CN110034834A - A kind of Interference Detection and inhibit optimization method - Google Patents
A kind of Interference Detection and inhibit optimization method Download PDFInfo
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- CN110034834A CN110034834A CN201910302667.XA CN201910302667A CN110034834A CN 110034834 A CN110034834 A CN 110034834A CN 201910302667 A CN201910302667 A CN 201910302667A CN 110034834 A CN110034834 A CN 110034834A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/336—Signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
- H04L25/03006—Arrangements for removing intersymbol interference
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
- H04L25/03006—Arrangements for removing intersymbol interference
- H04L25/03159—Arrangements for removing intersymbol interference operating in the frequency domain
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Abstract
The invention discloses a kind of Interference Detection and inhibit optimization method.The method comprising the steps of frequency-domain transform, Interference Detection, AF panel and time domain transformation, it wherein include that ambient noise estimation and adaptive determining interference detection threshold are carried out to transformed frequency domain digital signal in Interference Detection, the accuracy to interferer signal detection is improved, reduction is thus also helped because detection threshold setting inaccuracy causes interference with and inhibits possible noise specific loss.This method is versatile, especially suitable for the interferer signal detection in Direct Sequence Spread Spectrum Communication.
Description
Technical field
The present invention relates to signal of communication processing technology fields, more particularly to for a kind of Interference Detection and suppression of interference signal
Optimization method processed.
Background technique
In the radio communications such as satellite communication, mobile communication, signal of communication is often mixed into when receiving end receives
Various interference signals are conducive to be properly received signal of communication to detecting and eliminating for these interference signals.
In the prior art, due to that accurately estimating background noise comprising and interference detection threshold cannot immobilize and cause to dry
It disturbs detection and is difficult to being optimal.
Summary of the invention
The invention mainly solves the technical problem of providing a kind of Interference Detection and inhibit optimization method, solves the prior art
In to background noise estimation and interference detection threshold setting inaccuracy problem.
In order to solve the above technical problems, one technical scheme adopted by the invention is that providing a kind of Interference Detection and inhibiting excellent
Change method, comprising the following steps: frequency-domain transform, the signal of communication for being mixed with interference signal first pass through AD sampler and be transformed to input
Time-domain digital signal is transformed to frequency domain digital signal using FFT；Interference Detection carries out ambient noise to frequency domain digital signal
Then estimation determines interference detection threshold；The ambient noise is that mathematic expectaion is zero, and variance isStable Gaussian band limit
Noise, the one-dimensional distribution Rayleigh distributed of envelope；AF panel is greater than the Interference Detection in the frequency domain digital signal
The component of thresholding is then considered interference signal component, carries out inhibition processing to the interference signal component；Time domain transformation, the frequency
After interference signal component is suppressed in the digital signal of domain, is converted using IFFT, be reduced to output time-domain digital signal.
In Interference Detection of the present invention and inhibit the frequency point that white Gaussian noise is first calculated in another embodiment of optimization method
The mean μ of the amplitude of amount, then calculate the mean μ of frequency component half smallest point1, then calculate frequency component normal distribution
MeansquaredeviationσF, finally obtain Background Noise Power
In Interference Detection of the present invention and inhibit in another embodiment of optimization method, the frequency for first calculating white Gaussian noise
The mean μ of the amplitude of rate component includes:
If the amplitude x Rayleigh distributed of the frequency component of white Gaussian noise, then corresponding probability density function:
The probability-distribution function of amplitude x:
The mean value of amplitude x are as follows:
Calculate the mean μ of frequency component half smallest point1Include:
Take from small to large proportion be 1/2 frequency component data, enable F (A)=1/2, calculate amplitude x section [0,
A] on mean value, i.e. the mean μ of frequency component half smallest point1Are as follows:
And it as F (A)=1/2, obtains
Frequency component half smallest point mean μ as a result,1Relationship between mean μ are as follows: u/u1=3.4328.
In Interference Detection of the present invention and inhibit in another embodiment of optimization method, calculates the square of frequency component normal distribution
Poor σFMeet:According toObtain Background Noise Power
In Interference Detection of the present invention and inhibit to determine that interference detection threshold includes: first in another embodiment of optimization method
Step, enabling input time-domain digital signal is x (n)=s (n)+n (n), and s (n) is signal, and n (n) is noise；The DFT of N point sequence x (n)
Transformation are as follows:
The spectral line of output is X (k)=S (k)+N (k), and the DFT that S (k) is N point sequence s (n) is converted, and N (k) is N point sequence
The DFT transform of n (n)；
Second step, it is determining in glitch-free situation, | X (k) |2=| S (k)+N (k) |2Obey the index point that parameter is λ
Cloth hasIf adaptive threshold value is Th, then | X (k) |2It is lower than threshold T h
Probability p are as follows:
Third step is calculated, as Th=1/ λ, p=0.6321；As Th=2/ λ, p=0.8647；As Th=3/ λ
When, p=0.9502；As Th=4/ λ, p=0.9817；As Th=5/ λ, p=0.9933.
