Cramér-Rao Bounds for Polynomial Signal Estimation Using Sensors With AR(1) Drift

@article{Kar2012CramrRaoBF,
  title={Cram{\'e}r-Rao Bounds for Polynomial Signal Estimation Using Sensors With AR(1) Drift},
  author={Swarnendu Kar and Pramod K. Varshney and Marimuthu Swami Palaniswami},
  journal={IEEE Transactions on Signal Processing},
  year={2012},
  volume={60},
  pages={5494-5507}
}
We seek to characterize the estimation performance of a sensor network where the individual sensors exhibit the phenomenon of drift, i.e., a gradual change of the bias. Though estimation in the presence of random errors has been extensively studied in the literature, the loss of estimation performance due to systematic errors like drift have rarely been looked into. In this paper, we derive closed-form Fisher Information Matrix and subsequently Cramér-Rao bounds (up to reasonable approximation… 

Figures and Tables from this paper

Posterior Cramér-Rao bounds for discrete-time nonlinear filtering with finitely correlated noises

In this paper, a recursive formula of the mean-square error lower bound for the discrete-time nonlinear filtering problem when noises of dynamic systems are temporally correlated is derived based on

Posterior Cramér-Rao Bounds for Nonlinear Dynamic System with Colored Noises

A mean squared error lower bound for the discrete-time nonlinear filtering with colored noises is derived based on the posterior version of the Cramér-Rao inequality that can assess the achievable performance of suboptimal filtering techniques and the colored noise has a significantly effect on the lower bound and the performance of filters.

Geospatial Estimation-Based Auto Drift Correction in Wireless Sensor Networks

This article proposes a novel scheme that uses geospatial estimation-based interpolation techniques on measurements from neighboring sensors to collaboratively predict the value of phenomenon being observed and iteratively correct the sensor drift by means of a Kalman filter.

Cramér-Rao Bounds for a Coupled Mixture of Polynomial Phase and Sinusoidal FM Signals

This letter introduces a new coupled mixture of polynomial phase signal (PPS) and sinusoidal frequency modulated (FM) signal, motivated by real-world applications, for example, contactless linear

Controlled collaboration for linear coherent estimation in wireless sensor networks

  • Swarnendu KarP. Varshney
  • Computer Science
    2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
  • 2012
A wireless sensor network consisting of multiple nodes that are coordinated by a fusion center (FC) in order to estimate a common signal of interest is considered, and it is shown that it is always beneficial to sample as frequently as possible, despite the fact that the samples get increasingly noisy due to the power-constrained nature of the problem.

Collaborative Estimation in Distributed Sensor Networks

This dissertation provides an analytical framework linking the state of calibration to the overall uncertainty of the inferred parameters, and approximate measures of estimation accuracy are derived as a function of physical properties of sensors – namely the drift strength, correlation (Markov) factor and the time-elapsed since last calibration.

Cramér–Rao Bounds for a Coupled Mixture of Polynomial Phase and Sinusoidal FM Signals

This letter introduces a new coupled mixture of polynomial phase signal (PPS) and sinusoidal frequency modulated (FM) signal, motivated by real-world applications, for example, contactless linear

References

SHOWING 1-10 OF 47 REFERENCES

The Cramer-Rao lower bound for signals with constant amplitude and polynomial phase

The authors derive the Cramer-Rao lower bound (CRLB) for complex signals with constant amplitude and polynomial phase, measured in additive Gaussian white noise, which is found to be excellent in most cases.

Online drift correction in wireless sensor networks using spatio-temporal modeling

This paper presents a novel algorithm for detecting and correcting sensors drifts by utilising the spatio-temporal correlation between neigbouring sensors and demonstrates using real data obtained from the Intel Berkeley Laboratory that this algorithm successfully suppresses drifts developed in sensors and thereby prolongs the effective lifetime of the network.

Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case

A class of maximum-likelihood estimators that require transmitting just one bit per sensor to achieve an estimation variance close to that of the sample mean estimator of the deterministic mean-location parameter estimation when only quantized versions of the original observations are available.

Cramer-Rao bounds for deterministic signals in additive and multiplicative noise

  • A. Swami
  • Computer Science
    Signal Process.
  • 1996

Estimations of frequency and its drift rate

This paper presents an analysis of frequency and its drift rate estimation by the difference method, the least-squares method, and the Kalman filter. Error formulas are derived for all five noise

Universal decentralized estimation in a bandwidth constrained sensor network

  • Z. Luo
  • Computer Science
    IEEE Transactions on Information Theory
  • 2005
This correspondence investigates optimal local estimation and final fusion schemes under the constraint that the communication from each sensor to the fusion center must be a one-bit message.

Characterization and modeling of drift noise in Fourier transform spectroscopy: implications for signal processing and detection limits.

It is shown that the minimum detectable signal in an absorbance or transmission measurement degrades indefinitely with the time elapsed since background spectrum acquisition, and a frequency-dependent optimal spectrum averaging time is found to exist beyond which the Minimum detectable signal increases indefinitely.

Polynomial spline-approximation of Clarke's model

The results of this investigation show that local spline approximation is attractive for implementation from viewpoints of both low processing delay and small approximation error; the error can be very close to the minimum error provided by optimal splines.

Sampling Schemes for Sequential Detection With Dependent Observations

  • R. NiuP. Varshney
  • Computer Science, Mathematics
    IEEE Transactions on Signal Processing
  • 2010
It is theoretically proved that the scheme using groups of samples with the optimal signaling waveform is the most energy-efficient and under a constant power constraint.

Drift Compensation, Standards, and Calibration Methods

Many different methods for reducing the effects of drift are described, which try to compensate for the changes in sensor performance using mathematical models and thus maintaining the gas identification capability of the electronic nose.