Location estimation of mobile user in wireless sensor network based on Unscented Kalman Filter

In recent years, the rapid development of microelectronics, wireless communications, and electro-mechanical systems has occurred. The wireless sensor network (WSN) has been widely used in many applications. The localization of a mobile node is one of the key technologies for WSN. Among the factors that would affect the accuracy of mobile localization, non-line of sight (NLOS) propagation caused by a complicated environment plays a vital role. In this paper, we present a hierarchical voting based mixed filter (HVMF) localization method for a mobile node in a mixed line of sight (LOS) and NLOS environment. We firstly propose a condition detection and distance correction algorithm based on hierarchical voting. Then, a mixed square root unscented Kalman filter (SRUKF) and a particle filter (PF) are used to filter the larger measurement error. Finally, the filtered results are subjected to convex optimization and the maximum likelihood estimation to estimate the position of the mobile node. The proposed method does not require prior information about the statistical properties of the NLOS errors and operates in a 2D scenario. It can be applied to time of arrival (TOA), time difference of arrival (TDOA), received signal (RSS), and other measurement methods. The simulation results show that the HVMF algorithm can efficiently reduce the effect of NLOS errors and can achieve higher localization accuracy than the Kalman filter and PF. The proposed algorithm is robust to the NLOS errors.

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A Robust Indoor Mobile Localization Algorithm for Wireless Sensor Network in Mixed LOS/NLOS Environments

Journal of Sensors ◽

10.1155/2020/8854389 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Ou Yong Kang ◽

Cheng Long

Keyword(s):

Kalman Filter ◽

Wireless Sensor Network ◽

Sensor Network ◽

Unscented Kalman Filter ◽

Wireless Sensor ◽

Selection Strategy ◽

Time Of Arrival ◽

Multiple Model ◽

Localization Algorithm ◽

Extend Kalman Filter

Wireless sensor network (WSN) is a self-organizing network which is composed of a large number of cheap microsensor nodes deployed in the monitoring area and formed by wireless communication. Since it has the characteristics of rapid deployment and strong resistance to destruction, the WSN positioning technology has a wide application prospect. In WSN positioning, the nonline of sight (NLOS) is a very common phenomenon affecting accuracy. In this paper, we propose a NLOS correction method algorithm base on the time of arrival (TOA) to solve the NLOS problem. We firstly propose a tendency amendment algorithm in order to correct the NLOS error in geometry. Secondly, this paper propose a particle selection strategy to select the standard deviation of the particle swarm as the basis of evolution and combine the genetic evolution algorithm, the particle filter algorithm, and the unscented Kalman filter (UKF) algorithm. At the same time, we apply orthogon theory to the UKF to make it have the ability to deal with the target trajectory mutation. Finally we use maximum likelihood localization (ML) to determine the position of the mobile node (MN). The simulation and experimental results show that the proposed algorithm can perform better than the extend Kalman filter (EKF), Kalman filter (KF), and robust interactive multiple model (RIMM).

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Enhance the Performance of Wireless Sensor Network based on Software Define Network and Kalman Filter

Webology ◽

10.14704/web/v18si04/web18208 ◽

2021 ◽

Vol 18 (Special Issue 04) ◽

pp. 1436-1448

Author(s):

Jumana Suhail ◽

Dr. Khalida Sh. Rijab

Keyword(s):

Kalman Filter ◽

Wireless Sensor Network ◽

Sensor Network ◽

Average Density ◽

Wireless Sensor ◽

Buffer Memory ◽

Leach Protocol ◽

Subsequent Step ◽

Random Node ◽

Software Define Network

The paper proposes a methodology for estimating packet flowing at the sensor level in SDN-WSN based on the partial congestion controller with Kalman filter. Furthermore, the actual purpose of proposing such methodology for predicting in advance the subsequent step of packet flow, and that will consequently contribute in reducing the congestion that might happen. The model proposed (SDN with Kalman filter) is optimized using congestion controller, the methodology of proposed work, the first step random distributed of random node, the apply the Kmean cluster of select the head cluster node in, the connected the network based on LEACH protocol. in this work proposed SDN with Kalman filter for control on network and reduce error of data, where achieve by add buffer memory for each nodes and head cluster to store the data, and SDN control on transmit ion data and receiver data, before transmit apply the Kalman filter on data to reduce error data. The proposed technique, according to simulation findings, extends the network's lifetime by over 30% more than typical WSNs, the reduce the average density of memory to 20% than traditional WSN, and the increase the average capacity of memory to 20% than traditional WSN.

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A Research Cluster based Scheme for Node Positioning in Indoor Environment

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1016.0886s19 ◽

2019 ◽

Vol 8 (6S) ◽

pp. 79-83

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Indoor Environment ◽

Cluster Head ◽

Signal Strength ◽

Received Signal Strength ◽

Location Estimation ◽

Wireless Sensor ◽

Localization Error ◽

Network Efficiency

Location estimation in Wireless Sensor Network (WSN) is mandatory to achieve high network efficiency. Identifying the positions of sensors is an uphill task as monitoring nodes are involved in estimation and localization. Clustered Positioning for Indoor Environment (CPIE) is proposed for estimating the position of the sensors using a Cluster Head (CH) based mechanism. The CH estimates the number of neighbor nodes in each floor of the indoor environment. It sends the requests to the cluster members and the positions are estimated based on the Received Signal Strength Indicators (RSSIs) from the members of the cluster. The performance of the proposed scheme is analyzed for both stable and mobile conditions by varying the number of floors. Experimental results show that the propounded scheme offers better network efficiency and reduces delay and localization error

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Multivariate Regression for Location Estimation in Wireless Sensor Network

Journal of Convergence Information Technology ◽

10.4156/jcit.vol7.issue21.35 ◽

2012 ◽

Vol 7 (21) ◽

pp. 278-286

Author(s):

Xiaoyong Yan ◽

Huanyan Qian

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Multivariate Regression ◽

Location Estimation ◽

Wireless Sensor

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A Mobile Localization Method in Smart Indoor Environment Using Polynomial Fitting for Wireless Sensor Network

Journal of Sensors ◽

10.1155/2020/6787252 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Yan Wang ◽

Yang Yan ◽

Zhengjian Li ◽

Long Cheng

Keyword(s):

Kalman Filter ◽

Wireless Sensor Network ◽

Sensor Network ◽

Indoor Environment ◽

Detection Threshold ◽

Superior Performance ◽

Wireless Sensor ◽

Interacting Multiple Model ◽

Multiple Model ◽

Polynomial Fitting

The main factor affecting the localization accuracy is nonline of sight (NLOS) error which is caused by the complicated indoor environment such as obstacles and walls. To obviously alleviate NLOS effects, a polynomial fitting-based adjusted Kalman filter (PF-AKF) method in a wireless sensor network (WSN) framework is proposed in this paper. The method employs polynomial fitting to accomplish both NLOS identification and distance prediction. Rather than employing standard deviation of all historical data as NLOS detection threshold, the proposed method identifies NLOS via deviation between fitted curve and measurements. Then, it processes the measurements with adjusted Kalman filter (AKF), conducting weighting filter in the case of NLOS condition. Simulations compare the proposed method with Kalman filter (KF), adjusted Kalman filter (AKF), and Kalman-based interacting multiple model (K-IMM) algorithms, and the results demonstrate the superior performance of the proposed method. Moreover, experimental results obtained from a real indoor environment validate the simulation results.

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