487 research outputs found
Radar networks: A review of features and challenges
Networks of multiple radars are typically used for improving the coverage and
tracking accuracy. Recently, such networks have facilitated deployment of
commercial radars for civilian applications such as healthcare, gesture
recognition, home security, and autonomous automobiles. They exploit advanced
signal processing techniques together with efficient data fusion methods in
order to yield high performance of event detection and tracking. This paper
reviews outstanding features of radar networks, their challenges, and their
state-of-the-art solutions from the perspective of signal processing. Each
discussed subject can be evolved as a hot research topic.Comment: To appear soon in Information Fusio
Covariance-Based Estimation for Clustered Sensor Networks Subject to Random Deception Attacks
In this paper, a cluster-based approach is used to address the distributed fusion estimation
problem (filtering and fixed-point smoothing) for discrete-time stochastic signals in the presence of
random deception attacks. At each sampling time, measured outputs of the signal are provided by
a networked system, whose sensors are grouped into clusters. Each cluster is connected to a local
processor which gathers the measured outputs of its sensors and, in turn, the local processors of all
clusters are connected with a global fusion center. The proposed cluster-based fusion estimation
structure involves two stages. First, every single sensor in a cluster transmits its observations to the
corresponding local processor, where least-squares local estimators are designed by an innovation
approach. During this transmission, deception attacks to the sensor measurements may be randomly
launched by an adversary, with known probabilities of success that may be different at each sensor.
In the second stage, the local estimators are sent to the fusion center, where they are combined
to generate the proposed fusion estimators. The covariance-based design of the distributed fusion
filtering and fixed-point smoothing algorithms does not require full knowledge of the signal evolution
model, but only the first and second order moments of the processes involved in the observation
model. Simulations are provided to illustrate the theoretical results and analyze the effect of the
attack success probability on the estimation performance.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de
Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)
Networked distributed fusion estimation under uncertain outputs with random transmission delays, packet losses and multi-packet processing
This paper investigates the distributed fusion estimation problem for networked systems whose mul- tisensor measured outputs involve uncertainties modelled by random parameter matrices. Each sensor transmits its measured outputs to a local processor over different communication channels and random failures –one-step delays and packet dropouts–are assumed to occur during the transmission. White sequences of Bernoulli random variables with different probabilities are introduced to describe the ob- servations that are used to update the estimators at each sampling time. Due to the transmission failures, each local processor may receive either one or two data packets, or even nothing and, when the current measurement does not arrive on time, its predictor is used in the design of the estimators to compensate the lack of updated information. By using an innovation approach, local least-squares linear estimators (filter and fixed-point smoother) are obtained at the individual local processors, without requiring the signal evolution model. From these local estimators, distributed fusion filtering and smoothing estimators weighted by matrices are obtained in a unified way, by applying the least-squares criterion. A simula- tion study is presented to examine the performance of the estimators and the influence that both sensor uncertainties and transmission failures have on the estimation accuracy.This research is supported by Ministerio de Economía, Industria y Competitividad, Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional FEDER (grant no. MTM2017-84199-P)
Distributed Random Set Theoretic Soft/Hard Data Fusion
Research on multisensor data fusion aims at providing the enabling technology to combine
information from several sources in order to form a unifi ed picture. The literature
work on fusion of conventional data provided by non-human (hard) sensors is vast and
well-established. In comparison to conventional fusion systems where input data are generated
by calibrated electronic sensor systems with well-defi ned characteristics, research
on soft data fusion considers combining human-based data expressed preferably in unconstrained
natural language form. Fusion of soft and hard data is even more challenging, yet
necessary in some applications, and has received little attention in the past. Due to being
a rather new area of research, soft/hard data fusion is still in a
edging stage with even
its challenging problems yet to be adequately de fined and explored.
This dissertation develops a framework to enable fusion of both soft and hard data
with the Random Set (RS) theory as the underlying mathematical foundation. Random
set theory is an emerging theory within the data fusion community that, due to its powerful
representational and computational capabilities, is gaining more and more attention among
the data fusion researchers. Motivated by the unique characteristics of the random set
theory and the main challenge of soft/hard data fusion systems, i.e. the need for a unifying
framework capable of processing both unconventional soft data and conventional hard data,
this dissertation argues in favor of a random set theoretic approach as the first step towards
realizing a soft/hard data fusion framework.
Several challenging problems related to soft/hard fusion systems are addressed in the
proposed framework. First, an extension of the well-known Kalman lter within random
set theory, called Kalman evidential filter (KEF), is adopted as a common data processing
framework for both soft and hard data. Second, a novel ontology (syntax+semantics)
is developed to allow for modeling soft (human-generated) data assuming target tracking
as the application. Third, as soft/hard data fusion is mostly aimed at large networks of
information processing, a new approach is proposed to enable distributed estimation of
soft, as well as hard data, addressing the scalability requirement of such fusion systems.
Fourth, a method for modeling trust in the human agents is developed, which enables the
fusion system to protect itself from erroneous/misleading soft data through discounting
such data on-the-fly. Fifth, leveraging the recent developments in the RS theoretic data
fusion literature a novel soft data association algorithm is developed and deployed to extend
the proposed target tracking framework into multi-target tracking case. Finally, the
multi-target tracking framework is complemented by introducing a distributed classi fication
approach applicable to target classes described with soft human-generated data.
In addition, this dissertation presents a novel data-centric taxonomy of data fusion
methodologies. In particular, several categories of fusion algorithms have been identifi ed
and discussed based on the data-related challenging aspect(s) addressed. It is intended to
provide the reader with a generic and comprehensive view of the contemporary data fusion
literature, which could also serve as a reference for data fusion practitioners by providing
them with conducive design guidelines, in terms of algorithm choice, regarding the specifi c
data-related challenges expected in a given application
Multisensor data fusion
Thesis (M.Eng.Sc.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 199
Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition
The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future
Real-time detection of auditory : steady-state brainstem potentials evoked by auditory stimuli
The auditory steady-state response (ASSR) is advantageous against other hearing techniques because of its capability in providing objective and frequency specific information. The objectives are to reduce the lengthy test duration, and improve the signal detection rate and the robustness of the detection against the background noise and unwanted artefacts.Two prominent state estimation techniques of Luenberger observer and Kalman filter have been used in the development of the autonomous ASSR detection scheme. Both techniques are real-time implementable, while the challenges faced in the application of the observer and Kalman filter techniques are the very poor SNR (could be as low as −30dB) of ASSRs and unknown statistics of the noise. Dual-channel architecture is proposed, one is for the estimate of sinusoid and the other for the estimate of the background noise. Simulation and experimental studies were also conducted to evaluate the performances of the developed ASSR detection scheme, and to compare the new method with other conventional techniques. In general, both the state estimation techniques within the detection scheme produced comparable results as compared to the conventional techniques, but achieved significant measurement time reduction in some cases. A guide is given for the determination of the observer gains, while an adaptive algorithm has been used for adjustment of the gains in the Kalman filters.In order to enhance the robustness of the ASSR detection scheme with adaptive Kalman filters against possible artefacts (outliers), a multisensory data fusion approach is used to combine both standard mean operation and median operation in the ASSR detection algorithm. In addition, a self-tuned statistical-based thresholding using the regression technique is applied in the autonomous ASSR detection scheme. The scheme with adaptive Kalman filters is capable of estimating the variances of system and background noise to improve the ASSR detection rate
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