Damped Posterior Linearization Filter
Research output: Contribution to journal › Article › Scientific › peer-review
|Journal||IEEE Signal Processing Letters|
|Early online date||13 Feb 2018|
|Publication status||Published - 2018|
|Publication type||A1 Journal article-refereed|
In this letter, we propose an iterative Kalman type algorithm based on posterior linearization. The proposed algorithm uses a nested loop structure to optimize the mean of the estimate in the inner loop and update the covariance, which is a computationally more expensive operation, only in the outer loop. The optimization of the mean update is done using a damped algorithm to avoid divergence. Our simulations show that the proposed algorithm is more accurate than existing iterative Kalman filters.
- Bayesian state estimation, Computational modeling, Convergence, Cost function, estimation, Kalman filters, Noise measurement, nonlinear, Signal processing algorithms