e-ISSN 2231-8542
ISSN 1511-3701
Guduru Naga Divya and Sanagapallea Koteswara Rao
Pertanika Journal of Tropical Agricultural Science, Volume 29, Issue 3, July 2021
DOI: https://doi.org/10.47836/pjst.29.3.07
Keywords: Bearings-only tracking, cubature Kalman filter, information filter, state estimation, stochastic signal processing
Published on: 31 July 2021
Bearings-only tracking plays a pivotal role in passive underwater surveillance. Using noisy sonar bearing measurements, the target motion parameters (TMP) are extensively estimated using the extended Kalman filter (EKF) because of its simplicity and low computational load. The EKF utilizes the first order approximation of the nonlinear system in estimation of the TMP that degrades the accuracy of estimation due to the elimination of the higher order terms. In this paper, the cubature Kalman filter (CKF) that captures the system nonlinearity upto third order is proposed to estimate the TMP. The CKF is further extended using the information filter (IF) to provide decentralized data fusion, hence the filter is termed as cubature information filter (CIF). The results are generated using Matlab with Gaussian assumption of noise in measurements. Monte-Carlo simulation is done and the results demonstrate that the CIF accuracy is same as that of UKF and this indicates the usefulness of the algorithm for state estimation in underwater with the required accuracy.
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ISSN 1511-3701
e-ISSN 2231-8542