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In this short paper, we look at a real-world implementation of an algorithm to enhance localization of a moving object in an underwater distributed antenna system. We utilize OFDM modems which are capable of estimating the Doppler shift of a message when it is received and combine this with a time-di↵erence-of-arrival (TDOA) estimate. This combination serves to both enhance the position estimate and provide an accurate estimate of the velocity of the mobile network element, thus providing a full-state point estimate. A Kalman Filter (KF) is implemented to compare the accuracy of the full-state and position-only estimates. Basic simulations show that the full-state estimate provides an improvement, and this is verified using data from several pool tests.

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In this paper, we consider a problem of localizing a moving object in the context of an underwater distributed antenna system. All the distributed nodes record the time-of-arrivals of a single message from the moving object and obtain the Doppler speed estimates used in the decoding process. Conventional methods have relied only on the time-of-arrival measurements to obtain position estimates, on top of which filtering methods can be further applied for tracking purposes. In this work, we explore the usefulness of Doppler speed information. The combination of time-of-arrival measurements and Doppler speed estimates associated with a single message enhances the accuracy of position estimation and provides an estimate of the velocity of the mobile object. A Kalman Filter (KF) and a Probabilistic Data Association Filter (PDAF) are further implemented processing the point estimates. Simulations show that the proposed Doppler-aided methods improve both the point estimation and tracking filter performance, which is verified using data from pool tests.

No abstract