Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Linear optimal FIR estimation of discrete time-invariant state-space models
IEEE Transactions on Signal Processing
An Iterative Kalman-Like Algorithm Ignoring Noise and Initial Conditions
IEEE Transactions on Signal Processing
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The trade-off between the first- and second-order extended unbiased finite impulse response filters (EFIR1 and EFIR2, respectively) is examined for suboptimal estimation of nonlinear discrete-time state-space models with additive white noise. An important applied feature of the unbiased FIR filter is that it does not require noise statistics and initial errors. Based upon the tracking problem solved in a horizontal plane for a moving object, we show that in most of the cases, the difference between the EFIR1 and EFIR2 filter outputs appears to be negligible. In a few cases, the second-order approximation can improve a local performance. But it can also deteriorate it in some others or produce mixed effect. We therefore can give no definitive recommendations about practical usefulness of the EFIR2.