In Interference Detection of the present invention and inhibit in another embodiment of optimization method, self adaptive detection threshold setting are as follows:
In Interference Detection of the present invention and inhibit in another embodiment of optimization method, initial detecting thresholding setting are as follows: Th0≈5
μ。
In Interference Detection of the present invention and inhibit in another embodiment of optimization method, when the time-domain digital signal of input passes through
It is sequence Y (K) that ambient noise estimation is carried out to Y (K), determines detection threshold Th for the first time after FFT transform05 μ of ≈, then by sequence
The amplitude of each frequency component in Y (K) and detection threshold Th for the first time0It is compared, when the amplitude for having frequency component is greater than 5 μ,
Then to the frequency component carry out AF panel processing, and for the amplitude of frequency component be less than or equal to 5 μ when, then without into
The processing of row AF panel, after each frequency component in sequence Y (K) is thus carried out Threshold detection for the first time and AF panel processing,
Sequence X (K) is obtained, adaptive disturbance Threshold detection, self adaptive detection threshold at this moment are then carried out are as follows:Then the amplitude of each frequency component in sequence X (K) is compared with threshold T h, when
When thering is the amplitude of frequency component to be greater than threshold T h, then AF panel processing is carried out to the frequency component, it then will be by interference
The frequency component of processing is inhibited to be incorporated into again again sequence X (K), continuation is compared with current self adaptive detection threshold Th, directly
Amplitude to all frequency components is less than or equal to current self adaptive detection threshold Th, then at this time carries out sequence X (K) again
It is exported after IFFT transformation.
The beneficial effects of the present invention are: the invention discloses a kind of Interference Detection and inhibiting optimization method.This method includes
Step has frequency-domain transform, Interference Detection, AF panel and time domain transformation, wherein including to transformed frequency domain in Interference Detection
Digital signal carries out ambient noise estimation and adaptive determining interference detection threshold, improves to the accurate of interferer signal detection
Thus degree also helps reduction because detection threshold setting inaccuracy causes interference with and inhibits possible noise specific loss.The party
Method is versatile, especially suitable for the interferer signal detection in Direct Sequence Spread Spectrum Communication.
Detailed description of the invention
Fig. 1 is the principle composition figure of Interference Detection and inhibition one embodiment of optimization method according to the present invention；
Fig. 2 is the flow chart of Interference Detection and inhibition another embodiment of optimization method according to the present invention；
Fig. 3 is the overlapping windowing process composition figure of Interference Detection and inhibition another embodiment of optimization method according to the present invention；
Fig. 4 is the overlapping windowing process explanatory diagram of Interference Detection and inhibition another embodiment of optimization method according to the present invention；
Fig. 5 is the overlapping windowing process schematic diagram of Interference Detection and inhibition another embodiment of optimization method according to the present invention；
Fig. 6 is the flow chart of Interference Detection and inhibition another embodiment of optimization method according to the present invention；
Fig. 7 is the AF panel thresholding and the bit error rate of Interference Detection and inhibition another embodiment of optimization method according to the present invention
Relational graph；
Fig. 8 is the sunken amplitude and bit error rate relation of Interference Detection and inhibition another embodiment of optimization method according to the present invention
Figure；
Fig. 9 is that the adding window degree of overlapping and the bit error rate of Interference Detection and inhibition another embodiment of optimization method according to the present invention are closed
System's figure；
Figure 10 is the principle composition figure of Interference Detection and inhibition another embodiment of optimization method according to the present invention.
Specific embodiment
To facilitate the understanding of the present invention, in the following with reference to the drawings and specific embodiments, the present invention will be described in more detail.
A better embodiment of the invention is given in the attached drawing.But the invention can be realized in many different forms, and unlimited
In this specification described embodiment.On the contrary, purpose of providing these embodiments is makes to the disclosure
Understand more thorough and comprehensive.
It should be noted that unless otherwise defined, all technical and scientific terms used in this specification with belong to
The normally understood meaning of those skilled in the art of the invention is identical.Used term in the description of the invention
It is the purpose in order to describe specific embodiment, is not intended to the limitation present invention.Term "and/or" packet used in this specification
Include any and all combinations of one or more related listed items.
