CN112636781B - Method for detecting digital modulation optimal interference signal - Google Patents

Method for detecting digital modulation optimal interference signal Download PDF

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CN112636781B
CN112636781B CN202011535266.8A CN202011535266A CN112636781B CN 112636781 B CN112636781 B CN 112636781B CN 202011535266 A CN202011535266 A CN 202011535266A CN 112636781 B CN112636781 B CN 112636781B
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冯伟
王健全
萧洒
孙远欣
唐万斌
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University of Electronic Science and Technology of China
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    • H04B1/69—Spread spectrum techniques
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention provides a method for detecting an optimal interference signal of digital modulation, belonging to the technical field of digital communication. Extracting and combining pulse interference signals, and performing characteristic analysis on a high-order square spectrum of the combined signals so as to judge whether the signals are digital modulation optimal interference signals; the method is simple and easy to realize, and the detection rate of the digital modulation optimal interference signal is high.

Description

Method for detecting digital modulation optimal interference signal
Technical Field
The invention belongs to the technical field of digital communication, and particularly relates to a method for detecting an optimal interference signal of digital modulation.
Background
Conventional jamming techniques can be divided into jamming and spoofing. The interference suppression means that the interference party uses a high-power interference signal to drown out a useful signal of the communication party, and narrow-band aiming interference and wide-band blocking interference such as relatively common single-tone interference and sweep frequency interference belong to the interference suppression. Spoofed interference refers to interference in which an interferer disrupts the enemy's normal communication by generating or retransmitting an enemy signal. The deceptive jamming needs to fully recognize the enemy signal, the implementation condition is harsh, and the deceptive jamming is less in practical use. In recent years, with the development of signal processing techniques and the increasing level of integrated circuit computation, more and more interference types have been designed and implemented. These new interference types include digital modulation-optimized interference, code-targeted interference, etc., which are more ambiguous, covert, and efficient to design than conventional interference types. In addition, with the rise of machine learning, the current interference algorithm is developing towards artificial intelligence, and an interference signal which changes constantly can be designed according to the surrounding electromagnetic environment so as to achieve the optimal interference effect.
In order to normally communicate in the presence of interference electromagnetic spectrum, an anti-interference technology for dealing with a certain interference signal is designed, firstly, a current interference pattern needs to be fully recognized, an efficient anti-interference scheme can be provided after the interference pattern of an interference party is determined, and the interference recognition can be the first step of realizing an efficient anti-interference means. Interference identification first needs to intercept the interference signal, and a common method includes intercepting the interference signal in a silent period and separating and intercepting the interference signal in a communication process by using a blind source separation method. When detecting the intercepted interference signal, the conventional interference detection method includes a time domain energy detection algorithm and a frequency domain transformation detection algorithm. Wherein the time domain energy detection algorithm can only detect the presence of interfering signals. The frequency domain transformation detection algorithm is to perform characteristic identification on the frequency spectrum of the interference signal and then further judge the interference type. For traditional single tone, multi-tone and partial bandwidth interference, the frequency domain transformation detection algorithm can accurately identify, but for a novel interference type, only the existence of an interference signal can be judged through the frequency spectrum characteristic, and accurate interference type identification cannot be achieved. Under these new types of interferences, it is very important to accurately identify these interferences to realize an efficient anti-interference method.
The digital modulation optimal interference is used as a novel interference waveform, can reduce the bit error rate of a communication party to the maximum extent by adjusting the modulation constellation points of digital amplitude and phase under the condition of limited power, and has the characteristics of flexible interference, low average power and the like. For the optimal interference of the digital modulation, no method capable of accurately detecting and identifying exists at present. The detection and identification of the interference waveform are the premise for proposing the corresponding optimal anti-interference scheme.
Therefore, how to detect and identify the digital modulation optimal interference signal becomes an urgent problem to be solved.
