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Journal articles on the topic 'Acoustic equalization'

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Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Song, Hee-Chun. "Bidirectional equalization for underwater acoustic communications." Journal of the Acoustical Society of America 132, no. 3 (September 2012): 2016. http://dx.doi.org/10.1121/1.4755462.

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2

Isvan, Osman Kemal. "DUAL MODE EARPHONE WITH ACOUSTIC EQUALIZATION." Journal of the Acoustical Society of America 132, no. 2 (2012): 1238. http://dx.doi.org/10.1121/1.4742634.

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3

Song, H. C. "Bidirectional equalization for underwater acoustic communication." Journal of the Acoustical Society of America 131, no. 4 (April 2012): EL342—EL347. http://dx.doi.org/10.1121/1.3695075.

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4

Liu, Yan, and Yuan Min Li. "Novel DFE for Underwater Acoustic Channels." Advanced Materials Research 760-762 (September 2013): 691–94. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.691.

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In underwater acoustic communication systems, the channel equalization community has recently given much attention to decision feedback equalization (DFE). It is because that the DFE offers intersymbol interference (ISI) cancellation with reduced noise enhancement. However, its key algorithm such as constant modulus algorithm (CMA) has moderate convergence rate and steady-state mean square error (MSE), which is not sufficient for the receive system of communication. So a new cost function is defined and then a novel DFE based on such cost function is proposed. The efficiency of the proposed DFE is proved by computer simulations.

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5

Sun, Lin, Mei Wang, Guoheng Zhang, Haisen Li, and Lan Huang. "Filtered Multitone Modulation Underwater Acoustic Communications Using Low-Complexity Channel-Estimation-Based MMSE Turbo Equalization." Sensors 19, no. 12 (June 17, 2019): 2714. http://dx.doi.org/10.3390/s19122714.

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Filtered multitone (FMT) modulation divides the communication band into several subbands to shorten the span of symbols affected by multipath in underwater acoustic (UWA) communications. However, there is still intersymbol interference (ISI) in each subband of FMT modulation degrading communication performance. Therefore, ISI suppression techniques must be applied to FMT modulation UWA communications. The suppression performance of traditional adaptive equalization methods often exploited in FMT modulation UWA communications is limited when the effect of ISI spans tens of symbols or large constellation sizes are used. Turbo equalization consisting of adaptive equalization and channel decoding can improve equalization performance through information exchanging and iterative processes. To overcome the shortcoming of traditional minimum mean square error (MMSE) equalization and effectively suppress the ISI with relatively low computation complexity, an FMT modulation UWA communication using low-complexity channel-estimation-based (CE-based) MMSE turbo equalization is proposed in this paper. In the proposed method, turbo equalization is first exploited to suppress the ISI in FMT modulation UWA communications, and the equalizer coefficients of turbo equalization are adjusted using the low-complexity CE-based MMSE algorithm. The proposed method is analyzed in theory and verified by simulation analysis and real data collected in the experiment carried out in a pool with multipath propagation. The results demonstrate that the proposed method can achieve better communication performance with a higher bit rate than the FMT modulation UWA communication using traditional MMSE adaptive equalization.

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6

Xiao, Ying, and Rui Ruan. "CMA Blind Equalization with Quasi-Newton Algorithm in Underwater Acoustic Channels Based on Simplified Cost Function." Advanced Materials Research 989-994 (July 2014): 1865–68. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1865.

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The CMA cost function is simplified to meet the second norm form, and a new CMA blind equalization based on quasi-newton algorithm is proposed. Since the CMA cost function does not meet the second norm form, it is difficult to use quasi-newton algorithm to update the blind equalizer directly based on the cost function of CMA. If the cost function is simplified to meet the second norm form, it can use quasi-newton algorithm to update the blind equalizer directly. Thus, the convergence rate and convergence precision of CMA blind equalization can be improved effectively. Simulation results under the acoustic channels show that CMA blind equalization with quasi-newton algorithm based on the simplified cost function has faster convergence rate and less steady state residual error, which has practical value in the blind equalization of fast time-varying underwater acoustic channels

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7

Proakis, J. G. "Adaptive equalization techniques for acoustic telemetry channels." IEEE Journal of Oceanic Engineering 16, no. 1 (1991): 21–31. http://dx.doi.org/10.1109/48.64882.

