Abstract
In this paper, we reconstruct signals in heterogeneous sensor network (HSN) with distributed compressive sensing (DCS). Combining different types of measurement matrices and different numbers of measurements, we investigate three different scenarios in which HSN is used to acquiring signals for the first time. In the first scenario, there are two different types of measurement matrices. One is Gaussian measurement and the other is Fourier measurement, and each sensor applies the same numbers of measurements. In the second scenario, all sensors use the same type of measurement matrices but the number of measurements are different from each other. The third scenario combines different types of measurement matrix and distinct numbers of measurements. Our simulation results show that in Scenario I, when the common sparsity is considerable, the DCS scheme can reduce the number of measurements. In Scenario II, the reconstruction situation becomes better with the increase of the number of measurements. In both Scenario I and III, joint decoding that use different types of measurement matrices performs better than that of all-Gaussian measurement matrices, but it performs worse than that of all-Fourier measurement matrices. Therefore, DSC is a good compromise between reconstruction percentage and the number of measurements in HSN.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Eldar YC, Kutyniok G (2012) Compressed sensing: theory and applications. Cambridge University Press
Baraniuk RG (2007) Compressive sensing. IEEE Sig Process Mag 24(4)
Tropp J, Gilbert AC et al (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theor 53(12):4655–4666
Liang Q (2010) Compressive sensing for radar sensor networks. In: Global telecommunications conference (GLOBECOM, 2010) IEEE, pp 1–5
Hu F, Hao Q (2012) Intelligent sensor networks: the integration of sensor networks. CRC Press, Signal Processing and Machine Learning
Baron D, Duarte MF, Wakin MB, Sarvotham S, Baraniuk RG (2009) Distributed compressive sensing. arXiv:0901.3403
Liang J, Wang Z, Liang Q (2011) Adaptive sensor selection for multitarget detection in heterogeneous sensor networks. Ad Hoc Sens Wireless Netw 12(3–4):325–342
Liang J, Huo Y, Mao C (2015) Multitarget detection in heterogeneous radar sensor network with energy constraint. Sig Process. doi:10.1016/j.sigpro.2015.07.020
Xu W, Lin J, Niu K, He Z (2012) A joint recovery algorithm for distributed compressed sensing. Trans Emerg Telecommun Technol 23(6):550–559
Schnelle SR, Laska JN, Hegde C, Duarte MF, Davenport M et al (2010) Texas hold’em algorithms for distributed compressive sensing. In: IEEE international conference on acoustics speech and signal processing (ICASSP), 2010, pp 2886–2889
Baron D, Duarte MF, Wakin MB, Sarvotham S, Baraniuk RG (2006) Distributed compressive sensing. Tech Rep TREE0612
Acknowledgments
This work was supported by the National Natural Science Foundation of China Project No. 61102140, Doctoral Fund of Ministry of Education of China Project No. 20110185120003, and the Fundamental Research Funds for the Central Universities Project No. ZYGX2012J015.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mao, C., Zhu, F., Liu, H., Liang, J. (2016). Signals Reconstruction in Heterogeneous Sensor Network with Distributed Compressive Sensing. In: Liang, Q., Mu, J., Wang, W., Zhang, B. (eds) Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 386. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49831-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-662-49831-6_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-49829-3
Online ISBN: 978-3-662-49831-6
eBook Packages: EngineeringEngineering (R0)