Communications and Network, 2009, 52-56
doi:10.4236/cn.2009.11008 Published Online August 2009 (http://www.scirp.org/journal/cn)
Copyright © 2009 SciRes CN
The Identification of Frequency Hopping Signal
Using Compressive Sensing
Jia YUAN1, Pengwu TIAN2, Hongyi YU
Department of Communication Engineering, Institute of Information Technology, Zhengzhou, Henan, China
Email: 1 firstname.lastname@example.org; 2 email@example.com
Abstract: Compressive sensing (CS) creates a new framework of signal reconstruction or approximation
from a smaller set of incoherent projection compared with the traditional Nyquist-rate sampling theory. Re-
cently, it has been shown that CS can solve some signal processing problems given incoherent measurements
without ever reconstructing the signals. Moreover, the number of measurements necessary for most compres-
sive signal processing application such as detection, estimation and classification is lower than that necessary
for signal reconstruction. Based on CS, this paper presents a novel identification algorithm of frequency hop-
ping (FH) signals. Given the hop interval, the FH signals can be identified and the hopping frequencies can be
estimated with a tiny number of measurements. Simulation results demonstrate that the method is effective
Keywords: compressive sensing, frequency hopping signal, identification
With so many good advantages such as anti-jam, anti-
interception, high security and so on, the technique of
frequency hopping spread spectrum (FHSS) has been
extensively applied in many areas, especially in military
domain. The detection and interception of FH signals can
be addressed in several methods of which wide band or
channelized receiver, time-frequency distribution, and
cyclostationary processing are typical ones [1-4]. For all
the methods above, the extremely large requirement of
measurements is one of the most serious disadvantages,
which can be a bottleneck in the application of identifi-
cation of high speed wide band FH signals. Recently,
there have been some active attempts on signal process-
ing with the advantage of CS for the sparse or compres-
sive signals [5-8]. However, most of them are limited
within the area of statistical inference tasks which need
the prior knowledge of the probability density distribu-
tion of signals. Besides, it is seldom to be studied on how
to develop the potential of CS to make processing of FH
signal which is one of the most important sparse or com-
This paper makes use of the sparsity of FH signals on
the local Fourier basis, and then presents a novel identi-
fication algorithm of FH signals with the compressive
measurements. Given the hop interval, the FH signals
can be identified and the hopping frequencies can be es-
timated without reconstructing the signals.
2. Compressive Sensing Background
2.1 Representation and Sparsity of Signal
Nyquist-rate sampling is the classical method to describe
a signal with its bandlimitedness, while CS aims to com-
pletely describe a signal with its sparsity or compressibil-
ity to reduce the required number of measurements .
A signal can be viewed as an column vector
in with elements
nn N. Let the ma-
have columns which form a
basis of vectors in . And then, any signal can be
where is the
N column vector of weighting co-
When we say that x is K-sparse, we mean that it is well
reconstructed or approximated by a linear combination of
just K basis vectors from , with
N. That is,
there are only K of the i
in (1) are nonzero and
NK are zero.
2.2 Incoherent Measurements
Consider a generalized linear measurement process of a
signal which is K-sparse. Let be an x
N where the rows of
THE IDENTIFICATION OF FREQUENCY HOPPING SIGNAL USING COMPRESSIVE SENSING 53
are incoherent with the columns of . The incoherent
measurements can be obtained by computing
products between and the rows of as in
yx. It can also be expressed as:
yx =s= s (2)
where is an
N matrix. It is proved that
dose not depend on the signal x and it can be con-
structed as a random matrix such as Gaussian matrix.
And the CS theory shows that there is an over-measuring
factor such that only
cK incoherent meas-
urements are required to reconstruct with high prob-
ability [9-11]. That is, only cK incoherent measure-
ments include all of the salient information in the
K-sparse signal , which provides the theory support on
the signal processing only given the incoherent meas-
urements without reconstructing the signals.
