Localization Performance Evaluation of Extended Kalman Filter in Wireless Sensors Network

Khan, Rafiullah; Khan, Shahbaz; Khan, Shahid A.; Khan, Mohammad Usman Ali

doi:10.1016/j.procs.2014.05.405

Cited by 36 publications

(35 citation statements)

References 5 publications

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“…There are many measurements parameter which can be used for determining the position of the node. Those are estimated based on the RSSI measurement, TOA (time of arrival or time difference of arrival [1], [2], [4], [13]. Many applications, such as tracking the package in warehouse require the deployment of mobile ad hoc networks.…”

Section: A Related Workmentioning

confidence: 99%

“…Typically, there are several measurement types of positioning methods, depending on obtaining the distance estimation between two nodes. The methods are RSSI (Received Signal Strength Indicators), AOA (Angle of Arrival), TDOA (Time Difference of Arrival) and TOA (Time of Arrival) [1]- [2], [4]. Although TOA and AOA have more accurate estimation compared to the other methods, this method require high-cost hardware which is making difficult implementation in some application [3].…”

Section: Introductionmentioning

confidence: 99%

“…When the mobile node is moving to different position, it will be communicated or received RSSI data from the other reference node based on its maximum coverage [7]. The position is obtained using trilateration method which is formed by intersection circle from three nearest distance of anchor nodes [4]- [6], [8].…”

Section: Introductionmentioning

confidence: 99%

“…Kalman filter and its nonlinear extension, i.e. extended Kalman filter (EKF) provide a feasible solution to mitigate propagation effects and therefore it improves the accuracy of mobile target tracking in the indoor wireless environment [4], [9]. The EKF performs well for systems with mildly nonlinear functions, but if the measurement data is strongly nonlinear (such as tracking system), the performance of the filter deteriorates [11].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System

Ainul

Kristalina

Sudarsono

2017

International Journal on Advanced Science, Engineering and Information Technology

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Tracking a mobile node using wireless sensor network (WSN) under cooperative system among anchor node and the mobile node, has been discussed in this work, interested to the indoor positioning applications. Developing an indoor location tracking system based on received signal strength indicator (RSSI) of WSN is considered cost effective and the simplest method. The suitable technique for estimating position out of RSSI measurements is the extended Kalman filter (EKF) which is especially used for nonlinear data as RSSI. In order to reduce the estimated errors from EKF algorithm, this work adopted forward data processing of the EKF algorithm to improve the accuracy of the filtering output, its called iterated extended Kalman filter (IEKF). However, using IEKF algorithm should know the stopping criterion value that is influenced to the maximum number iterations of this system. The number of iterations performed will be affected to the computation time although it can improve the estimation position. In this paper, we propose modified IEKF for mobile cooperative tracking system within only 4 iterations number. The illustrated results using RSSI measurements and simulation in MATLAB show that our proposed method has the capability to reduce error estimation percentage up to 19.3% , with MSE (mean square error) 0.88 m compared with a conventional IEKF algorithm with MSE 1.09 m. The time computation performance of our proposed method achieved in 3.55 seconds which is better than adding more iteration process.

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Section: A Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System

Ainul

Kristalina

Sudarsono

2017

International Journal on Advanced Science, Engineering and Information Technology

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“…Jianping et al used the KF algorithm for mobile node localization in the complex mine environment [17]. Moreover, Rafiullah Khan et al proposed a localization method based on extended KF algorithm [18].…”

Section: Introductionmentioning

confidence: 99%

Mobile node localization method based on KF-LSSVR algorithm

Zhang

Wang

et al. 2018

J Wireless Com Network

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As the mobile node localization algorithm in three-dimensional environment cannot meet the demand of actual application, a hybrid mobile node localization algorithm for a wireless sensor network (WSN) in three-dimensional environment is proposed in this paper, which is based on the least squares support vector regression (LSSVR) and Kalman filter (KF). The proposed algorithm firstly constructs the LSSVR localization model by sampling measurement area and training sample sets. Then, the KF model is used to iterate and correct the measured distance in order to obtain the distance between the unknown node and each anchor node. Finally, the LSSVR localization model is employed to obtain the estimated location of the unknown node. The experiments were conducted and the experimental results were analyzed according to ranging errors, anchor node density, communication radius, moving speed, and node localization errors. Simulation results show that the proposed algorithm using a joint KF and LSSVR algorithm is superior to the KF algorithm and the LSSVR algorithm, and it can reduce the localization errors and improve the localization accuracy.

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Location prediction algorithm for a nonlinear vehicular movement in VANET using extended Kalman filter

Jaiswal

Jaidhar

2016

Wireless Netw

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Localization Performance Evaluation of Extended Kalman Filter in Wireless Sensors Network

Cited by 36 publications

References 5 publications

Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System

Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System

Mobile node localization method based on KF-LSSVR algorithm

Location prediction algorithm for a nonlinear vehicular movement in VANET using extended Kalman filter

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