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Keywords = probabilistic noise interval detection

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14 pages, 6164 KB  
Article
Probabilistic Noise Detection and Weighted Non-Negative Matrix Factorization-Based Noise Reduction Methods for Snapping Shrimp Noise
by Suhyeon Park, Jongwon Seok and Jungpyo Hong
J. Mar. Sci. Eng. 2025, 13(1), 96; https://doi.org/10.3390/jmse13010096 - 7 Jan 2025
Cited by 1 | Viewed by 1396
Abstract
Snapping Shrimps (SSs) live in warm marine areas. Snapping Shrimps Noise (SSN), loud sounds generated by these underwater creatures, serves as a major source of in performance degradation by decreasing the Signal-to-Noise Ratio (SNR) for underwater acoustic communication and target detection. Thus, we [...] Read more.
Snapping Shrimps (SSs) live in warm marine areas. Snapping Shrimps Noise (SSN), loud sounds generated by these underwater creatures, serves as a major source of in performance degradation by decreasing the Signal-to-Noise Ratio (SNR) for underwater acoustic communication and target detection. Thus, we propose a unified solution for SSN detection and reduction in this paper. First, Signal Presence Probability (SPP) is calculated for SSN detection, and then the SPP is provided to Non-negative Matrix Factorization (NMF) as a weight for SSN reduction. In the proposed method, SPP acts as a key factor for SSN detection and reduction. To verify the effectiveness of the proposed method, the SAVEX-15 dataset, real ocean data containing SSN, is used. As a result of SSN detection, it was confirmed that SPP presented the highest performance in the Receiver Operating Characteristics curve, and we achieved 0.014 higher Area Under the Curve compared to competing methods. In addition, Continuous Wave and Linear Frequency Modulation signals were set as target signals and combined with the SAVEX-15 data for evaluation of noise reduction performance. As a result, the performance of the SPP-weighted NMF (WNMF) presented at least 2 dB higher SNR and SDR while maintaining less LSD compared to the Optimally Modified Log Spectral Amplitude estimator and NMF. Full article
(This article belongs to the Section Ocean Engineering)
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18 pages, 933 KB  
Article
Robust Control for the Detection Threshold of CFAR Process in Cluttered Environments
by Jeong Hoon Shin and Youngjin Choi
Sensors 2020, 20(14), 3904; https://doi.org/10.3390/s20143904 - 13 Jul 2020
Cited by 1 | Viewed by 6066
Abstract
The constant false alarm rate (CFAR) process is essential for target detection in radar systems. Although the detection performance of the CFAR process is normally guaranteed in noise-limited environments, it may be dramatically degraded in clutter-limited environments since the probabilistic characteristics for clutter [...] Read more.
The constant false alarm rate (CFAR) process is essential for target detection in radar systems. Although the detection performance of the CFAR process is normally guaranteed in noise-limited environments, it may be dramatically degraded in clutter-limited environments since the probabilistic characteristics for clutter are unknown. Therefore, sophisticated CFAR processes that suppress the effect of clutter can be used in actual applications. However, these methods have the fundamental limitation of detection performance because there is no feedback structure in terms of the probability of false alarm for determining the detection threshold. This paper presents a robust control scheme for adjusting the detection threshold of the CFAR process while estimating the clutter measurement density (CMD) that uses only the measurement sets over a finite time interval in order to adapt to time-varying cluttered environments, and the probability of target existence with finite measurement sets required for estimating CMD is derived. The improved performance of the proposed method was verified by simulation experiments for heterogeneous situations. Full article
(This article belongs to the Special Issue Multi-Sensor Fusion for Object Detection and Tracking)
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