2015 International Conference on Intelligent Information Hiding and Multimedia Signal Processing
A Method of the Detection of Frequency-hopping Signal Based on Channelized
Receiver in the Complicated Electromagnetic Environment
Yi-jia Zhang
Rui-ying Liu, Hua-jun Song
Science and Technology on Communication Information
Security Control Laboratory
Jiangnan Electronic Communication Research Institute
Jiaxing, China
e-mail: 17081912@qq.com
College of Information and Control Engineering
China University of Petroleum (East China)
Qingdao, China
e-mail: huajun.song@upc.edu.cn
Abstract—A new method is studied and designed in this paper to
strengthen the adaptability and practicability of frequencyhopping (FH) signal detection under the complicated
electromagnetic environment. The proposed method includes
four processing algorithms: channelization, estimation of the
background noise, selection of the FH channel, and FH signal
detection. Through this method, FH signals can be effectively
detected under the complicated environment even with strong
interference. The simulation and results suggest this method
improves the accuracy of FH signal detection and signal-to-noise
ratio greatly.
II.
PRACTICE
The Complicated Electromagnetic Environment refers to a
kind of battlefield environment, in which various
electromagnetic activities interweave intricately, signals
superimpose together densely and also the power distribution
varies in a certain airspace, time domain and frequency domain
[3]. Besides, it has a significant impact on beneficial
electromagnetic activities. The FH radios mainly work in the
frequency band of 30-520 MHz at home and abroad. Systems
with wireless signal emission ability in this frequency band are
radar, civilian radio, FM radio and Television. The band
mainly contains fixed frequency signal, pulse signal, time
division signal, burst signal, etc. Each signal has its own
characteristics on its bandwidth and occurrence number as
shown in the following table.
Keywords-complicated
electromagnetic
environment;
channelization; FH signal detection strong interference;
channelized receiver; FH signal detection
I.
INTRODUCTION
With the development of communication technology, the
data captured by the broadband radio spectrum monitoring
receiver may contain all kinds of signals under increasingly
intensive communication environment, such as FH signal, burst
signal , narrowband communication signal under fixed carrier
frequency,
spread
spectrum
signal,
and
other
interference signals[1]. Such signal cannot be directly applied
to signal analysis of radio spectrum monitoring system, such as
modulation recognition, demodulation and decoding, etc.
Therefore, the monitoring system need to sort broadband
signals, eliminate interference signal from wideband data, and
estimate parameters in real time for interested communication
signals before the signal analysis [2]. Then the monitoring
system can isolate interested communication signals from
wideband data based on these parameters.
At present, the supported signals of most frequency
spectrum monitoring systems are only conventional fixed
frequency signal, and not the FH signals. FH communication
system has been widely adopted in many fields for its antijamming performance, low intercept characteristics and
preferable network-building ability. Thus, the research on
method of FH signal detection has become one of the hottest
researches in related field. This paper designs an algorithm for
FH signal detection based on multilevel channelized processing,
which can achieve the FH signal detection under the
complicated electromagnetic environment after receiving
signals with all probability.
978-1-5090-0188-0/15 $31.00 © 2015 IEEE
978-0-7695-5668-0/15
DOI 10.1109/IIH-MSP.2015.65
COMPLEX ELECTROMAGNETIC ENVIRONMENT IN
TABLE I.
THE FEATURE OF
COMPLEX ELECTROMAGNETIC ENVIRONMENT SIGNAL
Signal type
Feature of
Signal
frequency
domain
narrow-band
Feature of
Signal
Application
continuous
broadband
sparse
civilian radio, FM
radio,
and Radio
&Television
radar
time-sharing
signal
narrow-band
frequent
burst signal
random
burst
Frequencyhopping signal
narrow-band
sparse
fixed
frequency
signal
pulse signal
mobile communicati
on, satellite commu
nication
military
communication
tactical
antijamming communic
ation
From table 1, one single frequency is present sparsely in
FH signal; also the instantaneous bandwidth is narrow.
Therefore we can distinguish other signals to achieve
background signal denoising and FH signal detection according
to these characteristics.
294
III.
