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ROV enhancements

Aided inertial navigation system
Lead Scientist/Project Manager: Michael B. Matthews
Lead Engineer: Paul McGill

The term sensor fusion describes the process of assimilating measurements from a suite of sensors with widely varying sampling resolutions and latencies. By assimilation we mean combining the observational information into an optimal state estimate of the observed system. The sensors may exhibit time-varying behavior or state-dependent performance. They may be dynamically qualified within complex operating envelopes, or they may be dynamically removed from operation altogether as a result of component failure or scheduled servicing. New sensors may be dynamically added to the system in the absence of complete configuration information. Given such a dynamic suite of sensors, how do we determine in real time the optimal state estimate of the system?

Vehicle navigation is an extreme example of the dynamic sensor fusion problem. In the navigation problem, we wish to estimate the position and velocity of the vehicle given measurements from a suite of inertial, geomagnetic, and acoustic sensors. Each sensor is highly state dependent, each operates only in a small region of the state space, and each provides measurements of only a subset of the system states. Furthermore, the sampling rates and measurement latencies of the various sensors differ by two orders of magnitude. Given this dynamic situation, how do we estimate the position and velocity of the vehicle?

In the Kalman filter approach to the state estimation problem, the characteristics of each sensor are incorporated into the filter by augmenting the system model with a model of the sensor. The Kalman gain is computed from the augmented system model, and the state estimate is computed from the Kalman gain as a linear combination of both model predictions and sensor measurements. In the dynamic sensor problem described above, the situation becomes very complex as the system model is dynamically augmented and reduced with changing sensor complement.

Dynamic sensor fusion is a very general problem in the sense that basic theoretical results are generally applicable to a broad class of estimation problems including the multi-resolution field estimation problem currently under study. The practical implementation of these results may greatly influence the realization of the recommended MBARI ocean observing systems (MOOS) concept.

We propose (1) to study the aforementioned problem of dynamic sensor fusion from within the context of vehicle navigation, and (2) to design, build, and test a ring-laser-gyro-based navigation sensor data concentrator for ROV Tiburon