method based on windowing according to the present invention is disclosed. The method comprises the following steps:
first step S101: the method comprises the steps of frequency domain transformation, wherein a communication signal mixed with an interference signal is transformed into an input time domain digital signal through an AD sampler, and the input time domain digital signal is transformed into a frequency domain digital signal after FFT transformation;
second step S102: interference detection, namely performing background noise estimation on the frequency domain digital signal and then determining an interference detection threshold;
the third step S103: and interference suppression, wherein the component of the frequency domain digital signal which is greater than the interference detection threshold is regarded as an interference signal component, and the interference signal component is suppressed to eliminate interference signal energy.
Here, for the detected interference signal component, the amplitude of the interference signal component needs to be modified, which is also called threshold processing, and this process inevitably brings loss to the useful communication signal while processing the interference, resulting in a reduction in the output signal-to-noise ratio.
The fourth step S104: and time domain transformation, namely, after the interference signal component in the frequency domain digital signal is suppressed, carrying out IFFT transformation on the frequency domain digital signal to restore the frequency domain digital signal into an output time domain digital signal.
Preferably, the windowing process is further performed on the input time-domain digital signal before the frequency-domain transform in the first step S101.
Here, it is an important improvement to perform windowing, and if the windowing is not performed, performing an N-point FFT operation is equivalent to a rectangular window with an N-point added, and its first side lobe is only 13dB lower than the main lobe, i.e. the degree of side lobe suppression is only-13 dB, and for an interference signal several tens of dB greater than the communication signal, its side lobe is also much greater than the communication signal. Thus, from the frequency domain, the entire communication signal frequency domain is contaminated by interference. Therefore, windowing is required to focus more energy on the main lobe of the communication signal and reduce the amplitude of the side lobe. In time domain, the windowing process is to weight the input data, and the window function coefficients are gradually attenuated from the center to the two ends, so that the two ends of the data segment are smooth, and the frequency spectrum leakage is reduced. However, the window function is attenuated towards both ends, so that the input signal is distorted, and additional signal-to-noise ratio loss is brought.
Preferably, x (k) is the input time domain digital signal sequence:
x(k)＝Ap(k)+n(k)
wherein, p (k) is a PN sequence with equal probability value of +/-l, and the length of the PN sequence is N, which indicates that the sequence is a direct sequence spread spectrum signal; n (k) is zero mean and variance
Figure BDA0001930549070000061
And a is the signal amplitude.
Windowing x (k), the window function being w (k), the sequence after windowing:
xw(k)＝Ap(k)w(k)+n(k)w(k)
for xw(k) And performing correlated despreading with the length of N and integrating to obtain:
Figure BDA0001930549070000062
mean and variance of the sequence z:
Figure BDA0001930549070000063
the signal-to-noise ratio of the windowed sequence is:
Figure BDA0001930549070000064
the correlated output signal-to-noise ratio without windowing is:
Figure BDA0001930549070000065
the signal-to-noise ratio loss due to windowing is:
Figure BDA0001930549070000066
it can be seen that the loss of the snr is related to the coefficients of the window functions, and that different window functions bring different snr losses.
Table 1 shows the characteristics of several window functions and the resulting snr loss, and also compares the relative sidelobe peak amplitude and main lobe bandwidth of each window function. At a given length, the rectangular window has its main lobe narrowest, but its relative side lobe amplitude is greatest. From top to bottom, the main lobe of the various windows becomes wider and the relative side lobe amplitudes become smaller.
TABLE 1 fundamental characteristics of the windowing function and resulting loss of SNR
Figure BDA0001930549070000071
Most energy of the narrow-band interference signal can be limited in a plurality of limited spectral lines by selecting a window function with lower side lobes, so that the number of spectral lines needing to be suppressed is reduced, and the influence of interference on a useful signal is reduced to the greatest extent; meanwhile, the distortion of the useful signal can be reduced by selecting a smaller main lobe broadband.
The choice of the window function in practical applications requires consideration of the trade-off between sidelobe attenuation and main lobe bandwidth. Since the lower the side lobe is, the wider the main lobe is, and the more the impairment to the useful signal is at the same time as suppressing the narrowband interference, when selecting the window function, an appropriate window function is selected in accordance with the dynamic range of the received signal of the receiver and the intensity of the narrowband interference that needs to be suppressed.
