504 research outputs found
Adaptive Non-myopic Quantizer Design for Target Tracking in Wireless Sensor Networks
In this paper, we investigate the problem of nonmyopic (multi-step ahead)
quantizer design for target tracking using a wireless sensor network. Adopting
the alternative conditional posterior Cramer-Rao lower bound (A-CPCRLB) as the
optimization metric, we theoretically show that this problem can be temporally
decomposed over a certain time window. Based on sequential Monte-Carlo methods
for tracking, i.e., particle filters, we design the local quantizer adaptively
by solving a particlebased non-linear optimization problem which is well suited
for the use of interior-point algorithm and easily embedded in the filtering
process. Simulation results are provided to illustrate the effectiveness of our
proposed approach.Comment: Submitted to 2013 Asilomar Conference on Signals, Systems, and
Computer
A Joint Model and Data Driven Method for Distributed Estimation
This paper considers the problem of distributed estimation in wireless sensor
networks (WSN), which is anticipated to support a wide range of applications
such as the environmental monitoring, weather forecasting, and location
estimation. To this end, we propose a joint model and data driven distributed
estimation method by designing the optimal quantizers and fusion center (FC)
based on the Bayesian and minimum mean square error (MMSE) criterions. First,
universal mean square error (MSE) lower bound for the quantization-based
distributed estimation is derived and adopted as the design metric for the
quantizers. Then, the optimality of the mean-fusion operation for the FC with
MMSE criterion is proved. Next, by exploiting different levels of the statistic
information of the desired parameter and observation noise, a joint model and
data driven method is proposed to train parts of the quantizer and FC modules
as deep neural networks (DNNs), and two loss functions derived from the MMSE
criterion are adopted for the sequential training scheme. Furthermore, we
extend the above results to the case with multi-bit quantizers, considering
both the parallel and one-hot quantization schemes. Finally, simulation results
reveal that the proposed method outperforms the state-of-the-art schemes in
typical scenarios.Comment: in IEEE Internet of Things Journa
On Distributed Linear Estimation With Observation Model Uncertainties
We consider distributed estimation of a Gaussian source in a heterogenous
bandwidth constrained sensor network, where the source is corrupted by
independent multiplicative and additive observation noises, with incomplete
statistical knowledge of the multiplicative noise. For multi-bit quantizers, we
derive the closed-form mean-square-error (MSE) expression for the linear
minimum MSE (LMMSE) estimator at the FC. For both error-free and erroneous
communication channels, we propose several rate allocation methods named as
longest root to leaf path, greedy and integer relaxation to (i) minimize the
MSE given a network bandwidth constraint, and (ii) minimize the required
network bandwidth given a target MSE. We also derive the Bayesian Cramer-Rao
lower bound (CRLB) and compare the MSE performance of our proposed methods
against the CRLB. Our results corroborate that, for low power multiplicative
observation noises and adequate network bandwidth, the gaps between the MSE of
our proposed methods and the CRLB are negligible, while the performance of
other methods like individual rate allocation and uniform is not satisfactory
- …