CN101951276A - Method for detecting and suppressing Gaussian fitting linear frequency-modulated jamming in direct sequence spread spectrum (DSSS) communication system - Google Patents

Abstract

The invention provides a method for detecting and suppressing Gaussian fitting linear frequency-modulated jamming in a direct sequence spread spectrum (DSSS) communication system. The characteristic that data frequency spectrum transmitted in an information channel in the DSSS communication system is similar to white noise is used, if the linear frequency-modulated jamming is exerted, a strong peak value exists on an instantaneous energy frequency spectrum and an obvious peak value line exists in a time-frequency energy distribution plane. At the receiving end of the DSSS communication system, the extreme point of each time point in the time-frequency energy distribution plane is tracked, so that the track of the peak value line is obtained. A linear regression technology is used to acquire parameters of the peak value so as to roughly estimate linear frequency-modulated slope rate and rotation angle alpha of fractional Fourier transform. According to the parameter alpha, the fractional Fourier transform is carried out on a signal, and an iterative Gaussian fitting process is used to exactly search to obtain the optimal alpha. The wave absorbing processing is performed in the optimal fractional Fourier transform domain so as to suppress the linear frequency-modulated jamming. The de-noised signals are output to the follow-up process.

Description

Gauss curve fitting linear frequency modulation Interference Detection and inhibition method in the direct-sequence communications system

Technical field

The invention belongs to wireless data transmission, communication Anti-Jamming Technique field, what relate to is detection and the inhibition method of in the direct sequence spread spectrum communication (abbreviation direct-sequence spread-spectrum communication) linear frequency modulation being disturbed.

Background technology

In the communications field, disturb and suppress always to be the focus that the researcher pays close attention to.Present stage has been obtained in a large number comparatively mature theory and achievement on Suppression of narrow band interference.The introducing of multiple research tools such as adaptive-filtering, transform domain technology, the sign indicating number ancillary technique more inhibition of narrow band interference provides how reliable and effective means.But along with deepening continuously and the raising of practical application request of research, broad-band interference suppresses to become again research focus new on this direction.In this case, existing treatment technology ubiquitous defective on broad-band interference suppresses makes people begin to seek the purpose that new handling implement realizes suppressing broad-band interference.

Spread spectrum technic has big capacity, anti-interference, low intercepting and capturing rate and can realize code division multiple access advantages such as (CDMA), is widely used, and becomes the technical foundation of next generation mobile communication.In the spread spectrum communication system, (Directsequence spread spectrum, DSSS) The Application of Technology is the most general for direct sequence spread spectrum.There is very strong antijamming capability in the DSSS system, and still, when the intensity of external disturbance had surpassed the jamming margin of system, the performance of system will sharply descend, and at this moment, must introduce corresponding interference protection measure, normally before despreading signal is carried out preliminary treatment.At present, the achievement in research in this field mostly concentrates in the inhibition of narrow band interference, and in recent years, the non-stationary in broadband disturbs the influence of spread spectrum system is more and more caused people's attention, and its common form is that linear frequency modulation (LFM:line frequency modulate) disturbs.With respect to the single frequency sinusoidal ripple, linear frequency modulation disturbs more obvious to the influence of DSSS system.

Disturb at linear frequency modulation, proposed the method that a lot of inhibition are disturbed.There is distinct disadvantage in prior art.The linear frequency modulation that Fourier transfer pair broadband becomes when especially quick disturbs powerless.Therefore the time-frequency distributions technology can't discern a plurality of interference owing to there is cross term.

The fractional order conversion is a kind of new time frequency analyzing tool that causes that in recent years people pay close attention to, and is that fractional order thought is promoted the new variation that obtains in various traditional conversion.Fractional Fourier Transform (FRFT:Fractional Fouriertransform) then is a kind of special fractional order conversion, and fractional order thought also is to obtain from its popularization to traditional F ourier conversion.Suppressing special broad-band interference-linear frequency modulation in the Fractional Fourier territory disturbs existing corresponding algorithm to propose.Its basic ideas are to be variable with anglec of rotation α, observation signal is carried out Fractional Fourier Transform continuously, form the two-dimensional parameter (α of signal energy in fractional order or anglec of rotation α and chirp rate μ formation, μ) the Two dimensional Distribution on the plane, the two-dimensional search that carries out peak point by threshold value on this plane is realized the detection and the parameter Estimation of signal.The significant disadvantages of these algorithms is exactly that the hunting zone is big, and computation burden is excessive.

Summary of the invention

The object of the present invention is to provide fast linear frequency modulation Interference Detection and inhibition method in a kind of direct-sequence communications system that can overcome problems such as existing linear frequency modulation disturbance restraining method search volume based on Fractional Fourier Transform is big, heavy computational burden.

The object of the present invention is achieved like this:

(1) divide frame, will block from the data f (x) that receiver obtains short, necessarily overlapping Frame f arranged ₁, f ₂..., f _K-1, f _K

(2) to each frame f _i(x), suppose that linear frequency modulation disturbs existence, utilize Fourier transition structure two dimension time-frequency figure in short-term,, utilize linear regression technique to draw the anglec of rotation α that the peak line equation parameter promptly roughly estimates chirp slope and Fractional Fourier Transform by tracking to the peak line track;

(3) verify hypothesis and parameter optimization, carry out Fractional Fourier Transform according to parameter alpha, if exist remarkable peak value then to detect success, and parameter is carried out the iteration optimizing, utilize an iteration Gauss curve fitting process to carry out limited search and obtain the exact value that linear frequency modulation disturbs the anglec of rotation α ' of slope and Fractional Fourier Transform with accurate estimation;

(4) denoising is carried out wave absorption and is handled in the Fractional Fourier Transform territory, remove and disturb;

(5) judge whether to have handled all Frames;

(6) signal goes overlapping and reorganization, if treated all data f _i(x), then go overlapping and the reorganization processing, obtain interference signals the result data.

