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We are interested in information planning of structures represented by sparse graphical models where measurements correspond to a limited number of nodes. Choosing a set of measurements, which better describe spatiotemporal phenomena is a fundamental task whose optimal solution becomes intractable as the number of measurements grows. Krause et al. (2005) and Williams et al. (2007) have shown that...
Crowdsourcing provides a cheap but efficient approach for large-scale data and information collection. However, human judgments are inherently noisy, ambiguous and sometimes biased, and should be calibrated by additional (usually much more expensive) expert or true labels. In this work, we study the optimal allocation of the true labels to best calibrate the crowdsourced labels. We frame the problem...
We consider the setting where we are given multiple signal-plus-noise matrices. The signal matrices are modeled as low-rank with the same factors (or eigenvectors) but arbitrary (modulo a fixed ordering) eigen-SNRs. One motivating example is the determination of community structure from multiple, independent adjacency matrices. The objective is to combine them linearly so that the eigenvectors of...
We consider the problem of distributed detection of a radioactive source using a network of emission count sensors. Sensor nodes observe their environment and a central fusion node attempts to detect a change in the joint probability distribution due to the appearance of a hazardous source at an unknown time and location. We consider a minimax-type distributed change-point detection problem that minimizes...
In sparse target detection problems, it has been shown that significant gains can be achieved by adaptive sensing. We generalize previous work on adaptive sensing to (a) include targets of multiple classes with different levels of mission importance and (b) account for multiple sensor models. New optimization policies are developed to simultaneously locate, classify and estimate a sparse number of...
Detecting and classifying anomalies for Maritime Situation Awareness gets a lot of benefit from the combination of multiple sources, correlating their output for detecting inconsistencies in vessels' behaviour. Adequate uncertainty representation and processing is crucial for this higher-level task where the operator analyses information correlating with his background knowledge. This paper addresses...
Data fusion in heterogeneous environments plays a major role in assisting end users by providing them with an increased situational awareness so that decisions can be made about events in the field. Heterogeneous fusion involves combining different types of soft and hard data such that the situation or the resulting output is more precise, accurate, complete or easy to comprehend by decision makers...
Mathematical and uncertainty modelling is an important component of data fusion (the fusion of unprocessed sensor data) and information fusion (the fusion of processed or interpreted data). If uncertainties in the modelling process are not or are incorrectly accounted for, fusion processes may provide under- or overconfident results, or in some cases incorrect results. These are often owing to incorrect...
The uncertainty representation and reasoning evaluation framework (URREF) ontology discusses an organization, categorization, taxonomy and definitions of uncertainty. Uncertainty can be either subjective or objective pending its source. For example, the information's origin can affect the evaluation, processing, and results from information fusion systems. In this paper we explore the coordination...
Bayesian Networks have various applications including medical and technical diagnosis, financial scoring, and target behavior/pattern recognition. Bayesian Classification Networks fuse evidence from heterogeneous and homogeneous sources and calculate classification results. For many reasons, pieces of evidence from different sources can carry apparently contradicting information and in these cases...
State estimation of a non-linear system perturbed by non Gaussian noise is a challenging task. Typical solutions like EKF/UKF could fail while Monte Carlo methods, even though more accurate, are computationally expensive. Recently proposed log homotopy based particle flow filter, also known as Daum-Huang filter (DHF) provides an alternative way of non-linear state estimation. There have been a number...
This paper introduces the Langevin Monte Carlo Filter (LMCF), a particle filter with a Markov chain Monte Carlo algorithm which draws proposals by simulating Hamiltonian dynamics. This approach is well suited to non-linear filtering problems in high dimensional state spaces where the bootstrap filter requires an impracticably large number of particles. The simulation of Hamiltonian dynamics is motivated...
For linear systems, the optimal filtering is provided by the celebrated Kalman filter. For nonlinear systems, only suboptimal filters can be obtained in general. The Extended Kalman filter (EKF) is such a suboptimal filter. It helped the promotion of the Kalman filter. With the development of more advanced nonlinear filters, however, the EKF is receiving less and less attention because it performs...
In this paper, a semi-parametric model for RSS measurements is introduced that can be used to predict coverage in cellular radio networks. The model is composed of an empirical log-distance model and a deterministic antenna gain model that accounts for possible non-uniform base station antenna radiation. A least-squares estimator is proposed to jointly estimate the path loss and antenna gain model...
Due to the difficult characterization of the propagation model, most studies on racking of mobile nodes assume the correct knowledge of the power-distance gradients or the path-loss exponents (PLEs). In this paper, we first investigate the impact of erroneous PLEs on positioning of a wireless nodes when both distance and bearing measurements are available. Thus, an analytical expression of the mean...
This paper proposes a novel method to obtain robust and accurate object segmentations from 3D Light Detection and Ranging (LIDAR) data points. The method exploits motion information simultaneously estimated by a tracking algorithm in order to resolve ambiguities in complex dynamic scenes. Typical approaches for tracking multiple objects in LIDAR data follow three steps; point cloud segmentation, object...
This paper evaluates the performance of traditional Monte Carlo (FMC) for the nonlinear propagation of initial uncertainty in the two-body problem: an essential task in space situational awareness. This is done in light of a newly developed Markov chain Monte Carlo (MCMC) based particle approach that combines the benefits of MCMC sampling with the method of characteristics (MOC) for solving first...
Surveillance systems require advanced algorithms able to make decisions without a human operator or with minimal assistance from human operators. In this paper we propose a novel approach for dynamic topic modeling to detect abnormal behaviour in video sequences. The topic model describes activities and behaviours in the scene assuming behaviour temporal dynamics. The new inference scheme based on...
Target tracking faces the challenge in coping with large volumes of data which requires efficient methods for real time applications. The complexity considered in this paper is when there is a large number of measurements which are required to be processed at each time step. Sequential Markov chain Monte Carlo (MCMC) has been shown to be a promising approach to target tracking in complex environments,...
In this work we describe a system and propose a novel algorithm for moving object detection and tracking based on video feed. Apart of many well-known algorithms, it performs detection in unsupervised style, using velocity criteria for the objects detection. The algorithm utilises data from a single camera and Inertial Measurement Unit (IMU) sensors and performs fusion of video and sensory data captured...
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