In order to resolve the state estimation problem of nonlinear/non-Gaussian systems,a new kind of quadrature Kalman particle filter(QKPF)is proposed.In this new algorithm, quadrature Kalman filter(QKF)is used for generating the impor- tance density function.It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian- Hermite quadrature points.It need not compute the Jacobian matrix and is easy to be implemented.Moreover,the importantce density function integrates the latest measurements into system state transition density,so the approximation to the system poste- rior density is improved.The theoretical analysis and experimen- tal results show that,compared with the unscented partcle filter (UPF),the estimation accuracy of the new particle filter is improved almost by 18%,and its calculation cost is decreased a little.So, QKPF is an effective nonlinear filtering algorithm.

A new approach for improving the throughputs of multi-
channel packet radio systems is proposed.Based on the charac-
teristics of multi-code CDMA technology,the scheme factitiously
improves the transmission bit rate of a terminal by compressing
the packet transmission time and thereby increases the number
of the orthogonal spreading codes used by the terminal.By this
means,the average interference level of the system is reduced
and the system capacity is improved.Simulation results show that
the proposed scheme exhibits larger throughput compared with
the traditional multi-code CDMA slotted Aloha systems.

A new monostatic array system taking advantage of
diverse waveforms to improve the performance of underwater tar-
get localization is proposed.Unlike the coherent signals between
different elements in common active array,the transmitted signals
from different elements here are spatially orthogonal waveforms
which allow for array processing in the transit mode and result in
an extension of array aperture.The mathematical derivation of
Capon estimator for this sonar system is described in detail.And
the performance of this orthogonal-waveform based sonar is an-
alyzed and compared with that of its phased-array counterpart by
water tank experiments.Experimental results show that this sonar
system could achieve 12 dB?15 dB additional array gain over its
phased-array counterpart,which means a doubling of maximum
detection range.Moreover,the angular resolution is significantly
improved at lower SNR.

In imaging on moving target,it is easy to get space-
variant blurred image.In order to recover the image and gain
recognizable target,an approach to recover the space-variant
blurred image is presented based on image segmentation.Be-
cause of motion blur’s convolution process,the pixels of observed
image’s target and background will be displaced and piled up to
produce two superposition regions.As a result,the neighbor-
ing pixels in the superposition regions will have similar grey level
change.According to the pixel’s motion-blur character,the target’s
blurred edge of superposition region could be detected.Canny
operator can be recurred to detect the target edge which parallels
the motion blur direction.Then in the segmentation process,the
whole target image which has the character of integral convolution
between motion blur and real target image can be obtained.At
last,the target image is restored by deconvolution algorithms with
adding zeros.The restoration result indicates that the approach
can effectively solve the kind of problem of space-variant motion
blurred image restoration.

A computationally efficient soft-output detector with
lattice-reduction(LR)for the multiple-input multiple-output(MIMO)
systems is proposed.In the proposed scheme,the sorted QR de-
composition is applied on the lattice-reduced equivalent channel
to obtain the tree structure.With the aid of the boundary control,
the stack algorithm searches a small part of the whole search
tree to generate a handful of candidate lists in the reduced lattice.
The proposed soft-output algorithm achieves near-optimal perfor-
mance in a coded MIMO system and the associated computational
complexity is substantially lower than that of previously proposed
methods.

The mixture of factor analyzers(MFA)can accurately
describe high resolution range profile(HRRP)statistical charac-
teristics.But how to determine the proper number of the models
is a problem.This paper develops a variational Bayesian mixture
of factor analyzers(VBMFA)model.This procedure can obtain a
lower bound on the Bayesian integral using the Jensen’s inequality.
An analytical solution of the Bayesian integral could be obtained
by a hypothesis that latent variables in the model are indepen-
dent.During computing the parameters of the model,birth-death
moves are utilized to determine the optimal number of model au-
tomatically.Experimental results for measured data show that the
VBMFA method has better recognition performance than FA and
MFA method.

A novel polarimetric calibration method for new target
property measurement radar system is presented.Its applica-
tion in the real radar system is also discussed.The analysis
indicates that instantaneous polarization radar(IPR)has inherent
cross-polarization measurement error.The proposed method can
effectively eliminate this error,and thus enhance the polarization
scattering matrix(PSM)measurement precision.The phase error
caused by digital receiver’s direct IF sampling and mixing of two
orthogonal polarization channels can be removed.Consequently,
the inherent error of target polarization scattering measurement of
the instantaneous polarization radar system is well revised.It has
good reference value for further ploarimetric calibration and high
practical application prospect.

