© The Institution of Engineering and Technology
In the problem of the parameter estimation of frequencyhopping (FH) signals, most of existing works can only provide the unauthorised detection of the FH signals or estimate the part of parameters of FH signals, therefore cannot provide sufficient information to demodulate signals for message deciphering applications in a noncooperative communications. This study proposes a new method based on reassigned smoothed pseudo Wigner–Ville distribution (SPWVD) and maximumlikelihood estimation for joint signal parameter estimation of FH communications with Mary frequencyshiftkeyed (MFSK) orthogonal modulation. With the good time frequency concentration and restraining crossterm ability of reassigned SPWVD, this algorithm can efficiently estimate the parameters of FH signals which include hopping frequencies, hopping rate, hopping sequence and modulation type without making any assumption about the alphabet of hopping frequencies or the synchronisation. The algorithm has improved estimation accuracy, reduced estimation rootmeansquared error and obtained better performance than that of the approach based on conventional time–frequency distribution. The simulation results are presented to evaluate the performances of the proposed algorithm. The rootmeansquared error of hopping frequency estimation is <10^{−3} for signaltonoise ratio (SNR) of >−2 dB. The percentage of correct modulation recognition goes to 90% when SNR >3 dB.
References


1)

B.K. Levitt ,
U. Cheng ,
A. Polydoros ,
M.K. Simon
.
Optimum detection of slow frequencyhopped signals.
IEEE Trans. Commun.
,
1990 
2000

2)

M.K. Simon ,
U. Cheng ,
L. Aydin ,
A. Polydoros ,
B.K. Levitt
.
Hop timing estimation for noncoherent frequencyhopped MFSK intercept receivers.
IEEE Trans. Commun.
,
1144 
1154

3)

Aydin, L., Polydoros, A.: `Hop timing estimation for FH signals using a coarsely channelized receiver', Military Communications Conf., October 1994, p. 775–779.

4)

L.E. Miller ,
J.S. Lee ,
D.J. Torrieri
.
Frequencyhopping signal detection using partial band coverage.
IEEE Trans. Aerosp. Electron. Syst.
,
2 ,
540 
553

5)

C.C. Ko ,
W. Zhi ,
F. Chin
.
MLbased frequency estimation and synchronization of frequency hopping signals.
IEEE Trans. Signal Process.
,
2 ,
403 
410

6)

P.L. Shui ,
H.Y. Shang ,
Y.B. Zhao
.
Instantaneous frequency estimation based on directionally smoothed pseudoWignerVille distribution bank.
IET Radar Sonar Navig.
,
4 ,
317 
325

7)

L. Cohen
.
Timefrequency distributions – a review.
IEEE Proc.
,
7 ,
941 
981

8)

L. Cohen
.
(1995)
Timefrequency analysis.

9)

Flandrin, P.: `Some features of timefrequency representations of multicomponent signals', IEEE Int. Conf. on Acoustic, Speech, and Signal Processing, 1984, p. 266–269.

10)

Y. Wang ,
Y.C. Jiang
.
New time–frequency distribution based on the polynomial Wigner–Ville distribution and L class of Wigner–Ville distribution.
IET Signal Process.
,
2 ,
130 
136

11)

Barbarossa, S., Scaglione, A.: `Parameter estimation of spread spectrum frequencyhopping signals using timefrequency distributions', First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communication, April 1997, p. 213–216.

12)

X. Zhang ,
X. Du ,
L. Zhu
.
Time frequency analysis of frequency hopping signals based on Gabor spectrum method.
J. Data Acquis. Process.
,
2 ,
150 
154

13)

J. Zhao ,
Z. Zhang ,
L. Lai
.
Blind parameter estimation of frequencyhopping signals based on timefrequency analysis.
J. Circuits Syst.
,
3 ,
46 
50

14)

J. Lv ,
J. Luo
.
Applications of PWVD in timefrequency analysis of FH signal.
J. Ballistics
,
2 ,
93 
96

15)

F. Auger ,
P. Flandrin
.
Improving the readability of time frequency and time scale representations by the reassignment method.
IEEE Trans. Signal Process.
,
1068 
1089

16)

A. PapandreouSuppappola
.
(2003)
Applications in timefrequency signal processing.

17)

X. Wu ,
T. Liu
.
Spectral decomposition of seismic data with reassigned smoothed pseudo WignerVille distribution.
J. Appl. Geophys.
,
386 
393

18)

Sejdic, E., Ozertem, U., Djurovic, I., Erdogmus, D.: `A new approach for the reassignment of timefrequency representations', IEEE Int. Conf. on Acoustics, Speech and Signal Processing, April 2009, p. 2997–3000.

19)

S.S. Soliman ,
S.Z. Hsue
.
Signal classification using statistical moments.
IEEE Trans. Commun.
,
908 
916

20)

B.F. Beidas ,
C.L. Weber
.
Highorder correlationbased approach to modulation classification of digitally frequencymodulated signals.
IEEE J. Sel. Areas Commun.
,
1 ,
89 
101

21)

A. Swami ,
B.M. Sadler
.
Hierarchical digital modulation classification using cumulants.
IEEE Trans. Commun.
,
416 
429

22)

Zhang, D., Wang, X.: `MPSK signal modulation recognition based on wavelet transformation', Int. Conf. on Networking and Digital Society, 2009, p. 202–205.

23)

Xu, Y., Ge, L., Wang, B.: `Fast independent component analysis based digital modulation recognition method', Int. Conf. on Communication Software and Networks, 2009, p. 704–707.

24)

A.K. Nandi ,
E.E. Azzouz
.
Algorithms for automatic recognition of communication signals.
IEEE Trans. Commun.
,
4 ,
431 
436

25)

A. Mertins
.
(1999)
Signal analysis: wavelets, filter banks, timefrequency transforms and applications.

26)

H.L. Van Trees
.
(2001)
Detection, estimation, and modulation theory. part I.

27)

J.G. Proakis
.
(1995)
Digital communications.
http://iet.metastore.ingenta.com/content/journals/10.1049/ietcom.2010.0318
Related content
content/journals/10.1049/ietcom.2010.0318
pub_keyword,iet_inspecKeyword,pub_concept
6
6