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Quantized Kalman Filtering of Linear Stochastic Systems pp. 269-288 $100.00
Authors:  (Keyou You, Lihua Xie, Nanyang Technological University, Singapore)
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
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. 

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