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kavoosi V, Dehghani M J, Javidan R. Three Dimensional Localization of an Unknown Target Using Two Heterogeneous Sensors. JSDP 2020; 17 (1) :147-158
URL: http://jsdp.rcisp.ac.ir/article-1-860-en.html
Shiraz University of Technology
Abstract:   (2603 Views)
Heterogeneous wireless sensor networks consist of some different types of sensor nodes deployed in a particular area. Different sensor types can measure different quantity of a source and using the combination of different measurement techniques, the minimum number of necessary sensors is reduced in localization problems. In this paper, we focus on the single source localization in a heterogeneous sensor network containing two types of passive anchor-nodes: Omni-directional and vector sensors. An omni-directional sensor can simply measure the received signal strength (RSS) without any additional hardware. In other side, an acoustic vector sensor (AVS) consists of a velocity-sensor triad and an optional acoustic pressure-sensor, all spatially collocated in a point-like geometry. The velocity-sensor triad has an intrinsic ability in direction finding process. Moreover, despite its directivity, a velocity-sensor triad can isotropically measure the received signal strength and has a potential to be used in RSS-based ranging methods.
Employing a heterogeneous sensor-pair consisting of one vector and one omni-directional sensor, this study tries to obtain unambiguity estimation for the location of an unknown source in a three-dimensional (3D) space. Using a velocity-sensor triad as an AVS, it is possible to determine the direction of arrival (DOA) of the source without any restriction on the spectrum of the emitted signal. However, the range estimation is a challenging problem when the target is closer to the omnidirectional sensor than the vector sensor. The existence method proposed for such configuration suffers from a fundamental limitation, namely the localization coverage. Indeed, this algorithm cannot provide an estimate for the target range in 50 percent of target locations due to its dependency to the relative sensor-target geometry.
In general, our proposed method for the considered problem can be summarized as follows: Initially, we assume that the target's DOA is estimated using the velocity-sensor triad’s data. Then, considering the estimated DOA and employing the RSS measured by two sensors, we propose a computationally efficient algorithm for uniquely estimation of the target range. To this end, the ratio of RSS measured by two sensors is defined and, then, shown that this power ratio can be expressed as a monotonic function of the target range. Finally, the bisection search method is proposed to find an estimate for the target range. Since the proposed algorithm is based on bisection search method, a solution for the range of the target independent of its location is guaranteed. Moreover, a set of future aspects and trends is identified that might be interesting for future research in this area. Having a low computational complexity, the proposed method can enhance the coverage area mostly two times of that explored by the existence method. The simulated data confirms the speed and accuracy of developed algorithm and shows its robustness against various target ranges and different sensor spacing.
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Type of Study: Research | Subject: Paper
Received: 2018/04/26 | Accepted: 2019/06/19 | Published: 2020/06/21 | ePublished: 2020/06/21

References
1. [1] G. Han, J. Jiang, L. Shu, Y. Xu, and F. Wang, "Localization algorithms of underwater wireless sensor networks: a survey," Sensors, vol. 12, no.2, pp. 2026-2061. 2012. [DOI:10.3390/s120202026] [PMID] [PMCID]
2. [2] D. Li, and Y. H. Hu, "Energy-based collaborative source localization using acoustic microsensor array," EURASIP Journal on Applied Signal Processing, Vol. 2003, No. 4, pp. 321-337, 2003. [DOI:10.1155/S1110865703212075]
3. [3] Y. I. Wu, and K. T. Wong, "Acoustic near-field source-localization by two passive anchor-nodes," IEEE Transactions on Aerospace and Electronic Systems, vol. 48, no. 1, 2012. [DOI:10.1109/TAES.2012.6129627]
4. [4] Z. X Yao, and J. Y. Hui, "Four approaches to DOA estimation based on a single vector hydrophone," Ocean Engineering, vol. 24, pp.122-127, 2006.
