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CN102006252A - Single-tone signal identification method - Google Patents

Single-tone signal identification method Download PDF

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CN102006252A
CN102006252A CN2010105652427A CN201010565242A CN102006252A CN 102006252 A CN102006252 A CN 102006252A CN 2010105652427 A CN2010105652427 A CN 2010105652427A CN 201010565242 A CN201010565242 A CN 201010565242A CN 102006252 A CN102006252 A CN 102006252A
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signal
complex
tone signal
tone
passband
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CN102006252B (en
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王甲峰
李兵
任亚博
权友波
岳旸
李蕾
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Institute of Electronic Engineering of CAEP
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Abstract

The invention discloses a single-tone signal identification method, which is characterized by comprising the following steps of: converting a received actual pass band signal into a multiplexing pass band signal, and converting the multiplexing pass band signal into a multiplexing base band signal; judging whether the actual pass band signal is a single-tone signal through detection, wherein the detection is to acquire characteristic values of a related matrix by estimating an autocorrelation matrix of the multiplexing base band signal and sort the characteristic values according to a descending order; calculating a judgment amount through the characteristic values and comparing with a judged threshold value, if the judgment amount is smaller than the threshold value, determining that the signal is the single-tone signal, otherwise, the signal is a modulation signal. By converting the pass band signal into the base band signal and identifying the single-tone signal according to time domain characteristics of the base band signal, the probability of identifying a low-speed modulation signal into the single-tone signal is reduced, the false-alarm probability is low, and the method also can be applied in the fields of research, such as frequency spectrum monitoring, communication reconnaissance and the like.

Description

Single tone signal identification method
Technical Field
The invention relates to a signal identification method, in particular to a single tone signal identification method for distinguishing a single tone signal from a modulation signal.
Background
Signal recognition is a very widely used signal analysis technique, which is a prerequisite for further analysis of signals. In signal recognition, it is often necessary to distinguish a monophonic signal from a modulated signal. A single tone signal, also called a single frequency signal, is a signal with a constant frequency, which may be a sine signal or a cosine signal.
There are two main methods for identifying single-tone signals: the method comprises the steps of firstly, estimating the signal bandwidth, comparing the estimated signal bandwidth with a set threshold, judging as a single-tone signal if the estimated signal bandwidth is smaller than the set threshold, and judging as a modulation signal if the estimated signal bandwidth is not smaller than the set threshold; and the other is an identification method based on the frequency domain peak-to-average ratio, namely, the ratio of the maximum value to the average value of the signal amplitude spectrum is calculated and compared with a set threshold value, if the ratio is greater than the threshold value, the signal is judged to be a single-tone signal, and if the ratio is not greater than the threshold value, the signal is judged to be a modulation signal. Both methods are performed in the frequency domain, which has the common disadvantage that it is easy to identify the low-rate modulated signal as a single tone signal, thereby causing a false alarm.
Disclosure of Invention
The invention provides a single-tone signal identification method for solving the technical defects of the two methods, firstly, a passband signal is converted into a baseband signal, and then the single-tone signal is identified according to the time domain characteristic of the baseband signal.
The technical scheme of the invention is as follows:
a single tone signal identification method is characterized by comprising the following steps: firstly, converting a received real passband signal into a complex passband signal, converting the complex passband signal into a complex baseband signal, and detecting to obtain whether the real passband signal is a single-tone signal; the detection is to obtain the eigenvalue of the autocorrelation matrix by estimating the autocorrelation matrix of the complex baseband signal, and arrange the eigenvalue in descending order; then, the decision quantity is calculated by the characteristic valueAnd comparing the signal with a decision threshold, if the signal is smaller than the threshold, judging the signal to be a single tone signal, and otherwise, judging the signal to be a modulation signal.
The method comprises the following specific steps:
(1) the received real passband signal is converted to a complex passband signal.
(2) Converting the complex passband signal into a complex baseband signal through down conversion, and removing an average value;
down-conversion is the process of changing an input signal having a certain frequency to an output signal having a lower frequency (generally without changing the information content and modulation of the signal).
