## 1 Introduction
Although single and dual-antenna GNSS/INS sensors both utilize dynamic alignment, dual-antenna systems almost always provide more precise heading estimates, along with increased performance and robustness. However, the use of dual-antenna GNSS/INS sensors within commercial UAS-lidar systems is fairly uncommon due to the increased system complexity, added weight, higher cost, and additional calibration requirements. Thus, the focus of this study is to understand the impacts of using a single-antenna versus a dual-antenna GNSS/INS MEMS-based sensor on the positional precision of a UAS-lidar generated point cloud and to illustrate the potential practical benefits of a dual-antenna system to contrast with the potential drawbacks. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristic** & **GNSS** & **INS** \\ \hline Accuracy of navigation solution & High long-term accuracy but noisy in short-term & High short-term accuracy but deteriorates with time \\ \hline Initial conditions & Not required & Required \\ \hline Orientation information & Typically not available 1 & Available \\ \hline Sensitive to gravity & No & Yes \\ \hline Self-contained & No & Yes \\ \hline Jamming immunity & No & Yes \\ \hline Output data rate & Low & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of GNSS and INS characteristics (Source: [9]). Only a few studies have been published that partially address the impact of GNSS/INS heading estimates on the positional precision of UAS-lidar point clouds. In [14], a theoretical analysis was performed that investigated and quantified the effects of trajectory errors, lidar observation errors, and beam incidence angles on point cloud coordinate accuracies. The introduced error values and flight parameters (e.g., flying height) were typical of those found within commercial UAS-lidar systems and were experienced during mapping operations. The study concluded that error in the yaw (heading) angle had the greatest impact on the horizontal accuracy of a point cloud. In [15], an empirical accuracy assessment of a typical UAS-lidar system utilizing a single-antenna GNSS/INS sensor was presented. A UAS-lidar point cloud collected during a low-altitude/low-speed flight was compared to a reference point cloud generated by a terrestrial laser scanner (TLS). An analysis of the GNSS/INS orientation estimates showed lower than expected accuracy in the azimuth (heading) estimates and was attributed to poor kinematic alignment of the initial orientation. As a result, the accuracy of the UAS-lidar point cloud was decreased. The study concluded that absolute accuracy of the UAS-lidar point cloud was no worse than 10 cm and suggested the precision was as good as 2 cm. In [14] and [15], the theoretical and experimental findings indicated that the precision of the heading orientation estimates had the largest impact on the positional precision of a UAS-lidar point cloud. To build upon these findings, this study conducted a controlled UAS-lidar mapping experiment to examine how the precision of heading orientation estimates from single and dual-antenna systems affects the positional precision of a resulting point cloud. Specifically, the objectives of this study are to:  
1. provide the theoretical foundations behind different heading determination techniques used by GNSS/INS sensors;
2. detail the practical considerations for implementing these heading determination techniques during UAS-lidar field operations (e.g., INS initialization procedures and mission planning considerations);
3. compare the precisions of heading orientation estimates reported by a single-antenna and a dual-antenna GNSS/INS sensor at different UAS flying speeds; and
4. assess the positional precisions of UAS-lidar point clouds generated from a single-antenna and a dual-antenna GNSS/INS sensor.