Hidden Markov Model based classification approach for multiple dynamic vehicles in wireless sensor networks
ABSTRACT It is challenging to classify multiple dynamic targets in wireless sensor networks based on the timevarying and continuous signals. In this paper, multiple ground vehicles passing through a region are observed by audio sensor arrays and efficiently classified. Hidden Markov Model (HMM) is utilized as a framework for classification based on multiple hypothesis testing with maximum likelihood approach. The states in the HMM represent various combinations of vehicles of different types. With a sequence of observations, Viterbi algorithm is used at each sensor node to estimate the most likely sequence of states. This enables efficient local estimation of the number of source targets (vehicles). Then, each sensor node sends the state sequence to a manager node, where a collaborative algorithm fuses the estimates and makes a hard decision on vehicle number and types. The HMM is employed to effectively model the multiplevehicle classification problem, and simulation results show that the approach can decrease classification error rate.

Article: MultiTarget Classification Using Acoustic Signatures in Wireless Sensor Networks: A survey
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ABSTRACT: Classification of ground vehicles based on acoustic signals using wireless sensor networks is a crucial task in many applications such as battlefield surveillance, border monitoring, and traffic control. Different signal processing algorithms and techniques that are used in classification of ground moving vehicles in wireless sensor networks are surveyed in this paper. Feature extraction techniques and classifiers are discussed for single and multiple vehicles based on acoustic signals. This paper divides the corresponding literature into three main areas: feature extraction, classification techniques, and collaboration and information fusion techniques. The open research issues in these areas are also pointed out in this paper. This paper evaluates five different classifiers using two different feature extraction methods. The first one is based on the spectrum analysis and the other one is based on wavelet packet transform.Signal Processing : An International Journal. 01/2010;
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Hidden Markov Model Based Classification Approach for Multiple
Dynamic Vehicles in Wireless Sensor Networks
Ahmad Aljaafreh, Student Member, IEEE and Liang Dong, Senior Member, IEEE
Abstract—It is challenging to classify multiple dynamic
targets in wireless sensor networks based on the timevarying
and continuous signals. In this paper, multiple ground vehicles
passing through a region are observed by audio sensor arrays
and efficiently classified. Hidden Markov Model (HMM) is
utilized as a framework for classification based on multiple
hypothesis testing with maximum likelihood approach. The
states in the HMM represent various combinations of vehicles
of different types. With a sequence of observations, Viterbi
algorithm is used at each sensor node to estimate the most
likely sequence of states. This enables efficient local estimation
of the number of source targets (vehicles). Then, each sensor
node sends the state sequence to a manager node, where a
collaborative algorithm fuses the estimates and makes a hard
decision on vehicle number and types. The HMM is employed
to effectively model the multiplevehicle classification problem,
and simulation results show that the approach can decrease
classification error rate.
I. INTRODUCTION
W
geographical area, where each sensor node has a restricted
computation capability, memory, wireless communication,
and power supply. In general, the objective of WSNs is to
monitor, control, or track objects, processes, or events [1].
Fig 3 shows a one cluster of WSN. In WSNs, observed
data could be processed at the sensor node itself; distributed
over the network; or at the gateway node. Most often, nodes
are batterypowered which makes power the most significant
constraint in WSNs . The power consumed as a result of
the typical data processing tasks executed at the sensor
nodes is less than the power consumed for intersensor com
munication. This motivates researches and practitioners to
consider decentralized data processing algorithms more than
the centralized ones. Multipletarget classification in Multiple
moving target classification is a real challenge [2] because of
the dynamicity and mobility of targets. The dynamicity of the
targets refers to the evolution of the number of targets over
time. Furthermore, limited observations, power, computa
tional and communication constraints within and between the
sensor nodes make it a more challenging problem. Multiple
target classification can be modeled as a Blind Source Sepa
ration (BSS) problem [3]. Independent Component Analysis
IRELESS Sensor Network (WSN) is, by definition,
a network of sensor nodes that are spread across a
This work was supported in part by the DENSO North America Founda
tion and by the Faculty Research and Creative Activities Award of Western
Michigan University.
