Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Multiuser Detection
On the use of stochastic resonance in sine detection
Signal Processing
Design of detectors based on stochastic resonance
Signal Processing
Stochastic resonance in noisy threshold neurons
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Convex Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Detection of weak signals using adaptive stochastic resonance
ICASSP '95 Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Optimal noise benefits in Neyman-Pearson and inequality-constrained statistical signal detection
IEEE Transactions on Signal Processing
Stochastic resonance and improvement by noise in optimal detection strategies
Digital Signal Processing
Noise Enhanced Parameter Estimation
IEEE Transactions on Signal Processing - Part II
Theory of the Stochastic Resonance Effect in Signal Detection—Part II: Variable Detectors
IEEE Transactions on Signal Processing - Part II
Stochastic resonance in locally optimal detectors
IEEE Transactions on Signal Processing
Stochastic resonance in discrete time nonlinear AR(1) models
IEEE Transactions on Signal Processing
Theory of the Stochastic Resonance Effect in Signal Detection: Part I—Fixed Detectors
IEEE Transactions on Signal Processing - Part I
On UWB Impulse Radio Receivers Derived by Modeling MAI as a Gaussian Mixture Process
IEEE Transactions on Wireless Communications
Rapid search algorithms for code acquisition in UWB impulse radio communications
IEEE Journal on Selected Areas in Communications
Adaptive stochastic resonance in noisy neurons based on mutual information
IEEE Transactions on Neural Networks
Stochastic resonance in binary composite hypothesis-testing problems in the Neyman-Pearson framework
Digital Signal Processing
Effects of multiscale noise tuning on stochastic resonance for weak signal detection
Digital Signal Processing
Stochastic signaling in the presence of channel state information uncertainty
Digital Signal Processing
Weak signal detection: Condition for noise induced enhancement
Digital Signal Processing
Hi-index | 35.68 |
Performance of some suboptimal detectors can be enhanced by adding independent noise to their observations. In this paper, the effects of additive noise are investigated according to the restricted Bayes criterion, which provides a generalization of the Bayes and minimax criteria. Based on a generic M -ary composite hypothesis-testing formulation, the optimal probability distribution of additive noise is investigated. Also, sufficient conditions under which the performance of a detector can or cannot be improved via additive noise are derived. In addition, simple hypothesis-testing problems are studied in more detail, and additional improvability conditions that are specific to simple hypotheses are obtained. Furthermore, the optimal probability distribution of the additive noise is shown to include at most M mass points in a simple M -ary hypothesis-testing problem under certain conditions. Then, global optimization, analytical and convex relaxation approaches are considered to obtain the optimal noise distribution. Finally, detection examples are presented to investigate the theoretical results.