5. [5] Y. I. Wu, K. T. Wong and S.-K. Lau, "The acoustic vector-sensor's near-field array-manifold," IEEE Transactions on Signal Processing, vol. 58, no. 7, pp. 3946-3951, July 2010. [DOI:10.1109/TSP.2010.2047393]
6. [6] X. Zhong and A. B. Premkumar, "Particle filtering approaches for multiple acoustic source detection and 2-D direction of arrival estimation using a single acoustic vector sensor," IEEE Transactions on Signal Processing, vol. 60, no. 9, pp. 4719-4733, 2012. [DOI:10.1109/TSP.2012.2199987]
7. [7] V. N. Hari, A. B. Premkumar, and X. Zhong, "A decoupled approach for near-field source localization using a single acoustic vector sensor," Circuits, Systems, and signal Processing, vol. 32, no. 2, pp 843-859, 2013. [DOI:10.1007/s00034-012-9508-9]
8. [8] M. Laaraiedh, "Contributions on hybrid localization techniques for heterogeneous wireless networks," Ph.D. Thesis, University of Rennes, 2010.
9. [9] Z. M. Saric, D. D. Kukolj, and N. D. Teslic, "Acoustic source localization in wireless sensor network," Circuits, Systems, and Signal Processing, vol. 29, no. 5, pp.837-856, 2010. [DOI:10.1007/s00034-010-9187-3]
10. [10] J. Wang, J. Chen, and D. Cabric, "Cramer-Rao bounds for joint RSS/DoA-based primary-user localization in cognitive radio networks," IEEE Transactions on Wireless Communications, vol. 12, no. 3, pp.1363-1375, 2013. [DOI:10.1109/TWC.2013.012513.120966]
11. [11] W. Meng, L. Xie, and W. Xiao, "Optimality analysis of sensor-source geometries in heterogeneous sensor networks," IEEE Transactions on Wireless Communication, vol. 12, no. 4, pp. 1958-1967, 2013. [DOI:10.1109/TWC.2013.021213.121269]
12. [12] S. Tomic, M. Beko, R. Dinis, and L. Bernardo, "On target localization using combined RSS and AoA measurements," Sensors, vol. 18, no. 4, 2018. [DOI:10.3390/s18041266] [PMID] [PMCID]
13. [13] S. Wang, B. R. Jackson, and R. Inkol, "Hybrid RSS/AOA emitter location estimation based on least squares and maximum likelihood criteria," in 26th Biennial Symposium onCommunications (QBSC), 2012, pp. 24-29. [DOI:10.1109/QBSC.2012.6221344]
14. [14] L. Gazzah, L. Najjar, and H. Besbes, "Selective hybrid RSS/AOA weighting algorithm for NLOS intra cell localization," IEEE WCNC, Turkey, Istanbul, 2014, pp. 2546-2551. [DOI:10.1109/WCNC.2014.6952789]
15. [15] L. Gazzah, L. Najjar, and H. Besbes, "Hybrid RSSD/AoA cooperative localization for 4G wireless networks with uncooperative emitters," in International Wireless Communications and Mobile Computing Conference (IWCMC), 2015, pp. 874-879. [DOI:10.1109/IWCMC.2015.7289198]
16. [16] V. Kavoosi, M. J. Dehghani, and R. Javidan, "Selective geometry for near-field three-dimensional localization using one-pair sensor," IET Radar Sonar and Navigation, vol. 10, no. 5, pp. 844-849, 2015. [DOI:10.1049/iet-rsn.2014.0345]
17. [17] X. Sheng, and Y. Hu, "Energy based acoustic source localization," In IPSN'03: Proceedings of the 6th International Conference on Information Processing in Sensor Networks, Springer Berlin Heidelberg, Germany, 2003, pp. 285-300. [DOI:10.1007/3-540-36978-3_19]
18. [18] A. Quarteroni, R. Sacco, and F. Saleri, Numerical mathematics, Springer Science & Business Media, vol. 37, 2010.
19. [19] X. Zhong, and A. B. Premkumar, "Particle filtering approaches for multiple acoustic source detection and 2-D direction of arrival estimation using a single acoustic vector sensor," IEEE Transactions on Signal Processing, vol. 60, no. 9, pp. 4719-4733, 2012. [DOI:10.1109/TSP.2012.2199987]
20. [20] Z. Baoping, G. R. Wood, and W. P. Baritompa, "Multidimensional bisection: the performance and the context," Journal of Global Optimization, vol.3, no. 3, pp. 337-358, 1993 [DOI:10.1007/BF01096775]

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