Transfer Alignment Technique for Shipboard Missile Strapdown Inertial Navigation System using an Adaptive Kalman Filter

Abstract

Missile Guidance system needs accurate estimates from Inertial Navigation System (INS) for guiding
 the vehicle towards the target. In this paper a target point, specified before launch, in a battlefield scenario
 is considered for a landmark using missile Strap Down Inertial Navigation System (SDINS) aided by Master
 INS (MINS) placed on a moving platform. Azimuth information of the missile is one of the most critical
 navigation states for estimation on the moving platform before launching the missile for precise impact.
 An Adaptive Kalman Filter (AKF) based on the error state model is formulated. The 7-state AKF with 4-measurement forms the core, where the filter gain of the innovation sequence (measurements) is
 evaluated. This approach of adaptively computing the gain is tested in a laboratory, on a van and in a ship
 trial, culminating in a successful guided missile launch. Mean and the covariance of the measurement
 residuals were used in a unique way to compute adaptive gain after the accumulation of initial samples.
 A Master INS (with advanced Gyros) whose accuracy is much higher than the accuracy of the missile’s
 SDINS is used for velocity matching algorithm before the launch with execution of an S-maneuver for generation
 of accelerations towards observing the states more appropriately. Estimated error states were used in a feedback mode to get near the true orientation of the Missile’s slave INS. Error quaternions are used for this purpose in the feedback and the gains were selected using offline matrix Riccati equation solution in a discrete domain as used in the modern control system. The results were very encouraging with less than 5 arc minutes of error in azimuth.