کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7133227 | 1461822 | 2018 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Magnetic anomaly detection based on stochastic resonance
ترجمه فارسی عنوان
تشخیص آنومالی مغناطیسی بر اساس رزونانس تصادفی
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کلمات کلیدی
تشخیص آنومالی مغناطیسی، رزونانس تصادفی، تست فرضیه دوتایی، نسبت سیگنال به نویز،
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
الکتروشیمی
چکیده انگلیسی
Orthonormal basis function (OBF) decomposition method and minimum entropy (ME) detector are two typical methods of magnetic anomaly detection. The OBF detector works effectively only when the assumptions of the target signal and noise are appropriate, and the ME detector is limited by low signal-to-noise ratio (SNR). In order to improve magnetic anomaly detection performance in the case of low SNR and no prior information of the target signal, we proposed a novel detector by using stochastic resonance (SR) method and named it SR detector in the paper. The SR detector consists of a bistable SR system and a corresponding receiver. Firstly, the noise is used to enhance the magnetic anomaly signal with the help of SR system, instead of being suppressed in a traditional method; then the anomaly signal can be detected more effectively by the receiver. Experimental results show that the SR detector did work well, its detection probability approximated to 70% even if the input SNR was â3â¯dB, and was about 100% if the input SNR was 0dB, when the false alarm rate (FAR) was 1.5%. Furthermore, the SR detector provided higher detection probability than the ME detector under the same basis. In a word, due to the good detection performance and simple implementation, the SR detector would be more attractive in practice.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Sensors and Actuators A: Physical - Volume 278, 1 August 2018, Pages 11-17
Journal: Sensors and Actuators A: Physical - Volume 278, 1 August 2018, Pages 11-17
نویسندگان
Chengbiao Wan, Mengchun Pan, Qi Zhang, Fenghe Wu, Long Pan, Xiaoyong Sun,