Disclosure of Invention
In view of the problems in the background art, the present invention is directed to a method for detecting an optimal interference signal in digital modulation. The method extracts and combines the pulse interference signals, and performs characteristic analysis on the high-order square spectrum of the combined signals, so as to judge whether the signals are digital modulation optimal interference signals. The method is simple and easy to realize, and the detection rate of the digital modulation optimal interference signal is high.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a method for detecting an optimal interference signal of digital modulation comprises the following steps:
step 1, carrying out down-conversion and sampling on an interference signal: at a receiving end, firstly, the interference signal on the radio frequency is converted to a baseband, then the interference signal is sampled to obtain a sampled sequence Xrt；
Step 2, firstly aligning the sequence XrtLow-pass filtering, and then according to the symbol rate R of the communication party, the sequence XrtDown-sampling is carried out to obtain a sequence O after down-samplingt；
And 3, extracting and combining the interference signals, wherein the interference signals are dispersed, and the signals are directly identified to have higher missed detection probability, so that the interference signals dispersed in the time domain are extracted and combined firstly, and the specific process comprises the following steps: firstly, using a sliding filter to sample the sequence O after the down sampling of the step 2tSmooth filtering is carried out, so that misjudgment caused by burrs in the signals during detection can be reduced; then passes through the average power P of the noise floornCalculating high threshold value Th of signal detection according to high and low threshold factorshighAnd a lower threshold value Thlow(ii) a After obtaining two detection thresholds, calculating the position of the interference signal by adopting a double-threshold detection algorithm; finally in the sequence OtExtracting corresponding interference signals, and splicing the extracted interference signals to obtain a sequence Qt；
Step 4, obtaining sequence QtThe high power spectrum comprises the following specific processes: firstly, the sequence Q spliced in the step 3tPerforming a complex power operation to obtainQtPower of the square and fourth power of
Figure BDA0002853162830000021
And
Figure BDA0002853162830000022
secondly, to
Figure BDA0002853162830000023
And
Figure BDA0002853162830000024
performing fast Fourier transform to obtain
Figure BDA0002853162830000025
And
Figure BDA0002853162830000026
then to
Figure BDA0002853162830000027
And
Figure BDA0002853162830000028
taking the square of the modulus to respectively obtain QtThe quadratic and quartic spectra of (a);
step 5, carrying out modulation identification on the interference signal, wherein the specific process is as follows: judging whether the spectral line of the quadratic spectrum has an obvious peak value at the zero frequency, if so, judging the interference to be BPSK modulation, and turning to the step 6; if the spectral line of the quadratic spectrum has no obvious peak value at the zero frequency, but the spectral line of the quartic spectrum has an obvious peak value at the zero frequency, the interference is QPSK modulation, and the step 6 is carried out; if the spectral lines of the quadratic spectrum and the quartic spectrum have no obvious peak value at the zero frequency, judging that the interference signal is in an unrecognized modulation mode;
step 6, judging whether the interference signal is the digital modulation optimal interference signal, wherein the specific process is as follows: switching the modulation modes of the communication party, and judging the interference as the optimal interference signal of digital modulation if the modulation modes of a plurality of interference signals change; otherwise, it is a normal modulation signal.
Further, the sequence O obtained after down-sampling in step 2tThe data rate of (1) is preferably 4-16 times, preferably 8 times of the symbol rate R, so that the interference signal cannot be analyzed completely due to too narrow analysis bandwidth, and the complexity is not too high.
Further, average power P of noise floor in step 3nA part of noise can be collected and estimated under the condition of no data transmission.
Further, in step 4, sequence Q is first alignedtThe sequence Q may be first aligned before performing the complex exponentiationtDividing every n sampling points into a plurality of groups, performing complex power operation on each group, performing fast Fourier transform on the result of each group of power operation to obtain a quadratic spectrum or a quartic spectrum, and finally averaging the obtained quadratic spectrum or quartic spectrum result to realize the final smoothing accuracy of the quadratic spectrum or the quartic spectrum.