THE ALGORITHM PRINCIPLE
S (n)
D↓
h0 (m)
Z 1
D↓
Z
(1) m e
h0 (m)
e
1
2D
h0 (m)
2↓
x
y1 (m)
DFT
ĂĂ
ĂĂ
j m
2
j
x1 (m)
ĂĂ
ĂĂ
ĂĂ
(1) m e
y0 ( m)
j
0
2D
2↓
1
ĂĂ
Z 1
D↓
(1)( D 1) e
j
D1
( m)
y D1 (m)
( D 1)
2D
Figure 2. The mathematical model of real signal channelized receiver
AD data
IQ data which the sampling
rate is 800 kHz, the
bandwidth is 600 kHz ,and the
channel spacing is 400 kHz
FPGA1
The detection of frequencyhopping signal
channelization
e
j m
2
x 0 ( m)
ĂĂ
A. The overall flow chart
Firstly, the ultra-short wave signal should be converted to
intermediate frequency signal, and then several roads of IQ
data can be output after the channelized processing of
intermediate frequency signal. The channelized data will be
input into the estimation module of background noise which
can obtain the background parameters of current bandwidth
through cumulative calculation. For background removal
operation, the obtained parameters will be input into the
selection module of FH channel and the detection module of
FH signal. Then, data after channelized processing should be
input into selection module of FH channel, and the actual FH
signal channel can be obtained through the background
removal and channel selection. Finally the channelized IQ data,
selection results of FH channel, and estimation results of
background noise should be input into FH channel for FH
signal detection.
2↓
256 channels
800k
The selection of frequency-hopping
channel
Downconversion
Downconversion
The estimation of signal
background
FPGA2
2 times decimationand and
half band filter
Figure 1. The overall flow chart
1024 channels
400k
B. Channelized receiver
The original AD data within certain bandwidth which
belongs to UHF will be collected, and then be channelized. The
poly-phase filtering channel receiver (PPCR) is adopted in the
channelized receive processing, whose mathematical model is
shown in the following formula [4].
D 1
yk (m) [ x p (2m) e
j
( 2 D 1)
p
2D
p 0
x' p (2m) x p (2m)e
( 2 D 1) j 2D
D 1
] x' p (2m)e
j 2D kp
D 1
, DFT [ x' p (2m)] x' p (2m)e
Downconversion
FPGA3
2 times decimation and half
band filter
DFT [ x' p (2m)]
p 0
p
Downconversion
(1)
4096 channels
200k
j 2D kp
p 0
According to the above formula, the mathematical model of
real-signal poly-phase filter channelized receiver can be
obtained as figure 2. In figure 2, when D is even, the
m
product term of the first multiplier should be set as (1) e
j
2
FPGA4
N times interpolation
M times decimation and filter
4096 channels
200k*N/M
Chanelized
IQ data
m
;
j m
otherwise it should be changed to e 2 . This filters group
divides the whole frequency band into several channels
according to the instantaneous bandwidth of signal. It can
output IQ data which is used for interception, analysis and
demodulation after channelization.
Figure 3. The block diagram of multilevel channelization
Although channelized processing owns the total probability
to receive signals, the implementation of channelized receiver
is very difficult on the hardware platform when there are a
large number of hopping points in the signals. Also, the cost
would be greatly increased even if it could be implemented [5].
There are numerous frequency points in the FH signals under
the real environment. In order to process FH signal, it will be
divided into 2400 sub-channels when the FH bandwidth is 60
MHz and the frequency space is 25 KHz. This procedure puts
forward large demand on data transmission and calculation in
295
the hardware implementation. Therefore, a structure of
multilevel channelized processing is designed in this paper, in
order to reduce the data transmission and calculation. It can
also conduct the distributed data processing (DDP) in several
FPGA; the algorithm block is shown in the following diagram.
The whole process from AD data to the final outputs is divided
into four levels; the processing algorithms can be assigned to 4
FPGAs to achieve distributed data processing step by step. This
method can reduce the amount of total computation and data
processing in each FPGA, and has higher availability and
feasibility. The process is illustrated in figure 3.
The estimation of channelization
carrier
frequency
offset
carrier
frequency
correction
modulus
Remove the
back ground
background
C. The estimation of signal background
M points
occurrence
number of
signal
Threshold
carrier
frequency
correction
Channelized
data
accumulate of M
points
modulus
X
carrier
frequency
offset
delay of M
points
M points
peak hold
Y
Y
X
The calculation of peak
max(X+Y)*α+(1+α)*Z
The calculation of
occurrence
number(X+Y)*α+(1+α)*
Z
Z
Z
Peak
Occurrence
number
X
Y
signal background
calculation
X+Y+(1+α)*Z
Z
Figure 5. The block diagram of the selection of FH channels
Background
value
E. The detection of FH signals
The peak value and occurrence number of the signal after
filtering need to be compared with the amplitude threshold and
rate threshold. For example, if there are 256 frequency points
in the frequency table, the occurrence probability on each
channel is 1/256, and the rate threshold can be set less than
1/256 (e.g. 1/200). The FH channels will be selected when the
peak value of FH signal is greater than the amplitude threshold
and the probability of FH occurrence is less than the rate
threshold. In general, the FH signals are discontinuously
distributed in a certain bandwidth, the distribution of actual FH
channels can be obtained after removing some false-alarm
channels artificially. This can help shield the FH channels from
the interference channels. The FH detection can be done within
the selected channels so as to improve the accuracy of FH
signal detection [9]. The process is illustrated in figure 6.