Further preferably, in order to compensate for the snr loss caused by windowing, the frequency domain digital signal needs to be subjected to overlap windowing. The overlap-and-window process has the advantage of reducing the loss of signal-to-noise ratio due to windowing, at the cost of increased computational complexity. And, the main factors affecting the snr loss are the window function type, the window length, and the windowing overlap.
From the selection of the type of the window function, when the relative amplitude of the side lobe of the window function is low, the main lobe bandwidth is wider, more frequency points need to be processed, and when the main lobe bandwidth is narrow, the relative amplitude of the side lobe is high, the frequency spectrum leakage is serious, and more frequency points are polluted, so when the window function is selected, the dynamic range of the received signal and the intensity of the narrow-band interference signal needing to be suppressed need to be considered, a proper window function is selected, and the loss of the signal-to-noise ratio is reduced. For the windowing overlapping degree, the larger the windowing overlapping proportion is, the smaller the signal-to-noise ratio loss is, but the implementation complexity is high, the hardware resource consumption is high, and compromise consideration is required.
In further correspondence with the overlap windowing process, in conjunction with fig. 3, it is preferable that the output time-domain digital signal is also subjected to a de-overlap process after the time-domain transform at the fourth step S104.
The windowing causes distortion of the desired signal and the input data is overlap windowed in order to compensate for the loss of signal to noise ratio caused by the windowing. Assuming that the data length into which the signal sequence is segmented is N, the overlap factor r (0 ≦ r < 1), the overlap windowing principle is shown in FIG. 4. Here, the data sequence inputted after windowing is segmented, the data length of each segment is N, and when overlapping is performed, the data length of each segment is also N, but the two segments of data overlap each other, and the overlap length is Nr. Therefore, another problem to be considered when performing overlap windowing on signals is how to combine two paths of data into one path of data as a final output result.
Preferably, there are two methods for the overlap windowing process: selection methods and addition methods. For example, 1/2 overlap windowing is performed, and the selection method is to take 1/2 blocks of data at the center of the window of each signal, discard 1/4 blocks of data on the left and right sides, and combine 1/2 blocks of data on the upper and lower sides into a complete data sequence. The addition rule is to add the overlapping portions of two pieces of data as the final output signal.
The loss in signal-to-noise ratio of the overlap output of the selection and addition method with the overlap factor of 1/2 is analyzed below. Preferably, in the selection method output mode, the output sequence after the overlapping windowing is as follows:
Figure BDA0001930549070000081
wherein
Figure BDA0001930549070000082
w1(k) The signal weighted value under the output of the selection method is known from the derivation result of the signal-to-noise ratio loss, and the signal-to-noise ratio loss under the output mode of the selection method is as follows:
Figure BDA0001930549070000083
preferably, in the additive output mode, the output sequence after the overlapping windowing is:
Figure BDA0001930549070000091
wherein
Figure BDA0001930549070000092
w2(k) Is the weighted value of the signal under the output of the summation, the loss of the signal-to-noise ratio of the summation is as follows:
Figure BDA0001930549070000093
table 2 shows the snr loss due to the selection and summation outputs at different overlap factors.
TABLE 2 SNR loss for different overlap factors for two data synthesis modes
Figure BDA0001930549070000094
As can be seen from table 2, the same overlap factor results in a lower signal-to-noise ratio for the summation method than for the selection method, and this difference can be intuitively explained from the time domain. Considering hardware implementation, the addition output is more than the selection output by Nxr times of addition operation; for a non-rectangular window, theoretically, the larger the overlap ratio of two consecutive segments of data is, the smaller the introduced windowing loss is, the larger the corresponding computation amount is, and the implementation is not easy, and the selection of the overlap ratio in practical application depends on hardware conditions and system design performance requirements.
Preferably, the selected addition and subtraction factor is 1/2, and another embodiment of the interference detection and suppression method based on overlap windowing is shown in fig. 5, where the input time domain digital signal is divided into two input sequences, a first input sequence x (N) is subjected to windowing, and then to FFT conversion, interference detection, interference suppression and IFFT conversion to obtain a first output sequence, a second input sequence is subjected to N/2 delay x (N + N/2) and then to windowing, and then is subjected to FFT conversion, interference detection, interference suppression and IFFT conversion to obtain a second output sequence, and the first output sequence and the second output sequence are added to obtain the output time domain digital signal. Here, the first path is a waveform obtained by windowing a single-path signal, and the second path is a waveform obtained by delaying and windowing a signal x (N) by N/2. The data fidelity is highest in the center of the window, and the closer to the ends of the window, the more severe the distortion. However, the two signals are complementary, and the place with the most serious distortion of one signal can be compensated by the other signal. By time domain overlap windowing, useful signals can be better recovered when IFFT conversion is performed after interference cancellation. Preferably, Blackman overlap windowing with a length N of 1024 is chosen to verify the effect of overlap windowing on signal distortion reduction, with an overlap factor of 1/2, the number of overlap windowing being 2.
