Corpus ID: 236469313

On Optimal Quantization in Sequential Detection

  title={On Optimal Quantization in Sequential Detection},
  author={Michael Fauss and Manuel S. Stein and H. Vincent Poor},
The problem of designing optimal quantization rules for sequential detectors is investigated. First, it is shown that this task can be solved within the general framework of active sequential detection. Using this approach, the optimal sequential detector and the corresponding quantizer are characterized and their properties are briefly discussed. In particular, it is shown that designing optimal quantization rules requires solving a nonconvex optimization problem, which can lead to issues in… Expand

Figures and Tables from this paper


On Optimal Quantization Rules for Some Problems in Sequential Decentralized Detection
An asymptotic approximation to the optimal cost of stationary quantization rules is developed and exploited to show that stationary quantizers are not optimal in a broad class of settings. Expand
On optimal quantization rules for sequential decision problems
A negative answer to the question whether optimal local decision functions for the Bayesian formulation of sequential decentralized detection can be found within the class of stationary rules is provided by exploiting an asymptotic approximation to the optimal cost of stationary quantization rules, and the asymmetry of the Kullback-Leibler divergences. Expand
Fine quantization in signal detection and estimation
  • H. Poor
  • Mathematics, Computer Science
  • IEEE Trans. Inf. Theory
  • 1988
Several applications of this result in specific problems of signal detection and estimation being developed are developed, and some numerical results that illustrate the asymptotic behavior of the divergence in these applications are given. Expand
Quantization for Sequential Signal Detection
Using a sequential probability ratio test (SPRT), the performances of optimum quantizers are compared to systems with unquantized data and the relation between these asymptotic relative efficiencies and those of fixed-sample-size detectors is noted. Expand
Quantization Effect on the Log-Likelihood Ratio and Its Application to Decentralized Sequential Detection
  • Y. Wang, Y. Mei
  • Mathematics, Computer Science
  • IEEE Transactions on Signal Processing
  • 2013
It is shown via the convex domination technique that quantization may result in an increase in the case of the second moment of the log-likelihood ratio, but the increase is bounded above by 2/e. Expand
On the performance degradation from one-bit quantized detection
This correspondence examines the Chernoff exponent and discovers a nontrivial lower bound on the relative efficiency of an optimized one-bit quantized detector as compared to unquantized, and examines the case of finite sample size to discover a family of nontrivially bounds. Expand
On the relationship between measures of discrimination and the performance of suboptimal detectors
The problem of designing and analyzing suboptimal detectors via statistical distance measures is considered. As a preliminary result, we show that only the minimum and maximum probability of errorExpand
Quantization in multisensor random signal detection
  • Rick S. Blum
  • Mathematics, Computer Science
  • IEEE Trans. Inf. Theory
  • 1995
Numerical results indicate it is sometimes best for each quantizer to use different size alphabets when a quantizer is located at each sensor, and the best schemes for originally quantizing the observations are studied for the case of asymptotically large observation sample sizes. Expand
Quantizing for minimum distortion
  • J. Max
  • Mathematics, Computer Science
  • IRE Trans. Inf. Theory
  • 1960
This paper discusses the problem of the minimization of the distortion of a signal by a quantizer when the number of output levels of the quantizer is fixed and an algorithm is developed to simplify their numerical solution. Expand
A generalized sequential sign detector for binary hypothesis testing
A generalized sequential sign detector for detecting binary signals in stationary, first-order Markov dependent noise is studied and results are given to show that the proposed detector exploits the correlation in the noise and results in quicker detection. Expand