The present invention can also comprise:

1, described minute frame further comprises:

1) determines to contain the length of the pending signal f (x) that linear frequency modulation disturbs, represent with L;

2) determine that analysis frame is data frame length fr;

3) determine the front and back overlapping parameter 2*p of two frames (%), then lap 2* Δ p=fr*2*p%, and 4*p%＜1, p%=10%;

4) sliding step before and after between two frames is d=fr-2* Δ p;

5) respectively mend the data 0 that length is Δ p before and after signal f (x), generate new data f (x), length is 2* Δ p+L;

6) if 2* Δ p+ (K-1) * d＜2 Δs p+L＜2* Δ p+K*d then mends (2* Δ p+K*d)-(2* Δ p+L)=K*d-L data 0 again behind newly-generated f (x), constitute new f (x), length is 2* Δ p+K*d;

7) f (x) is divided into K frame f ₁, f ₂..., f _K-1, f _K,

f _i(x)：f((i-1)*d+1)，...，f(2*Δp+i*d)。

2, to each frame f _i(x), carrying out linear frequency modulation parameter prediction meter further comprises:

1) to f _i(x) do fourier conversion in short-term

Wherein w (n) is a window function, a kind of in rectangular window, Gaussian window, Hanning or the Hamming window;

2) obtain corresponding energy spectrogram

spectrogram(m，ω)＝|F(m，ω)| ²；

3) spectrogram (m, ω) time-frequency plane are followed the trail of the maximum curve, time-frequency plane spectrogram (m, ω) on, choose, write down each time point

On the maximum point position, constitute set P (m), 1≤m≤M;

4) if exist linear frequency modulation to disturb, it is adjacent that the some position among P (t-1) and the P (t) must be arranged, and utilizes the abutment points among the set P (m) to constitute line segment, with the method for fitting of a polynomial, tries to achieve the coarse value that the line segment parameter is a slope

Otherwise illustrate not exist linear frequency modulation to disturb in this frame that i=i+1 changes judging whether to have handled all Frame steps then.

3, described hypothesis verification and parameter optimization are based on the hypothesis verification and the parameter optimization of iteration Gauss curve fitting, and concrete grammar is:

1) utilize a stage to obtain

To f _i(x) carry out corresponding Fractional Fourier Transform

Obtain

Wherein

2) if Then exist linear FM signal to disturb, forward 3 to); If

Then do not exist linear FM signal to disturb, or the linear frequency modulation interfering energy is very for a short time is not enough to influence systematic function, forwards to and judge whether to have handled all Frame steps; Wherein μ is Average σ is Variance, a is a regulatory factor;

3), the excursion of k is decided to be if exist linear frequency modulation to disturb Iterations ite=1 is set;

4) the parameter iteration optimizing begins

To two slope value k _Head, k _End, make corresponding Fractional Fourier Transform respectively, obtain

$\max \left({\left|F_{p_{\mathrm{end}}}\left(f_i\left(x\right)\right)\right|}^2\right);$

If | k _Head-k _End|＜Δ, $\mathrm{\Δ}=\frac{|k_{\mathrm{hend}}-k_{\mathrm{end}}|}{5};$

If

If T=0.01, and iterations ite＜10 then forward 7 to) finishing iteration;

5) Gaussian function match

Calculate k _m∈ (k _Head, k _End):

$k_m=\frac{\max \left({\left|F_{p_{\mathrm{head}}}\left(f_i\left(x\right)\right)\right|}^2\right)}{\max \left({\left|F_{p_{\mathrm{head}}}\left(f_i\left(x\right)\right)\right|}^2\right)+\max \left({\left|F_{p_{\mathrm{end}}}\left(f_i\left(x\right)\right)\right|}^2\right)}\·k_{\mathrm{head}}+\frac{\max \left({\left|F_{p_{\mathrm{end}}}\left(f_i\left(x\right)\right)\right|}^2\right)}{\max \left({\left|F_{p_{\mathrm{head}}}\left(f_i\left(x\right)\right)\right|}^2\right)+\max \left({\left|F_{p_{\mathrm{end}}}\left(f_i\left(x\right)\right)\right|}^2\right)}\·k_{\mathrm{end}}$

If the Gaussian function form is:

$y=A\·e^{-\frac{{(x-b)}^2}{c}}$

It is taken the logarithm obtains:

$\log \left(y\right)=-\frac{1}{c}{x}^2+\frac{2b}{c}x+\log \left(A\right)-\frac{b^2}{c}$

The same with chirped slope estimation, be worth (y according to the observation _i, x _i), come estimated parameter A, b, c with linear regression method; With Slope Parameters and corresponding Fractional Fourier Transform territory extreme value, as 3 couples of input value in of Gaussian function match (x, y)={ (k _Head, M _Head), (k _m, M _m), (k _End, M _End) then estimate 3 parameters in the Gaussian function