Further preferably, in the interference detection, the background noise estimation is the most basic condition for the interference detection, and the background noise power is set to be
Figure BDA0001930549070000101
And performing FFT after windowing, so that the components of the interference frequency are concentrated in the main lobe. The frequency bands of the interference signals do not occupy more than half of the signal bandwidth, and the useful communication signals are so small that they are buried in noise, so at frequencies where there are no interferenceThe band is mainly the frequency component of gaussian white noise. Mathematical expectation is zero and variance is
Figure BDA0001930549070000102
The one-dimensional distribution of the envelope of the stationary gaussian band-limited noise follows rayleigh distribution. Since the frequency component of Gaussian white noise follows a normal distribution with a variance of
Figure BDA0001930549070000103
Therefore, the amplitude of the frequency component obeys Rayleigh distribution, the mean value of the amplitude of the whole frequency component can be obtained according to the statistical characteristics of the Rayleigh distribution, the amplitude variance of the frequency domain component of the background noise is estimated according to the relation between the mean value of the Rayleigh distribution and the normal distribution variance, and the time domain variance, namely the background noise power, is obtained according to the property of Fourier transform.
Here, assuming that the amplitude x of the frequency component of gaussian white noise follows rayleigh distribution, its probability density function:
Figure BDA0001930549070000104
probability distribution function of amplitude x:
Figure BDA0001930549070000111
the mean of the amplitudes x is:
Figure BDA0001930549070000112
the frequency component data with 1/2 ratio is taken from small to large, F (A) is 1/2, if the number of points obtained in the frequency domain is large, half of the minimum points are selected, and the frequency component data falls in the interval [0, A]In (1). It can be calculated that the variable x is in the interval [0, A]The mean value of (d) is recorded as μ1I.e. the mean of the minimum points of half of the frequency components, comparing mu and mu1The relationship between the two can be obtained,in practice, the average value of the entire frequency component can be derived from the undisturbed frequency component. When F (A) is 1/2, obtain
Figure BDA0001930549070000113
μ1Comprises the following steps:
Figure BDA0001930549070000114
relationship u/u between half minimum point mean of frequency component and mean of ensemble13.4328, i.e. obtaining mu1Then multiplied by 3.4328 to calculate mu. Therefore, the average of the entire frequency component is 3.4328 times the average of the minimum point of half of the frequency component, which is derived from the statistical properties of the rayleigh distribution.
Then according to the mean value of Rayleigh distribution
Figure BDA0001930549070000115
σFIs the mean square error of the normal distribution of the frequency components, based on
Figure BDA0001930549070000116
The background noise power is obtained.
Thus, μ can be determined first and then1Then find σ againFFinally, find out
Figure BDA0001930549070000117
At this point, the estimation of the background noise is completed.
Further preferably, the interference detection threshold design directly affects the anti-interference performance, and the design of the interference detection threshold is the key of the whole interference detection and elimination. On the one hand, the actual interference and signal are time-varying, so the interference detection threshold should not be fixed, and the threshold should be selected based on the statistical characteristics of the received signal. On the other hand, in order to overcome the disadvantage that the threshold detection algorithm is not strong in adaptability to the environment, it is necessary to be able to dynamically and adaptively detect the interference signal by multi-threshold interference.
Preferably, the mean value is obtained by counting x (k) after FFT of the input time domain digital signal
Figure BDA0001930549070000121
Sum mean square error
Figure BDA0001930549070000122
N is the number of spectral lines, ζnIs the amplitude of the corresponding spectral line. And further determining an interference threshold, and filtering out interference signals higher than the threshold. The threshold of the first moment algorithm is defined as Th ═ theta mu, and theta is an optimization coefficient of the interference detection threshold. The threshold of the second moment algorithm is defined as Th ═ μ + β σ, β is a threshold optimization factor, and can be selected from a preset weighting factor set according to different channel environments (such as fading, multipath delay and the like).
