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In many engineering applications, both state and parameter models are nonlinear. We consider a recursive algorithm for joint state and parameter estimation where the gradient of the prediction error is used to tune the approximate nonlinear filter adaptively. We apply cubature Kalman filter to derive the recursive state and parameter update steps and discuss the computational complexity of the overall...
Distributed Kalman filters are often used in multisensor target tracking where the fusion center receives local estimates and fuses them to obtain the global target state estimate. With such a fusion architecture, each local tracker can communicate less frequently with the fusion center than the local filter update rate. The global target state estimate via track fusion is usually less accurate than...
In this paper, we compare several nonlinear filtering methods, namely, extended Kalman filter (EKF), unscented filter (UF), particle filter (PF), and linear minimum mean square error (LMMSE) filter for a ballistic target tracking problem. We cast EKF and UF into a general linear recursive estimation framework and reveal their pros and cons. We pinpoint using the LMMSE filter for possible analytical...
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