Further, in step 5, if the peak value of the spectral line of the quadratic spectrum at the zero frequency is greater than twice of the peak value at 1/2T at 1/2 doubling frequency f, the spectral line of the quadratic spectrum is considered to have an obvious peak value at the zero frequency; if the peak value of the spectral line of the fourth power spectrum at the zero frequency is more than twice of the peak value at 1/2T of 1/2 doubling frequency f, the spectral line of the fourth power spectrum is considered to have an obvious peak value at the zero frequency.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. the invention extracts and combines the pulse interference signals, and performs characteristic analysis on the high-order square spectrum of the combined signals, thereby judging whether the signals are digital modulation optimal interference signals. The method innovatively completes the detection of the digital modulation optimal interference signal through the identification of the modulation mode.
2. The method has the advantages that the high-order square spectrum characteristic of the digital modulation optimal interference signal is obvious after combination, so the method has high detection rate; in addition, the method uses fast Fourier transform to simplify calculation when calculating the high power spectrum, and is more convenient to realize.
Drawings
FIG. 1 is a block diagram of the detection method of the present invention.
FIG. 2 is a schematic diagram of a high-order square spectrum in the detection method of the present invention.
Fig. 3 is a diagram of the performance of digital modulation optimum interference detection according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the following embodiments and accompanying drawings.
In this example, the communication party establishes communication at a symbol rate of 500 kbaud/sec by using a QPSK modulation method, the interference party transmits an interference signal after the communication is stable, and the receiving end can identify the digitally modulated optimal interference signal from the interference signal type set according to the interference characteristics. The interference signal comprises conventional interference such as multi-tone interference, noise interference and the like, and also comprises special interference modes such as digital modulation optimal interference, channel coding aiming interference and the like. The multi-tone interference is an interference signal formed by superposing a plurality of sine waves with different frequencies, and has no modulation characteristic; the noise interference is Gaussian distributed band-limited random noise interference; the channel coding targeted interference is a pulsed noise interference. The interference signal form and relevant parameters output by the interference recognition are transmitted to an anti-interference module for further decision and corresponding schemes.
The optimal interference signal of digital modulation can select the optimal modulation interference waveform according to the modulation mode of the signal of the communication party, the corresponding relation is shown in the table I,
watch 1
Communication side signal Modulation mode for digitally modulating optimal interference signal
BPSK BPSK
QPSK QPSK
QAM QPSK
The modulation modes of the digital modulation optimal interference signal are only BPSK and QPSK, the interference signal aiming at the low-order modulation BPSK of the communication signal is the BPSK modulation mode, and the rest interference signals aiming at the high-order modulation of the communication signal are all the QPSK modulation modes.
FIG. 1 is a block diagram of the detection method of the present invention. The following describes the process of detecting and identifying the digital modulation optimal interference signal by the receiving end in detail with reference to fig. 1,
a method for detecting an optimal interference signal of digital modulation comprises the following steps:
step 1, when a receiving end detects that the error rate obviously rises under the action of interference so as to influence the normal transmission of language service and character service in the communication process, the receiving end sends a signaling for stopping communication to a sending end through an undisturbed signaling control channel, and the sending end stops sending data after receiving the signaling and enters a silent period;
step 2, when the communication party enters a silent period, the receiving end starts to carry out down-conversion and sampling on the interference signal: at a receiving end, firstly, the interference signal on the radio frequency is converted to a baseband through down conversion, then the interference signal is sampled by controlling an ADC (analog to digital converter), and a sampled sequence Xr is obtainedtWherein the ADC sampling rate is 20 Msps/s;
step 3, using a low-pass filter g to pair the sequence XrtFiltering, wherein the filtered data is Xrt' the bandwidth of the filter g is kept consistent with the bandwidth of the communication signalThe low-pass filter adopts an FIR filter, the bandwidth of g is 500kHz, and the order is set to 150 orders;
