Please wait a minute...
Chin. Phys. B, 2015, Vol. 24(2): 020702    DOI: 10.1088/1674-1056/24/2/020702
GENERAL Prev   Next  

Estimation of spatially distributed processes using mobile sensor networks with missing measurements

Jiang Zheng-Xian (江正仙)a b c, Cui Bao-Tong (崔宝同)a b
a Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China;
b School of IoT Engineering, Jiangnan University, Wuxi 214122, China;
c School of Science, Jiangnan University, Wuxi 214122, China
Abstract  This paper investigates the estimation problem for a spatially distributed process described by a partial differential equation with missing measurements. The randomly missing measurements are introduced in order to better reflect the reality in the sensor network. To improve the estimation performance for the spatially distributed process, a network of sensors which are allowed to move within the spatial domain is used. We aim to design an estimator which is used to approximate the distributed process and the mobile trajectories for sensors such that, for all possible missing measurements, the estimation error system is globally asymptotically stable in the mean square sense. By constructing Lyapunov functionals and using inequality analysis, the guidance scheme of every sensor and the convergence of the estimation error system are obtained. Finally, a numerical example is given to verify the effectiveness of the proposed estimator utilizing the proposed guidance scheme for sensors.
Keywords:  estimation      spatially distributed process      mobile sensor network      missing measurements  
Received:  27 March 2014      Revised:  01 September 2014      Accepted manuscript online: 
PACS:  07.07.Df (Sensors (chemical, optical, electrical, movement, gas, etc.); remote sensing)  
  02.30.Jr (Partial differential equations)  
  84.40.Ua (Telecommunications: signal transmission and processing; communication satellites)  
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61174021, 61473136, and 61104155) and the 111 Project (Grant No. B12018).
Corresponding Authors:  Jiang Zheng-Xian     E-mail:  zhengxian@jiangnan.edu.cn

Cite this article: 

Jiang Zheng-Xian (江正仙), Cui Bao-Tong (崔宝同) Estimation of spatially distributed processes using mobile sensor networks with missing measurements 2015 Chin. Phys. B 24 020702