(3) According to the formula
Figure 583721DEST_PATH_IMAGE002
Estimating a covariance matrix, where M is a covariance matrix dimension,
Figure 2010105652427100002DEST_PATH_IMAGE003
a covariance matrix is represented by a matrix of covariance,Hrepresenting a conjugate transpose, it is clear
Figure 687812DEST_PATH_IMAGE003
Has a dimension ofM。
(4) After the covariance matrix is obtained, the eigenvalue of the covariance matrix is solved to obtain
Figure 467550DEST_PATH_IMAGE004
Wherein
Figure 2010105652427100002DEST_PATH_IMAGE005
The method is expressed by calculating the characteristic value,
Figure 800442DEST_PATH_IMAGE006
as vectors of eigenvalues，
Figure 2010105652427100002DEST_PATH_IMAGE007
Are non-negative real numbers and the eigenvalues are sorted in descending order.
At this point, the covariance dimensionMAnd taking 4. Mean covariance dimensionMWhen taking 4, the first three characteristic values are taken to calculate the decision quantity
Figure 614814DEST_PATH_IMAGE001
Then according to the formula
Figure 125692DEST_PATH_IMAGE008
And calculating the identification judgment amount.
According to the decision quantity obtained by recognition according to the criterion
Figure 2010105652427100002DEST_PATH_IMAGE009
Making a recognition decision wherein
Figure 330409DEST_PATH_IMAGE001
In order to identify the decision quantity,is a decision threshold;is the 1 st eigenvalue of the complex baseband signal covariance matrix eigenvalue vector,
Figure 385138DEST_PATH_IMAGE012
in order to be the second characteristic value,is the third characteristic value.
The real passband signal is converted to a complex passband signal, which can be expressed as:
Figure 378502DEST_PATH_IMAGE014
wherein,
Figure 2010105652427100002DEST_PATH_IMAGE015
，
Figure 70515DEST_PATH_IMAGE016
is the carrier frequency and is,
Figure 2010105652427100002DEST_PATH_IMAGE017
for the purpose of frequency offset,
Figure 490083DEST_PATH_IMAGE018
in order to modulate the phase of the light,
Figure 2010105652427100002DEST_PATH_IMAGE019
in order to modulate the amplitude of the signal,
Figure 13468DEST_PATH_IMAGE020
is the phase of the carrier wave and is,
Figure 2010105652427100002DEST_PATH_IMAGE021
in order to shape the function of the pulse,
Figure 380995DEST_PATH_IMAGE022
in the form of a symbol period, the symbol period,
Figure 2010105652427100002DEST_PATH_IMAGE023
is the number of symbols;
Figure 809571DEST_PATH_IMAGE024
complex gaussian noise; according to
Figure 2010105652427100002DEST_PATH_IMAGE025
、
Figure 22378DEST_PATH_IMAGE018
And
Figure 665849DEST_PATH_IMAGE019
the value taking method can obtain different modulation signals such as amplitude modulation (ASK), phase modulation (PSK), frequency modulation (FSK), Quadrature Amplitude Modulation (QAM) and the like.
For a single-tone signal, the signal is,
Figure 1015DEST_PATH_IMAGE026
，
Figure 2010105652427100002DEST_PATH_IMAGE027
and is and
Figure 418352DEST_PATH_IMAGE018
、
Figure 434850DEST_PATH_IMAGE019
are all constant and, therefore, can be expressed as,
it is obvious thatIs thatSpecific examples of (3).
The complex baseband signal corresponding to the complex passband signal is:
Figure 2010105652427100002DEST_PATH_IMAGE031
wherein
Figure 842063DEST_PATH_IMAGE032
Complex Gaussian noise for base bandAn acoustic signal.
Similarly, the complex baseband signal corresponding to the single tone signal is,
Figure 2010105652427100002DEST_PATH_IMAGE033
wherein,
Figure 662252DEST_PATH_IMAGE020
is a constant number of times that the number of the first,
Figure 14736DEST_PATH_IMAGE034
is also constant, therefore
Figure 2010105652427100002DEST_PATH_IMAGE035
Zero mean complex Gaussian noise
Figure 645700DEST_PATH_IMAGE032
And complex constantAddition, it is obviousWhich is also a baseband complex gaussian noise signal.