Put into parameter sets

Wherein

$M_{\mathrm{end}}=\max \left({\left|F_{p_{\mathrm{end}}}\right|}^2\right);$

If in the set of Gaussian function fitting parameter, continuous two values change little, promptly

Then Forward 7 to) finishing iteration;

6) hill-climbing algorithm

If

K then _End=k _Head, k _Head=k _Head-Δ forwards 4 to);

If

K then _Head=k _End, k _End=k _End+ Δ forwards 4 to);

If

K then _Head=k _m, otherwise k _End=k _m, iterations ite=ite+1 forwards 4 to);

7) iteration finishes, and obtains

4, described denoising further comprises:

1) to f _i(x) make p rank Fractional Fourier Transform, obtain F _p(f _i(x));

2) obtain the extreme point position

3) utilize Gaussian window w _gRight

Carry out smoothly, obtain new

w _g～N (0,1) is a standardized normal distribution, and its window length is

4) utilize

At l _MLeft side δ neighborhood be monotone increasing, and at l _MRight side δ neighborhood be the rule of monotone decreasing, right

Carry out second differnce, obtain l _MNear two valley points position l _V1, l _V2, and l is arranged _V1＜l _M＜l _V2

5) with F _p(f _i(x)) at [l _V1, l _V2] to be changed to 0 be that wave absorption is handled to interval data, suppress linear frequency modulation and disturb,

$X_p\left(u\right)=F_p\left(u\right)=F_p\left(f_i\left(x\right)\right)=\left\{\begin{array}{cc}{F}_p\left(u\right)& \mathrm{if}(u<l_{v1})\mathrm{or}(u>l_{v2})\\ 0& ({\mathrm{<figure-callout\; id="1"\; label="l\; v"\; filenames="HSA00000291046000012.png,HSA00000291046000021.png"\; state="\{\{state\}\}">l</figure-callout>}}_v\&le;u\&le;l_{v2})\end{array}\right.;$

6) to the F behind the wave absorption _p(f _i(x)) do-p rank Fractional Fourier Transform, obtain

5, judged whether all Frames treated, and whether also existed linear frequency modulation to disturb to each frame cycle criterion to comprise:

1) whether the judgment data frame crosses the border, and exceeds process range, if i＞K then forwards signal to and goes overlapping and reorganization;

2) with Frame f _i(x) forward to again each frame f _i(x), carry out linear frequency modulation parameter prediction meter.

6, described signal goes overlapping and recombinates further to comprise:

1) will

Go overlappingly by rule, constitute the signal after going to disturb

Step 1, remove

A last Δ p data;

Step 2, remove

Each Δ p data of head and the tail, 1＜i＜K;

Step 3, remove A preceding Δ p data;

Step 4, will remove redundant

Head and the tail connect, and constitute

2) remove

Head and the tail replenish 0, remove anterior Δ p 0, remove afterbody K*d-L+ Δ p 0;

3) signal after obtaining disturbing with isometric the going of input signal f (x)

The present invention has overcome that existing linear frequency modulation disturbance restraining method search volume based on Fractional Fourier Transform is big, the shortcoming of heavy computational burden.We's ratio juris mainly is to utilize in the direct-sequence communications system at the similar white noise of data spectrum of channel and have the characteristics of spreading gain.Jamming-to-signal ratio (disturbing the energy ratio with signal) if less than spreading gain, does not then need to carry out special processing, and system can correctly receive the decode data; In believing that if ratio is greater than spreading gain, data are disturbed by linear FM signal obviously in channel, then the energy that disturbs of linear frequency modulation obviously is better than signal and very strong peak value must be arranged on the instantaneous energy frequency spectrum, must the time-have tangible peak line correspondence linear FM signal on the frequency Energy distribution plane.Therefore can be by tracking on time-frequency Energy distribution plane to the peak line track, utilize linear regression technique just can draw the anglec of rotation α that the peak line equation parameter can roughly estimate chirp slope and Fractional Fourier Transform, carry out Fractional Fourier Transform according to parameter then, if exist remarkable peak value then to detect success (existing linear FM signal to disturb), utilize an iteration Gauss curve fitting process to carry out limited search then and obtain the exact value that linear frequency modulation disturbs the anglec of rotation α ' of slope and Fractional Fourier Transform with accurate estimation.Certainly exist the corresponding linear frequency modulation of a peak value in Fractional Fourier Transform territory, α ' rank and disturb, carry out carrying out corresponding Fractional Fourier inverse transformation after wave absorption is handled, can remove linear frequency modulation and disturb.

The simple argumentation of basic principle of the present invention and foundation:

(1) relation of Fractional Fourier Transform and LFM

(background technology) as previously mentioned, the LFM signal has the energy accumulating characteristic in corresponding Fractional Fourier Transform territory, can produce pulse peak, so the suitable unknown parameter LFM signal of handling of Fractional Fourier Transform, separating finished to signal of communication and LFM interference.Subject matter is how to find with LFM corresponding fractional-order in parameter space fast.

