An improved node localization based on adaptive iterated unscented Kalman filter for WSN

Ou, Xianhua; Wu, Xianqing; He, Xiongxiong; Chen, Zhongtian; Yu, Qun-ai

doi:10.1109/iciea.2015.7334145

Cited by 5 publications

(5 citation statements)

References 19 publications

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“…But, if the channel is in the NLoS environment, the EKF shows a high error in localization because of measurement data deviation [35]. For mobile node localization, a variety of NLoS approaches have been presented [36][37][38][39][40]. The method of unscented transformation is used for the standard KF to produce the UKF, which attains a better estimation than the other methods.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

Ullah

Zhu

et al. 2020

Wireless Communications and Mobile Computing

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Mobile robot localization has attracted substantial consideration from the scientists during the last two decades. Mobile robot localization is the basics of successful navigation in a mobile network. Localization plays a key role to attain a high accuracy in mobile robot localization and robustness in vehicular localization. For this purpose, a mobile robot localization technique is evaluated to accomplish a high accuracy. This paper provides the performance evaluation of three localization techniques named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). In this work, three localization techniques are proposed. The performance of these three localization techniques is evaluated and analyzed while considering various aspects of localization. These aspects include localization coverage, time consumption, and velocity. The abovementioned localization techniques present a good accuracy and sound performance compared to other techniques.

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Section: Related Workmentioning

confidence: 99%

Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

Ullah

Zhu

et al. 2020

Wireless Communications and Mobile Computing

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“…However, when the channel is in NLOS, the EKF algorithm displays large localization errors due to the deviation of measurement data. Many NLOS mitigation methods [2,3,4,5,6,7,8,9,10,11] have been proposed for the location of the mobile nodes. In [2], the unscented transformation is applied to the standard Kalman filter system to generate the unscented Kalman filter (UKF), which achieves great estimation performance.…”

Section: Related Workmentioning

confidence: 99%

“…In [2], the unscented transformation is applied to the standard Kalman filter system to generate the unscented Kalman filter (UKF), which achieves great estimation performance. An adaptive iterated unscented Kalman filter (AIUKF) is presented in [3] for target positioning, which improves the performance by combining iterative strategy and adaptive factor. In [4], the particle filter (PF) based on the Monte Carlo method [5,6,7] is used for positioning.…”

Section: Related Workmentioning

confidence: 99%

A Fusion Localization Method based on a Robust Extended Kalman Filter and Track-Quality for Wireless Sensor Networks

Wang

Jie

Cheng

2019

Sensors

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As one of the most essential technologies, wireless sensor networks (WSNs) integrate sensor technology, embedded computing technology, and modern network and communication technology, which have become research hotspots in recent years. The localization technique, one of the key techniques for WSN research, determines the application prospects of WSNs to a great extent. The positioning errors of wireless sensor networks are mainly caused by the non-line of sight (NLOS) propagation, occurring in complicated channel environments such as the indoor conditions. Traditional techniques such as the extended Kalman filter (EKF) perform unsatisfactorily in the case of NLOS. In contrast, the robust extended Kalman filter (REKF) acquires accurate position estimates by applying the robust techniques to the EKF in NLOS environments while losing efficiency in LOS. Therefore it is very hard to achieve high performance with a single filter in both LOS and NLOS environments. In this paper, a localization method using a robust extended Kalman filter and track-quality-based (REKF-TQ) fusion algorithm is proposed to mitigate the effect of NLOS errors. Firstly, the EKF and REKF are used in parallel to obtain the location estimates of mobile nodes. After that, we regard the position estimates as observation vectors, which can be implemented to calculate the residuals in the Kalman filter (KF) process. Then two KFs with a new observation vector and equation are used to further filter the estimates, respectively. At last, the acquired position estimates are combined by the fusion algorithm based on the track quality to get the final position vector of mobile node, which will serve as the state vector of both KFs at the next time step. Simulation results illustrate that the TQ-REKF algorithm yields better positioning accuracy than the EKF and REKF in the NLOS environment. Moreover, the proposed algorithm achieves higher accuracy than interacting multiple model algorithm (IMM) with EKF and REKF.

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“…The KF achieves this goal by deriving analytics equations based on multivariate normal distributions and linear projections. For this reason, KF is excellent in estimating information in linear and normal distribution environments [ 8 , 9 , 10 , 11 , 12 , 13 ]. Conversely, PF utilizes a set of discrete points or particles.…”

Section: Literature Reviewmentioning

confidence: 99%

Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization

Harith

Noor

Idris

et al. 2018

Sensors

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The majority of the Wireless Sensor Network (WSN) localization methods utilize a large number of nodes to achieve high localization accuracy. However, there are many unnecessary data redundancies that contributes to high computation, communication, and energy cost between these nodes. Therefore, we propose the Intersection and Complement Set (IACS) method to reduce these redundant data by selecting the most significant neighbor nodes for the localization process. Through duplication cleaning and average filtering steps, the proposed IACS selects the normal nodes with unique intersection and complement sets in the first and second hop neighbors to localize the unknown node. If the intersection or complement sets of the normal nodes are duplicated, IACS only selects the node with the shortest distance to the blind node and nodes that have total elements larger than the average of the intersection or complement sets. The proposed IACS is tested in various simulation settings and compared with MSL* and LCC. The performance of all methods is investigated using the default settings and a different number of degree of irregularity, normal node density, maximum velocity of sensor node and number of samples. From the simulation, IACS successfully reduced 25% of computation cost, 25% of communication cost and 6% of energy consumption compared to MSL*, while 15% of computation cost, 13% of communication cost and 3% of energy consumption compared to LCC.

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An improved node localization based on adaptive iterated unscented Kalman filter for WSN

Cited by 5 publications

References 19 publications

Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques

A Fusion Localization Method based on a Robust Extended Kalman Filter and Track-Quality for Wireless Sensor Networks

Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization

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