In conjunction with Fig. 1 schematic diagram and Fig. 2 flow chart, open Interference Detection of the present invention and the stream for inhibiting one embodiment of optimization method
Cheng Tu.The method comprising the steps of:
First step S101: frequency-domain transform, the signal of communication for being mixed with interference signal first pass through AD sampler and are transformed to input
Time-domain digital signal, the input time-domain digital signal are transformed to frequency domain digital signal after FFT transform；
Second step S102: Interference Detection carries out ambient noise estimation to frequency domain digital signal, then determines Interference Detection door
Limit, the ambient noise is that mathematic expectaion is zero, and variance isStable Gaussian band-limited noise, envelope one-dimensional distribution clothes
From rayleigh distributed；
Third step S103: AF panel, the component that the interference detection threshold is greater than in the frequency domain digital signal are then recognized
To be interference signal component, inhibition processing is carried out to the interference signal component, to eliminate interference signal energy.
Here, the interference signal component for detecting, it is necessary to the amplitude of interference signal component is modified, also referred to as
Be threshold process, this process handle interference while loss inevitably can be brought to useful signal of communication, lead
Output signal-to-noise ratio is caused to reduce.
4th step S104: time domain converts, after interference signal component is suppressed in the frequency domain digital signal, using IFFT
Transformation, is reduced to output time-domain digital signal.
Preferably, windowing process also is carried out to the input time-domain digital signal before first step S101 frequency-domain transform.
Here, it is a critically important improvement by windowing process, if not windowing process, carries out the FFT operation of N point just
It is equivalent to the rectangular window for having added a N point, its first secondary lobe is only 13dB lower than main lobe, i.e., Sidelobe Suppression degree only has -13dB, right
For the interference signal of tens dB bigger than signal of communication, its secondary lobe is also more much bigger than signal of communication.Come in this way from frequency domain
It sees, entire signal of communication frequency domain is all disturbed to be polluted.Therefore it needs to carry out windowing process, assembles the main lobe of signal of communication more
Multipotency amount, sidelobe magnitudes reduce.From time domain, windowing process is exactly to be weighted to input data, and window function coefficient is therefrom
The heart is gradually decayed to both ends, guarantees that data segment both ends are smooth, to reduce spectrum leakage.But since window function is decayed to both ends,
Cause input signal to be distorted, additional snr loss can be brought.
Preferably, x (k) is the time-domain digital signal sequence of input:
X (k)=Ap (k)+n (k)
Wherein, p (k) is the PN sequence of equiprobability value ± l, the length is N, shows the sequence for Direct Sequence Spread Spectrum letter
Number；N (k) be mean value be zero, variance isGaussian sequence, A is signal amplitude.
Windowing process is carried out to x (k), window function is w (k), the sequence after adding window:
xw(k)=Ap (k) w (k)+n (k) w (k)
To xw(k) it carries out the coherently despreading that length is N and integrates:
The mean value and variance of sequence z:
Sequence signal-to-noise ratio after adding window are as follows:
The not correlation output signal-to-noise ratio of adding window are as follows:
So adding window bring snr loss are as follows:
As it can be seen that the loss of signal-to-noise ratio is related with the coefficient of window function, different window function bring snr loss is different.
Table 1 gives the characteristic for commonly using several window functions and its caused snr loss, while also compared each window letter
Several opposite side lobe peak amplitudes and main lobe bandwidth.In given length, the main lobe of rectangular window is most narrow, but its opposite sidelobe magnitudes
It is maximum.From top to bottom, the main lobe of various windows broadens, and opposite sidelobe magnitudes become smaller.
1 window function fundamental characteristics of table and caused snr loss
It is a limited number of by selecting the lower window function of secondary lobe that can be limited to most of energy of narrow-band interference signal
Within root spectral line, to reduce the radical for needing the spectral line inhibited, farthest reduce influence of the interference to useful signal；Together
The lesser main lobe broadband Shi Xuanyong can reduce the distortion of useful signal.
The selection of window function needs to consider the compromise of side lobe attenuation and main lobe broadband in practical application.The lower window letter of secondary lobe
Number, main lobe is wider, and it is also bigger to the damage of useful signal while interference to inhibit narrowband, therefore when selecting window function, to tie
It closes the dynamic range of the reception signal of receiver and the intensity for needing that narrowband is inhibited to interfere selects suitable window function.
It is further preferred that needing to carry out the frequency domain digital signal in order to compensate for noise specific loss caused by adding window
It is overlapped windowing process.Windowing process is overlapped to increase computational complexity as cost, advantage is the damage for reducing adding window to signal-to-noise ratio
Consumption.Also, influence the fenestrate type function of principal element of noise specific loss, window length, adding window degree of overlapping.
From the point of view of the selection of window function type, when low due to the secondary lobe relative amplitude of window function, main lobe bandwidth is wider, needs to locate
The frequency point of reason is more, and secondary lobe relative amplitude is high when main lobe narrow bandwidth, and spectral leakage is serious, and contaminated frequency point is more, therefore
When selecting window function, the intensity for the narrow-band interference signal that the dynamic range for receiving signal and needs inhibit, selection are considered
Suitable window function reduces the loss to signal-to-noise ratio.And for adding window degree of overlapping, adding window overlap proportion is bigger, damages to signal-to-noise ratio
Consume smaller, but implementation complexity is big and hardware resource consumption is more, and need to compromise consideration.