(2) Fast estimation of the characteristics of spread spectrum communication and parameter

Spread spectrum communication system has spreading gain.In the spread spectrum communication system at the similar white noise of data spectrum of channel.Jamming-to-signal ratio (disturbing the energy ratio with signal) if less than spreading gain, does not then need to carry out special processing, and system can correctly receive the decode data; If jamming-to-signal ratio is greater than spreading gain, data are disturbed by linear FM signal obviously in channel, then the energy that disturbs of linear frequency modulation obviously is better than signal and very strong peak value must be arranged on the instantaneous energy frequency spectrum, must the time-have tangible peak line correspondence linear FM signal on the frequency Energy distribution plane.Therefore can be by tracking on time-frequency Energy distribution plane to the peak line track, utilize linear regression technique just can draw the anglec of rotation α that the peak line equation parameter can roughly estimate chirp slope and Fractional Fourier Transform, carry out Fractional Fourier Transform according to parameter then, if exist remarkable peak value then to detect success (existing linear FM signal to disturb), utilize an iteration Gauss curve fitting process to carry out limited search then and obtain the exact value that linear frequency modulation disturbs the anglec of rotation α ' of slope and Fractional Fourier Transform with accurate estimation.Certainly exist the corresponding linear frequency modulation of a peak value in Fractional Fourier Transform territory, α ' rank and disturb, carry out carrying out corresponding Fractional Fourier inverse transformation after wave absorption is handled, can remove linear frequency modulation and disturb.

(3) parameter optimization: iteration Gauss curve fitting-hill-climbing algorithm

A LFM signal is an impulse function in suitable fractional order Fourier domain only.Therefore FRFT is that the LFM signal of k has best aggregation properties to given frequency modulation rate in certain fractional order Fourier domain, and

Area fraction rank Fourier domain maximum is monotone increasing,

Area fraction rank Fourier domain maximum is monotone decreasing.This has different monotonicities and similar with point-symmetric characteristics of extreme value and normal distribution (Gaussian function) in the optimal value both sides.Therefore utilize this characteristic, proposed a kind of detection and parameter estimation algorithm-Gauss curve fitting optimizing algorithm of LFM signal.

(4) divide frame: windowing is overlapping

Generally, the data of input can be a lot, directly carry out treatment system and bear very heavy.Therefore need be with data truncation, segmentation (divided data frame) is handled and is helped to reduce computational load.The process of data truncation is equivalent to data are multiplied by a rectangular window in time domain, is equivalent to the convolution of the frequency spectrum and the rectangular window frequency spectrum of data at frequency domain.By the frequency spectrum knowledge of finite-length rectangular window, we know that data and rectangular window must cause spectrum leakage at frequency domain in time domain multiplication.This leakage is at the edge that mainly influences data when time domain is returned in the frequency domain inverse conversion.Therefore divide frame to adopt the limited overlapping method of front and back frame to initial data, eliminate this adverse effect.The present invention in an embodiment, the Duplication of front and back frame is 20% (being back 20% preceding 20% overlapping with next frame of former frame), has just eliminated the influence of spectrum leakage fully.

(5) to the Adaptive Suppression of LFM at the pulse peak in Fourier territory, corresponding scores rank

More than introduced of influence and the elimination of spectrum leakage problem to time domain.Disturb because will suppress LFM in the Fractional Fourier territory, therefore also must pay close attention to the influence of spectrum leakage the Fractional Fourier frequency spectrum.Cause the arrowband power expansion to contiguous scope at Fractional Fourier territory spectrum leakage, so LFM no longer is corresponding single-point pulse in Fourier territory, corresponding scores rank, but the pulse peak with sideband.Will suppress the LFM interference in the Fractional Fourier territory and will suppress the whole pulse peak that sideband arranged corresponding with it, under the intensity of pulse peak and the width condition of unknown, the effect that simple threshold value method suppresses LFM is limited.The pulse peak that sideband is arranged that utilization of the present invention is corresponding with LFM, seek the zone of the balance point (local minizing points of peak value both sides) of signal energy and LFM interfering energy in the monotonicity of peak point both sides spectrum energy as wave absorption (inhibition peak value), frequency spectrum in the balance point interval all is changed to 0, reaches wave absorption and suppress the purpose that LFM disturbs.

(6) many LFM disturb and suppress

The present invention utilizes the method for estimating the straight line parameter on the time-frequency energy plane that Fourier constructs in short-term, utilizes the parameter of estimating to suppress the LFM interference in the Fractional Fourier territory again.If there are many LFM to disturb, just determine its intensity difference by the jamming-to-signal ratio of each interference in time-frequency energy plane upward peak, will produce the peak line of varying strength.Utilize this characteristic, the present invention adopts the method for circulation, suppresses a LFM the strongest at every turn and disturbs, and reaches and suppresses the purpose that many LFM disturb.

Aforesaid spread spectrum communication neutral line frequency modulation Interference Detection and inhibition method have following feature: (1) is carried out the branch frame with the long data section and is handled in order to shorten data length, to reduce computation burden; (2) influence to time domain of spectrum leakage that data truncation causes has been eliminated in an amount of overlapping processing of Frame; (3) utilize the LFM signal to have the energy accumulating characteristic, detect the LFM signal and go and disturb in corresponding Fractional Fourier Transform territory; (4) utilize in the spread spectrum communication system at the similar white noise of data spectrum of channel and have the characteristic of spreading gain, reach parameter Fast estimation purpose; (5) the present invention proposes the Adaptive Suppression method at the pulse peak in Fourier territory, corresponding scores rank, utilize the sideband searching signal energy of pulse peak and the balance point of LFM interfering energy, determine the wave absorption zone, reach wave absorption and suppress the purpose that LFM disturbs at LFM.(6) the present invention can disturb a plurality of LFM and suppress.

Description of drawings

Fig. 1 is the time-frequency figure of LFM signal.

Fig. 2 be with Fig. 1 in the Fourier Transform of Fractional Order territory of LFM signal parameter coupling.

