Abstract
In this work, a particle filter based on innovative adaptive estimation for tracking a moving target in 2D is proposed. Aiming to mitigate the uncertainty or lack of knowledge of the process and measurement noise covariance matrices, the particle filter is allied to an innovative adaptive estimation. For such purpose, the difference between the theoretical and measured innovation covariances is defined as an approximation that uses the average of a moving estimation window for the innovation sequence calculus. This difference is computed continuously, using innovative adaptive estimation based on the maximum likelihood theory to dynamically adjust the covariances of the particle filter. To illustrate the efficiency and applicability of the proposed filter, simulations are carried out for estimating the state of a moving target considering different scenarios. The simulation results show that the proposed filter performs well in terms of robustness compared to extended Kalman filter and classic particle filter. It is shown that, although the simultaneous adaptation of noise covariance matrices can generate instability in some cases, more accurate estimates can be obtained in others.
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Pinheiro, C.M., Serrepe Ranno, M.M., das Chagas de Souza, F. (2022). Tracking a Moving Target in 2D Using a Particle Filter Based on Innovative Adaptive Estimation. In: Brito Palma, L., Neves-Silva, R., Gomes, L. (eds) CONTROLO 2022. CONTROLO 2022. Lecture Notes in Electrical Engineering, vol 930. Springer, Cham. https://doi.org/10.1007/978-3-031-10047-5_56
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