425 Received 02 June 2015, revised 25 August 2015, online published 10 November 2015 Defence Science Journal, Vol. 65, No. 6, November 2015, pp. 425-430, DOI : 10.14429/dsj.65.8874  2015, DESIDOC Fusion of Redundant Aided-inertial Sensors with Decentralised Kalman Filter for Autonomous Underwater Vehicle Navigation Vaibhav Awale # and Hari B. Hablani * # Department of Aerospace Engineering, Indian Institute of Technology Bombay, Mumbai - 400 076, India * Department of Aerospace Engineering, Indian Institute of Technology, Gandhinagar - 382 424, India * E-mail: hbhablani@iitgn.ac.in ABSTRACT Most submarines carry more than one set of inertial navigation system (INS) for redundancy and reliability. Apart from INS systems, the submarine carries other sensors that provide different navigation information. A major challenge is to combine these sensors and INS estimates in an optimal and robust manner for navigation. This issue has been addressed by Farrell 1 . The same approach is used in this paper to combine different sensor measurements along with INS system. However, since more than one INS system is available onboard, it would be better to use multiple INS systems at the same time to obtain a better estimate of states and to provide autonomy in the event of failure of one INS system. This would require us to combine the estimates obtained from local flters (one set of INS system integrated with external sensors), in some optimal way to provide a global estimate. Individual sensor and IMU measurements cannot be accessed in this scenario. Also, autonomous operation requires no sharing of information among local flters. Hence a decentralised Kalman flter approach is considered for combining the estimates of local flters to give a global estimate. This estimate would not be optimal, however. A better optimal estimate can be obtained by accessing individual measurements and augmenting the state vector in Kalman flter, but in that case, corruption of one INS system will lead to failure of the whole flter. Hence to ensure satisfactory performance of the flter even in the event of failure of some INS system, a decentralised Kalman fltering approach is considered. Keywords: Decentralised Kalman flter, inertial navigation, redundant navigation, autonomous underwater vehicle navigation NomeNClATURe p AUV position vector v e Velocity reative to earth a Acceleration f Specifc force vector g Gravity vector w Angular rate vector q Tangent top late form frame Euler angle three-tuple b a Accelerometer r bias vector b g Gyro bias vector j,q,y Roll, pitch, and yaw Euler angles f Latitude a b R transformation matrix from coordinate system a to b SUBSCRipTS t = tangent p = platform e = earth fixed frame 1. iNTRoDUCTioN The current major oceanic vehicles carry more than one set of inertial navigation system (INS). The primary reason to have multiple INS systems is to provide reliability in the event of failure. However before a failure occurs, navigation solution from different INSs may be fused to obtain a better estimate of position, velocity and attitude. The submarine carries different navigation systems, each of them providing their optimal estimate of vehicle’s navigation state. There is no luxury of having access to individual sensor measurements. Only the navigation system and their navigation solution are available. The main objective is fusing these different navigation solutions from different navigation systems to get best estimate of navigation solution. This leads us to the design of a decentralised Kalman flter, in which local flters provide local estimates and a master flter is used to combine all these local estimates to arrive at an optimal global estimate. Study of multiple INSs systems for an oceanic vehicle is done by Christopher 5 , et al. Development of a robust algorithm to account for delay in operation of acoustic sensors in an extended Kalman flter is done by Miller & Farrell 1 . The analysis presented herein is adopted from Farrell 2 . 2. KiNemATiC moDel The platform frame represents a frame attached to the vehicle, and the IMU is mounted on the platform.