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Journal articles on the topic 'Trilateration'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Jondhale, Satish R., Amruta S. Jondhale, Pallavi S. Deshpande, and Jaime Lloret. "Improved trilateration for indoor localization: Neural network and centroid-based approach." International Journal of Distributed Sensor Networks 17, no. 11 (2021): 155014772110539. http://dx.doi.org/10.1177/15501477211053997.

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Location awareness is the key to success to many location-based services applications such as indoor navigation, elderly tracking, emergency management, and so on. Trilateration-based localization using received signal strength measurements is widely used in wireless sensor network–based localization and tracking systems due to its simplicity and low computational cost. However, localization accuracy obtained with the trilateration technique is generally very poor because of fluctuating nature of received signal strength measurements. The reason behind such notorious behavior of received signa

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2

Zhou, Yu, Wenfei Liu, Xionghui Lu, and Xu Zhong. "Single-Camera Trilateration." Applied Sciences 9, no. 24 (2019): 5374. http://dx.doi.org/10.3390/app9245374.

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This paper presents a single-camera trilateration scheme which estimates the instantaneous 3D pose of a regular forward-looking camera from a single image of landmarks at known positions. Derived on the basis of the classical pinhole camera model and principles of perspective geometry, the proposed algorithm estimates the camera position and orientation successively. It provides a convenient self-localization tool for mobile robots and vehicles equipped with onboard cameras. Performance analysis has been conducted through extensive simulations with representative examples, which provides an in

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3

Rossow, Mark P. "Trilateration Computer Program." Journal of Surveying Engineering 131, no. 3 (2005): 94–96. http://dx.doi.org/10.1061/(asce)0733-9453(2005)131:3(94).

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4

Ainul, Rafina Destiarti. "An enhanced trilateration algorithm for indoor RSSI based positioning system using zigbee protocol." JURNAL INFOTEL 14, no. 4 (2022): 301–6. http://dx.doi.org/10.20895/infotel.v14i4.822.

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Location information of the object based on wireless communication will play crucial role in several Wireless Sensor Network (WSN) applications. Some applications need to know the exact position of the object. The advantages of Zigbee as the part of Radio Frequency (RF) technology such as low cost, high scalability, high availability, and supporting topology for Indoor positioning system (IPS). In this paper, we propose IPS using Received Signal Strength Indicator (RSSI) based Zigbee protocol. The proposed approach is based on the enhancement of Trilateration algorithm. Basically, the main con

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Luo, Qinghua, Kexin Yang, Xiaozhen Yan, Jianfeng Li, Chenxu Wang, and Zhiquan Zhou. "An Improved Trilateration Positioning Algorithm with Anchor Node Combination and K-Means Clustering." Sensors 22, no. 16 (2022): 6085. http://dx.doi.org/10.3390/s22166085.

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As a classic positioning algorithm with a simple principle and low computational complexity, the trilateration positioning algorithm utilizes the coordinates of three anchor nodes to determine the position of an unknown node, which is widely applied in various positioning scenes. However, due to the environmental noise, environmental interference, the distance estimation error, the uncertainty of anchor nodes’ coordinates, and other negative factors, the positioning error increases significantly. For this problem, we propose a new trilateration algorithm based on the combination and K-Means cl

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6

Joséphine Mpole Boyokame, Ch. G. Lionel Nkouka Moukengue, Flory Lidinga Mobonda, N. Meni Babakidi, J. G. Cimbela Kasongo, and Maguiraga Madiassa. "Method of locating a target with a Mimo radar distributed at the international airport of Ndjili in Kinshasa." World Journal of Advanced Research and Reviews 18, no. 2 (2023): 952–62. http://dx.doi.org/10.30574/wjarr.2023.18.2.0900.

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This system is based on a UWB wave distributed radar using a trilateration algorithm for localization. Before describing the complete system, we detail some signal processing techniques on the UWB signals acquired directly at the output of the reception antenna. To apply the trilateration algorithm, the signals coming from the reception antennas must first be shaped. The necessary processing is to improve the signal to noise ratio (S/N), to extract the signal envelope and to determine the propagation time. Once the propagation time is known, the trilateration algorithm is used to locate the ta

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7

Vincent, François, Eric Chaumette, Christophe Charbonnieras, Jonathan Israel, Marion Aubault, and Franck Barbiero. "Asymptotically efficient GNSS trilateration." Signal Processing 133 (April 2017): 270–77. http://dx.doi.org/10.1016/j.sigpro.2016.11.027.