When an interference signal with power far larger than that of a spread spectrum signal and background noise exists in an input time domain digital signal, through FFT (fast Fourier transform), the output of a sub-band with interference does not meet Gaussian distribution, the output power of the sub-band polluted by strong interference is far larger than that of the sub-band polluted by weak interference or not polluted by interference, and the missed detection of part of the interfered sub-band is caused, so that the signal-to-noise ratio of an output decision variable is reduced, and the error rate of a system is increased. In addition, the common interference detection algorithm has low adaptability to the environment, and the characteristics of the channel and the interference and the conditions of multipath delay, fading and the like of the signal in the transmission process need to be estimated in advance when the threshold value is selected each time.
The self-adaptive threshold needs to meet two conditions, namely when no interference signal exists, the self-adaptive threshold value is higher than spectral lines of most communication signals, and useful spectral lines of the communication signals cannot be used as interference processing, so that the false alarm probability is minimum; secondly, when there is an interference signal, the adaptive threshold value should be lower than the spectral lines of all interference signals and higher than the spectral lines of most communication signals, so that the detection probability is maximum.
To satisfy these two conditions, the distribution of the spectral lines is first analyzed. Let x (n) ═ s (n) + n (n), s (n) be a signal, and n (n) be noise. The adaptive multi-threshold interference detection method here considers that the desired signal s (n) in the received signal is swamped by the channel noise n (n), for example, for a direct sequence spread spectrum signal. The DFT of the N-point sequence x (N) is defined as:
Figure BDA0001930549070000123
according to the property of fourier transform, the output spectral line is x (k) ═ s (k) + N (k), s (k) is DFT of N-point sequence s (N), and N (k) is DFT of N-point sequence N (N). The DFT transform may also be implemented by an FFT transform. Without interference, | X (k) non-woven2＝|S(k)+N(k)|2Subject to an exponential distribution with a parameter λ, having
Figure BDA0001930549070000131
Setting the adaptive threshold value as Th, then | X (k) & gtY2The probability p lower than the threshold Th is:
Figure BDA0001930549070000132
it can be calculated that when Th is 1/λ, p is 0.6321; when Th is 2/lambda, p is 0.8647; when Th is 3/lambda, p is 0.9502; when Th is 4/lambda, p is 0.9817; when Th is 5/λ, p is 0.9933.
Assuming that the output signal is a superposition of the useful communication signal and noise, the square of the amplitudes of the N spectral lines after the discrete fourier transform should follow an exponential distribution with a parameter λ, according to the above analysis. In the case of a non-interfering signal, the probability of the square of the N spectral line amplitudes being less than 5/λ is 0.9933, i.e. the probability of being greater than 5/λ is only 0.0067. It can be considered that the spectral lines of the N spectral lines whose square of the amplitude is greater than 5/λ are almost nonexistent. Then if there is interference, the spectral line whose square of the amplitude of the spectral line after the discrete fourier transform is greater than 5/λ can be considered as the spectral line containing the interference, and the interference is processed by using a zeroing or clamping algorithm.
However, in practical tests it has been found that when the gate is limited to the theoretical optimum value of 5/λ, the probability of treating the communication signal as interference is almost zero, i.e. the false alarm probability is lowest. However, after the strong interference is subjected to the clamping processing, the interference suppression effect is not thorough, and the detection probability of the weak interference is reduced. Therefore, from comprehensive factors such as false alarm probability, strong interference suppression effect, weak interference detection probability and the like, limiting the gate to 3/lambda is a compromise choice. Therefore, if the discrete fourier transform of the N-point input sequence x (N) is known as x (k), then in practical applications, when the number of points N of the DFT transform is large, the estimate of the statistical mean 1/λ may be replaced by the mean of the sum of squared spectral line amplitudes, and the adaptive threshold may be:
Figure BDA0001930549070000133
in addition, there is a problem that the first interference threshold determination in the dynamic adaptive multi-threshold interference detection technology cannot be performed by itself, because the above analysis assumes that there is no interference signal or at least needs to ensure that there is no strong interference, but actually the signals are all superimposed with interference signals, so other means are needed for the first interference threshold determination.