step 4, according to symbol rate R of communication party, sequence Xr is pairedt' Down-sampling to get the sequence O after down-samplingtThe specific process is as follows: because the ADC samples the filtered sampling point Xr in the step 3t' the rate is 20M/s, much higher than the symbol rate of 500k/s, and the complexity of the system is increased by direct processing, so that the low-pass filtered samples Xr are normalized to 8 samples per symbolt' Down-sampling is performed at a rate of 4M/s for the down-sampled samples, and O for the sequence after down-samplingt；
And step 5, extracting and combining the interference signals, wherein the interference signals are dispersed, and the signals are directly identified to have higher missed detection probability, so that the interference signals dispersed in the time domain are extracted and combined firstly, and the specific process is as follows: firstly, the sequence O after the down sampling in the step 4 is processed by a moving average methodtPerforming smooth filtering, so that misjudgment caused by burrs in the signal during detection can be reduced, and the order of the moving average filter is set to be 8; then passes through the average power P of the noise floornMultiplying the obtained data with a low threshold factor and a high threshold factor respectively to obtain a high threshold Th of pulse detectionhighAnd a lower threshold value ThlowWherein the low threshold factor is 1.5 and the high threshold is set to 2.3; after obtaining two detection thresholds, calculating the position of the interference signal by adopting a double-threshold detection algorithm, namely traversing and searching the data after sliding filtration, firstly searching the high threshold, finding the initial position of the first pulse, and when the power of the data is greater than the high threshold ThhighWhen the power of the signal reaches the minimum power of the pulse signal, the data point is considered as the starting point of the pulse signal, then the low threshold is searched, the ending position of the first pulse is found, and when the power of the data is smaller than the low threshold ThlowWhen the power of the signal is reduced to the noise level, the position is considered as the end point of the pulse signal, and the positions of all the pulse signals are found out in the mode; after the position of the pulse signal is found, the data of the pulse signal is extractedThen sequentially spliced to obtain a sequence Qt；
Step 6, obtaining a sequence QtThe high power spectrum comprises the following specific processes: firstly, the sequence Q spliced in the step 5tGrouping every 2048 sampling points into a plurality of groups, and obtaining the 2048 sampling points Q of each groupt,iFirstly, according to the complex algorithm, Q is pairedt,iPerforming an exponentiation operation, calculated to Qt,iQuadratic square value of
Figure BDA0002853162830000051
And quartic square value
Figure BDA0002853162830000052
Then to
Figure BDA0002853162830000053
And
Figure BDA0002853162830000054
performing fast Fourier transform to obtain
Figure BDA0002853162830000055
And
Figure BDA0002853162830000056
last pair of
Figure BDA0002853162830000057
And
Figure BDA0002853162830000058
taking the square of the modulus to respectively obtain Qt,iThe quadratic and quartic spectra of (a); in order to make the calculated high-order square spectrum smoother and more accurate, the calculated 64 continuous groups (i is 1,2, 64) of high-order square spectrums are averaged, and finally, the smoothed high-order square spectrum is obtained; the high-order square spectrum diagrams of the two modulation modes are shown in FIG. 2;
and 7, modulating and identifying the interference signal, wherein the specific process is as follows: judging whether the peak value of the spectral line of the quadratic spectrum smoothed in the step 6 at the zero frequency is more than 2 times of the peak value of the spectral line of the quadratic spectrum at 1/2 frequency multiplication, if so, judging that the interference is BPSK modulation interference, and if the peak value of the spectral line of the quadratic spectrum at the zero frequency is more than 2 times of the peak value of the spectral line of the quadratic spectrum at the 1/2 frequency multiplication; if the spectral line of the quadratic spectrum has no obvious peak value at the zero frequency, but the peak value of the spectral line of the quartic spectrum at the zero frequency is more than 2 times of the peak value at 1/2 frequency doubling, the interference is QPSK modulation interference; otherwise, judging as other types of interference modes; in the embodiment, the interference signal modulation mode is QPSK, so that only a relatively obvious peak value of a quartic spectrum at a zero frequency is detected during high-power spectrum detection, and the final identification result is QPSK modulation;
step 8, judging whether the interference signal is the digital modulation optimal interference signal, and the specific process is as follows: and the communication party switches the modulation mode into BPSK, detects the modulation mode of the interference signal by re-adopting the steps, if the identification result is BPSK modulation interference, the interference signal is self-adaptively changed along with the conversion of the modulation mode of the communication party, and the interference is judged to be digital modulation optimal interference if the identification result is the BPSK modulation interference and accords with the characteristics of the digital modulation optimal interference signal, otherwise, the interference is judged to be other types of interference.