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8

Joséphine, Mpole Boyokame, G. Lionel Nkouka Moukengue Ch., Lidinga Mobonda Flory, Meni Babakidi N., G. Cimbela Kasongo J., and Madiassa Maguiraga. "Method of locating a target with a Mimo radar distributed at the international airport of Ndjili in Kinshasa." World Journal of Advanced Research and Reviews 18, no. 2 (2023): 952–60. https://doi.org/10.5281/zenodo.8406324.

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This system is based on a UWB wave distributed radar using a trilateration algorithm for localization. Before describing the complete system, we detail some signal processing techniques on the UWB signals acquired directly at the output of the reception antenna. To apply the trilateration algorithm, the signals coming from the reception antennas must first be shaped. The necessary processing is to improve the signal to noise ratio (S/N), to extract the signal envelope and to determine the propagation time. Once the propagation time is known, the trilateration algorithm is used to locate the ta

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9

Qureshi, Umair Mujtaba, Zuneera Umair, and Gerhard Petrus Hancke. "Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision." Sensors 19, no. 15 (2019): 3282. http://dx.doi.org/10.3390/s19153282.

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Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localizat

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10

Zhou, Yu. "A closed-form algorithm for the least-squares trilateration problem." Robotica 29, no. 3 (2010): 375–89. http://dx.doi.org/10.1017/s0263574710000196.

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SUMMARYTrilateration is the most adopted external reference-based localization technique for mobile robots, given the correspondence of external references. The nonlinear least-squares trilateration formulation provides an optimal position estimate from a general number (greater than or equal to the dimension of the environment) of reference points and corresponding distance measurements. This paper presents a novel closed-form solution to the nonlinear least-squares trilateration problem. The performance of the proposed algorithm in dealing with erroneous inputs of reference points and distan

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11

Heydarishahreza, Navid, and Nirwan Ansari. "Mobile Node Localization in Wireless Networks: Path-Loss Model, Trilateration, and Error Mitigation in a 5G Sub-6 GHz Scenario." Journal of Networking and Network Applications 3, no. 3 (2023): 129–36. http://dx.doi.org/10.33969/j-nana.2023.030304.

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In this paper, we proffer a novel technique designed for low-cost and computationally light localization of mobile nodes in an urban terrain, by leveraging the extended COST 231 Hata Path-Loss (PL) model and the Trilateration technique. Our approach accounts for the possibility of a Non-Line-of-Sight (NLoS) scenario in a medium-sized city, wherein one of the three reference nodes required for the trilateration approach encounters NLoS impediments. Our proposed method proceeds with localization by utilizing solely two Line-of-Sight (LoS) reference nodes, while integrating the localization syste

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12

Subhan, Fazli, Halabi Hasbullah, and Khalid Ashraf. "Kalman Filter-Based Hybrid Indoor Position Estimation Technique in Bluetooth Networks." International Journal of Navigation and Observation 2013 (September 22, 2013): 1–13. http://dx.doi.org/10.1155/2013/570964.

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This paper presents an extended Kalman filter-based hybrid indoor position estimation technique which is based on integration of fingerprinting and trilateration approach. In this paper, Euclidian distance formula is used for the first time instead of radio propagation model to convert the received signal to distance estimates. This technique combines the features of fingerprinting and trilateration approach in a more simple and robust way. The proposed hybrid technique works in two stages. In the first stage, it uses an online phase of fingerprinting and calculates nearest neighbors (NN) of t

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13

Thomas, F., and L. Ros. "Revisiting trilateration for robot localization." IEEE Transactions on Robotics 21, no. 1 (2005): 93–101. http://dx.doi.org/10.1109/tro.2004.833793.

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14

Lee, Michael C., and Placid M. Ferreira. "Auto-triangulation and auto-trilateration." Precision Engineering 26, no. 3 (2002): 250–62. http://dx.doi.org/10.1016/s0141-6359(02)00109-5.