A. Aljaafreh and L. Dong are with the Department of Electrical and
Computer Engineering, Western Michigan University, Kalamazoo, MI 49008
USA (email: ahmad.f.aljaafreh@wmich.edu, liang.dong@wmich.edu).
(ICA) can be utilized for such a problem. Most of the recent
literature assumes a given number of sources; thus, making
the aforementioned challenge easier to solve. Unfortunately,
this assumption is unrealistic in many applications of wireless
sensor networks. Some recent publications decouple the
problem into two subproblems, namely: the model order
estimation problem and the blind source separation problem.
Ref.[4] discusses the problem of source estimation in sensor
network for multiple target detection. In the literature, many
researchers utilized ICA for source separation while others
utilized statistical methods as in [5] where the authors pre
sented a particle filtering based approach for multiple vehicle
acoustic signals separation in wireless sensor networks. The
previously mentioned techniques are based on data fusion.
In these techniques, each sensor node detects the targets,
extracts the features and sends the data to the manager
node. The manager node is responsible for source separation,
number estimation, and classification of the sources. The
computation and communication overhead induced by such
a centralized approachs inadvertently limits the lifetime of
the sensor network.
Classification of multiple targets without signals or sources
separation based on multiple hypothesis testing is an efficient
way of classification [6]. Ref. [7] proposed a distributed
classifiers based on modeling each target as a zero mean
stationary Gaussian random process and so the mixture sig
nals. A multi hypothesis test based on maximum likelihood
is the base of the classifier. In this paper, we are proposing
an algorithm to classify multiple dynamic targets based on
HMM. HMM decreases the number of hypothesis that is
needed to be tested at every classification query. Which
decreases the computation overhead. On the other hand,
emerging hypothesis transition probability with hypothesis
likelihood increases the classification precision. The remain
der of this paper is organized as follows. Section 2 formulate
the problem mathematically. Section 3 describes modeling
the problem as HMM. Simulation environment is described
in Section 4. Section 5 presents the results and discussions.
And finally conclusions are described in section 6.
II. PROBLEM FORMULATION
Multiple ground vehicles as multiple targets are to be
classified in a particular cluster region of a WSN. In this
paper, any vehicle that enters the cluster region is assumed
to be sensed by all the sensor nodes within this cluster.
Each sensor node estimates the number and types of vehicles
currently present in the region and the final decision is made
5409781424464524/10/$26.00 ©2010 IEEE
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collectively by all the sensor nodes within the region. We
assume that the maximum number of distinct vehicles that
may exist in one cluster region at the same time M is known.
Then the number of hypotheses is N = 2M. The hypotheses
correspond to the various possibilities for the presence or
absence of different vehicles. Let hi denote hypothesis i,
i = 0,...,N −1. Observation xkis a feature vector obtained
by a sensor node at time k. The feather vector can be related
to the spectrum of a mixture of maximum M vehicle sounds.
According to Bayes theorem, hiis the maximum likelihood
hypothesis given xkif p(hixk) > p(hjxk),∀i ?= j. So far, the
decision about the hypothesis at any given event is based on
the observation at that event without any relation with the
previous observations as in [7]. In fact, the class to which
the feature vector xibelongs to also depends on the previous
event class. The classification decision at any instant of time
depends on the previous decision and the current observation.
Therefore, the classification problem is a context dependent
problem and it can be modeled by HMM.
In contextdependant Bayesian classification, a sequence
of decisions is needed instead of a single one, and the
decisions depend on each other. Let X : {x1,x2,...,xt}
be a sequence of feature vectors of observations. And let
Hi: {hi1,hi2,...,hit} be a sequence of classes. According
to Bayes theorem, X is classified to Hiif
p(HiX) > p(HjX),∀i ?= j.
