## 3 Tactical Calculation Methodology and Situational Awareness Evaluation Algorithms  
Timely delivered information and prepared decisions are crucial for individual soldiers and commanders, as this delivers the means for achieving decision superiority [1,12,13]. In order to achieve the required situational awareness [14], battlespace information must be accessible in real time. Therefore, mCOP was developed to render tactical information with respect to a current user's location (supporting current combat situation recognition) using an augmented reality view of the surrounding combat environment (Figures 3-5). All acquired data are fused and then used to estimate some major characteristics of the combat situation. Combat situational awareness in a real-world combat situation cannot be easily developed using a traditional 2D map view, as it does not provide direct spatial \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Unit (Type) Name** & **Potential** & **Estimated Time of Arrival (ETA)** & **Threat Level (THL)** & **Distance** & **Bearing** \\ \hline Motorized infantry battalion & 143.17 & 25 min & 0\% & 11.821 m & 236.44 \\ Mechanized infantry battalion & 130.93 & 32 min & 0\% & 15.103 m & 315.25 \\ (Armored) tank battalion & 18.7 & 18 min & 0\% & 8376 m & 293.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Red force (enemy unit) military potential and mission information. Figure 2: Recognized combat situation (TOPO map) merged from 10 separate reports (comparative and competitive data fusion strategies) taken from five different recon reports (**a**). Merged Common Operational Picture product taken from gathered reports rendered at command level (**b**). orientation, an enriched view of units, or threat-level semantics. Therefore, a tactical augmented reality view (TARV) was proposed and constructed. A TARV provides sensor data fusion products (location, azimuth, distance, and threat-level calculations) in the form of one picture, upon which the application evaluates the positions of allied and enemy forces and projects them onto the current camera view, presenting location and orientation data (Figure 4). Elements of TARV are calculated based on Global Positioning System (GPS) and magnetometer data that determine the observer's viewport and location, which additionally can present (Figure 4) the following: (1) location and GPS data precision, (2) altitude, (3) compass and current direction, (4) viewport angle, and (5) an overview map compass. This information can be further semantically processed to recommend the most efficient course of action or movement route. TARV is a construct developed through the usage of battlefield data, a user's location, and data derived from handheld device sensor fusions and the camera view. To create an AR view, there is a need to describe the vertical and horizontal field of the camera's view, calculated as a visible angle one meter away from the camera. When the viewing angles are specified, a maximum visible distance of units, POIs, and measurement points supplemented with Head-Up Display (HUD) controls and environmental data produce TARV. Algorithms [15; 16; 17] evaluate the visibility and characteristics of a rendered battlespace entity based on the formula \(\mathit{visible}=\mathit{bearingTo}(\mathit{unit})\leq\frac{\mathit{ azimuth}}{2}\ \wedge\ \mathit{distanceTo}(\mathit{unit})\leq max\), which can be adjusted using the max distance parameter and viewing criteria (threat factor, distance, and entity potential). The position of the battlespace entity marker is calculated using the following formulas: \(x=\frac{\mathit{width}}{2}+\frac{\mathit{bearingTo}(\mathit{unit})-\mathit{ azimuth}}{\mathit{horizontalFOV}}\cdot\mathit{width}\), \(y=\frac{\mathit{height}}{2}+\frac{\mathit{pitchTo}(\mathit{unit})-\mathit{ pitch}}{\mathit{verticalFOV}}\cdot\mathit{height}\). In order to stabilize the position of each and every entity and to compensate for projection movement, a linear filter is applied to process the orientation data.