Fig. 3 is the flow chart of linear frequency modulation disturbance restraining method.

Fig. 4 is a data framing process schematic diagram.

Fig. 5 is signal f _iThe map of magnitudes of p rank Fractional Fourier Transform (x).

Fig. 6 is signal f _iThe amplitude maximum point neighborhood figure of p rank Fractional Fourier Transform (x).

Fig. 7 is a superimposed structure

Embodiment

For example the present invention is done description in more detail below in conjunction with accompanying drawing:

This method theory diagram comprises following 6 stages referring to Fig. 3: (1) divides frame, with data f (x) block short, certain overlapping Frame f arranged ₁, f ₂..., f _K-1, f _K(2) to each frame f _i(x), suppose that linear frequency modulation disturbs existence, utilize Fourier transition structure two dimension time-frequency figure in short-term, by tracking, utilize linear regression technique just can draw the anglec of rotation α that the peak line equation parameter can roughly estimate chirp slope and Fractional Fourier Transform to the peak line track; (3) verify hypothesis and parameter optimization: carry out Fractional Fourier Transform according to parameter alpha, if exist remarkable peak value then to detect success, and parameter is carried out the iteration optimizing, utilize an iteration Gauss curve fitting process to carry out limited search and obtain the exact value that linear frequency modulation disturbs the anglec of rotation α ' of slope and Fractional Fourier Transform with accurate estimation; (4) denoising: carry out wave absorption in the Fractional Fourier Transform territory and handle, remove and disturb; (5) judge whether to have handled all Frames; (6) signal goes overlapping and reorganization, if treated all data f _i(x), then go overlapping and the reorganization processing, obtain interference signals the result data.

Stage 1: divide frame

Generally, the data of input can be a lot, directly carry out treatment system and bear very heavy.Therefore need be with data truncation, segmentation (divided data frame) is handled and is helped to reduce computational load.Process as shown in Figure 4.

(1) determines to contain the length of the pending signal f (x) that linear frequency modulation disturbs, represent L=120000 in this example with L;

(2) determine to analyze frame length fr, fr=24000 in this example;

(3) determine the front and back overlapping parameter 2*p of two frames (%), then lap 2* Δ p=fr*2*p%, and 4*p%＜1 establishes p%=10% in this example, lap 2* Δ p=fr*2*p%=4800;

(4) sliding step before and after between two frames is d=fr-2* Δ p, d=24000-4800=19200 in this example;

(5) respectively mend the data 0 that length is Δ p before and after signal f (x), generate new data f (x), length is 2* Δ p+L, and f (x) length newly-generated in this example is 2* Δ p+L=124800;

(6) if 2* Δ p+ (K-1) * d＜2 Δs p+L＜2* Δ p+K*d then mends (2* Δ p+K*d)-(2* Δ p+L)=K*d-L data 0 again behind newly-generated f (x), constitute new f (x), length is 2* Δ p+K*d;

7*19200-120000=14400 data 0 are then mended in (4800+6*19200=120000)＜124800＜(4800+7*19200=139200) in this example in f (x) back, constituting new length is the data f (x) of 2* Δ p+K*d=139200

(7) f (x) is divided into K frame f ₁, f ₂..., f _K-1, f _K, and f _i(x): f ((i-1) * d+1) ..., f (2* Δ p+i*d)

The length that in this example previous step is produced is that the f (x) of 2* Δ p+K*d=4800+7*19200=139200 is divided into 7 frames, and is as follows:

f ₁(x)：f(1)，...，f(24000)

f ₂(x)：f(19200+1)，...，f(43200)

f ₃(x)：f(38400+1)，...，f(62400)

f ₄(x)：f(57600+1)，...，f(81600)

f ₅(x)：f(76800+1)，...，f(100800)

f ₆(x)：f(96000+1)，...，f(120000)

f ₆(x)：f(11520+1)，...，f(139200)

Stage 2: to each frame f _i(x), carry out linear frequency modulation parameter prediction meter

(1) to f _i(x) do fourier conversion in short-term

Wherein w (n) is a window function, can select rectangular window, Gaussian window, Hanning or Hamming window etc.

W in this example (n) is a rectangular window, and the long M of window is that (log2 (fr)-3) power of 2 rounds, so the Matlab language description is M=2^floor (log2 (fr)-3).

(2) obtain corresponding energy spectrogram

spectrogram(m，ω)＝|F(m，ω)| ² (15)

(3) (m, ω) time-frequency plane are followed the trail of the maximum curve at spectrogram.Time-frequency plane spectrogram (m, ω) on, choose, write down each time point

On the maximum point position, constitute set P (m), 1≤m≤M.

(4) if exist linear frequency modulation to disturb, it is adjacent that the some position among P (t-1) and the p (t) must be arranged, and can utilize the abutment points among the set P (m) to constitute line segment.Can try to achieve the coarse value of line segment parameter (slope) with the method (introduction of the scape technology of specifically passing away neutral line regression analysis) of fitting of a polynomial Otherwise illustrate not exist linear frequency modulation to disturb in this frame, i=i+1 changes the stage 5 then;

Stage 3: based on the hypothesis verification and the parameter optimization of iteration Gauss curve fitting

By the definition of FRFT as can be known, a LFM signal is an impulse function in suitable fractional order Fourier domain only.Therefore FRFT has best aggregation properties to given LFM signal in certain fractional order Fourier domain, and

The zone is a monotone increasing,

The zone is a monotone decreasing, and this has different monotonicities and similar with point-symmetric characteristics of extreme value and normal distribution (Gaussian function) in the optimal value both sides.Therefore utilize this characteristic, proposed a kind of detection and parameter estimation algorithm-Gauss curve fitting optimizing algorithm of LFM signal.

