17-03-2014, 06:10 PM
Abstract
Distributed PF (DPF) was used due to the
limitation of nodes’ computing capacity inferring to
the target tracking in a wireless sensor network (WSN).
In this paper, a novel filtering method - DPF* in WSN
is proposed. Instead of transferring value and weight
of particles, Gaussian mixture model (GMM) is used to
approximate the posteriori distribution, and only
GMM parameters need to be transferred which can
reduce the bandwidth and power consumption. In
order to use sampling information effectively, when
target moving to the next cluster head region, the
GMM parameters are transfer to the next cluster head,
and combine with the new local GMM parameters to
compose the new GMM parameters incrementally.
The proposed DPF* is compared to some other DPF
for WSN target tracking, and the experimental results
show that not the precision is improved.
Index Terms – WSN, target tracking, distributed
particle filtering.
1. Introduction
Particle filter is one of the widely used tracking
algorithms in non-linear/ Gaussian dynamic systems.
When using such algorithm in sensor networks the
energy cost related to computation in each sensor node
and communication between sensor nodes is
significant. Currently there are several distributed
particle filters [1-3], in which the distributed nature is
achieved by either transmitting local statistics of
particles to a centralized unit or using the parameters
passing method. Transmitting local statistics of
particles to a centralized unit is not an efficient
approach. Failure of the centralized unit is vital to the
entire network. In the parameters passing method, the
algorithms construct a path through the networks,
which passes through all nodes. Global statistics of
particles are accumulated by adding local statistics in
each node through a forward pass. Then there needs a
backward pass, which runs the important sampling and
selection steps in each sensor node by using the
accumulated global statistics.
In this paper, a novel filtering method –DPF* for
target tracking in WSN is proposed. There are two
keys in the proposed algorithm. Firstly, instead of
transferring value and weight of particles, Gaussian
mixture model (GMM) is used to approximate the
posteriori distribution, and only GMM parameters
need to be transferred which can reduce the bandwidth
and power consumption. Secondly, in order to use
sampling information effectively, when target moving
to the next cluster head region, the GMM parameters
are transfer to the next cluster head, and combine with
the new local GMM parameters to compose the new
GMM parameters incrementally.
The remaining of the paper is organized as follows:
a brief description of PF and DPF in WSN are
presented in Section 2. The details of the new PF this
paper proposed –DPF* is presents in Section 3. In
Section 4 the proposed algorithm is compared to other
DPFs and finally, we give some concluding remarks in
section 5.
Distributed PF (DPF) was used due to the
limitation of nodes’ computing capacity inferring to
the target tracking in a wireless sensor network (WSN).
In this paper, a novel filtering method - DPF* in WSN
is proposed. Instead of transferring value and weight
of particles, Gaussian mixture model (GMM) is used to
approximate the posteriori distribution, and only
GMM parameters need to be transferred which can
reduce the bandwidth and power consumption. In
order to use sampling information effectively, when
target moving to the next cluster head region, the
GMM parameters are transfer to the next cluster head,
and combine with the new local GMM parameters to
compose the new GMM parameters incrementally.
The proposed DPF* is compared to some other DPF
for WSN target tracking, and the experimental results
show that not the precision is improved.
Index Terms – WSN, target tracking, distributed
particle filtering.
1. Introduction
Particle filter is one of the widely used tracking
algorithms in non-linear/ Gaussian dynamic systems.
When using such algorithm in sensor networks the
energy cost related to computation in each sensor node
and communication between sensor nodes is
significant. Currently there are several distributed
particle filters [1-3], in which the distributed nature is
achieved by either transmitting local statistics of
particles to a centralized unit or using the parameters
passing method. Transmitting local statistics of
particles to a centralized unit is not an efficient
approach. Failure of the centralized unit is vital to the
entire network. In the parameters passing method, the
algorithms construct a path through the networks,
which passes through all nodes. Global statistics of
particles are accumulated by adding local statistics in
each node through a forward pass. Then there needs a
backward pass, which runs the important sampling and
selection steps in each sensor node by using the
accumulated global statistics.
In this paper, a novel filtering method –DPF* for
target tracking in WSN is proposed. There are two
keys in the proposed algorithm. Firstly, instead of
transferring value and weight of particles, Gaussian
mixture model (GMM) is used to approximate the
posteriori distribution, and only GMM parameters
need to be transferred which can reduce the bandwidth
and power consumption. Secondly, in order to use
sampling information effectively, when target moving
to the next cluster head region, the GMM parameters
are transfer to the next cluster head, and combine with
the new local GMM parameters to compose the new
GMM parameters incrementally.
The remaining of the paper is organized as follows:
a brief description of PF and DPF in WSN are
presented in Section 2. The details of the new PF this
paper proposed –DPF* is presents in Section 3. In
Section 4 the proposed algorithm is compared to other
DPFs and finally, we give some concluding remarks in
section 5.