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Geographic location of nodes is very useful for a sensor network. A novel and practical Radio Frequency (RF)-based localization algorithm called Kcdlocation has been presented for some specific sensor network topologies, such as the grid and linear types. The Kcdlocation algorithm is adapted to those wireless sensor networks deployed with known coordinate database. It examines the ordered sequence of neighboring unknown nodes relative to some anchor nodes so that they are ranked to compute the location of unknown nodes by wireless localization measurements. After the task of distinguishing neighboring unknown nodes is completed through the ranging technology, the correct position for each sensor node would be identified. The localization scalability and fault tolerant performance of this algorithm have been evaluated from the perspective of practicability. Its performances for the real circumstance are verified through different experiments with several RF channels and deployment parameters. The node localization accuracy of Kcdlocation in a 5 * 4 grid network reaches 100% in a flat open square.

This paper focuses on direction finding of a signal source using time-difference-of-arrival (TDOA) measurements at a 3-D acoustic sensor array. Two evolutionary computation methods are proposed to solve the direction finding problem, which are the genetic algorithm and the particle swarm optimization algorithm. Sound speed is used in the development of the algorithms, which is estimated based on observed weather parameters and initial direction estimation results from the least square (LS) estimator which is presently the key method in TDOA-based direction finding. All reference-free TDOA measurements are adopted in defining cost function to improve performance. To guarantee fast convergence, an LS estimator is also utilized to provide initial direction estimates for the two swarm intelligent algorithms. Simulation results demonstrate that the proposed methods with a full TDOA set are superior to the Cramer–Rao lower bound with a limited set of reference-based TDOA measurement, significantly outperforming the LS estimator. Extensive field experiments were conducted and there is good agreement between the experimental results and simulation results. [ABSTRACT FROM PUBLISHER]