Target Tracking Using Machine Learning and Kalman Filter in Wireless Sensor Networks

Mahfouz, Sandy; Mourad-Chehade, Farah; Honeiné, Paul; Farah, Joumana; Snoussi, Hichem

doi:10.1109/jsen.2014.2332098

Cited by 110 publications

(74 citation statements)

References 28 publications

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“…d est (1,2,3) is the distance estimation of the mobile node to the anchor node that was obtained from PLE value eq. (17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27).…”

Section: E Proposed Modified Iekf Algorithmmentioning

confidence: 99%

“…Another application of WSN is target tracking system that can be viewed as a sequential localization. The target tracking system is estimating directly the object which is requiring a real-time location estimation algorithm [3].…”

Section: Introductionmentioning

confidence: 99%

“…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]. RSSI-based positioning methods are used as far as they require no complex measurement hardware which is measured the power received from the transmitted radio signal antenna [5].…”

Section: Introductionmentioning

confidence: 99%

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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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“…d est (1,2,3) is the distance estimation of the mobile node to the anchor node that was obtained from PLE value eq. (17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27).…”

Section: E Proposed Modified Iekf Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…In paper [12], author proposed RSSI scheme for target movement detection using the signal strength of sensor nodes. In WSN, sensor nodes continuously communicate with each other by exchanging the messages.…”

Section: ) Bss Algorithm 2) Clustering and Selecting Algorithmmentioning

confidence: 99%

Energy Efficient Target Tracking Scheme for Wireless Sensor Networks

2016

IJSR

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Target tracking in WSN has a lot of applications in military fields and surveillance purpose. Target tracking in WSN is more challenging because WSNs have issues such as limited battery power, unpredictable environments, high mobility of nodes as well as targets and failure of sensor nodes at runtime etc. This paper proposes a new polygon based target-tracking scheme, which is a predictive, and cluster based scheme. In this scheme, the clusters are polygon shaped, so the whole network is arranged in the form of polygons. The sensor nodes are interconnected with their neighboring nodes to form the edges of the polygons. Proposed scheme is more energy efficient than the existing schemes because only selected nodes of the polygon are kept active during the tracking process rather than keeping all the nodes active. Edge detection algorithm is used to send the message to the sensors of the next polygon and activate them before target's arrival in that polygon. A-star algorithm is used to choose the optimal nodes and to find the optimal routing path from source node to the sink. This routing scheme improves the energy efficiency as well as tracking accuracy of WSN by forwarding the target-tracking message via the optimal shortest path.

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“…In recent years, target tracking in wireless sensor networks (WSN) has received great attention in many fields such as mobile station localization, search-rescue, robotic navigation, and autonomous surveillance [1][2][3][4][5]. Target tracking can be viewed as a sequential localization problem via noisy measurements.…”

Section: Introductionmentioning

confidence: 99%

RSS-based joint detection and tracking in mixed LOS and NLOS environments

Jia

2015

Digital Signal Processing

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Target Tracking Using Machine Learning and Kalman Filter in Wireless Sensor Networks

Cited by 110 publications

References 28 publications

Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System

Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System

Energy Efficient Target Tracking Scheme for Wireless Sensor Networks

RSS-based joint detection and tracking in mixed LOS and NLOS environments

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