# Use of Real-Time GNSS-RF Data to Characterize the Swing Movements of Forestry Equipment
## 4 Discussion  
Analysis of the field data returned both expected and surprising results. As anticipated, the 2.5 s transmission interval data returned the highest proportion of correct element classifications. However, the 5.0 s transmission interval returned comparable correctly classified proportions. With 2.5 and 5.0 s transmission intervals capturing 94.4% and 91.7% of the respective overall cycles with at least one correct element for both Swing Unload and Return at the Grapple, accuracy exceeding that described by [PERSON] and [PERSON] and similar to that described by [PERSON] et al. was obtained when classifying machine cycles using GNSS data [20; 24]. The ability to correctly classify at least one observation of each element within each trial was reduced when analyzing the Heel data. The associated percentages of success for the 2.5, 5.0 and 10.0 s intervals at the Heel were 91.7%, 88.9% and 58.3%, respectively. Even so, if, for some operational reason, the transponders were placed at the heel rather than the grapple, a transmission interval of 2.5 or even 5.0 s would provide useful data, depending on accuracy requirements. When addressing the potential for GNSS-RF transponders to accurately capture angle measurements, a consistent trend of angle underestimation was found across all angles exceeding 180 degrees, though at the smaller angle measures this underestimation is decreased and in some cases there is predicted angle overestimation (Figure 5). The reason for this underestimation at the larger observed angle measures is likely due to transponder data packets representing the machine swing not capturing the full extent of the swing arc. For the GNSS location to represent the true arc angle, the transponder has to record a data packet the exact moment the full swing extent is reached or during any pause at the full extent. The time of a pause at full extent is a greater proportion of the overall time at shorter arcs, resulting in a higher inclusion probability of a data packet representing full swing extent. Therefore, the full extent would be represented more often for smaller swing angles and would result in lower underestimation of swing angles. As seen in Figure 5, any intersection of the 1:1 line and the time interval trend line represents a location where the predicted angles derived from the GNSS-RF data match observed angles. Additionally, Figure 5 and Table 4 indicate overall that the 2.5 s interval is most successful in capturing data to derive accurate angle measures. In our analysis, there was a trend of decreased GNSS measurement accuracy at the Heel location when compared to the Grapple location. The movement of the transponders around a smaller swing circumference means the transponders are less likely to be able to accurately and concisely plot varying points because the range of the movements is shorter. This is due to the inherent accuracy of GNSS. For example, GNSS accuracy of \(\pm 1\) m will have a greater impact on accurately capturing movements when the overall distance traveled 5 m as opposed to 15 m or 20 m. One possible solution to help account for this error when the swing arc is smaller is to incorporate additional sensors and mechanisms to capture grapple and other intricate movements to assist in defining elements [8]. These would augment GNSS data in ways that get around constraints related to GNSS accuracy. At the longer GNSS-RF transmission intervals, minute machine boom movements are not as accurately captured. This is seen by observing both the positional data itself, as well as the successful classification proportions. This is especially clear for the 10.0 s interval data. As suggested by [PERSON] and [PERSON], the 10.0 s interval may be beneficial for analysis of general machine movements across the landscape [10]. However, precise and intricate machine movements are best captured and analyzed using higher frequency data transmission of locations.