A Whitening-Based Tracking Algorithm for Autonomous Underwater Vehicles

Development of environmental niche models for use in underwater vehicle navigation

“…The aforementioned compromise between limiting time spent underwater in favour of positional accuracy, or the converse, illustrates the need for a novel methodology of localisation underwater. Previously, work by Reis and colleagues proposed augmenting traditional terrain-based navigation (TBN) in an underwater environment to improve localisation techniques devoid of a GPS signal [29,34,35]. TBN was initially developed for the localisation of long-range missiles and involves taking repeated altimeter measurements and comparing those to altimeter data in a stored array.…”

“…Reis et al [29,35] incorporated an array of sensors to augment altimeter data in traditional TBN to shorten the convergence time for localisation and enable navigation over regions with low bathymetric relief. The principle of augmenting TBN with in situ measurements of physical water data is the novel contribution of this work and where the concept of an environmental niche model emerges into the application of navigation for underwater robots.…”

“…However, our ATBN map is optimised to have the lowest global autocorrelation; a sequence of values has a high probability of being unique. For details on how this is accomplished, see [29,35]. Specifically, suppose an underwater robot is given a map of the ocean environment.…”

“…The goal of the AUV is to move within that environment, collecting water parameter data, e.g. temperature, salinity, dissolved oxygen, etc., combine these parameters using our innovative weighting scheme, see [29,35], and eliminate uncertainty about where it is located in the map.…”

“…This allows us to track the position of the vehicle within small intervals of time (less than an hour) until the GPS signal returns. Details of this decorrelation can be found in [35,42].…”