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15

MACHIDA, Ken-ichi, and Chuji MORI. "Adjustment of trilateration net by condition equations and characteristics of errors in trilateration chains." Doboku Gakkai Ronbunshu, no. 401 (1989): 51–60. http://dx.doi.org/10.2208/jscej.1989.51.

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16

Brida, Peter, and Juraj Machaj. "A Novel Enhanced Positioning Trilateration Algorithm Implemented for Medical Implant In-Body Localization." International Journal of Antennas and Propagation 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/819695.

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Medical implants based on wireless communication will play crucial role in healthcare systems. Some applications need to know the exact position of each implant. RF positioning seems to be an effective approach for implant localization. The two most common positioning data typically used for RF positioning are received signal strength and time of flight of a radio signal between transmitter and receivers (medical implant and network of reference devices with known position). This leads to positioning methods: received signal strength (RSS) and time of arrival (ToA). Both methods are based on t

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17

Yang, Junhua, Yong Li, and Wei Cheng. "An improved geometric algorithm for indoor localization." International Journal of Distributed Sensor Networks 14, no. 3 (2018): 155014771876737. http://dx.doi.org/10.1177/1550147718767376.

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Indoor localization system using receive signal strength indicator from wireless access point has attracted lots of attention recently. Geometric method is one of the most widely used spatial graph algorithms to locate object in an indoor environment, but it does not achieve good results when it is applied to a limited amount of valid data, especially when using the trilateration method. On the other hand, localization based on fingerprint can achieve high accuracy but need to pay heavy manual labor for fingerprint database establishment. In this article, we propose a bilateral greed iteration

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18

Ye, Jing Zhi, Ling Zhao, and Wen Feng Luo. "Performances of Localization Algorithms in a Prototype WSN System." Advanced Materials Research 457-458 (January 2012): 723–27. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.723.

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The proliferation of wireless sensor networks (WSNs) has fostered the demand of context aware applications, among which localization plays a significant role. Localization in WSN is facing the challenges of (1) error and noise; (2) dynamic environments; (3) data packet loss. However, most presented localization algorithms are verified just by simulation but practical systems. To validate the localization algorithms realistically, a prototype WSN system equipped with a group of sensor nodes is deployed in this paper. A trilateration based optimization approaches and the Extended Kalman Filter (

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19

Sung, Li-Yu, and David D. Jackson. "Three-dimensional adjustment of trilateration data." Geophysical Research Letters 12, no. 1 (1985): 25–28. http://dx.doi.org/10.1029/gl012i001p00025.

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20

Danial, Naguib F. "Adjustment of Space Trilateration by STRUDL." Journal of Surveying Engineering 113, no. 1 (1987): 11–27. http://dx.doi.org/10.1061/(asce)0733-9453(1987)113:1(11).

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21

Danial, N. F. "SMALL TRILATERATION NETS IN THREE DIMENSIONS." Survey Review 28, no. 216 (1985): 75–88. http://dx.doi.org/10.1179/sre.1985.28.216.75.

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22

Navidi, William, William S. Murphy, and Willy Hereman. "Statistical methods in surveying by trilateration." Computational Statistics & Data Analysis 27, no. 2 (1998): 209–27. http://dx.doi.org/10.1016/s0167-9473(97)00053-4.

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23

Ahmad, Tanveer, Xue Jun Li, Boon-Chong Seet, and Juan-Carlos Cano. "Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks." Electronics 9, no. 5 (2020): 738. http://dx.doi.org/10.3390/electronics9050738.

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In this paper, we proposed a new wireless localization technique based on the ideology of social network analysis (SNA), to study the different properties of networks as a graph. Centrality is a main concept in SNA, so we propose using closeness centrality (CC) as a measurement to denote the importance of the node inside the network due to its geo-location to others. The node with highest degree of CC is chosen as a cluster heads, then each cluster head can form its trilateration process to collect data from its cluster. The selection of closest cluster based on CC values, and the unknown node

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24

Krishnamoorthy, Vinoth Kumar, Usha Nandini Duraisamy, Amruta S. Jondhale, Jaime Lloret, and Balaji Venkatesalu Ramasamy. "Energy-Constrained Target Localization Scheme for Wireless Sensor Networks Using Radial Basis Function Neural Network." International Journal of Distributed Sensor Networks 2023 (March 30, 2023): 1–12. http://dx.doi.org/10.1155/2023/1426430.