It is further corresponding with overlapping windowing process in conjunction with Fig. 3, it is preferred that after the transformation of the 4th step S104 time domain also
Overlap processing is carried out to output time-domain digital signal.
Windowing process will cause the distortion of useful signal, in order to compensate for noise specific loss caused by adding window, to input data
Carry out overlapping adding window.Assuming that the data length that signal sequence is segmented into is N, overlap factor r (0≤r < 1), overlapping adding window is former
Reason is as shown in Figure 4.Here the data sequence inputted after adding window is segmented, and each section of data length is N, and is worked as and be overlapped
When, the length of every one piece of data is also N, but two segment data of front and back has coincidence, and being overlapped length is Nr.Therefore, when to letter
After number carrying out overlapping adding window, another question in need of consideration is how two paths of data to be synthesized a circuit-switched data as finally
Export result.
Preferably, there are two types of methods for overlapping windowing process: back-and-forth method and additive process.By taking 1/2 overlapping adding window as an example, back-and-forth method
It is 1/2 piece of data of the window center of the road Qu Mei signal, gives up each 1/4 piece of data of the right and left, by 1/2 piece of upper and lower two-way
Data form a complete data sequence.Sum rule is to be added the lap of two segment datas to believe as final output
Number.
Lower surface analysis overlap factor is the snr loss of the back-and-forth method and additive process overlapping output in the case of 1/2.It is preferred that
, the output sequence under the back-and-forth method way of output, after being overlapped adding window are as follows:
Wherein
w1It (k) is the lower signal weighting value of back-and-forth method output, by the derivation result of snr loss it is found that back-and-forth method output
Snr loss under mode are as follows:
Preferably, the output sequence under the additive process way of output, after being overlapped adding window are as follows:
Wherein
w2(k) be additive process output under signal weighting value, the snr loss of additive process such as following formula:
Table 2 gives the output of the back-and-forth method under different overlap factors and additive process exports bring snr loss feelings
Condition.
The letter to credit ratio of 2 two kinds of Data Synthesis mode difference overlap factors of table is lost
As known from Table 2, when overlap factor is identical, additive process is smaller than back-and-forth method bring signal-to-noise ratio, this species diversity can be from
Time domain is intuitively explained.In view of hardware realization, output is added than selection and exports more N × r sub-addition operations；For non-square
For shape window, theoretically, continuous two segment datas overlap proportion is bigger, and smaller, corresponding operand is lost in the adding window of introducing
It is bigger, it is not easy to realize, the selection of overlap proportion depends on hardware condition and the requirement of system design performance in practical application.
Preferably, the overlap factor of selection is 1/2 additive process Data Synthesis mode, the interference based on overlapping windowing process
Detection and another embodiment of suppressing method are as shown in figure 5, the input time-domain digital signal is divided into two-way list entries, the first via
List entries x (n) is after windowing process, using obtaining the after FFT transform, Interference Detection, AF panel and IFFT transformation
Output sequence all the way, the second road list entries first passes through N/2 delay x (n+N/2) and carries out windowing process again, then also over FFT
The second road output sequence, the first via output sequence and second are obtained after transformation, Interference Detection, AF panel and IFFT transformation
Road output sequence obtains the output time-domain digital signal after being added.Here, the first via is the waveform after one-channel signal adding window, the
Two tunnels are signal x (n) waveforms after N/2 postpones adding window.The data fidelity of window center be it is highest, closer to window two
End is distorted more serious.But two paths of signals is complementary, wherein distortion most serious place can use benefit of in addition coming all the way
It repays.Useful signal can preferably be restored by being overlapped adding window when interference carries out IFFT transformation after eliminating by time domain.Preferably, it selects
The Blackman for being N=1024 with length is overlapped adding window to verify the effect that overlapping adding window reduces distorted signals, overlap factor 1/
2, overlapping adding window number is 2.
It is further preferred that ambient noise estimation is the fundamental prerequisite of Interference Detection, if background is made an uproar in Interference Detection
Acoustical power size isFFT is after windowing process, then the component of interfering frequency concentrates in main lobe.If interference signal
The non-seizing signal bandwidth of frequency band more than half, and useful signal of communication very little and be submerged in noise, so not interfering with
Frequency band in mainly white Gaussian noise frequency component.Mathematic expectaion is zero, and variance isStable Gaussian band-limited noise,
The one-dimensional distribution of its envelope is Rayleigh distributed.Because the frequency component of white Gaussian noise is Normal Distribution, variance
ForSo the amplitude Rayleigh distributed of frequency component, can obtain entire frequency point according to the statistical property of rayleigh distributed
The mean value of discharge amplitude, and ambient noise frequency domain components amplitude is estimated by the mean value of rayleigh distributed and the relationship of Variance of Normal Distribution
Variance size, and the size of time domain variance namely the size of Background Noise Power have just been obtained according to the property of Fourier transformation.