From the above analysis, the problem of identifying the monophonic signal and the modulated signal is converted into the problem of signal detection in gaussian noise by converting the passband signal to the baseband, i.e., no signal represents that the passband is a monophonic signal, otherwise the passband is a modulated signal.
There are many signal detection methods in gaussian noise, and a signal detection method based on eigenvalue decomposition of a signal covariance matrix is selected here.
By using
Figure 2010105652427100002DEST_PATH_IMAGE037
Complex baseband signals representing the mean-removed modulated signal and the single-tone signal in unison, i.e.
Figure 101455DEST_PATH_IMAGE038
or
The digitized complex baseband signal is represented as
Figure 214904DEST_PATH_IMAGE040
WhereinnThe sampling point serial number is a non-negative integer.
Partitioning complex baseband signalsSegments of data length each
Figure 77818DEST_PATH_IMAGE042
Wherein the first
Figure 302126DEST_PATH_IMAGE044
The vector of segment data may be represented as,
Figure 2010105652427100002DEST_PATH_IMAGE045
then can obtain
Figure 52038DEST_PATH_IMAGE040
Covariance matrix of (2):
Figure 274072DEST_PATH_IMAGE002
。
when the signal is a noise signal, the noise signal,
Figure 686599DEST_PATH_IMAGE007
approximately distributed on a straight line; and when the signal is not a noise signal,
Figure 714598DEST_PATH_IMAGE007
can be divided into two sets, approximately distributed on two straight lines with greatly different slopes, so that
Figure 83131DEST_PATH_IMAGE046
There will be a distinct discontinuity in the curve. Thus can be based on
Figure 272804DEST_PATH_IMAGE046
The presence or absence of a signal is detected by the presence or absence of a sharp discontinuity in the curve.
Figure 172627DEST_PATH_IMAGE046
Position of the discontinuity of the curve and
Figure 676421DEST_PATH_IMAGE003
dimension of (2)MIt is related. In order to be able to use a uniform decision criterion,Mit is not suitable for getting too big, practice shows thatM=And 4, the detection effect is better. When only one element in the first set is present
Figure 978089DEST_PATH_IMAGE011
And the other eigenvalues belong to the second set.
The invention has the following beneficial effects:
the method converts the passband signals into baseband signals, and then identifies the single-tone signals according to the time domain characteristics of the baseband signals, thereby reducing the possibility of identifying the low-rate modulation signals into the single-tone signals and having very low false alarm probability; the method can also be applied to the research fields of frequency spectrum monitoring, communication reconnaissance and the like.
Drawings
FIG. 1 is a flow chart of the recognition of the present invention
FIG. 2 is a graph illustrating the results of the experiment for identifying a signal-to-noise ratio of 6dB according to the present invention.
Detailed Description
A method for identifying a single tone signal comprises the following steps:
(1) the received real passband signal is converted to a complex passband signal.
(2) Converting the complex passband signal into a complex baseband signal through down conversion, and removing an average value;
(3) according to the formula
Figure 21219DEST_PATH_IMAGE002
Estimating a covariance matrix, where M is a covariance matrix dimension, which takes 4,
Figure 408338DEST_PATH_IMAGE003
a covariance matrix is represented by a matrix of covariance,Hrepresenting a conjugate transpose, it is clear
Figure 778140DEST_PATH_IMAGE003
Dimension of (D)M。
(4) After the covariance matrix is obtained, the eigenvalue of the covariance matrix is solved to obtain
Figure 606419DEST_PATH_IMAGE004
Wherein
Figure 403473DEST_PATH_IMAGE005
The method is expressed by calculating the characteristic value,
Figure 12309DEST_PATH_IMAGE006
in order to be a vector of the eigenvalues,the real numbers are non-negative real numbers, and the characteristic values are arranged in descending order;
(5) according to the formula
Figure 383434DEST_PATH_IMAGE008
Calculating an identification decision quantity;
(6) according to the standard
Figure 351390DEST_PATH_IMAGE009
Making a recognition decision wherein
Figure 385205DEST_PATH_IMAGE001
In order to identify the decision quantity,
Figure 96809DEST_PATH_IMAGE010
is a decision threshold;is the 1 st eigenvalue of the complex baseband signal covariance matrix eigenvalue vector,
Figure 789269DEST_PATH_IMAGE012
in order to be the second characteristic value,
Figure 372697DEST_PATH_IMAGE013
is the third characteristic value.
