Receding Horizon Unbiased FIR Filters and Their Application to Sea Target Tracking

Skorohod, Boris A.

doi:10.1155/2018/1803623

Cited by 4 publications

(2 citation statements)

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“…The disadvantage of this method is the lack of proof of its convergence. This paper deals with finite impulse response (FIR) filters for state estimation of linear discrete systems which are extensively employed in a variety of applications see for instance [30][31][32][33][34][35][36][37][38][39][40]. Unlike the KF, they allow to avoid the divergence and unsatisfactory object tracking connected with temporary perturbations, errors in the noise statistics setting, abrupt object changes [1,14,15].…”

Section: Introductionmentioning

confidence: 99%

“…In [56], it is proposed to jointly assess the state of the system and noise statistics using the optimal FIR with a fixed given size of sliding window and sequential noise statistics estimation method. Another approach ensuring optimality and unbiasedness in a finite number of steps is described in [32,57,58]. The receding horizon optimal unbiased FIR filter suggested by the authors uses known statistical information for parts of state vector components at starting points of sliding windows and a learning cycle is not required for its the initialization.…”

Section: Introductionmentioning

confidence: 99%

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Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

Skorohod

2023

Journal of Robotics and Control

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It is known, that unlike the Kalman filter (KF) finite impulse response (FIR) filters allow to avoid the divergence and unsatisfactory object tracking connected with temporary perturbations and abrupt object changes. The main challenge is to provide the appropriate choice of a sliding window size for them. In this paper, the new finite impulse response (FIR) filtering algorithm with the adaptive horizon size selection is proposed. The algorithm uses the receding horizon optimal (RHOFIR) filter which receives estimates, an abrupt change detector and an adaptive recurrent mechanism for choosing the window size. Monotonicity and asymptotic properties of the estimation error covariance matrix and the RHOFIR filter gain are established. These results form a solid foundation for justifying the principal possibility to tune the filter gain using them and the developed adaptation mechanism. The proposed algorithm (the ARHOFIR filter) allows reducing the impact of disturbances by varying adaptively the sliding window size. The possibility of this follows from the fact that the window size affects the filter characteristics in different ways. The ARHOFIR filter chooses a large horizon size in the absence of abrupt disturbances and a little during the time intervals of their action. Due to this, it has better transient characteristics compared to the KF and RHOFIR filter at intervals where there is temporary uncertainty and may provide the same accuracy of estimates as the KF in their absence. By simulation, it is shown that the ARHOFIR filter is more robust than the KF and RHOFIR filter for the temporarily uncertain systems.

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