• Publications
  • Influence
The unscented Kalman filter for nonlinear estimation
This paper points out the flaws in using the extended Kalman filter (EKE) and introduces an improvement, the unscented Kalman filter (UKF), proposed by Julier and Uhlman (1997). A central and vital
The Unscented Particle Filter
TLDR
This paper proposes a new particle filter based on sequential importance sampling that outperforms standard particle filtering and other nonlinear filtering methods very substantially and is in agreement with the theoretical convergence proof for the algorithm.
Sigma-point kalman filters for probabilistic inference in dynamic state-space models
TLDR
This work has consistently shown that there are large performance benefits to be gained by applying Sigma-Point Kalman filters to areas where EKFs have been used as the de facto standard in the past, as well as in new areas where the use of the EKF is impossible.
The square-root unscented Kalman filter for state and parameter-estimation
TLDR
The square-root unscented Kalman filter (SR-UKF) is introduced which is also O(L/sup 3/) for general state estimation and O( L/sup 2/) for parameter estimation and has the added benefit of numerical stability and guaranteed positive semi-definiteness of the state covariances.
Sigma-Point Kalman Filters for Nonlinear Estimation and Sensor-Fusion: Applications to Integrated Navigation
TLDR
A probabilistic framework, called Sigma-point Kalman Filters (SPKF) was applied to the problem domain addressed by the extended Kalman Filter, and the SPKF-based sensor latency compensation technique is used to demonstrate the lagged GPS measurements.
RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers
  • A. S. Paul, E. Wan
  • Computer Science
    IEEE Journal of Selected Topics in Signal…
  • 20 October 2009
TLDR
The feasibility of building an indoor location tracking system that is cost effective for large scale deployments, can operate over existing Wi-Fi networks, and can provide flexibility to accommodate new sensor observations as they become available is evaluated.
Finite impulse response neural networks with applications in time series prediction
  • E. Wan
  • Computer Science
  • 2 January 1994
TLDR
A dynamic network is proposed which uses Finite Impulse Response (FIR) linear filters to model the processes of axonal transport, synaptic modulation, and membrane charge dissipation, and a unifying principle called Network Reciprocity is introduced.
Sigma-Point Kalman Filters for Integrated Navigation
TLDR
The improved state estimation performance of the SPKF is demonstrated by applying it to the problem of loosely coupled GPS/INS integration and an approximate 30% error reduction in both attitude and position estimates relative to the baseline EKF implementation is demonstrated.
Time series prediction by using a connectionist network with internal delay lines
TLDR
A neural network architecture, which models synapses as Finite Impulse Response (FIR) linear lters, is discussed for use in time series prediction and results show that the FIR network performed remarkably well on a chaotic laser intensity time series.
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