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8

Pelekanakis, Konstantinos, and Mandar Chitre. "Robust Equalization of Mobile Underwater Acoustic Channels." IEEE Journal of Oceanic Engineering 40, no. 4 (October 2015): 775–84. http://dx.doi.org/10.1109/joe.2015.2469895.

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9

Zheng, Yahong Rosa, Jingxian Wu, and Chengshan Xiao. "Turbo equalization for single-carrier underwater acoustic communications." IEEE Communications Magazine 53, no. 11 (November 2015): 79–87. http://dx.doi.org/10.1109/mcom.2015.7321975.

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10

Chen, Xi, Scott D. Sommerfeldt, and Timothy W. Leishman. "An adaptive equalization scheme using acoustic energy density." Journal of the Acoustical Society of America 115, no. 5 (May 2004): 2612. http://dx.doi.org/10.1121/1.4784781.

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11

Radlovic, B. D., R. C. Williamson, and R. A. Kennedy. "Equalization in an acoustic reverberant environment: robustness results." IEEE Transactions on Speech and Audio Processing 8, no. 3 (May 2000): 311–19. http://dx.doi.org/10.1109/89.841213.

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12

Gaubitch, Nikolay D., and Patrick A. Naylor. "Equalization of Multichannel Acoustic Systems in Oversampled Subbands." IEEE Transactions on Audio, Speech, and Language Processing 17, no. 6 (August 2009): 1061–70. http://dx.doi.org/10.1109/tasl.2009.2015692.

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13

Sun, Lin, Ming Yan, Haisen Li, and Yanjie Xu. "Joint Time-Reversal Space-Time Block Coding and Adaptive Equalization for Filtered Multitone Underwater Acoustic Communications." Sensors 20, no. 2 (January 9, 2020): 379. http://dx.doi.org/10.3390/s20020379.

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Underwater acoustic (UWA) sensor networks demand high-rate communications with high reliability between sensor nodes for massive data transmission. Filtered multitone (FMT) is an attractive multicarrier technique used in high-rate UWA communications, and can obviously shorten the span of intersymbol interference (ISI) with high spectral efficiency and low frequency offset sensitivity by dividing the communication band into several separated wide sub-bands without guard bands. The joint receive diversity and adaptive equalization scheme is often used as a general ISI suppression technique in FMT-UWA communications, but large receive array for high diversity gain has an adverse effect on the miniaturization of UWA sensor nodes. A time-reversal space-time block coding (TR-STBC) technique specially designed for frequency-selective fading channels can replace receive diversity with transmit diversity for high diversity gain, and therefore is helpful for ISI suppression with simple receive configuration. Moreover, the spatio-temporal matched filtering (MF) in TR-STBC decoding can mitigate ISI obviously, and therefore is of benefit to lessen the complexion of adaptive equalization for post-processing. In this paper, joint TR-STBC and adaptive equalization FMT-UWA communication method is proposed based on the merit of TR-STBC. The proposed method is analyzed in theory, and its performance is assessed using simulation analysis and real experimental data collected from an indoor pool communication trial. The validity of the proposed method is proved through comparing the proposed method with the joint single-input–single-output (SISO) and adaptive equalization method and the joint single-input–multiple-output (SIMO) and adaptive equalization method. The results show that the proposed method can achieve better communication performance than the joint SISO and adaptive equalization method, and can achieve similar performance with more simpler receive configuration as the joint SIMO and adaptive equalization method.

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14

Guo, Ye Cai, and Zheng Xin Liu. "Fuzzy Neural Network Blind Equalization Algorithm Based on Signal Transformation." Applied Mechanics and Materials 44-47 (December 2010): 4146–50. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.4146.

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To recover QAM signals at the receiver of blind equalizer, a Fuzzy C-means clustering Neural Network Blind Equalization Algorithm based on Signal Transformation (ST-FNN-BEA) is proposed. The proposed algorithm uses signal transformation method to debase the computational complexity of equalizer input signals and speed up the convergence rate, and makes use of fuzzy c-means clustering algorithm dividing the equalizer input signals into each cluster center with different membership values to improve the equalization performance. The proposed ST-FNN-BEA outperforms Neural Network Blind Equalization Algorithm (NN-BEA) and Neural Network Blind Equalization Algorithm based on Signal Transformation (ST-NN-BEA) in improving convergence rates and reducing mean square error. The performance of ST-FNN-BEA is proved by the computer simulation with underwater acoustic channels.