In this embodiment, a simplified approximate background noise estimation algorithm is used to assist in determining the first interference threshold. Since the desired signal s (n) in the received signal is drowned by the channel noise n (n), i.e. in the frequency domain, s (k) < n (k), and μ is the average of the entire undisturbed frequency components obtained by the background noise estimation algorithm, the first adaptive threshold can be Th 05 mu. Thus, the method at least ensures that most interference is suppressed, and further determines the threshold by using a dynamic self-adaptive multi-threshold interference detection algorithm. Fig. 6 shows a flow of an adaptive multi-threshold interference detection algorithm based on background noise estimation.
As shown in fig. 6, when the input time domain digital signal is FFT transformed to be represented as a sequence y (k), the background noise estimation is performed on y (k), and then the first detection threshold Th is determined05 mu, and then comparing the amplitude of each frequency component in the sequence Y (K) with a threshold value of 5 muWhen the amplitude of the frequency component is greater than 5 μ, the interference suppression processing is performed on the frequency component, for example, the amplitude of the frequency component is clipped, and when the amplitude of the frequency component is less than or equal to 5 μ, the interference suppression processing is not required. Therefore, each frequency component in Y (K) is subjected to threshold detection and interference suppression for the first time to obtain a sequence X (K), and then adaptive interference threshold detection is carried out, wherein the detection threshold at this time is as follows:
Figure BDA0001930549070000141
then comparing the amplitude of each frequency component in the sequence X (K) with a threshold value Th, when the amplitude of the frequency component is larger than Th, performing interference suppression processing on the frequency component, then re-encoding the frequency component subjected to the interference suppression processing into the sequence X (K), continuing to compare the frequency component with the current adaptive detection threshold Th (since the frequency component in the sequence X (K) is transformed, the threshold is correspondingly and adaptively adjusted), until the amplitude of all the frequency components is smaller than or equal to the current adaptive detection threshold Th, then performing IFFT conversion on the sequence X (K) and outputting the result.
Therefore, the interference detection problem is converted into a hypothesis test problem by the dynamic self-adaptive multi-threshold interference detection, a narrow-band interference component does not exist in a hypothesis receiving signal, then whether the interference component exists in the receiving signal is tested by setting a threshold value for the distribution obeyed by the transformed frequency spectrum, and the first interference detection threshold is determined by simplified background noise estimation. If the hypothesis is found to be established through detection, if no narrow-band interference exists in the received signal, the IFFT can be directly carried out on the spectral line, the spectral line is converted into a time domain, and then the data is sent to the next stage for processing. And if the detection finds that the hypothesis is not true, the interference exists in the received signal, the interference elimination processing is carried out on the spectral line of which the spectral line value is greater than the threshold value, then the average value of the processed spectral line is recalculated, and a new threshold is set to detect the spectral line until the spectral line of which the hypothesis is not true does not exist. Therefore, the interference frequency point is detected by setting the threshold in a multi-time self-adaptive manner, and the interference is removed by an interference elimination algorithm, so that the interference suppression is realized.
Further, the embodiments shown in fig. 6 and fig. 5 can be combined, so that the adaptive threshold detection can be performed on both paths of sequences in the process of the superimposition window according to the method shown in fig. 6.
Preferably, the effect of the above processing method can be checked by simulation. Fig. 7 shows the effect of different interference detection thresholds on the bit error rate. Taking the interference detection threshold as a variable, and analyzing the interference detection threshold to be 0-10 sigmaFThe corresponding bit error rate. As can be seen from fig. 7, the interference detection threshold takes 4 σFThe time error code rate is minimum. From the "3 σ rule" of normal distribution, it is known that Gaussian random variables are distributed in the interval (-3 σ) with a probability of 95%F,+3σF) In this case, the frequency component of the background noise is mainly distributed in the region (-3 σ)F,+3σF) When the threshold value of interference detection is less than 4 sigmaFWhen the frequency component of the partial communication signal is judged as interference and eliminated, the error rate is increased, and when the threshold value of the interference detection is 4 sigmaFTo 10 sigmaFWhen the interference detection threshold value is within the range of (3), the interference detection threshold value is larger than the amplitude value of the frequency component of the communication signal, the frequency component of the useful communication signal which is not interfered is reserved, and the range is smaller than the amplitude of the interference signal, and the frequency component of the interference signal can be effectively eliminated, so the interference elimination threshold value is 4 sigmaFTo 10 sigmaFWhen the bit error rate is within the range of (2), the bit error rate is within a normal range.
