11 research outputs found
Performance Analysis for Time-of-Arrival Estimation with Oversampled Low-Complexity 1-bit A/D Conversion
Analog-to-digtial (A/D) conversion plays a crucial role when it comes to the
design of energy-efficient and fast signal processing systems. As its
complexity grows exponentially with the number of output bits, significant
savings are possible when resorting to a minimum resolution of a single bit.
However, then the nonlinear effect which is introduced by the A/D converter
results in a pronounced performance loss, in particular for the case when the
receiver is operated outside the low signal-to-noise ratio (SNR) regime. By
trading the A/D resolution for a moderately faster sampling rate, we show that
for time-of-arrival (TOA) estimation under any SNR level it is possible to
obtain a low-complexity -bit receive system which features a smaller
performance degradation then the classical low SNR hard-limiting loss of
( dB). Key to this result is the employment of a lower bound for
the Fisher information matrix which enables us to approximate the estimation
performance for coarsely quantized receivers with correlated noise models in a
pessimistic way
Performance Analysis for Time-of-Arrival Estimation with Oversampled Low-Complexity 1-bit A/D Conversion
Analog-to-digtial (A/D) conversion plays a crucial role when it comes to the
design of energy-efficient and fast signal processing systems. As its
complexity grows exponentially with the number of output bits, significant
savings are possible when resorting to a minimum resolution of a single bit.
However, then the nonlinear effect which is introduced by the A/D converter
results in a pronounced performance loss, in particular for the case when the
receiver is operated outside the low signal-to-noise ratio (SNR) regime. By
trading the A/D resolution for a moderately faster sampling rate, we show that
for time-of-arrival (TOA) estimation under any SNR level it is possible to
obtain a low-complexity -bit receive system which features a smaller
performance degradation then the classical low SNR hard-limiting loss of
( dB). Key to this result is the employment of a lower bound for
the Fisher information matrix which enables us to approximate the estimation
performance for coarsely quantized receivers with correlated noise models in a
pessimistic way
Measurement-driven Quality Assessment of Nonlinear Systems by Exponential Replacement
We discuss the problem how to determine the quality of a nonlinear system
with respect to a measurement task. Due to amplification, filtering,
quantization and internal noise sources physical measurement equipment in
general exhibits a nonlinear and random input-to-output behaviour. This usually
makes it impossible to accurately describe the underlying statistical system
model. When the individual operations are all known and deterministic, one can
resort to approximations of the input-to-output function. The problem becomes
challenging when the processing chain is not exactly known or contains
nonlinear random effects. Then one has to approximate the output distribution
in an empirical way. Here we show that by measuring the first two sample
moments of an arbitrary set of output transformations in a calibrated setup,
the output distribution of the actual system can be approximated by an
equivalent exponential family distribution. This method has the property that
the resulting approximation of the statistical system model is guaranteed to be
pessimistic in an estimation theoretic sense. We show this by proving that an
equivalent exponential family distribution in general exhibits a lower Fisher
information measure than the original system model. With various examples and a
model matching step we demonstrate how this estimation theoretic aspect can be
exploited in practice in order to obtain a conservative measurement-driven
quality assessment method for nonlinear measurement systems.Comment: IEEE International Instrumentation and Measurement Technology
Conference (I2MTC), Taipei, Taiwan, 201