CN103905129A - Signal detection and signal information interpretation method based on spectral pattern analysis - Google Patents

Abstract

The invention discloses a signal detection and signal information interpretation method based on spectral pattern analysis. The method includes the steps of obtaining frequency spectrum data, a mean value spectrum and a mean value spectrum histogram, delimiting areas and determined areas, obtaining independent signals, a signal histogram and a signal principle value, and interpreting signal information. The signal detection and signal information interpretation method is high in adaptability, good in anti-noise performance, low in calculation complexity and free of priori knowledge requirements.

Description

Input and the signal message interpretation method analyzed based on spectral pattern

Technical field

The invention belongs to cognitive radio and digital processing field, particularly a kind of input and signal message interpretation method of analyzing based on spectral pattern.

Background technology

Along with the development of wireless communication technology, as the Wireless Telecom Equipment such as mobile phone, satellite communication terminal is generally used by people, what various wireless communication means were deep the incorporate mankind's various productions, life activity.Frequency-domain analysis based on Fourier transform is the core means of research radio communication always, and the frequency spectrum service condition of monitoring fixed frequency range is to ensure that wireless communication system normally moves, improves the important technical basis of wireless communication spectrum service efficiency.Frequency spectrum detection is mainly divided into two parts: input and signal message interpretation.Input refers to and from frequency spectrum, detects independently signal; Signal message interpretation refers to for the information such as its bandwidth of input, centre frequency, signal to noise ratio independently.

At present, signal detecting method mainly contains three classes:

The first kind detects for relevant.Knowing that under the architectural feature of signal (as pilot tone, leading or synchronization message etc.) prerequisite, the method that matched filter adds Threshold detection is optimum relevant detection detection method.Relevant detection can obtain accurate testing result, but its shortcoming is also clearly, must know the priori of signal, and frequency range very wide time, realize that to be permitted the relevant testing cost of eurypalynous signal too high, can realize hardly.

Equations of The Second Kind is energy measuring.In frequency range interested, measure the gross energy that receives signal in certain section of observation time, if energy is set thresholding lower than certain, state this frequency range no signal.Compared with relevant detection, the detection time that energy measuring need to be longer is to reach same perceived effect, and its noise robustness is low, and the frequency spectrum perception result in low signal-to-noise ratio environment is especially unreliable, poor for the conformability of noise.

The 3rd class is feature detection.In the time that some feature such as carrier frequency, modulation type or the Cyclic Prefix of signal is known, utilize the expectation of signal and periodicity (cyclostationary characteristic) that auto-correlation function presents, signal energy and noise energy can be made a distinction, break through the bottleneck of energy measuring.But its implementation complexity is far above energy measuring, and the statistics priori that need to understand signal is the main shortcoming of restriction feature detection.

At present, signal message interpretation method is mainly the analytical method based on signal spectrum.The information such as the bandwidth of its range value analytic signal based on signal, centre frequency, signal to noise ratio.But due to the interference of the factors such as noise, signal element, interference, the accuracy of the method is lower, and noise robustness is poor.

In a word, the problem that existing input and signal message interpretation technology exist is: bad adaptability, noise robustness is weak, computation complexity is high, need priori, and the application such as satellite communication, deep space communication especially changeable for background noise, communication mechanism is complicated, priori is difficult to obtain is difficult to be competent at.

Summary of the invention

The object of the present invention is to provide a kind of input and signal message interpretation method of analyzing based on spectral pattern, adaptability is good, noise robustness is strong, computation complexity is low, without priori requirement.

The technical solution that realizes the object of the invention is: a kind of input and signal message interpretation method of analyzing based on spectral pattern, comprises the following steps:

10) frequency spectrum data obtains: obtain frequency spectrum data from front end Fourier transform equipment;

20) average spectrum is obtained: frequency spectrum data is carried out to mean filter, generate average spectrum;

30) average spectrum histogram obtains: calculate average spectrum histogram according to average spectrum;

40) regional assignment: according to histogram calculation holding wire and noise line, delimit spectrum signal region, noise region and region undetermined;

50) regional determination undetermined: according to whether adjacent with the signal area condition that is linked as in region undetermined, judge region undetermined, if region undetermined and signal area are adjacent, determine that it is signal area, no, determine that it is noise region;

60) independent signal obtains: according to signal area information, it is segmented to independent signal rank from region rank;

70) signal histogram obtains: the each independent signal in ergodic signals region, and computational analysis obtains the histogram of each independent signal one by one;

80) signal main value obtains: according to signal histogram, calculate the signal main value of the each independent signal in signal area;

90) signal message interpretation: according to signal main value, search bandwidth and the centre frequency of signal, calculate signal power, and in conjunction with noise power spectral density, calculate the signal to noise ratio of signal.

