Nodes localization through data fusion in sensor network
ABSTRACT The location of nodes in sensor network is an important problem with application in resource allocation, location sensitive browsing, and emergency communications. A key problem in sensor network location is the creation of a method that is robust to measurement quantization and measurement noise and also has a reasonable implementation cost. The RSS and TDoA are the popular distance measurement methods, but can be easy affected by noise and not independent. This paper explores a covariance intersection (CI) that fuses together location estimations obtained from power propagation loss measurements and propagation time to obtain higher accuracy location estimate. The performances of Kalman filter type estimators are severely affected by the ignored cross covariance. CI algorithm provides a mechanism to fuse two or more random variables with unknown correlation such that the computed covariance of the new estimate is consistent; therefore CI method is quite suitable for sensor network application. In simulation result shows that position estimates are correct within small ranging distance with few initial master nodes of the system.
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ABSTRACT: This paper faces the problem of configuration and communication in a distributed radio sensor network, composed of identical sensors randomly placed in a two- or three-dimensional space. The reference is provided by objects with known positions called masters. Two architectures are shown; the first uses one master, the second three masters. The one master architecture makes it possible to identify and locate all the sensors in space and to calculate for each of them the lowest energy transmission path to reach the master. The three-master architecture locates, by triangulation, each sensor when a transmission of information occurs and cannot optimize energy consumption during sensor communication. On the other hand, it is also able to localize moving sensors or to handle dynamically changing sensor topologies. The results show that the three-master architecture is faster, but it implies an energy waste of about 30 times greater than the one-master architecture for a constellation of 50 sensors.IEEE Transactions on Instrumentation and Measurement 05/2004; · 1.36 Impact Factor
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ABSTRACT: This paper addresses the problem of obtaining a consistent estimate (or upper bound) of the covariance matrix when combining two quantities with unknown correlation. The combination is defined linearly with two gains. When the gains are chosen a priori, a family of consistent estimates is presented in the paper. The member in this family having minimal trace is said to be "family-optimal". When the gains are to be optimized in order to achieve minimal trace of the family-optimal estimate of the covariance matrix, it is proved that the global optimal solution is actually given by the Covariance Intersection Algorithm, which conducts the search only along a one-dimensional curve in the n-squared-dimensional space of combination gains. Keywords -- Consistent Estimation, Filtering, Kalman Filter, Unknown Correlation, Covariance Intersection, Data Fusion 1IEEE Transactions on Automatic Control 11/2002; · 2.72 Impact Factor
Conference Proceeding: Convex position estimation in wireless sensor networks[show abstract] [hide abstract]
ABSTRACT: A method for estimating unknown node positions in a sensor network based exclusively on connectivity-induced constraints is described. Known peer-to-peer communication in the network is modeled as a set of geometric constraints on the node positions. The global solution of a feasibility problem for these constraints yields estimates for the unknown positions of the nodes in the network. Providing that the constraints are tight enough, simulation illustrates that this estimate becomes close to the actual node positions. Additionally, a method for placing rectangular bounds around the possible positions for all unknown nodes in the network is given. The area of the bounding rectangles decreases as additional or tighter constraints are included in the problem. Specific models are suggested and simulated for isotropic and directional communication, representative of broadcast-based and optical transmission respectively, though the methods presented are not limited to these simple casesINFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE; 02/2001