Note that the Stansfield estimator involves the distances R j between sensors and target; however, as discussed in [16] and confirmed by simulation results (see section 5.2), the dependency of the estimator on R is quite weak.
In section 4, we address the problem of localization by combining DoA estimates of multiple sensors: we first derive the CRB of the achievable localization error, highlighting its dependence on distances and angles between sensors and target, and then we introduce practical algorithms: maximum-likelihood (ML), least-squares (LS), and Stansfield estimators.
Finally, target location can be achieved by the time difference analyzing, sensors layout and the relative position between sensors and targets.
Although LiDAR has proven to be an effective tool for mapping the forest canopy, its ability to probe all canopy elements is limited by the signal occlusion that occurs when multiple objects lie between the sensor and target area.
where θd,p represents the aspect angle between the sensor and target.
(a) The sensor orientation in a world coordinate system; (b) The slant and ground ranges between the sensor and target (side view); (c) The aspect orientation and the heading direction (top-down view); (d) Sensor-centered 3D coordinate system used for algorithm evaluation.
First, EPDM establishes the relation model and association relation between sensor nodes and target nodes.
(3) The coverage rate under certain condition can be determined through the state transition of nodes and the associated attributes between sensor nodes and target nodes.
The recognition between the imprinted sensor and target molecule was observed by measuring the variation amperometric response of the oxidation reduction probe, K3Fe CN 6, on electrode.
