基于偏移全网格的离格稀疏贝叶斯推理的密集时延估计研究

doi:10.1007/s12204-022-2464-z

摘要/Abstract

摘要： 对于密集时延估计，当多个真实时延都位于一个网格间隔内时，现有的稀疏贝叶斯学习/推理方法很难获得较高估计精度以满足应用要求。为了解决这个问题，本文提出一种称为偏移全网格的离格稀疏贝叶斯推理方法，此方法进行网格演进，根据离格在两个网格之间的位置迭代移动总网格。所提出的方法通过进一步重构最优网格并偏移离格向量来更新离格字典矩阵。实验结果表明：即使网格间隔大于真实时延间隙，该方法也比其他最先进的稀疏贝叶斯推理方法和多信号分类方法性能好。另外本文研究了时延估计的时域模型和频域模型。

关键词: 离格，稀疏贝叶斯推理，时延估计，偏移全网格

Abstract: For dense time delay estimation (TDE), when multiple time delays are located within a grid interval, it is difficult for the existing sparse Bayesian learning/inference (SBL/SBI) methods to obtain high estimation accuracy to meet the application requirements. To solve this problem, this paper proposes a method named off-grid sparse Bayesian inference - biased total grid (OGSBI-BTG), where a mesh evolution process is conducted to move the total grids iteratively based on the position of the off-grid between two grids. The proposed method updates the off-grid dictionary matrix by further reconstructing an optimum mesh and offsetting the off-grid vector. Experimental results demonstrate that the proposed approach performs better than other state-of-the-art SBI methods and multiple signal classification even when the grid interval is larger than the gap of true time delays. In this paper, the time domain model and frequency domain model of TDE are studied.

Key words: off-grid, sparse Bayesian inference (SBI), time delay estimation (TDE), biased total grids (BTG)

中图分类号:

TN928

魏爽，李文瑶，苏颖，刘睿. 基于偏移全网格的离格稀疏贝叶斯推理的密集时延估计研究[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(6): 763-771.

WEI Shuang (魏爽)， LI Wenyao (李文瑶)，SU Ying* (苏颖)， LIU Rui (刘睿). Off-Grid Sparse Bayesian Inference with Biased Total Grids for Dense Time Delay Estimation[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(6): 763-771.

