3,333 research outputs found
Environmental boundary tracking and estimation using multiple autonomous vehicles
In this paper, we develop a framework for environmental
boundary tracking and estimation by considering the
boundary as a hidden Markov model (HMM) with separated
observations collected from multiple sensing vehicles. For each
vehicle, a tracking algorithm is developed based on Page’s
cumulative sum algorithm (CUSUM), a method for change-point
detection, so that individual vehicles can autonomously
track the boundary in a density field with measurement noise.
Based on the data collected from sensing vehicles and prior
knowledge of the dynamic model of boundary evolvement, we
estimate the boundary by solving an optimization problem, in
which prediction and current observation are considered in the
cost function. Examples and simulation results are presented
to verify the efficiency of this approach
Distributed Target Tracking with Fading Channels over Underwater Wireless Sensor Networks
This paper investigates the problem of distributed target tracking via
underwater wireless sensor networks (UWSNs) with fading channels. The
degradation of signal quality due to wireless channel fading can significantly
impact network reliability and subsequently reduce the tracking accuracy. To
address this issue, we propose a modified distributed unscented Kalman filter
(DUKF) named DUKF-Fc, which takes into account the effects of measurement
fluctuation and transmission failure induced by channel fading. The channel
estimation error is also considered when designing the estimator and a
sufficient condition is established to ensure the stochastic boundedness of the
estimation error. The proposed filtering scheme is versatile and possesses wide
applicability to numerous real-world scenarios, e.g., tracking a maneuvering
underwater target with acoustic sensors. Simulation results demonstrate the
effectiveness of the proposed filtering algorithm. In addition, considering the
constraints of network energy resources, the issue of investigating a trade-off
between tracking performance and energy consumption is discussed accordingly.Comment: 12 pages, 6 figures, 6 table
GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model
Central to robot exploration and mapping is the task of persistent
localization in environmental fields characterized by spatially correlated
measurements. This paper presents a Gaussian process localization (GP-Localize)
algorithm that, in contrast to existing works, can exploit the spatially
correlated field measurements taken during a robot's exploration (instead of
relying on prior training data) for efficiently and scalably learning the GP
observation model online through our proposed novel online sparse GP. As a
result, GP-Localize is capable of achieving constant time and memory (i.e.,
independent of the size of the data) per filtering step, which demonstrates the
practical feasibility of using GPs for persistent robot localization and
autonomy. Empirical evaluation via simulated experiments with real-world
datasets and a real robot experiment shows that GP-Localize outperforms
existing GP localization algorithms.Comment: 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Extended
version with proofs, 10 page
Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey
Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
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