15 accepted papers
Seabed-to-space situational awareness (S3A) aims to organize, fuse, and synthesize the massive volume of information collected from heterogeneous sensors, i.e., underwater, terrestrial, and space-based sensors, and therefrom extract knowledge thence available to defence operators, enabling informed…
All practical sensing operations must work with quantized data. Along with measurements, each sensor is assumed to have some "label" value that is relevant to its stochastic measurement parameterization and must be communicated to the decision center. We are interested in cases that require very low…
Quickest detection problems are fairly common in surveillance applications, as framing surveillance alerts as a change in an observation sequence’s statistics is often apt. In this work, we consider the scenario where an appropriate statistical description of our observations is not available, neith…
Consider a test at a particular point in space for the existence of a point target using intensity measurements from passive sensors distributed uniformly around the test location. The distance from the test location of a particular sensor is relevant to the decision making, and is considered "label…
In this paper, we address the problem of predicting vessel trajectories based on Automatic Identification System (AIS) data. The goal is to learn the predictive distribution of maritime traffic patterns using historical data during the training phase, in order to be able to forecast future target tr…
Piecewise mean-reverting stochastic processes have been recently proposed and validated as an effective model for long-term object prediction. In this paper, we exploit the Ornstein-Uhlenbeck (OU) dynamic model to represent an anomaly as any deviation of the long-run mean velocity from the nominal c…
Unlabeled detection is an emerging paradigm for modern decentralized decision systems faced with big-data applications, and for all those applications in which data must be fused without exploiting their identity, due to the lack of provenance labels, or to uncontrolled data shuffling. Our focus her…
Suppose that the energy made available to a sensor network at the beginning of a time slot is proportional to the success of the network inferential task during the previous slot. And further, assume that such energy is to be apportioned to charge the individual sensors, such that the more energy on…
Many threats in the form of human actions (terrorist attacks, military actions, etc.) can be modeled by someone with relevant expert knowledge. A model would be a hypothesis or guess as to how a threat would develop and what kind of observable evidence it would produce along the way. We present a me…
Consider a binary hypothesis where data are independent and identically distributed under the null hypothesis, and known only to be independent under the alternative. The statistician observes an n- vector X <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlin…
In modern heterogeneous sensor networks huge volumes of information rapidly flow across the system, and it is often too difficult or costly to associate data to the sensors that produced them. Then, the set of observations appears to be unlabeled: What comes from whom? We study the classical problem…
There is good reason to model an asymmetric threat (a structured action such as a terrorist attack) as an hmm whose observations are cluttered. Recently a Bernoulli filter was presented that can process cluttered observations ("transactions") and is capable of detecting if there is an HMM present, a…
Parametric estimation for the generative social sensing model proposed in [19,20] is addressed. First, we provide a detailed analysis of the estimation performance bounds, in terms of the Fisher information matrix, with emphasis on the fundamental scaling laws as the number of network agents and/or…
Hidden Markov Models (HMMs) are powerful statistical techniques with many applications, and in this paper they are used for modeling asymmetric threats. The observations generated by such HMMs are generally cluttered with observations that are not related to the HMM. In this paper a Bernoulli filter…
In practical target tracking problems, the target detection performance of the sensors may be unknown and may change rapidly with time. In this work we develop a target tracking procedure able to adapt and react to time-varying changes of the detection capability for a network of sensors. The propos…