Stochastic Resonance Effect in Optimal Decision Solution Under Neyman–Pearson Criterion

Yang, Ting; Liu, Yu; Yang, Shiju; Liu, Shujun

doi:10.1007/s00034-020-01644-y

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…Besides, T k has a similar conclusion with T m in H 1 according to Equations ( 12), (16), and ( 29), so its distribution can be described as the chi-square distribution as follows:…”

Section: Identification Criterionmentioning

confidence: 99%

“…The likelihood-ratio test (LRT) assesses the goodness of fit of two competing statistical models based on the ratio of their likelihoods in the binary classification problem. According to the Neyman-Pearson theory, the false alarm probability of LRT can be controlled [16][17][18]. The Neyman-Pearson (NP) detector results if the false alarm probability is constrained to be less than or equal to a specified maximum value while minimising the probability of missed detection [19,20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Chaff identification method based on Range‐Doppler imaging feature

Wang

Chen

Zhu

et al. 2022

IET Radar Sonar & Navi

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Chaff is widely used in electronic countermeasures as an effective passive jamming method. This study proposes a chaff identification method that integrates the distribution of distance, Doppler frequency, and power in Range-Doppler imaging. The traditional radar signal processing method estimates the distance and Doppler frequency, and the mean-shift algorithm is combined to complete the clustering after the constant false alarm detection. Then, the target and chaff cloud models are modelled in three dimensions: radar cross-section, range, and Doppler frequency. Using the Neyman-Pearson criterion, three chaff likelihood ratio test detectors are designed based on these assumptions. The three detectors can effectively identify the chaff, and then the theoretical detection probability and influencing factors are analysed. At the same time, the Kullback-Leibler divergence is used to describe the distribution difference between the data to be detected and the theoretical target and chaff. Besides, the different classifiers are used to identify the features of the differences in Kullback-Leibler divergence in three dimensions. Finally, the real-life chaff datasets verified the excellent recognition rate of the authors' method for the chaff.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Besides, T k has a similar conclusion with T m in H 1 according to Equations ( 12), (16), and ( 29), so its distribution can be described as the chi-square distribution as follows:…”

Section: Identification Criterionmentioning

confidence: 99%