参考文献 28

[7]	HASHEMI H S, RIVAZ H. Global time-delay estimation in ultrasound elastography [J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2017, 64(10): 1625-1636.
[1]	PAN J J, LE BASTARD C, WANG Y D, et al. Time-delay estimation using ground-penetrating radar with a support vector regression-based linear prediction method [J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(5): 2833-2840.
[8]	QUAZI A. An overview on the time delay estimate in active and passive systems for target localization [J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1981, 29(3): 527-533.
[2]	THOMAS B, HUNTER A, DUGELAY S. Phase wrap error correction by random sample consensus with application to synthetic aperture sonar micronavigation [J]. IEEE Journal of Oceanic Engineering, 2021, 46(1): 221-235.
[9]	DEVI M, DHABALE N R, DEVISHREE. Active and passive radar-location systems for the calculation of coordinates [C]//2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). Bengaluru: IEEE, 2017: 770-773.
[3]	FATTAHI H, SIMONS M, AGRAM P. InSAR timeseries estimation of the ionospheric phase delay: An extension of the split range-spectrum technique [J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(10): 5984-5996.
[10]	KLEMBOWSKI W, KAWALEC A, WIZNER W. Critical views on present passive radars performance ascompared with that of active radars [C]//2013 14th International Radar Symposium (IRS). Dresden: IEEE, 2013: 131-135.
[4]	LAGUNA P, GARDE A, GIRALDO B F, et al. Eigenvalue-based time delay estimation of repetitive biomedical signals [J]. Digital Signal Processing, 2018, 75: 107-119.
[11]	LI J, ZHU J D, FENG Z H, et al. Passive multipath time delay estimation using MCMC methods [J]. Circuits, Systems, and Signal Processing, 2015, 34(12): 3897-3913.
[5]	GUO Y R, LIU Z J. Time-delay-estimation-liked detection algorithm for LoRa signals over multipath channels [J]. IEEE Wireless Communications Letters, 2020, 9(7): 1093-1096.
[12]	BOSSE J, KRASNOV O, YAROVOY A. Direct target localization and deghosting in active radar network [J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(4): 3139-3150.
[6]	KOTHANDARAMAN M, LAW Z, GNANAMUTHU E M A, et al. An adaptive ICA-based cross-correlation techniques for water pipeline leakage localization utilizing acousto-optic sensors [J]. IEEE Sensors Journal, 2020, 20(17): 10021-10031.
[13]	MURPHY S M, SCRUTTON J G E, HINES P C. Experimental implementation of an echo repeater for continuous active sonar [J]. IEEE Journal of Oceanic Engineering, 2016, 42(2): 289-297.
[7]	HASHEMI H S, RIVAZ H. Global time-delay estimation in ultrasound elastography [J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2017, 64(10): 1625-1636.
[14]	ZHANG Q Q, ZHANG L H. An improved delay algorithm based on generalized cross correlation [C]//2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference. Chongqing: IEEE, 2017: 395- 399.
[8]	QUAZI A. An overview on the time delay estimate in active and passive systems for target localization [J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1981, 29(3): 527-533.
[15]	TOFEL G, CZOPIK G, KAWALEC A. Signal time delay estimation using square correlation method and wavelet analysis [C]//2018 19th International Radar Symposium (IRS). Bonn: IEEE, 2018: 1-9.
[9]	DEVI M, DHABALE N R, DEVISHREE. Active and passive radar-location systems for the calculation of coordinates [C]//2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). Bengaluru: IEEE, 2017: 770-773.
[16]	GE F X, SHEN D X, PENG Y N, et al. Superresolution time delay estimation in multipath environments [J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2007, 54(9): 1977-1986.
[10]	KLEMBOWSKI W, KAWALEC A, WIZNER W. Critical views on present passive radars performance ascompared with that of active radars [C]//2013 14th International Radar Symposium (IRS). Dresden: IEEE, 2013: 131-135.
[17]	OZIEWICZ M. On application of MUSIC algorithm to time delay estimation in OFDM channels [J]. IEEE Transactions on Broadcasting, 2005, 51(2): 249-255.
[11]	LI J, ZHU J D, FENG Z H, et al. Passive multipath time delay estimation using MCMC methods [J]. Circuits, Systems, and Signal Processing, 2015, 34(12): 3897-3913.
[18]	SHUTIN D, BUCHGRABER T, KULKARNI S R, et al. Fast variational sparse Bayesian learning with automatic relevance determination for superimposed signals [J]. IEEE Transactions on Signal Processing, 2011, 59(12): 6257-6261.
[12]	BOSSE J, KRASNOV O, YAROVOY A. Direct target localization and deghosting in active radar network [J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(4): 3139-3150.
