This paper investigates potential improvement of nonparametric detection performance via addition of noise and evaluates the performance of noise modified nonparametric detectors. Detection performance comparisons are made between the original detectors and noise modified detectors. Conditions for improvability as well as the optimum additive noise distributions of the widely used sign detector, the Wilcoxon detector, and the dead-zone limiter detector are derived. Finally, a simple and fast learning algorithm to find the optimal noise distribution solely based on received data is presented. A near-optimal solution can be found quickly based on a relatively small dataset.

In this letter, we present a novel instance of the stochastic resonance effect in sequential detection. For a general binary hypotheses sequential detection problem, the detection performance is evaluated in terms of the expected sample size under both hypotheses. Improvability conditions are established for an injected noise to reduce at least one of the expected sample sizes for a sequential detection system using stochastic resonance. The optimal noise is also determined under such criteria. An illustrative example is presented where performance comparisons are made between the original detector and different noise modified detectors.

This paper considers the decentralized fusion problem involving local sensor detection as well as the fusion of decisions transmitted over non-ideal transmission channels in a wireless sensor network. Prime emphasis is given to the enhancement of several fusion rules using a recently developed stochastic resonance methodology applied at the local sensors. Further, it is shown that the optimal form of the stochastic resonance probability mass density for the decentralized sensor fusion problem retains the same form as that previously developed for the single sensor case.

Stochastic-resonance (SR), a nonlinear physical phenomenon in which the performance of some nonlinear systems can be enhanced by adding suitable noise, has been observed and applied in many areas. However, it has not been shown whether or not this phenomenon plays a role in distributed detection. It seems counterintuitive that adding additional noise to the received decisions at the fusion center can improve detection performance. However, in this paper, we demonstrate the existence of the SR phenomenon in decision fusion by examples. An explanation for its existence is provided

This paper considers the stochastic resonance (SR) effect in the two hypotheses signal detection problem. Performance of a SR enhanced detector is derived in terms of the probability of detection PDand the probability of false alarm PFA. Furthermore, the conditions required for potential performance improvement using SR are developed. Expression for the optimal stochastic resonance noise pdf which renders the maximum po without increasing PFAis derived. By further strengthening the conditions, this approach yields the constant false alarm rate (CFAR) receiver. Finally, detector performance comparisons are made between the optimal SR noise, Gaussian, uniform and optimal symmetric pdf noises

In this paper, we introduce a class of training-efficient adaptive signal detectors that exploit a parametric model taking into account the non-stationarity of HSI data in the spectral dimension. A maximum likelihood (ML) estimator is presented for estimation of the parameters associated with the proposed parametric model. Several important issues are discussed, including model order selection, training screening, and time-series based whitening and detection, which are intrinsic parts of the proposed parametric adaptive detectors. Experimental results using real HSI data reveal that the proposed parametric detectors are more training-efficient and outperform conventional covariance-matrix based detectors when the training size is limited.

The problem of reducing the probability of decision error of an existing binary receiver that is suboptimal using the ideas of stochastic resonance is solved. The optimal probability density function of the random variable that should be added to the input is found to be a Dirac delta function, and hence, the optimal random variable is a constant. The constant to be added depends upon the decision regions and the probability density functions under the two hypotheses and is illustrated with an example. Also, an approximate procedure for the constant determination is derived for the mean-shifted binary hypothesis testing problem

Hyperspectral imaging (HSI) sensors can provide very fine spectral resolution that allows remote identification of ground objects smaller than a full pixel. Traditional approaches to the so-called subpixel target signal detection problem involve the estimation of the sample covariance matrix of the background from target-free training pixels. This entails a large training requirement and high complexity. In this paper, we investigate parametric adaptive modeling and detection for HSI applications. To deal with nonstationarity in the spectral dimension that is characteristic of HSI data, we introduce a sliding-window based time-varying (TV) autoregressive (AR) modeling and detection technique, by which the spectral data is sliced into overlapping subvectors for parameter estimation and signal whitening. Experimental results using real HSI data show that the proposed parametric technique outperforms conventional detection schemes, especially when the training size is small.

The Gaussian multichannel binary detection problem is considered. A multichannel generalized likelihood ratio is implemented using a model-based approach where the signal is assumed to be characterized by an autoregressive vector process. Detection performance is obtained for the special case where the underlying processes are assumed to have known autoregressive process parameters. Specifically, results for two-channel signal vectors containing various temporal and cross-channel correlation are obtained using a Monte Carlo procedure. These results are plotted versus signal-to-noise ratio and are shown to be bounded by available optimal detection curves. The two-channel detection results are shown to decrease as (S/N)/sub 2/ decreases and approach the superior angle channel performance asymptotically. A likelihood ratio for a more general class of processes with correlated Gaussian quadrature components is noted.>