The present invention compared with prior art, its remarkable advantage:

1, noise circumstance strong adaptability: compare existing input and signal message interpretation method, this method adopts adaptive region to divide, and can adapt to various noise circumstances, has significantly improved the noise adaptation of input and signal message interpretation.

2, input accuracy is high: compare the existing signal detecting method based on energy measuring, this method adopts the region partitioning method that comprises region undetermined, the setting in region undetermined makes it judge the amplitude information that not only can use frequency itself, can also use its surrounding enviroment information, thereby improve the accuracy of input.

3, signal message interpretation accuracy is high: compare existing signal message interpretation method, this method is on the basis of mean filter, adopt the signal message interpretation method based on main value, can reduce the impact on information judging of noise and signal fluctuation, thereby improve the accuracy of signal message interpretation.

4, computation complexity is low: compare existing input and signal message interpretation method, this method is without as complicated calculations such as computation cycles spectrums, the mean filter adopting in method, the consideration that all has reduction amount of calculation based on methods such as histogrammic analyses, make the method computation complexity lower, can realize frequency spectrum high speed analysis.

5, without priori demand: compare existing signal detecting method, this method is without as prioris such as signal modulation system, frequency, bandwidth.

Below in conjunction with the drawings and specific embodiments, the present invention is done further and illustrated.

Brief description of the drawings

Fig. 1 the present invention is based on the input of spectral pattern analysis and the flow chart of signal message interpretation method.

Fig. 2 A, 2B are the simulated example figure (2A is frequency spectrum, and 2B is average spectrum) of mean filter.

Fig. 3 is average spectrum histogram exemplary plot.

Fig. 4 A, 4B are the exemplary plot (4A is frequency spectrum, and 4B is average spectrum) of signal, noise, region undetermined division.

Fig. 5 is the exemplary plot of subdivision result.

Fig. 6 A, 6B are the result exemplary plot (6A is frequency spectrum, and 6B is average spectrum) of signal message interpretation.

Embodiment

As shown in Figure 1, the present invention is based on input and signal message interpretation method that spectral pattern is analyzed, comprise the following steps:

10) frequency spectrum data obtains: obtain frequency spectrum data from front end Fourier transform equipment;

Described frequency spectrum data obtains in (10) step, and described frequency spectrum data is amplitude spectrum or power spectrum.

20) average spectrum is obtained: frequency spectrum data is carried out to mean filter, generate average spectrum;

Described average spectrum is obtained (20) step and is specially: use length is L _meanaverage window convolution frequency spectrum data, generate average corresponding to frequency spectrum data spectrum.(L _meanbe generally odd number)

If former frequency spectrum SP,

P _i∈SP(i＝1...N _spec (1)

For the amplitude of i frequency in frequency spectrum, N _spec=card (SP) counts for frequency spectrum, and wherein card () operator represents the number of getting set element.Order:

Pm _i∈SPm(i＝1...N _spec) (2)(2)

For the amplitude of i frequency of average spectrum, Pm _imeet

${\mathrm{Pm}}_i=\frac{{\mathrm{\Σ}}_{i-L_h}^{i+L_h}{p}_i}{L_{\mathrm{mean}}}---\left(3\right)$

Wherein

$L_h=\frac{L_{\mathrm{mean}}-1}{2}---\left(4\right)$

According to formula (3) traversal frequency spectrum SP, calculate and generate average spectrum SPm.The exemplary plot of mean filter as shown in Figure 2.In figure, ordinate all uses true value but not logarithm value.

