State Estimation for Pursuer Guidance using Interacting Multiple Model based Augmented Extended Kalman filter

Kashyap, SK and Shantha Kumar, N and Naidu, VPS and Girija, G and Raol, JR (2007) State Estimation for Pursuer Guidance using Interacting Multiple Model based Augmented Extended Kalman filter. In: International Radar Symposium India, IRSI 2007, 10-13 Dec 2007, Bangalore, India.

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In pursuers or interceptors, active radar seeker is used to measure relative range, relative range rate, LOS angles and rates between pursuer and evader. Using these measurements which are contaminated by high degree of noise due to Glint, Radar Cross Section (RCS) fluctuation and thermal noise, the true LOS rates have to be estimated for PN (Proportional navigation) guidance of the pursuer [1]. For advanced proportional navigation (APN) guidance, target acceleration is also required. To achieve all these, an estimator or seeker filter is required which processes the seeker measurements recursively to obtain the signals required for guidance of pursuer towards evader. The design of the estimator is complex because not only the cumulative effects of the noise are highly non-Gaussian and time correlated but there is a periodic loss of seeker measurements due to eclipsing effects. Also the seeker measurements are available in inner Gimbal frame necessitating the use of an extended Kalman filter (EKF) for the estimator. In this paper, in order to handle the non Gaussian noise, an augmented EKF (AEKF) has been evolved using the models of RCS and glint noise effects as additional states. In any pursuer-evader engagement, the evader would execute maneuvers to avoid the pursuer. In order to track these maneuvering targets, Interacting multiple model or IMM [2] is generally used. IMM is essentially an adaptive estimator which is based on assumption that a finite number of models are required to characterize the target at all times and which takes care of model switching implicitly. In this paper, for pursuer-evader engagement applications, the IMM based on the soft switching between a set of pre-defined target models such as constant velocity, constant acceleration, constant jerk [3] has been implemented. At each instant of time, mode probability is calculated for each model using residual vector and innovation covariance matrix. Each of the mode matched filters used in IMM in this paper are based on the AEKF. It is expected that the estimator operating in closed loop for pursuer guidance applications, will produce low miss distances in any engagement scenario [4]. This could be achieved if the estimator produces estimates of LOS rates which have well attenuated noise characteristics with minimum lag. This paper presents the development and performance evaluation of an IMM based AEKF in closed-loop using simulated data of typical pursuer-evader engagement scenarios. The key contribution of this paper is the introduction of augmented states to handle glint noise and RCS fluctuation in an IMM framework. Also, the estimator provides an estimate of the target acceleration that could be used for Augmented Proportional Navigation based pursuer guidance. The performance of novel algorithm is evaluated in terms of estimated states compared with true ones, residuals w.r.t. theoretical bounds, attenuation factor, miss distance and time to intercept.

Item Type: Conference or Workshop Item (Paper)
Subjects: ENGINEERING > Communications and Radar
Depositing User: Dr Sudesh Kumar Kashyap
Date Deposited: 01 Jul 2010 06:59
Last Modified: 07 Jul 2010 06:18

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