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The EKF is a first-order approximation, which is achieved by linearizing the system about the current state estimate (i.e., the mean). In some cases, the EKF is not stable due to nonlinearities. For example, if the system is highly nonlinear, then the EKF might not work well. In contrast, the UKF uses the unscented transform , which is a function to estimate ...


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I still couldn't derive it, but maybe I could prove it thanks to willSapgreen. Information matrix of distribution $p(x_t, Y^+, Y^0|Y^-=0)$ is $H_t'$; $$H_t'=S_{x_t,Y^+,Y^0}S^T_{x_t,Y^+,Y^0}H_tS^T_{x_t,Y^+,Y^0}S_{x_t,Y^+,Y^0}$$ This matrix can be written as $$H_t' = \left(\begin{array}{cc} H_{x_t,Y^+,x_t,Y^+}&H_{x_t,Y^+,Y^0}\\H_{Y^0,x_t,Y^+}&H_{Y^0,Y^...


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