Soft-decision detection of weak tonals for passive sonar using track-before-detect method

Zhang, Dianlun; Gao, Linsen; Sun, Dong; Teng, Tingting

doi:10.1016/j.apacoust.2021.108549

Cited by 12 publications

(4 citation statements)

References 31 publications

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“…The main idea is to generate a group of random samples (particles) with relevant weights through Monte Carlo simulation to approximate the posterior distribution, and then estimate the target state on this basis. The specific implementation steps can be referred to [10]. When there are multiple tones, M filters are work together in M frequency bins to give the detection and estimation results to each tonal.…”

Section: Implementation By Particle Filtermentioning

confidence: 99%

“…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]. Due to the fact that the estimation of the target state in the spectrogram is a nonlinear problem, the particle filter method may be superior in dealing with such problems [11].…”

Section: Introductionmentioning

confidence: 99%

“…If the data information from multiple frames can be combined to generate measurements, the target SNR will be improved, thus the detection probability can be increased accordingly. This study aims to design a multi-frame non-coherent TBD (MFNC-TBD) method to improve the detection performance of single frame TBD (SF-TBD) algorithm [10]. The novel measurement model likelihood ratio function are established through non-coherent integration of multi-frame data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Implementation By Particle Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

“…However, practical sonar systems often encounter strong reverberation interference. In this low signal-to-reverberation ratio (SRR) environment, only setting a lower threshold can ensure that the target is not missed, but it also causes a lot of false alarms [5,6]. The higher false alarm rate adversely affects target associations, thereby increasing the risk of tracker drift during the tracking process.…”

Section: Introductionmentioning

confidence: 99%

Tracking-by-Detection Algorithm for Underwater Target Based on Improved Multi-Kernel Correlation Filter

Yue,

Xu,

Yang

2024

Remote Sensing

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Joint detection and tracking of weak underwater targets are challenging problems whose complexity is intensified when the target is disturbed by reverberation. In the low signal-to-reverberation ratio (SRR) environment, the traditional detection and tracking methods perform poorly in tracking robustness because they only consider the target motion characteristics. Recently, the kernel correlation filter (KCF) based on target features has received lots of attention and gained great success in visual tracking. We propose an improved multi-kernel correlation filter (IMKCF) tracking-by-detection algorithm by introducing the KCF into the field of underwater weak target detection and tracking. It is composed of the tracking-by-detection, the adaptive reliability check, and the re-detection modules. Specifically, the tracking-by-detection part is built on the multi-kernel correlation filter (MKCF), and it uses multi-frame data weighted averaging to update. The reliability check helps keep the tracker from corruption. The re-detection module, integrated with a Kalman filter, identifies target positions when the tracking is unreliable. Finally, the experimental data processing and analysis show that the proposed method outperforms the single-kernel methods and some traditional tracking methods.

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Underwater weak moving target detection method based on wideband Multi-pulse coherent integration

Sun,

Hou,

Teng

2023

Applied Acoustics

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Soft-decision detection of weak tonals for passive sonar using track-before-detect method

Cited by 12 publications

References 31 publications

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

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

Tracking-by-Detection Algorithm for Underwater Target Based on Improved Multi-Kernel Correlation Filter

Underwater weak moving target detection method based on wideband Multi-pulse coherent integration

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