(1) utilize a stage to obtain To f _i(x) carry out corresponding Fractional Fourier Transform

Obtain

Wherein

(2) if Then exist linear FM signal to disturb, forward (3) to; If

Then do not exist linear FM signal to disturb, or the linear frequency modulation interfering energy is very for a short time is not enough to influence systematic function, can forwards the stage 5 to.Wherein μ is

Average

σ is

Variance, a is regulatory factor (can be made as spreading gain).

(3), the excursion of k is decided to be if exist linear frequency modulation to disturb

Iterations ite=1 is set;

(4) the parameter iteration optimizing begins

To two slope value k _Head, k _End, make corresponding Fractional Fourier Transform respectively, obtain

$\max \left({\left|F_{p_{\mathrm{end}}}\left(f_i\left(x\right)\right)\right|}^2\right);$

If | k _Head-k _End|＜Δ,

If

Usually establish T=0.01, and iterations ite＜10, then forward to

(7) finishing iteration;

(5) Gaussian function match

Calculate k _m∈ (k _Head, k _End):

$k_m=\frac{\max \left({\left|F_{p_{\mathrm{head}}}\left(f_i\left(x\right)\right)\right|}^2\right)}{\max \left({\left|F_{p_{\mathrm{head}}}\left(f_i\left(x\right)\right)\right|}^2\right)+\max \left({\left|F_{p_{\mathrm{end}}}\left(f_i\left(x\right)\right)\right|}^2\right)}\&CenterDot;k_{\mathrm{head}}+\frac{\max \left({\left|F_{p_{\mathrm{end}}}\left(f_i\left(x\right)\right)\right|}^2\right)}{\max \left({\left|F_{p_{\mathrm{head}}}\left(f_i\left(x\right)\right)\right|}^2\right)+\max \left({\left|F_{p_{\mathrm{end}}}\left(f_i\left(x\right)\right)\right|}^2\right)}\&CenterDot;k_{\mathrm{end}}$

If the Gaussian function form is:

$y=A\&CenterDot;e^{-\frac{{(x-b)}^2}{c}}$

It is taken the logarithm obtains:

$\log \left(y\right)=-\frac{1}{c}{x}^2+\frac{2b}{c}x+\log \left(A\right)-\frac{b^2}{c}$

The same with chirped slope estimation, also can be worth (y according to the observation _i, x _i), come estimated parameter A, b, c with linear regression method.With Slope Parameters and corresponding Fractional Fourier Transform territory extreme value, as 3 couples of input value in of Gaussian function match (x, y)={ (k _Head, M _Head), (k _m, M _m), (k _End, M _End) then just in time can estimate 3 parameters in the Gaussian function

Put into parameter sets

Wherein $M_m=\max \left({\left|F_{p_m}\right|}^2\right),$ $M_{\mathrm{end}}=\max \left({\left|F_{p_{\mathrm{end}}}\right|}^2\right).$

If in the set of Gaussian function fitting parameter, continuous two values change little, promptly

Then Forward (7) finishing iteration to;

(6) hill-climbing algorithm

If

K then _End=k _Head, k _Head=k _Head-Δ forwards (4) to:

If

K then _Head=k _End, k _End=k _End+ Δ forwards (4) to;

If

K then _Head=k _m, otherwise k _End=k _m, iterations ite=ite+1 forwards (4) to;

(7) iteration finishes, and obtains

Stage 4: denoising

(1) to f _i(x) make p rank Fractional Fourier Transform, obtain F _p(f _i(x));

(2) obtain the extreme point position

(3) utilize Gaussian window w _gRight

Carry out smoothly, obtain new

w _g～N (0,1) is a standardized normal distribution, and its window length is

(4) utilize

At l _MLeft side δ neighborhood be monotone increasing, and at l _MRight side δ neighborhood be the rule (as shown in Figure 5 and Figure 6) of monotone decreasing, right

Carry out second differnce, obtain l _MNear two valley points position l _V1, l _V2, and l is arranged _V1＜l _M＜l _V2

(5) with F _p(f _i(x)) at [l _V1, l _V2] interval data are changed to 0 (wave absorption processings), can suppress the linear frequency modulation interference.

$X_p\left(u\right)=F_p\left(u\right)=F_p\left(f_i\left(x\right)\right)=\left\{\begin{array}{cc}{F}_p\left(u\right)& \mathrm{if}(u<l_{v1})\mathrm{or}(u>l_{v2})\\ 0& ({\mathrm{<figure-callout\; id="1"\; label="l\; v"\; filenames="HSA00000291046000012.png,HSA00000291046000021.png"\; state="\{\{state\}\}">l</figure-callout>}}_v\&le;u\&le;l_{v2})\end{array}\right.$

(6) to the F behind the wave absorption _p(f _i(x)) do-p rank Fractional Fourier Transform, obtain

Stage 5: judged whether all Frames treated, and whether also existed linear frequency modulation to disturb to each frame cycle criterion

(1) whether the judgment data frame crosses the border (exceeding process range), if i＞K then forwards the stage 6 to

(2) with Frame f _i(x) forward the stage 2 again to;