Fig. 8 shows the error rate of the clipping process for interference in interference suppression. The amplitude of the interference frequency component is analyzed to be 0 to 30 sigma by using the trap amplitude value as a variableFThe corresponding error rate is shown in fig. 8, where the trap amplitude is equal to the background noise amplitude, the error rate is within the normal range, and when the trap amplitude is 2 σFTo 6 sigmaFWhen the trap amplitude is larger than 10 sigma, the error rate is minimumFIn the process, the interference power is eliminated too little, and the error rate rapidly becomes larger and exceeds the normal range along with the increase of the trap amplitude value.
Fig. 9 shows the effect of the windowing overlap degree on the error rate, and the error rate is analyzed when the windowing overlap degree is 10% to 100% by taking the windowing overlap degree as a variable, and as can be seen from fig. 9, the error rates corresponding to the windowing overlap degree in the range of (0, 50%) and the range of (50%, 100%) are symmetrical, and the error rate is the smallest when the windowing overlap degree is 1/2. Due to the influence of windowing, the signal time domain waveform presents the shape of a window, the output signal subjected to interference suppression still presents the current situation of the window, when the windowing overlapping degree is 1/2, two paths of signals are orthogonal, and the signal time domain waveform after addition becomes gentle. Corresponding to the previously described embodiment shown in fig. 5.
Therefore, based on the simulation analysis of fig. 7 to 9, when the interference cancellation threshold value takes 4 σFThe trap amplitude is 2 sigmaFTo 6 sigmaFAnd when the windowing overlapping degree is 1/2, the algorithm can obtain the best anti-interference performance.
Further, after the interference detection and interference suppression processing, the amplitude (or power) of the output time-domain digital signal output from the time-domain transform and the amplitude (or power) of the input time-domain digital signal are changed, and therefore, it is desirable that the original amplitude (or power) of the input time-domain digital signal can be maintained after the processing, or that the amplitude (or power) of the output time-domain digital signal can be controlled to a stable power value for output. As shown in fig. 10, on the basis of the embodiment shown in fig. 3, a function of detecting the amplitude of the input time-domain digital signal and the amplitude of the output time-domain digital signal, that is, an amplitude detection function, is added, so that a gain control signal is further generated and output by comparing the amplitudes of the input and output signals, and the gain control signal is output to an amplifier that performs gain control on the output time-domain digital signal, so that the amplitude of the output time-domain digital signal can be controlled, and therefore, fig. 10 also adds a function of performing gain control on the output time-domain digital signal as compared with fig. 3, so that the amplitude of the output time-domain digital signal can be gain controlled as needed.
Therefore, the invention discloses an interference detection and suppression method based on windowing processing. The method comprises the steps of frequency domain transformation, interference detection, interference suppression and time domain transformation, wherein overlapping windowing is carried out after the frequency domain transformation, overlap removing processing is carried out after the time domain transformation, in addition, background noise estimation and self-adaption determination of an interference detection threshold can be carried out on a transformed frequency domain digital signal in the interference detection, the accuracy of interference signal detection is improved, and the signal-to-noise ratio loss brought by the windowing processing is reduced. The method has strong universality and is particularly suitable for detecting the interference signals in satellite communication and microwave communication.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent structural changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to other related technical fields, are included in the scope of the present invention.

