383 research outputs found
Adaptive Quantizers for Estimation
In this paper, adaptive estimation based on noisy quantized observations is
studied. A low complexity adaptive algorithm using a quantizer with adjustable
input gain and offset is presented. Three possible scalar models for the
parameter to be estimated are considered: constant, Wiener process and Wiener
process with deterministic drift. After showing that the algorithm is
asymptotically unbiased for estimating a constant, it is shown, in the three
cases, that the asymptotic mean squared error depends on the Fisher information
for the quantized measurements. It is also shown that the loss of performance
due to quantization depends approximately on the ratio of the Fisher
information for quantized and continuous measurements. At the end of the paper
the theoretical results are validated through simulation under two different
classes of noise, generalized Gaussian noise and Student's-t noise
Multiple Parameter Estimation With Quantized Channel Output
We present a general problem formulation for optimal parameter estimation
based on quantized observations, with application to antenna array
communication and processing (channel estimation, time-of-arrival (TOA) and
direction-of-arrival (DOA) estimation). The work is of interest in the case
when low resolution A/D-converters (ADCs) have to be used to enable higher
sampling rate and to simplify the hardware. An Expectation-Maximization (EM)
based algorithm is proposed for solving this problem in a general setting.
Besides, we derive the Cramer-Rao Bound (CRB) and discuss the effects of
quantization and the optimal choice of the ADC characteristic. Numerical and
analytical analysis reveals that reliable estimation may still be possible even
when the quantization is very coarse.Comment: 9 pages, 9 figures, International ITG Workshop on Smart Antennas -
WSA 2010, Bremen, German
CRLB Based Optimal Noise Enhanced Parameter Estimation Using Quantized Observations
Cataloged from PDF version of article.In this letter, optimal additive noise is characterized
for parameter estimation based on quantized observations. First,
optimal probability distribution of noise that should be added to
observations is formulated in terms of a Cramer–Rao lower bound
(CRLB) minimization problem. Then, it is proven that optimal additive
“noise” can be represented by a constant signal level, which
means that randomization of additive signal levels is not needed
for CRLB minimization. In addition, the results are extended to
the cases in which there exists prior information about the unknown
parameter and the aim is to minimize the Bayesian CRLB
(BCRLB). Finally, a numerical example is presented to explain the
theoretical results
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