Multi-sensor integrated filtering for highly dynamic system using recursive moving horizon estimation technique

Gul, Haris Ubaid; Kai, Yang; Khan, M Sadiq Ali

doi:10.1109/ibcast.2017.7868078

Cited by 2 publications

(2 citation statements)

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“…Our simulation results show that the FHMLE tends to produce estimates that, while having a smaller estimation error can be less smooth than those obtained from a UKF. In moving horizon estimation (MHE), this is often resolved by adding to the optimization cost terms that penalize the distance between the current estimate and the one obtained with the previous window of measurements . Experimenting with this option and understanding its implications in the context of maximum likelihood estimation is an important topic for future research.…”

Section: Discussionmentioning

confidence: 99%

“…Our finite horizon MLE is heavily inspired by the literature on Moving Horizon Estimation (MHE) , which also considers a finite window of measurements, but typically also includes a penalty term to account to the “missing” measurements. A sliding window approach to GNSS/INS integration appeared in . The effect of changing the length of the sliding window is considered in .…”

Section: Introductionmentioning

confidence: 99%

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Finite Horizon Maximum Likelihood Estimation for Integrated Navigation with RF Beacon Measurements

Shankar

Ezal

Hespanha

2019

Asian Journal of Control

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We develop a Finite Horizon Maximum Likelihood Estimator (FHMLE) that fuses Inertial Measurement Unit (IMU) and radio frequency (RF) measurements over a sliding window of finite length for three-dimensional navigation. Available RF data includes pseudo-ranges, angles of transmission (AoT), and Doppler shift measurements. The navigation estimates are obtained by solving a finite-dimensional nonlinear optimization using a primal-dual interior point algorithm (PDIP). The benefits of the proposed estimation method are highlighted using simulations results comparing the FHMLE approach with an Unscented Kalman Filter (UKF), in a scenario where an aircraft approaches a carrier, with RF measurements from beacons aboard the carrier, and low-cost IMU measurements aboard the aircraft. When the Geometric Dilution of Precision is large, we found that the FHMLE is able to achieve smaller estimation errors than the UKF, which tends to carry a bias throughout the trajectory.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%