Here, if the amplitude x Rayleigh distributed of the frequency component of white Gaussian noise, then its probability density function:
The probability-distribution function of amplitude x:
The mean value of amplitude x are as follows:
Taking proportion from small to large is 1/2 frequency component data, F (A)=1/2 is enabled, if in the acquirement point of frequency domain
There are many number, then selecting half smallest point, then can fall in section [0, A].Mean value of the variable x on section [0, A] can be calculated,
It is denoted as μ1, the as mean value of frequency component half smallest point compares μ and μ1, both available relationship, and then can be
The mean value of entire frequency component is derived in practical application from undisturbed frequency component.As F (A)=1/2, obtainμ1Are as follows:
Relationship u/u between frequency component half smallest point mean value and the mean value of entirety1=3.4328, that is, obtaining μ1Afterwards
μ can be calculated multiplied by 3.4328.So showing that the mean value of entire frequency component is frequency component by the statistical property of rayleigh distributed
3.4328 times of half smallest point mean value.
Further according to the mean value of rayleigh distributedσFFor the mean square deviation of frequency component normal distribution, according toObtain Background Noise Power.
Therefore μ can be first found out, then finds out μ1, then find out σF, finally find outSo far, ambient noise is completed
Estimation.
It is further preferred that the quality of the threshold scheme of Interference Detection directly affects interference free performance, interference detection threshold
Design be the key that entire Interference Detection and elimination.On the one hand, interference and signal in practice is all time-varying, so interference
Detection threshold should not be fixed and invariable, and selecting for thresholding should be to receive the statistical property of signal as foundation.On the other hand,
For the disadvantage for overcoming Threshold detection algorithm not strong to environmental suitability, it is desirable to be able to the interference of dynamic self-adapting multi-threshold Interference Detection
Signal.
Preferably, by counting to the X (k) after input time-domain digital signal progress FFT transform, mean value is obtainedAnd mean square errorN is spectral line number, ζnFor the amplitude of corresponding spectral line.In turn
It determines interference threshold, filters off the interference signal for being higher by thresholding.Wherein the threshold de of first moment algorithm is Th=θ μ, and θ is interference
The optimized coefficients of detection threshold.The threshold de of second moment algorithm is Th=μ+β * σ, and β is thresholding Optimization Factor, can be according to difference
Channel circumstance situations such as (such as decline, multidiameter) concentrate and choose from preset weighted factor.
When inputting in time-domain digital signal there are when the interference signal that power is much larger than spread-spectrum signal and ambient noise, pass through
FFT transform exports there are the subband of interference and is unsatisfactory for Gaussian Profile, and the subband output power polluted by strong jamming is much larger than quilt
The subband of weak jamming pollution or not disturbed pollution, causes the missing inspection that part is disturbed subband, so that output judgment variables
Signal-to-noise ratio becomes smaller, and error rate of system increases.In addition, common Interference Detection algorithm is not strong to the adaptability of environment, choose every time
Threshold value all needs in advance the characteristics of to channel, interference and signal to pre-estimate situations such as multidiameter, decline in transmission process.
Adaptive threshold needs to meet two conditions, first is that adaptive threshold value should when not interfering with signal
Higher than the spectral line of most signals of communication, the spectral line of useful signal of communication cannot be handled as interference, make false-alarm probability
It is minimum；Second is that adaptive threshold value should be lower than the spectral line of all interference signals when having interference signal, and it is higher than exhausted
The spectral line of most of signals of communication keeps detection probability maximum.
To meet the two conditions, the distribution of spectral line is analyzed first.Assuming that input time-domain digital signal is x (n)
=s (n)+n (n), s (n) are signal, and n (n) is noise.Here Adaptive multi-threshold interference detection method is thought to receive signal
In desired signal s (n) flooded by interchannel noise n (n), such as be directed to Direct Sequence Spread Spectrum Signal.N point sequence x's (n)
DFT is defined as:
According to the property of Fourier transformation, the spectral line of output is X (k)=S (k)+N (k), and S (k) is N point sequence s's (n)
DFT, N (k) are the DFT of N point sequence n (n).The DFT transform can also be realized by FFT transform.In glitch-free situation,
|X(k)|2=| S (k)+N (k) |2The exponential distribution that parameter is λ is obeyed, is hadIf from
Adaptation threshold value is Th, then | X (k) |2The Probability p lower than threshold T h are as follows:
It can be calculated, as Th=1/ λ, p=0.6321；As Th=2/ λ, p=0.8647；As Th=3/ λ
When, p=0.9502；As Th=4/ λ, p=0.9817；As Th=5/ λ, p=0.9933.