Figure 4. The block diagram of the background noise estimation
As shown in figure 4, Firstly, The data obtained by
channelized processing also need to conduct carrier frequency
offset correction in order that the instantaneous frequency of
every FH signal hop can accurately point at the sub channel
center [6]. Secondly, modulus operation would be done on the
IQ data of each sample point, i.e. I 2 Q 2 . Then, the IQ data of
M sample points are cumulatively summed to obtain X, and the
IQ data of the next M sample points which is after the first M
sample points are also cumulatively summed to get Y. Finally,
the background noise calculation should be conducted on X, Y
and the earlier background value Z. The background noise can
) * Z ; the coefficient
be calculated by X Y (1
in the
formula should be selected from 0 to 1 according to the
practical signal background. This algorithm is equivalent to
conduct filtering smoothly on the background value in a certain
time for obtaining the signal background value of each channel
[7].
The occurrence time of estimated signal
Peak
X
Amplitude
threshold
D. The selection of FH channel
As shown in figure 5, the signal background can be
removed from the channelized data. Then, all the channelized
data should be dealt with peak holding and the statistics of
occurrence number. Peak holding is for obtaining the
amplitude information of FH signals, and the statistics of hops'
occurrence number is for obtaining the rate information of FH
signals. The appearance of FH signals can be judged when the
amplitude value is greater than a certain value and the signal
rate value less than a certain value. Then the obtained signal
should be dealt with smoothing filtering of the M points for the
preparation of FH signal detection [8].
Channel
shielding
Z
Rate
threshold
carrier
frequency
offset
296
Yt
(X>Xt)&(Y<Y
t)
The selection
of channel
~Z
Y
Occurrence
number
Channelized
data
carrier frequency
correction
Xt
modulu
Remove
signal
background
background
FH signal
detection
Figure 6. The block diagram of the detection of FH signal
IV.
THE SIMULATION RESULTS AND DISCUSSION
F. Test equipment
power
amplifier
antenna
power
amplifier
The transmitter
Figure 8. Occurrence rate and amplitude of signals
Signal
generator
For generating
interferences
V.
In summary, this paper puts forward a FH signal detection
algorithm based on channelization under strong interference
environment. The method includes four processing algorithms:
channelized receiver, estimation of background noise, selection
of FH channel and detection of FH signal. It can process and
detect FH signal with strong interference under complex
environment. The simulation of algorithm proves that this
algorithm can obtain FH channel with strong interference under
the complex electromagnetic, and can also greatly improve the
adaptability and practicability of the FH signal algorithm.
antenna
The receiver
Figure 7. The Diagram of the test equipment
As shown in figure 7, the transmitter generates FH signal
by signal generator and transmitting equipment. The signal will
be input to the power amplifier and then radiate the FH signals
into air through the antenna. The hop speed of the FH signal is
609 hop/s and modulation style is 2CPM. The receiver should
get signals using the antenna by channelization, background
signals estimation, FH channel selection and FH signal
estimation processing, etc. After all these processing, the FH
signal detection will be achieved.
ACKNOWLEDGMENT
This work was partially supported by the National Natural
Science Fund Project of China under grant 61271001 and
61171150, the Fundamental Research Funds for the Central
Universities of China under Grant No. 14CX05039A and
Zhejiang Provincial Natural Science Foundation of China
under grant R1110006.
G. Generation of strong interferences
Signal generator can used to generate strong interferences
which include fixed frequency signals, time division signal and
burst signal. Table 2 shows parameters of these interference
signals.
TABLE II.
Background
simulation
radar
FH signal
FH radio
fixed
frequenc
y signals
time
division
signal
Communicat
ion radio
Data
system
REFERENCES
[1]
PARAMETERS OF INTERFERENCE SIGNALS
Signal
type
burst
signal
link
frequency
100MHz
and
200MH
300340MHz
450MHz
bandw
idth
5MHz
modulati
on type
MSK
Jump
speed
10
[2]
[3]
48KHz
2CPM
50KHz
FSK
100KH
z
BPSK
609
[4]
[5]
410MHz
and
500MHz
CONCLUSION
1000
[6]
As shown in figure 8, the statistics of occurrence rate and
peak holding need to be done on all signals within the test
bandwidth under strong interference background. The
simulation result is shown as the following figure in which
fixed frequency signal and time division signal have higher
occurrence rate, but FH signal and burst signal have lower
occurrence rate. Therefore, the fixed frequency signals and
time division signal can be identified and eliminated by its
occurrence frequency. In practical condition, FH signal
generally owns continuous channel and wide jump width. The
actual FH signal can be obtained after eliminating pulse signal
and burst signal on the basis of these characteristics.
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[8]
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