(1)
p(HiX)(><)p(HjX) ≡ p(XHi)p(Hi)(><)p(XHj)p(Hj)
where (><) denotes comparing and ≡ denotes equivalent
to. According to the Markov chain model,
(2)
p(Hi) = p(hi1)
N
?
k=2
p(hikhik−1)
(3)
We assume that {xi} are mutually independent and so are
the probability distributions of the classes. Therefore,
p(XHi) =
N
?
k=1
p(xkhi)
(4)
Based on Equ. 2, 3, and 4, we have
p(XHi)p(Hi) = p(hi1)p(x1hi1)
N
?
k=2
p(hikhik−1)p(xkhik)
(5)
It is computationally expensive to find the maximum value
of equation (5) in bruteforce task. Thus, Viterbi algorithm
is appropriate to solve such a problem of HMM. Given a
sequence of observation the most likelihood classes is corre
sponded to the optimal path. We define the cost of transition
from hypothesis hikto hypothesis hik−1as d(hik,hik−1)
d(hik,hik−1) = p(hikhik−1)p(xkhik)
d(hi1,hi0) = p(hi1)p(xihi1)
(6)
(7)
Feature vector of observation of each class i is modeled as a
multi variate normal distribution with mean and covariance
matrix known. The maximum cost corresponds to the optimal
path. The hypotheses along the optimal path result in the
observation sequence X. Based on Bellman’s principle the
cost in Equations (6) and (7) can be computed online.
III. HIDDEN MARKOV MODEL
HMM has a specific discrete number of unobserved states,
each state has a transition probability to any other state
and an initial probability. The last parameter of HMM is
the probability density function of the observation for each
state. The state parameters of the HMM are the numbers of
targets of each class. For instance, if we have two classes
and the maximum number of sources that can be sensed by
any sensor at any instant of time is three, then the number
of states are eight if the targets are distinct, and ten if not
distinct as in Fig. 1. T, W, and 0 represent class T, class
W, and no vehicle respectively. Each state represents the
number of targets for each class. For instance state TTW
means that there are two targets of class T and one target
of class W. We assume that the states are equiprobable. This
assumption is a reasonable one since it will be the worst
scenario compared to trained ones. This means that the state
transition probabilities will be equal for all possible states as
in Table I. Therefor the initial probabilities are as follows
Pi(00T) = Pi(00W) = Pi(000) =1
Other states initial probabilities are zeros, since we assume
that there will one change at a time. Which means that the
vehicles enter and exit from the sensor range in a dynamic
manner. So the sensor observe one vehicle or nothing at
time zero then it goes to possible states as in Fig. 1. This
assumption is reasonable because it will approach to the
right hypothesis even two vehicles or more enter the sensor
range at the same time. It misclassifies it in the first step
as one of the initial sates, but it will classify it correctly in
the second step. Such cases have a very low probabilities.
All of the above contributes in decreasing the computation
overhead for multiple hypothesis testing, because the only
hypotheses that need to be tested depend on the transition
probabilities. So there is no need to test a hypothesis that has
zero transition probability. The important parameter of HMM
is the output probability density function of each state. This
distribution is assumed as a multi variate normal distribution
with mean and covariance matrix that are estimated based
on maximum likelihood. Mixture of different sources is
generated by simulation. The maximum of Equations (6)
for all hypothesis at every stage is the maximin likelihood
hypothesis. Simulation results show that the correct classifi
cation error based on our solution is less than classification
with maximum likelihood without modeling the problem as
a context dependant classification problem.
3
IV. SIMULATION ENVIRONMENT
We developed our simulation environment using Matlab
for one network cluster region (300 × 300) as in Fig. 2.
541
Page 3
Fig. 1.
