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2022 | OriginalPaper | Buchkapitel

5. KF-Based Target L&T Using RSSI

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Abstract

The KF is the one of the most widely used approaches to solve the problem of target L&T using RSSI measurements. However, very few existing KF-based research works address the issues such as abrupt changes in the target velocity and variation in the target trajectories. The existing KF-based L&T algorithms though perform well for one type of target track; it is guaranteed to perform if the target track changes or the monitoring area is enlarged. It is very difficult to achieve high tracking accuracy with the help of traditional techniques such as trilateration alone in this context. Hence, the combination of the trilateration and KF can be a very good option to deal with the abovementioned issues. This chapter presents two range-based KF algorithms, namely, trilateration+KF and trilateration+UKF, to deal with these important issues. The chapter discusses the performance of the trilateration+KF and trilateration+UKF L&T algorithms for the changes in the target velocity trajectory, target trajectory, as well as WSN monitoring area. The proposed techniques are tested for linear as well as nonlinear target trajectories. The WSN monitoring (simulation) area is varied from 100 m × 100 m to 200 m × 200 m. To understand the effect of abrupt variations in the target velocity, we varied velocity abruptly in the range of −2 to 7 m/s at specific time instances in all the three cases. The overall target L&T performance is evaluated in terms of the localization error and RMSE. The results confirmed that the proposed target L&T algorithms are able to track the moving target with the help of WSN, irrespective of the dynamicity of the given RF environment.

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Literatur
1.
Zurück zum Zitat S.R. Jondhale, R.S. Deshpande, Kalman filtering framework based real time target tracking in wireless sensor networks using generalized regression neural networks. IEEE Sensors J. 19, 224–233 (2018)CrossRef S.R. Jondhale, R.S. Deshpande, Kalman filtering framework based real time target tracking in wireless sensor networks using generalized regression neural networks. IEEE Sensors J. 19, 224–233 (2018)CrossRef
3.
Zurück zum Zitat S.R. Jondhale, R.S. Deshpande, Modified Kalman filtering framework based real time target tracking against environmental dynamicity in wireless sensor networks. Ad Hoc Sensor Wirel. Netw. 40(1–2), 119–143 (2018) S.R. Jondhale, R.S. Deshpande, Modified Kalman filtering framework based real time target tracking against environmental dynamicity in wireless sensor networks. Ad Hoc Sensor Wirel. Netw. 40(1–2), 119–143 (2018)
Metadaten
Titel
KF-Based Target L&T Using RSSI
verfasst von
Satish R. Jondhale
R. Maheswar
Jaime Lloret
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-030-74061-0_5

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