The influence of frequency modulation(FM)interfer-
ence on correlation detection performance of the pseudo random
code continuous wave(PRC-CW)radar is analyzed.It is found
that the correlation output deteriorates greatly when the FM inter-
ference power exceeds the anti-jamming limit of the radar.Accord-
ing to the fact that the PRC-CW radar echo is a wideband pseudo
random signal occupying the whole TF plane,while the FM in-
terference only concentrates in a small portion,a new method is
proposed based on adaptive short-time Fourier transform(STFT)
and time-varying filtering for FM interference suppression.This
method filters the received signal by using a binary mask to excise
only the portion of the TF plane corrupted by the interference.Two
types of interference,linear FM(LFM)and sinusoidal FM(SFM),
under different signal-to-jamming ratio(SJR)are studied.It is
shown that the proposed method can effectively suppress the FM
interference and improve the performance of target detection.

The aim of this paper is to discuss the approximate rea-
soning problems with interval-valued fuzzy environments based
on the fully implicational idea.First,this paper constructs a class
of interval-valued fuzzy implications by means of a type of impli-
cations and a parameter on the unit interval,then uses them to
establish fully implicational reasoning methods for interval-valued
fuzzy modus ponens(IFMP)and interval-valued fuzzy modus tol-
lens(IFMT)problems.At the same time the reversibility properties
of these methods are analyzed and the reversible conditions are
given.It is shown that the existing unified forms ofα-triple I(the
abbreviation of triple implications)methods for FMP and FMT can
be seen as the particular cases of our methods for IFMP and IFMT.

A class of latent ancestral graph for modelling the
dependence structure of structural vector autoregressive(VAR)
model affected by latent variables is proposed.The graphs are
mixed graphs with possibly two kind of edges,namely directed and
bidirected edges.The vertex set denotes random variables at dif-
ferent times.In Gaussian case,the latent ancestral graph leads to
a simple parameterization model.A modified iterative conditional
fitting algorithm is presented to obtain maximum likelihood esti-
mation of the parameters.Furthermore,a log-likelihood criterion
is used to select the most appropriate models.Simulations are
performed using illustrative examples and results are provided to
demonstrate the validity of the methods.

A global convergent algorithm is proposed to solve
bilevel linear fractional-linear programming,which is a special
class of bilevel programming.In our algorithm,replacing the lower
level problem by its dual gap equaling to zero,the bilevel linear
fractional-linear programming is transformed into a traditional sin-
gle level programming problem,which can be transformed into a
series of linear fractional programming problem.Thus,the modi-
fied convex simplex method is used to solve the infinite linear
fractional programming to obtain the global convergent solution of
the original bilevel linear fractional-linear programming.Finally,an
example demonstrates the feasibility of the proposed algorithm.

The group decision making problem with linguistic pref-
erence relations is studied.The concept of additive consistent
linguistic preference relation is defined,and then some properties
of the additive consistent linguistic preference relation are studied.
In order to rank the alternatives in the group decision making with
the linguistic preference relations,the weighted average is first
utilized to combine the group linguistic preference relations to one
linguistic preference relation,and then the transformation function
is proposed to transform the linguistic preference relation to the
multiplicative preference relation,and thus the Saaty’s eigenvec-
tor method(EM)of multiplicative preference relation is utilized to
rank the alternatives in group decision making with the linguistic
preference relations.Finally,an illustrative numerical example is
given to verify the proposed method.A comparative study to the
linguistic ordered weighted averaging(LOWA)operator method is
also demonstrated.

A new method for discretization of continuous attributes
is put forward to overcome the limitation of the traditional rough
sets,which cannot deal with continuous attributes.The method is
based on an improved algorithm to produce candidate cut points
and an algorithm of reduction based on variable precision rough
information entropy.With the guarantee of consistency of decision
system,the method can reduce the number of cut points and im-
prove efficiency of reduction.Adopting variable precision rough
information entropy as measure criterion,it has a good tolerance
to noise.Experiments show that the algorithm yields satisfying
reduction results.

