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JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 10, NO. 2, JUNE 2012 163
AbstractTo meet the actual requirement of
automatic monitoring of the shortwave signals under
wide band ranges, a technique for automatic recognition
is studied in this paper. And basing upon the spectrum
and modulation characters of amplitude modulation
(AM) signals, an automatic recognition scheme for AM
signals is proposed. The proposed scheme is achieved by
a joint judgment with four different characteristic
parameters. Experiment results indicate that the
proposed scheme can effectively recognize AM signals in
practice.
Index TermsAmplitude modulation, automatic
recognition, characteristic parameters, shortwave radio.
1. IntroductionNowadays a lot of studies have been devoted to the
algorithms for modulation recognition of analog signals. In
[1] to [8], a series of parameters based on the instantaneousamplitude and phase were proposed to identify the different
modulation types: amplitude modulation (AM), single side
band (SSB), frequency modulation (FM), double side
band (DSB), and so on. However, the situation in the
modulation recognition algorithms mentioned above and
that in the actual application are different. Furthermore,
digital signals are usually accrued in the shortwave bands.
Therefore, a lot of tasks should be done to this field.
A new AM signal recognition scheme is proposed in
this paper. The scheme is based on the spectrum and
modulation characters of AM signals. In this way, a fast
recognition of AM signals can be obtained by characteristic
parameters calculation. Experimental results show that the
proposed scheme can effectively improve the performance
of automatic AM sorting work.
The rest of this paper is organized as follows. Section 2
demonstrates a theoretical analysis for AM signal
Manuscript received May 31, 2012; revised June 11, 2011; presented at
2012 2nd International Conference on Signal, Image Processing and
Applications, Hong Kong, August 34, 2012.
X.-F. Zhang, L. Chang, P.-M. Ren, and R. Liu are with State Radio
Monitoring Center, Beijing 100027, China (e-mail: [email protected];
[email protected]; [email protected];[email protected]).Digital Object Identifier: 10.3969/j.issn.1674-862X.2012.02.013
identification and an AM signal recognition scheme based
on characteristic parameters is proposed in this section. In
Section 3, simulation results and analysis are presented.
Finally, Section 4 concludes this paper.
2. Algorithm for AM signalIdentification
In this section, the theoretical analysis on AM
recognition algorithms is illustrated in detail. To introduce
recognition algorithms, four characteristic parameters are
taken into consideration. Finally, a new scheme for AM
signal sorting is proposed.
2.1 Power Spectrum Symmetry
Considering that some signals show a certain sort of
symmetry on spectrum distribution at the range of its 3 dB
bandwidth, a power spectrum symmetry equation[5] can be
defined as
1L U
L U
P PS
P P
=
+
(1)
where PL and PU are the upper and lower bands of power
spectrum, which are symmetrical about the center
frequency. Since these parameters can be used to measure
the symmetrical degree of the overall spectrum,
symmetrical signals can be easily distinguished from
unsymmetrical signals by S1. The value of S1 is inversely
proportional to symmetry.
It is known that signals, like AM, Morse, and single
carrier, do show good symmetry. However, sometimes thehighest amplitude of phase shift keying (PSK) and
frequence shift keying (FSK) maybe just located in the
center of the whole spectrum. Taking everything mentioned
above into account, the threshold is usually set around 0.1
to 0.2.
2.2 Maximum Difference Based on the Normalized
Power Spectrum Symmetry
In comparison with the parameters S1, the parameterS2
is used to further describe the symmetry of spectrum
distribution, which can be recognized as a stricter parameter.
The mathematical formulation of this different symmetryrelationship is described by
Automatic Recognition Algorithm of AM Signals
Based on Spectrum and Modulation CharactersXiao-Fei Zhang, Liang Chang, Pei-Ming Ren, and Rong Liu
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JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 10, NO. 2, JUNE 2012164
(a)
(b)
(c)
Fig. 1. Instantaneous amplitude of various signals after filtering:
(a) CW signal, (b) MORSE signal, and (c) AM signal.
( ) ( ){ }2 maxS S n k S n k = + (2)
where S(n) is the maximum value of the spectrum center,
S(n+k) and S(nk) (k=1, 2, , n1) are respectively
represent the left amplitude and right amplitude.
The basic ideal of parameter S2 is similar to that of
parameterS1. It extracts the largest amplitude differences of
the normalized power spectrum symmetry in order to avoid
the presence of outliers. By this way, the symmetrical
signals are easily sorted.
In the ideal case, the parameter S2 can strictlydistinguish AM, Morse, and carrier waves (CW) from other
signals. Additionally, the computational complexity of
parameterS2 is very high. However, both the reliability and
the computational complexity can be greatly improved by a
joint judgment with the parameterS1.
Usually, if the value of parameterS1 is above the given
threshold, it can be judged that the signal is none of AM,
Morse, and CW. On the contrary, we need the parameterS2
for further judgment. Once the value of parameterS2 is less
than the set threshold, it is sure that the filtered signals are
one of these three signals.
2.3 Statistics based on Instantaneous Amplitude
Distribution
After filtering signals by parameters S1 and S2, the
remaining signals may be one of AM, Morse, and CW.
Another parameter S3 is used to distinguish Morse from
AM and CW signals. The Morse signal is a turn-off signal
while the AM and CW signals are constant signals, so thedistinction can be caused by the different forms of
amplitude distribution.