Stage 6: signal goes overlapping and reorganization

(1) will

Go overlappingly by rule, constitute the signal after going to disturb Process as shown in Figure 7;

The rule 1, remove

A last Δ p data;

The rule 2, remove

Each Δ p data of head and the tail, 1＜i＜K;

The rule 3, remove

A preceding Δ p data;

Rule 4, will remove redundant

Head and the tail connect, and constitute

In this example,

Remove a last Δ p=2400 data, from

Arrive P=2400 data of Δ before and after respectively removing,

A Δ p=2400 data before removing are so constitute Length is 7*fr-12* Δ p=7*24000-12*2400=139200

(2) remove

0 (remove anterior Δ p 0, remove afterbody K*d-L+ Δ p 0) that head and the tail replenish;

Removing previous step in this example produces

Preceding Δ p=2400 number reach afterbody K*d-L+ Δ p=7*19200-120000+2400=16800 according to this, then

Length become 139200-16800-2400=120000

(3) signal after obtaining disturbing with isometric the going of input signal f (x)

The interference signal of going of the final output of this example is

Its length equals the length 120000 of input signal f (x).

Claims (7)

1. Gauss curve fitting linear frequency modulation Interference Detection and inhibition method in the direct-sequence communications system is characterized in that:

(1) divide frame, will block from the data f (x) that receiver obtains short, necessarily overlapping Frame f arranged ₁, f ₂..., f _K-1, f _K

(2) to each frame f _i(x), suppose that linear frequency modulation disturbs existence, utilize Fourier transition structure two dimension time-frequency figure in short-term,, utilize linear regression technique to draw the anglec of rotation α that the peak line equation parameter promptly roughly estimates chirp slope and Fractional Fourier Transform by tracking to the peak line track;

(3) verify hypothesis and parameter optimization, carry out Fractional Fourier Transform according to parameter alpha, if exist remarkable peak value then to detect success, and parameter is carried out the iteration optimizing, utilize an iteration Gauss curve fitting process to carry out limited search and obtain the exact value that linear frequency modulation disturbs the anglec of rotation α ' of slope and Fractional Fourier Transform with accurate estimation;

(4) denoising is carried out wave absorption and is handled in the Fractional Fourier Transform territory, remove and disturb;

(5) judge whether to have handled all Frames;

(6) signal goes overlapping and reorganization, if treated all data f _i(x), then go overlapping and the reorganization processing, obtain interference signals the result data.

2. Gauss curve fitting linear frequency modulation Interference Detection and inhibition method in the direct-sequence communications system according to claim 1 is characterized in that described minute frame further comprises:

1) determines to contain the length of the pending signal f (x) that linear frequency modulation disturbs, represent with L;

2) determine that analysis frame is data frame length fr;

3) determine the front and back overlapping parameter 2*p of two frames (%), then lap 2* Δ p=fr*2*p%, and 4*p%＜1, p%=10%;

4) sliding step before and after between two frames is d=fr-2* Δ p;

5) respectively mend the data 0 that length is Δ p before and after signal f (x), generate new data f (x), length is 2* Δ p+L;

6) if 2* Δ p+ (K-1) * d＜2 Δs p+L＜2* Δ p+K*d then mends (2* Δ p+K*d)-(2* Δ p+L)=K*d-L data 0 again behind newly-generated f (x), constitute new f (x), length is 2* Δ p+K*d;

7) f (x) is divided into K frame f ₁, f ₂..., f _K-1, f _K,

f _i(x)：f((i-1)*d+1)，...，f(2*Δp+i*d)。

3. Gauss curve fitting linear frequency modulation Interference Detection and inhibition method in the direct-sequence communications system according to claim 2 is characterized in that each frame f _i(x), carrying out linear frequency modulation parameter prediction meter further comprises:

1) to f _i(x) do fourier conversion in short-term

Wherein w (n) is a window function, a kind of in rectangular window, Gaussian window, Hanning or the Hamming window;

2) obtain corresponding energy spectrogram

spectrogram(m，ω)＝|F(m，ω)| ²；

3) spectrogram (m, ω) time-frequency plane are followed the trail of the maximum curve, time-frequency plane spectrogram (m, ω) on, choose, write down each time point

On the maximum point position, constitute set P (m), 1≤m≤M;

4) if exist linear frequency modulation to disturb, it is adjacent that the some position among P (t-1) and the P (t) must be arranged, and utilizes the abutment points among the set P (m) to constitute line segment, with the method for fitting of a polynomial, tries to achieve the coarse value that the line segment parameter is a slope

Otherwise illustrate not exist linear frequency modulation to disturb in this frame that i=i+1 changes judging whether to have handled all Frame steps then.