Claims (8)

1. An interference detection and suppression method based on windowing processing is characterized by comprising the following steps:
the method comprises the steps of frequency domain transformation, namely transforming a communication signal mixed with an interference signal into an input time domain digital signal through an AD sampler, windowing the input time domain digital signal, and then transforming the input time domain digital signal into a frequency domain digital signal through FFT;
interference detection, namely performing background noise estimation on the frequency domain digital signal and then determining an interference detection threshold;
interference suppression, wherein the component of the frequency domain digital signal which is greater than the interference detection threshold is regarded as an interference signal component, and the interference signal component is suppressed;
time domain transformation, namely, after the interference signal component in the frequency domain digital signal is suppressed, carrying out IFFT transformation on the frequency domain digital signal to restore the frequency domain digital signal into an output time domain digital signal;
in the interference detection, an input time domain digital signal is represented as a sequence Y (k) after FFT (fast Fourier transform), and background noise estimation is carried out on the sequence Y (k), wherein the background noise power is
Figure FDA0002966379010000011
After windowing processing, FFT is carried out, then the components of the interference frequency are concentrated in the main lobe and then are distributed according to the mean value of Rayleigh distribution
Figure FDA0002966379010000012
σFIs the mean square error of the normal distribution of the frequency components, based on
Figure FDA0002966379010000013
Obtaining background noise power; then, the first detection threshold Th is determined05 mu is approximately reserved, then the amplitude of each frequency component in the sequence Y (k) is compared with a threshold value of 5 mu, and mu is the mean value of the whole frequency component when the frequency component is not interfered, which is obtained by background noise estimation; in interference suppression, when the amplitude of a frequency component is greater than 5 mu, performing interference suppression processing on the frequency component, and when the amplitude of the frequency component is less than or equal to 5 mu, not performing interference suppression processing, thereby performing threshold detection on each frequency component in Y (k) for the first time, and obtaining a sequence X (k) after the interference suppression processing;
then, self-adaptive interference threshold detection is carried out, and the detection threshold at this moment is as follows:
Figure FDA0002966379010000014
n is the number of points of the sequence X (k), 1/λ is the statistical average; in the following interference suppression, comparing the amplitude of each frequency component in the sequence X (k) with a threshold value Th, when the amplitude of the frequency component is greater than Th, performing interference suppression processing on the frequency component, recoding the frequency component subjected to the interference suppression processing into the sequence X (k), and meanwhile, adaptively adjusting the threshold Th correspondingly, and continuing to compare with the adaptive detection threshold Th until the amplitudes of all the frequency components are less than or equal to the current adaptive detection threshold Th; then, the time domain transformation is performed, and at this time, the sequence x (k) is output after being subjected to the IFFT transformation.
2. The windowing based interference detection and suppression method according to claim 1, wherein said windowing uses a window function comprising windowing said input time domain digital signal using a buttlett window, a hanning window, a Hamming window, a Blackman window, or a Blackman-Karris window.
3. The windowing based interference detection and suppression method according to claim 1, wherein said input time domain digital signal is the sequence x (k):
x(k)＝Ap(k)+n(k)
wherein, p (k) is a PN sequence with equal probability value of +/-l, and the length of the PN sequence is N; n (k) is zero mean and variance
Figure FDA0002966379010000021
A is the amplitude of the communication signal;
performing windowing on x (k), wherein the window function is w (k), and the sequence after windowing is as follows:
xw(k)＝Ap(k)w(k)+n(k)w(k)。
4. the method of claim 1, wherein the windowing is an overlap windowing corresponding to a de-overlap processing of the time domain digital signal output by the IFFT in the time domain transform.
5. The method of claim 4, wherein the data sequence of the input time-domain digital signal is equally segmented, each data segment has a length N, and when performing the overlap windowing, the overlapping length of the adjacent data segments is Nr, r is an overlap factor, and r is greater than or equal to 0 and less than 1.
6. The method of claim 5, wherein the overlap factor r is 1/2, and the input time-domain digital signal is a sequence x (k) ap (k) + N (k), where p (k) is a PN sequence with equal probability ± l, and the length of p (k) is N; n (k) is zero mean and variance
Figure FDA0002966379010000031
White gaussian noise ofSequence, a is the amplitude of the communication signal;
the output sequence after the overlapping windowing is
Figure FDA0002966379010000032
Figure FDA0002966379010000033
Wherein the content of the first and second substances,
Figure FDA0002966379010000034
w (k) is a window function.
7. The method of claim 6, wherein the overlap factor r is 1/2, and the input time-domain digital signal is a sequence x (k) ap (k) + N (k), where p (k) is a PN sequence with equal probability ± l, and the length is N; n (k) is zero mean and variance
Figure FDA0002966379010000035
A is the amplitude of the communication signal;
the output sequence after the overlapping windowing is
Figure FDA0002966379010000036
Figure FDA0002966379010000037
Wherein the content of the first and second substances,
Figure FDA0002966379010000038
w (k) is a window function.
8. The interference detection and suppression method based on windowing processing according to claim 7, wherein the input time domain digital signal is divided into two input sequences, the first input sequence is subjected to windowing processing and then subjected to FFT conversion, interference detection, interference suppression and IFFT conversion to obtain a first output sequence, the second input sequence is subjected to windowing processing after N/2 delay, and then subjected to FFT conversion, interference detection, interference suppression and IFFT conversion to obtain a second output sequence, and the first output sequence and the second output sequence are added to obtain the output time domain digital signal.
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