Assuming that output signal is formed by stacking by useful signal of communication with noise, then according to above-mentioned analysis, discrete fourier
N root spectral line amplitude square should obey the exponential distribution that parameter is λ after transformation.In the case where state no interference signal, N root breadth of spectral line
The probability square less than 5/ λ of degree is 0.9933, i.e., the probability greater than 5/ λ is only 0.0067.It is considered that in N root spectral line
The spectral line square greater than 5/ λ of amplitude is almost to be not present.If that with the presence of interference, it is believed that direct computation of DFT
The spectral line square greater than 5/ λ of spectral line amplitude is containing noisy spectral line, using zero or clamped algorithm to interference after leaf transformation
It is handled.
But found in actual test, when thresholding is scheduled on 5/ λ of theoretical optimal value, although signal of communication as interfere into
The probability of row processing is almost nil, i.e., false-alarm probability is minimum.But after carrying out clamped process to strong jamming, the effect of AF panel
Fruit is simultaneously not thorough, in addition, can decline to the detection probability of weak jamming.Therefore from false-alarm probability, strong jamming inhibitory effect and weak
The composite factors such as Interference Detection probability are set out, and it is a selection for compromise that thresholding, which is scheduled on 3/ λ,.So if known N point list entries
The discrete Fourier transform of x (n) is X (k), then in practical applications, when the points N of DFT transform is larger, assembly average
The estimated value of 1/ λ can be replaced with the mean value of breadth of spectral line degree quadratic sum, and adaptive threshold is desirable are as follows:
It is exactly the interference for the first time in dynamic self-adapting multi-threshold interference detection technique in addition, there is also an issue here
Thresholding determination can not be completed by itself, do not interfered with signal because analysis above all assumes that or at least to be ensured without strong
Interference threshold is determined under conditions of interference, but actually signal is all often superposition interference signal, so interference threshold for the first time
It determines and needs by other means.
It will assist determining interference threshold for the first time using simplified approximate background noise Estimation Algorithm in the present embodiment.Due to
The desired signal s (n) received in signal is flooded by interchannel noise n (n), that is to say, that having S (k) < < N (k), μ in frequency domain is benefit
With ambient noise algorithm for estimating obtain it is undisturbed when entire frequency component mean value, therefore adaptive threshold is desirable for the first time
For Th0≈5μ.It has at least ensured that most interference are suppressed in this way, the multi-threshold of dynamic self-adapting is recycled to interfere inspection
Method of determining and calculating further determines that thresholding.Fig. 6 gives the Adaptive multi-threshold Interference Detection algorithm stream based on ambient noise estimation
Journey.
As shown in fig. 6, being carried out when the time-domain digital signal of input is expressed as sequence Y (K) after FFT transform to Y (K)
Ambient noise estimation, then determines detection threshold Th for the first time05 μ of ≈, then by the amplitude of each frequency component in sequence Y (K) with
5 μ of threshold value is compared, and when the amplitude for having frequency component is greater than 5 μ, then carries out AF panel processing, example to the frequency component
Such as the amplitude of the frequency component is carried out to fall into width processing, and the amplitude of frequency component is less than and equal to 5 μ when is then not necessarily to carry out
AF panel processing.Thus sequence is obtained after each frequency component in Y (K) being carried out Threshold detection for the first time and AF panel processing
X (K) then carries out adaptive disturbance Threshold detection, detection threshold at this moment are as follows:Then will
The amplitude of each frequency component in sequence X (K) is compared with threshold T h, when the amplitude for having frequency component is greater than Th, then
AF panel processing is carried out to the frequency component, then will be incorporated into again sequence X by the frequency component of AF panel processing
(K), continue compared with current self adaptive detection threshold Th (since the frequency component in sequence X (K) has transformation, the thresholding pair
Also should adaptively be adjusted), until the amplitude of all frequency components is less than or equal to current self adaptive detection threshold
Th is exported after sequence X (K) is then carried out IFFT transformation again at this time.
It is asked it can thus be seen that Interference Detection problem is converted hypothesis testing by dynamic self-adapting multi-threshold Interference Detection
Topic, it is assumed that receive and narrowband interference components are not present in signal, threshold value is then arranged by the distribution obeyed frequency spectrum after transformation
It receives to examine with the presence or absence of interference components in signal, interference detection threshold for the first time is determined by simplified ambient noise estimation.
If finding to assume to set up by detecting, it is believed that receive and narrowband interference is not present in signal, directly can carry out IFFT to spectral line, become
Time domain is changed to, then handles data feeding rear stage.If detection discovery is assumed invalid, then it is assumed that receive and deposited in signal
It is interfering, the spectral line for being greater than threshold value to spectral line value carries out interference elimination treatment, and then recalculating that treated again, spectral line is equal
Value, is arranged new thresholding and detects to spectral line, until making to assume that invalid spectral line is not present.Pass through so repeatedly certainly
The setting thresholding of adaptation come detect interference frequency point, by interference cancellation algorithm remove interfere, to realize AF panel.