30) average spectrum histogram obtains: calculate average spectrum histogram according to average spectrum;

Described average spectrum histogram obtains (30) step and is specially:

First travel through average spectrum, find out average spectrum maximum, minimum value, then calculate L according to maximum, minimum value _hislayer average spectrum histogram, every layer of corresponding numerical value of histogram represents the number of the point of amplitude within the scope of this layer in average spectrum, and the number of plies is higher, and within the scope of this layer, some amplitude is larger.

If the histogram number of plies is L _his.The maximin of computation of mean values spectrum:

$\left\{\begin{array}{c}{A}_{\max }=\max \left(\mathrm{SPm}\right)\\ A_{\min }=\min \left(\mathrm{SPm}\right)\end{array}\right.---\left(5\right)$

Histogram interlamellar spacing is:

$A_{\mathrm{level}}=\frac{A_{\max }-A_{\min }}{L_{\mathrm{his}}}---\left(6\right)$

Definition PH _i(0 < i < L _his) be histogram i layer point set,

PH _i＝{Pm _i|A _min+i×A _level＞Pm _i＞A _min+(i-1)×A _level} (7)

Every layer of histogram count into:

$N_{L_i}=\mathrm{card}\left({\mathrm{PH}}_i\right)---\left(9\right)$

The histogram that calculates average spectrum according to formula (5-8), histogrammic exemplary plot as shown in Figure 3.

40) regional assignment: according to histogram calculation holding wire and noise line, delimit spectrum signal region, noise region and region undetermined;

In subsequent calculations, think, the average of amplitude more than holding wire spectrum frequency is to determine signal frequency point, and the average spectrum frequency of amplitude below noise line is to determine noise frequency, and amplitude is frequency undetermined between noise line and holding wire.

Described regional assignment (40) step comprises:

41) noise floor is searched: search the noise floor in histogram;

Described noise floor is searched (41) step and is specially:

Definition L _noisebe an integer, think 1...L in histogram _noiselayer is noise floor, upwards searches, until L from histogram bottom _noisemeet:

$(N_{L_1}+...+N_{L_{\mathrm{noise}}})>N_{\mathrm{spec}}\&times;{\mathrm{\&beta;}}_{\mathrm{noi}}---\left(9\right),$

Wherein

represent L in histogram _iwhat layer comprised counts, N _specexpression average spectrum is always counted, β _noi∈ [0,1] is noise accounting coefficient.

42) holding wire noise line delimited: delimit holding wire and noise line according to noise floor;

Described holding wire noise line delimited (42) step and be specially:

Calculating noise layer average:

mean _noise＝||PN||1 (10)，

Wherein

$\mathrm{PN}=\left\{P_i\right|P_i\&Element;{\mathrm{PH}}_{L_1...L_{\mathrm{noise}}}\}---\left(11\right),$

PH _ifor histogram i layer point set.With mean _noiseas benchmark, calculate holding wire and noise line, making noise factor is α _noi, signal coefficient is α _sig, holding wire is:

line _sig＝α _sig×mean _noise (12)，

Noise line is:

line _noi＝α _noi×mean _noise (13)。

43) signal area noise region and region undetermined obtain: traversal frequency spectrum, obtains signal area, noise region and region undetermined according to holding wire and noise line;

Described signal area noise region and region undetermined obtain (43) step and are specially:

Make signal area SPm _sigfor:

SPm _sig＝{Pm _i|Pm _i＞line _sig} (14),

Noise region SPm _noifor:

SPm _noi＝{Pm _i|Pm _i＜line _noi} (15),

Region SPm undetermined _undfor:

SPm _und＝{Pm _i|line _sig≥Pm _i≥line _noi} (16)。

The exemplary plot that region is divided as shown in Figure 4.In the picture of bottom, there are two lines, the straight line representation signal line of top, the straight line of below represents noise line, more than holding wire is signal area, in the picture abscissa of top, mark with dark color, below noise line is noise region, and between holding wire and noise line is region undetermined.

