Pulse Ranging Method Based on Active Virtual Time Reversal in Underwater Multi-Path Channel

Zhang, Zhichen; Wang, Haiyan; Yao, Hongxun

doi:10.3390/jmse8110883

Cited by 3 publications

(5 citation statements)

References 29 publications

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“…Evaluating Equation (27) with Equation ( 23) produces the value c = 0. Evaluating Equation (28) with Equation ( 22) produces the value d = 0. Evaluating Equation (27) with Equation (25) produces Equation ( 29), while evaluating Equation (28) with Equation ( 24) produces Equation (30).…”

Section: Terminal Transversality Of the Enpoint Lagrangianmentioning

confidence: 99%

“…This manuscript seeks to use the same notion, assertion of the first principals (via Pontryagin’s formulation of Hamiltonian systems) in the context of inertial motion estimation fused with sensor measurements (that are presumed to be noisy). Noise in sensors is a serious issue elaborated by Oliveiera et al [ 27 ] for background noise of acoustic sensors and by Zhang et al [ 28 ] for accuracy of pulse ranging measurement in underwater multi-path environments. Barker et al [ 29 ] evaluated impacts on doppler radar measurements beneath moving ice.…”

Section: Introductionmentioning

confidence: 99%

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Virtual Sensoring of Motion Using Pontryagin’s Treatment of Hamiltonian Systems

Sands

2021

Sensors

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To aid the development of future unmanned naval vessels, this manuscript investigates algorithm options for combining physical (noisy) sensors and computational models to provide additional information about system states, inputs, and parameters emphasizing deterministic options rather than stochastic ones. The computational model is formulated using Pontryagin’s treatment of Hamiltonian systems resulting in optimal and near-optimal results dependent upon the algorithm option chosen. Feedback is proposed to re-initialize the initial values of a reformulated two-point boundary value problem rather than using state feedback to form errors that are corrected by tuned estimators. Four algorithm options are proposed with two optional branches, and all of these are compared to three manifestations of classical estimation methods including linear-quadratic optimal. Over ten-thousand simulations were run to evaluate each proposed method’s vulnerability to variations in plant parameters amidst typically noisy state and rate sensors. The proposed methods achieved 69–72% improved state estimation, 29–33% improved rate improvement, while simultaneously achieving mathematically minimal costs of utilization in guidance, navigation, and control decision criteria. The next stage of research is indicated throughout the manuscript: investigation of the proposed methods’ efficacy amidst unknown wave disturbances.

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Section: Terminal Transversality Of the Enpoint Lagrangianmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Virtual Sensoring of Motion Using Pontryagin’s Treatment of Hamiltonian Systems

Sands

2021

Sensors

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“…The simulated underwater acoustic channel is generated by the acoustic toolbox Bellhop [41]. The simulation environment is as follows: shallow water wave-guide, the sound speed profile is shown in Figure 3, the bottom is to be modeled as an acoustic elastic half-space, the density is 2 g/cm 3 and other parameters are presented in Table 1. The impulse response of the simulated channel is shown in Figure 4.…”

Section: Experiments 41 Simulation Experimentsmentioning

confidence: 99%

“…In addition, LFM signal has the lower side-lobe after pulse compression, and its Doppler frequency is not sensitive. Thus, the LFM signal is often applied in some detection or ranging systems [2,3] by using matched filtering to compress the received signal. It is easy to detect an LFM signal in a high signal-to-noise environment.…”

Section: Introductionmentioning

confidence: 99%

Time Reversal and Fractional Fourier Transform-Based Method for LFM Signal Detection in Underwater Multi-Path Channel

2021

Self Cite

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Fractional Fourier transform (FrFT) is a useful tool to detect linear frequency modulated (LFM) signal. However, the detection performance of the FrFT-based method will deteriorate drastically in underwater multi-path environment. This paper proposes a novel method based on time-reversal and fractional Fourier transform (TR-FrFT) to solve this problem. We make use of the focusing ability of time-reversal to mitigate the influence of multi-path, and then improve the detection performance of FrFT. Simulated results show that, compared to FrFT, the difference between peak value and maximum pseudo-peak value of the signal processed by TR-FrFT is improved by 8.75 dB. Lake experiments results indicate that, the difference between peak value and maximum pseudo-peak value of the signal processed by TR-FrFT is improved by 7.6 dB. The detection performance curves of FrFT and TR-FrFT detectors with simulated data and lake experiments data verify the effectiveness of proposed method.

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“…To concentrate the multipath energy, channel estimation methods are applied to acquire the multipath structures. Then, time reversal mirror (TRM) is used to achieve adaptive focusing [10] [11] [12]. Tian used robust orthogonal matching pursuit to get multipath information and improved the detection performance in multipath environment affected by impulsive noise [13].…”

Section: Introductionmentioning

confidence: 99%