In step 6 in this example, since the zero frequency of the result obtained by the fast fourier transform corresponds to the position where the sequence starts, the negative frequency is shifted to the rear, and for the convenience of identification, the part of the negative frequency can be shifted to the front, so that the obtained high power spectrum is easier to identify. The calculated high-order square spectrum is shown in fig. 2.
In a simulation test, the signal-to-noise ratio of the communication party is set to be 20dB, the digital modulation optimal interference signal in the real-time instance of the interference party interferes, and the detection rate of the receiving party on the digital modulation optimal interference signal is as shown in fig. 3, which can be obtained from the figure, and under the condition that the instantaneous JNR is 12dB, the detection rate on the digital modulation optimal interference signal is close to 100%.
While the invention has been described with reference to specific embodiments, any feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise; all of the disclosed features, or all of the method or process steps, may be combined in any combination, except mutually exclusive features and/or steps.

Claims (6)

1. A method for detecting digitally modulated optimal interfering signals, comprising the steps of:
step 1, carrying out down-conversion and sampling on an interference signal: at a receiving end, firstly, the interference signal on the radio frequency is converted to a baseband, then the interference signal is sampled to obtain a sampled sequence Xrt；
Step 2, firstly aligning the sequence XrtLow-pass filtering, and then according to the symbol rate R of the communication party, the sequence XrtDown-sampling is carried out to obtain a sequence O after down-samplingt；
Step 3, extracting and combining the interference signals, firstly extracting and combining the interference signals scattered on a time domain, and the specific process is as follows: firstly, using a sliding filter to sample the sequence O after the down sampling of the step 2tCarrying out smooth filtering; then passes through the average power P of the noise floornCalculating high threshold value Th of signal detection according to high and low threshold factorshighAnd a lower threshold value Thlow(ii) a After obtaining two detection thresholds, calculating the position of the interference signal by adopting a double-threshold detection algorithm; finally in the sequence OtExtracting corresponding interference signals, and splicing the extracted interference signals to obtain a sequence Qt；
Step 4, obtaining sequence QtThe high power spectrum comprises the following specific processes: firstly, the sequence Q spliced in the step 3tPerforming complex exponentiation to obtain QtPower of the square and fourth power of
Figure FDA0003394059620000011
And
Figure FDA0003394059620000012
secondly, to
Figure FDA0003394059620000013
And
Figure FDA0003394059620000014
performing fast Fourier transform to obtain
Figure FDA0003394059620000015
And
Figure FDA0003394059620000016
then to
Figure FDA0003394059620000017
And
Figure FDA0003394059620000018
taking the square of the modulus to respectively obtain QtThe quadratic and quartic spectra of (a);
step 5, carrying out modulation identification on the interference signal, wherein the specific process is as follows: judging whether the spectral line of the quadratic spectrum has an obvious peak value at the zero frequency, if so, judging the interference to be BPSK modulation, and turning to the step 6; if the spectral line of the quadratic spectrum has no obvious peak value at the zero frequency, but the spectral line of the quartic spectrum has an obvious peak value at the zero frequency, the interference is QPSK modulation, and the step 6 is carried out; if the spectral line of the fourth power spectrum has no obvious peak value at the zero frequency, judging that the interference signal is in an unrecognized modulation mode;
step 6, judging whether the interference signal is the digital modulation optimal interference signal, wherein the specific process is as follows: switching the modulation modes of the communication party, and judging the interference as the optimal interference signal of digital modulation if the modulation modes of a plurality of interference signals change; otherwise, the signal is a conventional modulation signal; wherein, the digital modulation optimal interference signal can be adaptively changed along with the conversion of the modulation mode of the communication party.
2. The detection method according to claim 1, wherein the sequence O obtained after down-sampling in step 2tThe data rate of (a) is 4-16 times the symbol rate R.
3. The detection method according to claim 2, wherein the sequence O obtained after down-sampling in step 2tIs 8 times the symbol rate R.