for mixtures signals of three maximum targets number
HMM states flow diagram for two classes. Class T and class W
TABLE I
STATE TRANSITION PROBABILITY
States
000
00T
00W
0TW
0TT
0WW
TTT
TTW
TWW
WWW
000
0.33
0.25
0.25
00T
0.33
0.25
00W
0.33
0TW0TT0WWTTTTTWTWWWWW
000
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
00.25
0.25
0.2
0.25
00.25
0.2
0
0
0.25
0
0
0
0
0
0
0
0.20
0
0.2
0.33
0.2
0
0
0
0
0
0
00
0
0
0.330.330
0.2500.2500
0
0.250.25
0
0
0
0
0.5
0.33
0
0
0.50
0
0
0
0
0.33
0.33
0
0
0
0.33
0
0
0.33
0.5
0
0
0.33
00 0.5
Where this cluster is consist of a grid of different numbers of
sensor nodes. Sensing range for all sensor nodes will be the
same. Sensing range is chosen to enable all sensor nodes in
one cluster region to observe the same targets with different
attenuation. The sensing range is represented by a radius
of a circle. When any target enter this circle, the simulator
will pick a random real life vehicle sound according to
the vehicle type. Where the vehicle type and number are
chosen randomly. Then this sound will be attenuated based
on the distance between the target and the sensor node. After
that, a mixture is linearly formed based on the number of
targets. Then each sensor node extracts the feature from the
acoustic signal based on discrete spectrum. This mixture
is classified by each sensor node. Classification decision
is sent to the manger node where decision fusion will be
accomplished. Sensor nodes are deployed uniformity as in
Fig.2. Simulator is built such that multiple targets can enter
the region of simulation from one direction. Entry location
and entry angle are selected randomly. Targets speed and
directions are modeled according to GaussMarkov mobility
model. GaussMarkov mobility model parameters are chosen
such that to avoid sharp updates in speed and direction.
Each sensor node calculates the maximum likelihood state
based on HMM at every discrete time t. State transition cost
as in equation (6) is calculated only for states that have
nonzero transition probability as in Fig.1 then the maximum
of all cost is corresponded to the maximum likelihood state
0 50 100 150200250300
0
50
100
150
200
250
300
Sensor Node
Vehicle One Track
Vehicle Two Track
Fig. 2.Four sensors in one cluster simulation region
Fig. 3. One Cluster of Wireless Sensor Network
or hypothesis.
V. RESULTS AND DISCUSSIONS
inthispaper,
life vehicle sounds
http://www.ece.wisc.edu/sensit. Fig.4 displays the result of
running the simulator hundreds of times. Our experiment
is conducted for two distinct vehicles. Simulation results
that are shown in Fig.4 shows that the correct classification
error rate is declining with the sensor density in both cases
with and without HMM. It is clear that this error is less
in the case of HMM framework. Results are based on
majority voting distributed algorithm for all the sensors
local decisions in the region of interest. All sensors observe
the same number at any instant of time with different
attenuation factors. Fig.4 shows how efficient it is to model
such kind of problem using HMM and solve it by Viterbi
algorithm. HMM based classification approach reduces
the computation overhead for multiple hypothesis testing.
because the only hypotheses that need to be tested are the
ones that have not zero state transition probability. For
Results,
with
are based
that
onsimulation
availablerealis at
542
Page 4
0 0.20.40.6 0.81 1.21.4 1.61.8
−4
x 10
0.26
0.28
0.3
0.32
0.34
0.36
0.38
0.4
0.42
0.44
0.46
Sensors Density
Correct classification Error Rate
Without HMM
With HMM
Fig. 4.
classification system with and without HMM framework in one cluster of
WSN.
Performance evaluation of Distributed Maximum Likelihood
distinct targets, the number of hypothesis are 2Mwhere M
is the maximum number of targets that can be exist within
the sensor range at the same time. In our approach only
M + 1 hypothesis need to be tested at each time step.
VI. CONCLUSIONS
In this paper, we propose an idea of modeling a dis
tributed multiple hypothesis classification problem by HMM.
Classification of multiple dynamic vehicles in WSNs can
be modeled as a context dependant classification problem.
The number of moving vehicles of each class is considered
as the state, and each state depends on the previous state.
This makes it appropriate to model the system with HMM.
Given a sequence of observation, Viterbi algorithm is used to
find the maximum likelihood sequence of states. Simulation
results based on real vehicle sounds show that using HMM
framework decreases the classification error rate. The other
benefit of HMM is the reduction of the computation overhead
for multiple hypothesis testing. The only hypotheses that
need to be tested depend on the state transition probabilities,
therefore the hypotheses that need to be tested are the ones
that have none zero transition probabilities.
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