319,288 research outputs found
Decentralized Distributed Expert Assisted Learning (D2EAL) approach for cooperative target-tracking
This paper addresses the problem of cooperative target tracking using a
heterogeneous multi-robot system, where the robots are communicating over a
dynamic communication network, and heterogeneity is in terms of different types
of sensors and prediction algorithms installed in the robots. The problem is
cast into a distributed learning framework, where robots are considered as
'agents' connected over a dynamic communication network. Their prediction
algorithms are considered as 'experts' giving their look-ahead predictions of
the target's trajectory. In this paper, a novel Decentralized Distributed
Expert-Assisted Learning (D2EAL) algorithm is proposed, which improves the
overall tracking performance by enabling each robot to improve its look-ahead
prediction of the target's trajectory by its information sharing, and running a
weighted information fusion process combined with online learning of weights
based on a prediction loss metric. Theoretical analysis of D2EAL is carried
out, which involves the analysis of worst-case bounds on cumulative prediction
loss, and weights convergence analysis. Simulation studies show that in adverse
scenarios involving large dynamic bias or drift in the expert predictions,
D2EAL outperforms well-known covariance-based estimate/prediction fusion
methods, both in terms of prediction performance and scalability
Distributed Target Tracking and Synchronization in Wireless Sensor Networks
Wireless sensor networks provide useful information for various applications but pose challenges in scalable information processing and network maintenance. This dissertation focuses on statistical methods for distributed information fusion and sensor synchronization for target tracking in wireless sensor networks.
We perform target tracking using particle filtering. For scalability, we extend centralized particle filtering to distributed particle filtering via distributed fusion of local estimates provided by individual sensors. We derive a distributed fusion rule from Bayes\u27 theorem and implement it via average consensus. We approximate each local estimate as a Gaussian mixture and develop a sampling-based approach to the nonlinear fusion of Gaussian mixtures. By using the sampling-based approach in the fusion of Gaussian mixtures, we do not require each Gaussian mixture to have a uniform number of mixture components, and thus give each sensor the flexibility to adaptively learn a Gaussian mixture model with the optimal number of mixture components, based on its local information. Given such flexibility, we develop an adaptive method for Gaussian mixture fitting through a combination of hierarchical clustering and the expectation-maximization algorithm. Using numerical examples, we show that the proposed distributed particle filtering algorithm improves the accuracy and communication efficiency of distributed target tracking, and that the proposed adaptive Gaussian mixture learning method improves the accuracy and computational efficiency of distributed target tracking.
We also consider the synchronization problem of a wireless sensor network. When sensors in a network are not synchronized, we model their relative clock offsets as unknown parameters in a state-space model that connects sensor observations to target state transition. We formulate the synchronization problem as a joint state and parameter estimation problem and solve it via the expectation-maximization algorithm to find the maximum likelihood solution for the unknown parameters, without knowledge of the target states. We also study the performance of the expectation-maximization algorithm under the Monte Carlo approximations used by particle filtering in target tracking. Numerical examples show that the proposed synchronization method converges to the ground truth, and that sensor synchronization significantly improves the accuracy of target tracking
Audio-Visual Speaker Tracking: Progress, Challenges, and Future Directions
Audio-visual speaker tracking has drawn increasing attention over the past
few years due to its academic values and wide application. Audio and visual
modalities can provide complementary information for localization and tracking.
With audio and visual information, the Bayesian-based filter can solve the
problem of data association, audio-visual fusion and track management. In this
paper, we conduct a comprehensive overview of audio-visual speaker tracking. To
our knowledge, this is the first extensive survey over the past five years. We
introduce the family of Bayesian filters and summarize the methods for
obtaining audio-visual measurements. In addition, the existing trackers and
their performance on AV16.3 dataset are summarized. In the past few years, deep
learning techniques have thrived, which also boosts the development of audio
visual speaker tracking. The influence of deep learning techniques in terms of
measurement extraction and state estimation is also discussed. At last, we
discuss the connections between audio-visual speaker tracking and other areas
such as speech separation and distributed speaker tracking
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