Research article
Application of Cubature Information Filter for Underwater Target Path Estimation
https://doi.org/10.47836/pjst.29.3.07KeywordsBearings-only tracking, cubature Kalman filter, information filter, state estimation, stochastic signal processing
Article content
Abstract
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.
Supporting literature
References
- Arasaratnam, I., & Chandra, K. B. (2015). Cubature information filters: Theory and applications to multisensor fusion. In Multisensor Data Fusion (pp. 216-229). CRC Press.
- Arasaratnam, I., & Haykin, S. (2009). Cubature kalman filters. IEEE Transactions on Automatic Control, 54(6), 1254-1269. https://doi.org/10.1109/TAC.2009.2019800
- Ding, Z., & Balaji, B. (2012, October 22-25). Comparison of the unscented and cubature Kalman filters for radar tracking applications. In IET International Conference on Radar Systems (Radar 2012). Glasgow, UK. https://doi.org/10.1049/cp.2012.1695
- Jiang, H., & Cai, Y. (2018). Adaptive fifth-degree cubature information filter for multi-sensor bearings-only tracking. Sensors, 18(10), Article 3241. https://doi.org/10.3390/s18103241
- Koteswararao, S. (2018). Bearings-only tracking: Observer maneuver recommendation. IETE Journal of Research, 67(2), 193-204. https://doi.org/10.1080/03772063.2018.1535917
- Mutambara, A. G. O. (1998). Decentralized estimation and control for multisensor systems. CRC Press.
- Pakki, K., Chandra, B., Gu, D. W., & Postlethwaite, I. (2011). Cubature information filter and its applications. In Proceedings of the 2011 American Control Conference (pp. 3609-3614). IEEE Conference Publication. https://doi.org/10.1109/ACC.2011.5990913
- Wan, E. A., & Van Der Merwe, R. (2000, October). The unscented Kalman filter for nonlinear estimation. In Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No. 00EX373) (pp. 153-158). IEEE Conference Publication. https://doi.org/10.1109/ASSPCC.2000.882463
