An Improved PSO Localization Algorithm for UWB Sensor Networks
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An Improved PSO Localization Algorithm for UWB Sensor Networks Bin Xia1 · Tao Liu1 · Tian Ding1 · ZhiQiang Wang1 Accepted: 11 November 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Traditional particle swarm optimization (PSO) localization method can achieve good localization accuracy with the advantages of fewer parameters and simpler calculations. However, its localization performance is constrained by the limited ranging information available between the anchor node and the unknown node. Thus, an improved PSO localization method is proposed to further improve the localization performance. Firstly, we use the ranging information between the unknown node and the anchor node to make an initial positioning through the traditional PSO method. Then, based on the initial positioning, all ranging information is employed to calculate a more precise location. Compared with the traditional PSO method, the proposed algorithm can reduce the impact of the ranging error and improve localization performance. Keywords Particle swarm optimization · Localization algorithm · Non line of sight
1 Introduction The progresses in ultrawide band (UWB) communications and microsystem technologies have resulted in the rapid development of UWB sensor network [1–3] in recent decades. In the UWB network, the data is carried by low-power ultra short pulses which has a fractional bandwidth greater than 20% or at least 500 MHz for − 10 dB bandwidth. The lower power permits the network’s coexistence with different types of wireless sensor networks, while the short pulse provides high time resolution, which could help to enhance the positioning precision. Moreover, the UWB network offers the possibility to reduce the size of the transceiver architecture and increase the service time. One of its wide applications is the source localization, which plays an important role in health monitoring [4], target tracking [5], and robot techniques [6]. Therefore, many range-based localization algorithms have been proposed in numerous studies. In the range-based localization methods, positioning equations are based on the ranging information which is obtained by techniques such as received signal strength indicator (RSSI) [7, 8], arrival of angle [9], and time of arrival (TOA) [10, 11]. Many methods for * Bin Xia [email protected] 1
School of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China
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solving positioning equations have been studied. Ren et al. [12] proposed a genetic algorithm and RSSI quantization scheme for localization. Lu et al. [13] presented a Taylor series algorithm based on semidefinite programming. First, the semidefinite programming method was used to obtain the coarse position of the target node. Then, the refined location was obtained by the Taylor series algorithm. Hijazi et al. [14] proposed a gradient descent localization algorithm based on the RSSI information. Wu et al. [15] presented a particle swarm optimizati
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