Abstract
Primary sensor used in any modern air combat tactical aerospace vehicle for engaging moving or fixed targets is a seeker. The on-board sensor i.e. seeker gives the target measurements with which tactical aerospace vehicles are capable of tracking the target and adjust its path to hit the target. Tactical aerospace vehicles are equipped with Radio Frequency (RF) passive seekers in the nose cone to track RF signals from the emitters (ground Radars). Seeker gives target information in terms of two gimbal angles in elevation and azimuth planes. It does not provide closing range, velocity and LOS rates which are required for Proportional Navigation (PN) guidance law for steering the vehicle towards target. The main aim of the research is to estimate the Line of Sight (LOS) rates from the noisy measurement of elevation and azimuth gimbal angles of a seeker. A four state process model is used to implement Extended Kalman Filter (EKF) which estimates unknown LOS rates from the available measurements. The state consists of gimbal angles and LOS rates in elevation and azimuth planes. The vehicle mathematical model is developed along with control, guidance and navigation models to validate the performance of EKF in the closed loop. Influence of initial errors on miss distance is investigated. The design of EKF algorithm is tuned for faster convergence.
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Acknowledgements
The Authors express gratitude to shri BHVS Narayana Murthy, Director, RCI and shri L Sobhan Kumar, Outstanding Scientist and Director, DHILS, RCI for their encouragement and support to write this paper. The authors thank Mr MVKS Prasad and Mr Samir Patel, RCI, DRDO for their continuous support. The authors also thank Mr P.K. Tiwari, Mr Anil Kumar and Mr Vijay Hirwani, DRDL, DRDO for their valuable inputs.
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Kranthi Kumar, R., Sandhya, R., Laxman, R., Chandrakanth, A. (2020). LOS Rate Estimation Using Extended Kalman Filter. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-030-24318-0_5
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DOI: https://doi.org/10.1007/978-3-030-24318-0_5
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