Nova Publishers
My Account Nova Publishers Shopping Cart
HomeBooksSeriesJournalsReference CollectionseBooksInformationSalesImprintsFor Authors
            
  Top » Catalog » Books » Mathematics and Statistics » Kalman Filtering Chapters » My Account  |  Cart Contents  |  Checkout   
Quick Find
  
Use keywords to find the product you are looking for.
Advanced Search
What's New? more
Doxycycline: Medical Uses and Effects
$73.80
Shopping Cart more
0 items
Information
Shipping & Returns
Privacy Notice
Conditions of Use
Contact Us
Notifications more
NotificationsNotify me of updates to Quantized Kalman Filtering of Linear Stochastic Systems pp. 269-288
Tell A Friend
 
Tell someone you know about this product.
Quantized Kalman Filtering of Linear Stochastic Systems pp. 269-288 $100.00
Authors:  (Keyou You, Lihua Xie, Nanyang Technological University, Singapore)
Abstract:
In recent years, networked systems such as wireless sensor networks (WSNs) have gained
popularity in the research community due to their broad potential military and civilian applications.
WSNs are generally composed of a large number of low-quality sensors equipped
with limited communication capabilities and limited energy. However, a collective of these
nodes can form a powerful network for information gathering and processing. Due to limited
communication capacity and also for the sake of energy saving, the number of bits of
information to be transmitted between nodes is quite restrictive. Therefore, signals such as
sensor measurements are to be severely quantized before their transmissions. This introduces
interesting and challenging problems such as what information is to be transmitted
and how many bits are needed to represent the information in order to achieve a given
performance.
Quantized estimation has been extensively investigated in literature such as [4, 6, 9, 11–
13, 16] where various quantization schemes have been addressed. Luo studied the static
parameter estimation under severe bandwidth constraints with each sensor’s observation
quantized to one or a few bits in [9]. The resulting estimators turn out to exhibit comparable
variances with that of the estimator relying on un-quantized observations. Note that
one of the major challenges of quantized estimation is that quantization is highly nonlinear
and there lack of efficient filtering methods for nonlinear systems. By applying the particle
filter to quantized measurements, a quantized version of particle filter is proposed by reconstructing
the required probability density [6]. Unfortunately, the filtering performance
is poor under a low number of quantization levels. With severe quantization, e.g., binary
quantization, a dynamic quantization scheme based on feedback from the estimation center
is designed for the state estimation of a hidden Markov model in [4]. The main disadvantage
is that the solution involves a rather complicated on-line optimization and does not lead
to a recursive filter. 


Available Options:
Version:
This Item Is Currently Unavailable.
Special Focus Titles
01.Violent Communication and Bullying in Early Childhood Education
02.Cultural Considerations in Intervention with Women and Children Exposed to Intimate Partner Violence
03.Chronic Disease and Disability: The Pediatric Lung
04.Fruit and Vegetable Consumption and Health: New Research
05.Fire and the Sword: Understanding the Impact and Challenge of Organized Islamism. Volume 2

Nova Science Publishers
© Copyright 2004 - 2020

Quantized Kalman Filtering of Linear Stochastic Systems pp. 269-288