A study on the Delta-Bullington irregular terrain radiofrequency propagation model : assessing model suitability for use in decision support tools

Modeling the propagation of radiofrequency signals over irregular terrain is both challenging and critically important in numerous Army applications. One application of particular importance is the performance and radio connectivity of sensors deployed in scenarios where the terrain and the environment significantly impact signal propagation. This report investigates both the performance of and the algorithms and assumptions underlying the Delta-Bullington irregular terrain radiofrequency propagation model discussed in International Telecommunications Union Recommendation P.526-15. The aim is to determine its suitability for use within sensor-planning decision support tools. After reviewing free-space, spherical earth diffraction, and terrain obstacle diffraction losses, the report dis-cusses several important tests of the model, including reciprocity and geographic continuity of propagation loss over large areas of rugged terrain. Overall, the Delta-Bullington model performed well, providing reasonably rapid and geographically continuous propagation loss estimates with computational demands appropriate for operational use.

Configuration and Enablement of Vision Sensor Solutions Through a Combined Simulation Based Process Chain

Machine vision solutions can perform within a wide range of applications and are commonly used to verify the operation of production systems. They offer the potential to automatically record assembly states and derive information, but simultaneously require a high effort of planning, configuration and implementation. This generally leads to an iterative, expert based implementation with long process times and sets major barriers for many companies. Furthermore the implementation is task specific and needs to be repeated with every variation of product, environment or process. Therefore a novel concept of a simulation-based process chain for both—configuration and enablement—of machine vision systems is presented in this paper. It combines related work of sensor planning algorithms with new methods of training data generation and detailed task specific analysis for assembly applications.

Mobile sensors' patrol path planning in unobservable border region

PurposeThe border control becomes challenging when a protected region is large and there is a limited number of border patrols. This research paper proposes a novel heuristic-based patrol path planning scheme in order to efficiently patrol with resource scarcity.Design/methodology/approachThe trespasser influencing score, which is determined from the environmental characteristics and trespassing statistic of the region, is used as a heuristic for measuring a chance of approaching a trespasser. The patrol plan is occasionally updated with a new trespassing statistic during a border operation. The performance of the proposed patrol path planning scheme was evaluated and compared with other patrol path planning schemes by the empirical experiment under different scenarios.FindingsThe result from the experiment indicates that the proposed patrol planning outperforms other patrol path planning schemes in terms of the trespasser detection rate, when more environment-aware trespassers are in the region.Research limitations/implicationsThe experiment was conducted through simulated agents in simulated environment, which were assumed to mimic real behavior and environment.Originality/valueThis research paper contributes a heuristic-based patrol path planning scheme that applies the environmental characteristics and dynamic statistic of the region, as well as a border surveillance problem model that would be useful for mobile sensor planning in a border surveillance application.