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A blind signal detection algorithm for passive location system based on troposcatter

Published online by Cambridge University Press:  11 September 2018

Zan Liu Open the ORCID record for Zan Liu [Opens in a new window] ,
Xihong Chen ,
Qiang Liu  and
Zedong Xie
Zan Liu*
Affiliation:
Air and Missile Defense College, Air Force Engineering University, Xi'an, 710051, Shaanxi, People's Republic of China
Xihong Chen
Affiliation:
Air and Missile Defense College, Air Force Engineering University, Xi'an, 710051, Shaanxi, People's Republic of China
Qiang Liu
Affiliation:
Air and Missile Defense College, Air Force Engineering University, Xi'an, 710051, Shaanxi, People's Republic of China
Zedong Xie
Affiliation:
Air and Missile Defense College, Air Force Engineering University, Xi'an, 710051, Shaanxi, People's Republic of China
*
Author for correspondence: Zan Liu, E-mail: kgdliuzan@163.com
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Abstract

To improve detection performance of passive location system based on troposcatter, we propose a blind signal detection algorithm. According to our algorithm, complementary ensemble empirical mode decomposition decomposes the received signal into several intrinsic mode functions (IMFs). To reconstruct the signal and background noises, difference between the entropy of adjacent IMFs is utilized as a standard. Different IMFs are utilized to estimate threshold of energy detection algorithm and energy level of received signal. Simulation examples indicate that the proposed algorithm can blindly and effectively detect the signal.

Keywords

Complementary ensemble empirical mode decompositionenergy detectionentropy theorypassive location systemtroposcatter
Type
Research Papers
Information
International Journal of Microwave and Wireless Technologies , Volume 10 , Issue 10 , December 2018 , pp. 1128 - 1133
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2018 

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References

1.Wang, Z, Wang, M, Wang, Q, Cheng, Z and Zhang, X (2017) Receiving antenna mode of troposcatter passive ranging based on the signal group delay. IET Microwaves, Antennas & Propagation 11, 121–128.Google Scholar
2.Wang, M, Wang, Z, Wang, J and Cheng, Z (2017) Fading correlation modelling for troposcatter microwave propagation in array antenna detection applications. IET Microwaves, Antennas & Propagation 11, 833–843.Google Scholar
3.Wang, Q, Wang, Z, Cheng, Z and Wang, M (2014) The troposcatter array signal receiving model and processing algorithm. 12th International Conference on Signal Processing (ICSP), Hangzhou, China, pp. 283–287.Google Scholar
4.Yang, F, Xu, Q and Li, B (2017) Ship detection from optical satellite image based on saliency segmentation and structure-LBP feature. IEEE Geoscience & Remote Sensing Letters 14, 1–5.Google Scholar
5.Dinc, E and Akan, OB (2015) A ray-based channel modeling approach for MIMO troposcatter beyond-line-of-sight (b-LoS) communications. IEEE Transactions on Communications 63, 1690–1699.Google Scholar
6.Luini, L, Riva, C, Emiliani, L and Capsoni, C (2016) Worst-month tropospheric attenuation prediction: application of a new approach. European Conference on Antennas & Propagation, Davos, Switzerland, pp. 1–5.Google Scholar
7.Bae, S, So, J and Kim, H (2017) On optimal cooperative sensing with energy detection in cognitive radio. Sensors 17, 1–15.Google Scholar
8.Verma, P and Singh, B (2016) Overcoming sensing failure problem in double threshold based cooperative spectrum sensing. Optik 127, 4200–4204.Google Scholar
9.Liu, X, Zhang, C and Tan, X (2014) Double-threshold cooperative detection for cognitive radio based on weighing. Wireless Communications and Mobile Computing 14, 1231–1243.Google Scholar
10.Joshi, DR, Popescu, DC and Dobre, OA (2010) Adaptive spectrum sensing with noise variance estimation for dynamic cognitive radio systems. 44th Annual Conference on Information Sciences and Systems, Princeton, USA, pp. 1–5.Google Scholar
11.Song, X, Zhou, C, Hepburn, DM and Zhang, G (2017) Second generation wavelet transform for data denoising in PD measurement. IEEE Transactions on Dielectrics & Electrical Insulation 14, 1531–1537.Google Scholar
12.Zhuang, M and Wang, Z (2017) Troposcatter array signal detection based on frequency and spatial fading correlation. Electronics Letters 53, 1564–1566.Google Scholar
13.de Paula, A and Panazio, C (2014) Cooperative spectrum sensing under unreliable reporting channels. Wireless Networks 20, 1399–1407.Google Scholar
14.Yue, W, Zheng, B, Meng, Q, Cui, J and Xie, P (2011) Robust cooperative spectrum sensing schemes for fading channels in cognitive radio networks. Science China (Information Sciences) 54, 348–359.Google Scholar
15.Chatziantoniou, E, Allen, B, Velisavljevic, V, Karadimas, P and Coon, J (2017) Energy detection based spectrum sensing over two-wave with diffuse power fading channels. IEEE Transactions on Vehicular Technology 66, 868–874.Google Scholar
16.Li, C, Chen, X and Liu, X (2018) Cognitive tropospheric scatter communication. IEEE Transactions on Vehicular Technology 67, 1482–1491.Google Scholar
17.Ahuja, B and Kaur, G (2017) Design of an improved spectrum sensing technique using dynamic double threshold for cognitive radio networks. Wireless Personal Communications 3, 1–24.Google Scholar
18.Li, J, Wang, J, Zhang, X and Tang, W (2017). Empirical mode decomposition based on instantaneous frequency boundary. Electronics Letters 53, 781–783.Google Scholar
19.Liu, Z, Cui, Y and Li, W (2017) A classification method for complex power quality disturbances using EEMD and rank wavelet SVM. IEEE Transactions on Smart Grid 6, 1678–1685.Google Scholar
20.Xu, Y, Luo, M, Li, T and Song, G (2017) ECG signal de-noising and baseline wander correction based on CEEMDAN and wavelet threshold. Sensors 17, 1–16.Google Scholar
21.Sharifi, R and Langari, R (2017) Nonlinear sensor fault diagnosis using mixture of probabilistic PCA models. Mechanical Systems & Signal Processing 85, 511–521.Google Scholar
22.Lim, M and Yuen, PC (2016) Entropy measurement for biometric verification systems. IEEE Transaction on Cybernetics 46, 1065–1077.Google Scholar