[19]	YANG Z, XIE L H, ZHANG C S. Off-grid direction of arrival estimation using sparse Bayesian inference [J]. IEEE Transactions on Signal Processing, 2013, 61(1): 38-43.
[13]	MURPHY S M, SCRUTTON J G E, HINES P C. Experimental implementation of an echo repeater for continuous active sonar [J]. IEEE Journal of Oceanic Engineering, 2016, 42(2): 289-297.
[20]	DAI J S, BAO X, XU W C, et al. Root sparse Bayesian learning for off-grid DOA estimation [J]. IEEE Signal Processing Letters, 2017, 24(1): 46-50.
[14]	ZHANG Q Q, ZHANG L H. An improved delay algorithm based on generalized cross correlation [C]//2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference. Chongqing: IEEE, 2017: 395- 399.
[21]	DAI J S, SO H C. Sparse Bayesian learning approach for outlier-resistant direction-of-arrival estimation [J]. IEEE Transactions on Signal Processing, 2018, 66(3): 744-756.
[15]	TOFEL G, CZOPIK G, KAWALEC A. Signal time delay estimation using square correlation method and wavelet analysis [C]//2018 19th International Radar Symposium (IRS). Bonn: IEEE, 2018: 1-9.
[22]	ZHANG Y, YE Z F, XU X. Off-grid direction of arrival estimation based on weighted sparse Bayesian learning [C]//2014 International Conference on Audio, Language and Image Processing. Shanghai: IEEE, 2014: 547-550.
[16]	GE F X, SHEN D X, PENG Y N, et al. Superresolution time delay estimation in multipath environments [J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2007, 54(9): 1977-1986.
[23]	TAN Z, NEHORAI A. Sparse direction of arrival estimation using co-prime arrays with off-grid targets [J]. IEEE Signal Processing Letters, 2014, 21(1): 26-29.
[17]	OZIEWICZ M. On application of MUSIC algorithm to time delay estimation in OFDM channels [J]. IEEE Transactions on Broadcasting, 2005, 51(2): 249-255.
[24]	ZHANG P, BA B, CUI W J. High-resolution time of arrival estimation with small samples considering timevarying channel fading coefficient correlation in OFDM [J]. IEEE Access, 2019, 7: 19579-19589.
[18]	SHUTIN D, BUCHGRABER T, KULKARNI S R, et al. Fast variational sparse Bayesian learning with automatic relevance determination for superimposed signals [J]. IEEE Transactions on Signal Processing, 2011, 59(12): 6257-6261.
[25]	CHEN F F, DAI J S, HU N, et al. Sparse Bayesian learning for off-grid DOA estimation with nested arrays [J]. Digital Signal Processing, 2018, 82: 187-193.
[19]	YANG Z, XIE L H, ZHANG C S. Off-grid direction of arrival estimation using sparse Bayesian inference [J]. IEEE Transactions on Signal Processing, 2013, 61(1): 38-43.
[26]	MCLACHLAN G J, KRISHNAN T. The EM algorithm and extensions [M]. Hoboken, NJ: John Wiley & Sons, Inc., 2008.
[20]	DAI J S, BAO X, XU W C, et al. Root sparse Bayesian learning for off-grid DOA estimation [J]. IEEE Signal Processing Letters, 2017, 24(1): 46-50.
[27]	MAEDA S I, ISHII S. Convergence analysis of the EM algorithm and joint minimization of free energy [C]//2007 IEEE Workshop on Machine Learning for Signal Processing. Thessaloniki: IEEE, 2007: 318-323.
[21]	DAI J S, SO H C. Sparse Bayesian learning approach for outlier-resistant direction-of-arrival estimation [J]. IEEE Transactions on Signal Processing, 2018, 66(3): 744-756.
[28]	LI Y Y. Research on time delay estimation technologies for extremely weak signal in multipath environments [D]. Wuhan: Huazhong University of Science and Technology, 2009 (in Chinese).
[22]	ZHANG Y, YE Z F, XU X. Off-grid direction of arrival estimation based on weighted sparse Bayesian learning [C]//2014 International Conference on Audio, Language and Image Processing. Shanghai: IEEE, 2014: 547-550.
[23]	TAN Z, NEHORAI A. Sparse direction of arrival estimation using co-prime arrays with off-grid targets [J]. IEEE Signal Processing Letters, 2014, 21(1): 26-29.
[24]	ZHANG P, BA B, CUI W J. High-resolution time of arrival estimation with small samples considering timevarying channel fading coefficient correlation in OFDM [J]. IEEE Access, 2019, 7: 19579-19589.
[25]	CHEN F F, DAI J S, HU N, et al. Sparse Bayesian learning for off-grid DOA estimation with nested arrays [J]. Digital Signal Processing, 2018, 82: 187-193.
[26]	MCLACHLAN G J, KRISHNAN T. The EM algorithm and extensions [M]. Hoboken, NJ: John Wiley & Sons, Inc., 2008.
[27]	MAEDA S I, ISHII S. Convergence analysis of the EM algorithm and joint minimization of free energy [C]//2007 IEEE Workshop on Machine Learning for Signal Processing. Thessaloniki: IEEE, 2007: 318-323.
[28]	LI Y Y. Research on time delay estimation technologies for extremely weak signal in multipath environments [D]. Wuhan: Huazhong University of Science and Technology, 2009 (in Chinese).