8 Papers
47 Citations
Quan Pan is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Adaptive filter & Sensor fusion. The author has an hindex of 6, co-authored 8 publications. Previous affiliations of Quan Pan include Northwestern Polytechnic University.
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Papers
Object separation by polarimetric and spectral imagery fusion
TL;DR: A Polarimetric imagery fusion algorithm is first proposed based on the degree of linear polarization modulation to distinguish different objects, and the spectral and polarimetric information, which can be extracted from the specular and diffuse reflected light, is fused by using the HSI color space mapping for more robust object separation.
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Multi-rate optimal state estimation
Yan Liang,Tongwen Chen,Quan Pan +2 more
TL;DR: This article formulates a multi-rate linear minimum mean squared error (LMMSE) state estimation problem, which includes four rates as follows: the state updating rate in the model, the measurement sampling rate, the estimate updating rate and the estimate output rate.
74
Multiresolution modeling and estimation of multisensor data
TL;DR: This paper presents a multiresolution multisensor data fusion scheme for dynamic systems to be observed by several sensors of different resolutions that satisfies the requirements of discrete Kalman filtering and offers an optimal estimation algorithm of the system.
Adaptive Filtering for Stochastic Systems With Generalized Disturbance Inputs
TL;DR: This letter presents a new class of discrete-time linear stochastic systems with the statistically-constrained disturbance input, which can represent an arbitrary linear combination of dynamic, random, and deterministic disturbance inputs to generalize the complicated modeling error encountered in actual applications.
A quadratic programming based cluster correspondence projection algorithm for fast point matching
TL;DR: A quadratic programming based cluster correspondence projection (QPCCP) algorithm, where the optimal correspondences are searched via gradient descent and the constraints on the correspondence are satisfied by projection onto appropriate convex set.
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