A Parameter Estimation Method of Non-instantaneous Diffusion Point Source Based on Finite Rate of Innovation

FU Ning; SHEN Mengyao; WEI Zhiliang; QIAO Liyan

doi:10.11999/JEIT210540

Volume 44 Issue 8

Aug. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(8): 2739-2748

FU Ning, SHEN Mengyao, WEI Zhiliang, QIAO Liyan. A Parameter Estimation Method of Non-instantaneous Diffusion Point Source Based on Finite Rate of Innovation[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2739-2748. doi: 10.11999/JEIT210540

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FU Ning, SHEN Mengyao, WEI Zhiliang, QIAO Liyan. A Parameter Estimation Method of Non-instantaneous Diffusion Point Source Based on Finite Rate of Innovation[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2739-2748. doi: 10.11999/JEIT210540

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A Parameter Estimation Method of Non-instantaneous Diffusion Point Source Based on Finite Rate of Innovation

doi: 10.11999/JEIT210540 cstr: 32379.14.JEIT210540

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China

Funds: The National Natural Science Foundation of China (62071149, 61671177)

Received Date: 2021-06-08
Accepted Date: 2022-03-31
Rev Recd Date: 2022-03-10

Available Online: 2022-04-08

Publish Date: 2022-08-17

Abstract

Abstract

Many physical phenomena can be described by the diffusion equations, such as the emission of chimney pollutants, chemical substance leakage, etc. Therefore, the estimation of diffusion source parameters is of great significance in practical applications. Currently, most of the proposed methods for estimating parameters of diffusion sources are aimed at instantaneous point source signals. For non-instantaneous actual diffusion processes, there is a problem of model mismatch. In this paper, the diffusion source model is extended to variable pulse-width signals, and the parameter estimation algorithm of corresponding non-instantaneous point sources are proposed. In this algorithm, the actual measurement value is obtained by sampling with the wireless sensor network, a combination coefficient is found to combine linearly the actual measurement value into an exponential function, and then the combined data is analyzed according to the Finite Rate of Innovation (FRI) sampling theory by using the annihilation filter method to solve the diffusion source parameters. The simulation results analyze the performance factors that affect parameter recovery, including noise, the number of sensors, etc., and the accuracy of the non-instantaneous diffusion point source parameter estimation method is validated.
- Diffusion source,
- Parameter estimation,
- Finite Rate of Innovation (FRI),
- Pulse of variable width

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References(21)