With the salient information included in the incoherent
measurements, there have been several kinds of recon-
struction algorithms including 1 minimization, greedy
algorithm, matching pursuit and so on [12-15]. Since this
paper is concentrated on FH signal identification without
signal reconstruction, we don’t discuss reconstruction
algorithms in detail here.
3. Compressive Identification for FH Signal
With the good sparsity of FH signals on the local Fourier
basis, we now show that incoherent measurements can be
used to solve the identification problem without ever
reconstructing the signal. In this process, it is able to save
significantly on the number of measurements required.
3.1 Compressive Identification Problem Setup
FH signals are sparse in a time-frequency representation
as short-time Fourier transform, and they are always
wideband when there is no prior restriction on the fre-
quencies of the local sinusoid . Therefore, the meas-
urements obtained with the traditional Nyquist-rate sam-
pling could be excessive and hard to meet with the pre-
sent ability of hardware instrument.
Now, consider a FH signal which consists of a se-
quence of windowed sinusoids with frequencies distrib-
uted between f1 and f2 Hz. The bandwidth of this signal is
B=f2-f1 Hz, which asks for sampling above the Nyquist
rate of 2(f2-f1) Hz to avoid aliasing. However, the expres-
sion of the signal at any single hop is extremely simple: it
consists of only one sinusoid of which bandwidth is
extremely less than B . Hence, CS could make identi-
Figure 1. Hop intervals and observation intervals in the condition
fication of FH signals possible with a sampling rate that
is extremely less than the Nyquist rate.
Let the observation interval equal to the hop interval.
If the start of the FH signal can be captured exactly, the
signal can be observed synchronously as depicted in Fig-
ure 1 and it has 1-sparse representation on the local Fou-
rier basis within each of hop interval. Otherwise, as de-
picted in Figure 2, the signal within each of hop interval
will have 2-sparse representation since only two of the
hopping frequencies appear in every single observation
We observe yx
instead of and our goal is to
identify the FH signal and estimate its hopping frequen-
cies with and its connection with .
3.2 1-Sparse Compressive Identification
The amplitudes of Fourier coefficients of some FH signal
within an observation interval have been shown in Figure
3 which dedicates that all the coefficients are almost zero
except for only one single large coefficient.
Figure 2. Hop intervals and observation intervals in the condition
Figure 3. The amplitudes of Fourier coefficients of some FH signal
within an observation interval in the condition of 1-sparse
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54 THE IDENTIFICATION OF FREQUENCY HOPPING SIGNAL USING COMPRESSIVE SENSING
Figure 4. Measurement process in the condition of 1-sparse
The process of 1-sparse compressive measurement is
depicted in Figure 4. We aim to find the position of non-
indicating the hopping frequency of a particular
Since is obtained by multiplying the nonzero
by its corresponding column vector
p, the hopping
frequency can be estimated given and . A direct
method to estimate the position of nonzero
search for the position of
which can be decided by
calculating the angles between y and each column vector
of in the vector space as only the angle between y
p is zero in the ideal condition. Since
a random Gaussian matrix if
is chosen to be a ran-
dom Gaussian matrix, the angle between y and another
column vector of is also zero with extremely low
probability. Taking account of the effect of noise, we
design the estimation algorithm of hopping frequency as
1) Obtain the incoherent measurements with
2) Calculate the cosine of angles between and each
i in the vector space
denotes conjugate transpose.
3) Select the column vector that maximizes cos( ,y)
and define the position of this vector as estimation of
ˆargmax cos(, y)
After several intervals of observation and estimation of
hopping frequencies, the time-frequency curve of the
signal can be obtained and the FH signal has been identi-
fied in the condition of 1-sparse.