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15

Zhang, Xin, and Xiao-ji Zhang. "Iterative Block Decision Feedback Equalization for Underwater Acoustic Channels." Journal of Electronics & Information Technology 35, no. 3 (January 21, 2014): 683–88. http://dx.doi.org/10.3724/sp.j.1146.2012.00948.

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16

Lin, Chun-Dan, Seongwook Lee, and Jong Rak Yoon. "Adaptive Equalization for Underwater Acoustic Communication in Multipath Channel." Japanese Journal of Applied Physics 45, no. 5B (May 25, 2006): 4856–58. http://dx.doi.org/10.1143/jjap.45.4856.

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17

Blair, Ballard J., and James C. Preisig. "Subspace dimension estimation for equalization of underwater acoustic channels." Journal of the Acoustical Society of America 129, no. 4 (April 2011): 2666. http://dx.doi.org/10.1121/1.3588919.

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18

Bong-Gee Song and J. A. Ritcey. "Spatial diversity equalization for MIMO ocean acoustic communication channels." IEEE Journal of Oceanic Engineering 21, no. 4 (1996): 505–12. http://dx.doi.org/10.1109/48.544060.

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19

Dromey, Christopher, Emily Heaton, and J. Arden Hopkin. "The Acoustic Effects of Vowel Equalization Training in Singers." Journal of Voice 25, no. 6 (November 2011): 678–82. http://dx.doi.org/10.1016/j.jvoice.2010.09.003.

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20

Stojanovic, M., J. Catipovic, and J. G. Proakis. "Adaptive multichannel combining and equalization for underwater acoustic communications." Journal of the Acoustical Society of America 94, no. 3 (September 1993): 1621–31. http://dx.doi.org/10.1121/1.408135.

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21

Xi, Junyi, Shefeng Yan, Lijun Xu, Zhen Zhang, and Di Zeng. "Frequency–Time Domain Turbo Equalization for Underwater Acoustic Communications." IEEE Journal of Oceanic Engineering 45, no. 2 (April 2020): 665–79. http://dx.doi.org/10.1109/joe.2019.2891171.

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22

Talantzis, Fotios, and Darren B. Ward. "Robustness of multichannel equalization in an acoustic reverberant environment." Journal of the Acoustical Society of America 114, no. 2 (August 2003): 833–41. http://dx.doi.org/10.1121/1.1594189.

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23

Rao, Wei, Ye Cai Guo, Min Chen, Wen Qun Tan, Jian Bing Liu, Fei Xia, Li Fan, and Hui Jun Xu. "New Concurrent Fractionally-Spaced Blind Equalization." Advanced Materials Research 108-111 (May 2010): 363–68. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.363.

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The paper proposes a concurrent constant modulus algorithm (CMA) and decision-directed (DD) scheme for fractionally-spaced blind equalization. The proposed algorithm makes full use of the advantages of CMA and DD algorithm. A novel rule to control the adjustment of DD’s tap weights vector is proposed which avoids the hard switch between CMA and DD in practice. Simulations with underwater acoustic channels are used to compare the proposed algorithm with the famous CMA. And the simulation results show that the proposed algorithm has faster convergence rate and lower steady state mean square error.

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24

Sun, Jianqiu, Xingguang Li, Kang Chen, Wei Cui, and Ming Chu. "A Novel CMA+DD_LMS Blind Equalization Algorithm for Underwater Acoustic Communication." Computer Journal 63, no. 6 (April 20, 2020): 974–81. http://dx.doi.org/10.1093/comjnl/bxaa013.

