364 research outputs found
Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking
Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering target in a multisensor environment. A novel scheme for distributed tracking is employed that utilizes a nonlinear target model and estimates from local (sensor-based) estimators. The resulting estimation problem is highly nonlinear and thus quite challenging. In order to evaluate the performance capabilities of the architecture considered, advanced sampling-based nonlinear filters are implemented: particle filter (PF), unscented Kalman filter (UKF), and unscented particle filter (UPF). Results from extensive Monte Carlo simulations using different configurations of these algorithms are obtained to compare their effectiveness for solving the distributed target tracking problem
Comparison of Sampling-Based Algorithms for Multisensor Distributed Target Tracking
Nonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering target in a multisensor environment. A novel scheme for distributed tracking is employed that utilizes a nonlinear target model and estimates from local (sensor-based) estimators. The resulting estimation problem is highly nonlinear and thus quite challenging. In order to evaluate the performance capabilities of the architecture considered, advanced sampling-based nonlinear filters are implemented: particle filter (PF), unscented Kalman filter (UKF), and unscented particle filter (UPF). Results from extensive Monte Carlo simulations using different configurations of these algorithms are obtained to compare their effectiveness for solving the distributed target tracking problem
Interacting Multiple Model Algorithm with the Unscented Particle Filter (UPF)
AbstractCombining interacting multiple model (IMM) and unscented particle filter (UPF), a new multiple model filtering algorithm is presented. Multiple models can be adapted to targets' high maneuvering. Particle filter can be used to deal with the nonlinear or non-Gaussian problems and the unscented Kalman filter (UKF) can improve the approximate accuracy. Compared with other interacting multiple model algorithms in the simulations, the results demonstrate the validity of the new filtering method
Iterated Filters for Nonlinear Transition Models
A new class of iterated linearization-based nonlinear filters, dubbed
dynamically iterated filters, is presented. Contrary to regular iterated
filters such as the iterated extended Kalman filter (IEKF), iterated unscented
Kalman filter (IUKF) and iterated posterior linearization filter (IPLF),
dynamically iterated filters also take nonlinearities in the transition model
into account. The general filtering algorithm is shown to essentially be a
(locally over one time step) iterated Rauch-Tung-Striebel smoother. Three
distinct versions of the dynamically iterated filters are especially
investigated: analogues to the IEKF, IUKF and IPLF. The developed algorithms
are evaluated on 25 different noise configurations of a tracking problem with a
nonlinear transition model and linear measurement model, a scenario where
conventional iterated filters are not useful. Even in this "simple" scenario,
the dynamically iterated filters are shown to have superior root mean-squared
error performance as compared with their respective baselines, the EKF and UKF.
Particularly, even though the EKF diverges in 22 out of 25 configurations, the
dynamically iterated EKF remains stable in 20 out of 25 scenarios, only
diverging under high noise.Comment: 8 pages. Accepted to IEEE International Conference on Information
Fusion 2023 (FUSION 2023). Copyright 2023 IEE
Statically Fused Converted Measurement Kalman Filters
This chapter presents a state estimation method without using of nonlinear recursive filters when Doppler measurement is incorporated into the tracking system. The commonly used motions, such as the constant velocity (CV), constant acceleration (CA), and constant turn (CT), are represented in a pseudo-state space, defined from the product of target true range and range rate, by linear pseudo-state equations. Then the linear converted Doppler measurement Kalman filter (CDMKF) is presented to extract pseudo-state from the converted Doppler measurement, constructed by the product of the range and Doppler measurements. The output of the CDMKF is fused statically with that of the converted position measurement (range and one or two angles) Kalman filter (CPMKF) to produce target Cartesian state estimates. The accuracy and consistence of the estimator can be both guaranteed, since linear Kalman filters are both used to extract information from position and Doppler measurements
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