Then, assuming that the modulation signal is a BPSK signal, the code rate is 1kbps, the carrier frequency is 10kHz, the sampling rate is 30ksps, the shaping coefficient is 0.35, and the sample length is 600 samples (20 symbols); the single tone signal is the same frequency as the BPSK signal, as well as the sampling rate and sample length.
As shown in fig. 2, the distribution of the identified decisions obtained by testing the steps of the method according to the invention 100 times for each of the two signals is given for a signal-to-noise ratio of 6 dB. As can be seen from fig. 2, forThe judgment amount of the single-tone signal identification basically fluctuates around 1, and the fluctuation is small; the decision quantity for identifying BPSK signals fluctuates greatly, but is much greater than 1. Thus the decision thresholdAlso very large, for example if taken
Figure 2010105652427100002DEST_PATH_IMAGE047
100% recognition accuracy was obtained in 200 trials.
The single-tone identification method provided by the invention converts the identification process into the baseband for carrying out, and utilizes the covariance matrix characteristic value to construct the identification characteristic quantity, so that the distance between the single-tone signal characteristic quantity value set and the modulation signal characteristic quantity value set is very large, and better identification performance is obtained.
In the identification method, the carrier frequency needs to be estimated firstly when the carrier frequency is unknown, the carrier frequency estimation precision is limited under the condition that the modulation mode of the modulation signal is unknown, but the frequency of the single-tone signal can be estimated accurately, and the aim of the invention is to distinguish the single-tone signal from the modulation signal, so the influence of the residual carrier on the final identification effect is small.

Claims (9)

1. A single tone signal identification method is characterized by comprising the following steps: firstly, converting a received real passband signal into a complex passband signal, converting the complex passband signal into a complex baseband signal, and then judging whether the real passband signal is a single-tone signal or not by detection; the detection is to obtain the eigenvalue of the autocorrelation matrix by estimating the autocorrelation matrix of the complex baseband signal, and arrange the eigenvalue in descending order; then, the decision quantity is calculated by the characteristic value
Figure 2010105652427100001DEST_PATH_IMAGE002
And comparing the signal with a decision threshold, if the signal is smaller than the threshold, judging the signal to be a single tone signal, and otherwise, judging the signal to be a modulation signal.
2. The method of claim 1, wherein the method comprises the steps of:
A. converting the received real passband signal into a complex passband signal;
B. converting the complex passband signal into a complex baseband signal through down conversion, and removing an average value;
C. according to the formula
Figure 2010105652427100001DEST_PATH_IMAGE004
Estimating a covariance matrix, where M is a covariance matrix dimension,
Figure 2010105652427100001DEST_PATH_IMAGE006
a covariance matrix is represented by a matrix of covariance,Hrepresents a conjugate transpose;
D. after the covariance matrix is obtained, solving the eigenvalue of the covariance matrix to obtain:
Figure 2010105652427100001DEST_PATH_IMAGE008
whereinThe method is expressed by calculating the characteristic value,
Figure 2010105652427100001DEST_PATH_IMAGE012
in order to be a vector of the eigenvalues,
Figure 2010105652427100001DEST_PATH_IMAGE014
are non-negative real numbers and the eigenvalues are sorted in descending order.
3. The tone signal identifying method of claim 2, wherein: the covariance dimensionMAnd taking 4.
4. The tone signal identification method of claim 3, wherein the tone signal is identified by a tone signal identification method：The covariance dimensionMWhen taking 4, the first three characteristic values are taken to calculate the decision quantity
Figure 406185DEST_PATH_IMAGE002
。
5. The tone signal identification method of claim 4, wherein the tone signal is identified by a single tone signal：When the covariance dimension M is 4, the formula is shown
Figure 2010105652427100001DEST_PATH_IMAGE016
And calculating the identification judgment amount.
