251,703 research outputs found
Filter Bank Fusion Frames
In this paper we characterize and construct novel oversampled filter banks
implementing fusion frames. A fusion frame is a sequence of orthogonal
projection operators whose sum can be inverted in a numerically stable way.
When properly designed, fusion frames can provide redundant encodings of
signals which are optimally robust against certain types of noise and erasures.
However, up to this point, few implementable constructions of such frames were
known; we show how to construct them using oversampled filter banks. In this
work, we first provide polyphase domain characterizations of filter bank fusion
frames. We then use these characterizations to construct filter bank fusion
frame versions of discrete wavelet and Gabor transforms, emphasizing those
specific finite impulse response filters whose frequency responses are
well-behaved.Comment: keywords: filter banks, frames, tight, fusion, erasures, polyphas
Optimal multisensor data fusion for linear systems with missing measurements
Multisensor data fusion has attracted a lot of research in recent years. It has been widely used in many applications especially military applications for target tracking and identification. In this paper, we will handle the multisensor data fusion problem for systems suffering from the possibility of missing measurements. We present the optimal recursive fusion filter for measurements obtained from two sensors subject to random intermittent measurements. The noise covariance in the observation process is allowed to be singular which requires the use of generalized inverse. Illustration example shows the effectiveness of the proposed filter in the measurements loss case compared to the available optimal linear fusion methods.<br /
KFHE-HOMER: A multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter
Multi-label classification allows a datapoint to be labelled with more than
one class at the same time. In spite of their success in multi-class
classification problems, ensemble methods based on approaches other than
bagging have not been widely explored for multi-label classification problems.
The Kalman Filter-based Heuristic Ensemble (KFHE) is a recent ensemble method
that exploits the sensor fusion properties of the Kalman filter to combine
several classifier models, and that has been shown to be very effective. This
article proposes KFHE-HOMER, an extension of the KFHE ensemble approach to the
multi-label domain. KFHE-HOMER sequentially trains multiple HOMER multi-label
classifiers and aggregates their outputs using the sensor fusion properties of
the Kalman filter. Experiments described in this article show that KFHE-HOMER
performs consistently better than existing multi-label methods including
existing approaches based on ensembles.Comment: The paper is under consideration at Pattern Recognition Letters,
Elsevie
Tracking filter and multi-sensor data fusion
In this paper factorization filtering, fusion filtering strategy and related algorithms are presented. Some results of implementation and validation using realistic data are given
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