50) regional determination undetermined: according to whether adjacent with the signal area condition that is linked as in region undetermined, judge region undetermined, if region undetermined and signal area are adjacent, determine that it is signal area, no, determine that it is noise region;

Make Pm _i∈ SPm _undit is a frequency undetermined.If

$\&Exists;({\mathrm{Pm}}_i\&Element;{\mathrm{SPm}}_{\mathrm{sig}})---\left(17\right)$

And l meets

L=max{l|l < i & (Pm _i∈ SPm _sigor Pm _i∈ SPm _noi) (18)

Or

$\&Exists;({\mathrm{Pm}}_r\&Element;{\mathrm{SPm}}_{\mathrm{sig}})---\left(19\right)$

And l meets

R=min{r|r > i & (Pm _r∈ SPm _sigor Pm _r∈ SPm _noi) (20)

Assert P _ifor signal frequency point, otherwise regard as noise frequency.After regional determination undetermined, in frequency spectrum, only there is signal area SPm _sigand noise region SPm _noi.

60) independent signal obtains: according to signal area information, it is segmented to independent signal rank from region rank;

Described independent signal obtains (60) step and is specially:

Analytic signal region, in ergodic signals region institute a little, any two points in its region, if there is not noise region therebetween, judge that it belongs to same signal, otherwise think that it belongs to unlike signal, thereby signal area is segmented to independent signal rank from region rank.

Make Pm _i, Pm _jfor two points in signal area, and (i < j), if:

$\&Exists;{\mathrm{Pm}}_n\&Element;{\mathrm{SPm}}_{\mathrm{noi}}(i<n<j)---\left(21\right)$

Judge Pm _iwith Pm _jbelong to two signals, otherwise, judge that it belongs to same signal.Subdivision result exemplary plot is as Fig. 5, and independently rectilinear frame represents the independent signal segmenting out.

70) signal histogram obtains: the each independent signal in ergodic signals region, and computational analysis obtains the histogram of each independent signal one by one;

For each independent signal, adopt the method calculating Ls as step 2 _hislayer histogram, order

for signal histogram Ls _ithe point value of layer, As _min, As _maxbe respectively signal minimum, maximum.

80) signal main value obtains: according to signal histogram, calculate the signal main value of the each independent signal in signal area;

Described signal main value obtains (80) step and is specially:

Making maximum layer of counting in signal histogram is main value layer, and determines signal principal value interval according to this main value layer upper and lower limit, in principal value interval average a little as signal main value.

Signal principal value interval layer is defined as

${\mathrm{Ls}}_{\mathrm{main}}=\left\{{\mathrm{Ls}}_{\mathrm{main}}\right|\max \left(\right\{{\mathrm{Ns}}_{{\mathrm{Ls}}_i}|0<i\&le;{\mathrm{Ls}}_{\mathrm{his}}\})\}---\left(22\right)$

Principal value interval is limited to up and down so:

$\left\{\begin{array}{c}{\mathrm{As}}_{\mathrm{upLine}}={\mathrm{As}}_{\min }+{\mathrm{Ls}}_{\mathrm{main}}\&times;\frac{{\mathrm{As}}_{\max }-{\mathrm{As}}_{\min }}{{\mathrm{Ls}}_{\mathrm{his}}}\\ {\mathrm{As}}_{\mathrm{downLine}}={\mathrm{As}}_{\min }+({\mathrm{Ls}}_{\mathrm{main}}-1)\&times;\frac{{\mathrm{As}}_{\max }-{\mathrm{As}}_{\min }}{{\mathrm{Ls}}_{\mathrm{his}}}\end{array}\right.---\left(23\right)$

Principal value interval point set is ${\mathrm{PH}}_{{\mathrm{Ls}}_{\mathrm{main}}}=\left\{{\mathrm{Pm}}_i\right|{\mathrm{As}}_{\mathrm{upLine}}>{\mathrm{Pm}}_i>{\mathrm{As}}_{\mathrm{downLine}}\}.$ Signal main value

${\mathrm{As}}_{\mathrm{main}}=\frac{{\left|\right|{\mathrm{PH}}_{{\mathrm{Ls}}_{\mathrm{main}}}\left|\right|}_1}{N_{{\mathrm{Ls}}_{\mathrm{main}}}}---\left(24\right)$

Wherein $N_{{\mathrm{Ls}}_{\mathrm{main}}}=\mathrm{card}\left({\mathrm{PH}}_{{\mathrm{Ls}}_{\mathrm{main}}}\right).$

90) signal message interpretation: according to signal main value, search bandwidth and the centre frequency of signal, calculate signal power, and in conjunction with noise power spectral density, calculate the signal to noise ratio of signal.

