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Scalar field mapping has many applications including environmental monitoring, search and rescue, etc. In such applications there is a need to achieve a certain level of confidence regarding the estimates at each location. In this paper, a cooperative and active sensing framework is developed to enable scalar field mapping using multiple mobile sensor nodes. The cooperative and active controller is...
A distributed estimation problem using sensor networks is considered. A parameter is observed in noise by sensors, phase-modulated using complex exponentials and transmitted over a Gaussian multiple-access channel to a fusion center. The received signal is used to estimate the parameter under observation. The system is evaluated using the asymptotic variance of the estimate. The relationship between...
A distributed network of sensors leverages its performance by aggregating information gathered by individual sensors. This process is referred to as sensor fusion. The primary goal of sensor fusion is to process and progressively refine information from multiple sensors to eventually create situation awareness (SA). Sensor fusion requires sensors to exchange data and information over a network with...
Recently target detection is widely regarded as a typical hot spot in research of sensor networks. A fast target detection algorithm is proposed by using the hypothesis testing (HT) method in the paper. The objective is to determine whether a target is present in a sensor network for decision-makers. Due to the nature of sensor networks, it is desirable to have a fast algorithm to accomplish the detection...
Because of sensors with different types and some other different characteristics in real deployment, there exist four kinds of interaction among sensors in information fusion for coverage analysis, including negative synergy, positive synergy, independency and interference. In order to handle the interaction properly, the discrete fuzzy measure theory is introduced to represent the interaction among...
We present a novel approach for sensor scheduling which is, in general, a NP-hard problem involving the selection of S out of N sensors such that an optimal filtering performance is attained. Our approach utilizes a heuristic measure that quantifies the incoherence of the vector space defined by the sensors with respect to the system principal directions. This in turn facilitates the formulation of...
We consider the problem of distributed test of statistical independence under communication constraints. While independence test is frequently encountered in various applications, distributed independence test is particularly useful for events detection in sensor networks: data correlation often occurs among sensor observations in the presence of a target. Focusing on the Gaussian case because of...
Information geometry, as a powerful though complex mathematical tool can provide additional insights in the analysis of sensor measurement. In this paper, the application of information geometry to the analysis of sensor networks is explored in an attempt to gain a better understanding of sensor system issues for target detection and tracking. In particular, the (integrated) Fisher information distance...
We propose a design approach for sensor networks based on formal linguistic representations of information. The approach exploits the concepts of space-time neighborhoods for dynamic sensor grid formation in the vicinity of an event, and symbolization and nonlinear filtering to formulate rigorous mathematical methods that capture the causal dynamics of distributed fusion processes. We formulate the...
Multi-sensor data fusion latency in Internet-based sensor applications is analyzed. The probabilistic estimation of periodic backlog (PEPB) technique presented takes into account the nature of sensor data generation and the time-scale invariant burstiness (i.e., self-similarity) of network traffic. The fusion application considered requires synchronizing a set of correlated data before fusion processing...
Based on the success of intelligent video surveillance systems, especially in the field of security, scalability and performance, the corresponding architecture and concept is extended to involve different types of sensors in addition to visual sensors. In this work an analytical study of the whole spectrum of sensor technologies and their characteristics and performance recommendation is presented...
This paper investigates the performance of belief propagation (BP) as a distributed solution to two combinatorial resource allocation problems arising in sensor networks: network formation and fusion center location. We model these problems by max-weight b-matching and uncapacitated facility location, respectively. Each of these is a classical optimization problem. For both problems, we (a) show how...
In this paper, we consider the distributed parameter estimation problem using one-bit quantized data from local sensors. Nonparametric distributed estimators are proposed based on knowledge of the moments of sensor noise. These estimators are shown to be either unbiased or asymptotically unbiased with bounded estimation variance for all possible parameter values. Relationship between the proposed...
In sensor networks applied to monitoring applications, individual sensors may perform preassigned or on-demand tasks, or missions. Data updates (info-pages) may be sent to sensors from a command center, via a time-division broadcast channel. Sensors are normally put in sleep mode when not actively listening, in order to conserve energy in their batteries. Hence, a schedule is required that specifies...
When an unknown target emits a radio signal, its position can be localized by a network of sensors (or radar receivers) using the time difference of arrival (TDOA) information. In this paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex optimization problem. In addition, we propose a robust target localization...
We consider the problem of approximating a family of isocontours in a sensor field with a topologically-equivalent family of simple polygons. Our algorithm is simple and distributed, it gracefully adapts to any user-specified representation size k, and it delivers a worst-case guarantee for the quality of approximation. In particular, we prove that the topology-respecting Hausdorff error in our k-vertex...
We formulate the target tracking based on received signal strength in the sensor networks using Bayesian network representation. Data fusion among the same type of sensors in an active sensor neighborhood is referred to as cross-sensor fusion, conceptualized as "cooperative fusion". This data fusion is embedded in the likelihood function derivation. Fusion of signals collected by multiple...
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