Many practical systems in physics,biology,engineer-
ing and information science exhibit impulsive dynamical behaviors
due to abrupt changes at certain instants during the dynami-
cal processes.The problems of finite-time stability analysis are
investigated for a class of Markovian switching stochastic sys-
tems,in which exist impulses at the switching instants.Multiple
Lyapunov techniques are used to derive sufficient conditions for
finite-time stochastic stability of the overall system.Furthermore,
a state feedback controller,which stabilizes the closed loop sys-
tems in the finite-time sense,is then addressed.Moreover,the
controller appears not only in the shift part but also in the diffu-
sion part of the underlying stochastic subsystem.The results are
reduced to feasibility problems involving linear matrix inequalities
(LMIs).A numerical example is presented to illustrate the proposed
methodology.

A method for maneuvering target tracking based on in-
ductive factor of posture information is proposed.A distinguished
characteristic of video frequency tracking is that it can capture
the target posture changes from its picture easily,and the posture
change means the motive model of the target will change.This
information is very important to predict the trace of maneuvering
target.Based on this idea,the quantified values of the target pos-
ture change are obtained using Hough algorithm,this key values
are defined as inductive factor of posture information,and then,the
multiple grey trace predict models are established and the degrees
of fuzzy subordinate values for every model are calculated with the
inductive factor,the maneuvering extent values are determined
by a new analysis method of stochastic differential equations for
each model used to modify the degree of fuzzy subordinate values,
these constitute the weighted values for every grey predict collec-
tion.Finally,the synthesis predicting weighted result is obtained.
The experimental results show that the new method is superior to
the conventional algorithm.

Model-set is utilized in state estimation for maneuver-
ing target tracking.Two minimal symmetric model-subsets are
designed and investigated by moment matching method,which
include hypersphere-symmetric model-subset and axis-symmetric
model-subset,if system mode is a random variable and obeys
certain probability distribution.They can be used as the fun-
damental model-subset for multiple models estimation with fixed
structure,variable structure and moving bank.The model-groups
constructed by above designed subsets are given,which give the
practical guidance for use of model-set in multiple models ap-
proach with a variable structure.Simulation results show that the
performances of two minimal model-set significantly outperform
the corresponding model-sets with fixed spacing.

A linear matrix inequality(LMI)-based sliding surface
design method for integral sliding mode control of uncertain time-
delay systems with mismatching uncertainties is proposed.The
uncertain time-delay system under consideration may have mis-
matching norm bounded uncertainties in the state matrix as well
as the input matrix.A sufficient condition for the existence of a
sliding surface is given to guarantee asymptotic stability of the full
order sliding mode dynamics.An LMI characterization of the slid-
ing surface is given,together with an integral sliding mode control
law guaranteeing the existence of a sliding mode from the initial
time.Finally,a simulation is given to show the effectiveness of the
proposed method.

Although it is known that exact sampling algorithm is
easy to construct and less sensitive to noise,the samples distri-
bution of the algorithm deviates from the target states distribution
due to the local dependent coupling problem.A new algorithm,
named exact sampling with directional threshold(ES-DT)is intro-
duced.The main advantage of the new algorithm,in comparison
with the traditional exact sampling algorithm,is that it can control
the sampling with a rejection strategy in Markov chain during the
path growth,and closely approach the ideal distribution based on
maintaining the target density.Simulation experiments show the
effectiveness of the proposed algorithm.

The exponential stability in mean square and stabiliza-
tion problems for Ito? stochastic switched systems with multiple
time-delays are investigated.The system possesses the norm-
bounded uncertainties and Markovian jumping parameters.By
using an effective descriptor model transformation of the system
and applying It?o’s differential formula and Moon’s inequality for
bounding cross terms,a new delay-dependent sufficient condi-
tion is derived in terms of linear matrix inequalities,and its states
feedback controller is designed.Numerical examples are given to
illustrate the efficiency and less conservation of the results.

The problem of designing fuzzy static output feedback
controller for T-S discrete-time fuzzy bilinear system(DFBS)is
presented.Based on parallel distribution compensation method,
some sufficient conditions are derived to guarantee the stability of
the overall fuzzy system.The stabilization conditions are further
formulated into linear matrix inequality(LMI)so that the desired
controller can be easily obtained by using the Matlab LMI toolbox.
In comparison with the existing results,the drawbacks,such as
coordinate transformation,same output matrices,have been elim-
inated.Finally,a simulation example shows that the approach is
effective.