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The indoor object tracking by utilizing received signal strength indicator (RSSI) measurements with the help of wireless sensor network (WSN) is an interesting and important topic in the domain of location-based applications. Without the knowledge of location, the measurements obtained with WSN are of no use. The trilateration is a widely used technique to get location updates of target based on RSSI measurements from WSN. However, it suffers with high location estimation errors arising due to random variations in RSSI measurements. This paper presents a range-free radial basis function neural

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25

Mardi Hardjianto, Hidayatul Ichwan,. "Optimasi Penempatan Lokasi Access Point dengan Metode Simulated Annealing dan Trilateration (Studi Kasus : Universitas Budi Luhur)." Respati 16, no. 2 (2021): 116. http://dx.doi.org/10.35842/jtir.v16i2.407.

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INTISASI Teknologi tanpa kabel yang lebih dikenal dengan Wi-Fi. Wi-Fi (Wirelles Fidelity) adalah koneksi tanpa kabel yang menghubungkan jaringan komputer, seperti ponsel yang menggunakan teknologi radio sehingga pengguna dapat melakukan transfer data dengan cepat dan aman. Kebutuhan akan Wi-Fi terlihat dengan jelas, baik di rumah, di kantor-kantor, di kampus, maupun pusat-pusat bisnis. Trafik pengguna Wi-Fi semakin tinggi, namun pemasangan Access Point belum tepat banyak access point yang terpasang yang pemasangannya tidak menentukan jarak antar access point. Menyebabkan penumpukan sinyal deng

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26

Yoon, Jae-hyuk, Hee-jin Kim, and Soon-kak Kwon. "Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator." Applied Sciences 14, no. 8 (2024): 3319. http://dx.doi.org/10.3390/app14083319.

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In this paper, we propose a proximity-based adaptive positioning algorithm to address the challenge of positioning errors in indoor localization based on RSSI (received signal strength indicator). When positioning by trilateration, if a receiver is close to one AP, the signals of other APs become rapidly unstable, so positioning accuracy is reduced. Therefore, this paper proposes an algorithm to identify the proximity state with AP and adaptively determine the positioning technique based on this state. The proposed algorithm consists of three steps: RSSI error correction, adaptive location est

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27

Jondhale, Satish R., Vijay Mohan, Bharat Bhushan Sharma, Jaime Lloret, and Shashikant V. Athawale. "Support Vector Regression for Mobile Target Localization in Indoor Environments." Sensors 22, no. 1 (2022): 358. http://dx.doi.org/10.3390/s22010358.

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Trilateration-based target localization using received signal strength (RSS) in a wireless sensor network (WSN) generally yields inaccurate location estimates due to high fluctuations in RSS measurements in indoor environments. Improving the localization accuracy in RSS-based systems has long been the focus of a substantial amount of research. This paper proposes two range-free algorithms based on RSS measurements, namely support vector regression (SVR) and SVR + Kalman filter (KF). Unlike trilateration, the proposed SVR-based localization scheme can directly estimate target locations using fi

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S.Al-Olimat, Hussein, Robert C. Green, Mansoor Alam, Vijay Devabhaktuni, and Wei Cheng. "Particle Swarm Optimized Power Consumption of Trilateration." International Journal in Foundations of Computer Science & Technology 4, no. 4 (2014): 1–19. http://dx.doi.org/10.5121/ijfcst.2014.4401.

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29

Hough, Michael E. "Precise Reentry Maneuver Estimation Using Radar Trilateration." Journal of Guidance, Control, and Dynamics 43, no. 3 (2020): 422–37. http://dx.doi.org/10.2514/1.g004513.

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30

Beck, Norman, J. Robert Duval, and Peter T. Taylor. "GPS Processing Methods: Comparison with Precise Trilateration." Journal of Surveying Engineering 115, no. 2 (1989): 181–97. http://dx.doi.org/10.1061/(asce)0733-9453(1989)115:2(181).

