Abstract
The multi-sensor information fusion particle filter (PF) has been put forward for nonlinear systems with correlated noises. The proposed algorithm uses the Taylor series expansion method, which makes the nonlinear measurement functions have a linear relationship by the intermediary function. A weighted measurement fusion PF (WMF-PF) was put forward for systems with correlated noises by applying the full rank decomposition and the weighted least square theory. Compared with the augmented optimal centralized fusion particle filter (CF-PF), it could greatly reduce the amount of calculation. Moreover, it showed asymptotic optimality as the Taylor series expansion increased. The simulation examples illustrate the effectiveness and correctness of the proposed algorithm.
Highlights
In the last few years, filtering algorithms for nonlinear systems have attracted a great deal of attention from many scholars due to their wide applications such as guidance, signal processing, aircraft attitude estimation, trajectory tracking, and so on [1,2,3,4,5,6,7,8]
We present a polynomial intermediary function obtained by the Taylor series expansion method that can overcome the constraints of Theorem 2, so that the weighted measurement fusion system (WMFS) can be obtained via Theorem 3
Taylor series expansion (WMF-PF2) tracking curve is shown in Figure 3 where it can be seen that the tracking effect was good
Summary
In the last few years, filtering algorithms for nonlinear systems have attracted a great deal of attention from many scholars due to their wide applications such as guidance, signal processing, aircraft attitude estimation, trajectory tracking, and so on [1,2,3,4,5,6,7,8]. This paper proposes a multi-sensor information fusion filter for nonlinear systems with correlated noise. In order to ensure the accuracy of the estimation, a kind of centralized fusion method, the weighted measurement fusion (WMF) algorithms, was adopted in this paper. A WMF algorithm was proposed for nonlinear systems with correlated noises by using the weighted least squares (WLS) method and Taylor series expansion. It can compress a high-dimensional measurement into a low-dimensional measurement for nonlinear measurements. The proposed filter could solve the fusion estimation problem for multi-sensor nonlinear systems with correlated measurement noises and the system noises.
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