[1] Akyildiz I F, Su W L, Sankarasubramniam Y and Cayirci E 2002 IEEE Commun. Mag. 40 102
[2] Cassandras C G and Li W 2005 Eur. J. Control 11 436
[3] Olfati-Saber R 2007 Proceedings of the 46th IEEE Conference on Decision and Control, December 12-14, 2007, New Orleans, LA, USA, p. 5492
[4] Yu W W, Chen G R, Wang Z D and Yang W 2009 IEEE Trans. Syst., Man, Cybern, Part B 39 1568
[5] Cattivelli F S and Sayed A H 2010 IEEE Trans. Autom. Control 55 2069
[6] Lakshmanan S, Park J H, Jung H Y and Balasubramaniam P 2012 Chin. Phys. B 21 100205
[7] Zhang C, Fei S M and Zhou X P 2012 Chin. Phys. B 21 120101
[8] Olfati-Saber R and Jalalkamali P 2012 IEEE Trans. Autom. Control 57 2609
[9] Wang Z D, Ho D W C, Liu Y R and Liu X H 2009 Automatica 45 684
[10] Wang Z D, Shen B and Liu X H 2012 Automatica 48 556
[11] Demetriou M A 2010 IEEE Trans. Autom. Control 55 1570
[12] Demetriou M A 2010 Automatica 46 300
[13] Demetriou M A and Hussein I I 2009 SIAM J. Control Optim. 48 266
[14] Tricaud C, Patan M, Uciński D and Chen Y Q 2008 Proceedings of the American Control Conference, June 11-13, 2008, Seattle, WA, USA, p. 663
[15] Ray P and Varshney P K 2009 IEEE Trans. Wireless Commun. 8 3162
[16] Uciński D and Chen Y Q 2008 Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, December 12-15, 2005, Sevill, Spain p. 5257
[17] Wu X D, Wang Y N, Liu W T and Zhu Z Y 2011 Chin. Phys. B 20 069201
[18] Nahi N 1969 IEEE Trans. Inf. Theory 15 457
[19] Sinopoli B, Schenato L, Franceschetti M, Poolla K, Jordan M I and Sastry S S 2004 IEEE Trans. Autom. Control 49 1453
[20] Liang J L, Wang Z D and Liu X H 2011 IEEE Trans. Neural Networks 22 486
[21] Curtain R F and Zwart H J 1995 An Introduction to Infinite Dimensional Linear Systems Theory (Berlin: Springer) p. 227
[22] Liu K 2006 Stability of Infinite Dimensional Stochastic Differential Equations with Applications (Boca Raton: Chapman and Hall/CRC) p. 268
[23] Khasminskii R 2011 Stochastic Stability of Differential Equations (Berlin: Springer) pp. 171-174
[24] Slotine J J E and Li W P 1991 Applied Nonlinear Control (New Jersey: Prentice Hall) pp. 123-124
[1] Feedback control and quantum error correction assisted quantum multi-parameter estimation
Hai-Yuan Hong(洪海源), Xiu-Juan Lu(鲁秀娟), and Sen Kuang(匡森). Chin. Phys. B, 2023, 32(4): 040603.
[2] Environmental parameter estimation with the two-level atom probes
Mengmeng Luo(罗萌萌), Wenxiao Liu(刘文晓), Yuetao Chen(陈悦涛), Shangbin Han(韩尚斌), and Shaoyan Gao(高韶燕). Chin. Phys. B, 2022, 31(5): 050304.
[3] Parameter estimation of continuous variable quantum key distribution system via artificial neural networks
Hao Luo(罗浩), Yi-Jun Wang(王一军), Wei Ye(叶炜), Hai Zhong(钟海), Yi-Yu Mao(毛宜钰), and Ying Guo(郭迎). Chin. Phys. B, 2022, 31(2): 020306.
[4] Robust H state estimation for a class of complex networks with dynamic event-triggered scheme against hybrid attacks
Yahan Deng(邓雅瀚), Zhongkai Mo(莫中凯), and Hongqian Lu(陆宏谦). Chin. Phys. B, 2022, 31(2): 020503.
[5] $\mathcal{H}_{\infty }$ state estimation for Markov jump neural networks with transition probabilities subject to the persistent dwell-time switching rule
Hao Shen(沈浩), Jia-Cheng Wu(吴佳成), Jian-Wei Xia(夏建伟), and Zhen Wang(王震). Chin. Phys. B, 2021, 30(6): 060203.
[6] Blind parameter estimation of pseudo-random binary code-linear frequency modulation signal based on Duffing oscillator at low SNR
Ke Wang(王珂), Xiaopeng Yan(闫晓鹏), Ze Li(李泽), Xinhong Hao(郝新红), and Honghai Yu(于洪海). Chin. Phys. B, 2021, 30(5): 050708.
[7] Efficient tensor decomposition method for noncircular source in colocated coprime MIMO radar
Qian-Peng Xie(谢前朋), Xiao-Yi Pan(潘小义), Shun-Ping Xiao(肖顺平). Chin. Phys. B, 2020, 29(5): 054304.
[8] Preliminary abnormal electrocardiogram segment screening method for Holter data based on long short-term memory networks
Siying Chen(陈偲颖), Hongxing Liu(刘红星). Chin. Phys. B, 2020, 29(4): 040701.
[9] Applicability of coupling strength estimation for linear chains of restricted access
He Feng(冯赫), Tian-Min Yan(阎天民), Yuhai Jiang(江玉海). Chin. Phys. B, 2020, 29(3): 030305.
[10] Dynamics analysis of chaotic maps: From perspective on parameter estimation by meta-heuristic algorithm
Yue-Xi Peng(彭越兮), Ke-Hui Sun(孙克辉), Shao-Bo He(贺少波). Chin. Phys. B, 2020, 29(3): 030502.
[11] Optimal parameter estimation of open quantum systems
Yinghua Ji(嵇英华), Qiang Ke(柯强), and Juju Hu(胡菊菊). Chin. Phys. B, 2020, 29(12): 120303.
[12] Optimal estimation of the amplitude of signal with known frequency in the presence of thermal noise
Jie Luo(罗杰), Jun Ke(柯俊), Yi-Chuan Liu(柳一川), Xiang-Li Zhang(张祥莉), Wei-Ming Yin(殷蔚明), Cheng-Gang Shao(邵成刚). Chin. Phys. B, 2019, 28(10): 100401.
[13] Correlation method estimation of the modulation signal in the weak equivalence principle test
Jie Luo(罗杰), Liang-Cheng Shen(沈良程), Cheng-Gang Shao(邵成刚), Qi Liu(刘祺), Hui-Jie Zhang(张惠捷). Chin. Phys. B, 2018, 27(8): 080402.
[14] Quantum estimation of detection efficiency with no-knowledge quantum feedback
Dong Xie(谢东), Chunling Xu(徐春玲). Chin. Phys. B, 2018, 27(6): 060303.
[15] Quantum parameter estimation in a spin-boson dephasing quantum system by periodical projective measurements
Le Yang(杨乐), Hong-Yi Dai(戴宏毅), Ming Zhang(张明). Chin. Phys. B, 2018, 27(4): 040601.
No Suggested Reading articles found!