A space-frequency joint detection and tracking method for line-spectrum components of underwater acoustic signals

Luo, Xinwei; Shen, Zihan

doi:10.1016/j.apacoust.2020.107609

Cited by 18 publications

(8 citation statements)

References 8 publications

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“…Luo et al [37][38][39] used the normalized spectrum of the signal as input and completed the space-frequency joint detection of the line spectrum of underwater acoustic signals using a restricted Boltzmann machine. Yang et al [40] proposed a new cooperative deep learning method for underwater acoustic target recognition by combining deep long-and short-term memory networks and deep self-coding neural networks.…”

Section: Underwater Target Radiation Noise Recognition Based On Deep ...mentioning

confidence: 99%

Present status and challenges of underwater acoustic target recognition technology: A review

Lei

Xiaofang²,

Wang³

et al. 2022

Front. Phys.

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Future naval warfare has placed high demands on underwater targets’ target detection, target recognition, and opposition resistance, among other things. However, the ocean’s complex underwater acoustic environment and the evolving “stealth” technology of underwater targets pose significant challenges to target detection systems, which has become a hot topic in the field of underwater acoustic signal processing in various countries. This study introduced the mechanism of underwater target radiation noise generation, analyzed the research progress and development of underwater target radiation noise recognition by applying machine learning from three perspectives: signal acquisition, feature extraction, and signal recognition at home and abroad, and elaborated on the challenges of underwater target-radiated noise recognition technology against the backdrop of rapid computing science development, and finally, an integrated signal processing method based on the fusion of traditional feature extraction methods and deep learning is proposed for underwater target radiation noise recognition, which improves the low recognition rate of traditional methods and also circumvents the problem of deep learning requiring high computational cost, and is an important direction for future hydroacoustic signal processing.

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Section: Underwater Target Radiation Noise Recognition Based On Deep ...mentioning

confidence: 99%

Present status and challenges of underwater acoustic target recognition technology: A review

Lei

Xiaofang²,

Wang³

et al. 2022

Front. Phys.

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“…Among them, the methods based on hidden Markov models (HMM) are widely used due to their superior detection performance for weak tones [5]. The frequency change of the tonal track is modeled as a first-order Markov model, then the occupancy probability in every discrete frequency bin or the target frequency trajectory can be obtained from the observations through the forward backward algorithm or the Viterbi algorithm [6], [7]. The third category aims to track the tones by using some filters, such as Kalman filter [8] and particle filter [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Multi-frame non-coherent track-before-detect method for weak tones in passive sonar

Zhang,

Piao,

Guo

et al. 2024

J. Phys.: Conf. Ser.

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In order to solve the problem of performance degradation of track-before-detect (TBD) methods using single frame information when received signals exhibit a large fluctuation. A TBD method utilizing multi-frame information is proposed. Specifically, the novel measurement model and likelihood ratio function based on the non-coherent accumulation of multi-frame data are established. Multi-frame integration greatly improves the signal-to-noise ratio (SNR) of measurements. Furthermore, the derived likelihood ratio function can more effectively describe the statistical characteristics of the spectrum measurements. Combining these two advantages can improve the detection performance of weak tones for passive sonar. The simulation and experimental data results both demonstrate the advantages of the proposed method.

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“…A hidden Markov model is established on the omnidirectional LOFAR spectrum to estimate the frequency time series of the target signal in reference [8]. In reference [9], a 2-D hidden Markov model is established on the frequency-azimuth (FRAZ) spectrum to track the target azimuth and frequency simultaneously, which requires less prior information and has better detection performance. However, it is likely to be affected by high energy interference and noise on the FRAZ spectrum.…”

Section: Introductionmentioning

confidence: 99%

Line spectrum detection and motion parameters estimation for underwater moving target

Shi,

Piao,

Guo

2024

J. Phys.: Conf. Ser.

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For the detection of underwater moving target with constant speed at low signal-to-noise ratio (SNR), a track-before-detect method is proposed based on motion parameters estimation and hidden Markov model (HMM). Optimization algorithm is used for the moving target motion parameter estimation with cost function obtained by energy accumulation along line spectrum tracked with 1-D Hidden Markov model in spatially filtered LOFAR spectrum. This method is suitable for detecting weak targets with fast changes in azimuth.

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A space-frequency joint detection and tracking method for line-spectrum components of underwater acoustic signals

Cited by 18 publications

References 8 publications

Present status and challenges of underwater acoustic target recognition technology: A review

Present status and challenges of underwater acoustic target recognition technology: A review

Multi-frame non-coherent track-before-detect method for weak tones in passive sonar

Line spectrum detection and motion parameters estimation for underwater moving target

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