Further, Fig. 6 and embodiment illustrated in fig. 5 can also be combined, two in windowing process can be folded with counterweight in this way
Road sequence can carry out adaptive threshold detecting according to method shown in fig. 6.
Preferably, validity check can be carried out to above-mentioned processing method by emulation.Fig. 7 shows that disturbance detects door
Limit the influence to the bit error rate.Using interference detection threshold as variable, Analysis interference detection threshold is 0 to 10 σFWhen corresponding error code
Rate.As can be seen from Figure 7, interference detection threshold value takes 4 σFWhen the bit error rate it is minimum.Gaussian random is known by " the 3 σ rule " of normal distribution
Variable is with 95% probability distribution in section (- 3 σF,+3σF) inner, the frequency component of ambient noise is mainly distributed on section (- 3 σF,
+3σF) inner, when interference detection threshold value is less than 4 σFWhen, the frequency component of section communication signal can be judged as interfering and being disappeared
It removes, the bit error rate is caused to become larger, when interference detection threshold value is in 4 σFTo 10 σFIn the range of when, interference detection threshold value be greater than communication
Signal frequency component range value remains the frequency component of not disturbed useful signal of communication, while this range is opposite
It is smaller in interference signal amplitude, interference signal frequency component can be effectively eliminated, so interference eliminates threshold value in 4 σFTo 10 σF
In the range of when, the bit error rate is in normal range (NR).
Fig. 8 is shown in AF panel, fall into interference the bit error rate of width processing.Using sunken amplitude as variable, divide
The amplitude of analysis interference frequency component falls into as 0 to 30 σFWhen the corresponding bit error rate fall into amplitude and ambient noise width as can be seen from Figure 8
When spending suitable, the bit error rate is in normal range (NR), when sunken amplitude is 2 σFTo 6 σFWhen, the bit error rate is minimum, when sunken amplitude is greater than 10 σF
When, jamming power is eliminated very few, and with the increase of sunken amplitude, the bit error rate becomes larger rapidly beyond normal range (NR).
Fig. 9 shows influence of the adding window degree of overlapping to the bit error rate, using adding window degree of overlapping as variable, analyzes adding window degree of overlapping
Corresponding bit error rate situation when being 10% to 100%, it can be seen in fig. 9 that adding window degree of overlapping in (0,50%) range and
The corresponding bit error rate is symmetrical in (50%, 100%) range, and when adding window degree of overlapping is 1/2, the bit error rate is minimum.Due to adding
The influence of window, time domain plethysmographic signal is presented the shape of window, the status of window can still be presented by the output signal of AF panel, when adding
When window degree of overlapping is 1/2, two paths of signals is orthogonal, and time domain plethysmographic signal becomes flat after addition.With aforementioned embodiment illustrated in fig. 5 phase
It is corresponding.
Therefore, the simulation analysis based on Fig. 7 to Fig. 9 eliminates threshold value when interference and takes 4 σF, sunken amplitude takes 2 σFTo 6 σF, add
When window degree of overlapping is 1/2, algorithm can get best anti-interference ability performance.
Further, after above-mentioned Interference Detection and AF panel processing, from the output time-domain number of time domain transformation output
The amplitude (either power) of word signal and the amplitude (or power) of input time-domain digital signal are changed, and are wished to thus
Enough amplitudes (or power) that original input time-domain digital signal can also be kept after above-mentioned processing, it is desirable alternatively to export
The amplitude (or power) of time-domain digital signal can be controlled in a stable performance number output.As shown in Figure 10, in Fig. 3 institute
Show to increase on the basis of embodiment and the amplitude of input time-domain digital signal and the amplitude of output time-domain digital signal are examined
The function of survey, i.e. amplitude detection function can be compared further by the amplitude detection to input and output signal in this way, be generated
A gain control signal is exported, which is output to the amplification that gain regulation is carried out to output time-domain digital signal
Device can thus regulate and control the amplitude of output time-domain digital signal, thus Figure 10 with respect to Fig. 3 also add to output when
Domain digital signal carry out gain regulation function, it is possible thereby to make the amplitude of output time-domain digital signal can according to need into
Row gain regulation.
It can be seen that the invention discloses a kind of Interference Detection and inhibiting optimization method.The method comprising the steps of frequency domain
Transformation, Interference Detection, AF panel and time domain transformation, wherein in Interference Detection include to transformed frequency domain digital signal into
The estimation of row ambient noise and adaptive determining interference detection threshold, improve the accuracy to interferer signal detection, thus also
Inhibit possible noise specific loss because detection threshold setting inaccuracy causes interference with conducive to reducing.This method is versatile,
Especially suitable for the interferer signal detection in Direct Sequence Spread Spectrum Communication.
