Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold

Alaie, Hamed Komari; Farsi, Hassan

doi:10.3390/app8010061

Cited by 18 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The basic approach is "one-class" classifications, in which all data from already known classes are considered a single class. According to [13], using the results of the analysis of acoustic signals on the time and frequency domains combined with Least Mean Square as input for the SVM model gives significantly improved results compared to comparable models. Yang's research team [14] (2018) used unsorted SONAR data for pre-training to increase the effectiveness of supervised learning.…”

Section: Introductionmentioning

confidence: 99%

Classification of Surface Vehicle Propeller Cavitation Noise Using Spectrogram Processing in Combination with Convolution Neural Network

Bach¹,

Vu²,

Nguyen

2021

Sensors

View full text Add to dashboard Cite

This paper proposes a method to enhance the quality of detecting and classifying surface vehicle propeller cavitation noise (VPCN) in shallow water by using the improved Detection Envelope Modulation On Noise (DEMON) algorithm in combination with the modified Convolution Neural Network (CNN). To improve the quality of the VPCN spectrogram signal, we apply the DEMON algorithm while analyzing the amplitude variation (AV) to detect the fundamental frequencies of the VPCN signal. To enhance the performance of the traditional CNN, we adapt the size of the sliding window in accordance with the properties of the VPCN spectrogram data, and also reconstruct the CNN layer structure. As for the results, the fundamental frequencies contented in the VPCN spectrogram data can be detected. The analytical results based on the measured data show that the accuracy of the VPCN classification obtained by the proposed method is above 90%, which is higher than those obtained by traditional methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Classification of Surface Vehicle Propeller Cavitation Noise Using Spectrogram Processing in Combination with Convolution Neural Network

Bach¹,

Vu²,

Nguyen

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In the past few years, the use of sonar equipment is bursting forth. Because of the extreme fading of radio frequencies and visual signals in the water medium, acoustic waves are mostly considered powerful methods to sense underwater objects and targets [4]. The sound waves can travel farther in water than the radio waves or radar [5].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, in the passive sonar system, the underwater audio signals, which are received by hydrophones, are pre-processed and then analyzed to detect the required parameters. The passive sonar systems use waves and their undesirable vibrations to spot the vessels [4]. They are applied mainly to diagnose noise from oceanic objects like submarines, ships, and aquatic animals.…”

Section: Introductionmentioning

confidence: 99%

“…Sounds are not generated from the vessels only. The factors like vibration signals from the sea bottom, fishes, and other aquatic creatures confuse target detection [4]. So, the effective sonar signal processing is required, and for the effective sonar signal processing, one needs an understanding of the characteristics of sound propagation in the underwater environment [14].…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

2020

View full text Add to dashboard Cite

Underwater acoustics has been implemented mostly in the field of sound navigation and ranging (SONAR) procedures for submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in the underwater atmosphere. With the rapid development in science and technology, the advancement in sonar systems has increased, resulting in a decrement in underwater casualties. The sonar signal processing and automatic target recognition using sonar signals or imagery is itself a challenging process. Meanwhile, highly advanced data-driven machine-learning and deep learning-based methods are being implemented for acquiring several types of information from underwater sound data. This paper reviews the recent sonar automatic target recognition, tracking, or detection works using deep learning algorithms. A thorough study of the available works is done, and the operating procedure, results, and other necessary details regarding the data acquisition process, the dataset used, and the information regarding hyper-parameters is presented in this article. This paper will be of great assistance for upcoming scholars to start their work on sonar automatic target recognition.

show abstract

“…Passive sonar only includes a receiver without a transmitter and is used to detect the sound emitted by the target, which has the advantages of high concealment and a long detection distance. Therefore, passive sonar is widely used to detect, locate, and classify different targets on or under the surface of water, such as vessels and submarines [2,3], as illustrated in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Passive Detection of Ship-Radiated Acoustic Signal Using Coherent Integration of Cross-Power Spectrum with Doppler and Time Delay Compensations

Guo

Piao

Guo

et al. 2020

Sensors

View full text Add to dashboard Cite

Passive sonar is widely used for target detection, identification and classification based on the target radiated acoustic signal. Under the influence of Doppler, generated by relative motion between the moving target and the sonar array, the received ship-radiated acoustic signals are non-stationary and time-varying, which has a negative effect on target detection and other fields. In order to reduce the influence of Doppler and improve the performance of target detection, a coherent integration method based on cross-power spectrum is proposed in this paper. It can be concluded that the frequency shift and phase change in the cross-power spectrum obtained by each pair of data segments can be corrected with the compensations of time scale (Doppler) factor and time delay. Moreover, the time scale factor and time delay can be estimated from the amplitude and phase of the original cross-power spectrum, respectively. Therefore, coherent integration can be implemented with the compensated cross-power spectra. Simulation and experimental data processing results show that the proposed method can provide sufficient processing gains and effectively extract the discrete spectra for the detection of moving targets.

show abstract

Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold

Cited by 18 publications

References 15 publications

Classification of Surface Vehicle Propeller Cavitation Noise Using Spectrogram Processing in Combination with Convolution Neural Network

Classification of Surface Vehicle Propeller Cavitation Noise Using Spectrogram Processing in Combination with Convolution Neural Network

A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

Passive Detection of Ship-Radiated Acoustic Signal Using Coherent Integration of Cross-Power Spectrum with Doppler and Time Delay Compensations

Contact Info

Product

Resources

About