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ONLINE ESTIMATION AND SMOOTHING OF A TARGET TRAJECTORY IN MIXED STATIONARY/MOVING CONDITIONS

Citation Author(s):
Angelo Coluccia, Alessio Fascista, Giuseppe Ricci
Submitted by:
Alessio Fascista
Last updated:
11 May 2019 - 12:32pm
Document Type:
Poster
Document Year:
2019
Event:
Presenters:
Alessio Fascista
Paper Code:
3143

Abstract

A novel maximum likelihood trajectory estimation algorithm for targets in mixed stationary/moving conditions is presented. The proposed approach is able to estimate position and velocity of the target over arbitrary complex trajectories, while explicitly taking into account the possibility of stop&go motion. Moreover, a novel trajectory reconstruction method based on the theory of Bezier curve is developed for online smoothing of the trajectory, which keeps the advantages of Bayesian smoothing while introducing only a fixed lag in the estimation process. The performance assessment, conducted on both simulated and real data, shows that the proposed approach can outperform classical Kalman filter and Rauch-Tung-Striebel smoother techniques.

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Poster ICASSP 2019

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