4. Gauss curve fitting linear frequency modulation Interference Detection and inhibition method in the direct-sequence communications system according to claim 3 is characterized in that described hypothesis verification and parameter optimization are based on the hypothesis verification and the parameter optimization of iteration Gauss curve fitting, and concrete grammar is:

1) utilize a stage to obtain

To f _i(x) carry out corresponding Fractional Fourier Transform

Obtain

Wherein

2) if

Then exist linear FM signal to disturb, forward 3 to); If

Then do not exist linear FM signal to disturb, or the linear frequency modulation interfering energy is very for a short time is not enough to influence systematic function, forwards to and judge whether to have handled all Frame steps; Wherein μ is

Average

σ is

Variance, a is a regulatory factor;

3), the excursion of k is decided to be if exist linear frequency modulation to disturb

Iterations ite=1 is set;

4) the parameter iteration optimizing begins

To two slope value k _Head, k _End, make corresponding Fractional Fourier Transform respectively, obtain

$\max \left({\left|F_{p_{\mathrm{end}}}\left(f_i\left(x\right)\right)\right|}^2\right);$

If | k _Head-k _End|＜Δ, $\mathrm{\&Delta;}=\frac{|k_{\mathrm{hend}}-k_{\mathrm{end}}|}{5};$

If If T=0.01, and iterations ite＜10 then forward 7 to) finishing iteration;

5) Gaussian function match

Calculate k _m∈ (k _Head, k _End):

$k_m=\frac{\max \left({\left|F_{p_{\mathrm{head}}}\left(f_i\left(x\right)\right)\right|}^2\right)}{\max \left({\left|F_{p_{\mathrm{head}}}\left(f_i\left(x\right)\right)\right|}^2\right)+\max \left({\left|F_{p_{\mathrm{end}}}\left(f_i\left(x\right)\right)\right|}^2\right)}\&CenterDot;k_{\mathrm{head}}+\frac{\max \left({\left|F_{p_{\mathrm{end}}}\left(f_i\left(x\right)\right)\right|}^2\right)}{\max \left({\left|F_{p_{\mathrm{head}}}\left(f_i\left(x\right)\right)\right|}^2\right)+\max \left({\left|F_{p_{\mathrm{end}}}\left(f_i\left(x\right)\right)\right|}^2\right)}\&CenterDot;k_{\mathrm{end}}$

If the Gaussian function form is:

$y=A\&CenterDot;e^{-\frac{{(x-b)}^2}{c}}$

It is taken the logarithm obtains:

$\log \left(y\right)=-\frac{1}{c}{x}^2+\frac{2b}{c}x+\log \left(A\right)-\frac{b^2}{c}$

The same with chirped slope estimation, be worth (y according to the observation _i, x _i), come estimated parameter A, b, c with linear regression method; With Slope Parameters and corresponding Fractional Fourier Transform territory extreme value, as 3 couples of input value in of Gaussian function match (x, y)={ (k _Head, M _Head), (k _m, M _m), (k _End, M _End) then estimate 3 parameters in the Gaussian function

Put into parameter sets Wherein

$M_{\mathrm{end}}=\max \left({\left|F_{p_{\mathrm{end}}}\right|}^2\right);$

If in the set of Gaussian function fitting parameter, continuous two values change little, promptly

Then

Forward 7 to) finishing iteration;

6) hill-climbing algorithm

If

K then _End=k _Head, k _Head=k _Head-Δ forwards 4 to);

If

K then _Head=k _End, k _End=k _End+ Δ forwards 4 to);

If

K then _Head=k _m, otherwise k _End=k _m, iterations ite=ite+1 forwards 4 to);

7) iteration finishes, and obtains

5. Gauss curve fitting linear frequency modulation Interference Detection and inhibition method in the direct-sequence communications system according to claim 4 is characterized in that described denoising further comprises:

1) to f _i(x) make p rank Fractional Fourier Transform, obtain F _p(f _i(x));

2) obtain the extreme point position

3) utilize Gaussian window w _gRight Carry out smoothly, obtain new

w _g～N (0,1) is a standardized normal distribution, and its window length is

4) utilize

At l _MLeft side δ neighborhood be monotone increasing, and at l _MRight side δ neighborhood be the rule of monotone decreasing, right

Carry out second differnce, obtain l _MNear two valley points position l _V1, l _V2, and l is arranged _V1＜l _M＜l _V2

5) with F _p(f _i(x)) at [l _V1, l _V2] to be changed to 0 be that wave absorption is handled to interval data, suppress linear frequency modulation and disturb,

$X_p\left(u\right)=F_p\left(u\right)=F_p\left(f_i\left(x\right)\right)=\left\{\begin{array}{cc}{F}_p\left(u\right)& \mathrm{if}(u<l_{v1})\mathrm{or}(u>l_{v2})\\ 0& (l_{v1}\&le;u\&le;l_{v2})\end{array}\right.;$

6) to the F behind the wave absorption _p(f _i(x)) do-p rank Fractional Fourier Transform, obtain

6. whether Gauss curve fitting linear frequency modulation Interference Detection and inhibition method is characterized in that judging whether treated all Frames in the direct-sequence communications system according to claim 5, and also exist the linear frequency modulation interference to comprise to each frame cycle criterion:

1) whether the judgment data frame crosses the border, and exceeds process range, if i＞K then forwards signal to and goes overlapping and reorganization;

2) with Frame f _i(x) forward to again each frame f _i(x), carry out linear frequency modulation parameter prediction meter.

7. Gauss curve fitting linear frequency modulation Interference Detection and inhibition method in the direct-sequence communications system according to claim 5 is characterized in that described signal goes overlapping and reorganization further comprises:

1) will

Go overlappingly by rule, constitute the signal after going to disturb

Step 1, remove

A last Δ p data;

Step 2, remove

Each Δ p data of head and the tail, 1＜i＜K;

Step 3, remove

A preceding Δ p data;

Step 4, will remove redundant

Head and the tail connect, and constitute

2) remove

Head and the tail replenish 0, remove anterior Δ p 0, remove afterbody K*d-L+ Δ p 0;

3) signal after obtaining disturbing with isometric the going of input signal f (x)

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