There is a pretreatment in the experiment. Since the
bandwidth of Morse signal is very narrow, it is filtered by
adopting a very narrow band-pass filter to minimize the
impact of noise. The bandwidth herein is set to 20Hz. After
filtering, the instantaneous amplitude of different signals
are shown as Fig. 1.
It can be seen from Fig. 1 that the CW signal is
basically turned to be a constant envelope signal after
filtering. On the contrary, the envelope of the AM signal is
not constant and it changes slowly. At the same time, Morsesignal shows an apparent turn-off feature. So the three
signals can be distinguished by different amplitude
distributions.
The Morse signal can be separated from AM and CW
signals by computing amplitude distribution statistics. In
this experiment, the normalized amplitude is divided intoM
intervals, and the total amplitude distribution number in
each interval can be denoted as N1, N2, , NM. For the
Morse signal, it is in the off state for nearly half of the
time. So the number ofN1, which belongs to the lowest
value, should be the largest. But there is no such feature for
AM and CW signals. Therefore, the Morse signal can beeasily identified by counting whether the interval with the
largest number of points corresponds to the lowest
amplitude interval, that is
( ){ }3 argmax iS n N= (3)
where n is the number of points whose corresponding
amplitudes are in the interval Ni, the function arg max{}
means the value of i when n gets the maximum value. So,
when S3=1, the signal is surly to be Morse, otherwise it may
be AM or CW signals alternatively.
2.4 Constant Envelope Feature ParameterFor AM and CW signals, we can distinguish them by
2.5 3 3.5 4 4.5 5 5.5
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2.5 3 3.5 4 4.5 5 5.5
t(s104)
Normalizedamplitude
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x 104
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Normalizedamplitude
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
t(s104)
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Normalizedamplitude
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ZHANG et al.: Automatic Recognition Algorithm of AM Signals based on Spectrum and Modulation Characters 165
judging whether it is a constant envelope signal. Setting a
constant envelope signal as {Sa(k)}, the parameterS4 can be
defined as
( ){ }4 Var aS S k= (4)
where Var{} means the variance operator.
The threshold is set to be 0.1. If S4 is larger than the
threshold, the signal modulation can be judged as AM.
According to the aforementioned analysis, this paper
proposes an AM signal recognition scheme based on
characteristic parameters. Fig. 2 illustrates the flow of the
proposed scheme. It is worth noting that this is one possible
scheme, we need to adopt specific process according to the
characteristics of practical signals. For example, we can
extend the processing time to make a repetitive judgment to
improve the reliability.
3. Simulation ResultsIn this section, 22 signals are tested in the experiment.
The modulation type of these signals includes 8-channel
quaternary phase shift keying (QPSK), CW, upper sideband
(USB), FSK, AM, SSB, and Morse. As the signal-to-noise
ratio (SNR) of the acquired signals is relatively high, we
can add noise in order to access each parameter. The
thresholds (T) of parameters 1, 2, and 4 are set to 0.05, 0.2,
and 0.1, respectively.
As is shown in Table 1, the SNR is set to 0 dB, 5dB
and 10dB, respectively. Then we can obtain the number of
each signal with different SNR. Meanwhile, for each SNR,
we will get two statistics: one with the threshold T, the
other with the threshold =T =T =T =T =T =T T =T >T =T >T
AM 3 0 3 0 3 0 3
CW 2 0 2 0 2 0 2
MORSE 2 2 0 2 0 2 0
Table 4: Judgment result ofS4
SNR 0dB 5dB 10dB
TYPE No. >=T =T =T Threshold 1
Threshold 2 >Threshold 2
>Threshold 4>Threshold 4
ParameterS1
ParameterS2
ParameterS3 ParameterS4
Thre-
shold 1
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JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 10, NO. 2, JUNE 2012166
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Modulation identification algorithm, Machinery &
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[8] H.-P Tao, Research of Communication signal modulation
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Xiao-Fei Zhang was born in Henan Province,
China in 1978. He received the B.S. degree and
M.S degree from the Beijing University of
Technology in 2000 and 2006, respectively,
both in electrical engineering. He is currenly
working with the State Radio Monitoring
Center as a senior engineer. His research
interests include VHF/UHF monitoring and
interference location technologies.
Liang Chang was born in Beijing, China in
1981. He received the B.S. degree and the
Ph.D. degree from Tsinghua University in
2003 and 2008, respectively, both in electrical
engineering. He is currenly working with the
State Radio Monitoring Center as a seniorengineer. His research interests include signal
processing in spectrum monitoring and
satellite monitoring thechnologies.
Pei-Ming Ren was born in Shanxi Province,
China in 1985. He received the B.S. degree
and M.S. degree from the Communication
University of China (CUC), Beijing in 2009
and 2011, respectively. He is currenly
working with the State Radio Monitoring
Center as an assistant engineer. His researchinterests include spectrum monitoring, signal
processing, and imaging processing.
Rong Liu was born in Hubei Province, China
in 1986. She received the B.S. degree from
the Hefei University of Technology (HFUT),
Hefei in 2009 and the M.S. degree from
Beijing Jiaotong University (BJTU), Beijing
in 2011. She is currenly working with the
State Radio Monitoring Center as an assistant
engineer. Her research interests include signalprocessing and information theory.