References

[1]	BENSALEH M S, SAIDA R, KACEM Y H, et al. Wireless Sensor Network Design Methodologies: A Survey[J]. Journal of Sensors, 2020,, 2020(1): 1–13. doi: 10.1155/2020/9592836
[2]	蒋俊正, 李杨剑, 赵海兵, 等. 一种大规模传感器网络节点分布式定位算法[J]. 电子与信息学报, 2019, 41(12): 3022–3028. doi: 10.11999/JEIT181101 JIANG Junzheng, LI Yangjian, ZHAO Haibing, et al. A distributed node localization algorithm for large scale sensor networks[J]. Journal of Electronics &Information Technology, 2019, 41(12): 3022–3028. doi: 10.11999/JEIT181101
[3]	VAN WATERSCHOOT T and LEUS G. Static field estimation using a wireless sensor network based on the finite element method[C]. The 4th IEEE International Workshop on Computational Advances in Multi-sensor Adaptive Processing, San Juan, USA, 2011: 369–372.
[4]	ZHANG Yong, WANG Tong, SHI Yu, et al. Joint variational Bayesian based localization estimation algorithm on distributed gas source sensor network[J]. Computer Communications, 2020, 154: 262–268. doi: 10.1016/j.comcom.2020.02.060
[5]	RANIERI J, CHEBIRA A, LU Y M, et al. Sampling and reconstructing diffusion fields with localized sources[C]. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing, Prague, Czech Republic, 2011: 4016–4019.
[6]	ROSTAMI M, CHEUNG N M, and QUEK T Q S. Compressed sensing of diffusion fields under heat equation constraint[C]. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, Canada, 2013: 4271–4274.
[7]	ZHANG Yuexin and ZHANG Jianjun. K-coverage: A monitor node selection algorithm for diffusion source localizations[J]. Research Briefs on Information & Communication Technology Evolution, 2020, 6(8): 1–12. doi: 10.22667/ReBiCTE.2020.12.01.008
[8]	ALEXANDRU R, BLU T, and DRAGOTTI P L. D-SLAM: Diffusion source localization and trajectory mapping[C]. 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020: 5600–5604.
[9]	WANG Zhixiao, SUN Chengcheng, RUI Xiaobin, et al. Localization of multiple diffusion sources based on overlapping community detection[J]. Knowledge-Based Systems, 2021, 226: 106613. doi: 10.1016/j.knosys.2020.106613
[10]	FLEGG M B, MUÑOZ M A, SMITH-MILES K, et al. Parameter estimation for a point-source diffusion-decay morphogen model[J]. Journal of Mathematical Biology, 2020, 80(7): 2227–2255. doi: 10.1007/s00285-020-01494-x
[11]	VETTERLI M, MARZILIANO P, and BLU T. Sampling signals with finite rate of innovation[J]. IEEE Transactions on Signal Processing, 2002, 50(6): 1417–1428. doi: 10.1109/TSP.2002.1003065
[12]	DOKMANIć I, RANIERI J, CHEBIRA A, et al. Sensor networks for diffusion fields: Detection of sources in space and time[C]. The 49th Annual Allerton Conference on Communication, Control, and Computing, Monticello, USA, 2011: 1552–1558.
[13]	LU Y M, DRAGOTTI P L, and VETTERLI M. Localizing point sources in diffusion fields from spatiotemporal samples[C]. The 9th International Conference on Sampling Theory and Applications, Singapore, 2011.
[14]	RANIERI J, DOKMANIć I, CHEBIRA A, et al. Sampling and reconstruction of time-varying atmospheric emissions[C]. 2012 IEEE International Conference on Acoustics, Speech and Signal Processing, Kyoto, Japan, 2012: 3673–3676.
[15]	MURRAY-BRUCE J and DRAGOTTI P L. Spatio-temporal sampling and reconstruction of diffusion fields induced by point sources[C]. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing, Florence, Italy, 2014: 31–35.
[16]	MURRAY-BRUCE J and DRAGOTTI P L. Estimating localized sources of diffusion fields using spatiotemporal sensor measurements[J]. IEEE Transactions on Signal Processing, 2015, 63(12): 3018–3031. doi: 10.1109/TSP.2015.2419187
[17]	MURRAY-BRUCE J and DRAGOTTI P L. A Sampling framework for solving physics-driven inverse source problems[J]. IEEE Transactions on Signal Processing, 2017, 65(24): 6365–6380. doi: 10.1109/TSP.2017.2742983
[18]	BAECHLER G, SCHOLEFIELD A, BABOULAZ L, et al. Sampling and exact reconstruction of pulses with variable width[J]. IEEE Transactions on Signal Processing, 2017, 65(10): 2629–2644. doi: 10.1109/TSP.2017.2669900
[19]	王亚军, 李明, 刘高峰. 基于改进指数再生采样核的有限新息率采样系统[J]. 电子与信息学报, 2013, 35(9): 2088–2093. doi: 10.3724/SP.J.1146.2013.00059 WANG Yajun, LI Ming, and LIU Gaofeng. Finite rate of innovation sampling system based on modified exponential reproducing sampling kernel[J]. Journal of Electronics &Information Technology, 2013, 35(9): 2088–2093. doi: 10.3724/SP.J.1146.2013.00059
[20]	王亚军, 李明, 刘高峰. 复杂脉冲序列的有限新息率采样方法[J]. 电子与信息学报, 2013, 35(7): 1606–1611. doi: 10.3724/SP.J.1146.2012.01329 WANG Yajun, LI Ming, and LIU Gaofeng. Sampling complex pulse streams with finite rate of innovation methods[J]. Journal of Electronics &Information Technology, 2013, 35(7): 1606–1611. doi: 10.3724/SP.J.1146.2012.01329
[21]	URIGÜEN J A, BLU T, and DRAGOTTI P L. FRI sampling with arbitrary kernels[J]. IEEE Transactions on Signal Processing, 2013, 61(21): 5310–5323. doi: 10.1109/TSP.2013.2278152