Make signal point set sig _i, main value As _main, spectral resolution f _g, definition signal left and right starting point is respectively:

Wherein α is bandwidth discriminant coefficient, is that power spectrum gets 0.707 for frequency spectrum, and frequency spectrum is that amplitude spectrum gets 0.5.If I _l, I _rin Spm, the sequence of corresponding point is respectively I _sl, I _sr, the three dB bandwidth of signal

B _i＝(I _sr-I _sl)×f _g (26)(26)

Signal center frequency is:

$f_{\mathrm{ci}}=I_{\mathrm{sl}}\&times;f_g+\frac{B_i}{2}---\left(27\right)$

Signal-to-Noise is:

${\mathrm{SNR}}_i=\frac{{\left|\right|{\mathrm{sig}}_1\left|\right|}_1}{{\left|\right|\mathrm{PN}\left|\right|}_1}---\left(28\right)$

The exemplary plot of signal message interpretation as shown in Figure 6.As can be seen from the figure the accuracy of signal bandwidth and centre frequency interpretation.

For the feasibility of checking the method, the feature of the method is described, do emulation for input and signal message interpretation respectively.

Input accuracy will directly affect the effect of follow-up signal information judging, and for analytical method performance accurately, simulating scheme is for the factor design that may affect signal detecting result.Because frequency spectrum occupancy, signal to noise ratio may exert an influence to signal detecting result, be two simulation parameters so set it.Adopt the effect of signal-detection probability measure algorithm.For every group of parameter, for statistical signal detection probability accurately, under the condition of random noise, random sources, generate 1000 frame frequencies spectrums, and independent detection checking.Can find out in frequency spectrum occupancy and be less than 0.8, when signal to noise ratio is greater than 0dB, detection probability is 1, all correctly detects.Only have in the time that frequency spectrum occupancy is more than or equal to 0.8, noise line and holding wire move on can be to some extent, impact analysis result a little.

Signal message interpretation simulation objectives be the method for inspection to the correct judgement of signal message, for parser performance accurately, this simulating scheme is for the factor design that may affect signal message sentence read result.Because Signal-to-Noise may exert an influence to signal message sentence read result, so set it for simulation parameter.For every group of parameter, in order to add up accurately arrowband Interference Detection probability, under the condition of random noise, random sources, generate 5000 frame frequency spectrums, and independent detection checking.Wherein bandwidth error and centre frequency unit of error are percentage, signal to noise ratio error dB.There is result can find out accuracy and the stability of this method under various state of signal-to-noise.

The present invention adopts adaptive region division methods, based on spectral magnitude, spectrum division is become to signal, noise and region undetermined.The advantage of this design is, the judgement in region undetermined, not only can use the amplitude information of frequency itself, can also use its surrounding enviroment information, this has promoted the accuracy of differentiation and the toughness of algorithm greatly, and adopt the information judging method based on signal main value, make the interpretation of the information such as signal bandwidth, centre frequency, signal to noise ratio have good anti-fluctuation and noise robustness.

The invention provides a kind of input and signal message interpretation method of analyzing based on spectral pattern; should be understood that; for those skilled in the art; under the premise without departing from the principles of the invention; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.In addition, in the present embodiment not clear and definite each part all available prior art realized.

Claims (10)

1. input and the signal message interpretation method analyzed based on spectral pattern, is characterized in that, comprises the following steps:

10) frequency spectrum data obtains: obtain frequency spectrum data from front end Fourier transform equipment;

20) average spectrum is obtained: frequency spectrum data is carried out to mean filter, generate average spectrum;

30) average spectrum histogram obtains: calculate average spectrum histogram according to average spectrum;

40) regional assignment: according to histogram calculation holding wire and noise line, delimit spectrum signal region, noise region and region undetermined;

50) regional determination undetermined: according to whether adjacent with the signal area condition that is linked as in region undetermined, judge region undetermined, if region undetermined and signal area are adjacent, determine that it is signal area, no, determine that it is noise region;

60) independent signal obtains: according to signal area information, it is segmented to independent signal rank from region rank;

70) signal histogram obtains: the each independent signal in ergodic signals region, and computational analysis obtains the histogram of each independent signal one by one;

80) signal main value obtains: according to signal histogram, calculate the signal main value of the each independent signal in signal area;

90) signal message interpretation: according to signal main value, search bandwidth and the centre frequency of signal, calculate signal power, and in conjunction with noise power spectral density, calculate the signal to noise ratio of signal.

