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Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System

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@article{IJASEIT2657,
   author = {Rafina Destiarti Ainul and Prima Kristalina and Amang Sudarsono},
   title = {Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {3},
   year = {2017},
   pages = {980--992},
   keywords = {Mobile cooperative tracking; WSN; RSSI; EKF; IEKF; modified IEKF.},
   abstract = {

Tracking a mobile node using wireless sensor network (WSN) under cooperative system among anchor node and 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 non linear 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 ilustrated results using RSSI measurements and simulation in MATLAB show that our propose method have capability to reduce error estimation percentage up to 19.3% , with MSE (mean square error) 0.88 m compared with conventional IEKF algorithm with MSE 1.09 m. The time computation perfomance of our propose method achived in 3.55 seconds which is better than adding more iteration process.    

 

 
},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2657},    doi = {10.18517/ijaseit.7.3.2657} }

EndNote

%A Ainul, Rafina Destiarti
%A Kristalina, Prima
%A Sudarsono, Amang
%D 2017
%T Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System
%B 2017
%9 Mobile cooperative tracking; WSN; RSSI; EKF; IEKF; modified IEKF.
%! Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System
%K Mobile cooperative tracking; WSN; RSSI; EKF; IEKF; modified IEKF.
%X 

Tracking a mobile node using wireless sensor network (WSN) under cooperative system among anchor node and 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 non linear 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 ilustrated results using RSSI measurements and simulation in MATLAB show that our propose method have capability to reduce error estimation percentage up to 19.3% , with MSE (mean square error) 0.88 m compared with conventional IEKF algorithm with MSE 1.09 m. The time computation perfomance of our propose method achived in 3.55 seconds which is better than adding more iteration process.    

 

 
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2657 %R doi:10.18517/ijaseit.7.3.2657 %J International Journal on Advanced Science, Engineering and Information Technology %V 7 %N 3 %@ 2088-5334

IEEE

Rafina Destiarti Ainul,Prima Kristalina and Amang Sudarsono,"Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 3, pp. 980-992, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.3.2657.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Ainul, Rafina Destiarti
AU  - Kristalina, Prima
AU  - Sudarsono, Amang
PY  - 2017
TI  - Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 3
Y2  - 2017
SP  - 980
EP  - 992
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Mobile cooperative tracking; WSN; RSSI; EKF; IEKF; modified IEKF.
N2  - 

Tracking a mobile node using wireless sensor network (WSN) under cooperative system among anchor node and 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 non linear 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 ilustrated results using RSSI measurements and simulation in MATLAB show that our propose method have capability to reduce error estimation percentage up to 19.3% , with MSE (mean square error) 0.88 m compared with conventional IEKF algorithm with MSE 1.09 m. The time computation perfomance of our propose method achived in 3.55 seconds which is better than adding more iteration process.    

 

 
UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2657 DO - 10.18517/ijaseit.7.3.2657

RefWorks

RT Journal Article
ID 2657
A1 Ainul, Rafina Destiarti
A1 Kristalina, Prima
A1 Sudarsono, Amang
T1 Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 3
YR 2017
SP 980
OP 992
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Mobile cooperative tracking; WSN; RSSI; EKF; IEKF; modified IEKF.
AB 

Tracking a mobile node using wireless sensor network (WSN) under cooperative system among anchor node and 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 non linear 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 ilustrated results using RSSI measurements and simulation in MATLAB show that our propose method have capability to reduce error estimation percentage up to 19.3% , with MSE (mean square error) 0.88 m compared with conventional IEKF algorithm with MSE 1.09 m. The time computation perfomance of our propose method achived in 3.55 seconds which is better than adding more iteration process.    

 

 
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2657 DO - 10.18517/ijaseit.7.3.2657