3.3 2 Sparse Compressive Identification
Different from the condition of 1-sparse, Figure 5 shows
that there are two large coefficients within an observation
interval as each observation interval covers parts of two
hop intervals in the condition of 2-sparse depicted in Fig-
Figure 5. The amplitudes of Fourier coefficients of some FH signal
within an observation interval in the condition of 2-sparse
The process of 2-sparse compressive measurement is
shown is Figure 3 which dedicates that is a linear
combination of two column vectors
responding to the two nonzero coefficients 1p
indicating the two hopping frequencies within a particu-
lar observation interval. And y is also a linear combina-
tion of another two column vectors of with ex-
tremely low probability, since is a random Gaussian
Therefore, the two hopping frequencies can be esti-
mated by deciding the subspace comprised of 1
. The estimation algorithm is as follows:
1) Obtain the incoherent measurements with
2) Calculate the orthogonal projection of onto
the subspace comprised of any two column vectors
P is orthogonal projector expressed by:
3) Select the two column vectors that maximize
onto the corresponding subspace, and define the posi-
tions of these two vectors as estimation of the two hop-
[,]arg max( )ij
Taking account of the repetition of hopping frequen-
cies within two consecutive observation intervals in the
condition of 2-sparse, we can use the estimation results
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THE IDENTIFICATION OF FREQUENCY HOPPING SIGNAL USING COMPRESSIVE SENSING 55
Figure 6. Measurement process in the condition of 2-sparse
of the former interval in the latter one. Only in the first
interval, the algorithm is a kind of two-dimensional
search as two column vectors have to be selected mean-
while. And in the successive intervals, it can be executed
as a one-dimensional search (twice) as one column vec-
tor can be confirmed in according to the position infor-
mation of two selected vectors of the former interval.
This iterative processing can effectively reduce the
computation, but obviously the error propagation can
also be introduced. To solve this problem, an updating
window is designed to separate the whole observation
time into several segments of intervals. And in the first
interval of every updating window, the two-dimensional
search is executed all over again.
As the condition of 1-sparse, the time-frequency curve
of the FH signal can also be obtained after several ob-
servation intervals, and the signal can be identified.
4. Simulation Results
To demonstrate the feasibility and effectiveness of the
proposed algorithm, a wideband FH signal submerged in
additive Gaussian white noise (AWGN) is considered to
make the simulation experiments. This FH signal has ten
hopping frequencies which are distributed uniformly
between 20MHz and 200MHz, and the hop interval is
1ms, i.e. 1000 hops per second. The other main simula-
tion parameters are as follows: 2048-point local Fourier
basis is chosen to be , random Gaussian matrix is
chosen to be , and the number of observation inter-
vals is set to 2000. Each experiment is made in the con-
dition of both 1-sparse and 2-sparse.
First, the estimation performance of hopping fre-
quency is evaluated by normalized mean square error
(NMSE) through several intervals of observation, where
NMSE is expressed by
is the estimation of hopping frequency that
expressed by in the th observation interval and
represents the number of observation intervals
which is set to 2000 here.
Figure 7 and Figure 8 show the performance curves of
1-sparse and 2-sparse respectively.
Figure 7. MSE of estimation with SNR in the condition of 1-sparse,
where N=2048 and M represents the number of measurements used
in this experiment experiments
Figure 8. MSE of estimation with SNR in the condition of 2-sparse,
where N=2048 and M also represents the number of measurements
used in this experiment experiments. And the length of updating
window is set to 40
Some conclusions can be demonstrated from Figure 7
and Figure 8. First, the hopping frequencies can be effec-
tively estimated with a tiny number of measurements
when SNR is higher than 8dB. Second, the performance
of estimation degrades with the decrease of
cially in low SNR. And finally, the performance of 1-
sparse is better than that of 2-sparse.
Next, the estimated time-frequency curves of the FH
signal of 1-sparse and 2-sparse are depicted in Figure 9
and Figure 10 respectively when and SNR
From the Figure 9 and Figure 10, it is shown that the
estimated time-frequency curve is quite close to the real
one and the FH signal can be effectively identified, espe-
cially in the condition of 1-sparse.
opyright © 2009 SciRes CN
56 THE IDENTIFICATION OF FREQUENCY HOPPING SIGNAL USING COMPRESSIVE SENSING
Copyright © 2009 SciRes CN
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