4. The detection method according to claim 1, wherein the average power P of the noise floor in step 3nAnd acquiring partial noise under the condition of no data transmission for estimation.
5. The detection method of claim 1, wherein the sequence Q is first aligned in step 4tThe sequence Q may be first aligned before performing the complex exponentiationtDividing every n sampling points into a plurality of groups, then performing complex power operation on each group, performing fast Fourier transform on the result of the power operation, and finally solving the average value of the obtained quadratic spectrum or quartic spectrum result.
6. The detection method according to claim 1, wherein in step 5, if the peak value of the line of the quadratic spectrum at zero frequency is greater than twice the peak value at 1/2T at 1/2 doubling frequency f, the line of the quadratic spectrum is considered to have an obvious peak value at zero frequency; if the peak value of the spectral line of the fourth power spectrum at the zero frequency is more than twice of the peak value at 1/2T of 1/2 doubling frequency f, the spectral line of the fourth power spectrum is considered to have an obvious peak value at the zero frequency.
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6263306B1 (en) * 1999-02-26 2001-07-17 Lucent Technologies Inc. Speech processing technique for use in speech recognition and speech coding
WO2001078242A2 (en) * 1999-04-28 2001-10-18 Isco International, Inc. Interference detection, identification, extraction and reporting
CN101674270A (en) * 2009-10-16 2010-03-17 电子科技大学 Identification method of low signal-to-noise ratio phase-shift keying modulating signal
CN103220241A (en) * 2013-03-29 2013-07-24 南京信息职业技术学院 Method for extracting box-dimension features from signals at low signal-to-noise ratio condition
CN106330385A (en) * 2016-08-29 2017-01-11 电子科技大学 Interference type identification method
CN108683622A (en) * 2018-05-22 2018-10-19 电子科技大学 A kind of Modulation Identification method for composing flat coefficient based on signal square
CN109490848A (en) * 2018-11-07 2019-03-19 国科电雷(北京)电子装备技术有限公司 A kind of length radar pulse signal detection method based on two-stage channelizing

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8180634B2 (en) * 2008-02-21 2012-05-15 QNX Software Systems, Limited System that detects and identifies periodic interference
CN105119862B (en) * 2015-07-22 2019-02-19 中国电子科技集团公司第三十六研究所 A kind of identification of signal modulation method and system
CN107689815A (en) * 2016-08-04 2018-02-13 北京遥感设备研究所 A kind of MSK band spread receivers Adaptive Anti-jamming detection threshold determines method

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6263306B1 (en) * 1999-02-26 2001-07-17 Lucent Technologies Inc. Speech processing technique for use in speech recognition and speech coding
WO2001078242A2 (en) * 1999-04-28 2001-10-18 Isco International, Inc. Interference detection, identification, extraction and reporting
CN101674270A (en) * 2009-10-16 2010-03-17 电子科技大学 Identification method of low signal-to-noise ratio phase-shift keying modulating signal
CN103220241A (en) * 2013-03-29 2013-07-24 南京信息职业技术学院 Method for extracting box-dimension features from signals at low signal-to-noise ratio condition
CN106330385A (en) * 2016-08-29 2017-01-11 电子科技大学 Interference type identification method
CN108683622A (en) * 2018-05-22 2018-10-19 电子科技大学 A kind of Modulation Identification method for composing flat coefficient based on signal square
CN109490848A (en) * 2018-11-07 2019-03-19 国科电雷(北京)电子装备技术有限公司 A kind of length radar pulse signal detection method based on two-stage channelizing

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
A Double Threshold Cooperative GNSS Interference Detection Algorithm Based on Fuzzy Logic;SHASHA ZHAI 等;《IEEE Access》;20200929;第8卷;全文 *
GNSS自适应双门限协同干扰检测算法;周平 等;《现代电子技术》;20191201;全文 *
基于传感器网络的分布式信号调制识别技术研究;段皓宇;《中国优秀博硕士学位论文全文数据库(硕士) 信息科技辑》;20130615(第6期);全文 *

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