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Abstract The performance of underwater acoustic communication system is affected seriously by inter-symbol interference caused by multipath effects. Therefore, a novel blind equalization algorithm based on constant modulus algorithm (CMA) and decision-directed least mean square (DD_LMS) is adopted to improve the equalization ability of the system. Firstly, the LMS algorithm is improved by introducing inverse hyperbolic sine function and three adjustment factors to control step-size and the appropriate parameter values are set through the simulation of three adjustment factors. Secondly, the error values of the step-size function are replaced with error expectations to improve the anti-noise performance. Finally, the improved step-size function is introduced into the CMA and DD_LMS algorithm and the difference of the iteration error of adjacent k times is used as the switching condition of the dual mode algorithm. The results show that the algorithm has good equalization and anti-noise performance at both high and low signal-to-noise ratio (SNR), especially at low SNR, its steady-state error is ~10 dB lower than the traditional CMA and its convergence speed is ~15% higher than the traditional CMA. This algorithm can be used to effectively improve the communication efficiency of the communication system of underwater robots, which has good application value.

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25

Guo, Ye Cai, and Zhi Chao Zhang. "Wavelet Vector Machines Blind Equalization Algorithm Based on Variable Segmentation Error Function." Applied Mechanics and Materials 44-47 (December 2010): 3210–14. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3210.

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To overcome the disadvantage of constant modulus algorithm's slow convergence and local minimum, this paper presents a wavelet vector machine blind equalization algorithm based on variable segmentation error function. This proposed algorithm uses support vector machine to optimize the initial weight vector, then, it switches to Wavelet Constant Modulus blind equalization Algorithm(WCMA) with odd symmetry variable segmentation error function. The computer simulation with underwater acoustic channel demonstrates that the proposed algorithm has fast convergence rate and small mean square error.

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26

Zhu, Anfu. "Time-Varying Channel Equalization in Underwater Acoustic OFDM Communication System." Radioelectronics and Communications Systems 63, no. 8 (August 2020): 405–17. http://dx.doi.org/10.3103/s0735272720080038.

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27

Zhao, Liang, Wei-qing Zhu, and Min Zhu. "An Adaptive Equalization Algorithm for Underwater Acoustic Coherent Communication System." Journal of Electronics & Information Technology 30, no. 3 (March 1, 2011): 648–51. http://dx.doi.org/10.3724/sp.j.1146.2006.01229.

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28

Kari, Dariush, Nuri Denizcan Vanli, and Suleyman S. Kozat. "Adaptive and efficient nonlinear channel equalization for underwater acoustic communication." Physical Communication 24 (September 2017): 83–93. http://dx.doi.org/10.1016/j.phycom.2017.06.001.

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29

Kim, Lae-Hoon, Mark Hasegawa-Johnson, Jun-Seok Lim, and Koeng-Mo Sung. "Acoustic model for robustness analysis of optimal multipoint room equalization." Journal of the Acoustical Society of America 123, no. 4 (April 2008): 2043–53. http://dx.doi.org/10.1121/1.2837285.

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30

Kodrasi, Ina, and Simon Doclo. "Signal-Dependent Penalty Functions for Robust Acoustic Multi-Channel Equalization." IEEE/ACM Transactions on Audio, Speech, and Language Processing 25, no. 7 (July 2017): 1512–25. http://dx.doi.org/10.1109/taslp.2017.2699326.

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31

Marcoux, Caitlyn N., Bindu Chandna, and Ballard J. Blair. "Blind equalization and automatic modulation classification of underwater acoustic signals." Journal of the Acoustical Society of America 144, no. 3 (September 2018): 1729. http://dx.doi.org/10.1121/1.5067670.

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32

Sifferlen, J. F., H. C. Song, W. S. Hodgkiss, W. A. Kuperman, and J. M. Stevenson. "An Iterative Equalization and Decoding Approach for Underwater Acoustic Communication." IEEE Journal of Oceanic Engineering 33, no. 2 (April 2008): 182–97. http://dx.doi.org/10.1109/joe.2008.923552.

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33

Ahfir, Maamar, Izzet Kale, and Daoud Berkani. "An Alternative Approach to the Balanced Model Truncation Algorithm for Acoustic Minimum-Phase Inverse Filters Order Reduction." ISRN Signal Processing 2011 (April 13, 2011): 1–6. http://dx.doi.org/10.5402/2011/971051.