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31

Retscher, Guenther, Jonathan Kleine, and Lisa Whitemore. "Trilateration Approaches for Indoor Wi-Fi Positioning." E3S Web of Conferences 94 (2019): 02002. http://dx.doi.org/10.1051/e3sconf/20199402002.

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In smartphones several sensors and receivers are embedded which enable positioning in Location-based Services and other navigation applications. They include GNSS receivers and Wireless Fidelity (Wi-Fi) cards as well as inertial sensors, such as accelerometers, gyroscope and magnetometer. In this paper, indoor Wi-Fi positioning is studied based on trilateration. Three methods are investigated which are a resection, a calculation of the center of gravity point and a differential approach. The first approach is a commonly employed resection using the ranges to the Wi-Fi Access Points (APs) as ra

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32

Yang, Zheng, and Yunhao Liu. "Quality of Trilateration: Confidence-Based Iterative Localization." IEEE Transactions on Parallel and Distributed Systems 21, no. 5 (2010): 631–40. http://dx.doi.org/10.1109/tpds.2009.90.

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33

Berle, F. J. "Mixed triangulation/trilateration technique for emitter location." IEE Proceedings F Communications, Radar and Signal Processing 133, no. 7 (1986): 638. http://dx.doi.org/10.1049/ip-f-1.1986.0101.

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34

Misra, Rajiv, Shailendra Shukla, and Vivek Chandel. "Lightweight Localization Using Trilateration for Sensor Networks." International Journal of Wireless Information Networks 21, no. 2 (2014): 89–100. http://dx.doi.org/10.1007/s10776-014-0239-7.

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35

Huang, Ke, Ke He, and Xuecheng Du. "A Hybrid Method to Improve the BLE-Based Indoor Positioning in a Dense Bluetooth Environment." Sensors 19, no. 2 (2019): 424. http://dx.doi.org/10.3390/s19020424.

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Indoor positioning using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. A number of efforts have been exerted to improve the performance of BLE-based indoor positioning. However, few studies pay attention to the BLE-based indoor positioning in a dense Bluetooth environment, where the propagation of BLE signals become more complex and more fluctuant. In this paper, we draw attention to the problems resulting from the dense Bluetooth environment, and it turns out that the dense Bluetooth environment would result in a high received s

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36

Mahadev Mahajan. "Localizing Mobile Target using Kalman Filtering and Least Square Boosting Ensemble Learning based Approach." Panamerican Mathematical Journal 34, no. 1 (2024): 36–51. http://dx.doi.org/10.52783/pmj.v34.i1.904.

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Trilateration-based target localization using received signal strengths (RSS) within wireless sensor networks (WSN) often results in inaccurate location estimates due to the considerable fluctuations in RSS measurements encountered in indoor environments. Enhancing the precision of RSS-based localization systems has been a primary area of interest in extensive research efforts. This study introduces two range-free algorithms, Ensemble Learning (EL) and EL+KF, which leverage RSS measurements for localization. In contrast to trilateration, the EL- based localization approach enables the direct e

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37

Lu, Kezhong, Xiaohua Xiang, Dian Zhang, Rui Mao, and Yuhong Feng. "Localization Algorithm Based on Maximum a Posteriori in Wireless Sensor Networks." International Journal of Distributed Sensor Networks 8, no. 1 (2011): 260302. http://dx.doi.org/10.1155/2012/260302.

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Many applications and protocols in wireless sensor networks need to know the locations of sensor nodes. A low-cost method to localize sensor nodes is to use received signal strength indication (RSSI) ranging technique together with the least-squares trilateration. However, the average localization error of this method is large due to the large ranging error of RSSI ranging technique. To reduce the average localization error, we propose a localization algorithm based on maximum a posteriori. This algorithm uses the Baye's formula to deduce the probability density of each sensor node's distribut

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38

Simamora, Yohannes S. M., Nahdia Fadilatur Rachmach, Muhammad Yaasir Rizqon, Kheri Agus Suseno, and Muhammad Nursyams Hilmi. "Revisiting Trilateration Method Based on Time-of-Flight Measurements for Navigation." Jurnal Riset Multidisiplin dan Inovasi Teknologi 2, no. 01 (2023): 207–14. http://dx.doi.org/10.59653/jimat.v2i01.432.