2. input and the signal message interpretation method of analyzing based on spectral pattern according to claim 1, is characterized in that: described frequency spectrum data obtains in (10) step, and described frequency spectrum data is amplitude spectrum or power spectrum.

3. input and the signal message interpretation method of analyzing based on spectral pattern according to claim 1, is characterized in that, described average spectrum is obtained (20) step and is specially: use length is L _meanaverage window convolution frequency spectrum data, generate average corresponding to frequency spectrum data spectrum.

4. input and the signal message interpretation method of analyzing based on spectral pattern according to claim 1, is characterized in that, described average spectrum histogram obtains (30) step and is specially:

First travel through average spectrum, find out average spectrum maximum, minimum value, then calculate L according to maximum, minimum value _hislayer average spectrum histogram, every layer of corresponding numerical value of histogram represents the number of the point of amplitude within the scope of this layer in average spectrum, and the number of plies is higher, and within the scope of this layer, some amplitude is larger.

5. input and the signal message interpretation method of analyzing based on spectral pattern according to claim 1, is characterized in that, described regional assignment (40) step comprises:

41) noise floor is searched: search the noise floor in histogram;

42) holding wire noise line delimited: delimit holding wire and noise line according to noise floor;

43) signal area noise region and region undetermined obtain: traversal frequency spectrum, obtains signal area, noise region and region undetermined according to holding wire and noise line.

6. input and the signal message interpretation method of analyzing based on spectral pattern according to claim 1, is characterized in that, described noise floor is searched (41) step and is specially:

Definition L _noisebe an integer, think 1...L in histogram _noiselayer is noise floor, upwards searches, until L from histogram bottom _noisemeet:

Wherein

represent L in histogram _iwhat layer comprised counts, N _specexpression average spectrum is always counted, β _noi∈ [0,1] is noise accounting coefficient.

7. input and the signal message interpretation method of analyzing based on spectral pattern according to claim 1, is characterized in that, described holding wire noise line delimited (42) step and is specially:

Calculating noise layer average:

mean _noise＝||PN|| ₁ (10)，

Wherein

PH _ifor histogram i layer point set.With mean _noiseas benchmark, calculate holding wire and noise line, making noise factor is α _noi, signal coefficient is α _sig, holding wire is:

line _sig＝α _sig×mean _noise (12)，

Noise line is:

line _noi＝α _noi×mean _noise (13)。

8. input and the signal message interpretation method of analyzing based on spectral pattern according to claim 1, is characterized in that, described signal area noise region and region undetermined obtain (43) step and be specially:

Make signal area SPm _sigfor:

SPm _sig＝{Pm _i|Pm _i＞line _sig} (14)，

Noise region SPm _noifor:

SPm _noi＝{Pm _i|Pm _i＜line _noi} (15)，

Region SPm undetermined _undfor:

SPm _und＝{Pm _i|line _sig≥Pm _i≥line _noi} (16)。

9. input and the signal message interpretation method of analyzing based on spectral pattern according to claim 1, is characterized in that, described independent signal obtains (60) step and is specially:

Analytic signal region, in ergodic signals region institute a little, any two points in its region, if there is not noise region therebetween, judge that it belongs to same signal, otherwise think that it belongs to unlike signal, thereby signal area is segmented to independent signal rank from region rank.

10. input and the signal message interpretation method of analyzing based on spectral pattern according to claim 1, is characterized in that, described signal main value obtains (80) step and is specially:

Making maximum layer of counting in signal histogram is main value layer, and determines signal principal value interval according to this main value layer upper and lower limit, in principal value interval average a little as signal main value.

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