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We propose an alternative approach to the Balanced Model Truncation method (standard method). This approach reduces substantially the order of minimum-phase inverse filters for equalizing room acoustics. This method is based on a property of the filter z transform function, which modifies the corresponding FIR coefficients before the application of the standard technique to the modified FIR coefficients filter version. In the standard technique, the Hankel singular values plot is the chief guide for a user for the selection of a reduced filter order. Results for minimum-phase inverse filter corresponding to partial equalization of measured acoustic impulse response show the superiority of the proposed method over the standard technique, in terms of reduced filters order selection.

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34

Liu, Lanjun, Hao Zhao, Ming Li, Lin Zhou, Jiucai Jin, Jie Zhang, Zhichao Lv, Hui Ren, and Jicun Mao. "Modelling and Simulation of Pseudo-Noise Sequence-Based Underwater Acoustic OSDM Communication System." Applied Sciences 9, no. 10 (May 19, 2019): 2063. http://dx.doi.org/10.3390/app9102063.

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Orthogonal signal division multiplex (OSDM) is an emerging signal modulation technology which has a lower peak-to-average power ratio (PAPR) and a flexible subcarrier system architecture. Particularly, it can be seen as a bridge between the single-carrier modulation and the orthogonal frequency division multiplexing (OFDM) modulation in the frequency domain. Aiming at the development trend and demand of underwater acoustic hybrid and adaptive modulation communication technology, a pseudo-noise (PN) sequence-based underwater acoustic OSDM communication system is proposed in this paper. A data frame structure with PN sequence is designed to solve the multipath and Doppler effect of underwater acoustic channel. On the basis of the PN sequence, a compressive sensing method based on the orthogonal matching pursuit (OMP) algorithm and the minimum mean square error (MMSE) algorithm is designed for channel estimation and equalization. On the basis of the system construction, the relationship among the OSDM vector length M, the OSDM subcarrier number N, and the underwater acoustic channel length is further studied for adaptive modulation of underwater acoustic communication. Finally, the proposed system is verified by simulation. The OSDM system has lower and controllable PAPR. When the OSDM vector length M is bigger than the channel length, and the system subcarrier flexibility is guaranteed, the bit error rate (BER) of the OSDM system is lower than that of the OFDM system and the single-carrier system. The PN sequence-based compressive sensing channel estimation and equalization with the OMP and MMSE algorithms has a good performance to resist the multipath effect of underwater acoustic channel.

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35

Zhu, Ting Ting, and Xiao Tao Jiao. "A New Blind Equalization Algorithm Suitable for Sparse Underwater Acoustic Channel." Applied Mechanics and Materials 195-196 (August 2012): 149–53. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.149.

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The Constant Modulus Algorithms is a mature algorithm for a long time. The researchers are fond of its solidity. But in the area of shallow sea or blue sea, the CMA has poor constringency. In this passage, based on a improved blind equalization algorithm, we proposed a new algorithm which suitable for sparse underwater acoustic channel. The computer simulation results demonstrate fast convergence and fewer accounts.

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36

Yang, Zhuangchun, Tianyi Liang, Zhourong Deng, and Youwen Zhang. "Improved proportionate FONLMS algorithm based direct adaptive Turbo equalization for MIMO underwater acoustic communications." MATEC Web of Conferences 283 (2019): 07003. http://dx.doi.org/10.1051/matecconf/201928307003.

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In this paper, a novel normalized least mean squares (NLMS) algorithm that jointly updates the efficient of the linear equalizer and soft interference canceller (SIC) in an adaptive turbo equalizer for multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications. To exploit the sparsity of MIMO UWA channels and enhance the convergence speed of adaptive equalization, improved proportionate fast self-optimized NLMS algorithm (IPFONLMS), is proposed to well adapt to sparse channel with the similar complexity as improve proportionate NLMS (IPNLMS) algorithm. Then we extend the proposed algorithm to the adaptive turbo equalization for MIMO UWA communications. The performance of the proposed adaptive algorithm is evaluated by numerical results. Simulation results show that the improved data efficiency and bit error ratio (BER) performance of the proposed receiver is achieved over adaptive turbo equalizer based on the IPNLMS algorithm.

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37

Lee, Tae-Jin, and Ki-Man Kim. "A Study on the Equalization for Low Power Underwater Acoustic Communication." Journal of Korean navigation and port research 36, no. 3 (April 30, 2012): 169–73. http://dx.doi.org/10.5394/kinpr.2012.36.3.169.

