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Light Linear RSS-Based Sensor Localization with Unknown Transmit Power by Tikhonov-regularization | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 8، دوره 53، شماره 1، شهریور 2021، صفحه 77-86 اصل مقاله (1.06 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2021.19103.5380 | ||
| نویسندگان | ||
| Hassan Nazari1؛ M. R. Danaee* 2 | ||
| 1Department of Electronic and Electrical Engineering, Malek Ashtar University of Technology, Tehran, Iran | ||
| 2EE Dept., Imam Hossein Comprehensive University (IHCU), Tehran, Iran | ||
| چکیده | ||
| In this paper, we introduce a new linear received signal strength-based estimator for unknown node localization which its accuracy at low Signal-to-noise ratio (SNR) is better than many linear estimators and can compete with estimators based on the convex optimization, but it is much lighter than convex optimization-based estimators. The main ingredients in our proposed linear position estimator are to reformulate the localization problem in terms of Tikhonov-regularization and introduce a biased noise variable. The way that we apply for this reformulation avoids any possible linear approximation in which target position variables are involved, thus saving fair amount of information. The proposed algorithm is also indifferent to the transmit power and thus, applicable to either known or unknown transmit power scenarios. Simulation results show the efficacy of the proposed algorithm in comparison to the other methods for both typical RSS-based measurement data model and the modified model for indoor application. | ||
| کلیدواژه‌ها | ||
| Received signal strength (RSS)؛ node localization؛ linear least squares؛ Tikhonov-regularization؛ wireless؛ sensor network (WSN) | ||
| مراجع | ||
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[1] Ian F Akyildiz, Weilian Su, Yogesh Sankarasubramaniam, and Erdal Cayirci. Wireless sensor networks: a survey. Computer networks, 38(4):393–422, 2002.
[2] N.H. Nguyen, and K. Do˘gançay. Closed-form algebraic solutions for angle-of arrival source localization with Bayesian priors. IEEE Transactions on Wireless Communications, 18(8):3827–3842, 2019.
[3] F. Pérez.Cruz, P.M. Olmos, M.M. Zhang, and H. Huang. Probabilistic time of arrival localization. IEEE Signal Processing Letters, 26(11):1683–1687, 2019.
[4] Y. Zhao, Z. Li, B. Hao, and J. Shi. Sensor selection for TDOA-based localization in wireless sensor networks with non-line-of-sight condition. IEEE Transactions on Vehicular Technology, 68(10):9935–9950, 2019.
[5] C.H. Hsieh, J.Y. Chen, and B.H. Nien. Deep learning-based indoor localization using received signal strength and channel state information. IEEE access, 7:33256–33267, 2019.
[6] L.A.C., Najarro, I. Song, S. Tomic, and K. Kim. Fast Localization with Unknown Transmit Power and Path-Loss Exponent in WSNs Based on RSS Measurements. IEEE Communications Letters, 2020.
[7] Guoqiang Mao, Barıs¸ Fidan, and Brian DO Anderson. Wireless sensor network localization techniques. Computer networks, 51(10):2529–2553, 2007.
[8] M. Bouabdellah, E. Ghribi, and N. Kaabouch. RSS-Based Localization with Maximum Likelihood Estimation for PUE Attacker Detection in Cognitive Radio Networks. In: 2019 IEEE International Conference on Electro Information Technology (EIT).
[9] S. Chang, Y. Li, Y. He, and H. Wang. Target localization in underwater acoustic sensor networks using RSS measurements, Applied Sciences, 8(2):1–14, 2018.
[10] Steven M Kay. Fundamentals of statistical signal processing. Prentice Hall PTR, 1993.
[11] Mou Wu, Naixue Xiong, and Liansheng Tan. Adaptive range-based target localization using diffusion Gauss-Newton method in industrial environments. IEEE Transactions on Industrial Informatics, 2019.
[12] Amir Beck, Petre Stoica, and Jian Li. Exact and approximate solutions of source localization problems. IEEE Transactions on signal processing, 56(5):1770–1778, 2008.
[13] Slavisa Tomic, Marko Beko, and Rui Dinis. RSS-based localization in wireless sensor networks using convex relaxation: Noncooperative and cooperative schemes. IEEE Transactions on Vehicular Technology, 64(5):2037–2050, 2015.
[14] Reza Monir Vaghefi, Mohammad Reza Gholami, R Michael Buehrer, and Erik G Strom. Cooperative received signal strength-based sensor localization with unknown transmit powers. IEEE Transactions on Signal Processing, 61(6):1389–1403, 2013.
[15] M. Grant, and S. Boyd. CVX: Matlab software for disciplined convex programming, version 2.1, 2014.
[16] S. Xu, Y. Ou, and W. Zheng .Optimal sensor-target geometries for 3-D static target localization using received-signal-strength measurements. IEEE Signal Processing Letters, 26(7):966–970, 2019.
[17] S. Xu. Optimal Sensor Placement for Target Localization Using Hybrid RSS, AOA and TOA Measurements. IEEE Communications Letters, 2020.
[18] Yaming Xu, Jianguo Zhou, and Peng Zhang. RSS-based source localization when path-loss model parameters are unknown. IEEE communications letters, 18(6):1055–1058, 2014.
[19] A. Achroufene, Y. Amirat, and A. Chibani. RSS-based indoor localization using belief function theory. IEEE Transactions on Automation Science and Engineering, 16(3):1163–1180, 2019.
[20] Gang Wang and Kehu Yang. A new approach to sensor node localization using RSS measurements in wireless sensor networks. IEEE transactions on wireless communications, 10(5):1389–1395, 2011.
[21] S. Boyd, and L., Vandenberghe. Convex optimization. Cambridge university press, 2004.
[22] Yongchang Hu and Geert Leus. Robust differential received signal strength-based localization. IEEE Transactions on Signal Processing, 65(12):3261–3276, 2017.
[23] Danaee MR, and Behnia F. Improved Least Squares Approaches for Differential Received Signal Strength-Based Localization with Unknown Transmit Power. Wireless Personal Communications, 110(3):1373–1401, 2020.
[24] J. Li, K. Doğançay, N. H. Nguyen, and Y. Wei Law. Drss-Based Localisation Using Weighted Instrumental Variables and Selective Power Measurement. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, 4876-4880.
[25] M.R. Danaee. One‐to‐one non‐linear transformation for RSS‐based localization with unknown transmit power. IET Communications, 2021; 1– 15. https://doi.org/10.1049/cmu2.12094 | ||
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