Theory of the Stochastic Resonance Effect in Signal Detection: Part I—Fixed Detectors

Chen, Hao; Varshney, Pramod K.; Kay, S.; Michels, J.H.

doi:10.1109/tsp.2007.893757

Cited by 260 publications

(371 citation statements)

References 38 publications

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“…(20) is exactly that defined in (12), and we are in fact faced with the earlier discussed sequential test. Clearly, L n is the cumulative sum of the sequence of iid random variables log [f 1 (y i )/f 0 (y i )], whence application of Wald's equality yields (25) The numerator can be expanded by conditioning, and then approximated by Wald's approximations: (26) where the last approximation follows from eqs. (18) and (19).…”

Section: Sequential Probability Ratio Testmentioning

confidence: 99%

“…,∞, represents the sensor number, X i is the observation made at node i (observations are iid), and the pdf f X (x) is an even function. According to formulation (7), the SPRT for this problem would be (53) but, in many cases of interest, implementing such an optimal SPRT is unfeasible [25]. Therefore, we consider sub-optimal sequential detectors and, as explained before, we also contaminate the original observations with iid noise: this latter effect amounts to consider the noise contaminated observables Y i = X i + W i , in place of the original X i .…”

Section: Problem Formalizationmentioning

confidence: 99%

“…2.3. First, Wald's equality (25), under hypothesis 1 , yields, in the regime of small P e , The two above functions allow us to write compact formulas for the system analysis. Indeed, in [24] it is found that (60)…”

Section: Problem Formalizationmentioning

confidence: 99%

See 2 more Smart Citations

Elements of Sequential Detection with Applications to Sensor Networks

Maranò¹,

Matta²

2009

Sensor and Data Fusion

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Section: Sequential Probability Ratio Testmentioning

confidence: 99%

Section: Problem Formalizationmentioning

confidence: 99%

See 1 more Smart Citation

Elements of Sequential Detection with Applications to Sensor Networks

Maranò¹,

Matta²

2009

Sensor and Data Fusion

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“…The term was first coined by Benzi et al in 1981 [4,5] and since then, SR has been observed in numerous systems across many fields [6,7,8]. As more systems were seen to exhibit SR, several generalized theories on the subject have thus been presented [9,10,11,12,13], but essentially, SR means that the system performance is enhanced by the means of noise.…”

Section: Introductionmentioning

confidence: 99%

Stochastic resonator to detect bipolar binary pulse amplitude modulated signals; analysis, parameter‐induced SR designs and sine‐induced SR

Gunes¹,

Higgins

Leeson³

2016

IET signal process.

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A Stochastic Resonator has been considered as an alternative signal processing tool because of its noise-induced performance enhancement ability. Here, the resonator parameters, steady states and transition time of the system are redefined for BPAM signals such that the region in which the resonator benefits from noise can be identified. Simple parameter-induced stochastic resonance (PSR) designs are then built, based on this analysis in order to configure the resonator in the optimum region. Furthermore, Sine-induced SR based on using a periodic signal instead of noise is introduced to enhance the system performance and compared with noise-enhanced SR (NSR). It is shown that Sine-induced SR provides a performance enhancement as it needs less power and does not require an adjustment relevant to the background noise. The results indicate that a resonator improves the receiver performance by eliminating noise if its parameters and BPAM characteristics are set accurately as given in the PSR designs, otherwise the resonator can benefit from either a noise as in NSR, or a sine wave as proposed.

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“…In other words, for certain detectors, addition of controlled "noise" can improve detection performance. Such noise benefits can be in various forms, such as an increase in output signal-tonoise ratio (SNR) [4], a decrease in probability of error [5], or an increase in probability of detection under a false-alarm rate constraint [3], [6], [7].…”

Section: Introductionmentioning

confidence: 99%