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38

Zheng, Yahong Rosa, Chengshan Xiao, T. C. Yang, and Wen-Bin Yang. "Frequency-domain channel estimation and equalization for shallow-water acoustic communications." Physical Communication 3, no. 1 (March 2010): 48–63. http://dx.doi.org/10.1016/j.phycom.2009.08.010.

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39

Wang, Chunhui, Xueli Sheng, Lina Fan, Jia Lu, and Weijia Dong. "Space-time block code with equalization technology for underwater acoustic channels." Journal of the Acoustical Society of America 136, no. 4 (October 2014): 2148. http://dx.doi.org/10.1121/1.4899758.

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40

Zhao, Shiduo, Shefeng Yan, and Junyi Xi. "Adaptive Turbo Equalization for Differential OFDM Systems in Underwater Acoustic Communications." IEEE Transactions on Vehicular Technology 69, no. 11 (November 2020): 13937–41. http://dx.doi.org/10.1109/tvt.2020.3017778.

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41

Kodrasi, Ina, and Simon Doclo. "Joint Dereverberation and Noise Reduction Based on Acoustic Multi-Channel Equalization." IEEE/ACM Transactions on Audio, Speech, and Language Processing 24, no. 4 (April 2016): 680–93. http://dx.doi.org/10.1109/taslp.2016.2518804.

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42

Tu, Xingbin, Aijun Song, and Xiaomei Xu. "Prefix-Free Frequency Domain Equalization for Underwater Acoustic Single Carrier Transmissions." IEEE Access 6 (2018): 2578–88. http://dx.doi.org/10.1109/access.2017.2784388.

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43

Chen, Xi, Timothy Leishman, and Scott Sommerfeldt. "Equalization of a one‐dimensional sound field using acoustic energy density." Journal of the Acoustical Society of America 114, no. 4 (October 2003): 2460. http://dx.doi.org/10.1121/1.4779712.

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44

Zeng, Wen-Jun, and Wen Xu. "Low complexity estimation and equalization of doubly spread underwater acoustic channels." Journal of the Acoustical Society of America 131, no. 4 (April 2012): 3275. http://dx.doi.org/10.1121/1.4708244.

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45

Singer, Andrew. "Adaptive equalization, tracking, and decoding for high-rate underwater acoustic communications." Journal of the Acoustical Society of America 131, no. 4 (April 2012): 3276. http://dx.doi.org/10.1121/1.4708247.

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46

Ramadan, K., M. I. Dessouky, M. Elkordy, S. Elagooz, and F. E. A. bd El-Samie. "Joint Equalization and Coding Schemes for Underwater Acoustic MIMO-OFDM Systems." Menoufia Journal of Electronic Engineering Research 27, no. 1 (January 1, 2018): 33–52. http://dx.doi.org/10.21608/mjeer.2018.64379.

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47

Tao, Jun, Yahong Rosa Zheng, Chengshan Xiao, and T. C. Yang. "Robust MIMO Underwater Acoustic Communications Using Turbo Block Decision-Feedback Equalization." IEEE Journal of Oceanic Engineering 35, no. 4 (October 2010): 948–60. http://dx.doi.org/10.1109/joe.2010.2077831.

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48

Tao, Jun, Yanbo Wu, Xiao Han, and Konstantinos Pelekanakis. "Sparse Direct Adaptive Equalization for Single-Carrier MIMO Underwater Acoustic Communications." IEEE Journal of Oceanic Engineering 45, no. 4 (October 2020): 1622–31. http://dx.doi.org/10.1109/joe.2019.2946679.

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49

Bharitkar, Sunil, and Chris Kyriakakis. "Visualization of Multiple Listener Room Acoustic Equalization With the Sammon Map." IEEE Transactions on Audio, Speech and Language Processing 15, no. 2 (February 2007): 542–51. http://dx.doi.org/10.1109/tasl.2006.881683.

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50

He, Chengbing, Lianyou Jing, Rui Xi, Han Wang, Fei Hua, Qianqian Dang, and Qunfei Zhang. "Time-Frequency Domain Turbo Equalization for Single-Carrier Underwater Acoustic Communications." IEEE Access 7 (2019): 73324–35. http://dx.doi.org/10.1109/access.2019.2919757.

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