Improving Self-Alignment of Strapdown INS using Measurement Augmentation Arunasish Acharya School of Education Technology Jadavpur University Kolkata, India. arunasisha@yahoo.com Smita Sadhu Electrical Engineering Department Jadavpur University Kolkata, India. ssadhuju@gmail.com T.K.Ghoshal Electrical Engineering Department Jadavpur University Kolkata, India. tkghoshal@gmail.com Abstract - An alternative solution to the velocity based self-alignment of strapdown inertial navigation system is proposed. It is well known that a simple Kalman filter based solution to the above problem fails to provide accurate azimuth alignment due to the inherent lack of observability of the model in the presence of instrument bias. Earlier researchers use external digital filters to obtain improved estimation of selected states and substitute these into the filter. The current paper demonstrates that a simple augmentation of the output vector with the inertial measurement unit signals and an extended Kalman filter would yield similar or better alignment performance compared to such ad hoc additional digital filters. The proposed method improves the convergence rate of azimuth attitude error even in the presence of gyro bias and makes it relatively independent of the gyro noise. Results of comparative performance of the two filters using Monte Carlo simulation have been provided. Keywords: Self-Alignment, Strapdown Inertial Navigation System, Filtering, Extended Kalman filter. 1 Introduction This paper addresses self-alignment (that is without external aids) of a strapdown inertial navigation system (INS) [1,2] in near stationary condition, where the knowledge of the local gravity (vertical), latitude and earth’s spin rate are utilized and additional instrumentation is not necessary. Such initial self-alignment in stationary platform is often used for tactical missiles and autonomous ground vehicles. Use of “raw” IMU (inertial measurement unit, comprising of rate gyro and accelerometer) outputs alone can provide only a coarse alignment due to poor signal to noise ratio of IMU’s on stationary platforms [3]. For “fine” alignment, it is customary to use the velocity outputs from the INS (after the due integrations are performed by the navigation software) and from a previously aligned reference INS in a so-named “transfer alignment” [1] configuration, employing Kalman filter (KF) based estimators. For “self-alignment” (that is without the aid of a reference IMU) in stationary base and in presence of instrument biases, velocity based scheme produces unacceptable results, especially in correcting azimuth orientation errors due to observability problem [4, 5, 6]. As to be demonstrated subsequently, the poor observability manifests as inaccurate alignment (the azimuth misalignment error being large and roughly proportional to gyro bias) coupled with slow convergence in azimuth. To overcome the observability problem, both [5] and [6] use ad hoc first order digital filters on the gyro measurement signal or on the estimated tilt axes alignment errors respectively. Such filtered output is algebraically combined and substituted in the KF update equations to obtain a more accurate estimate of the azimuth misalignment and a faster convergence. In [6], in particular, the improved estimate of the azimuth misalignment is obtained through approximate algebraic relations involving north axis error and east axis error rate. As the true value of these quantities are unknown, noisy estimates of the azimuth error were filtered and then a KF was applied. The technique used in [5], on the other hand, utilizes filtered gyro measurement to reconstruct estimates about the gyro biases. These values are then substituted to the KF update equations. The present work reports an alternative possibility of using the gyro outputs from the IMU, along with the velocity outputs in a nonlinear state estimation framework, without taking recourse to ad hoc digital filtering and demonstrates that improvement in both convergence rate and residual alignment are possible compared to a (linear) KF implementation. Performance of the proposed scheme has been compared with that obtained in [5]. For a better and more objective comparison, root mean square error (RMSE), its sensitivity to change in instrument bias and measurement error covariances and its convergence rates have been computed for the proposed and earlier scheme [5]. 12th International Conference on Information Fusion Seattle, WA, USA, July 6-9, 2009 978-0-9824438-0-4 ©2009 ISIF 1783