Experimental investigation and comparison of nonlinear Kalman filters

One of the most important problems when designing controller is how to deal with all kinds of uncertainties, which, along with the high nonlinearities of most real systems, makes it difficult to guarantee the desired closed loop performance. Recently, nonlinear Kalman-class filter has been extensively researched and several well-known algorithms, including Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Adaptive Unscented Kalman Filter (AUKF), have been reported to be applicable in some cases. In this paper, on the basis of the moving target cooperative observation problem, performances of these nonlinear filter algorithms are analyzed and tested on a multi-flying-robot testbed, and the experimental results are listed to show the advantages and disadvantages of them.

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