The above description is only an embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalent structure transformation made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant technical fields,
Similarly it is included within the scope of the present invention.
Claims (8)
1. a kind of Interference Detection and inhibition optimization method, which comprises the following steps:
Frequency-domain transform, the signal of communication for being mixed with interference signal first pass through AD sampler and are transformed to input time-domain digital signal, then
It is frequency domain digital signal by FFT transform；
Interference Detection carries out ambient noise estimation to frequency domain digital signal, then determines interference detection threshold, the ambient noise
It is zero for mathematic expectaion, variance isStable Gaussian band-limited noise, the one-dimensional distribution Rayleigh distributed of envelope；
AF panel, the component that the interference detection threshold is greater than in the frequency domain digital signal are then considered interference signal point
Amount, carries out inhibition processing to the interference signal component；
Time domain converts, and after interference signal component is suppressed in the frequency domain digital signal, converts using IFFT, is reduced to export
Time-domain digital signal.
2. Interference Detection according to claim 1 and inhibition optimization method, which is characterized in that estimate in the ambient noise
In, the mean μ of the amplitude of the frequency component of white Gaussian noise is first calculated, then calculate the mean value of frequency component half smallest point
μ1, then calculate the meansquaredeviationσ of frequency component normal distributionF, finally obtain Background Noise Power
3. Interference Detection according to claim 2 and inhibition optimization method, which is characterized in that described to calculate Gauss white noise
The mean μ of the amplitude of the frequency component of sound includes:
If the amplitude x Rayleigh distributed of the frequency component of white Gaussian noise, then corresponding probability density function:
The probability-distribution function of amplitude x:
The mean value of amplitude x are as follows:
Calculate the mean μ of frequency component half smallest point1Include:
Taking proportion from small to large is 1/2 frequency component data, is enabled F (A)=1/2, calculates amplitude x on section [0, A]
Mean value, i.e. the mean μ of frequency component half smallest point1Are as follows:
And it as F (A)=1/2, obtains
Thus frequency component half smallest point mean μ is obtained1Relationship between mean μ are as follows: u/u1=3.4328.
4. Interference Detection according to claim 3 and inhibition optimization method, which is characterized in that calculate frequency component normal state point
The meansquaredeviationσ of clothFMeet:According toObtain Background Noise Power
5. Interference Detection according to claim 4 and inhibition optimization method, which is characterized in that determine interference detection threshold packet
It includes:
The first step, input time-domain digital signal are x (n)=s (n)+n (n), and s (n) is signal, and n (n) is noise；N point sequence x
(n) DFT transform are as follows:
The spectral line of output is X (k)=S (k)+N (k), and S (k) is the DFT transform of N point sequence s (n), and N (k) is N point sequence n (n)
DFT transform；
Second step, it is determining in glitch-free situation, | X (k) |2=| S (k)+N (k) |2The exponential distribution that parameter is λ is obeyed, is hadIf adaptive threshold value is Th, then | X (k) |2The Probability p lower than threshold T h
Are as follows:
Third step is calculated, as Th=1/ λ, p=0.6321；As Th=2/ λ, p=0.8647；As Th=3/ λ, p
=0.9502；As Th=4/ λ, p=0.9817；As Th=5/ λ, p=0.9933.
6. Interference Detection according to claim 5 and inhibition optimization method, which is characterized in that self adaptive detection threshold setting
Are as follows:
7. Interference Detection according to claim 6 and inhibition optimization method, which is characterized in that the setting of initial detecting thresholding
Are as follows: Th0≈5μ。
8. Interference Detection according to claim 7 and inhibition optimization method, which is characterized in that when the time-domain digital of input is believed
It is sequence Y (K) that ambient noise estimation is carried out to Y (K), determines detection threshold Th for the first time number after FFT transform0≈5μ；
Then, by the amplitude of each frequency component in sequence Y (K) and detection threshold Th for the first time0It is compared, when there is frequency component
Amplitude when being greater than 5 μ, then AF panel processing is carried out to the frequency component, and the amplitude of frequency component is less than or is waited
When 5 μ, then without carrying out AF panel processing, thus by each frequency component in sequence Y (K) carry out Threshold detection for the first time and
After AF panel processing, sequence X (K) is obtained；
Carry out adaptive disturbance Threshold detection, self adaptive detection threshold at this moment are as follows:By sequence
The amplitude of each frequency component in column X (K) is compared with threshold T h, when the amplitude for having frequency component is greater than threshold T h
When, then AF panel processing is carried out to the frequency component, then will be incorporated into again again by the frequency component of AF panel processing
Sequence X (K), continuation are compared with current self adaptive detection threshold Th, be less than up to the amplitude of all frequency component or
Equal to current self adaptive detection threshold Th, then exported after sequence X (K) being carried out IFFT transformation again at this time.
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