6. The tone signal identification method of claim 5, wherein the tone signal is identified by a single tone signal：According to the calculated decision quantity and the criterion
Figure 2010105652427100001DEST_PATH_IMAGE018
Making a recognition decision wherein
Figure 261009DEST_PATH_IMAGE002
In order to identify the decision quantity,
Figure 2010105652427100001DEST_PATH_IMAGE020
is a decision threshold;
Figure 2010105652427100001DEST_PATH_IMAGE022
is the 1 st eigenvalue of the complex baseband signal covariance matrix eigenvalue vector,
Figure 2010105652427100001DEST_PATH_IMAGE024
in order to be the second characteristic value,
Figure 2010105652427100001DEST_PATH_IMAGE026
is the third characteristic value.
7. The tone signal identifying method of claim 2, wherein: the real passband signal is converted to a complex passband signal, denoted as:
Figure 2010105652427100001DEST_PATH_IMAGE028
wherein,，is the carrier frequency and is,
Figure 2010105652427100001DEST_PATH_IMAGE034
for the purpose of frequency offset,
Figure 2010105652427100001DEST_PATH_IMAGE036
in order to modulate the phase of the light,
Figure 2010105652427100001DEST_PATH_IMAGE038
in order to modulate the amplitude of the signal,
Figure 2010105652427100001DEST_PATH_IMAGE040
is the phase of the carrier wave and is,
Figure 2010105652427100001DEST_PATH_IMAGE042
in order to shape the function of the pulse,
Figure 2010105652427100001DEST_PATH_IMAGE044
in the form of a symbol period, the symbol period,
Figure 2010105652427100001DEST_PATH_IMAGE046
is the number of symbols;
Figure 2010105652427100001DEST_PATH_IMAGE048
complex gaussian noise;
according to
Figure 2010105652427100001DEST_PATH_IMAGE050
、
Figure 666845DEST_PATH_IMAGE036
And
Figure 999737DEST_PATH_IMAGE038
the value taking method obtains different modulation signals such as amplitude modulation (ASK), phase modulation (PSK), frequency modulation (FSK), Quadrature Amplitude Modulation (QAM) and the like;
for a single-tone signal, the signal is,
Figure 2010105652427100001DEST_PATH_IMAGE052
，
Figure 2010105652427100001DEST_PATH_IMAGE054
and is and
Figure 751792DEST_PATH_IMAGE036
、
Figure 574255DEST_PATH_IMAGE038
are all constants, expressed as:
Figure 2010105652427100001DEST_PATH_IMAGE056
。
8. the tone signal identifying method of claim 2, wherein: the complex baseband signal corresponding to the complex passband signal is:wherein
Figure DEST_PATH_IMAGE060
A complex Gaussian noise signal of a baseband;
the complex baseband signal corresponding to the single-tone signal is:
Figure DEST_PATH_IMAGE062
whereinis a constant number of times that the number of the first,
Figure DEST_PATH_IMAGE064
is also a constant, and is,
Figure DEST_PATH_IMAGE066
which is also a baseband complex gaussian noise signal.
9. The tone signal identifying method of claim 4, wherein: signal detection method using complex signal covariance matrix eigenvalue decomposition, usingA complex baseband signal that collectively represents the de-averaged modulated signal and the single-tone signal, namely: or
Figure DEST_PATH_IMAGE072
let the digitized complex baseband signal be represented as
Figure DEST_PATH_IMAGE074
WhereinnThe number of the sampling point is a non-negative integer;
partitioning complex baseband signals
Figure DEST_PATH_IMAGE076
Segments of data length eachWherein the first
Figure DEST_PATH_IMAGE080
The vector of segment data can be represented as:
Figure DEST_PATH_IMAGE082
then can obtain
Figure 603970DEST_PATH_IMAGE074
Covariance matrix of (2):
Figure 272849DEST_PATH_IMAGE004
。
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CN110896308A (en) * 2019-10-31 2020-03-20 中国工程物理研究院电子工程研究所 Single tone signal reconstruction method
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