Underwater moving target detection using track-before-detect method with low power and high refresh rate signal

Zhang, Dianlun; Gao, Linsen; Teng, Tingting; Jia, Zixuan

doi:10.1016/j.apacoust.2020.107750

Cited by 16 publications

(5 citation statements)

References 29 publications

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“…Generally, when the maximum likelihood estimation (MLE) criterion is used to calculate the threshold

, no target information is introduced and

is constant; this makes the detection probability of moving targets lower than that of stationary targets. Given this, the idea of track-before-detect [ 17 ] was used for reference in this study, and the estimate-before-detect method was used to calculate the threshold to achieve more reasonable detection of stationary and moving targets in the background of reverberation. That is, the target’s speed was informative, and the target’s distance was a nuisance.…”

Section: Joint Design Of the Ptfm Waveform And Receiver Filtermentioning

confidence: 99%

A Reverberation Suppression Method Based on the Joint Design of a PTFM Waveform and Receiver Filter

Lei

Liu

Duan

et al. 2022

Entropy

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Transmitting waveform design and signal processing method optimization are effective ways to improve a sonar system’s detection performance. In this study, the spectrum and ambiguity function characteristics of pulse trains of frequency modulation (PTFM) signals were deduced and analyzed to address the problem of serious reverberation interference in the detection of low-speed targets in shallow water environments. The action mechanisms of PTFM signal parameters on the comb spectrum and bed of nails ambiguity function were identified. PTFM signal parameters were designed according to reverberation suppression requirements. The threshold was calculated using the estimate-before-detect method, and the comb spectrum waveform cognitive filtering detection algorithm is proposed. The simulation and lake experimental results show that the PTFM signals’ reverberation suppression ability for low-speed targets was better than it was for stationary or high-speed targets. The proposed method has good universality, which can improve the output signal-to-reverberation ratio (SRR) by more than 6 dB.

show abstract

“…Generally, when the maximum likelihood estimation (MLE) criterion is used to calculate the threshold

, no target information is introduced and

Section: Joint Design Of the Ptfm Waveform And Receiver Filtermentioning

confidence: 99%

A Reverberation Suppression Method Based on the Joint Design of a PTFM Waveform and Receiver Filter

Lei

Liu

Duan

et al. 2022

Entropy

View full text Add to dashboard Cite

show abstract

“…In track-before-detect (TBD) algorithms, unthresholded or low-threshold preprocessed sensor data in several consecutive frames are jointly used for simultaneous detection and tracking of low SNR targets. They have been studied extensively and proven to be effective in optical and infrared images [2][3][4], radars [5][6][7][8] and sonars [9][10][11]. The current TBD approaches can be classified into two groups: the single frame recursive TBD [12][13][14][15] and the multi-frame or batch style TBD (MF-TBD) [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Adaptive pseudo‐spectrum based track‐before‐detect for targets with uncertain existence

Li,

Zhou

2023

IET Radar Sonar & Navi

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In the existing velocity filtering based track‐before‐detect (VF‐TBD), target existence is generally assumed to be constant, and multiple successive frames in a sliding window are jointly processed. However, when the target existence changes abruptly in a processing batch, the incorporation of noise‐only frames may corrupt target energy integration. This results in degraded detection and estimation performances with serious decision delays. A pseudo‐spectrum based VF‐TBD is proposed to effectively detect and track targets with existence uncertainty. An adaptive integration strategy is developed to realise effective energy accumulation with only informative frames by matching target appearance and disappearance times in addition to the velocity. The change of target existence can be discovered in time, and the decision delay is reduced. An estimation strategy based on the maximum output SNR is presented to supply accurate estimations of not only target motion parameters (including the position and velocity) but also time parameters (including appearance and disappearance times). The target output envelope, SNR gain and receiver operating characteristic curve of targets with uncertain existence are analysed theoretically. Numerical simulations exhibit the validity of the proposed method.

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“…Therefore, underwater target detection based on AUV has important research significance. Accurate identification of underwater targets is a difficulty in underwater target detection by AUV, which is also an important research content in computer vision [6]. Particle swarm optimization (PSO) algorithm mainly includes optical, acoustic and magnetic detection technologies.…”

Section: Introductionmentioning

confidence: 99%

Underwater Image Processing and Target Detection from Particle Swarm Optimization Algorithm

Zhang,

Bi,

2023

Preprint

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The underwater image obtained is difficult to satisfy human visual perception because of the particle scattering and water absorption phenomena when visible light propagates underwater. In underwater images, light absorption easily leads to image distortion and reduction of image contrast and brightness. Therefore, this work aims to improve the quality of underwater image processing, reduce the distortion rate of underwater images, and further improve the efficiency of underwater image extraction, processing, and tracking. This work combines intelligent blockchain technology in emerging multimedia industries with existing image processing technology to improve the target detection capability of image processing algorithms. Firstly, the theory of visual saliency analysis (VSA) is studied. The steps of image processing using VSA are analyzed. Based on the original Itti model, the visual significance detection step is optimized. Then, the theoretical basis and operation steps of particle swarm optimization (PSO) algorithm in intelligent blockchain technology are studied. VSA theory is combined with PSO to design underwater image processing algorithms and target detection optimization algorithms for underwater images. The experimental results show that: (1) the method has a higher F value and lower Mean Absolute Error. (2) Compared with the original image, the restored image entropy through this method is greatly improved, and the information in the image increases. Therefore, this method has good performance. Besides, this method performs well in image definition, color, and brightness. The quality of the restored image through this method is better than that of other algorithms. (3) Compared with similar algorithms, the relative errors of this method are reduced by 2.56%, 3.24% and 3.89%, respectively. The results show that the method has high accuracy. The research results can provide a reference for future underwater image processing and target detection research. In addition, the designed underwater image processing and target detection and tracking algorithms can improve the detection efficiency and accuracy of underwater targets and help to accurately obtain underwater target images.

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Underwater moving target detection using track-before-detect method with low power and high refresh rate signal

Cited by 16 publications

References 29 publications

A Reverberation Suppression Method Based on the Joint Design of a PTFM Waveform and Receiver Filter

A Reverberation Suppression Method Based on the Joint Design of a PTFM Waveform and Receiver Filter

Adaptive pseudo‐spectrum based track‐before‐detect for targets with uncertain existence

Underwater Image Processing and Target Detection from Particle Swarm Optimization Algorithm

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