A novel heuristic search algorithm called seeker op-
timization algorithm(SOA)is proposed for the real-parameter
optimization.The proposed SOA is based on simulating the act of
human searching.In the SOA,search direction is based on empir-
ical gradients by evaluating the response to the position changes,
while step length is based on uncertainty reasoning by using a
simple fuzzy rule.The effectiveness of the SOA is evaluated by
using a challenging set of typically complex functions in compari-
son to differential evolution(DE)and three modified particle swarm
optimization(PSO)algorithms.The simulation results show that
the performance of the SOA is superior or comparable to that of
the other algorithms.

The pruning algorithms for sparse least squares support
vector regression machine are common methods,and easily com-
prehensible,but the computational burden in the training phase
is heavy due to the retraining in performing the pruning process,
which is not favorable for their applications.To this end,an im-
proved scheme is proposed to accelerate sparse least squares
support vector regression machine.A major advantage of this
new scheme is based on the iterative methodology,which uses
the previous training results instead of retraining,and its feasibility
is strictly verified theoretically.Finally,experiments on bench-
mark data sets corroborate a significant saving of the training time
with the same number of support vectors and predictive accuracy
compared with the original pruning algorithms,and this speedup
scheme is also extended to classification problem.

Since the simulation underwater acoustic signal is used
in the semi-object simulation experiment of underwater weapons,
it has great impression upon simulation fidelity.It is asked that
whether simulation signals can replace the real signal effectually.
Considering the randomness of signals,the interval estimation of
feature parameters of simulation signals is made.By comparing
the obtained confidence interval with the corresponding accept
interval,the concept of similarity coefficient of simulation signals is
given.By making a statistical analysis for similarity coefficient,the
uniformity information of simulation signals is extracted,and the
fuzzy number which expresses the fuzzy uniformity level of simu-
lation signals is obtained.The analysis method on fuzzy uniformity
of simulation underwater acoustic signals is presented.It is indi-
cated by the application in simulation of target radiated-noises that
the method is suitable and effectual for the simulation research on
underwater acoustic signals,and the analysis result may provide
support for decision-making relative to perfecting simulation sys-
tems and applying simulation signals.

Fuzzy c-means(FCM)algorithm is one of the most
popular methods for image segmentation.However,the standard
FCM algorithm is sensitive to noise because of not taking into
account the spatial information in the image.An improved FCM
algorithm is proposed to improve the antinoise performance of
FCM algorithm.The new algorithm is formulated by incorporating
the spatial neighborhood information into the membership function
for clustering.The distribution statistics of the neighborhood pixels
and the prior probability are used to form a new membership func-
tion.It is not only effective to remove the noise spots but also can
reduce the misclassified pixels.Experimental results indicate that
the proposed algorithm is more accurate and robust to noise than
the standard FCM algorithm.

Ant colony optimization(ACO)is a new heuristic algo-
rithm which has been proven a successful technique and applied
to a number of combinatorial optimization problems.The traveling
salesman problem(TSP)is among the most important combinato-
rial problems.An ACO algorithm based on scout characteristic is
proposed for solving the stagnation behavior and premature con-
vergence problem of the basic ACO algorithm on TSP.The main
idea is to partition artificial ants into two groups:scout ants and
common ants.The common ants work according to the search
manner of basic ant colony algorithm,but scout ants have some
differences from common ants,they calculate each route’s muta-
tion probability of the current optimal solution using path evaluation
model and search around the optimal solution according to the
mutation probability.Simulation on TSP shows that the improved
algorithm has high efficiency and robustness.

Quantum key distribution(QKD)is used in quantum
cryptographic systems to exchange secret key between parties
who need to communicate secretly.According to the structure
of European Secoqc QKD network,a QKD protocol is proposed.
Entanglement swapping between Einstein-Podolsky-Rosen(EPR)
pairs can be used to exchange message bits in two remote places.
Based on this idea,n+1 EPR pairs are used as logical quan-
tum channel(for n nodes per routing),while measurements of
Bell operator are transmitted by classical channel.Random space
quantum channel selection is exploited in our protocol to improve
the probability of revealing Eve.Compared with traditional EPR
protocol,the proposed protocol exhibits many features,which are
minutely described.