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SLIM-Detector for Undersampling Preamble in Underwater Acoustic Communications

Published: 13 February 2020 Publication History

Abstract

The preamble is the signal to trigger the receiver to change from the standby mode to the working mode. Preamble detection is a very important part for underwater acoustic communications, since if the preamble signal is not detected or the receiver is triggered wrongly by inteferences, the whole communication will fail[6][7].
Matched filter (MF) [5] is widely used for sonar detection. It correlates the transmitted with received waveform to determine whether and when a target appears. The MF has a very simple formulation, and is optimal in additive white noise in probing a stationary target since it gives the highest output signal-to-noise ratio (SNR) among all FIR filters in White Gaussian Noise (WGN). The Page test (PT)[1] is used to improve the detection performance in the nonstationary underwater channel with the slowly changing noise level and multiple echoes for active sonar systems. Besides, the normalized matched filter (NMF)[3] relies on the matching of the signal shapes for detection, minimizes the influence of unknown SNR which is more practical than the MF in complicated underwater environments. Other anti-interference detectors for preamble in underwater multipath channel are proposed [7][4][9] to further improve the detection.
All the above traditional detectors are based on assumption that the Nyquist frequency is absolutely met. However, the underwater systems usually need to be low power-consuming, especially in standby mode. And undersampling processing can lower the power, cost and even storage for preamble in standby mode. Hence undersampling preamble detection calls for studying for underwater acoustic modems. Undersampling makes signal aliasing and lose much feature, hence it leads traditional detection failure.
Doppler-insensitive waveforms[10] such as hyperbolic frequency modulation (HFM) or linear frequency modulation (LFM) are often used as the probing waveforms for target detection in radar and sonar applications and preambles in communications. Undersampling processing can seriously weaken the detection of these wideband signals.
In this work, we propose a new detector named Sparse Learning via Iterative Minimization(SLIM)-detector. SLIM is proposed for sonar location in [8]. It can collect echoes from Multiple-Input Multiple-Output (MIMO). In our work we use this principle to collect and strengthen energy from underwater multipath channel. With SLIM-detector, energy of preamble is strengthened after iterations in SLIM, then collected. Even if the undersampling makes the signal aliasing, we can still separate preamble from different interferences and noise.
SLIM-detector works different from SLIM. The initialization part of SLIM-detector is similar to NMF which presents a rough model for detection. The iterative part of SLIM-detector is the main part of SLIM estimator. In this part, the strongest paths become stronger after each iteration and the weak paths become weaker. This can also help us distinguish real paths of preamble signals from false alarms under aliasing caused by undersampling. One of the challenges to put this detector into practical use is that the original assumption for SLIM is under WGN. Therefore interferences can bring outliers which can lead the detection into failure. As we know, normalization can lower the false matching for MF. Here we add another normalization step after the iterations to further lower the false alarms of interferences. Another challenge to make the detector practical is that a reasonable signal template is crucial. The iterations make the matching very sensitive. This is a benefit if the template is good enough, but can also be a weakness when the template is too rough. A good template should try to cover all the possible delays and Doppler-shifts of preamble signals. However this will lead to a large dimensional signal template and low efficiency. Here also comes to why we choose HFMs as our preamble signals: it is Doppler-insensitive, and the Doppler-shift in it can be converted into a time-delay. Hence we only need to consider the coverage of the possible delay in signal template. Besides, the threshold is also difficult to obtained in practice. The normalization step helps us to limit it into a small range. Empiric value can be recommended from simulations and experiments.
The experimental results show the performance of SLIM-detector under undersampling condition. In the experimental data, the HFM signal with 5kHz bandwidth comes from the test in Qiandao Lake, Zhejiang, China, in 2017, full of multipath. The impulsive interference data sets were collected from the sea experiment conducted in May 2013, in the South China Sea, near Kaohsiung City, Taiwan.Other data sets of interferences are collected from the Mobile Acoustic Communication Experiment (MACE10), which was held in June 2010 off the coast of Martha's Vineyard, Massachusetts. After downsampling, the sampling frequency of received preamble becomes 5kHz. It is much lower than Nyquist frequency. SNR is --6 dB. In Figure 1, SLIM-detector is robust against undersampling, while other conventional detectors totally fail in some interferences due to the aliasing effect. Results show SLIM-detector can be used as an efficient detector for the undersampling preamble signal.

References

[1]
D.A. Abraham and P.K. Willett. 2002. Active sonar detection in shallow water using the Page test. IEEE Journal of Oceanic Engineering 27, 1 (2002), 35--46.
[2]
Christian R. Berger, Shengli Zhou, James C. Preisig, and Peter Willett. 2010. Sparse channel estimation for multicarrier underwater acoustic communication: from subspace methods to compressed sensing. IEEE Transactions on Signal Processing 58, 3 (2010), 1708--1721.
[3]
Roee Diamant. 2016. Computationally Efficient Calculations of Target Performance of the Normalized Matched Filter Detector for Hydrocoustic Signals. (2016).
[4]
H.Jin, W.Li, X.Wang, Y.Zhang, S.Yu, and Q.Shi. 2018. Preamble Detection for Underwater Acoustic Communications based on Convolutional Neural Networks. In Proc. of IEEE OCEANS. Kobe, 1--6.
[5]
Steven M. Kay. 1998. Fundamentals of Statistical Signal Processing, Volume II: Detection Theory. PTR Prentice hall.
[6]
Wei Li, Hanzhi Lu, and Yanyan Zuo. 2014. Parallel Array Bistable Stochastic Resonance System with Independent Input and Its Signal-to-Noise Ratio Improvement. Mathematical Problems in Engineering 2014, 1 (2014), 1--14.
[7]
Wei Li, Shengli Zhou, Peter Willett, and Qinyu Zhang. 2017. Preamble Detection for Underwater Acoustic Communications Based on Sparse Channel Identification. IEEE Journal of Oceanic Engineering PP, 99 (2017), 1--13.
[8]
Jun Ling, Luzhou Xu, and Jian Li. 2014. Adaptive Range-Doppler Imaging and Target Parameter Estimation in Multistatic Active Sonar Systems. IEEE Journal of Oceanic Engineering 39, 2 (2014), 290--302.
[9]
M.Xin, W.Li, X.Wang, Y.Zhang, and L.Xu. 2018. Preamble Design with HFMs for Underwater Acoustic Communications. In Proc. of IEEE OCEANS. Kobe, 1--5.
[10]
A. W Rihaczek. 1966. Doppler-tolerant signal waveforms. Proc. IEEE 54, 6 (1966), 849--857.
[11]
Shengli Zhou and Zhaohui Wang. 2014. OFDM for Underwater Acoustic Communications. Wiley Publishing.

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WUWNet '19: Proceedings of the 14th International Conference on Underwater Networks & Systems
October 2019
210 pages
ISBN:9781450377409
DOI:10.1145/3366486
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Publication History

Published: 13 February 2020

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Author Tags

  1. SLIM
  2. Underwater acoustic communications
  3. preamble detection
  4. undersampling

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  • Extended-abstract
  • Research
  • Refereed limited

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  • National Natural Science foundation of Guangdong Province
  • National Natural Science foundation of China

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WUWNET'19

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Overall Acceptance Rate 84 of 180 submissions, 47%

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