Abstract
There are shortcomings of the RSSI-based measurements in localisation and tracking in real time and further applying prediction on those values can really lead to an increased error distribution due to environmental noise and path loss issues during location extraction. In order to improve on prediction from the previously known coordinates which are in turn calculated via the signal strength correspondence with the distances, we have proposed a scheme which uses an interpolation technique for skilful path and velocity prediction and is more powerful than the traditionally used newton’s interpolation as it even works for un-spaced discrete values with its counterpart less efficient in that case. We are using the more efficient Lagrange’s Interpolation scheme. Apart from that we are keeping a re-testifying phase to assure the current moment path of target node is logically in resonance with the coordinates we are finding consecutively finding by RSSI-based measurements and obviously the signal strength value dataset need to be filtered at the required step we are using a very powerful filter used nowadays along with the machine learning algorithms i.e. the Kalman Filter which smoothly tracks the noise distribution pattern as we are assuming a Gaussian noise distribution in RSSI-values scheme.
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Mishra, P., Tripathi, A., Bajpai, A., Tiwari, N. (2021). Hybrid Approach for Trajectory Identification of Mobile Node via Lagrange Interpolation and Kalman Filtering Framework. In: Abraham, A., Siarry, P., Ma, K., Kaklauskas, A. (eds) Intelligent Systems Design and Applications. ISDA 2019. Advances in Intelligent Systems and Computing, vol 1181. Springer, Cham. https://doi.org/10.1007/978-3-030-49342-4_51
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