357 research outputs found
A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks
Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally,
conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002
and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140
Sequential Bayesian inference for static parameters in dynamic state space models
A method for sequential Bayesian inference of the static parameters of a
dynamic state space model is proposed. The method is based on the observation
that many dynamic state space models have a relatively small number of static
parameters (or hyper-parameters), so that in principle the posterior can be
computed and stored on a discrete grid of practical size which can be tracked
dynamically. Further to this, this approach is able to use any existing
methodology which computes the filtering and prediction distributions of the
state process. Kalman filter and its extensions to non-linear/non-Gaussian
situations have been used in this paper. This is illustrated using several
applications: linear Gaussian model, Binomial model, stochastic volatility
model and the extremely non-linear univariate non-stationary growth model.
Performance has been compared to both existing on-line method and off-line
methods
Counter-Adversarial Learning with Inverse Unscented Kalman Filter
In counter-adversarial systems, to infer the strategy of an intelligent
adversarial agent, the defender agent needs to cognitively sense the
information that the adversary has gathered about the latter. Prior works on
the problem employ linear Gaussian state-space models and solve this inverse
cognition problem by designing inverse stochastic filters. However, in
practice, counter-adversarial systems are generally highly nonlinear. In this
paper, we address this scenario by formulating inverse cognition as a nonlinear
Gaussian state-space model, wherein the adversary employs an unscented Kalman
filter (UKF) to estimate the defender's state with reduced linearization
errors. To estimate the adversary's estimate of the defender, we propose and
develop an inverse UKF (IUKF) system. We then derive theoretical guarantees for
the stochastic stability of IUKF in the mean-squared boundedness sense.
Numerical experiments for multiple practical applications show that the
estimation error of IUKF converges and closely follows the recursive
Cram\'{e}r-Rao lower bound.Comment: 6 pages, 1 figur
Two-Channel Extended Kalman Filtering with Intermittent Measurements
We consider two nonlinear state estimation problems in a setting where an
extended Kalman filter receives measurements from two sets of sensors via two
channels (2C). In the stochastic-2C problem, the channels drop measurements
stochastically, whereas in 2C scheduling, the estimator chooses when to read
each channel. In the first problem, we generalize linear-case 2C analysis to
obtain -- for a given pair of channel arrival rates -- boundedness conditions
for the trace of the error covariance, as well as a worst-case upper bound. For
scheduling, an optimization problem is solved to find arrival rates that
balance low channel usage with low trace bounds, and channels are read
deterministically with the expected periods corresponding to these arrival
rates. We validate both solutions in simulations for linear and nonlinear
dynamics; as well as in a real experiment with an underwater robot whose
position is being intermittently found in a UAV camera image
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
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
Distributed estimation over a low-cost sensor network: a review of state-of-the-art
Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted
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