A State Observation Technique for Highly Compressed Source Coding of Autonomous Underwater Vehicle Position

Abstract

Abstract-A novel technique is presented for using state observers in conjunction with an entropy source encoder to enable highly compressed telemetry of autonomous underwater vehicle (AUV) position vectors. In this work, both the sending vehicle and receiving vehicle or human operator are equipped with a shared real-time simulation of the sender's state based on the prior transmitted positions. Thus, only the innovation between the sender's actual state and the shared state need be sent over the link, such as a very low throughput acoustic modem. The distribution of this innovation can be modeled a priori or assembled adaptively. This distribution is then paired with an arithmetic entropy encoder, producing a very low cost representation of the vehicle's position vector.This system was analyzed on experimental data from the GLINT10 and AGAVE07 expeditions involving two different classes of AUVs performing a diverse number of maneuvers, and implemented on a fielded vehicle in the MBAT12 experiment. Using an adaptive probability distribution in combination with either of two state observer models, greater than 90% compression, relative to a 32-bit integer baseline, was achieved.

An Embedded Tactical Decision Aid Framework for Environmentally Adaptive Autonomous Underwater Vehicle Communication and Navigation

“…For all chosen weights, only β 6 and β 7 go beyond the a priori distribution when matching the microlens, further suggesting that it is an anomalous feature. The majority of chosen weights for all observations fall within the a priori distribution such that a probability distribution encoding may be of practical use [68]. The exceptions to this pattern, most evident in β 6 , may be easily remedied by extending the encoding domain.…”

UComms: A Conference and Workshop on Underwater Communications, Channel Modeling, and Validation

“…It is natural, then, to integrate communications with ranging and position fixing to provide important updates to the network, enabling efficient control and routing of assets. Schneider and Schmidt [15] look at smart ways of compressing regular navigational reports using an entropy encoder to track the innovations of a state model of vehicle position, and they show how considerable savings ( 90%) can be made with such adaptive compression techniques.…”