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

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

Information Aided Navigation: A Review

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.Comment: 8 figures, 3 table