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This paper revisits trilateration in three-dimensional positioning. Specifically, range between a positioning target and the reference points through time-of-flight (ToF) measurements. In a ToF, range is yielded by multiplying the time required by a wave to travel between two points and its propagation speed. Position of the target can be then estimated once the number of references are adequate, i.e. at least three for two-dimensional positioning and four for three-dimensional one. In this paper, the positioning is considered for navigation where the target moves following a trajectory whilst

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Kim, Sun-Gwan, Tae-Hoon Kim, and Sung-Woo Tak. "Performance Evaluation of RSSI-based Trilateration Localization Methods." Journal of the Korean Institute of Information and Communication Engineering 15, no. 11 (2011): 2488–92. http://dx.doi.org/10.6109/jkiice.2011.15.11.2488.

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40

Pande, Simeon, and Kwame S. Ibwe. "Robust Trilateration Based Algorithm for Indoor Positioning Systems." Tanzania Journal of Science 47, no. 3 (2021): 1195–210. http://dx.doi.org/10.4314/tjs.v47i3.28.

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Abstract Indoor Positioning Systems (IPS) plays crucial roles in indoor environment items positioning used in self-navigating robots and helping hands. To obtain position information, positioning algorithms employing Received Signal Strength Indicator (RSSI) are of great benefits since they reuse the existing radio wireless infrastructures for indoor positioning. However, the changes in the indoor environment decrease the overall accuracy of the developed indoor positioning algorithms. To cope with the challenge of environmental dependency in indoor positioning, a robust algorithm using

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41

Le, Nam Tuan, and Yeong Min Jang. "Photography Trilateration Indoor Localization with Image Sensor Communication." Sensors 19, no. 15 (2019): 3290. http://dx.doi.org/10.3390/s19153290.

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Localization has become an important aspect in a wide range of mobile services with the integration of the Internet of things and service on demand. Numerous mechanisms have been proposed for localization, most of which are based on the estimation of distances. Depending on the channel modeling, each mechanism has its advantages and limitations on deployment, exhibiting different performances in terms of error rates and implementation. With the development of technology, these limitations are rapidly overcome with hybrid systems and enhancement schemes. The successful approach depends on the a

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Filonenko, Viacheslav, Charlie Cullen, and James Carswell. "Indoor Positioning for Smartphones Using Asynchronous Ultrasound Trilateration." ISPRS International Journal of Geo-Information 2, no. 3 (2013): 598–620. http://dx.doi.org/10.3390/ijgi2030598.

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43

Mok, E., and G. Retscher. "Location determination using WiFi fingerprinting versus WiFi trilateration." Journal of Location Based Services 1, no. 2 (2007): 145–59. http://dx.doi.org/10.1080/17489720701781905.

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44

Hu, W. C., and Wilson H. Tang. "Automated Least-Squares Adjustment of Triangulation-Trilateration Figures." Journal of Surveying Engineering 127, no. 4 (2001): 133–42. http://dx.doi.org/10.1061/(asce)0733-9453(2001)127:4(133).

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45

Plets, David, Yousef Almadani, Sander Bastiaens, Muhammad Ijaz, Luc Martens, and Wout Joseph. "Efficient 3D trilateration algorithm for visible light positioning." Journal of Optics 21, no. 5 (2019): 05LT01. http://dx.doi.org/10.1088/2040-8986/ab1389.

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46

Fang, Bertrand T. "Trilateration and extension to Global Positioning System navigation." Journal of Guidance, Control, and Dynamics 9, no. 6 (1986): 715–17. http://dx.doi.org/10.2514/3.20169.

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47

Oliva, Gabriele, Stefano Panzieri, Federica Pascucci, and Roberto Setola. "Sensor Networks Localization: Extending Trilateration via Shadow Edges." IEEE Transactions on Automatic Control 60, no. 10 (2015): 2752–55. http://dx.doi.org/10.1109/tac.2015.2404253.

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48

Ruiz, Daniel, Jesús Ureña, Juan C. García, Carmen Pérez, José M. Villadangos, and Enrique García. "Efficient trilateration algorithm using time differences of arrival." Sensors and Actuators A: Physical 193 (April 2013): 220–32. http://dx.doi.org/10.1016/j.sna.2012.12.021.

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