INtroductIoNVarious missile guidance laws have been developed and studied since the World War II. The proportional navigation 1 (PN) is quite celebrated among these laws, due to its simplicity and efficiency. First studies on modern guidance laws based on modern control and estimation theory were conducted during 1960's. However at that time, implementation of these methods was not feasible because of the computational restrictions. Yet, with the proceeding technology and increasing capability of the targets, the aforementioned attitude towards modern laws began to change during late 1970's 2-8 . The problem of coping with modern day targets (e.g., intercepting a hostile aircraft) with advanced manoeuverability skills, calls for utilisation of estimation techniques within guidance algorithms. By making use of these estimation methods, it is possible to predict the future trajectory of the target. The basic idea behind predictive guidance is enabling the interceptor to take advantage of the estimated future trajectory of the target and modify the guidance law according to these estimations. Talole & Banavar 9 have shown that the PN law modified with predictive control is superior to the PN law itself in terms of control effort. Prabhakar 10 , et al. demonstrated that predictive guidance is capable of exhibiting a significantly improved performance. However, most of the existing predictive guidance laws assume full or partial knowledge of the target's dynamics. In this paper, estimations are based on a learning process, which utilises the noisy measurement of the target positions by passing them through a recursive least squares (RLS) estimation algorithm. This study presents a predictive guidance scheme for tactical missiles. The modern day targets, with improved manoeuverability, have revealed insufficient performance of the conventional guidance laws. The underlying cause of this poor performance is the reactive nature of the conventional guidance laws such as proportional navigation (PN) and pure pursuit (PP). Predictive guidance offers an alternative approach to the classical methods by taking proactive actions by estimating target's future trajectory. However, most of the existing predictive guidance approaches assume that the interceptor have a model of the target dynamics. A guidance strategy is developed in this study, that can learn the target dynamics iteratively and adapt the interceptor actions accordingly. A recursive least squares (RLS) estimation algorithm is employed for learning and estimating the possible future target positions, and a fixed horizon nonlinear program is employed for selecting the optimal interception action. Monte-Carlo simulations show that the guidance algorithm introduced in this work demonstrates a significantly improved performance compared to the alternatives in terms of interception time and miss distance.
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