Study on robust space-time adaptive reverberation suppressing

Hao-ke, Zhan; Qu-li, MA; Li, Hailin

doi:10.1109/icosp.2010.5655206

Cited by 1 publication

(2 citation statements)

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“…to design narrow beamwidth [1], reduce the spatial size of the resolving unit to increase the signal-to-reverberation ratio (SRR). Moreover, the essence of the waveform design method is to increase the product of pulse width bandwidth and improve the matched filter gain [2].…”

Section: Fu Et Al Eurasip Journal On Advances In Signalmentioning

confidence: 99%

“…Considering the small amplitude of the multiple scattering, the multiple scattering effect is ignored. Under the assumption of the narrow-band waveform, the reverberation can be described as (1) where N(t) is the number of scatterers contributing to the reverberation at time t. A i ,τ i and φ i , denote the amplitude, time delay, and Doppler shift of the i-th scatterer echo, respectively. s(t) denotes the waveform.…”

Section: Reverberation Modelingmentioning

confidence: 99%

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Model-based optimal action selection for Dyna-Q reverberation suppression cognitive sonar

Fu,

Ma,

Feng

et al. 2023

EURASIP J. Adv. Signal Process.

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The Doppler shift of low-speed targets is frequently disturbed by the reverberation Doppler spread clutter under the shallow sea. The clutter is generated by underwater scatterers, which increases the difficulty of Doppler estimation. To solve this problem, a reverberation target resolution function based on the Doppler spread clutter statistical model is proposed in this paper. Through the width of reverberation Doppler clutter, this function adjusts the waveform parameters by determining whether the target is discriminable. In addition, the reverberation Doppler spread clutter is time-spatial varying and affected by grazing angle, waves, wind speed, fish and other effects. Thus, the sonar waveform parameters need to be adjusted constantly. Therefore, this paper combines the cognitive sonar based on reinforcement learning with the reverberation target resolution function to evaluate different waveforms in different environments. Consequently, the sonar can adjust the waveform parameters in real-time and obtain the optimal waveform in different environments. Meanwhile, in this paper, the action selection strategy of Dyna-Q reinforcement learning is optimized, and the model-based maximum action selection Dyna-Q algorithm (Dyna-Q-Max-Action) is proposed. Compared with the traditional Dyna-Q and Q-learning algorithms, the proposed algorithm needs fewer episodes. Finally, numerical simulation verified the effectiveness of the proposed algorithm.

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Section: Fu Et Al Eurasip Journal On Advances In Signalmentioning

confidence: 99%

Section: Reverberation Modelingmentioning

confidence: 99%